Miracle-dz/UniFAR-FTUs huggingface dataset:
Crewed Missions to Mars: Modeling the Impact of Astrophysical Charged Particles on Astronauts and Their Health

The impact of exposure to astrophysical ionizing radiation on astronaut health is one of the main concerns in planning crewed missions to Mars. Astronauts will be exposed to energetic charged particles from Galactic and Solar origin for a prolonged period with little protection from a thin spacecraft shield in transit and from the rarefied Martian atmosphere when on the surface. Adverse impacts on astronaut health include, for example, Acute Radiation Syndrome, damage to the nervous system, and increased cancer risk. We rely on medical studies to assess the impact of enhanced radiation dose levels on various physiological systems and the overall health of astronauts. Using a combination of radiation measurements and numerical modeling with the GEANT4 package, we calculate the distribution of radiation dose in various human body organs for various expected scenarios simulated with a model human Dimitra Atri Center for Space Science, New York University Abu Dhabi, Saadiyat Island, PO Box 129188, Abu Dhabi, UAE Blue Marble Space Institute of Science, 600 1st Avenue, Seattle, WA 98104, USA E-mail: atri@nyu.edu Caitlin MacArthur Pathology and Molecular Medicine, University of Otago, Wellington, 23A Mein Street, Newtown, Wellington 6242, New Zealand ∗ equal contribution author Sriram Devata International Institute of Information Technology, Gachibowli, Hyderabad, Telangana 500032, India ∗ equal contribution author Konstantin Herbst Institut für Experimentelle und Angewandte Physik, Christian-Albrechts Universität zu Kiel, Leibnizstraße 11, D-24118 Kiel, Germany Dionysios Gakis Department of Physics, University of Patras, Patras, 26504, Greece Shireen Mathur Blue Marble Space Institute of Science, Seattle, WA 98154, USA Maria Villarreal-Gomez Universidad Industrial de Santander, Bucaramanga, Santander 680002, Colombia Giulia Carla Bassani Polytechnic University of Turin, 10129 Torino (TO), Italy Roberto Parisi Department of Medicine and Surgery, Università degli Studi di Salerno, Fisciano, Salerno 84084, Italy Azza Al Bakr National Space Science and Technology Centre, Al Ain, UAE Tammy Witzens Ira A. Fulton Schools of Engineering, Arizona State University, PO Box 879309, Temepe, AZ 85287, USA ar X iv :2 20 8. 00 89 2v 2 [ ph ys ic s. sp ac eph ] 5 J an 2 02 3
Development of Photoelectron Microscope with Compact X-Ray Source Generated by Line-Focused Laser Irradiation

A laboratory-sized x-ray photoelectron microscope was constructed using a compact x-ray source produced by line-focused laser irradiation. The system is a scanning type photoelectron microscope in which the x-ray beam is micro-focused via Schwarzschild optics. A compact laser-plasma x-ray source has been developed consisting of a YAG laser, a line-focus lens assembly, an Al tape-target driver and a debris prevention system. The 13.1 nm x-rays were delivered from the line plasma whose length was 0.6 to 11 mm with higher intensity than that from a point-focused source. Schwarzschild optics having a designed demagnification of 224 and coated with Mo/Si multilayers for 13.1 nm x-rays, was set on the beamline at a distance 1 m from the source. The electron energy analyzer was a spherical capacitor analyzer with a photoelectron image detection system that was suited for detection of a burst of photoelectrons excited by pulsed x-rays of ns-order duration. Photoelectron spectra were obtained having two peaks from As 3d and Ga 3d electrons, when a GaAs wafer was used as a sample. Spatial resolution of less than 5˜3 μm was confirmed from the variation of the As 3d electron intensity along the position of the GaAs sample coated with a photo-resist test pattern.
Internal Waves in the Ocean

Salinity, temperature, and pressure gradients all cause the density of sea water to vary with depth in the ocean, and the density gradient affects the motion of the waters. A quantity N, having the units radians per second, can be defined using the density gradient, the velocity of sound, and the acceleration of gravity.The simplest motions have the form of horizontally progressive waves of frequency ω, wave number κ, and velocity V. If h is the amplitude of the vertical displacement of the water and z the vertical coordinate, then V2(d2h/dz2) + [N2(z) − ω2]h = 0; this equation is formally identical with Schrodinger's wave equation. The stream function of these waves is φ = Vh(z) sin (κx − ωt), and the variable part of the pressure is − ρ dφ/dz, while the vorticity is R = −N2φ/V2. The wave may be described as a lattice of vortices moving with velocity V. In the ocean N(z) ordinarily has one or two maxima called thermoclines. The analogy with the quantum‐mechanical problems of one and two potential minima .
Evaluation of BioFire Respiratory Panel 2 plus for Detection of Bordetella pertussis in Nasopharyngeal Swab Specimens from Children with Clinically Suspected Pertussis

In recent years, there have been some epidemic or local outbreaks of pertussis in countries with high vaccination rates. One of the crucial factors in controlling pertussis is early diagnosis, which is based on specific laboratory measurements, including culture, serological tests, and PCR assays. ABSTRACT The objective of this study was to compare the performances of BioFire Respiratory Panel 2 (RP2) plus, quantitative real-time PCR (qPCR), and culture for the detection of Bordetella pertussis in nasopharyngeal swab (NPS) specimens. Consecutive NPS specimens were collected from patients with clinically suspected pertussis from 1 March 1 to 31 July 2018 in Shenzhen Children’s Hospital. All the specimens were tested in parallel by RP2 plus, qPCR, and culture methods. A total of 464 children were enrolled in this study. The positive pertussis rates of culture, RP2 plus, and qPCR were 23.1%, 39.0%, and 38.4%, respectively. Compared to the combined reference standard, the sensitivity, specificity, positive predictive value, and negative predictive values were, respectively, 56.6% (95% confidence interval [CI], 49.2 to 63.7%), 100% (98.3 to 100%), 100% (95.7 to 100%), and 77.0% (72.2 to 81.2%) for culture, 89.9% (84.5 to 93.7%), 96.0% (92.8 to 97.9%), 93.9% (89.1 to 96.8%), and 93.3% (89.5 to 95.8%) for RP2 plus, and 86.8% (80.9 to 91.1%), 94.9% (91.4 to 97.1%), 92.1% (86.9 to 95.5%), and 91.3% (87.2 to 94.2%) for qPCR. The most prevalent codetected pathogen was human rhinovirus/enterovirus (n = 99, 52.4%), followed by parainfluenza virus (n =32, 16.9%) and respiratory syncytial virus (n = 29, 15.3%), in children with B. pertussis present, which was consistent with the top three pathogens previously found in children with B. pertussis absent. Turnaround times for RP2 plus, qPCR, and culture were 2 h, 8 h, and 120 h, respectively. RP2 plus quickly and accurately detected B. pertussis, providing valuable information for an early clinical diagnosis and optimal choice of therapy. IMPORTANCE In recent years, there have been some epidemic or local outbreaks of pertussis in countries with high vaccination rates. One of the crucial factors in controlling pertussis is early diagnosis, which is based on specific laboratory measurements, including culture, serological tests, and PCR assays. Compared to culture and serological tests, PCR is more suitable for clinical application, with a fast detection speed of several hours independent of the disease stage and individual vaccination status. BioFire Respiratory Panel 2 plus, a multiplex PCR assay for simultaneously detecting 22 respiratory pathogens, facilitates the quick detection of Bordetella pertussis and coinfecting respiratory pathogens. It also provides valuable information for an early clinical diagnosis and optimal choice of therapy for children with clinically suspected pertussis.
Repurposing Carbamazepine To Treat Gonococcal Infection in Women: Oral Delivery for Control of Epilepsy Generates Therapeutically Effective Levels in Vaginal Secretions

Neisseria gonorrhoeae has developed resistance to all previous antibiotics used for treatment. This highlights a crucial need for novel antimicrobials to treat gonococcal infections. ABSTRACT Neisseria gonorrhoeae has developed resistance to all previous antibiotics used for treatment. This highlights a crucial need for novel antimicrobials to treat gonococcal infections. We previously showed that carbamazepine (Cz), one of the most commonly prescribed antiepileptic drugs, can block the interaction between gonococcal pili and the I-domain region of human complement receptor 3 (CR3)—an interaction that is vital for infection of the female cervix. We also show that Cz can completely clear an established N. gonorrhoeae infection of primary human cervical cells. In this study, we quantified Cz in serum, saliva, and vaginal fluid collected from 16 women who were, or were not, regularly taking Cz. We detected Cz in lower reproductive tract mucosal secretions in the test group (women taking Cz) at potentially therapeutic levels using a competitive ELISA. Furthermore, we found that Cz concentrations present in vaginal fluid from women taking this drug were sufficient to result in a greater than 99% reduction (within 24 h) in the number of viable gonococci recovered from ex vivo, human, primary cervical cell infections. These data provide strong support for the further development of Cz as a novel, host-targeted therapy to treat gonococcal cervicitis.
Local-Scale Variability of Solar Radiation in a Mountainous Region

Abstract Simultaneous measurements of horizontal global solar irradiance and other meteorological parameters have been taken over three years at a dense radiometric network. The network is located on a mountainous region in southeastern Spain (37°N, 3°W) covering elevations from 456 to 3398 m above mean sea level. This dataset has been used to study the spatial variability at a local scale. The great heterogeneity of the area is reflected in the great spatial variability encountered. The purpose of the study is to obtain an estimate of the small-scale solar variability of the daily, hourly, and shorter-scale solar irradiance values. Following previous results at a similar spatial scale, no mathematical function could be obtained between the variability and the interstation distance. Nevertheless limiting the analysis to pairing of the same hillside, the authors found a correlation between variability and elevation difference between pairs of stations. To quantify the influence of temporal scale on the spa.
Answering Software Deployment Questions via Neural Machine Reading at Scale

As software systems continue to grow in complexity and scale, deploying and delivering them becomes increasingly difficult. In this work, we develop DeployQA, a novel QA bot that automatically answers software deployment questions over user manuals and Stack Overflow posts. DeployQA is built upon RoBERTa. To bridge the gap between natural language and the domain of software deployment, we propose three adaptations in terms of vocabulary, pre-training, and fine-tuning, respectively. We evaluate our approach on our constructed DeQuAD dataset. The results show that DeployQA remarkably outperforms baseline methods by leveraging the three domain adaptation strategies. Repository: https://github.com/Smallqqqq/DeployQA Video: https://www.youtube. com/watch? v=TVf9w8gD3Ho
AroMa: Evaluating Deep Learning Systems for Stealthy Integrity Attacks on Multi-tenant Accelerators

Multi-tenant applications have been proliferating in recent years, supported by the emergence of computing-as-service paradigms. Unfortunately, multi-tenancy induces new security vulnerabilities due to spatial or temporal co-location of applications with possibly malicious intent. In this article, we consider a special class of stealthy integrity attacks on multi-tenant deep learning accelerators. One interesting conclusion is that it is possible to perform targeted integrity attacks on kernel weights of deep learning systems such that it remains functional but mis-labels specific categories of input data through standard RowHammer attacks by only changing 0.0009% of the total weights. We develop an automated framework, AroMa, to evaluate the impact of multi-tenancy on security of deep learning accelerators against integrity attacks on memory systems. We present extensive evaluations on AroMa to demonstrate its effectiveness.
BuildSonic: Detecting and Repairing Performance-Related Configuration Smells for Continuous Integration Builds

Despite the benefits, continuous integration (CI) can incur high costs. One of the well-recognized costs is long build time, which greatly affects the speed of software development and increases the cost in computational resources. While there exist configuration options in the CI infrastructure to accelerate builds, the CI infrastructure is often not optimally configured, leading to CI configuration smells. Attempts have been made to detect or repair CI configuration smells. However, none of them is specifically designed to improve build performance in CI. In this paper, we first create a catalog of 20 performance-related CI configuration smells (PCSs) in three tools (i.e., Travis CI, Maven and Gradle) of the CI infrastructure for Java projects. Then, we propose an automated approach, named BuildSonic, to detect and repair 15 types of PCSs by analyzing configuration files. We have conducted large-scale experiments to evaluate BuildSonic. We detected 20,318 PCSs in 99.0% of the 4,140 Java projects, with a precision of 0.998 and a recall of 0.991. We submitted 1,138 pull requests for sampled PCSs of each PCS type, 246 and 11 of which have been respectively merged and accepted by developers. We successfully triggered CI builds before and after merging 288 pull requests, and observed an average build performance improvement of 12.4% after repairing a PCS.
Horntinuum: Autonomous Testing using Constrained Horn Clauses

To test a program, one could benefit from an effective approach to enumerate traces using a symbolic representation of the program. Similar to other approaches to automated reasoning about software, test case generation could then follow a paradigm of delegating complex symbolic reasoning to constraint solvers. Intermediate languages, such as the ones based on constrained Horn clauses (CHC) offer a high degree of flexibility for the designers of various toolchains, and in particular, designers of test case generators. Having a test-case generator developed for CHC, it only remains to develop a CHC encoder for a programming language of choice, or to use an existing one. Horntinuum is the first tool that uses CHC for test-case generation. For maximizing a branch coverage, Horntinuum aims to enumerate test cases, such that the programs if executed under these tests visit as many branches as possible. Branches in the code are commonly attributed to the conditional statements or loops. For testing a loop-free program, possible test cases for all the branches can be identified by symbolic execution. If a conditional is placed inside or after a loop, test-case generation immediately becomes challenging and boils down to finding a proper unrolling of the loop. Horntinuum takes a set of symbolic constraints as input that could be produced by encoding a desired program in a programming language (potentially any, and currently C and in parts Solidity) that has a proper encoding to CHCs. Formally, a CHC is a logic formula involving uninterpreted predicates. In our application, each CHC encodes a straight-line block of code, and uninterpreted predicates encode points of control-flow divergence or convergence. Valid interpretations for these predicates, if found, represent reachability information (are known as invariants) and are useful in pruning the search space explored by test-case generators. If an invariant shows that a branch can never be taken, then it is guaranteed that no test can ever reach the branch, and thus a test-case generator can safely proceed to discovery of the next test case.
Evolving Ranking-Based Failure Proximities for Better Clustering in Fault Isolation

Failures that are not related to a specific fault can reduce the effectiveness of fault localization in multi-fault scenarios. To tackle this challenge, researchers and practitioners typically cluster failures (e.g., failed test cases) into several disjoint groups, with those caused by the same fault grouped together. In such a fault isolation process that requires input in a mathematical form, ranking-based failure proximity (R-proximity) is widely used to model failed test cases. In R-proximity, each failed test case is represented as a suspiciousness ranking list of program statements through a fingerprinting function (i.e., a risk evaluation formula, REF). Although many off-the-shelf REFs have been integrated into R-proximity, they were designed for single-fault localization originally. To the best of our knowledge, no REF has been developed to serve as a fingerprinting function of R-proximity in multi-fault scenarios. For better clustering failures in fault isolation, in this paper, we present a genetic programming-based framework along with a sophisticated fitness function, for evolving REFs with the goal of more properly representing failures in multi-fault scenarios. By using a small set of programs for training, we get a collection of REFs that can obtain good results applicable in a larger and more general scale of scenarios. The best one of them outperforms the state-of-the-art by 50.72% and 47.41% in faults number estimation and clustering effectiveness, respectively. Our framework is highly configurable for further use, and the evolved formulas can be directly applied in future failure representation tasks without any retraining.
RESTCluster: Automated Crash Clustering for RESTful API

RESTful API has been adopted by many other notable companies to provide cloud services. Quality assurance of RESTful API is essential. Several automated RESTful API testing techniques have been proposed to overcome this problem. However, automated tools often generate a large number of failed test cases. Since validating each test case is a lot of work for developers, automatic failure clustering is a promising solution to help debug cloud services. In this paper, we propose RESTCluster, a two-phase crash clustering approach. The preliminary evaluation result indicates that RESTCluster can achieve 100% precision in different sizes of subjects with a high recall.
Evaluating neurorehabilitation exercises captured with commodity sensors and machine-learning framework

During the last decades, disease-related disabilities, primarily caused by stroke have increased worldwide. Neurorehabilitation exercise therapy plays a vital role in the recovery of such disabilities. However, due to global demographic changes and the increasing number of stroke patients, therapists are facing difficulties in coping with the demand. Consequently, the necessity for appropriate technical support to help the therapists for providing helpful progress feedback to the patients is becoming evident. So far, such technological systems are not yet available for clinical usage. Moreover, there is still a lack of research demonstrating the possibility of pursuing the therapeutic exercises by the patients themselves at their homes using non-invasive commodity sensors. In this work, we design a system pipeline containing commodity cameras by which the patients would be able to record their exercises at home; we also evaluate and analyze the acquired data using an off-the-shelf machine-learning framework. The medical experts can utilize our system to monitor the patients’ progress over the prescribed duration of the therapy. Here, rather than using specialized sensors with the body to acquire the movement information of the body joints, which some of the existing works use, we use a machine-learning framework to acquire this information. Our evaluation process demonstrates the situations in which these activities can be reliably acquired with commodity RGB cameras; moreover, the challenging aspects of the acquisition which can affect the accuracy of recognition of the framework are discussed and analyzed.
A Review of AI-augmented End-to-End Test Automation Tools

Software testing is a process of evaluating and verifying whether a software product still works as expected, and it is repetitive, laborious, and time-consuming. To address this problem, automation tools have been developed to automate testing activities and enhance quality and delivery time. However, automation tools become less effective with continuous integration and continuous delivery (CI/CD) pipelines when the system under test is constantly changing. Recent advances in artificial intelligence and machine learning (AI/ML) present the potential for addressing important challenges in test automation. AI/ML can be applied to automate various testing activities such as detecting bugs and errors, maintaining existing test cases, or generating new test cases much faster than humans. In this study, we will outline testing activities where AI has significantly impacted and greatly enhanced the testing process. Based on that, we identify primary AI techniques that are used in each testing activity. Further, we conduct a comprehensive study of test automation tools to provide a clear look at the role of AI/ML technology in industrial testing tools. The results of this paper help researchers and practitioners understand the current state of AI/ML applied to software testing, which is the first important step towards achieving successful and efficient software testing.
Checking LTL Satisfiability via End-to-end Learning

Linear temporal logic (LTL) satisfiability checking is a fundamental and hard (PSPACE-complete) problem. In this paper, we explore checking LTL satisfiability via end-to-end learning, so that we can take only polynomial time to check LTL satisfiability. Existing approaches have shown that it is possible to leverage end-to-end neural networks to predict the Boolean satisfiability problem with performance considerably higher than random guessing. Inspired by these approaches, we study two interesting questions: can end-to-end neural networks check LTL satisfiability, and can neural networks capture the semantics of LTL? To this end, we train different neural networks for keeping three logical properties of LTL, i.e., recursive property, permutation invariance, and sequentiality. We demonstrate that neural networks can indeed capture some effective biases for checking LTL satisfiability. Besides, designing a special neural network keeping the logical properties of LTL can provide a better inductive bias. We also show the competitive results of neural networks compared with state-of-the-art approaches, i.e., nuXmv and Aalta, on large scale datasets.
Future scoping of truly Human-Centric IoT and Intelligent Networks: A Foresight Approach

As IoT and network technology become increasingly pervasive, intelligent, and thus more powerful, it will be crucially important for future, sustainable and Human-Centric IoT scenarios that humans and humanity stay in control and can exercise meaningful agency. However, despite a growing understanding that this is of crucial importance, there is no clear shared vision between the multiple stakeholders of what is preferable and how to get there. This paper therefore reports on the first intelligence gathering that is part of a larger foresight study in this respect. More concretely, we report on a horizon scanning study and expert interviews (N=9) conducted in Norway, aimed at identifying emerging issues, trends and developments that may affect future scenarios related to human-centric intelligent networks and IoT environments.