Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware-Enforced Integrity and Provenance for Distributed Code Deployments

Deployed microservices must adhere to a multitude of application-level security requirements and regulatory constraints imposed by mutually distrusting application principals--software developers, cloud providers, and even data owners. Although these principals wish to enforce their individual security requirements, they do not currently have a common way of easily identifying, expressing and automatically enforcing these requirements at deployment time. CDI (Code Deployment Integrity) is a security policy framework that enables distributed application principals to establish trust in deployed code through high-integrity provenance information. We observe that principals expect the software supply chain to preserve certain code security properties throughout the creation of an executable bundle, even if the code is transformed or inspected through various tools (e.g., compilation inserts stack canaries for memory safety). Our key insight in designing CDI is that even if application principals do not trust each other directly, they can trust a microservice bundle to meet their security policies if they can trust the tools involved in creating the bundle.
SOME UNIFORM BOUNDS FOR ELLIPTIC CURVES OVER Q

We give explicit uniform bounds for several quantities relevant to the study of Galois representations attached to elliptic curves E/Q. We consider in particular the subgroup of scalars in the image of Galois, the first Galois cohomology group with values in the torsion of E, and the Kummer extensions generated by points of infinite order in E(Q).
Analytical, Statistical Approximate Solution of Dissipative and Nondissipative Binary-Single Stellar Encounters

We present a statistical approximate solution of the bound, non-hierarchical three-body problem, and extend it to a general analysis of encounters between hard binary systems and single stars. Any such encounter terminates when one of the three stars is ejected to infinity, leaving behind a remnant binary; the problem of binary-single star-scattering consists of finding the probability distribution of the orbital parameters of the remnant binary, as a function of the total energy and the total angular momentum. Here, we model the encounter as a series of close, non-hierarchical, triple approaches, interspersed with hierarchical phases, in which the system consists of an inner binary and a star that orbits it -- this turns the evolution of the entire encounter to a random walk between consecutive hierarchical phases. We use the solution of the bound, non-hierarchical three-body problem to find the walker's transition probabilities, which we generalise to situations in which tidal interactions are important. Besides tides, any dissipative process may be incorporated into the random walk model, as it is completely general. Our approximate solution can reproduce the results of the extensive body of past numerical simulations, and can account for different environments and different dissipative effects. Therefore, this model can effectively replace the need for direct few-body integrations for the study of binary-single encounters in any environment. Furthermore, it allows for a simply inclusion of dissipative forces typically not accounted for in full N-body integration schemes.
Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications

Traffic near-crash events serve as critical data sources for various smart transportation applications, such as being surrogate safety measures for traffic safety research and corner case data for automated vehicle testing. However, there are several key challenges for near-crash detection. First, extracting near-crashes from original data sources requires significant computing, communication, and storage resources. Also, existing methods lack efficiency and transferability, which bottlenecks prospective large-scale applications. To this end, this paper leverages the power of edge computing to address these challenges by processing the video streams from existing dashcams onboard in a real-time manner. We design a multi-thread system architecture that operates on edge devices and model the bounding boxes generated by object detection and tracking in linear complexity. The method is insensitive to camera parameters and backward compatible with different vehicles. The edge computing system has been evaluated with recorded videos and real-world tests on two cars and four buses for over ten thousand hours. It filters out irrelevant videos in real-time thereby saving labor cost, processing time, network bandwidth, and data storage. It collects not only event videos but also other valuable data such as road user type, event location, time to collision, vehicle trajectory, vehicle speed, brake switch, and throttle. The experiments demonstrate the promising performance of the system regarding efficiency, accuracy, reliability, and transferability. It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.
On convergence of numerical solutions for the compressible MHD system with exactly divergence-free magnetic field

We study a general convergence theory for the analysis of numerical solutions to a magnetohydrodynamic system describing the time evolution of compressible, viscous, electrically conducting fluids in space dimension $d$$(=2,3)$. First, we introduce the concept of dissipative weak (DW) solutions and prove the weak–strong uniqueness property for DW solutions, meaning a DW solution coincides with a classical solution emanating from the same initial data on the lifespan of the latter. Next, we introduce the concept of consistent approximations and prove the convergence of consistent approximations towards the DW solution, as well as the classical solution. Interpreting the consistent approximation as the energy stability and consistency of numerical solutions, we have built a nonlinear variant of the celebrated Lax equivalence theorem. Finally, as an application of this theory, we show the convergence analysis of two numerical methods.
PR ] 2 4 O ct 2 00 5 Another approach to Brownian motion

For the partial sums Sn = Y1 + . + Yn of a centered stationary strongly mixing sequence {Yi} with finite second moment, the well-known sufficient conditions for the central limit theorem are that Var(Sn)/n is slowly varying as n → ∞ and the sequence {S2 n/σn} is uniformly integrable (σn = Var(Sn)). The conditions are checkable under various mixing conditions and they lead to the central limit theorem under the normalization σn(see, Denker (1986) and Peligrad (1986) for a survey). Dehling, Denker and Philipp (1986) proved an interesting central limit theorem using the nontraditional normalization ρn = E|Sn|. One of their results, Theorem 3, roughly states that if both sequences σn/n and ρn/ √ n are slowly varying as n → ∞, then the central limit theorem holds. On the other hand, Braverman, Mallows and Shepp (1995) showed that if the absolute moments of partial sums of i.i. d. symmetric variables are equal to those of normal variables, then the marginals have normal distribution. This fact suggested the conjecture that probably the absolute moments alone characterize the homogeneous process with independent increments (see Bryc (2002) for a discussion on this topic and related conjectures). Our main interest in this topic is to prove some of these conjectures and to apply them to understand the nature of the intricate normalization in Dehling, Denker and Philipp (1986).
A New Chemical Approach to Durable Flame-Retardant Cotton Fabrics1

Recent studies of fire accidents in Switzerland involving individuals' wearing apparel have illustrated the importance of a durable flame-retardant finish for cotton textiles. Knowledge of the dynamics and chemistry of pyrolysis and combustion is considered essential to understand the mechanism of flame-retardant systems. A series of N-methylol compounds of dialkyl phosphonocarboxylic acid amides were assessed in relation to established systems based on APO and THPC. A number of these compounds were found suitable for durable flame-retardant finishes on cotton. One compound was chosen to illustrate their performance. It showed excellent fastness to laundering and dry cleaning with acceptable effects on the properties of the fabric.
Estimation of Tolerable Daily Intake (TDI) for Immunological Effects of Aflatoxin

Aflatoxins are toxic chemicals produced by the fungi Aspergillus flavus and Aspergillus parasiticus. In warm climates, these fungi frequently contaminate crops such as maize, peanuts, tree nuts, and sunflower seeds. In many tropical and subtropical regions of the world, populations are coexposed to dietary aflatoxin and multiple infectious pathogens in food, water, and the environment. There is increasing evidence that aflatoxin compromises the immune system, which could increase infectious disease risk in vulnerable populations. Our aim was to conduct a dose–response assessment on a noncarcinogenic endpoint of aflatoxin: immunotoxicological effects. We sought to determine a noncarcinogenic tolerable daily intake (TDI) of aflatoxin, based on the existing data surrounding aflatoxin and biomarkers of immune suppression. To conduct the dose response assessment, mammalian studies were assessed for appropriateness of doses (relevant to potential human exposures) as well as goodness of data, and two appropriate mouse studies that examined decreases in leukocyte counts were selected to generate dose response curves. From these, we determined benchmark dose lower confidence limits (BMDL) as points of departure to estimate a range of TDIs for aflatoxin‐related immune impairment: 0.017–0.082 μg/kg bw/day. As aflatoxin is a genotoxic carcinogen, and regulations concerning its presence in food have largely focused on its carcinogenic effects, international risk assessment bodies such as the Joint Expert Committee on Food Additives (JECFA) have never established a TDI for aflatoxin. Our work highlights the importance of the noncarcinogenic effects of aflatoxin that may have broader public health impacts, to inform regulatory standard‐setting.
Investigation of Porous Silicon/Carbon Composite as Anodes for Lithium Ion Batteries

3D porous silicon was synthesized by metal-assisted chemical etching process using commercially available polycrystalline silicon powders. After chemical etching in optimized solution, 3D porous silicon structures with pore size of about 130 nm and specific surface area of about 4.85 m/g was obtained. Subsequently, the 3D porous silicon powders treated with ball milling and heat carbonization processes were coated with amorphous carbon and utilized as the anode electrode material for lithium ion battery. The combination of the 3D porous structure and a carbon coating layer can accommodate large mechanical strains by providing the empty space of the pores to alleviate the volume change, and by increasing the electrical conductivity with the carbon layer. The electrodes achieve an initial charge capacity of 3345 mAh/g with coulombic efficiency of 85.8% as well as a high reversible capacity of 1645 mAh/g after 55 cycles at 0.4 A/g. And it is capable to retain a capacity of 1174 mAh/g even at 4 A/g. Thus, this work introduces a novel and easy potential industrial method for fabrication Si/C materials for high-performance lithium ion battery.
White Wine Phenolics: Varietal and Processing Differences as Shown by HPLC

Grape skin extracts and white wines with and without pomace contact were made for two vintages from Chenin blanc, French Colombard, Semillon, and Thompson Seedless. HPLC separated at least 124 substances absorbing in the ultraviolet. Seven of these have been identified and quantitated as gallic acid, caftaric acid, coutaric acid, catechin, epicatechin, astilbin, and engeletin. The retention times for these substances on three different reversed phase columns were different, but in the same sequence with coefficients of variation for known standards (5-7 replicates) typically 1-2% for retention time and 1.2-8.9% for peak area. Detection at 280, 292, or 320 nm gave considerably different absolute and relative peak areas depending on the absorbance maxima of the substances, but as long as the absorbance was appreciable and separation complete, fairly constant amounts were calculated. Contamination of the catechin peak with a substance having more absorbance at longer wavelengths illustrated a difficulty with direct quantitation by peak area alone on a complex chromatogram. Composition was similar within a grape variety and differed between varieties. Vintage differed also, but less so, but pomace contact made vary large compositional differences. Evidence is presented that specified components are added by extraction from seeds or skins and others are augmented or depleted by reactions such as hydrolysis and oxidation during processing.
Fracture toughness of one- and two-dimensional nanoreinforced cement via scratch testing

Cement is the most widely consumed material globally, with the cement industry accounting for 8% of human-caused greenhouse gas emissions. Aiming for cement composites with a reduced carbon footprint, this study investigates the potential of nanomaterials to improve mechanical characteristics. An important question is to increase the fraction of carbon-based nanomaterials within cement matrices while controlling the microstructure and enhancing the mechanical performance. Specifically, this study investigates the fracture response of Portland cement reinforced with one- and two-dimensional carbon-based nanomaterials, such as carbon nanofibres, multiwalled carbon nanotubes, helical carbon nanotubes and graphene oxide nanoplatelets. Novel processing routes are shown to incorporate 0.1–0.5 wt% of nanomaterials into cement using a quadratic distribution of ultrasonic energy. Scratch testing is used to probe the fracture response by pushing a sphero-conical probe against the surface of the material under a linearly increasing vertical force. Fracture toughness is then computed using a nonlinear fracture mechanics model. Nanomaterials are shown to bridge nanoscale air voids, leading to pore refinement, and a decrease in the porosity and the water absorption. An improvement in fracture toughness is observed in cement nanocomposites, with a positive correlation between the fracture toughness and the mass fraction of nanofiller for graphene-reinforced cement. Moreover, for graphene-reinforced cement, the fracture toughness values are in the range of 0.701 to 0.717 MPam. Thus, this study illustrates the potential of nanomaterials to toughen cement while improving the microstructure and water resistance properties. This article is part of a discussion meeting issue ‘A cracking approach to inventing new tough materials: fracture stranger than friction’.
Research made simple: using multiperspective interpretative phenomenological analysis to explore healthcare phenomena

10.1136/ebnurs-2021-103428 Using interpretative phenomenological analysis to explore multiperspectives Interpretative phenomenological analysis (IPA) was originally developed in 1995 by Johnathan Smith as a method to undertake experiential research in psychology and has gained prominence across health and social sciences as a way to understand and interpret topics that are complex and emotionally laden, such as chronic illness experiences. 2 IPA aims to uncover what a lived experience means to the individual through a process of indepth reflective inquiry. The IPA draws on phenomenological thinking, with the purpose to return ‘to the things themselves’ (p168). However, IPA also acknowledges that we are each influenced by the worlds in which we live and the experiences we encounter. Therefore, IPA is an interpretative process between the researcher and researched, influenced predominantly by Heidegger’s interpretive phenomenology, hermeneutics and idiography. Within IPA, it is typical for researchers to select a small homogenous sample to explore the shared perspectives on a single phenomenon of interest. Within IPA studies, the focus has been on individual people living within diverse settings and populations such as chronic or longterm illnesses. The focus is on understandings of rich, lived experiences, and, given the small samples, IPA studies have typically not focused on those connected to the person living with diversity or disease. Recently, there has been an interest within IPA to suggest the value of capturing more complex data through multiple perspectives using designs and processes to address this shortcoming in IPA. This may involve the use of multiple participants and a range of data collection methods such as the use of dyads or focus groups. The aim of this paper is to explore the utility of IPA approaches using multiperspectives through focusing on a specific case study to illustrate this approach.
Prediction of the Work-Related Injuries Based on Neural Networks

Abstract Artificial neural networks (ANN) are a powerful tool in the decision-making process, especially in solving the complex problems with a large number of input data. The possibility to predict the work-related injuries in the underground coal mines, based on application of the neural networks, is analyzed in this work. the input data for the network were obtained based on a survey of 1300 respondents. After analyzing the input data influence on the network output, 14 most influential inputs were selected, with help of which the network correctly predicted whether the worker would suffer the work-related injury or not, with 80% precision. The two models were developed, based on the multilayer perceptron (MLP) and radial basis function (RBF) networks. The two models’ results were compared to each other. The sensitivity analysis was used to select the most influential parameters, like mine, age of miners, as well as their work experience. The parameters were further analyzed by use of the descriptive statistics. The selected parameters are direct indicators of problems that can cause injuries. The obtained results point to the fact that the work-related injuries can be successfully predicted by application of the artificial neural networks. The proposed models’ importance is reflected in the clear indicators for enforcing the stricter occupational safety and organizational measures in order to reduce the number of work-related injuries in underground mines.
Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers

For many, Graphics Processing Units (GPUs) provides a source of reliable computing power. Recently, Nvidia introduced its 9th generation HPC-grade GPUs, the Ampere 100 (A100), claiming significant performance improvements over previous generations, particularly for AI-workloads, as well as introducing new architectural features such as asynchronous data movement. But how well does the A100 perform on non-AI benchmarks, and can we expect the A100 to deliver the application improvements we have grown used to with previous GPU generations? In this paper, we benchmark the A100 GPU and compare it to four previous generations of GPUs, with a particular focus on empirically quantifying our derived performance expectations. We find that the A100 delivers less performance increase than previous generations for the well-known Rodinia benchmark suite; we show that some of these performance anomalies can be remedied through clever use of the new data-movement features, which we microbenchmark and demonstrate where (and more importantly, how) they should be used.
Queuing analysis of delayed differentiation system

ABSTRACT Delayed differentiation is one of the effective ways to cope with the challenge of product proliferation. In this research, we combine make-to-stock and make-to-order strategy to design a production system for delayed differentiation. In Stage 1, the common component is made-to-stock by an assembly line. In Stage 2, the final customization is made-to-order in a job shop environment. Given the service level, we use queueing theory to determine how to set up a proper inventory policy to manage the common component. We also derive approximations of the average inventory and the average customer waiting time. Conditions under which the approximations will be less accurate are discussed. Computer Simulation is used to validate the accuracy of the approximations. Managers can use these approximations to evaluate and configure the system in a timely manner. And the approximations could be the building block of more complex queuing systems.
Ulk4, a Newly Discovered Susceptibility Gene for Schizophrenia, Regulates Corticogenesis in Mice

Schizophrenia (SCZ) is a chronic and severe mental disease that affects around 1% of the population. The precise etiology of SCZ still remains largely unknown, and no conclusive mechanisms are firmly established. Recent advances in epidemiological and clinical investigation support an overwhelmingly strong neurodevelopmental origin for SCZ. Here, we demonstrated that Unc-51-like kinase 4 (Ulk4), a novel risk factor for major mental disorders including schizophrenia, is involved in the corticogenesis. Deletion of Ulk4 in mice led to significantly thinner layers of II–III, and V in the cerebral cortex, which was confirmed in conditional Ulk4 deletion mice achieved by Cre-loxp strategy. This abnormality might be caused by decreased intermediate neural progenitors and increased apoptosis. Thus, our data suggest that Ulk4 manipulates the behaviors of neural progenitors during brain development and, when functionally defective, leads to the reduction of specific cortical layers. This anomaly may increase predisposition to a range of neurodevelopmental disorders, including SCZ.
Approach to SARS-CoV-2 Vaccination in Patients With Multiple Sclerosis

For more than a year now, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the coronavirus disease (COVID-19) pandemic with high mortality and detrimental effects on society, economy, and individual lives. Great hopes are being placed on vaccination as one of the most potent escape strategies from the pandemic and multiple vaccines are already in clinical use. However, there is still a lot of insecurity about the safety and efficacy of vaccines in patients with autoimmune diseases like multiple sclerosis (MS), especially under treatment with immunomodulatory or immunosuppressive drugs. We propose strategic approaches to SARS-CoV-2 vaccination management in MS patients and encourage fellow physicians to measure the immune response in their patients. Notably, both humoral and cellular responses should be considered since the immunological equivalent for protection from SARS-CoV-2 after infection or vaccination still remains undefined and will most likely involve antiviral cellular immunity. It is important to gain insights into the vaccine response of immunocompromised patients in order to be able to deduce sensible strategies for vaccination in the future.