Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic and Chemical Inhibition of Sphingosine 1-Phosphate Lyase Results in Peripheral Lymphopenia and Alleviates Disease Development in Animal Models of Inflammation and Autoimmunity.

The ceramide metabolite sphingosine 1-phosphate (S1P) is an important modulator of a wide variety of physiologic functions via its action as a second messenger in intracellular signaling pathways and its extracellular interaction with specific G protein-coupled receptors on the cell surface. In the immune system, interaction of S1P with one of its receptors, S1P1, leads to inhibition of lymphocyte egress from primary and secondary lymphoid tissues resulting in peripheral lymphopenia, a mechanism with demonstrated effects in recent human clinical studies using the S1P1 receptor agonist, FTY720. S1P lyase (S1PL), which catalyzes the only irreversible step in sphingosine metabolism, the degradation of S1P to hexadecenal and ethanolamine phosphate, has recently been suggested to be a target of 2-acetyl-4-tetrahydroxybutylimidazole (THI), a compound known to increase tissue S1P levels and alter lymphocyte trafficking. In the current study, genetic inactivation of S1PL in knockout (KO) mice resulted in severe depletion of lymphocytes in the peripheral blood accompanied by accumulation of mature T cells in the thymus. This abnormal pattern of lymphocyte distribution suggests that results previously seen in THI treated mice were indeed due to THI’s inhibition of S1PL rather than another unknown activity. S1PL KO mice also demonstrated significantly reduced viability, perhaps due to increased numbers of myeloid cells which infiltrated the lungs. In a S1PL humanized allele, in which the human S1PL has been introduced into S1PL KO mice and restores ∼17% of wild type S1PL activity, the reduced viability trait is rescued while peripheral lymphopenia is maintained. Since the phenotypes are genetically separable by an allele that reduces S1PL activity, the lack of observed toxicity at doses of THI resulting in lymphopenia is likely due to its partial inhibition of S1PL activity at those doses, perhaps due to differences in S1P or S1P receptor expression or metabolism in different cell types. The potential for separation of lymphopenia and myeloid cell lung pathology effects by chemical inhibition was confirmed by chronic treatment of mice with the oral, small molecule S1PL inhibitor, LX2931, for 47 days during which no effects on viability were observed while the altered profile of lymphocyte migration seen in the partially inhibited humanized S1PL allele was maintained. Rats and monkeys treated with LX2931 also exhibited peripheral lymphopenia suggesting that the S1PL signaling pathway is conserved across mammalian species. Both S1PL KO and LX2931 treated mice were protected in the oxazolone challenge assay of delayed type hypersensitivity and the collagen induced model of rheumatoid arthritis, demonstrating that the reduction of peripheral lymphocytes previously observed with S1PL inhibition can beneficially affect the development of autoimmune disease.
Periodic Poling of X-Cut Thin-Film Lithium Niobate: The Route to Submicrometer Periods

Ultra-short poling periods of $1\ \mu \mathrm{m}$ and below in lithium niobate will allow nonlinear optical devices with operation to the UV regime or narrow-band counter-propagating single-photon generation. However, fabrication of such periods in bulk Lithium Niobate penetrating the complete modal areas of waveguides has been challenging. In this work, we demonstrate the fabrication of periodic domain grids with submicrometer periodicity in 300 nm x-cut thin-film lithium niobate. The poling was achieved through application of a single, shaped electrical pulse and electrodes fabricated with a combination of electron-and direct laser-writing lithography. The poling results were investigated with piezo-response force microscopy and second-harmonic microcopy and indicate the poled domains to penetrate fully across the complete film thickness. This will enable the fabrication of novel nonlinear optical devices combining the high efficiency of thin films with ultra-short domain periods.
SuperPointVO: A Lightweight Visual Odometry based on CNN Feature Extraction

In this paper, we propose a lightweight stereo visual odometry (SuperPointVO) based on feature extraction of convolutional neural network(CNN). Compared with the traditional indirect method of VO system, our system replace the hand-engineered feature extraction method with a CNN-based method. Based on the feature extraction network SuperPoint, we discard the redundant descriptor information it extracted, and expand the expression ability of the descriptor through NMS and grid sampling, making it more suitable for VO tasks. We build a complete stereo VO system without loop closing around the modified feature extractor. In the experiments, we evaluate the performance of the system on the KITTI dataset, which is close to other state-of-the-art stereo SLAM system. This shows that the accuracy and robustness of feature extraction methods based on deep learning are comparable to, or even better than the traditional methods in VO tasks.
FAst Detection Algorithm for 3D Keypoints (FADA-3K)

3D keypoint is widely used for object recognition and pose estimation with a 3D point cloud since it has robustness against measurement noise and occlusion. Many types of 3D keypoints and the corresponding detection methods of 3D keypoints have been proposed. It is essentially important to select suitable 3D keypoint depending on a target object, scene situation, and 3D measurement system. While there have been a lot of useful methods for detecting 3D keypoints, detecting time is an issue, especially for real-time applications, such as robot vision. The detecting of 3D keypoints tends to entail a trade-off between computation time and the robustness of the 3D keypoint. To solve these problems, we propose a FAst Detection Algorithm for 3D keypoints named FADA-3K. A user can select one of the suitable 3D keypoints and the corresponding detection method which has been already proposed since FADA-3K can be implemented as an add-on of the existing detection methods. Numerical experiments show that FADA-3K can achieve about nine times faster detection than conventional approaches to detecting 3D keypoints.
SINADRA: Towards a Framework for Assurable Situation-Aware Dynamic Risk Assessment of Autonomous Vehicles

Assuring an adequate level of safety is the key challenge for the approval of autonomous vehicles (AV). The full performance potential of AV cannot be exploited at present because traditional assurance methods at design time are based on a risk assessment involving worst-case assumptions about the operating environment. Dynamic Risk Assessment (DRA) is a novel technique that shifts this activity to runtime and enables the system itself to assess the risk of the current situation. However, existing DRA approaches neither consider environmental knowledge for risk assessments, as humans do, nor are they based on systematic design-time assurance methods. To overcome these issues, in this paper we introduce the model-based SINADRA framework for situation-aware dynamic risk assessment. It aims at the systematic synthesis of probabilistic runtime risk monitors employing tactical situational knowledge to imitate human risk reasoning with uncertain knowledge. To that end, a Bayesian network synthesis and assurance process is outlined for DRA in different operational design domains and integrated into an adaptive safety management architecture. The SINADRA monitor intends to provide an information basis at runtime to optimally balance residual risk and driving performance, in particular in non-worst-case situations.
Task Scheduling System Based on Consensus Algorithm in P2P Network

Centralized networks can perform task scheduling readily because they can easily collect system information and manage tasks. However, due to the existence of the central nodes, centralized networks have low fault tolerance. On the contrary, peer-to-peer(P2P) networks have higher fault tolerance, but they do not have global information without central nodes. Therefore, how to schedule tasks in a P2P network has become a problem and a challenge. This paper proposes a consensus algorithm to maintain task information in the P2P network and implements a scheduling system to solve this problem. At the same time, it combines the ease of container deployment and migration to improve the efficiency of task scheduling. Besides, the heartbeat mechanism between nodes achieves fault tolerance for nodes. The system can be used in network environments without central nodes, such as mobile Ad hoc networks.
Learning Analytics Perspective: Evidencing Learning from Digital Datasets in Cybersecurity Exercises

Cybersecurity exercises are gaining in popularity in university curricula and professional training paths and are seen as an effective teaching method. Such exercises provide digital datasets to facilitate a learning analytics approach such as by using the traces that learners leave behind to improve the learning process and environment. While there are various learning measurement efforts from digital datasets in the existing literature, a holistic learning analytics approach incorporated into cybersecurity exercises is still lacking. We propose a practical reference model for incorporating a learning analytics approach into the cybersecurity exercise life-cycle. To facilitate this application, we have performed an extensive review of existing academic research on applying learning analytics in the context of cybersecurity exercises. We specifically focus on the learning indicators used to measure empirical impact and training effectiveness that could indicate achievement of defined learning outcomes. This reference model and overview of existing learning analytics use cases and learning metrics in various types of exercises can help educators, organisers and cyber range developers. This results in more adaptive exercise design and measurement using evidence-based data and connects digital learning traces to skills and competencies.
A Score-Level Fusion of Fingerprint Matching With Fingerprint Liveness Detection

Fingerprint-based recognition is widely deployed in different domains. However, the traditional fingerprint recognition systems are vulnerable to presentation attack, which utilizes an artificial replica of the fingerprint to deceive the sensors. In such scenarios, Fingerprint Liveness Detection (FLD) is required to ensure the actual presence of a live fingerprint. In this paper, a fingerprint matching method fused with liveness detection is proposed. Firstly, the similarity between two fingerprint images is calculated based on Octantal Neatest-Neighborhood Structure (ONNS), where the closest minutia to the central minutia is found from each sector of octant. Secondly, the FLD score of the fingerprint image is obtained by using the modified Residual Network (Slim-ResCNN). Finally, a score-level fusion is performed on the results of fingerprint matching and FLD by generating interaction features and polynomial features as the score feature vector. To classify whether a fingerprint image is a genuine live fingerprint or a spoof attack (including impostor live and fake fingerprints), the score feature vector is processed using logistic regression (LR) classifiers. The proposed method won the first place in the Fingerprint Liveness Detection Competition 2019 with an overall accuracy of 96.88%, which indicates it can effectively protect the fingerprint recognition systems from spoof attacks.
A Zone Reference Model for Enterprise-Grade Data Lake Management

Data lakes are on the rise as data platforms for any kind of analytics, from data exploration to machine learning. They achieve the required flexibility by storing heterogeneous data in their raw format, and by avoiding the need for pre-defined use cases. However, storing only raw data is inefficient, as for many applications, the same data processing has to be applied repeatedly. To foster the reuse of processing steps, literature proposes to store data in different degrees of processing in addition to their raw format. To this end, data lakes are typically structured in zones. There exists various zone models, but they are varied, vague, and no assessments are given. It is unclear which of these zone models is applicable in a practical data lake implementation in enterprises. In this work, we assess existing zone models using requirements derived from multiple representative data analytics use cases of a real-world industry case. We identify the shortcomings of existing work and develop a zone reference model for enterprise-grade data lake management in a detailed manner. We assess the reference model’s applicability through a prototypical implementation for a real-world enterprise data lake use case. This assessment shows that the zone reference model meets the requirements relevant in practice and is ready for industry use.
Predicting Uncorrectable Memory Errors for Proactive Replacement: An Empirical Study on Large-Scale Field Data

Uncorrectable memory errors are the leading causes of server failures in datacenters. Predicting uncorrectable errors (UEs) using the historical correctable error (CE) information helps for proactive replacement of memory hardware before the catastrophic events happen. In this paper, we perform an empirical study of UE prediction on the large-scale field data from more than 30,000 contemporary servers in Tencent datacenters over an 8-month period. We demonstrate that the traditional approach based on CE rate works poorly with a low precision. We then leverage the detail micro-level CE information to design several new predictors. The comparative study shows that the new predictor based on column fault identification boosts the baseline precision for a factor of more than 300% and at the same time also improve the baseline recall substantially.
Electrical Properties of Al2O3/ZnO Metal–Insulator–Semiconductor Capacitors

An Al<sub>2</sub>O<sub>3</sub> thin film is assembled on an unin- tentionally doped ZnO for metal–insulator–semiconductor (MIS) capacitors. Leakage current density (<inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula>), capacitance–voltage (<inline-formula> <tex-math notation="LaTeX">${C} - {V}$ </tex-math></inline-formula>) properties, and their thermal stabilities are investigated. The <inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula>-values for the MIS capacitors before and after annealing at 300 °C are lower than <inline-formula> <tex-math notation="LaTeX">$2.0 \times 10^{-7}$ </tex-math></inline-formula> A/cm<sup>2</sup> at a gate voltage of −10.0~10.0V. Frequency-dependent <inline-formula> <tex-math notation="LaTeX">${C}-{V}$ </tex-math></inline-formula> characteristic shows a stable maximum capacitance for the MIS capacitor before annealing. However, it decreases dramatically at 1 MHz for that after annealing at 300 C, which is attributed to the change of ZnO channel resistance. Flat band voltage shifts in the–curves after annealing at 300 C are one order lower than those before annealing, indicating the decrease of positive fixed charge density in the Al<sub>2</sub>O<sub>3</sub> film. There are ledge phenomena for hysteresis characteristics in the curves, which result possibly from the delayed evacuation of minority carriers in deep depletion region. By considering the Fowler–Nordheim tunneling model, band configurations of the Al<sub>2</sub>O<sub>3</sub>/ZnO heterojunctions are clarified to be straddling gap structures with conduction band offsets larger than 1.3 eV.
Study of Temperature and Time Dependent Performance Alteration of Silver Ion-exchanged Waveguides in Glass

Single-mode glass waveguides for a wavelength of 1310 nm were designed in two different glasses with single-mode optical fiber coupling loss of 0.3 dB per interface. The temperature dependent diffusivity of both glasses is studied to explore the thermal alteration of the refractive index profile which can lead to less mode confinement and an increase in optical fiber coupling loss over time. For the model, diffusivity data is collected at higher temperature and extrapolated down to operation temperature range with a peak temperature of 110ºC. The data is verified with experimental data from long term experiments. We study the refractive index change of both glasses and the impact on optical performance over time. One of the selected glasses has almost no alteration and is meeting the data center lifetime requirements of five years. Waveguides were fabricated in both glasses and the lowest propagation loss was measured to be 0.048 dB/cm at a wavelength of 1310 nm.
Improved Field-Effect Mobility of In–Ga–Zn–O TFTs by Oxidized Metal Layer

The application of an aluminum (Al) capping layer on top of an In–Ga–Zn–O (IGZO) active layer is proposed to enhance the mobility of IGZO thin-film transistors (TFTs). The Al metal layer forms a very thin and dense oxide film on the surface that prevents further internal oxidation, and an additional AlOx interlayer between Al and IGZO is formed using oxygen in the IGZO back-channel region due to its strong oxidation power. The formation of an aluminum oxide interlayer induces an oxygen-deficient region in the active layer, inducing free carriers that enhance the field-effect mobility from 11.3 to 72.6 cm2/Vs. The device reliability under positive and negative bias stress in the dark is relatively unaffected by the presence of the Al capping layer; however, the stability under negative bias illumination stress is accelerated, likely originating from the ionization of oxygen vacant sites.
TCP-PPCC: Online-Learning Proximal Policy for Congestion Control

Effective network congestion control strategies are the key to secure the normal operation of complex and changeable networks. The fundamental assumptions of many existing TCP congestion control variants dominated by hand-crafted heuristic algorithms are no longer valid. We propose an algorithm called TCP-Proximal Policy Congestion Control (TCP-PPCC), which is based on deep reinforcement learning algorithm Proximal Policy Optimization (PPO). TCP-PPCC updates the policy offline from the features of the preceding network state and feedback from the current network environment and adjusts the congestion window online with the updated policy. The senders with TCP-PPCC can learn about the changes in network bandwidth more accurately and adjust the congestion window in time. We demonstrate the performance of TCP-PPCC by comparing it with the traditional congestion control algorithm NewReno in four network scenarios with the ns-3 simulator. The results show that in scenario 2, TCP-PPCC takes 58.75% improvement in average delay and 27.80% improvement in throughput compared with NewReno.
A Novel Broadcast Network Design for Routing in Mobile Ad-Hoc Network

MANET is an infrastructure-less, fixed topology less, multi-hop and bandwidth constraint wireless network. In MANET optimized control message broadcasting through an efficient routing can results in bandwidth utilization and efficient data transmission with minimum numbers of hops. An efficient routing technique using a broadcast network, designed by existing minimum cost spanning tree (MCST) is also unable to provide all-nodes-to-all shortest route and also have issues about data transmission efficiency. In this article, our broadcast network design approaches can provide a solution for optimized control message broadcast and data transmission efficiency by considering Kruskalś MCST based broadcast network as a base network. Initially, we consider high-frequency links (HF-Link) to be added in the base network by considering the Dijkstra algorithm. Secondly, the broadcast network is designed by including those links with the neighbors who have at least a fixed number of neighbors (CF-Link) and finally, only those links are added with the base network which gives a shorter path to all other nodes (ST-Link) in the network. Using Omnet++ we have compared individual broadcast network design with the base network and observe that a slight increment in control overhead in existing based techniques could increase data efficiency significantly through a given suboptimal path.
A 7-bit 900-MS/s 2-Then-3-bit/cycle SAR ADC With Background Offset Calibration

This article presents a 7-bit 900-MS/s multi-bit/cycle successive approximation register (SAR) analog-to-digital converter (ADC) with background offset calibration. Unlike prior works that adopt either four capacitive DACs (CDACs) or interpolated resistive DAC to enable 3-bit/cycle operation, the proposed technique uses only two CDACs to realize the same functionality. The number of CDACs is reduced by embedding gain inside the two-path comparators, resulting in reduced power, area, and input loading. In addition, the built-in redundancy relaxes the accuracy requirements for DAC settling and comparators. On-chip background offset calibration with narrow pulse charge sharing is used to further improve the ADC linearity. The prototype fabricated in 40-nm LP CMOS achieves an SNDR of 39.7 dB at Nyquist input and consumes 2.6 mW including on-chip calibration, leading to a Walden FoM of 36.6 fJ/conversion-step.
Monitoring Crop Growth During the Period of the Rapid Spread of COVID-19 in China by Remote Sensing

The status of crop growth under the influence of COVID-19 is an important information for evaluating the current food security in China. This article used the cloud computing platform of Google Earth Engine, to access and analyze Sentinel-2, MODIS, and other multisource remote sensing data in the last five years to monitor the growth of crops in China, especially in Hubei province, during the period of the rapid spread of COVID-19 (i.e., from late January to mid-March 2020), and compared with the growth over the same period under similar climate conditions in the past four years. We further analyzed the indirect effects of COVID-19 on crop growth. The results showed that: the area of the crops with better growth (51%) was much more than that with worse growth (22%); the crops with better and worse growth were mainly distributed in the North China Plain (the main planting areas of winter wheat in China) and the South China regions (such as Guangxi, Guangdong province), respectively. The area of the crops with a similar growth occupied 27%. In Hubei province, the area of the crops with better growth (61%) was also more than that with worse growth (27%). It was found that there was no obvious effect from COVID-19 on the overall growth of crops in China during the period from late January to mid-March 2020 and the growth of crops was much better than that during the same period in previous years. The findings in this study are helpful in evaluating the impact of the COVID-19 on China's agriculture, which are conducive to serve the relevant agricultural policy formulation and to ensure food security.
Cash, Cards or Cryptocurrencies? A Study of Payment Culture in Four Countries

Payment cultures around the globe are diverse and have significant implications on security, privacy and trust. We study usable security aspects of payment cultures in four culturally distinct societies. Based on a qualitative study in Germany and Iran, we developed an online survey and deployed it in Germany, Iran, China, and the United States. The results reveal significant differences between the studied countries. For example, we found that participants from Iran and China are more comfortable with credential sharing and German participants were most accepting towards cryptocurrencies. We suggest these kinds of differences in payment culture need to be considered in the context of HCI research when evaluating current payment mechanisms or designing new ones.