Miracle-dz/UniFAR-FTUs huggingface dataset:
Postmortem Computation of Pagerank on Temporal Graphs

Temporal graphs capture changes in relational data over time and have been of increasing interest to data analysts. Most research focuses on streaming algorithms that incrementally update an analysis to account for the changes in the graph. However, one can also be interested in understanding the nature of changes in the graph over time. In such a case, they perform a postmortem analysis on different points in time where all the data known in advance We study in this paper a postmortem analysis of Pagerank over-time on graphs that are defined by temporal relational event databases. A relation between two entities at a particular point in time will form an edge between these two entities and that will remain in the graph for a fixed period of time. While one can reuse a streaming algorithm for that purpose, leveraging the availability of all the data from the beginning can be beneficial. Postmortem analysis enables encoding the temporal graph with a more efficient graph representation. Also, it provides an additional level of parallelism since one can not only parallelize within a particular timestamp but also across different timestamps. We will show that depending on the properties of the temporal data, either parallelization can be better, and in some cases, a combination of both approaches is preferable. We experimentally show across 7 databases and across different temporal derivations of the graph that postmortem analysis can be between 50 times and 880 times faster than streaming analysis.
Pre and post operative care for cardiac catheterization, stress management: A study protocol

--- Background: Heart disease has been increasingly common in recent years, and cardiac care overall, including cardiac catheterization, has increased accordingly Heart catheterization is a difficult surgery for both patients and practitioners. It's important to recognise that there are a number of potential sources of stress, both mental and physical. while doing the operation The study's overarching goal is to design and pilot a stress management approach aimed at decreasing the Heart catheterization causes emotional and physical distress. Methods: The clinical trial used a double-blind, randomized, controlled design. randomised
Human Listeriosis

Listeria monocytogenes is a Gram-positive facultative intracellular pathogen that can cause severe invasive infections upon ingestion with contaminated food. Clinically, listerial disease, or listeriosis, most often presents as bacteremia, meningitis or meningoencephalitis, and pregnancy-associated infections manifesting as miscarriage or neonatal sepsis. SUMMARY Listeria monocytogenes is a Gram-positive facultative intracellular pathogen that can cause severe invasive infections upon ingestion with contaminated food. Clinically, listerial disease, or listeriosis, most often presents as bacteremia, meningitis or meningoencephalitis, and pregnancy-associated infections manifesting as miscarriage or neonatal sepsis. Invasive listeriosis is life-threatening and a main cause of foodborne illness leading to hospital admissions in Western countries. Sources of contamination can be identified through international surveillance systems for foodborne bacteria and strains’ genetic data sharing. Large-scale whole genome studies have increased our knowledge on the diversity and evolution of L. monocytogenes, while recent pathophysiological investigations have improved our mechanistic understanding of listeriosis. In this article, we present an overview of human listeriosis with particular focus on relevant features of the causative bacterium, epidemiology, risk groups, pathogenesis, clinical manifestations, and treatment and prevention.
Influenza Virus Infection Increases Host Susceptibility To Secondary Infection with Pseudomonas aeruginosa, and This Is Attributed To Neutrophil Dysfunction through Reduced Myeloperoxidase Activity

Secondary bacterial infection greatly increased the morbidity and mortality of influenza virus infection. To investigate the underlying mechanism by which influenza impairs the pulmonary defense against secondary Pseudomonas aeruginosa (P. aeruginosa) infection, we established a lethal mouse model in which to study secondary P. aeruginosa infection after influenza virus infection. We found a significant increase in host susceptibility to a secondary infection with P. aeruginosa in mice after an influenza virus infection, and this was accompanied by severe immunopathology and pulmonary inflammation. Importantly, we demonstrated that neutrophils were essential for P. aeruginosa clearance in secondarily infected mice. Further, we revealed that influenza impaired the phagocytosis and digestion functions of pulmonary neutrophils for P. aeruginosa clearance. We identified that the activity of reactive oxygen species (ROS) and the myeloperoxidase (MPO) activity of neutrophils in the lungs played an important role in antibacterial host defense in influenza-infected lungs. Hereby, influenza virus infection causes deficient MPO activity in neutrophils, and this contributes to the increased susceptibility to secondary P. aeruginosa infection. Treatment with Bacillus Calmette-Guerin polysaccharide nucleic acid (BCG-PSN) prior to secondary P. aeruginosa infection may improve the function of neutrophils, resulting in significantly reduced lethality during secondary P. aeruginosa infection. We also demonstrated that treatment with anti-influenza immune serum during the early stage of an influenza virus infection could decrease the disease severity of secondary P. aeruginosa infection. Our findings suggest that improving the MPO activity of neutrophils may provide a therapeutic strategy for viral-bacterial coinfection. IMPORTANCE A secondary bacterial infection, such as that of P. aeruginosa, often occurs after a pulmonary virus infection and contributes to severe disease. However, the underlying mechanisms responsible for viral-bacterial synergy in the lung remain largely unknown. In this study, we reported that influenza virus infection increases a host’s susceptibility to secondary infection by P. aeruginosa by reducing the MPO activity of neutrophils. We also demonstrated that treatment with BCG-PSN or anti-influenza immune serum prior to secondary P. aeruginosa infection can reduce the disease severity. Our findings suggest that improving the MPO activity of neutrophils may provide a therapeutic strategy for viral-bacterial coinfection.
On codes over Z_m (Corresp.)

The factorization of Abelian codes over Z_{m} , i.e., ideals in Z_{m},G,G a finite Abelian group, corresponding to a factorization of m and that of G as a product of cyclic groups are considered. Quasi-Abelian codes over Z_{m} are considered and it is shown that every quasi-Abelian code over Z_{m} is a direct sum of Abelian codes over Z_{m} . A factorization of Gray codes over Z_{m} is also considered.
Strategic radicality and inefficient investment of enterprises

Using the data of all A-share listed companies in Shanghai and Shenzhen stock exchanges from 2011 to 2020 as research samples, this paper analyzes the relationship between corporate strategic radicality and inefficient investment from the perspective of information asymmetry and agency problems. The results of this paper show that: Strategic aggressiveness is significantly positively correlated with inefficient investment; The more radical the strategy, the more over investment is; Moreover, a good corporate governance mechanism will reduce the positive correlation between the two. Further research finds that the economic consequence of the positive correlation between the two is to aggravate corporate risk.
The Realization Path of Artificial Intelligence In Adolescent Psychological Assessment

This paper reviews the latest research on adolescent psychological assessment and artificial intelligence applications at home and abroad. This paper uses artificial intelligence technology as the background to construct the realization path of the adolescent psychological assessment system: through the collection of personal data and family environment information, combined with the real-time information of individual emotional changes, use artificial intelligence technology to establish an assessment model for adolescent psychological problems. Big data builds an early warning mechanism, and then uses 5G technology for rapid information transmission and feedback, and timely intervention. At the same time, a technical and ethical reflection on the realization process of this path is carried out.
Conco-ERNIE: Complex User Intent Detect Model for Smart Healthcare Cognitive Bot

The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient’s utterance. To this end, we create a high-quality annotated Chinese Medical query (utterance) dataset, CMedQ (about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model Conco-ERNIE, using concept co-occurrence patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on CMedQ demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot, MedicalBot. To provide knowledge support for MedicalBot, we also build a Chinese medical graph, CMedKG (about 45k entities and 283k relationships).
Accuracy Analysis of Precise Point Positioning Using Predicted GPS Satellite Orbits

Abstract In this paper, near-real-time positioning accuracies of precise point positioning technique were analyzed using IGS predicted orbits. As a result, we could get the mean errors of 1~1.6 cm, standard deviation of 1~1.3cm from one year of GPS data. This results were similar level to positioning accuracy using the IGS rapid orbits. Positioning errors of >10cm showed 44% of observed days of orbital anomalies. When the orbital anomalies of the predicted orbits were shown, maximum error was 1.7 km, and maximum of mean errors was 308 m. From this study, we conclude that check and consideration were necessary before using the IGS predicted orbits.Key words : GPS, Predicted Orbits, Precise Point Positioning, Positioning Accuracy * bcdefg#>h i(Satellite Navigation Team, Korea Aerospace Research Institute) ‧ 1jk(First Author) : * W ‧ lmnk: 2012103n ‧ o h z{(GNSS, Global Navigation Satellite System) G*C|GPS(Global Positioning System)WP}~h,  /, 9, , ,( UKm,. GNSS'#$ hpBP#(Relative Positioning)! "#(Point Positioning)8CQ,. BP#2xBHJ XQ'*BPw) *hM8, b45'w#$ &'B ,. "##$ 'mk*! nJ XHJ*hM8IGS(International GNSS Service)b*/45 '* &'B ,. IGSb*GPS  (Precise Orbits), 9:(Rapid Orbits), 9:(Ultra-Rapid Orbits), -
Research on the Topic Evolution of Digital Economy Based on LDA

The paper uses the Latent Dirichlet Allocation (LDA) topic model to study the change law of hot topics in the field of digital economy and show the research topic evolution in this field over the last 30 years. The data consists of 1823 papers from various journals from China National Knowledge Infrastructure (CNKI). The main results of LDA are as follows. First, we find five research topics to cover this research field. Second, among these topics, we find that two topics show an upward trend in intensity and another two show an downward trend in intensity, and high-quality development and governance of digital economy are new topics in recent years. It is hoped that the findings could help scholars to overview the field of digital economy and grasp the emerging evolution trends.
Active vibration suppression of flexible structure using a LMI-based control patch

Based on the linear matrix inequality, a linear feedback control is presented to realize active vibration suppression of a class of flexible structure. By introducing an appropriate modal transformation, the controller design procedure can be simplified greatly. A specific Lyapunov function is adopted to induce the asymptotical stability of the flexible structure. Simulation results for flexible spacecraft are provided to illustrate the effectiveness of the proposed scheme.
New generalizations of Jensen’s functional equation

Let f be an unknown entire function of a complex variable, and let s, t be real variables. We consider Jensen's functional equation f(X+Y)=f(x)+f(y) 2 J 2 where x, y are complex variables. Replacing x and y by s and it in the above equation and taking the absolute values of the resulting equality one obtains the functional equation f(S+ it) =f(s)+f(it) The main purpose of this paper is to solve a new generalization of the above equation.
Toward Just-in-Time and Language-Agnostic Mutation Testing

Mutation Testing is a popular approach to determine the quality of a suite of unit tests. It is based on the idea that introducing faults into a system under test (SUT) should cause tests to fail, otherwise, the test suite might be of insufficient quality. In the language of mutation testing, such a fault is referred to as “mutation”, and an instance of the SUT’s code that contains the mutation is referred to as “mutant”. Mutation testing is computationally expensive and time-consuming. Reasons for this include, for example, a high number of mutations to consider, interrelations between these mutations, and mutant-associated costs such as the cost of mutant creation or the cost of checking whether any tests fail in response. Furthermore, implementing a reliable tool for automatic mutation testing is a significant effort for any language. As a result, mutation testing is only available for some languages. Present mutation tools often rely on modifying code or binary executables. We refer to this as “ahead-of-time” mutation testing. Oftentimes, they neither take dynamic information that is only available at run-time into account nor alter program behavior at run-time. However, mutating via the latter could save costs on mutant creation: If the corresponding module of code is compiled, only the mutated section of code needs to be recompiled. Run-time information (like previous execution results selected by an initial test run) could help to determine the utility of a mutant. Skipping mutants of low utility could have an impact on mutation testing efficiency. We refer to this approach as just-in-time mutation testing. In this paper, we provide a proof of concept for just-in-time and language-agnostic mutation testing. We present preliminary results of a feasibility study that explores the implementation of just-in-time mutation testing based on Truffle’s instrumentation API. Based on these results, future research can evaluate the implications of just-in-time and language-agnostic mutation testing.
Experiences with Contrastive Predictive Coding in Industrial Time-Series Classification

Multivariate time-series classification problems are found in many industrial settings; for example, fault detection in a manufacturing process by monitoring sensors signals. It is difficult to obtain large labeled datasets in these settings, for reasons such as limitations in the automatic recording, the need for expert root-cause analysis, and the very limited access to human experts. Therefore, methods that perform classification in a label efficient manner are useful for building and deploying machine learning models in the industrial setting. In this work, we apply a self-supervised learning method called Contrastive Predictive Coding (CPC) to classification tasks on three industrial multivariate time-series datasets. First, the CPC neural network (CPC base) is trained with a large number of unlabeled time-series data instances. Then, a standard supervised classifier such as a multi-layer perception (MLP) is trained on available labeled data using the output embeddings from the pre-trained CPC base. On all three classification datasets, we see increased label efficiency (ability to reach a goal accuracy level with less labeled examples). In the low data regime (10's or few 100's of labeled examples), the CPC pre-trained model achieves high accuracy with up to 15x less labels than a model trained only on labeled data. We also conduct experiments to evaluate the usefulness of CPC pre-trained classifiers as base models to start an active learning loop, and find that uncertainty sampling does not perform significantly better than random sampling during the initial queries.
Contrastive Learning of Electrodermal Activity Representations for Stress Detection

Electrodermal activity (EDA) is a biosignal that contains valuable information for monitoring health conditions related to sympathetic nervous system activity. Analyzing ambulatory EDA data is challenging because EDA measurements tend to be noisy and sparsely labeled. To address this problem, we present the first study of contrastive learning that examines approaches that are tailored to the EDA signal. We present a novel set of data augmentations that are tailored to EDA, and use them to generate positive examples for unsupervised contrastive learning. We evaluate our proposed approach on the downstream task of stress detection. We find that it outperforms baselines when used both for fine-tuning and for transfer learning, especially in regimes of high label sparsity. We verify that our novel EDA-specific augmentations add considerable value beyond those considered in prior work through a set of ablation experiments.
A Survey on Quantum-safe Blockchain System

Although blockchain technology is widely recognized for the near future, certain of its components are being challenged by another impending technology, quantum computing. Quantum-safe-blockchain is quickly becoming one of the essential future-proof blockchain designs. Quantum blockchain has a significantly greater scope than cryptocurrencies and cross-chain asset transactions. The majority of today’s cryptography algorithms will be susceptible to quantum attacks. In this paper, a comprehensive literature review is performed by considering research questions regarding how quantum computing will affect the classical blockchain mechanism. The survey is completed by responding to the research questions and highlighting research gaps.
Regularity and Structure of the Spatial Pattern of Blue Cones of Macaque Retina

Abstract Models were developed for describing the regular pattern of blue cones in macaque retina. Two functional descriptions were applied to patterns, one based on the cdf of interpoint distances, the other on the cdf of the central angles of Voronoi regions. Disordered triangular and square lattices and hard balls failed to model the blue-cone point pattern. An elastic-ball model was developed whose spatial properties fit well those of the blue-cone pattern. This model employed a hard core and a soft surrounding shell and is proposed as a valid model for the blue-cone pattern.
The utility and caveat of split-GAL4s in the study of neurodegeneration

ABSTRACT Parkinson’s disease (PD) is the second most common neurodegenerative disorder, afflicting over 1% of the population of age 60 y and above. The loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) is the primary cause of its characteristic motor symptoms. Studies using Drosophila melanogaster and other model systems have provided much insight into the pathogenesis of PD. However, little is known why certain cell types are selectively susceptible to degeneration in PD. Here, we describe an approach to identify vulnerable subpopulations of neurons in the genetic background linked to PD in Drosophila, using the split-GAL4 drivers that enable genetic manipulation of a small number of defined cell populations. We identify split-GAL4 lines that target neurons selectively vulnerable in a model of leucine-rich repeat kinase 2 (LRRK2)-linked familial PD, demonstrating the utility of this approach. We also show an unexpected caveat of the split-GAL4 system in ageing-related research: an age-dependent increase in the number of GAL4-labelled cells.
On the interstellar CO/+//CO abundance ratio.

A search at high sensitivity for interstellar absorption in the 4250-A line of CO(+) yields negative results toward 14 stars, despite the pervasive presence of large amounts of CO in the interstellar gas. This result is in accord with theory, but still lacks by a large factor sufficient sensitivity to test any details of the theory.
Effects of the number of periods on strain in superlattices

The effect of periods on the accumulation and release of stress in GaAs/AlInAs superlattices structure is reported here. It is observed that in GaAs/AlInAs superlattices, when the Indium (In) content is greater than 10%, stress accumulates monotonically as the number of period increases. In GaAs/AlInAs superlattices with an In content of 5%, the accumulated stress is larger when the number of periods is less than 10. However when the number of periods exceeds 10, it was observed that suddenly there is a significant increase in defects and stress release. However, with any further increase in period number, there is once again an accumulation of stress.