Miracle-dz/UniFAR-FTUs huggingface dataset:
The Incidence and Effect of Cytomegalovirus Disease on Mortality in Transplant Recipients and General Population: Real-world Nationwide Cohort Data

Background: In addition to the conventional opportunistic infections in solid organ transplantation (SOT) and hematopoietic stem cell transplantation (HSCT) recipients, cytomegalovirus (CMV) infection is associated with various chronic inflammatory diseases or poor outcomes in non-immunocompromised critically ill patients. To evaluate the burden or outcome of CMV replication in non-transplant individuals, we compared the incidence rates (IRs) for CMV disease and all-cause mortality between SOT recipients, HSCT recipients, and non-transplant population. Methods: The SOT (N=16,368) and HSCT (N=10,206) cohorts between 2010 and 2015 were established using the WHO ICD-10 from the whole population-based large database of the Health Insurance Review & Assessment Service (HIRA). CMV cases, defined as symptomatic disease with isolation of virus, DNA, pp65 antigen, and pathology except CMV syndrome, were extracted with the unique codes for relief of medical costs of HIRA in the same dataset. Cox's proportional hazard regression analyses and log-rank test in the Kaplan-Meier curves were performed to compare all-cause mortality between the three groups. Results: The CMV IRs adjusted by age and sex were significantly higher in the SOT (adjusted IR [95% confidence intervals], 33.1 [28.8-38.0] per 1,000 person-years) and HSCT recipients (5.1 [4.6-6.1] per 1,000 person-years) than in the whole population (0.58 [0.49-0.67] per 100,000 person-years). However, SOT recipients with CMV (18/283, 6.4%) had significantly lower all-cause mortality than non-transplant individuals with CMV (207/1,258, 16.5%) (adjusted hazard ratio [95% CI], 0.42 [0.25-0.67], log-rank P < 0.001). Conclusion: These data suggest that CMV disease in patients without transplants is associated with poor outcomes.
A Study on Minority Women 's Willingness to Participate in Tourism Poverty Alleviation and Its Influencing Factors A Case Study of Kazak in Xinyuan County

Tourism poverty alleviation is the attitude and driving force of potential tourism employment behavior, and also the forecast target of employment behavior. This paper investigates the willingness of 660 Kazak women in 33 rural areas of Xinyuan County to participate in tourism poverty alleviation. The Probit model is used to study the influencing factors of Kazakh women's willingness to participate in poverty alleviation. The results show that 51.9% of the respondents showed the willingness to participate in tourism poverty alleviation work. Among all influencing factors affecting Kazakh women’s willingness to participate in tourism poverty alleviation, the positive factors of Kazakh women' s employment intention include the Chinese level, ethnic food making skills, tourist souvenir making skills, government's attention to tourism poverty alleviation, economic income supporting the participation in tourism poverty alleviation, family support for working out, and parents' health level; age, tourism poverty alleviation accuracy, tourism poverty alleviation employment environment, distance between the residence and the scenic spot, the number children in the family, per capita grassland area showed negative effects; education years, halal diet care degree, singing and dancing skills, per capita cultivated land area did not pass the significant test.
SENSORS BASED VEHICLE CROWED SENSING IN BAD WEATHER CONDITIONS

Bad weather and road traffic accidents are the main causes of vehicle losses and deaths. The degree of severity may be influenced by a number of factors. Various methods and techniques were tested with different distances, conditions and speed, to recognize the reaction of drivers in changing visibility in fog. On the basis of various types of fog generated on artificial and camera images, new algorithms have been proposed in recent studies for visibility improvement techniques. The core thing should be the development of operational fog detection and alarming system, with respect to speed and headway. Although the Advanced camera based driving assistance systems have the functionality in relieving the driver but they have drawbacks in terms of image quality, computation, working at night and with bad weather conditions. The proposed research contributes by analyzing existing work, detecting bad weather by measuring visibility distance, detecting moving or stationary vehicle on road in fog and providing alert to drivers in danger distance between vehicles and other objects using sensors.
ZOMBIE COMPANIES: EXPERIMENTAL RESEARCH FROM REAL ESTATE LISTED SECTORS

This paper aims to identify factors affecting the ability of a listed company in the real estate industry to become zombie company (ZC) and how real estate enterprises themselves affect the performance of enterprises in such industry. The study uses secondary data collected from the financial statements of 48 listed real estate companies in Vietnam from 2008-2016. Empirical research results show that: (i) during the research period, the highest rate of zombies in real estate listed companies was 32.65% in 2013; (ii) Zombie companies have lower financial performance (ROA, ROE) than healthy companies, and healthy companies also have current solvency and total assets turnover. higher than zombie companies; Zombie companies tend to be smaller in scale than healthy companies; (iii) to reduce the risk of becoming a zombie, companies should expand capital scale, increase total asset turnover and increase production and business efficiency; (iv) zombies and revenue growth of zombies have a negative effect on the performance of the whole industry and thus negatively affect the economy.
The conflicting role of SIRT3 as therapeutic target in cancer

Mitochondria are increasingly recognized as key factors in cancer, affecting a multitude of tumor hallmarks. Likewise, sirtuin 3, a master regulator of mitochondrial function, has gained attention as a therapeutic target in cancer. As a main mediator of calorie restriction, sirtuin 3 inhibits anabolic reactions and cell proliferation, acting as a tumor suppressor. At the same time, sirtuin 3 protects the cell during nutrient stress by regulating cell death and DNA repair, thereby acting as an oncogene. Here we discuss this conflicting role of sirtuin 3 in cancer and evaluate the therapeutic potential of sirtuin 3 activators and inhibitors in different cancers.
Burden, Perceived Stigma and Coping Style of Caregivers of Patients with Schizophrenia and Bipolar Disorder

Background- Schizophrenia and Bipolar Disorder (BD) are two major psychotic disorders which cause considerable burden and limitations among the caregivers of the affected patients. Stigma can be a major obstacle to recovery and can limit opportunities of work and social functioning of patients and their family members. Coping with such patients can be taxing for the caregivers. Aims and Objectives- (1)To assess the objective and subjective impact of burden in caregivers of schizophrenia and bipolar disorder patients (2) To measure the perceived stigma in primary caregivers (3)To identify the coping mechanisms used by the caregivers Materials and Methods-This is a cross sectional hospital based study. The caregivers of a total of 88 patients of which 50 with schizophrenia and 38 with Bipolar Mood Disorder were taken as participants. The semi structured Proforma, The Burden Assessment Schedule (BAS), Brief COPE and Perceived Discrimination Devaluation Scale (PDD) translated and validated in Gujarati were administered. Results and Conclusion- The level of burden for schizophrenic caregivers is more as compared to the caregivers of BD. The caregivers of patients with schizophrenia were more stigmatized as compared to caregivers of BD patients. Females were more stigmatized than males in both the groups. The caregivers of both BD and schizophrenic patients used Active emotional coping to combat the situation.
Contrasting Roles of Checkpoint Proteins as Recombination Modulators at Fob1-<italic toggle='yes'>Ter</italic> Complexes with or without Fork Arrest

The replication terminator protein Fob1 of Saccharomyces cerevisiae specifically interacts with two tandem Ter sites (replication fork barriers) located in the nontranscribed spacer of ribosomal DNA (rDNA) to cause polar fork arrest. The Fob1-Ter complex is multifunctional and controls other DNA transactions such as recombination by multiple mechanisms. Here, we report on the regulatory roles of the checkpoint proteins in the initiation and progression of recombination at Fob1-Ter complexes. The checkpoint adapter proteins Tof1 and Csm3 either positively or negatively controlled recombination depending on whether it was provoked by polar fork arrest or by transcription, respectively. The absolute requirements for these proteins for inducing recombination at an active replication terminus most likely masked their negative modulatory role at a later step of the process. Other checkpoint proteins of the checkpoint adapter/mediator class such as Mrc1 and Rad9, which channel signals from the sensor to the effector kinase, tended to suppress recombination at Fob1-Ter complexes regardless of how it was initiated. We have also discovered that the checkpoint sensor kinase Mec1 and the effector Rad53 were positive modulators of recombination initiated by transcription but had little effect on recombination at Ter. The work also showed that the two pathways were Rad52 dependent but Rad51 independent. Since Ter sites occur in the intergenic spacer of rDNA from yeast to humans, the mechanism is likely to be of widespread occurrence.
Prediction-Constrained Hidden Markov Models for Semi-Supervised Classiﬁcation

We develop a new framework for training hidden Markov models that balances generative and discriminative goals. Our approach requires likelihood-based or Bayesian learning to meet task-speciﬁc prediction quality constraints, preventing model misspeciﬁcation from leading to poor subsequent predictions. When users specify appropriate loss functions to constrain predictions, our approach can enhance semi-supervised learning when labeled sequences are rare and boost accuracy when data has unbalanced labels.Via automatic differentiation we backpropagate gradients through dynamic programming computation of the marginal likelihood, making training feasible without auxiliary bounds or approximations. Our approach is effective for human activity modeling and healthcare intervention forecasting, delivering accuracies competitive with well-tuned neural networks for fully labeled data, and substantially better for partially labeled data. Simultaneously, our learned generative model illuminates the dynamical states driving predictions.
Noise-like pulses with 2.15 MHz repetition rate in an all-polarization-maintaining mode-locked fiber laser at the center wavelength of 1550 nm

We demonstrate a 2.15 MHz repetition-rate mode-locked figure-of-9 erbium-doped fiber laser at a center wavelength of 1550 nm by using an all-polarization-maintaining nonlinear amplifying loop mirror configuration. This laser can be self-started mode-locked and generate Gaussian-shape noise-like pulses (NLPs). The single pulse energy is approximately 11.68 nJ at a repetition rate of 2.15 MHz. Dispersion compensation fibers are added into the cavity to make the laser operate in a net normal dispersion regime and generate NLPs at the center wavelength of 1550 nm. These characteristics give it a great application potential, such as low spectral coherence interference, optical sensing and micromachining. In addition, we have investigated the evolution of these characteristics, contributing to further understanding the Gaussian-shape NLP.
Interference-based weak-value amplification effects in post-selected weak measurement

Weak measurement (WM) based on post-selection is a useful tool for precision metrology due to the ability of amplifying weak physical effects. In this paper, we reveal the underlying mechanism of the amplification in the post-selected WM based on the weak-value amplification (WVA) and the almost balanced weak-values amplification (ABWVA) strategies. We find that the amplification stems from the interference between the local oscillator and signal components which are respectively associated with the initial system state and the state disturbance induced by the weak coupling. The magnitude of the amplification is proportional to the interference strength which is dominated by the post-selection and the coupling strength. Furthermore, our theory can perfectly explain the amplification limit in the WVA strategy and the anomalous amplification in the ABWVA strategy.
Feature-Enhanced Data Classification Using B-center Function

Linear-kernel Support Vector Machine (SVM) classification algorithm needs to constantly calculate the similarity between the data to be tested and the sample during classification. If samples in the dataset are linearly indivisible, the accuracy of the algorithm will get worse. The feature weight calculation based on category information in Multi-layer Perceptron (MLP) algorithm is not accurate enough to express the relationship between features and categories, i.e. it cannot distinguish categories precisely when the features with the same category frequency. In order to improve the performance of MLP and linear-kernel SVM algorithms in classification, an improved function: the Base function, which is based on clustering, is proposed in this paper. Before running the classification algorithm, a series of possible centers is predicted, after which the Base function is used for data clustering to increase the dimension of data, and then samples with more features are passed to classification models. In the high-position Gaussian data space, the Base function can obviously improve the spatial distribution of samples. Experimental comparison in the thesis also proved that the Base function can improve the performance of MLP or SVM in classification effectively. The proposed feature-enhanced MLP is superior to the baseline with 32.6% accuracy promotion on the Gaussian dataset.
Onboard device for UAS remote identification

The unsupervised use of UAS creates threats to the security of general aviation and ground objects, including objects of critical information infrastructure. Equipping UAS with a remote identification device can be the solution to this problem. It should increase the sense of responsibility among UAS pilots and allow the public, aviators and law enforcement to identify and track UAS and their operators during flight. The goal of the article is the technical requirements specifications of a device for UAS remote identification. The existing technologies of UAS remote identification have been reviewed during this research, and the criteria of their choice have been defined. The result of the work takes into account the territorial features of Russia and the concept of the RUTM system. The authors propose the network model for UAS remote identification using the ADS-B direct broadcast technology to form the aircraft pilots’ situational awareness of the air situation near the vessel. ADS-B 1090 ES, cellular and satellite communications have been chosen as data transmission channels for the onboard UAS remote identification device. Based on this, the technical requirements have been developed for the onboard device for UAS remote identification.
SPECT bone imaging thyroid lesion segmentation

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83.
Machine Learning Algorithm for Classification

Recently, machine learning methods have a good performance in the field of classification tasks. Summarizing and comparing the performances of different classifiers in the application of their specific classification tasks has a reference significance. In this paper, five classical machine learning classifiers, including GMM, Random Forest, SVM, XGBoost, and Naive Bayes, are compared to show their computing characteristics. The advantages and disadvantages are analysed in this paper. Based on the different datasets, namely different specific classification tasks, the different classifiers perform similarly. However, the SVM-based classifier has the lowest accuracy while processing the text data to apply the text classification task. This result shows that if the classification task is difficult, the accuracy would not be high. This research summarizes the performances of different machine learning methods in the application of specific classification tasks. And this research has a reference significance for the machine learning-based classifiers.
Impact of channel mobility on design optimization of 600–3300 V-class high-speed GaN vertical-trench MOSFETs based on TCAD simulation

To simultaneously evaluate the switching performance and cost (required chip size) of GaN vertical-trench metal-oxide-semiconductor field-effect transistors, we calculated R on AR on Q g considering the Miller effect with various cell pitches, channel mobilities, and blocking voltages. When the blocking voltage was 600 V, optimized cell pitches of 8 and 12 μm minimized R on AR on Q g with channel mobilities of 100 and 200 cm2 V−1 s−1, respectively. Moreover, a wide range of cell pitches could maintain a low R on AR on Q g with a channel mobility of 200 cm2 V−1 s−1. This indicates that a channel mobility of 100 cm2 V−1 s−1 or higher, particularly 200 cm2 V−1 s−1, is desirable for a good switching performance and low cost.
Evidence for a Recent Collision in Saturn’s Irregular Moon Population

Using CFHT imaging data, we searched a 1.1 deg2 field on each side of Saturn down to magnitude m w ≃ 26.3, corresponding to diameters of D ≃ 3 km. We detected 120 objects, which were comoving with Saturn and are nearly certainly irregular moons. For example, all but one of our detections brighter than magnitude 25.5 link to known Saturnian irregulars, with 40 linkages that thus extend the orbital arc of previous discoveries. Extrapolating our sample's characterized detections (those for which we can debias the search) to the entire Saturnian irregular population, we estimate that there are 150 ± 30 moons down to D = 2.8 km, which is approximately three times as many irregular moons as Jupiter down to the same size. At the smallest sizes, from D = 3.8 down to 2.8 km, we find that the Saturnian irregular population exhibits a steep size distribution of the differential power-law index q=4.9−0.6+0.7 . We believe this steep size distribution is the signature of a relatively recent (few hundred Myr ago) collisional event in Saturn's retrograde irregular population.
An improved hybrid recommendation algorithm based on feature preference

In order to solve the problem of data matrix sparsity and cold start in the era of big data. This paper designs a collaborative filtering algorithm, which is an improved hybrid recommendation algorithm based on feature preference analysis. It combines the analysis of user feature preference and item feature, and then uses the traditional collaborative filtering idea to recommend the best scoring object to users. The experimental results show that the accuracy of the proposed algorithm is improved obviously.
Determination of mercury (II) by precipitation potentiometric titration

The article presents the results of potentiometric titration of the solution containing Hg (II) with the solution of sodium diethylditheocarbamate (NaDEDC). The efficiency of electrochemical analysis is defined by the presence of the most appropriate indicator system. The possibility of using carbon composite electrode (CCE) as a sensor for mercury ions determination has been explored. The conditions for potentiometric determination of Hg (II) (titrant, indicating electrode, pH, response time, range of concentrations, and background electrolyte) were studied. Titration curves were processed by the linearization method.
Human Learners Integrate Visual and Linguistic Information in Cross-Situational Verb Learning

Learning verbs is challenging because it is difficult to infer the precise meaning of a verb when there are a multitude of relations that one can derive from a single event. To study this verb learning challenge, we used children’s egocentric view collected from naturalistic toy-play interaction as learning materials and investigated how visual and linguistic information provided in individual naming moments as well as cross-situational information provided from multiple learning moments can help learners resolve this mapping problem using the Human Simulation Paradigm. Our results show that learners benefit from seeing children’s egocentric views compared to third-person observations. In addition, linguistic information can help learners identify the correct verb meaning by eliminating possible meanings that do not belong to the linguistic category. Learners are also able to integrate visual and linguistic information both within and across learning situations to reduce the ambiguity in the space of possible verb meanings.
A Comparison of the Performance of 2D and 3D Convolutional Neural Networks for Subsea Survey Video Classification

Utilising deep learning image classification to automatically annotate subsea pipeline video surveys can facilitate the tedious and labour-intensive process, resulting in significant time and cost savings. However, the classification of events on subsea survey videos (frame sequences) by models trained on individual frames have been proven to vary, leading to inaccuracies. The paper extends previous work on the automatic annotation of individual subsea survey frames by comparing the performance of 2D and 3D Convolutional Neural Networks (CNNs) in classifying frame sequences. The study explores the classification of burial, exposure, free span, field joint, and anode events. Sampling and regularization techniques are designed to address the challenges of an underwater inspection video dataset owing to the environment. Results show that a 2D CNN with rolling average can outperform a 3D CNN, achieving an Exact Match Ratio of 85% and F1-Score of 90%, whilst being more computationally efficient.
Low Resource ASR: The Surprising Effectiveness of High Resource Transliteration

Cross-lingual transfer of knowledge from high-resource languages to low-resource languages is an important research prob-lem in automatic speech recognition (ASR). We propose a new strategy of transfer learning by pretraining using large amounts of speech in the high-resource language but with its text transliterated to the target low-resource language. This simple map-ping of scripts explicitly encourages increased sharing between the output spaces of both languages and is surprisingly effective even when the high-resource and low-resource languages are from unrelated language families. The utility of our proposed technique is more evident in very low-resource scenarios, where better initializations are more beneﬁcial. We evaluate our technique on a transformer ASR architecture and the state-of-the-art wav2vec2.0 ASR architecture, with English as the high-resource language and six languages as low-resource targets. With access to 1 hour of target speech, we obtain relative WER reductions of up to 8.2% compared to existing transfer-learning approaches.