Miracle-dz/UniFAR-FTUs huggingface dataset:
nite automata with quantum and classical states

In order to discover how Quantum Mechanics would improve the power of computational devices, Quantum Computation was proposed by Feynman [6] in 1982 and since then some important breakthroughs in this area were achieved. The two main models, Quantum Turing Machines [4] and Quantum Circuits [5], allowed computer scientists, mathematicians and physicists to develop algorithms that extract computational power from the quantum structure of matter and compare their eciency with the classical ones. The most expressive results were Shor’s Algorithm [19], that factorizes integers in polynomial time, and Grover’s Algorithm [8], that presents a quadratic speedup for unordered database search problem. Within this context, the topic of Quantum Automata was proposed to study the inuence of Quantum Mechanics in simpler computational models. Several quantum counterparts of classical automata were proposed, whose dierences usually involve characteristics of the tape head and interaction with external elements, such as quantum and classical states and pushdown stacks. In this work, we consider two-way
THE STUDY ON FORECAST OF CLOUD AMOUNT WITH SVM AND ANN METHODS

The cloud amount forecast is carried out based on SVM and ANN methods,and comparisons are made between the two methods. Using the surface and high-level observations in Wuhan and numerical forecast field from ECMWF during May 1st 2001 to December 31st 2004,cross-validations are performed with random samples in different proportion. It shows that both models are able to forecast cloud amount with robustness. And then the models are rebuilt with the whole sample data to forecast the cloud amount in Wuhan from January 1st to May 31st 2005.Cross-validations and real time forecast reveal that SVM is better than ANN in the ability of forecasting cloud amount,especially in computing speed.
Improved rounding methods for binary and ordinal variables under multivariate normal imputation

Missing data are common in epidemiological studies. Multivariate normal imputation (MVNI) is a popular method of handling missing data that imputes missing values assuming a multivariate normal distribution. This presents a dilemma when imputing categorical variables as these are not normally distributed. Should the continuous imputations be rounded, and if so, which rounding method should be used? The objective of this study is to evaluate and compare existing methods and develop new methods of rounding categorical variables under MVNI. We focus on missingness in covariates rather than outcome variables. This is because MVNI generally has little or no benefit over complete case analysis if missingness is in an outcome variable only. A number of different rounding methods have been proposed for binary variables, including simple rounding, adaptive rounding and calibration. However, no studies to date have compared adaptive rounding with calibration. We performed a large simulation study in Stata to compare the above rounding methods with unrounded MVNI, and with a new method that we developed called proportional rounding. Proportional rounding produced similar results to adaptive rounding and calibration but was faster and easier to implement. To date, several rounding methods have been proposed for ordinal variables. Distance-based rounding (DBR) and projected distance-based rounding (PDBR) are indicator-based methods, while crude rounding, calibration and mean indicatorbased rounding (MIBR) are continuous methods. Previous studies have demonstrated the inadequacy of DBR, PDBR and crude rounding for rounding categorical variables with up to seven categories. Calibration and MIBR perform well in some settings but they are two-stage methods that are time-consuming to implement, particularly for large data sets. An alternative method of imputing ordinal variables is
A Landmark Algorithm for the Time-Dependent Shortest Path Problem

The shortest path problem is one of the most classical problem in combinatorial optimization problem which, given an edge-weighted graph and two vertices, asks to find a path between the two vertices of the minimum length. In this thesis, we consider a generalization of the shortest path problem in which the edge length is time-variable, which we call the time-dependent shortest path problem. This kind of problems has many applications in the fields of navigation systems and others. Since the first algorithm was proposed by Cooke and Halsey in 1966, many studies have been done for this problem. Currently the fastest algorithm is due to Dreyfus and others (1969–1990) who proposed a straightforward generalization of the famous Dijkstra algorithm that is originally developed for the classical shortest path problem. In this thesis, we give an even faster algorithm at a small amount of extra preprocessing cost. The proposed algorithm is based on the ALT algorithm proposed by Goldberg and Harrelson (2005) for the shortest path problem, in which the main idea is to use pre-calculated landmarks in determining the interim distance labels for vertices. Experimental results show our algorithms is several times faster than the generalized Dijkstra algorithm.
An O{VTVT· IE I) Algorithm. for Finding lIaDmum Matching in General Graphs

In this paper we present.an O(JiVi.IEI) algo­ rithm for finding a maximum matching in general graphs. This algorithm works in 'phases'. In each phase a maximal set of disjoint minimum len­ gth augmenting paths is found, and the existing matching is increased along these paths. Our contribution consists in deVising a spe­ cial way of handling blossoms, which enables an O(lEI) implementation of a phase. In each phase, the algorithm grows Breadth First Search trees at all unmatched vertices. When it detects the pres­ ence of a blossom, it does not 'shrink' the blossom immediately. Instead, it delays the shrinking in such a way that the first augmenting path found is of minimum length. Furthermore, it achieves the effect of shrinking a blossom by a special labeling procedure which enables it to fin~ an augmenting path through a blossom quickly.
Calculation of the Key Length for Quantum Key Distribution

Quantum key distribution (QKD) is based on the laws of quantum physics and therefore it can guarantee the highest level of security. It is used to establish the key that is used for further symmetrical encryption. Since QKD consists of several phases in which the key is reduced, it is necessary to define the equation by which the length of the raw key is calculated. In this paper, we analyse all QKD phases with an emphasis on the explanation of the process of shortening the initial key. The results are verified with a large number of tests using a quantum cryptography simulator. DOI: http://dx.doi. org/10.5755/j01.eee. 21.6. 13768
Experimental aeroacoustics of a two-strut, two-wheel landing gear in a propeller wake

The effects of an upstream propeller on the radiated noise of a two-wheel landing gear configuration were studied. Tests were conducted at a scale of 1:10.8 with a propeller advance ratio of 1, at Mach numbers less than 0.1 in the University of Toronto Acoustic Wind Tunnel Facility. Microphone measurements show that the propeller dominates the far-field noise signature for all tested configurations. A broadband noise increase of 10dBSPL was observed between 1 and 5 times the blade passing frequency (BPF) for M ≥ 0.045 when the drag strut is added. Spectra of the fluctuating surface pressure measurements displayed peaks at the BPF and were strongest for the configuration omitting the drag strut. The pressure fluctuations at the BPF on the wheel microphones were at least 30dB less than on the strut. Cross-spectra between one far-field microphone against all surface microphones are also presented. These preliminary measurements present a unique dataset for the validation of models used in the design of regional turboprop aircraft.
PERFORMANCE OF A HIGH-PERVEANCE ELECTRON GUN WITH A CONVEX CATHODE

Strongly magnetized electron gun with a convex cathode can provide the homogenous transverse current density distribution in a low temperature electron beam with the high perveance of 5-10 µA/V 3/2 . Such a gun was successfully tested with currents up to 10 A at the prototype set-up for beam-beam compensation in the Tevatron collider. In this article we present the test results. Electron guns of this type can be used in electron cooling and beam-beam compensation devices.
Supervised Sparse and Functional Principal Component Analysis

Principal component analysis (PCA) is an important tool for dimension reduction in multivariate analysis. Regularized PCA methods, such as sparse PCA and functional PCA, have been developed to incorporate special features in many real applications. Sometimes additional variables (referred to as supervision) are measured on the same set of samples, which can potentially drive low-rank structures of the primary data of interest. Classical PCA methods cannot make use of such supervision data. In this article, we propose a supervised sparse and functional principal component (SupSFPC) framework that can incorporate supervision information to recover underlying structures that are more interpretable. The framework unifies and generalizes several existing methods and flexibly adapts to the practical scenarios at hand. The SupSFPC model is formulated in a hierarchical fashion using latent variables. We develop an efficient modified expectation-maximization (EM) algorithm for parameter estimation. We also implement fast data-driven procedures for tuning parameter selection. Our comprehensive simulation and real data examples demonstrate the advantages of SupSFPC. Supplementary materials for this article are available online.
Discriminative model combination

Discriminative model combination is a new approach in the field of automatic speech recognition. It aims at an optimal integration of all given (acoustic and language) models into one log-linear posterior probability distribution. As opposed to the maximum entropy approach, the coefficients of the log-linear combination are estimated on training samples using discriminative methods to obtain an optimal classifier.
Density and Mass Distribution of the Spiral Galaxy NGC 3672

The density and mass of Spiral galaxy NGC 3672 has been studied as a function of the distance from the center of the galaxy. The mass of the three regions of the galaxy (nucleus, disk and halo) have been calculated and compared with other work. The total mass of NGC 3672 is found to be 1.22×10 Mo at a radius of 23.7 kpc.
Testing for a Unit Root in a Time Series with Mean Shifts

The study considers the ADF and KPSS tests for unit root testing in a time series characterized by a number of structural changes in its mean. Using the Monte Carlo simulation method the percentage points of the tests distributions are estimated. These two tests are biased towards non-rejection of the unit root. The bias of these tests appears to increase as the number of breaks in the series increases. The overall results in the study indicate that when a time series is subjected to a number of changes, provided the appropriate critical values are used, the unit root tests can erroneously reject the hypothesis of unit root. The tabulated critical values can be used in hypothesis testing.
Influence of oxygen vacancies on the photoresponse of β-Ga2O3/SiC n–n type heterojunctions

β-Ga2O3/4H-SiC n–n type heterojunctions have been fabricated by depositing high quality β-Ga2O3 films on c-axis orientation n-type 4H-SiC substrates using laser molecular beam expitaxy. The influences of oxygen vacancies on the junction performances are investigated. It is found that the existence of oxygen vacancies degrades the rectifying and photoresponse properties of the heterojunctions. A large rectification ratio of 1900, a high 254 nm ultraviolet photosensitivity of 6308% and a zero response of 365 nm ultraviolet have been achieved by reducing oxygen vacancies. It is supposed that the oxygen vacancies in Ga2O3 affect the depletion layer of the n–n junction greatly.
Routine respiratory metagenomics service for intensive care unit patients

Background: Respiratory metagenomics (RMg) needs evaluation in a pilot service setting to determine utility and inform implementation into clinical practice. Methods: Feasibility, performance and clinical impacts on antimicrobial prescribing and infection control were recorded during a pilot RMg service for patients with suspected lower respiratory tract infection (LRTI) on two general and one specialist respiratory intensive care units (ICU) at Guys & St Thomas NHS foundation Trust, London. Results: RMg was performed on 128 samples from 87 patients during the first 15-weeks providing same-day results for 110 samples (86%) with median turnaround time of 6.7hrs (IQR 6.1-7.5 hrs). RMg was 92% sensitive and 82% specific for clinically-relevant pathogens compared with routine testing. 48% of RMg results informed antimicrobial prescribing changes (22% escalation; 26% de-escalation) with escalation based on speciation in 20/24 cases and detection of acquired-resistance genes in 4/24 cases. Fastidious or unexpected organisms were reported in 21 samples including anaerobes (n=12), Mycobacterium tuberculosis, Tropheryma whipplei, cytomegalovirus and Legionella pneumophila ST1326, which was subsequently isolated from the bed-side water outlet. Application to consecutive severe community-acquired LRTI cases identified Staphylococcus aureus (two with SCCmec and three with luk F/S virulence determinants), Streptococcus pyogenes (emm1-M1uk clone), S. dysgalactiae subspecies equisimilis (STG62647A) and Aspergillus fumigatus with multiple treatments and public-health impacts. Conclusions: RMg provides frequent diverse benefits for treatment, infection control and public health. The combination of rapid comprehensive results, alongside revealing and characterising a hidden burden of infections makes the case for expediting routine service implementation
Disrupted seasonality and association of COVID-19 with medically attended respiratory syncytial virus infections among young children in the US: January 2010–January 2023

Respiratory syncytial virus (RSV) infections and hospitalizations surged sharply in 2022 among young children. To assess whether COVID-19 contributed to this surge, we leveraged a real-time nation-wide US database of electronic health records (EHRs) using time series analysis from January 1, 2010 through January 31, 2023, and propensity-score matched cohort comparisons for children aged 0-5 years with or without prior COVID-19 infection. Seasonal patterns of medically attended RSV infections were significantly disrupted during the COVID-19 pandemic. The monthly incidence rate for first-time medically attended cases, most of which were severe RSV-associated diseases, reached a historical high rate of 2,182 cases per 1,0000,000 person-days in November 2022, corresponding to a related increase of 143% compared to expected peak rate (rate ratio: 2.43, 95% CI: 2.25-2.63). Among 228,940 children aged 0-5 years, the risk for first-time medically attended RSV during 10/2022-12/2022 was 6.40% for children with prior COVID-19 infection, higher than 4.30% for the matched children without COVID-19 (risk ratio or RR: 1.40, 95% CI: 1.27-1.55); and among 99,105 children aged 0-1 year, the overall risk was 7.90% for those with prior COVID-19 infection, higher than 5.64% for matched children without (RR: 1.40, 95% CI: 1.21-1.62). These data provide evidence that COVID-19 contributed to the 2022 surge of severe pediatric RSV cases.
Protocol for a systematic review of long-term physical sequelae and financial burden of multidrug-resistant and extensively drug-resistant tuberculosis

Introduction Multidrug resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) are major public health threats that are significant causes of physical sequelae and financial consequences for infected people. Treatment for MDR- and XDR-TB are more toxic and take longer duration than for drug-susceptible-TB. As a result, the long-term sequelae are thought to be more common among patients with MDR- and XDR-TB than drug-susceptible-TB, but this is yet to be quantified. Hence, the aim of this systematic review and meta-analysis is to quantify the global burden and types of long-term physical sequelae and financial burden associated with both MDR- and XDR-TB. Method and analysis We will search CINHAL, MEDLINE, Embase, Scopus, and Web of science for studies that report physical and financial sequelae associated with rifampicin-resistant (RR), MDR- and XDR-TB or their treatments. The search will be conducted without time, language, and place restrictions. A random-effects meta-analysis will be conducted to estimate the pooled prevalence of each physical sequela. Heterogeneity will be measured using the Higgins I2 statistics. We will assess publication bias visually using the funnel plot and statistically using Egger’s test. Adjustments for publication basis will be made using Tweedie’s and Duval Trim and Fill analysis. Ethics and dissemination Since the study is based on published evidence, ethics approval is not required. The findings of the systematic review will be presented at various conferences and will be published in a peer-reviewed journal. Protocol registration The protocol is published in the PROSPERO with registration number CRD42021250909.
Facing the challenges of PROM implementation in Dutch dialysis care: Patients’ and professionals’ perspectives

Background Patient Reported Outcome Measures (PROMs) are increasingly used in routine clinical practice to facilitate patients in sharing and discussing health-related topics with their clinician. This study focuses on the implementation experiences of healthcare professionals and patients during the early implementation phase of the newly developed Dutch set of dialysis PROMs and aims to understand the process of early implementation of PROMs from the users’ perspectives. Methods This is a qualitative study among healthcare professionals (physicians and nursing staff: n = 13) and patients (n = 14) of which 12 were receiving haemodialysis and 2 peritoneal dialysis. Semi-structured interviews were used to understand the barriers and facilitators that both professionals and patients encounter when starting to implement PROMs. Results The early PROM implementation process is influenced by a variety of factors that we divided into barriers and facilitators. We identified four barriers: patient´s indifference to PROMs, scepticism on the benefits of aggregated PROM data, the limited treatment options open to doctors and organizational issues such as mergers, organizational problems and renovations. We also describe four facilitators: professional involvement and patient support, a growing understanding of the use of PROMs during the implementation, quick gains from using PROMs such as receiving instant feedback and a clear ambition on patient care such as a shared view on patient involvement and management support. Conclusions In this qualitative study carried out during the early implementation phase of the Dutch dialysis PROM set, we found that patients did not yet consider the PROM set to be a useful additional tool to share information with their doctor. This was despite the professionals’ primary reason for using PROMs being to improve patient–doctor communication. Furthermore, the perceived lack of intervention options was frustrating for some of the professionals. We found that nurses could be important enablers of further implementation because of their intensive relationship with dialysis patients.
Ultra-short objective alertness assessment: an adaptive duration version of the 3 minute PVT (PVT-BA) accurately tracks changes in psychomotor vigilance induced by sleep restriction

Abstract Study Objectives The Psychomotor Vigilance Test (PVT) is a widely used and sensitive assay of the cognitive deficits associated with sleep loss and circadian misalignment. As even shorter versions of the PVT are often considered too long, I developed and validated an adaptive duration version of the 3 min PVT (PVT-BA). Methods The PVT-BA algorithm was trained on data from 31 subjects participating in a total sleep deprivation protocol and validated in 43 subjects undergoing 5 days of partial sleep restriction under controlled laboratory conditions. With each subject response, the algorithm updated the odds of the test being high, medium or low performance based on lapses plus false starts on the full 3 min PVT-B. Results With a decision threshold of 99.619%, PVT-BA classified 95.1% of training data tests correctly without incorrect classifications across two performance categories (i.e. high as low or low as high) and resulted in an average test duration of 1 min 43 s with a minimum duration of 16.4 s. Agreement corrected for chance between PVT-B and PVT-BA was “almost perfect” for both the training (kappa = 0.92) and validation data (kappa = 0.85). Across the three performance categories and data sets, sensitivity averaged 92.2% (range 74.9–100%) and specificity averaged 96.0% (range 88.3–99.2%). Conclusions PVT-BA is an accurate adaptive version of PVT-B and, to my knowledge, the shortest version to date that maintains key properties of the standard 10 min duration PVT. PVT-BA will facilitate the use of the PVT in settings in which it was previously considered impractical.
Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets

To investigate the well-observed racial disparities in computer vision systems that analyze images of humans, researchers have turned to skin tone as a more objective annotation than race metadata for fairness performance evaluations. However, the current state of skin tone annotation procedures is highly varied. For instance, researchers use a range of untested scales and skin tone categories, have unclear annotation procedures, and provide inadequate analyses of uncertainty. In addition, little attention is paid to the positionality of the humans involved in the annotation process—both designers and annotators alike—and the historical and sociological context of skin tone in the United States. Our work is the first to investigate the skin tone annotation process as a sociotechnical project. We surveyed recent skin tone annotation procedures and conducted annotation experiments to examine how subjective understandings of skin tone are embedded in skin tone annotation procedures. Our systematic literature review revealed the uninterrogated association between skin tone and race and the limited effort to analyze annotator uncertainty in current procedures for skin tone annotation in computer vision evaluation. Our experiments demonstrated that design decisions in the annotation procedure such as the order in which the skin tone scale is presented or additional context in the image (i.e., presence of a face) significantly affected the resulting inter-annotator agreement and individual uncertainty of skin tone annotations. We call for greater reflexivity in the design, analysis, and documentation of procedures for evaluation using skin tone.
Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps

In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded.