Miracle-dz/UniFAR-FTUs huggingface dataset:
Haplotype Reconstruction Error as a Classical Misclassification Problem: Introducing Sensitivity and Specificity as Error Measures

Background Statistically reconstructing haplotypes from single nucleotide polymorphism (SNP) genotypes, can lead to falsely classified haplotypes. This can be an issue when interpreting haplotype association results or when selecting subjects with certain haplotypes for subsequent functional studies. It was our aim to quantify haplotype reconstruction error and to provide tools for it. Methods and Results By numerous simulation scenarios, we systematically investigated several error measures, including discrepancy, error rate, and R2, and introduced the sensitivity and specificity to this context. We exemplified several measures in the KORA study, a large population-based study from Southern Germany. We find that the specificity is slightly reduced only for common haplotypes, while the sensitivity was decreased for some, but not all rare haplotypes. The overall error rate was generally increasing with increasing number of loci, increasing minor allele frequency of SNPs, decreasing correlation between the alleles and increasing ambiguity. Conclusions We conclude that, with the analytical approach presented here, haplotype-specific error measures can be computed to gain insight into the haplotype uncertainty. This method provides the information, if a specific risk haplotype can be expected to be reconstructed with rather no or high misclassification and thus on the magnitude of expected bias in association estimates. We also illustrate that sensitivity and specificity separate two dimensions of the haplotype reconstruction error, which completely describe the misclassification matrix and thus provide the prerequisite for methods accounting for misclassification.
Asymptotic behavior of a multiplexer fed by a long-range dependent process

In this paper we study the asymptotic behavior of the tail of the stationary backlog distribution in a single server queue with constant service capacity c, fed by the so-called M/G/∞ input process or Cox input process. Asymptotic lower bounds are obtained for any distribution G and asymptotic upper bounds are derived when G is a subexponential distribution. We find the bounds to be tight in some instances, e.g. when G corresponds to either the Pareto or lognormal distribution and c − ρ < 1, where ρ is the arrival rate at the buffer.
A measure of quality for n-detection test sets

N-detection test sets are useful in improving the coverage of unmodeled faults. We introduce a measure of quality that allows us to compare two test sets in terms of their ability to detect unmodeled faults based on the concept of n-detections. Using this measure, we describe a procedure for ordering an n-detection test set for stuck-at faults such that the quality of a test set comprised of the first K tests of the test set is as high as possible. This is useful when only K tests of the test set can be accommodated in the tester memory or to help ensure that unmodeled faults are detected as early as possible during the test application process. We present experimental results demonstrating that the proposed ordering yields test sets with increased coverage of unmodeled faults.
An incremental speaker-adaptation technique for hybrid HMM-MLP recognizer

One of the problems of speaker-independent continuous speech recognition systems is their inability to cope with the inter-speaker variability. When we find test speakers with different characteristics from the ones presented in the training pool we observe a large degradation on the system performance. To overcome this problem speaker-adaptation techniques may be used to provide near speaker-dependent accuracy. In this work we present a speaker-adaptation technique applied to a hybrid HMM-MLP system for large vocabulary, continuous speech recognition. This technique is based on an architecture that employs a trainable linear input network (LIN) to map the speaker specific features input vectors to the speaker-independent system. This speaker-adaptation technique is evaluated in an incremental speaker-adaptation task using a Wall Street Journal (WSJ) database. Both supervised and unsupervised modes are evaluated. The results show that speaker-adaptation within the hybrid framework can substantially improve system performance.
Findings from the Sussex hate crime project

In this short report we summarise some of our key findings from the quantitative surveys and experiments (with over 1400 respondents) and which are yet to be published. Some of the data below on police perceptions are aggregated into two areas: London (serviced by the Metropolitan Police Service) and all other areas in England and Wales. This allows for a comparison between the country’s largest city (and largest police service) with the rest of the country
Spaces of Exception and Spaces of Normality: Towards a Relational Understanding of Violence and Peace in Muslim Mindanao

This article argues for a relational and historical understanding of the violent confict in Muslim Mindanao, the Philippines that goes beyond treating this region as a violent “space of exception,” but instead points at similarities and congruities with other parts of the Philippines. Te consequences of this observation are discussed in relation to the ongoing peace negotiations in the region. Rather than seeing these as efforts potentially transforming Muslim Mindanao into a peaceful “space of normality,” it is argued that the impact of these negotiations will remain confned, as they solely tackle the national confict scale and remain confned to only one type of armed organization.
A Social Rank Explanation of How Money Influences Health

Objective: Financial resources are a potent determinant of health, yet it remains unclear why this is the case. We aimed to identify whether the frequently observed association between absolute levels of monetary resources and health may occur because money acts an indirect proxy for a person’s social rank. Method: To address this question we examined over 230,000 observations on 40,400 adults drawn from two representative national panel studies; the British Household Panel Survey and the English Longitudinal Study of Ageing. We identified each person’s absolute income/wealth and their objective ranked position of income/wealth within a social reference-group. Absolute and rank income/wealth variables were then used to predict a series of self-reported and objectively recorded health outcomes in cross-sectional and longitudinal analyses. Results: As anticipated, those with higher levels of absolute income/wealth were found to have better health than others, after adjustment for age, gender, education, marital status, and labor force status. When evaluated simultaneously the ranked position of income/wealth but not absolute income/wealth predicted all health outcomes examined including: objective measures of allostatic load and obesity, the presence of long-standing illness, and ratings of health, physical functioning, role limitations, and pain. The health benefits of high rank were consistent in cross-sectional and longitudinal analyses and did not depend on the reference-group used to rank participants. Conclusions: This is the first study to demonstrate that social position rather than material conditions may explain the impact of money on human health.
Plasticity of the Arabidopsis Root System under Nutrient De fi ciencies 1 [ C ]

Plant roots show a particularly high variation in their morphological response to different nutrient deficiencies. Although such changes often determine the nutrient efficiency or stress tolerance of plants, it is surprising that a comprehensive and comparative analysis of rootmorphological responses to different nutrient deficiencies has not yet been conducted. Since one reason for this is an inherent difficulty in obtaining nutrient-deficient conditions in agar culture, we first identified conditions appropriate for producing nutrient-deficient plants on agar plates. Based on a careful selection of agar specifically for each nutrient being considered, we grewArabidopsis (Arabidopsis thaliana) plants at four levels of deficiency for 12 nutrients and quantified seven root traits. In combinationwithmeasurements of biomass andelemental concentrations,weobserved that thenutritional status and type of nutrient determined the extent and type of changes in root system architecture (RSA). The independent regulation of individual root traits further pointed to a differential sensitivity of root tissues to nutrient limitations. To capture the variation in RSA under different nutrient supplies, we used principal component analysis and developed a root plasticity chart representing the overall modulations in RSA under a given treatment. This systematic comparison of RSA responses to nutrient deficiencies provides a comprehensive view of the overall changes in root plasticity induced by the deficiency of single nutrients and provides a solid basis for the identification of nutrient-sensitive steps in the root developmental program.
Design of a Bi-Directional Dual Active Bridge Converter for Electric Vehicle Battery Charging

The increasing demand for EV s stems from the need to reduce fossil fuel dependency and mitigate environmental impacts. Efficient EV battery chargers require high-power converters and optimal charging algorithms. This study uses a Bi-Directional Dual Active Bridge Converter (DAB) with Single-Phase Shift control. The DAB adjusts the transformer's turn ratio to provide a wide output voltage range with a controllable phase shift ratio and duty cycle. It offers isolation, fewer passive components, and enables efficient soft-switching. Steady-state and dynamic analyses are conducted using the Generalized Averaging Technique. Control of DAB is done using a PI controller. Regulation of output voltage and output current is made by adjusting the phase angle of bridges for power flow in both directions MATLAB/SIMULINK simulations explore voltage mode control of the DAB for bi-directional power flow with a 5kW converter.
Convergence of gradient method with momentum for two-Layer feedforward neural networks

A gradient method with momentum for two-layer feedforward neural networks is considered. The learning rate is set to be a constant and the momentum factor an adaptive variable. Both the weak and strong convergence results are proved, as well as the convergence rates for the error function and for the weight. Compared to the existing convergence results, our results are more general since we do not require the error function to be quadratic.
Emerging Trends in AI-Driven Health Tech: A Comprehensive Review and Future Prospects

Purpose: The purpose of this research is to explore the integration of artificial intelligence (AI) in healthcare, specifically within the realm of health informatics (HI). The study aims to understand the impact of AI on patient treatment, research, and operational processes within healthcare systems. Additionally, it seeks to address the challenges posed by the increasing volume of unorganized and unstructured data generated by AI technologies in healthcare. Materials and Methods: This research employs a comprehensive analysis approach, utilizing complex health information systems, clinical images, and intricate language. It examines the current utilization of AI in healthcare, focusing on its effects on patient and clinician involvement in healthcare decision-making. The assessment emphasizes key skill areas of Health Informatics, including IT, health information systems, security and privacy, telemedicine, m-Health, consumer health informatics, and clinical informatics. Findings: The study identifies several significant findings regarding the role of AI in healthcare. It highlights how AI technologies contribute to the generation of unstructured data, posing challenges for research and analysis. Additionally, the research underscores AI's ability to enhance personalized medical guidance, identify complex illnesses, forecast negative health occurrences, and improve patient outcomes. Moreover, it discusses AI's impact on social media and mobile apps, emphasizing its potential to gather valuable insights from online sources for a deeper understanding of patient needs and behaviors. Implications to Theory, Practice and Policy: Based on the findings, the research suggests several recommendations for future research and progress in the field of AI usage in healthcare. These recommendations may include further exploration of AI applications in healthcare decision-making, addressing challenges related to unstructured data, enhancing security and privacy measures, and leveraging AI for improving patient outcomes and clinician engagement. Additionally, the study emphasizes the importance of ongoing research and development to maximize the potential benefits of AI in healthcare.
Unidirectional valve malfunction by the breakage or malposition of disc - two cases report -

Malfunction of the unidirectional valve in a breathing circuit system may cause hypercapnia from the rebreathing of expired gas, ventilation failure, and barotrauma. Capnography is a useful method for monitoring the integrity of the unidirectional valve. We experienced two cases of malfunction of a unidirectional valve which caused leakage and reverse flow, diagnosed early as a change of the capnographic waveform. One case was caused by expiratory unidirectional valve breakage. The other was caused by an incorrectly-assembled inspiratory unidirectional valve.
AI-Aided Diagnosis For Neurodegenerative Diseases: Prospects And Challenges

Neurodegenerative disease (ND) represents a chronic disease characterized by loss of neuron function and death, such as Alzheimer's disease, Parkinson's disease, etc. It’s difficult and complex to diagnose ND, due to the requirement for the synthesis of multiple biomarker data, such as genes, proteins, images, etc. The advent of artificial intelligence (AI) technology, particularly through machine learning (ML) and deep learning (DL) algorithms, introduces novel methodologies and tools for ND diagnosis. These methodologies extract pertinent information and patterns from massive, multidimensional, and non-linear data, assisting doctors to render more precise, prompt, and objective assessments. This article provides an overview of the updated implementation status of AI in ND diagnosis, assesses the advantages and contributions of AI to ND diagnosis, as well as the existing limitations and challenges, and offers insights to the future development direction of AI in ND diagnosis.
Classification improvement based on feature combination and topic vector model

In this paper, we demonstrate a feature processing procedure which emphasizes on the combination of original features with redundancy trimming steps. This procedure shows better classification result than traditional classification models. In our experiment, several key feature processing steps were proposed according to the type of the feature. These steps contains numerical to categorical feature value conversion, feature combination, feature redundancy discrimination, and latent structure discovery based on the concatenation of original features and extended feature set. The UCI machine learning repository is chosen as our demonstration to show the effect of our approach. In our preliminary result, it shows that the classification accuracy outperforms the traditional SVM classifier(SVM-only) while the ROC benchmark equals to the SVM-only scenario. This result is believed to be a promising one on the feature processing procedure research.
Programming education for primary school children using a textual programming language

In this research, a Textual Programming Language (TPL) is used in programming education for primary schoolchildren because of the following reasons: (1) it is more practical to use the programming languages similar to the ones used for developing real applications, (2) typing statements could be easier for primary schoolchildren than generally thought, (3) there exist programming environments such as Processing that are easy to use and produce very attractive graphical outcomes. Teaching material for programming education with Processing was developed. In this teaching material, cartoons were used to explain difficult concepts. The learners who use this teaching material were supposed to draw some computational figures with chosen colors. Trial experiments of programming education using this teaching material was conducted to a cohort of seven primary schoolchildren (six 4th grade and one 5th grade children) in two consecutive weekend classes (one hour each). Since the authors' aim of this programming education was to create a sense of fun and excitement in the children and inculcate a desire to engage with computing, the motivation of the children was assessed using the questionnaire based on the ARCS (Attention, Relevance, Confidence, and Satisfaction) motivation model. The results were encouraging and suggested that TPLs could be used in programming education for primary schoolchildren.
VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in Distributed IoT

The development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed Internet of Things (IoT). However, existing FL frameworks still suffer from invisible attacks in IoT environments, such as free-rider attacks, backdoor attacks, and model theft. In this paper, we propose a Verifiable Property Federated Learning (VPFL) framework to overcome the above invisible attacks. We present a black-box watermarking task distribution mechanism to prevent free-rider attacks by verifying the property of local models. Our adversarial fine-tuning embedding technique can not only eliminate backdoors in global models, but also simultaneously embed white-box watermarks into model parameters to prevent model theft. Comprehensive experimental evaluations demonstrate that our framework outperforms state-of-the-art schemes in terms of security and feasibility against invisible attacks.
NoFTL-KV: TacklingWrite-Amplification on KV-Stores with Native Storage Management

Modern persistent Key/Value stores are designed to meet the demand for high transactional throughput and high data-ingestion rates. Still, they rely on backwards-compatible storage stack and abstractions to ease space management, foster seamless proliferation and system integration. Their dependence on the traditional I/O stack has negative impact on performance, causes unacceptably high write-amplification, and limits the storage longevity. In the present paper we present NoFTL-KV, an approach that results in a lean I/O stack, integrating physical storage management natively in the Key/Value store. NoFTL-KV eliminates backwards compatibility, allowing the Key/Value store to directly consume the characteristics of modern storage technologies. NoFTLKV is implemented under RocksDB. The performance evaluation under LinkBench shows that NoFTL-KV improves transactional throughput by 33%, while response times improve up to 2.3x. Furthermore, NoFTL-KV reduces write-amplification 19x and improves storage longevity by imately the same factor.
Mild traumatic brain injuries: a 10-year follow-up.

OBJECTIVE AND DESIGN Long-term consequences of mild traumatic brain injuries were investigated based on a 10-year follow-up of patients from a previously-published randomized controlled study of mild traumatic brain injuries. One aim was to describe changes over time after mild traumatic brain injuries in terms of the extent of persisting post-concussion symptoms, life satisfaction, perceived health, activities of daily living, changes in life roles and sick leave. Another aim was to identify differences between the intervention and control groups. PATIENTS The intervention group comprised 142 persons and the control group 56 persons. METHODS Postal questionnaires with a response rate of 56%. RESULTS No differences over time were found for the intervention and control groups in terms of post-concussion symptoms. In the intervention group some variables in life satisfaction, perceived health and daily life were decreased. Some roles had changed over the years for both groups. No other differences between the intervention and control groups were found. However, in both groups sick leave decreased. CONCLUSION Early individual intervention by a qualified rehabilitation team does not appear to impact on the long-term outcome for persons with symptoms related to mild traumatic brain injuries. The status after approximately 3 weeks is indicative of the status after 10 years.
DeepFake Video Detection using Vision Transformer

: Technology is always a double-edged sword, and with the astonishing advancements in technology, it is expected that the DeepFake problem will become more common and serious. DeepFake has recently caused a lot of trouble because its flaws outweigh its advantages. Since DeepFake has such a significant influence on individuals deception, instability of principles and falsification of evidence. Instead of just affecting people, it led to multiple incidents that affected the image of entire nations. In this paper, a model that has been built to mitigate the negative effects of deepFake and maintain an individual's reputation by detecting the alteration of people's photographs and videos. A model with integrated vision transformer architectures Deep-ViT and Cross-ViT is designed to process pre-extracted faces from FF++ dataset. The model distinguishes between the real and fake faces in two different perspectives, subclass detection on each manipulation method and overall detection of all types. The proposed model achieves an outstanding results and the highest accuracy in FaceSwap manipulation method with 98%.
Assessing and improving the effectiveness of logs for the analysis of software faults

Event logs are the primary source of data to characterize the dependability behavior of a computing system during the operational phase. However, they are inadequate to provide evidence of software faults, which are nowadays among the main causes of system outages. This paper proposes an approach based on software fault injection to assess the effectiveness of logs to keep track of software faults triggered in the field. Injection results are used to provide guidelines to improve the ability of logging mechanisms to report the effects of software faults. The benefits of the approach are shown by means of experimental results on three widely used software systems.
KF-based LOS Guidance Law for Path Following of USV: Experiments and Performance Evaluation

In this paper, we present the experimental results of the recently proposed Kalman filter-based line-of-sight (KF-based LOS) path-following algorithm tailored for Unmanned Surface Vehicles (USVs), addressing a known challenge where traditional LOS methods lack robustness against external disturbances leading to sideslip and tracking errors. A series of experiments focusing on straight-line following has been conducted, with all data systematically recorded for further analysis. The vehicle’s performance is quantitatively assessed through well-known performance indices. The results demonstrate that the KF-based LOS method effectively compensates for sideslip, enabling a USV to accurately follow a straight line.