Miracle-dz/UniFAR-FTUs huggingface dataset:
COPD_A_260917 2015..2024

1Division of Molecular and Clinical Medicine, University of Dundee, Dundee DD1 9SY, UK; 2Department of Respiratory Medicine, Victoria Hospital, NHS Fife, Kirkcaldy KY2 5AH, UK; 3Scottish Centre for Respiratory Research, Medical Research Institute, University of Dundee, Dundee DD1 9SY, UK; 4Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; 5Imaging Science and Technology, University of Dundee, Dundee DD1 9SY, UK Background: Oxidative stress (OS) has been implicated in the development of pulmonary hypertension (PH) and ventricular hypertrophy. Xanthine oxidase is a well-recognised source of reactive oxygen species, which lead to OS. The aim of this proof of concept study was to assess whether allopurinol (xanthine oxidase inhibitor) would reduce right ventricular mass (RVM) in patients with PH-associated chronic lung disease (PH-CLD). Methods: We conducted a randomised, double-blind, parallel-group, placebo-controlled trial in patients with PH-CLD (93% COPD, 7% IPF) who were randomly assigned to receive allopurinol or placebo for 12 months. The primary outcome was the mean change in RVM, as assessed by cardiac magnetic resonance imaging (CMRI). Secondary outcomes included quality of life (QOL), spirometry and six-minute walk test (6MWT). Results: Seventy-one patients were recruited: mean age 71 years, mean pulmonary arterial pressure 30 mm Hg, FEV1 60% and resting SpO2 96%. After 12 months, there was no significant difference in the change in RVM from baseline (allopurinol 1.85g vs placebo 0.97g with mean difference 0.88g, CI −4.77 to 3.01, p =0.7). There were also no significant changes in other cardiac parameters measured on MRI, in QOL, spirometry and 6MWT. Subgroup analysis showed that allopurinol significantly reduced RVM compared to placebo with -6.16g vs 0.75g and mean difference 6.92g (CI 1.14 to 12.69, p = 0.02) in COPD patients with more severe airflow limitation. Conclusion: Allopurinol had no overall impact on patients with PH-CLD but had potential benefit in COPD patients with more severe airflow limitation.
VERI-ZEXE: Decentralized Private Computation with Universal Setup

Traditional blockchain systems execute program state transitions on-chain , requiring each network node participating in state-machine replication to re-compute every step of the program when validating transactions. This limits both scalability and privacy. Recently, Bowe et al . introduced a primitive called decentralized private computation (DPC) and provided an instantiation called Z EXE , which allows users to execute arbitrary computations off-chain without revealing the program logic to the network. Moreover, transaction validation takes only constant time, independent of the off-chain computation. However, Z EXE required a separate trusted setup for each application, which is highly impractical. Prior attempts to remove this per-application setup incurred significant performance loss. We propose a new DPC instantiation V ERI Z EXE that is highly efficient and requires only a single universal setup to support an arbitrary number of applications. Our benchmark improves the state-of-the-art by 9x in transaction generation time and by 3 . 4x in memory usage. Along the way, we also design efficient gadgets for variable-base multi-scalar multiplication and modular arithmetic within the P LONK constraint system, leading to a P LONK verifier gadget using only ∼ 21 k constraints.
Secure and Robust Key-Trapped Design-for-Security Architecture for Protecting Obfuscated Logic

—Having access to the scan chain of Integrated Circuits (ICs) is an integral requirement of the debug/testability process within the supply chain. However, the access to the scan chain raises big concerns regarding the security of the chip, particularly when the secret information, such as the key of logic obfuscation, is embedded/stored inside the chip. Hence, to relieve such concerns, numerous secure scan chain architectures have been proposed in the literature to show not only how to prevent any unauthorized access to the scan chain but also how to keep the availability of the scan chain for debug/testability. In this paper, we first provide a holistic overview of all secure scan chain architectures. Then, we discuss the key leakage possibility and some substantial architectural drawbacks that moderately affect both test flow and design constraints in the state-of-the-art published design-for-security (DFS) architectures. Then, we propose a new key-trapped DFS (kt-DFS) architecture for building a secure scan chain architecture while addressing the potential of key leakage. The proposed kt-DFS architecture allows the designer to perform the structural test with no limitation, enabling an untrusted foundry to utilize the scan chain for manufacturing fault testing without needing to access the scan chain. Finally, we evaluate and compare the proposed architecture with state-of-the-art ones in terms of security, testability time and complexity, and area/power/delay overhead.
Linear Communication in Malicious Majority MPC

The SPDZ multiparty computation protocol \citeC:DPSZ12 allows n parties to securely compute arithmetic circuits over a finite field, while tolerating up to n-1 active corruptions. A line of work building upon SPDZ has made considerable improvement to the protocol's performance, typically focusing on concrete efficiency. However, the communication complexity of each of these protocols is Ømega(n^2|C|). In this paper, we present a protocol that achieves \mathcalO (n|C|) communication. Our construction is very similar to those in the SPDZ family of protocols, but for one modular sub-routine for computing a verified sum. There are a handful of times in the SPDZ protocols in which the n parties wish to sum n public values. Rather than requiring each party to broadcast their input to all other parties, clearly it is cheaper to use some designated "dealer'' to compute and broadcast the sum. In prior work, it was assumed that the cost of verifying the correctness of these sums is O(n^2), erasing the benefit of using a dealer. We show how to amortize this cost over the computation of multiple sums, resulting in linear communication complexity whenever the circuit size is |C| > n.
A Study of Generalization Metrics for Natural Language Processing: Correlational Analysis and a Simpson's Paradox

A predictive model’s utility lies in its ability to generalize to data it has not seen. Unfortunately, it is difficult to reliably measure a model’s ability to generalize to unseen data since it requires reasoning about the model’s interactions with unknown environments. Generalization of deep learning models has been the subject of extensive study for years, but there has been a recent increase in the exploration of generalization metrics to predict the generalization of deep learning models. While prior work in generalization metrics has been dominated by computer vision, in this work, we conduct one of the first analyses of generalization metrics in natural language processing (NLP). We study 36 generalization metrics spanning various motivations/theories with the goal of understanding the degree to which each metric is appropriate for use in predicting the generalization of models common in NLP. We particularly focus on shape metrics (generalization metrics derived from the shape of the empirical distribution of eigenvalues of weight correlation matrices) and are among the first to consider out-of-distribution generalization when evaluating the effectiveness generalization metrics. We find that shape metrics are a promising category of generalization metrics, as they are the best metrics among those we consider at predicting generalization performance throughout training and show characteristics of being “ideal” generalization metrics. Interestingly, many of the generalization metrics we consider exhibit a behavior reminiscent of the Simpson’s paradox when related to generalization performance. Moreover, the generalization metrics we consider are generally robust to changes in data distribution. However, there are signs this robustness is limited.
Does Saving Strategies Enhance Sustainability of Table Banking Groups? Evidence from in Uasin Gishu County, Kenya. Saving Strategies Enhance Sustainability of Banking Groups? Evidence Uasin

Does Abstract Financial management strategies are crucial determinants of sustainability of table banking groups. It enables groups set clear goals, efficient utilization of resources, proper decisions in sourcing of finances and dividends decision making. The main purpose of this study therefore was to establish the relationship between saving strategies on sustainability of table banking groups was assessed. The study was founded on institution theory of asset accumulation. The study adopted the descriptive survey research design. The target population were all table bank groups in Kenya. The accessible population was 538 registered table bank groups in Uasin Gishu County. A sample of 230 groups was involved in the study. Two stage sampling technique was used to narrow down the sub-counties. Purposive sampling technique was used to select 3 sub-counties out of six sub-counties in Uasin Gishu County. Simple random sampling technique was used to select respondents for the actual study. A pilot study was conducted in order to test the validity and reliability of the research questionnaire. Content validity was used as a validity test while reliability was tested using Cronbach’s alpha coefficient. Self-administered questionnaires were used to collect data. Both descriptive and inferential statistics were used for data analysis. Descriptive statistical tools included frequencies, percentages, mean, variance and standard deviations. Inferential statistics included Pearsons Product Moment Correlation and multiple regression analysis. Findings were presented in tables and graphs. The study found out that savings strategies positively and significantly influence sustainability of table banking groups (β=0.163; p < 0.05. The findings of this study are expected to be employed by table banking groups to ensure business success, hence promoting sustainability and economic development. The study recommended that risk management strategies should be incorporated in financial management strategies. It was also recommended that theories anchored this
Performance of Contrast Adjustment in Face Recognition with Training Images under Lighting Variations

— This study investigates the contrast adjustment technique for facial recognition in training images under different lighting conditions. In many previous studies, contrast adjustment techniques are often used in the pre-processing stage of face recognition to reduce the effect of lighting on the image, so that performance of face recognition can be optimal. Lighting is one of the factors that greatly affects the facial recognition system, so research in this case continues to be carried out to get better problem-solving concept. In this study, the contrast adjustment technique uses the histogram equalization method which is also widely used in many other studies. This study was intended to test the reliability of this technique in facial recognition with training images in various lighting conditions. Experiments were carried out empirically to determine whether the technique showed good performance in all lighting conditions of the training image or not. The face recognition process uses the Robust Regression method. In a number of previous studies, this method has shown a very good performance for recognizing faces that are affected by lighting factors. One of the facial image datasets, namely the AR Face Database, was used for testing in this study. The images used are related to the lighting factor. The images are grouped into 4 categories, including low, medium, high, and very high (extreme) lighting conditions. Experimental scenarios were carried out by comparing the results of face recognition with using histogram equalization technique at the preprocessing stage and face recognition without using contrast adjustment techniques in every lighting condition. Based on the testing process that has been carried out, under low, medium, and high lighting conditions, the performance of contrast adjustment using histogram equalization technique in facial recognition pre-processing is better than the approach without contrast adjustment. While in extreme lighting condition, the use of histogram equalization technique in preprocessing (average accuracy of 93.17%) results in lower facial recognition performance than the approach without using histogram equalization (average accuracy of 94.67%).
Knowledge Production, Historical Reparation and Construction of Alternative Futures. Interview With Miguel de Barros

The COVID-19 pandemic dramatically exacerbated pre-existing social inequalities and added urgency to the awareness of the need for social transformation. Social movements for the decolonisation of knowledge and governance systems have gained new momentum and the demands for historical reparation and climate, health and food justice. Historical reparation has sometimes been equated only to restitution of material goods or financial compensation. Still, it is a much more complex endeavour that necessarily involves making knowledge production a more engaging and participatory process, inside and outside the academy, linking different knowledge to build fairer and more inclusive futures. This text stems from an interview de sociologist and “Leadership Social is a in and executive The interview unfolded on different occasions. The first part was in October 2019, before the and the second part 2 years later, in November 2021, address the challenges in knowledge production, historical reparation and social justice in the current planetary context.
with Cognate Antigen Allergic Encephalomyelitis upon Activation Suppressive Functions against Experimental Specific T Regulatory Cells Display Broad

To date, very few Ag-based regimens have been deﬁned that could expand T regulatory (Treg) cells to reverse autoimmunity. Additional understanding of Treg function with respect to speciﬁcity and broad suppression should help overcome these limita-tions. Ig-proteolipid protein (PLP)1, an Ig carrying a PLP1 peptide corresponding to amino acid residues 139-151 of PLP, displayed potent tolerogenic functions and proved effective against experimental allergic encephalomyelitis (EAE). In this study, we took advantage of the Ig-PLP1 system and the PLP1-speciﬁc TCR transgenic 5B6 mouse to deﬁne a regimen that could expand Ag-speciﬁc Treg cells in vivo and tested for effectiveness against autoimmunity involving diverse T cell speciﬁcities. The ﬁndings indicate that in vivo exposure to aggregated Ig-PLP1 drives PLP1-speciﬁc 5B6 TCR transgenic cells to evolve as Treg cells expressing CD25, CTLA-4, and Foxp3 and producing IL-10. These Treg cells were able to suppress PLP1 peptide-induced EAE in both SJL/J and F 1 (SJL/J (cid:1) C57BL/6) mice. However, despite being effective against disease induced with a CNS homogenate, the Treg cells were unable to counter EAE induced by a myelin basic protein or a myelin oligodendrocyte glycoprotein peptide. Nevertheless, activation with Ag before transfer into the host mice supports suppression of both myelin oligodendrocyte glycoprotein- and myelin basic protein peptide-induced EAE.
The Benefit Coordination Mechanism of Cloud Platforms for Rural Cultural Tourism Based on a Case Analysis of TikTok, AAuto, and Bilibili

: The benefit coordination mechanism of cloud platforms directly affects platform enterprises’ competitiveness and plays a vital role in enterprises’ sustainable development. In this study, we design a benefit coordination mechanism of cloud platforms for rural cultural tourism by summing-up the two dimensions of the proportion of online and offline user time and the degree of commercialization of TikTok, AAuto, and Bilibili. Research shows that cloud platforms’ four benefits are platform traffic, advertising penetration, community ecology, and commercial monetization capabilities. Their interaction is the key to benefitting from cloud platforms. The provided benefit mechanism of the cloud platform for rural cultural tourism can provide theoretical reference and decision-making support for platform enterprises.
Journal of Comparative Urban Law and Policy Journal of Comparative Urban Law and Policy The Enigma of Housing Choice The Enigma of Housing Choice

The U.S. faces a housing choice crisis. The growing shortage of affordable rental homes and looming mismatch between the homes offered for sale by baby boomers and the homes sought by the next generation of homeowners point to a need to fundamentally reshape the extent and diversity of the nation’s housing options. Housing and land-use policy experts have appealed to the aim of expanding housing choice to justify the removal of regulatory restrictions on certain housing types, the construction of affordable rental housing in transit-adjacent neighborhoods, the elimination of housing market discrimination on the basis of race and ethnicity, and the expansion of tenant-based rental vouchers in lowpoverty neighborhoods. Housing choice expansion is a battle being waged on multiple fronts in defense of a variety of causes, but conceptual confusion threatens to erode the foundation of this fragile alliance. This paper brings clarity to the cause by exploring the concept of housing choice, evaluating the normative arguments in favor of expanding housing choice, and proposing a refined conception of the goal to expand housing choice. I defend a conception of housing choice expansion that prioritizes effective housing choice and tenure and type neutrality. This conception provides a justification for several policy reforms, including an expanded demandside housing subsidy funded by eliminating homeownership tax incentives, enhancements to renters’ rights of occupancy, expansion of flexible tenure arrangements, and reform of local land-use practices.
MPC for Group Reconstruction Circuits

In this work, we generalize threshold Schnorr signatures, ElGamal encryption, and a wide variety of other functionalities, using a novel formalism of group reconstruction circuits (GRC)s. We construct a UC secure MPC protocol for computing these circuits on secret shared inputs, even in the presence of malicious parties. Applied to concrete circuits, our protocol yields threshold signature and encryption schemes with similar round complexity and concrete eﬃ-ciency to functionality-speciﬁc protocols. Our formalism also generalizes to other functionalities, such as polynomial commitments and openings.
Online prediction of battery discharge and flight mission assessment for electrical rotorcraft

In recent concept development and research effort on Unmanned Arial System (UAS) Traffic Management (UTM) and urban on demand mobility (ODM), electric Vertical Take-off and Landing (eVTOL) operations for cargo delivery and passenger transportation need to constantly check if their mission can be successfully completed given the current battery power supply. This onboard or ground-based mission evaluation algorithm is necessary because (1) eVTOL aircraft run on limited battery power; and (2) eVTOL aircraft are usually light weighted so they are subject to wind uncertainties in low-altitude airspace. In this work, the plan is to create an equivalent circuit model (ECM) that best represents the battery pack of a UAS, and then use flight testing to validate the accuracy of that model. Additionally, the ECM will be used to predict the UAS’s ability to complete a specific flight plan successfully. The expected significance of this research is to provide an online framework to constantly monitor and predict battery behavior for mission assessment, which is critical for low-altitude eVTOL operations.
Combinatorial Aspects of a Measure of Rank Correlation Due to Kendall and its Relation to Complete Signed Digraphs

: In this paper, we shall present an account of certain combinatorial aspects of a measure of rank correlation due to Kendall (1938) and point out its relation to the analysis of patterns of preference and indiﬀerence which in recent years, have been matters of intense discussion among the social psychologists because of their fundamental role in dealing with certain vital issues of social decision theory.
A Novel Integration of Hodrick-Prescott Filter and Harmonic Analysis with Machine Learning Methods to Enhance Time Series Prediction Accuracy of Daily and Monthly Wind Speeds

In this work, a new hybrid algorithm for modelling time series of daily and monthly wind speed is proposed. The method utilizes Hodrick-Prescott Filter (HPF) to decompose raw wind speed data into trend and cyclic components, and harmonic analysis (HA) is thereafter used to decompose the cyclic component into the periodic and stochastic sub-components. Machine learning (ML) methods are then used to model the time series of both the trend and stochastic components. The predicted wind speeds are finally summed from the individual predictions of the ML methods and harmonic analyses. To highlight the considerably higher predictive accuracy that results from the introduced data pre-treatments with HPF and HA, the proposed hybrid algorithm is compared against the traditional ML methods that are not subjected to the pre-treatments. The proposed hybrid algorithms are highly accurate relative to the traditional ML methods reflecting much higher coefficients of determination and correlation coefficients, and much lower error indices. Artificial neural networks (ANNs), linear regression with interactions (LRI), support vector machine (SVM), rational quadratic Gaussian process regression (RQGPR), fine regression trees (FRTs) and boosted ensembles of trees (BETs) are used as the illustrative machine learning methods. To guarantee both versatility and robustness, the methods are tested on example data drawn from both temperate and tropical conditions.
EVALUATION OF DICHLORVOS INHALATION ON THE LIVER HISTOLOGY AND SELECTED LIVER ENZYMES ACTIVITIES IN ADULT WISTAR RATS

Dichlorvos is one of the cheapest insecticides use to eradicate mosquitoes in Nigeria. It forms the active ingredient of the solution called Ota piapia or Madarar piapia . It binds irreversibly with acetylcholine receptors and inhibits its activity. It is metabolized in the liver and kidney. Oral ingestion of dichlorvos was commonest cause of toxicity reported however few literatures were reported on the effect of dichlorvos inhalation on the liver. The aim of this study was to evaluate the effects of dichlorvos inhalation on the liver histology and selected liver enzymes in Adult Wistar Rats. Twenty five apparently healthy Adult Wistar Rats were randomly selected and divided into five groups. Group one was used as control the rest were used as treatments for twenty eight days. The animals were sacrificed; their blood and liver tissues were collected for enzyme assay and routine histological technique respectively. Significant increase [ P ˂ 0.05] in the values of the Albumin, Alkaline Phosphatase, Aspartate Aminotransferase and Alanine Aminotransferase as well as variable degrees of apoptosis and steatosis were observed in dose dependent manner in the treatment groups. Dichlorvos inhalation damaged histo-architechture and altered enzymes activities of the liver.