Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Block Matching Optical Flow Algorithms

– The paper presents a study of existing Block Matching Optical Flow algorithms, which estimate the motion from an image sequence. The seven algorithms selected for our performance and comparative study (ES, TSS, SETSS, NTSS, FSS, DS and ARPS) are finding a minimum cost match between the current image reference block and one of the considered blocks in the next image. The benchmark used for the performance testing was the Middlebury Dataset, with the conclusions leading to a parameters and algorithm selection criteria.
RStorm: Developing and Testing Streaming Algorithms in R

Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams. However, in current production packages testing and evaluating streaming algorithms is cumbersome. This paper presents RStorm for the development and evaluation of streaming algorithms analogous to these production packages, but implemented fully in R. RStorm allows developers of streaming algorithms to quickly test, iterate, and evaluate various implementations of streaming algorithms. The paper provides both a canonical computer science example, the streaming word count, and examples of several statistical applications of RStorm.
User Personality Prediction on Facebook Social Media using Machine Learning

In recent years, Social network use is increasingly build-up. The various statistics are split widely through social media Such as Facebook, Twitter. Data about the person and what they communicate through the status updates are important for research in human personality. This paper intends to scrutinize the forecasting of personality traits of Facebook users bases on machine learning and part of the Big ﬁve model this experiment uses my personality data set of Facebook users are used for linguistic factors respective to personality correlation. We used the Data Prepossessing concept of data mining after that feature Extraction. Next, we will work on feature selection. The Personality Prediction system built in the XGboosting classiﬁcation model.
Big data in cloud computing and the related security issues

This paper discusses the security issues associated with cloud computing environment. Additionally, big data, Map Reduce and Hadoop environment have also been catered through this research. The use of big data applications is rising over time because of the benefits of managing massive amount of data easily. Both small scale and large scale organizations use the big data application. Furthermore, the solutions of the problems of cloud computing have been discussed along with Hadoop. This security is developing rapidly and it includes security of the information, computer security, security of the network, and data privacy. The use of cloud computing is evident in policies, technologies, controls, and big data tools that are being used currently. The technology of Cloud computing, big data and its applications are going to form the future of science.
Detection and Prediction of Active Tuberculosis Disease by a Whole-Blood Interferon- g Release Assay in HIV-1–Infected Individuals

Background. The sensitivity of whole-blood interferon- g release assays to detect or predict active tuberculosis in individuals infected with human immunodeﬁciency virus type 1 (HIV-1) has as yet not been determined. Methods. In this prospective, longitudinal, single-center study, 830 HIV-1–infected patients underwent testing with the QuantiFERON-TB Gold In-Tube (QFT-GIT) assay. Clinical screening for active tuberculosis was performed at least every 3 months for a median follow-up time of 19 months. Results. At baseline, the QFT-GIT assay yielded positive or indeterminate results in 44 (5.3%) and 47 (5.7%) of the 830 patients, respectively. A positive QFT-GIT assay result occurred at signiﬁcantly higher frequencies among black individuals than among white individuals (odds ratio, 4.84; 95% conﬁdence interval, 2.25–9.97; ), P ! 001 among patients from Africa than among patients from Austria (odds ratio, 6.57; 95% conﬁdence interval, 2.99– 14.25; ), and among patients from high-prevalence countries than among patients from low-prevalence P !
Investigating uncertainties in zooplankton composition shifts under climate change scenarios in the Mediterranean Sea

Ensemble niche modelling has become a common framework to predict changes in assemblages composition under climate change scenarios. The amount of uncertainty generated by the different components of this framework has rarely been assessed. In the marine realm forecasts have usually focused on taxa representing the top of the marine food-web, thus overlooking their basal component: the plankton. Calibrating environmental niche models at the global scale, we modelled the habitat suitability of 106 copepod species and estimated the dissimilarity between present and future zooplanktonic assemblages in the surface Mediterranean Sea. We identified the patterns (species replacement versus nestedness) driving the predicted dissimilarity, and quantified the relative contributions of different uncertainty sources: environmental niche models, greenhouse gas emission scenarios, circulation model configurations and species prevalence. Our results confirm that the choice of the niche modelling method is the greatest source of uncertainty in habitat suitability projections. Presence-only and presence–absence methods provided different visions of the niches, which subsequently lead to different future scenarios of biodiversity changes. Nestedness with decline in species richness is the pattern driving dissimilarity between present and future copepod assemblages. Our projections contrast with those reported for higher trophic levels, suggesting that different components of the pelagic food-web may respond discordantly to future climatic changes. In a context of climate change, ENMs
A concurrent engineering approach to develop BioMEMS employed in a deep brain stimulator integrated with a drug delivery system

This paper presents an Integrated Product Development (IPD) based model to specifically develop bio-medical micro-electro-mechanical-systems (BioMEMS). The concurrent engineering model is based on the IPD model phases, which are presented and formulated by the Integration DEFinition (IDEF) modelling language. To evaluate the IPD model, a case study concerning the development of a BioMEMS device for a deep brain stimulation (DBS) system was investigated. By following the relevant mechanisms and controls in the model, a design concept of a wireless headmounted DBS implant integrated with a drug delivery system (DDS) was conceived. The contribution of this paper is the IDEF model, which provides a road map to the product development team members in order to take a concurrent engineering approach to develop BioMEMS. The qualitative feedback received from the identified stakeholders, together with the quality of the case study employed, namely, an integrated DBS and DDS solution, indicate a degree of evidence that the model provides a sound basis in this direction.
Mechanical testing of glutaraldehyde cross-linked mitral valves. Part two: Elastic and viscoelastic properties of chordae tendineae

The aim of this study was to assess whether the mechanical properties of mitral valve chordae tendineae are sensitive to being cross-linked under load. A total 64 chordae were extracted from eight porcine hearts. Two chordae (posterior basal) from each heart were subjected to uniaxial ramp testing and six chordae (two strut, two anterior basal and two posterior basal) were subjected to dynamic mechanical analysis over frequencies between 0.5 and 10 Hz. Chordae were either cross-linked in tension or cross-linked in the absence of loading. Chordae cross-linked under load transitioned from high to low extension at a lower strain than cross-linked unloaded chordae (0.07 cf. 0.22), with greater pre-transitional (30.8 MPa cf. 5.78 MPa) and post-transitional (139 MPa cf. 74.1 MPa) moduli. The mean storage modulus of anterior strut chordae ranged from 48 to 54 MPa for cross-linked unloaded chordae, as compared to 53–61 MPa cross-linked loaded chordae. The mean loss modulus of anterior strut chordae ranged from 2.3 to 2.9 MPa for cross-linked unloaded chordae, as compared to 3.8–4.8 MPa cross-linked loaded chordae. The elastic and viscoelastic properties of chordae following glutaraldehyde cross-linking are dependent on the inclusion/exclusion of loading during the cross-linking process; with loading increasing the magnitude of the material properties measured.
Final Report - Implementation of RingBFT

Federated data management has been widely used in various scientific data management applications[10]. Due to the increasing number of byzantine attacks[1], Byzantine Fault-Tolerant (BFT) consensus protocols have become essential in distributed databases and related fields such as blockchain. When client transactions require access to a single-shard, the traditional BFT protocols like PBFT[3], are efficient on sharded-replicated blockchains. However, in many situations, cross-shard transactions are required[4, 9], and when client transactions require access to a cross-shard, the performance will face degradation. This paper[11] presents a novel meta-BFT protocol for sharded blockchains, RingBFT, which can significantly reduce the costs with cross-shard transactions while supporting large scale shards, improving throughput performance, and guaranteeing safety and liveliness. Our goal for the final project is to implement and evaluate the proposed protocol, and compare the results and performance demonstrated in the paper on the ResilientDB platform[5].
Diverse Outreach of Macroprudential Policy: An Indication of Policy Leak?

Despite increasing awareness of the importance of countercyclical policies to overcome financial system instability, the potential leak of such policies comes to attention due to economic agents’ risk-taking behavior. This paper aims to investigate the potential leaks of the policy. Using the Estimator General Method of Moments-difference (GMM-diff), we found evidence that macroprudential policies are less functional in controlling non-financial firms’ credit growth than household credit growth. The result amplifies hesitation about the effectiveness of macroprudential policy caused by potential leaks coming from non-financial firms’ risk-taking behavior. We also found that macroprudential policy in developing countries is less effective than in developed countries. Hence, the financial stability goal cannot rely solely on macroprudential policy. Instead, it needs support from other mutual policies, such as the capital control policy and transparent regulatory boundaries, to prevent partial risk shift from regulated financial institutions to unregulated, prevalent in the less developed financial system.
Multi-View Mesh Reconstruction with Neural Deferred Shading

We propose an analysis-by-synthesis method for fast multi-view 3D reconstruction of opaque objects with arbitrary materials and illumination. State-of-the-art methods use both neural surface representations and neural rendering. While flexible, neural surface representations are a significant bottleneck in optimization runtime. Instead, we represent surfaces as triangle meshes and build a differentiable rendering pipeline around triangle rasterization and neural shading. The renderer is used in a gradient descent optimization where both a triangle mesh and a neural shader are jointly optimized to reproduce the multi-view images. We evaluate our method on a public 3D reconstruction dataset and show that it can match the reconstruction accuracy of traditional baselines and neural approaches while surpassing them in optimization runtime. Additionally, we investigate the shader and find that it learns an interpretable representation of appearance, enabling applications such as 3D material editing.
Differences in transcriptional response underlie why prior vaccination exceeds prior infection in eliciting robust immune responses in Omicron infected outpatients (preprint)

Antibody response following Omicron infection is reported to be less robust than that to other variants. Here we investigated how prior vaccination and/or prior infection modulates that response. Disease severity, antibody responses and immune transcriptomes were characterized in four groups of Omicron-infected outpatients (n=83): unvaccinated/no prior infection, vaccinated/no prior infection, unvaccinated/prior infection and vaccinated/prior infection. The percentage of patients with asymptomatic or mild disease was highest in the vaccinated/no prior infection group (87%) and lowest in the unvaccinated/no prior infection group (47%). Significant anti-Omicron spike antibody levels and neutralizing activity were detected in the vaccinated group immediately after infection but were not present in the unvaccinated/no prior infection group. Within two weeks, antibody levels against Omicron, increased. Omicron neutralizing activity in the vaccinated group exceeded that of the prior infection group. No increase in neutralizing activity in the unvaccinated/no prior infection group was seen. The unvaccinated/prior infection group showed an intermediate response. We then investigated the early transcriptomic response following Omicron infection in these outpatient populations and compared it to that found in unvaccinated hospitalized patients with Alpha infection. Omicron infected patients showed a gradient of transcriptional response dependent upon prior vaccination and infection status that correlated with disease severity. Vaccinated patients showed a significantly blunted interferon response as compared to both unvaccinated Omicron infected outpatients and unvaccinated Alpha infected hospitalized patients typified by the response of specific gene classes such as OAS and IFIT that control anti-viral responses and IFI27, a predictor of disease outcome.
Bioactive Compounds and Mineral Contents of Telfairia occidentalis hooker fil. Grown in Urea Solutions

: The study focused on the use of Urea containing hydroponic solutions for growing Telfairia occidentalis Hooker fil and evaluated the bioactive compounds and mineral contents of T. occidentalis . They solutions varied in the amount of Urea granules (25g, 50g, 75g, 100g, 125g and 150g, respectively) dissolved in water and designated as M 25 U, M 50 U, M 75 U, M 100 U, M 125 U, M 150 U and Control, respectively. Two-week old seedlings of T. occidentalis raised using River-sand were transferred into the growth media; in four replicates. The bioactive compounds (nutritional composition, vitamins, and amino acids contents) and mineral content were determined 5 weeks after planting following standard procedures. Telfairia occidentalis grown in Control and M 25 U media had the highest energy level (1509.76 KJ/100g and 1496.70 KJ/100g, respectively) while the lowest energy content (1351.87 J/100g) was recorded at M 75 U growth medium. The study showed that T. occidentalis contains more of water-soluble vitamins than fat-soluble vitamins. The leaves grown in M 100 U medium had the highest total vitamins value. Also, 9 essential amino acids (EAA) and 11 non-essential amino acids (NEAA) were detected and quantified in the leaves of T. occidentalis grown in varying Urea solutions. The EAA contents were lower than the NEAA contents. Amongst the amino acids and growth media, glycine content (9.10%) of leaves was the most concentrated in M 50 U medium. The concentrations of N, Ca, Mg and K in the leaves were high compared Na, Mn, Fe, Zn and Cu in the growth media. The leaves grown in the Control medium had the lowest mineral content in most of the growth media .
Income Convergence or Divergence in the Aftermath of the COVID-19 Shock?

The paper extends the work of Deaton (2021) by exploring the period of post-crisis recovery in 2021-2024. The paper documents per-capita income divergence during the period of post-shock recovery, with countries at the bottom of the income distribution falling significantly behind. Findings suggest that higher COVID-19 vaccination rates and targeted virus containment measures are associated with faster recovery in per-capita incomes in the medium term. Evidence on the effectiveness of economic support policies for reducing cross-country income inequality, including fiscal and monetary policies, is mixed especially in the case of developing countries.
The Cuticle of Caenorhabditis elegans II. Stage-Specific Changes in Ultrastructure and Protein Composition during Postembryonic Development

The cuticle of the free-living nematode Caenwhabditis elegans is a proteinaceous extracellular structure that is replaced at each of four postembryonic molts by the underlying hypodermis. The cuticles of the adult and three juvenile stages (Ll, Dauer larva, L4) have been compared ultrastructurally and biochemically. Each cuticle has an annulated surface and comprises two main layers, an inner basal layer and an outer cortical layer. The adult cuticle has an additional clear layer which separates the basal and cortical layers and is traversed by regularly arranged columns of electron-dense material. The fine structure of the cortical layer is similar in cuticles from different stages while that of the basal layer is stage specific. Purified cuticles were obtained by sonication and treatment with sodium dodecyl sulfate (SDS) and their component proteins solubilized with a sulfhydryl reducing agent. The degree of cuticle solubility is stage specific and the insoluble structures for each cuticle were localized by electron microscopy. Analysis of %-labeled soluble cuticle proteins by SDS-polyacrylamide gel electrophoresis yields unique banding patterns for each stage. Most proteins are of high molecular weight (100-200 K) and are restricted to particular stages. Sixteen of the nineteen major proteins characterized are specifically degraded by bacterial collagenase. The results indicate that the different molts are not reiterative, but require the integration of both unique and shared gene functions. The potential use of stage-specific cuticle differences to identify and characterize regulatory genes controlling cuticle-type switching during development is discussed.
When International Blueprints Hit Local Realities : Bricolage Processes in Implementing IWRM in South Africa, Mongolia and Peru

: International targets such as the Sustainable Development Goals or those that are set as part of Integrated Water Resources Management (IWRM) programmes are, on the whole, universally accepted; however, they are often shaped mainly in the Global North. As a result, when these institutionally set targets conflict with pre-existing rules and norms in implementing states, implementation difficulties may result, as one can currently observe with regard to IWRM and SDG 6.5. Governance challenges that result from implementation gaps are often filled at the local level, where actors arrange for functional management processes despite institutional insecurity. Applying institutional bricolage theory, we investigate such processes for South Africa, Mongolia and Peru, focusing on how horizontal and vertical coordination, as well as participation, are achieved as key aspects of IWRM. By adopting an analytical frame focusing on institutions, discourses and power relations based on Frances Cleaver’s bricolage dimensions, we show how their governance and management arrangements have evolved. In the process of comparing the three cases, we consider what conclusions can be drawn from these arrangements with regard to facilitating institutional transfer processes. Our study shows that informal aspects of governance systems powerfully influence the interpretation of newly introduced policies. We find that efforts to implement international blueprints that neglect institutional legacies, sociocultural dynamics, and pre-existing inequalities are unlikely to result in arrangements that are suited to local realities.
Higher Category Theory and Hilbert’s Sixth Problem

In 1900 David Hilbert published his famous list of 23 problems. The sixth of them - the axiomatization of Physics - remains partially unsolved. In this work we will give a gentle introduction and a brief review to one of the most recent and formal approaches to this problem, based on synthetic higher categorical languages. This approach, developed by Baez, Schreiber, Sati, Fiorenza, Freed, Lurie and many others, provides a formalization to the notion of classical ﬁeld theory in terms of twisted diﬀerential cohomologies in cohesive ( ∞ , 1)-topos. Furthermore, following the Atiyah-Witten functorial style of topological quantum ﬁeld theories, it provides a nonperturbative quantization for classical ﬁeld theories whose underlying cohomology satisﬁes orientability and duality conditions. We follow a pedagogical and almost non-technical style trying to minimize the mathematical prerequisites. No categorical background is required.
A deep learning approach to face swap detection

—We propose the method for detecting an incident at face authentication when an imposter falsifies the client's face using a digital technique named Face Swap to cheat the system. The method is based on a convolutional neural network to get facial features and classify them. The proposed method can work with faces obtained with low quality and heavy lighting conditions. It is confirmed by experiments on a big test dataset. Experiments show that the accuracy reaches values over 98% for low-quality images and over 99% for high-quality images. Classification results are congruent to the best results shown by the other known methods tested on the same test dataset. The proposed method can be applied to improve the quality of face authentication systems.
Supplementary Materials to “A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution”

1. Detailed descriptions on the proposed recurrent positional encoding method (refer to Section 3.2 in the main paper). 2. Derivation of the triplex SSIM formula (refer to Section 3.3 in the main paper). 3. Best choices for the balancing parameters α and β for our loss function (refer to Section 3.4 in the main paper). 4. Degradation settings and visual comparisons on the three recognition datasets (refer to Section 4.4 in the main paper).
Exploring Consumer Mobile Payment Adoption: A Multi-Country Study

The increasing popularity of mobile technologies is reshaping human activities including financial transactions. This research focuses on the use and acceptance of NFC-enabled proximity mobile payment (m-payment). This research builds on previous research highlighting the various factors influencing the use and acceptance of m-payment through the Technology Acceptance Model (TAM) and Diffusion of Innovation of (DoI). The results indicate a statistically significant relationship between compatibility, personal innovativeness, and behavioural intention to use m-payment. The findings of this study confirm the influence of external factors, i.e., compatibility and personal innovativeness, determines the end-users’ perceived usefulness and perceived ease of use of m-payment which subsequently determines their attitude towards using m-payment and the intention to use m-payment