Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Retrieval with Relational Semantic Indexing Color and 1 Gray Images 2

7 Due to the development of digital technology large number of image is available in web and 8 personal database and it take more time to classify and organize them. In AIA assigns label to 9 image content with this image is automatically classified and desired image can be retrieved. 10 Image retrieval is the one of the growing research area. To retrieve image Text and content 11 based methods used. In recent research focus on annotation based retrieval. Image annotation 12 represents assigning keywords to image based on its contents and it use machine learning 13 techniques. Using image content with more relevant keywords leads fast indexing and retrieval 14 of image from large collection of image database. Many techniques have been proposed for the 15 last decades and it gives some improvement in retrieval performance. In this proposed work 16 Relational Semantic Indexing (RSI) based LQT technique reduces the search time and 17 increase the retrieval performance. This proposed method includes segmentation, feature 18 extraction, classification, and RSI based annotation steps. This proposed method compared 19 against IAIA, and LSH algorithms
Symptom Perceptions and Help-Seeking Behaviours of Omani Patients Diagnosed with Late-Stage Colorectal Cancer: A Qualitative Study

Objective: Colorectal cancer (CRC) is the fourth leading cause of mortality in Oman, with most patients diagnosed at advanced stages. Early diagnosis of CRC improves prognosis and survival rate. The aim of this study was to explore the symptom perceptions and help-seeking behaviours (HSBs) of Omani patients diagnosed with late-stage CRC. Methods: Semi-structured individual interviews were conducted with 16 patients. Results: Four main themes emerged, including normalisation and ignorance (patients felt healthy, perceived symptoms as not being serious and related to dietary habits, concealed them or prioritised work and family commitments), self-empowerment and self-management (patients were stubborn, employed ‘wait and see’ approach, used symptomatic or herbal treatments), disclosure and seeking help (patients disclosed symptoms to family members or friends, sought medical help only when symptoms worsened, visited faith healers or travelled abroad for treatment) and healthcare professionals (patients attributed treatment or diagnosis delays to lack of continuity of care, loss of trust in doctors or delays in referral). Conclusion: Patients attributed delays in CRC diagnosis to several factors based on their perceptions of symptoms. Most HSBs driven by sociocultural and emotional causes. Increased awareness of CRC symptoms and modifying HSBs can encourage early diagnosis. Prompting patients to disclose CRC-related symptoms may aid referral decisions.
Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border

On July 1st, 2020, members of the European Union gradually lifted earlier COVID-19 restrictions on non-essential travel. In response, we designed and deployed “EVA” – a novel, self-learning artificial intelligence system – across all Greek borders to identify asymptomatic travelers infected with SARS-CoV-2 based on demographic characteristics and results from previously tested travelers. EVA allocates Greece’s limited testing resources to (i) limit the importation of new cases and (ii) provide real-time estimates of COVID-19 prevalence to inform border policies. Counterfactual analysis shows that our system identified on average 1.85x as many asymptomatic, infected travelers as random surveillance testing, and up to 2-4x as many during peak travel. Moreover, for most countries, EVA identified atypically high prevalence 9-days earlier than machine learning systems based on publicly reported data. By adaptively adjusting border policies 9-days earlier, EVA prevented additional infected travelers from arriving. Finally, using EVA’s unique cross-country, large-scale dataset on prevalence in asymptomatic populations, we show that commonly used public data on cases/deaths/testing have limited predictive value for the prevalence among asymptomatic travelers, and furthermore exhibit strong country-specific idiosyncrasies. As herd immunity is still likely more than a year away [1], and travel protocols for the summer of 2021 are still being discussed, our insights raise serious concerns about internationally proposed border control policies [2] that are both country-agnostic and solely based on public data. Instead, our work paves the way for leveraging AI and real-time data for public health goals, such as border control during a pandemic.
Understanding the Complexity of Detecting Political Ads

Online political advertising has grown significantly over the last few years. To monitor online sponsored political discourse, companies such as Facebook, Google, and Twitter have created public Ad Libraries collecting the political ads that run on their platforms. Currently, both policymakers and platforms are debating further restrictions on political advertising to deter misuses. This paper investigates whether we can reliably distinguish political ads from non-political ads. We take an empirical approach to analyze what kind of ads are deemed political by ordinary people and what kind of ads lead to disagreement. Our results show a significant disagreement between what ad platforms, ordinary people, and advertisers consider political and suggest that this disagreement mainly comes from diverging opinions on which ads address social issues. Overall our results imply that it is important to consider social issue ads as political, but they also complicate political advertising regulations.
An Engineered Reporter Phage for the Fluorometric Detection of Escherichia coli in Ground Beef

Despite enhanced sanitation implementations, foodborne bacterial pathogens still remain a major threat to public health and generate high costs for the food industry. Reporter bacteriophage (phage) systems have been regarded as a powerful technology for diagnostic assays for their extraordinary specificity to target cells and cost-effectiveness. Our study introduced an enzyme-based fluorescent assay for detecting the presence of E. coli using the T7 phage engineered with the lacZ operon which encodes beta-galactosidase (β-gal). Both endogenous and overexpressed β-gal expression was monitored using a fluorescent-based method with 4-methylumbelliferyl β-d-galactopyranoside (MUG) as the substrate. The infection of E. coli with engineered phages resulted in a detection limit of 10 CFU/mL in ground beef juice after 7 h of incubation. In this study, we demonstrated that the overexpression of β-gal coupled with a fluorogenic substrate can provide a straightforward and sensitive approach to detect the potential biological contamination in food samples. The results also suggested that this system can be applied to detect E. coli strains isolated from environmental samples, indicating a broader range of bacterial detection.
Car Hacking and Defense Competition on In-Vehicle Network

—Cybersecurity competitions can promote the importance of security and discover talented researchers. We hosted the Car Hacking: Attack & Defense Challenge from September 14, 2020 to November 27, 2020, and many security companies and researchers participated. To the best of our knowledge, it is the ﬁrst competition to contest both attack and detection techniques on an in-vehicle network, speciﬁcally Controller Area Network (CAN). The participants developed various injection attacks and high-performance detection algorithms based on the real vehicle environment. Rule-based and ensemble tree-based models dominated the ﬁnal round. Also, time interval and data byte patterns worked as major features to detect attacks.
A Validated Reversed Phase HPLC Assay for the Determination of Gliclazide in Human Plasma

DOI: 10.21276/sjmps.2017. 3.10. 19 Abstract: A simple and precise reversed-phase high performance liquid chromatography (HPLC) method for the determination of gliclazide in human plasma was developed and validated. Using glipizide as an internal standard (IS), separation was achieved on Atlantis dC18 column. The mobile phase consisted of acetonitrile and water (pH= 2.5±0.2 adjusted with phosphoric acid) (55:45, v:v), and delivered at a flow rate of 1 ml/min. 0.5 ml plasma samples were deproteinized with dichloromethane and centrifuged. 100 μl supernatant clear solutions were injected to HPLC system. The eluent was monitored spectrophotometricly at 230 nm. No interference in blank plasma or of commonly used drugs was observed. The relationship between the concentration of gliclazide in plasma and peak height ratio of gliclazide to the IS was linear over the range of 0.05-10.0 μg/ml. Intra-day and inter-day coefficient of variation (CV) and bias were 3.1% to 6.8% and 3.8% to 9.3% respectively. Mean extraction recovery of gliclazide and the IS from plasma samples was 94% and 87% respectively. The method was applied to assess the stability of gliclazide under various conditions generally encountered in the clinical laboratory. Stability for processed and unprocessed samples was ≥ 90% and ≥ 93% respectively.
Polysemy and Synonymy Detection in Ontology Engineering

Polysemy, when a single term has multiple meanings, and synonymy, when multiple terms have the same meaning, are common phenomena in linguistics as well as in scientific knowledge. In ontology engineering, it is vital to detect the synonyms annotations and the multiple inheritances because of polysemy. The persistence of these issues in the semantic description of a knowledge domain causes problematic interoperability and data processing. The disambiguation of the entities, properties and relationships sense in a semantic web ontology significantly improves linked data generation and information retrieval. We explore the synonymy and polysemy in the setting of a cardiology terminology generated from textbooks on the basis of field coverage, professionals’ associations’ recommendations and bibliometrics, for the building of a cardiologic ontology. From 56,134 terms collected we found that 67.7% were unique. The indexed terms included single words, compound words and multi-word expressions. The frequency of their appearances in the combined master index was calculated and used as a marker of their significance. To cope with the linguistic polysemy and synonymy of terms, we examined them in WordNet, MeSH and BioPortal, as well as by latent semantic analysis (LSA) through singular value decomposition (SVD). Through these approaches we managed to identify and decipher semantic associations and relationships between the terms. We proposed a roadmap for ontology building from scratch by utilizing intrinsic and extrinsic knowledge resources and reuse of metadata. We anticipate that this approach is applicable in ontology engineering of different knowledge domains for relationships setting and linked data contextualization
Duke University Dissertation Template

Investigating Unfolded Proteins by Small-Angle X-Ray Scattering by Danna Li Department of Chemistry Duke University Date:_______________________ Approved: ___________________________ Terrence G.Oas, Supervisor ___________________________ Qiu Wang ___________________________ David Richardson ___________________________ Patrick Charbonneau An abstract of a thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Chemistry in the Graduate School of Duke University 2013 Copyright by Danna Li 2013
Prevalence and related risk factors of psychotic symptoms among inpatients infected with COVID-19 during the second wave pandemic

Background Possibility of psychotic symptoms and related risk factors among the normal person and infected patients during the outbreak of COVID-19 has been widely investigated in previous studies. With the arrival of the second wave of the epidemic in many countries around the word, the accordingly mental health outcomes were unclear yet, especially the mental health outcomes among infected patients. It aims to explore the prevalence of and related risk factors associated with psychotic symptoms in COVID-19 infected inpatients during the second wave. Method: A cross-sectional survey was conducted in five isolated wards of a designated hospital in Beijing, China, from 1 July to 15 July, 2020. The Mini-International Neuropsychiatric Interview (M.I.N.I) was conducted to assess the psychiatric disorders, and a serious of scales was used to measure self-reported psychotic symptoms and psychosomatic factors. Multivariate regression analysis was used to analyze the risk factors associated with psychotic symptoms. Results Among 199 infected participants, the prevalence of generalized anxiety symptoms is 51.3%, of depressive symptoms 41.2%, of PTSD symptoms 33.6%. Loneliness, hope, coping strategies, history of mental disorders were shared risk or protective factors across several psychotic symptoms. Perceived impact by COVID-19 is the specific risk factor associated with state anxiety symptoms. Conclusions The prevalence of symptoms of depression, anxiety and PTSS/PTSD is high among infected inpatients during the second wave of pandemic in Beijing. Clinical doctors must realize that infected inpatients during the second wave of pandemic are likely to suffer from depressive disorder, anxiety disorders and PTSS/PTSD, as well as some neuropsychiatric syndromes. Specific mental health care is urgently needed to help inpatients cope with the virus during the second wave of pandemic.
Risk-mitigating effects of being prompt and transparent

We study ex ante risk-taking by banks in the global market for syndicated U.S. dollar loans in response to U.S. policy rates and potential mitigants of such risk-taking. We focus on the mitigating effects of banks’ official supervision; activities restrictions and capital regulation; and private monitoring and external governance. We find remarkably robust evidence of a global risk-taking channel of U.S. monetary policy and of a significant dampening effect on this channel of certain supervisory powers—in particular, prompt corrective power that allows for early intervention, cease and desist orders, and suspension of capital payouts. In a way, we show that microprudential tools have systemic, macroprudential effects. We also find small prudential “leakages” because of shadow bank participation in loan originations. Reporting transparency, but not the other mitigants, have robust dampening effects too. We conclude that supervisory stringency and reporting transparency may materially reduce financial stability risks from corporate leveraged debt by slowing down its build-up.
Computational Study of Structure and Thermochemistry of Some Endo‐ and Exohedral Fullerene Derivatives

Abstract DFT calculations were applied to the problem of fullerene isomerism. Two classes of thermally stable fullerene derivatives are considered: endohedral metallofullerenes, which in some cases reveal carbon cage topology different from that observed in the empty fullerenes of the same size, and fullerene fluorides, which have uniquely rich number of theoretically possible isomers.
LATE Estimators under Costly Non-compliance in Student-College Matching Markets

A growing literature exploits a feature of centralized college admission systems where students with similar admission scores in a neighborhood of a school’s admission threshold are or are not offered admission based on small quasi-random differences in admission scores. Assuming that the students at the margin of admission differ only in the treatment assignment, this literature relies on admission scores to instrument for admission or graduation. We point out that non-compliance with the centralized matching assignment typically corresponds to enrolling in one’s preferred program a year after the initial assignment, introducing significant non-compliance costs. We show that with costly non-compliance, the exclusion restriction, the key assumption of the LATE theorem, is violated, leading to biased estimates when instrumenting for graduation, i.e., for a treatment taking place after non-compliance costs are incurred. We use data from a student-college matching market in Croatia to illustrate the empirical importance of this potential source of bias and propose a method inspired by Lee (2009), which recovers the treatment effect bounds under the assumption that the costs of non-compliance are not related to the treatment assignment. ∗CERGE-EI, a joint workplace of Center for Economic Research and Graduate Education, Charles University and the Economics Institute of the Czech Academy of Sciences, Politickych veznu 7, P.O. Box 882, 111 21 Prague 1, Czech Republic. E-mail: marin.drlje@cerge-ei.cz stepan.jurajda@cerge-ei.cz. Jurajda is a Research Fellow at IZA, Bonn. We thank Thomas Le Barbanchon, Michal Bauer, Alena Bicakova, Henry Farber, Randall K. Filer, Vasily Korovkin, Dejan Kovac, David Lee, Andreas Menzel, Nikolas Mittag, Daniel Munich, Christopher Neilson, Christian Ochsner, Seth Zimmerman, Jan Zápal, Zhuan Pei and Kresimir Zigic for their many useful comments. We are also grateful to the Ministry of Education of Croatia and ASHE (AZVO) for access to their administrative data.
American Journal on Intellectual and Developmental Disabilities The FMR1 Premutation Phenotype and Mother-Youth Synchrony in Fragile X Syndrome

A subset of mothers who carry the FMR1 premutation may express a unique phenotype. The relationship between the FMR1 phenotype and mother-child interaction in families with fragile X associated disorders has not been well characterized, despite the importance of highquality mother-child interaction for child development. This study examined the association between the FMR1 phenotype and the quality of interactions between mothers and their adolescent/young adult sons with fragile X syndrome. Mother-youth synchrony was coded from a dyadic interaction. Maternal anxiety and depression symptoms, executive function deficits, and pragmatic language difficulties were evaluated. Results indicated that pragmatic language was associated with mother-youth synchrony. These findings highlight the importance of familycentered intervention practices for families with fragile X-associated disorders.
Massage Therapy for Pulmonary Function in Patients Recovering From Covid-19: A Protocol for Systematic Review and Meta-analysis

Background:Starting in December 2019 in Wuhan (Hubei province, China), a novel coronavirus, designated SARS-CoV-2, has caused an international outbreak of a respiratory illness and rapidly evolved into a pandemic.Given the rapidly growing pandemic and the overwhelmedmedical system, the number of self‐quarantined and recovering patients is increasing.There is an urgentneed of alternative medicine to help patients relieve symptoms duringself‐quarantine, and possibly to help increase their chances of survivaland recovery from COVID-19.Massage (tuina) therapy is one of the widely employed complementary and alternative medicine interventions in the world.Long-term clinicalpractices and experiences have shown that massage therapy could significantly contribute to the healing of most respiratory conditions and lung disease.This systematic review and meta-analysis will summarize the current evidence of tuina (massage) used as an intervention for pulmonary function in COVID-19 recovering patients.Methods:We will search the following electronic databases for randomized controlled trials to evaluate the effectiveness and safety of massage therapy inimproving pulmonary function ofCOVID-19 recovering patients: Wanfang and Pubmed Database, CNKI, CENTRAL, CINAHL, EMBASE and MEDLINE. Each database will be searched from inception to June 2020. The entire process will include study selection, data extraction, risk of bias assessment and meta-analyses.Discussion:This proposed systematic review will evaluate the existing evidence and explore the potential roleof massage therapyon the effectiveness and safety in pulmonary function of COVID-19 recovering patients.The outcomes will include the improvement of pulmonary function and adverse effect.PROSPERO registration number:CRD42020192107
Achieving Diﬀerential Privacy in Secure Multiparty Computation

Collecting data about people’s health contributes to useful medical research, adapting health care programs or improving overall knowledge about us, human beings. Data is stored in databases, which could be queried by various organizations and institutions. As the databases are likely to contain sensitive data, it would be a violation of privacy if this information is disclosed somehow. In particular, statistics gathered from these databases are subject to disclosing private information. Diﬀerential privacy is a technique to preserve the privacy of the individuals in these situations, by generating randomness and perturbing statistics by adding noise. Secure multiparty computation techniques distributes the computation of the statistics and the noise for a more robust privacy guarantee. This thesis explores possibilities for generating randomness in a secure multiparty computation setting in order to generate the noise accordingly.
Wishing Everyone a Happy New Year !

In this paper we present a novel simulation technique for generating high quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission, with across track resolutions of any chosen magnitude. In essence, our model extends Generative Adversarial Networks (GANs) based architecture into a conditional recursive setting, that facilitates the continuity of the generated images. The data produced is continuous, realistically-looking, and can also be generated at least two times faster than the real speed of acquisition for the sonars with higher resolutions, such as EdgeTech. The seabed topography can be fully controlled by the user. The visual assessment tests demonstrate that humans cannot distinguish the simulated images from real. Moreover, experimental results suggest that in the absence of real data the autonomous recognition systems can benefit greatly from training with the synthetic data, produced by the R2D2-GANs. Second Prize (Certificate and $2000 prize) Matias Carandell, Universitat Politecnica de Catalunya, Spain Impact on the Wave Parameters Estimation of a Kinetic Energy Harvester Embedded into a Drifter Abstract—The effect of a Kinetic Energy Harvester (KEH) on the wave parameters estimation at a WAVY Ocean (WO) drifter The Student Poster Competition at Global OCEANS 2020The effect of a Kinetic Energy Harvester (KEH) on the wave parameters estimation at a WAVY Ocean (WO) drifter The Student Poster Competition at Global OCEANS 2020 Shyam Madhusudhana, OES Student Poster Competition Chair
NMVOC emissions vs. spaceborne HCHO columns

Evaluating the performance of pyrogenic and biogenic emission inventories against one decade of space-based formaldehyde columns T. Stavrakou, J.-F. Müller, I. De Smedt, M. Van Roozendael, G. R. van der Werf, L. Giglio, and A. Guenther Belgian Institute for Space Aeronomy, Avenue Circulaire 3, 1180, Brussels, Belgium Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands Science Systems and Applications, Inc. NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA National Center for Atmospheric Research, Boulder, CO 80303, USA