Miracle-dz/UniFAR-FTUs huggingface dataset:
A Statistical Approach to Two-Particle Bell Tests

Extensive experimental tests of the Bell inequality have been conducted over time and the test results are viewed as a testimony to quantum mechanics. In considering the close tie between quantum mechanics and statistical theory, this paper identifies the mistake in previous statistical explanation and uses an elegant statistical approach to derive general formulas for two-particle Bell tests, without invoking any wavefunctions. The results show that, for the special case where the spins/polarizations are in the same, opposite, or perpendicular directions, the general formulas derived in this paper convert to quantum predictions, which are confirmed by numerous experiments. The paper also investigates the linkages between the statistical and quantum predictions and finds that vector decomposition and probability law are at the heart of both approaches. Based on this finding, the paper explains statistically why the local hidden variable theory fails the Bell tests. The paper has important implications for quantum computing, quantum theory in general, and the role of randomism and realism in physics.
Designing for Aging across Borders: Craft, Body, Personhood

This position paper contributes to the HCI agenda on aging with a focus on novel design methods and interactive systems emphasizing personhood through body-centric and sensory stimulation approaches. It presents three case studies focusing on innovative technologies for cuing autobiographical memories in old age, supporting stimulation needs in dementia in the Western context, and for supporting weaving craft practices in developing context. We conclude with three design principles highlighting craft practices, bodily involvement, and personhood that can be seen from the lens of the individualist-collectivist cultures.
Exploiting Outlier Value Effects in Sparse Urban CrowdSensing

Sparse spatiotemporal data completion is crucial in Mobile CrowdSensing for urban application scenarios. In fact, accurate urban data completion can enhance data expression, improve urban analysis, and ultimately guide city planning. However, it is a non-trivial task to consider outlier values caused by the special events (e.g., parking peak, traffic congestion, or festival parade) in spatiotemporal data completion because of the following challenges: 1) the rarity and unpredictability, 2) the inconsistency compared to normal values, and 3) the complex spatiotemporal relations. In spite of the considerable improvements, recent deep learning-based methods overlook the existence of outlier values, which results in misidentifying these values. To this end, focusing on spatiotemporal data, we propose a matrix completion method that takes outlier value effects into consideration. Specifically, an outlier value model is proposed by adding a memory network and modifying the loss function to traditional matrix completion. Along this line, we extract the features of outlier values and further efficiently complete and predict the unsensed data. Finally, we conduct both qualitative and quantitative experiments on three different datasets, and the results demonstrate that the performance of our method outperforms the state-of-the-art baselines.
Running head: FOURTH GRADERS’ MULTIPLE-DOCUMENT COMPREHENSION Going Beyond Children’s Single-Text Comprehension: The Role of Fundamental and Higher–Level Skills in 4 Graders’ Multiple-Document Comprehension

Background. Children’s comprehension of single texts relies on both foundational and higher-level skills. These are also assumed to support multiple-document comprehension, but their relative importance has not been examined, to date. Multiple-document comprehension additionally requires the identification and use of information about each document’s source. Aims. This study examined multiple-document comprehension in primary school-aged children. It sought to determine the relative importance of skills proposed to be common to both singleand multiple-document comprehension (word reading fluency, verbal working memory, comprehension monitoring) and specific to the latter (source use). Single-text comprehension and prior topic knowledge were considered as moderator and control. Sample. Participants were 94 children in the fourth year (mean age = 9;7 years; 52% females).
The Crystal-Structure of Silver(I) (2-Carbamoylphenoxy)Acetate

The crystal structure of anhydrous silver(i) (2-carbamoylphenoxy)acetate has been determined by X-ray diffraction and refined to a residual of 0.041 for 1532 observed reflections. The complex forms crystals with space group C 2/c and Z 4 in a unit cell of dimensions a 20.789(6), b 5.202(1) c 17.318(6) A, s 105.89(2)°. The structure is based on a centrosymmetric bis-carboxylate bridged dimer [Ag-O 2.228(3), 2-266(3) A, O-Ag-O 155.8(1)° Ag…Ag 3-001(1) A] which is extended into an infinite zigzag polymer through the axial positions via the carbamoyl oxygens of adjacent ligands [Ag-O 2.494(3) A]. Symmetrical long-range interactions of phenyl-carbon to silver are also found in the structure [Ag-C 3.094, 3.078(4) A].
Predicting Unknown Adverse Drug Reactions Using an Unsupervised Node Embedding Algorithm

Defined as undesirable effects of a medication that occur during or after usual clinical use, Adverse Drug Reactions (ADRs) pose a major health risk and result in the hospitalization of millions of patients each year. While pre-marketing clinical trials evaluate the safety and efficacy of a new drug, post-marketing surveillance identifies and monitors ADRs that were not previously identified during trials. Traditionally, most approaches tend to focus on ADR detection in the post-marketing phase. Also current approaches mostly use supervised machine learning, requiring significant preprocessing of the data and feature engineering. I developed a customizable framework based on unsupervised learning that allows users to run prediction tasks on different types of labeled graph data. The framework first creates a knowledge graph from the data and then uses an unsupervised algorithm to create embeddings (vector representations) of the nodes in the knowledge graph, and finally runs the prediction task. The framework enables an embedding to be learned for any newly added node as long as it is connected with the other nodes, and users can create embeddings for any pre-marketed drug as long as its related drug attributes are present in the knowledge graph. Using DrugBank and FAERS, I created a knowledge graph of drugs and drug attributes. To emulate drugs in the pre-marketing stage, I removed all the drug-ADR edges in the test dataset. Then, I experimented with different parameters of the node embedding algorithm and three different classifiers namely MLP, KNN and random forest. The models were trained to predict 9 different ADR associations for any drug, and our results showed that the MLP classifier was the best model with an AUROC score of 0.79, which is comparable to existing approaches but with much greater customizability. This approach has potential to improve how ADRs are predicted and allow them to be detected at a far earlier stage thus improving patient safety. Thesis Supervisor: Lalana Kagal Title: Principal Research Scientist
Mitigation of chilling and freezing stresses through colonization with arbuscular mycorrhizal fungi in spring barley

Cold stress is an important limiting factor for cereal production. Barley is a host species for arbuscular mycorrhizal fungi (AMF) with a high genetic diversity in response to cold stress. In order to explore the mechanisms for the ameliorative effect of AMF under cold stress, an experiment was undertaken using completely randomized block design with three factors including temperature treatment, AMF inoculation and plant cultivar. Two spring barley cultivars with different cold tolerance (‘Reyhan’ as tolerant and ‘Torkaman’ as susceptible) were inoculated with two AMF species (Diversispora versiformis and Rhizophagus irregularis) and were grown under chilling (CH, 5 °C) and freezing (FR, ‒5 °C) temperatures for three weeks. Dry matter production, photosynthesis rate, and membrane integrity parameters decreased, while the antioxidant defense and the synthesis of phenolics were activated under CH stress. Inoculation of plants with AMF alleviated the adverse effects of CH stress on growth and membrane parameters, while exacerbated CH effect on the antioxidant system and phenolics accumulation. Plants could not survive FR stress when they were not cold acclimated through prior exposure to CH treatment, unless they were inoculated with AMF which resulted in survival rates almost similar to cold-acclimated plants. Our results suggested that AMF alleviated CH stress through reducing H2O2 and improved membrane integrity while the substituting effect of AMF for cold acclimation and increasing FR survival was mediated by the activation of antioxidant defense and phenolics synthesis and accumulation of proline.
HawkEDA: a tool for quantifying data integrity violations in event-driven microservices

A microservice architecture advocates for subdividing an application into small and independent components, each communicating via well-defined APIs or asynchronous events, to allow for higher scalability, availability, and fault isolation. However, the implementation of substantial amount of data management logic at the application-tier and the existence of functional dependencies cutting across microservices create a great barrier for developers to reason about application safety and performance trade-offs. To fill this gap, this work presents HawkEDA, the first data management tool that allows practitioners to experiment their microservice applications with different real-world workloads to quantify the amount of data integrity anomalies. In our demonstration, we present a case study of a popular open-source event-driven microservice to showcase the interface through which developers specify application semantics and the flexibility of HawkEDA.
A General Review of Human Face Image Detection using Machine Learning Classifier

: Face image detection is widely used technology has proven for many application like security camera, checking system at airports, retail marketing, people counting, mall etc. Face image detection is a very important field now a days because all over world used different face detection software application . This technology is only finding the human faces image in a frame video and counting the images. In this research, an efforts to use the haar cascades classifier which is comes from viola jones algorithm haar cascades method used the haar like feature by using this feature is to finding the efficient human feature like eye, nose, and forehead etc. In this study we uses the viola-jones algorithm for face detection. This system proposed two methodology to find the face, first phase is to detect face in clustered image using viola-jones. Second phase this algorithm is to extract the feature from face and classifies these feature using add boost classifier. This system, aim to determine the existence of the position of the human face in a frame. This study helps to person’s identification/identity in various application like home security, camera surveillance in any field. In this research discovering the how works the viola-jones algorithm, finding the human face accurately with contour and real time face detection. We use this system in many social media application.
Molecular identification of insecticide degradation by gut bacteria isolated from Helicoverpa armigera of Cotton plants

Old-World bollworm (Helicoverpa armigera) has attained the status of national pest owing to its devastative nature on commercial crops in India. The cotton bollworm H. armigera occurs as a major pest in many economically important crops, including cotton, pigeon pea, chickpea, pea, cowpea, sunflower, tomato, sorghum, pearl millet and other crops. The cotton grown in over 5% of the total cultivated area has consumed more than 55% of the total amount of pesticide used in India (Kranthi et al. 2001) A robust complement of prokaryotes is harbored in insects’ alimentary canals, facilitating nutrient availability, utilization, and detoxification of environmental toxins. Intestinal microorganisms play important role in the degradation of diet components of insects (Hayashi et al., 2007). However, the indigenous gut bacteria also play a role in withstanding and colonization of the gut by non-indigenous species including pathogens (Dillon and Dillon, 2004). Precious little is known about the microbial community inhabiting the guts of various higher organisms, though it is acknowledged that microbes play an important role in metabolism, growth, development and protection of their host. Lepidoptera species feed on plants and the biomass digestion is Abstract The cotton bollworm Helicoverpa armigera occurs as a major pest in many economically important crops, including cotton, pigeon pea, chickpea, pea, cowpea, sunflower, tomato, sorghum, pearl millet and other crops. Intestinal microorganisms play important role in the degradation of diet components of insects. In order to know the role of gut bacteria in insecticide resistance five insecticides Chlorpyriphos (20% EC), Cypermethrin (25% EC), Malathion (50% EC), Quinalphos (25% EC), Triazophos (40% EC), were selected for the insecticide degradation studies. All the bacterial isolates from the gut of lab and field populations of H. armigera were identified using 16S rRNA gene-based identification and tested for their growth on minimal salt medium (MSM) along with the selected insecticides. A total of 11 bacterial isolates were tested and among them, isolate CL4 (Rhodococcus sp.) was found to grow on minimal salt medium (MSM) and with chlorpyriphos and isolate CL2 (Enterococcus casseliflavus) was able to grow in MSM with chloropyriphos (C22H19Cl2NO3) and malathion (C10H19O6PS2) and no growth was seen in MSM without insecticide (control). Gas Chromatography analysis of the positive bacterial isolate cultures in MSM showed that the isolate CL4 (Rhodococcus sp.) was able to utilize 43.9% of chlorpyriphos and isolate CL2 (E.casseliflavus) was able to utilize 26% of chlorpyriphos and 57.1% of malathion in MSM broth cultures with comparison with the respective control cultures. Findings of the current work suggested that gut bacteria in the field populations of H. armigera plays a role in insecticide resistance.
Exploration and Veri cation of a Six-RNA Binding Proteins-based Prognosis Evaluating Model for Hepatocellular Carcinoma

Miao Chen Nanjing university of Chinese Medicine https://orcid.org/0000-0002-4718-376X Shujie Li Nanjing university of Chinese medicine, Nanjing, Jiangsu province, China Jiakang Zhang Nanjing university of Chinese medicine, Nanjing, Jiangsu province, China Jianbo Han Department of hepatobiliary surgery, the a liated Nanjing hospital of Nanjing university of Chinese medicine, Nanjing, Jiangsu province, China Lili Wang Clincal research center, the a liated Nanjing hospital of Nanjing university of Chinese medicine, Nanjing, Jiangsu province, China Dandan Yin Clincal research center, the a liated Nanjing hospital of Nanjing university of Chinese medicine, Nanjing, Jiangsu province, China Changhua Yi Clincal research center, the a liated Nanjing hospital of Nanjing university of Chinese medicine, Nanjing, Jiangsu province, China Yongxiang Yi (  18120148097@189.cn ) Department of hepatobiliary surgery, the a liated Nanjing hospital of Nanjing university of Chinese medicine, Nanjing, Jiangsu province, China https://orcid.org/0000-0001-6133-1207
A TRIDENT SCHOLAR PROJECT REPORT

The Maritime Continent (MC) is a region particularly susceptible to enhanced convective (thunderstorm) activity during the Madden-Julian Oscillation (MJO) active phase. As the ascending branch of the MJO envelope reaches the MC, the convective signal may either propagate eastward through the MC and reach the Pacific Ocean (an active-to-active event) or weaken over the MC, not reaching the Pacific Ocean (an active-to-inactive event). Accurately predicting MJO amplitude changes over the MC is currently challenging, partially due to a lack of full understanding of atmospheric conditions that favor active MJO propagation. Determining differences in the background atmospheric state among these active-to-active and active-toinactive events could aid understanding of the MJO, thereby increasing weather predictability in the MC and nearby regions critical to U.S. national security interests. To accomplish that objective, atmospheric conditions across two vertical levels of the atmosphere were explored in this study: (a) humidity and height in the troposphere, and (b) wind in the stratosphere. In both, the WheelerHendon Real-Time Multivariate MJO (RMM) Index was used to categorize MJO events over the MC from 1980-2017 into one of four event types based on their amplitude entering and exiting the region. Standard anomalies of specific humidity and geopotential height were then computed using data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim Reanalysis and compared among the four event types. Next, an empirical orthogonal function analysis was developed to identify phases of the Quasi-Biennial Oscillation (QBO) by direction and altitude of zonal wind centers. Impacts of the QBO on the upper troposphere and lower stratosphere that could serve as mechanisms for a QBO-MJO relationship were investigated. Finally, changes in MJO strength over the MC were compared to phase of the QBO in order to
Conversational and Image Recognition Chatbot

This paper proposes a chatbot framework that adopts a model which consists of natural language processing and image recognition technology. Based on this chatbot framework, neural encoder-decoder model is utilized with Late Fusion encoder[1] and 2 different decoders(generative and discriminate). The chatbot is able to detect object in the image, tell about and recognize the image, later on the chatbot is also able to answer the questions about this image. It is a task where an AI agent can hold meaningful dialogue with humans in natural language about a visual content, speciﬁcally images. The basic workﬂow is that given an Image (I), current question (Q) and a history of Question and answers (H), the agent should be able to generate the answer of the current question
Calculation of Everything by Q-theory

Abstract The purpose of this study is to describe the calculation algorithm for everything that was calculated in previous studies. All calculations are divided into kinetic state and steady state. When analyzing forces, kinetic state is correct, and when analyzing other particles, steady state is correct. The entire universe is the mixture of kinetic state and steady state at the ratio of dark energy. The origin of all things are three generation neutrinos, graviton, photon, and gluon. Other particles are a combination of the above 6 particles. Force is also a particle with mass, and from its analysis, the ratio of dark energy and dark matter is calculated as 2.692. Our universe is on a dimensional horizon within a 4D supermassive anti-black hole. From Schwarzschild formula, the ratio of dark matter and ordinary matter is calculated as 84.6%: 15.4%. The values of electron neutrino, light, weak force, gravitational force, up quark, charm quark, down quark, strange quark, H boson, cosmological constant, birth of life, stellar black hole, intermediate-mass black hole, supermassive black hole, the radius of Big Bang, and the mass of Big Bang were calculated as 0.1531 eV, 1.795E-36 eV, 1.010E-6, 5.904E-39, 2.254 MeV, 1277.7 MeV, 4.756 MeV, 95.16 MeV, 125.02 GeV, 1.0883E-52 /m2, 3.64E9 LY ago, 2.83 Mθ, 32.8 Mθ, 341 kMθ, 31.2 LY, 1.00E14 Mθ. The six generation Planck units are calculated, and from Planck mass and dark mass it is proved that universe expands at a constant velocity. The physical values of all multi-verse will be the same as above. Given the 4th digit of muon and tau neutrino masses, and the correct masses of top and bottom quarks, the calculations will be more accurate.
THE AUDITOR'S CHARACTERISTICS FOR BIG FOUR AUDIT COMPANIES: EMPIRICAL ANALYSIS IN THE BRAZILIAN MARKET

The phenomenon of globalization, the dependence on markets and the emergence of possible competitors, impose on organizations other forms of management and professionals with new capabilities. In this sense, it is crucial to identify the requirements most valued by companies, with regard to the skills of an auditor. The purpose of this article is to analyse the skills that accounting graduates must possess, in view of the characteristics sought by auditing companies when hiring their professionals. The sample consists of 62 questionnaires to professionals from Big Four audit companies in Brazil, a country with few studies in this area. The methodology used is a model of structural equations. The developed model consists of twenty observed variables, divided into three constructs, auditing, universities and graduates. The results show the importance of technical, accounting, technological knowledge and previous internships and/or jobs, as well as, of the institution that the graduate acquired the training. However, some behavioral characteristics (attendance, leadership) indicated less weight in the market demand process. The model presented thus corroborates the information for the selection process of professionals, as it indicates variables/characteristics in the binomial training/demand of the audit market.
Significance of Low-Oxygen Zone for Nitrogen Cycling in a Freshwater Lake : Production of N2O by Simultaneous Denitrification and Nitrification

Denitrifying and nitrifying activities were determined to elucidate the mechanism of N2O accumulation in the oxygen depleted zone of Lake Kizaki. Both activities were detected without any substrate enrichments in a zone of low-oxygen content (0.03-0.14 ml·-1), where N2O and NO2-were accumulated. The determined nitrifying activity approximately coincided with the NH4+ utilization estimated from the NH4+ budget in this zone during the observation interval. On the basis of these activities and the change in N2O concentation, it was suggested that the observed N2O accumulation had dual origins of nitrification and denitirification and that these productions were in a dynamic equilibrium with further reduction of N2O to N2. The low-oxygen zone was found to be an active site for nitrogen cycling and was characterized by the presence of dissolved Mn and the absences of dissolved Fe and sulfide.
Gender Disparity in Risk Factors of COVID-19 Mortality Rate

Incidence and mortality rates due to COVID-19 have varied widely in different parts of the world and placed a huge strain on hospital resources. Understanding the underlying reasons behind such variation is crucial to developing population-specific or even individual-specific management strategies. This paper presents a comprehensive analysis of incidence and mortality rates from data collected over a cumulative period of approximately 6.5 months from February to August 2020 across 411 districts of India, totalling over 2 million individuals. We identify the health factors which have both positive as well as negative correlates with high mortality rates, using data obtained from district-wise aggregated COVID-19 incidence and mortality rates and health data obtained from National Family Health Survey (NFHS). To obtain robust indicators, we apply both machine learning techniques as well as classical statistical methods and show that the same factors are identified by both methods. We also identify positive and negative correlates at multiple population scales by dividing the cohort into sub-cohorts formed from two Indian states which were further segregated by gender. We show that there is a disparity of risk factors among males and females. While obesity is the highest risk factor for men, anaemia is the highest risk factor for women. Hence, to better manage the health of a specific group of people, it is important to consider gender-wise heterogeneity in health risk factors which could contribute to differing vulnerabilities