Miracle-dz/UniFAR-FTUs huggingface dataset:
Deterministic Algorithms for the Lovasz Local Lemma

The Lovisz Local Lemma [6] (LLL) is a powerful result in probability theory that states that the probability that none of a set of bad events happens is nonzero if the probability of each event is small compared to the number of events that depend on it. It is often used in combination with the probabilistic method for non-constructive existence proofs. A prominent application is to k-CNF formulas, where LLL implies that, if every clause in the formula shares variables with at most d < 2k/e other clauses then such the formula has a satisfying assignment. Recently, a randomized algorithm to efficiently construct a satisfying assignment was given by Moser [17]. Subsequently Moser and Tardos [18] gave a randomized algorithm to construct the structures guaranteed by the LLL in a very general algorithmic framework. We address the main problem left open by Moser and Tardos of derandomizing these algorithms efficiently. Specifically, for a k-CNF formula with m clauses and d < 2k/(l+)/e for some c E (0, 1), we give an algorithm that finds a satisfying assignment in time O(m2(1+1/E)). This improves upon the deterministic algorithms of Moser and of MoserTardos with running times mn (k ) and mD(k 1/c) which are superpolynomial for k = w(1) and upon other previous algorithms which work only for d < 2k/ 16 /e. Our algorithm works efficiently for the asymmetric version of LLL under the algorithmic framework of Moser and Tardos [18] and is also parallelizable, i.e., has polylogarithmic running time using polynomially many processors. Thesis Supervisor: David R. Karger Title: Professor of Computer Science Bibliography of the Candidate Since September 2008 I have been a PhD student in the Theory Group of the MIT Computer Science Department. My advisor is David Karger. In 2007-2008 I was a visiting graduate student at the Theory Group of the Princeton's Computer Science Department where I worked with Robert E. Tarjan. From 2004 to 2007 I studied in Germany at the Technical University Munich where in October 2007 I completed the mathematics diploma. From 2006 to 2008 I also studied in the elite graduate program TopMath in Applied Mathematics in which I completed the TopMath Elite Bachelor of Science (2007) and the TopMath Master of Science with Honours (2009).
Initial Margin Requirements and Market Efficiency August 2021

We examine the association between margin requirements and the market’s efficiency in incorporating firm-specific and market-level public news. Combining the Fed’s 22 changes in margin requirements with a hand-collected sample of earnings announcements between 19341975, we show that higher margin requirements induce greater delay in incorporating earnings information into prices. We draw similar conclusions when we analyze the Hou and Moskowitz (2005) price delay measure, as well as indirect measures of leverage constraints over recent years. Further tests suggest that, despite the Fed’s expressed intent to curtail excess speculation, higher margin requirements restrict trading by arbitrageurs more than noise traders.
Standards for Reporting on Empirical Social Science Research in AERA Publications

The American Educational Research Association (AERA) is pleased to provide guidelines for reporting on empirical social science research in AERA publications. These guidelines apply to reports of education research grounded in the empirical traditions of the social sciences. They cover, but are not limited to, what are commonly called qualitative and quantitative methods. Other forms of scholarship equally important to education research include reviews of research; theoretical, conceptual, or method-ological essays; critiques of research traditions and practices; and scholarship more grounded in the humanities (e.g., history, philosophy , literary analysis, arts-based inquiry). The latter forms of scholarship are beyond the scope of this document. The aim of specifying reporting standards for empirical research in education is to assist researchers in the preparation of manuscripts that report such work, editors and reviewers in the consideration of these manuscripts for publication, and readers in learning from and building upon such publications. The primary audience for these standards is researchers who wish to publish reports of empirical research and who review such research for AERA publications. In adopting these standards, AERA emphasizes that the standards are intended to provide a framework of expectations, or rules of thumb, about what a report of empirical work ordinarily should address. The standards are not intended to define the conduct of empirical research. Although research reporting and research conduct are necessarily related, decisions about how to conduct empirical research are the researcher's responsibility. The standards are also not intended to define or determine the format for writing empirical work. Reports of different kinds of empirical research can take different forms, and authors working in different methodological or intellectual traditions may vary in the modes, order, or form of presentation. Finally, the acceptability of a research report does not rest on evidence of literal satisfaction of every standard in this document, and acceptability cannot be determined by using a checklist. In a given case, there may be a sound professional reason why a particular standard is inapplica-ble. The purpose in specifying these standards is to provide guidance about the kinds of information essential to understanding both the nature of the research and the importance of the results. While these standards are directed to authors, editors, reviewers, and readers of AERA journals, the substance of the standards and the breadth of methodological coverage are not particular to education research. Thus, in publishing these standards, the Association seeks to offer …
Length-Weight, Length-Length Relationship of the Spiny Eel, Macrognathus pancalus (Hamilton 1822) sampled from Ganges and Brahmaputra river basins, India

The freshwater spiny eel, Macrognathus pancalus is an inland water teleost fish commonly known as barred or striped spiny eel found in Asia. The sampling areas were selected which are spatially and geographically different and characterized by different environmental conditions in order to elucidation of ecotype. A total 345 specimens were collected from the sampling sites during January 2008 to December 2010. The weight of M. pancalus in the present study nearly the cube of its length in all cases as the values of regression coefficient „b‟ were found to be close to 3. The length weight relationship indicated the isometric growth in all the samples of striped spiny eel collected from different environmental condition. The coefficient of regression „b‟ were more in the riverine population (3.17±0.08) as compared to the populations of a large lake i.e. Beel (2.85±0.13). The coefficient of determination, (r 2 ) in all the cases was highly significant (p< 0.001). The relative condition factor (Kn) were also calculated and the average of condition factor in riverine and beel populations of spiny eel were found to be 0.50±0.09, and 0.47±0.05, respectively. The lengthlength relationship was highly correlated (r 2 = 0.99 at P<0.0001) for all the individuals of both habitats. A significant size difference was also noted between the river basin populations of M.pancalus. In the present study the length-weight, length-length relationship of M.pancalus in two different river basins was compared.
Voice Conversion for Lombard Speaking Style with Implicit and Explicit Acoustic Feature Conditioning

Text-to-Speech (TTS) systems in Lombard speaking style can improve the overall intelligibility of speech, useful for hearing loss and noisy conditions. However, training those models requires a large amount of data and the Lombard effect is challenging to record due to speaker and noise variability and tiring recording conditions. Voice conversion (VC) has been shown to be a useful augmentation technique to train TTS systems in the absence of recorded data from the target speaker in the target speaking style. In this paper, we are concerned with Lombard speaking style transfer. Our goal is to convert speaker identity while preserving the acoustic attributes that define the Lombard speaking style. We compare voice conversion models with implicit and explicit acoustic feature conditioning. We observe that our proposed implicit conditioning strategy achieves an intelligibility gain comparable to the model conditioned on explicit acoustic features, while also preserving speaker similarity.
FROM THE DESK OF THE EDITOR

In this new economy talent, ideas, energy and decision making power to employees are key to ultimate success of business. Employee provides benefit to business in some manner as the other tangible and intangible resources. Every company extended handsome salary and different benefits to the employees to retain them in competitive world. Prosperous companies come out with innovative compensation plan to its employer. Company spends crore of rupees in giving benefits to its employees. In this context, ICAI has framed Accounting Standard No 15 that discusses how the companies should give the treatment to the employees’ benefits and discloses various information in its annual report. The present study attempt to study the disclosure practices of various Fast Moving Consumer Goods companies. The results shows that there the companies follows the AS 15 as it is mandatory but there is no common disclosure practices among selected companies. The study found that Colgate Palmolive, Asian Paints, P&G and Philips India Ltd are the top companies in disclosure practices
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Introduction: Dental pulp is particularly susceptible to ischemic conditions (hypoxia and serum deprived) because it is commonly exposed to trauma, inflammation, chronic caries injury, and pulpitis. We investigated the apoptotic response of human dental pulp cells (HDPCs) to varying levels of oxygen and serum to mimic different degrees of ischemia, tested whether lysophosphatidic acid (LPA) could reverse ischemia-induced apoptosis, and investigated the possible mechanisms of LPA. Methods: HDPCs were cultured under conditions mimicking serum deprivation and ischemia for 2 days with or without LPA. Flow cytometry and JC-1 fluorescence were used to detect any apoptotic change. Western blotting was used to measure the expression of the apoptosis regulators B-cell lymphoma 2 (Bcl-2) and Bax, focal adhesion kinase (FAK), Src, extracellular signal-regulated kinase (ERK), and Akt. Results: Flow cytometry and JC-1 1⁄2Q2 immunofluorescence showed that ischemia could induce apoptosis of HDPCs in 2 days and treatment with LPA could reduce cell death significantly. To clarify the molecular mechanisms, Western blot results showed up-regulation of both proapoptotic Bax and antiapoptotic Bcl-2 during apoptosis. LPA functioned as an antiapoptotic cytokine by activation of the phosphorylation of FAK and ERK. No statistically significant difference was found in the activation levels of p-Src or p-Akt. Conclusions: A self-defense mechanism functioned during cell apoptosis. LPA could effectively rescue HDPCs from ischemia-induced apoptosis via regulation of Bax and Bcl-2 and the activation of phosphorylated FAK and phosphorylated ERK. LPA is a potent candidate for biological therapy of chronic pulpal inflammatory diseases. (J Endod 2013;-:1–6)
Many Voices Speak The River: Education in an Adventure- River- Landscape

A drag mechanism for a spinning reel which is provided at one of a stationary said member stationary with respect to a spool with a reference portion for the optimum braking force and at the other with a position setting portion corresponding to said reference portion, thereby indicating the position of an adjuster corresponding to the optimum braking force.
How Remote-Sensing Evapotranspiration Data Improve Hydrological Model Calibration in a Typical Basin of Qinghai-Tibetan Plateau Region

Many rivers in the East Asian Monsoon region originates from the Qinghai-Tibet Plateau (QTP), which provide huge amount of fresh water resources for downstream counties. As a region characterized by high altitude and cold weather, 25 distributed hydrological modelling provide valuable knowledge about water cycle and cryosphere of the QTP. However, the lack of streamflow data restricts the application of hydrological models in this data-sparse region. Previous studies have demonstrated the possibility of using remote sensing evapotranspiration (RS-ET) data to improve modelling. However, in the QTP, the mechanisms driving such improvements haven’t been understood thoroughly. In this study, such driving mechanisms were explored through the rainfall-runoff modelling of the Soil and Water Assessment Tool (SWAT) in the Yalong River 30 Basin of the QTP. Three experiments of model calibrations were conducted using streamflow data at the basin outlet, basins averaged RS-ET data of the Global Land Evaporation Amsterdam Model (GLEAM), and the combination of the both data, under the framework of the Generalized Likelihood Uncertainty Analysis (GLUE). The results show that compared with calibration using streamflow data solely, the Nash-Sutcliffe Efficiency of simulated streamflow at 50% quantiles for the calibration using both of streamflow and RS-ET data increased from 0.71 to 0.81 in the calibration period, while in the 35 validation period improved from 0.75 to 0.84, and more observations are embraced by the uncertainty bands. Similar improvements are also found for the ET estimates. Comparison of parameter posterior distributions among the three experiments demonstrated that calibration using both types of observations could increase the number of parameters that posterior distributions are different from assumed uniform prior distribution, indicating the degree of equifinality was reduced.
Post-Assay Chemical Enhancement for Highly Sensitive Lateral Flow Immunoassays: A Critical Review

Lateral flow immunoassay (LFIA) has found a broad application for testing in point-of-care (POC) settings. LFIA is performed using test strips—fully integrated multimembrane assemblies containing all reagents for assay performance. Migration of liquid sample along the test strip initiates the formation of labeled immunocomplexes, which are detected visually or instrumentally. The tradeoff of LFIA’s rapidity and user-friendliness is its relatively low sensitivity (high limit of detection), which restricts its applicability for detecting low-abundant targets. An increase in LFIA’s sensitivity has attracted many efforts and is often considered one of the primary directions in developing immunochemical POC assays. Post-assay enhancements based on chemical reactions facilitate high sensitivity. In this critical review, we explain the performance of post-assay chemical enhancements, discuss their advantages, limitations, compared limit of detection (LOD) improvements, and required time for the enhancement procedures. We raise concerns about the performance of enhanced LFIA and discuss the bottlenecks in the existing experiments. Finally, we suggest the experimental workflow for step-by-step development and validation of enhanced LFIA. This review summarizes the state-of-art of LFIA with chemical enhancement, offers ways to overcome existing limitations, and discusses future outlooks for highly sensitive testing in POC conditions.
Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunities

Trusted Research environments (TRE)s are safe and secure environments in which researchers can access sensitive data. With the growth and diversity of medical data such as Electronic Health Records (EHR), Medical Imaging and Genomic data, there is an increase in the use of Artificial Intelligence (AI) in general and the subfield of Machine Learning (ML) in particular in the healthcare domain. This generates the desire to disclose new types of outputs from TREs, such as trained machine learning models. Although specific guidelines and policies exists for statistical disclosure controls in TREs, they do not satisfactorily cover these new types of output request. In this paper, we define some of the challenges around the application and disclosure of machine learning for healthcare within TREs. We describe various vulnerabilities the introduction of AI brings to TREs. We also provide an introduction to the different types and levels of risks associated with the disclosure of trained ML models. We finally describe the new research opportunities in developing and adapting policies and tools for safely disclosing machine learning outputs from TREs.
Knowledge and Self-efficacy in Individuals with Type 2 Diabetes Mellitus

: Objective: To analyze the knowledge and self-efficacy of individuals with type 2 diabetes mellitus. Methodology: A cross-sectional and quantitative study was conducted in a public hospital in Recife, PE, with 86 individuals with diabetes. Sociodemographic, clinical, and laboratory variables were analyzed, in addition to knowledge and self-efficacy in diabetes, using the Diabetes Knowledge Scale and the Diabetes Management Self-Efficacy Scale, respectively. Descriptive statistics and Student's t-test for independent samples were used to verify homogeneity and compare means. Results: Most patients were female, under 60, with low education, low income, and inadequate diabetes control. Insufficient knowledge and low self-efficacy prevailed. Sufficient knowledge positively interfered with self-efficacy regarding physical exercise, and insufficient knowledge interfered with self-efficacy for blood glucose control. Individuals were self-efficacious regardless of age. Conclusion: Individuals with diabetes have insufficient knowledge and low self-efficacy, and nurses need to consider these aspects when planning nursing interventions aimed at these persons to promote self-care.
ISATIN/THIOSEMICARBAZONE HYBRIDS: FACILE SYNTHESIS, AND THEIR EVALUATION AS ANTI-PROLIFERATIVE AGENTS AND METABOLIC ENZYME INHIBITORS

We are reporting a novel series of thiosemicarbazone derivatives derived from isatin ( 1-6 ), structural determination, and investigation of the inhibitory properties against proliferative, carbonic anhydrase, and cholinesterase enzymes. The anti-proliferative effects of the compounds were measured by XTT assay against MCF-7 and MDA-MB-231 cancerous cell lines. Compound 3 showed significant cytotoxic effects on both MCF-7 and MDA-MB-231 cell lines, with IC 50 values of 8.19 μM and 23.41 μM, respectively. In addition, the compounds ( 1-6 ) inhibited the hCA I and II, their K i values 2.01 ± 0.35 – 21.55 ± 2.56 and 1.24 ± 0.33 – 25.03 ± 5.48 µM, respectively. AChE was also successfully inhibited by these compounds ( 1-6 ), with K i values ranging from 40.37 ± 8.23 to 125.43 ± 24.93 µM. The best K i values for 3 , 6 , and 4 for α-glycosidase were 564.35 ± 72.06, 594.38 ± 52.04, and 683.437 ± 66.58 µM, respectively. Binding affinities were determined to be -6.697 kcal/mol, -8.251 kcal/mol, -9.932 kcal/mol, and -4.946 kcal/mol for hCA I, hCA II, AChE, and α-glucosidase enzymes, respectively. These findings reveal that the formed compounds containing isatin moieties were crucial in the enzyme inhibition.
When One Pipeline Is Not Enough

Pipelines that operate on buffers often work well to mitigate the high latency inherent in interprocessor communication and in accessing data on disk. Running a single pipeline on each node works well when each pipeline stage consumes and produces data at the same rate. If a stage might consume data faster or slower than it produces data, a single pipeline becomes unwieldy. We describe how we have extended the FG programming environment to support multiple pipelines in two forms. When a node might send and receive data at different rates during interprocessor communication, we use disjoint pipelines that send and receive on each node. When a node consumes and produces data from different streams on the node, we use multiple pipelines that intersect at a particular stage. Experimental results for two out-of-core sorting algorithms—one based on columnsort and the other a distribution-based sort—demonstrate the value of multiple pipelines.
Integration of data from probability surveys and big found data for finite population inference using mass imputation

Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.
Novel Strategies in the Treatment of COPD

Asthma and chronic obstructive pulmonary disease (COPD) are highly prevalent respiratory diseases characterized by airway inflammation, airway obstruction and airway hyperresponsiveness. Whilst current therapies, such as β-agonists and glucocorticoids, may be effective at reducing symptoms, they do not reduce disease progression. Thus, there is a need to identify new therapeutic targets. In this review, we summarize the potential of novel targets or tools, including anti-inflammatories, phosphodiesterase inhibitors, kinase inhibitors, transient receptor potential channels, vitamin D and protease inhibitors, for the treatment of asthma and COPD.
Kinematic Modeling for Twin Spherical Balls Robot

— The goal is to build and stabilize a robot balancing on twin balls. The robot consists of six omni wheels in a special configuration standing on twin balls which gives it inverse pendulum dynamics. The robot is stabilized by rotating the wheels which makes it move in the xy-plane. First, the kinematics of omni wheels was investigated by studying different mounting configurations on platforms moving on the ground. The robot was built to verify and visualize the kinematics and special properties of omni wheels. The model is derived from energy equations and using Euler-Lagrange formulation. The energy equations are potential and kinetic energies. The obtained equations relate torque to Twin Spherical Balls Robot parameters.