Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Ownership Concentration on Bank Performance and Risk-taking: Evidence from East Asia

Motivated by agency theory, we explore the impact of ownership concentration on bank performance and risk-taking. Our results, based on banks in East Asia, reveal that more concentrated ownership is associated with poorer bank performance and lower risk-taking. In particular, a rise in ownership concentration by one standard deviation reduces bank profitability by nearly 17% and lowers the degree of risk-taking by about 10%. Our results also show that banks with more concentrated ownership experience higher operating costs. Further analysis shows that our results are not likely driven by unobservable bank characteristics, nor by reverse causality. Our results are important as they show that ownership concentration is a significant determinant of bank performance, operating costs, and risk-taking. JEL Classification: G21, G34
Inclusion Probability Proportional to Size Sampling: A Nonlinear Programming Approach to Ensure a Nonnegative and Stable Variance Estimator

Since 1943 when Hansen and Hurwitz first introduced the use of probability proportional to size (PPS) sampling, a number of procedures for selecting samples without replacement have been developed. Many of them are reviewed and compared in Brewer and Hanif (1983). One of the popular methods is an Inclusion Probability Proportional to Size (IPPS) sampling schemes used in combination with the Horvitz-Thompson (H-T) (1952) estimator for the population total. Jessen (1969) proposed four interesting IPPS sampling schemes and examined their properties. Nigam, Kumar, Gupta (1984) suggested an IPPS sampling scheme which is closely related to the methods of Jessen and provides a more stable variance estimator. However, their method uses binary block designs in an experimental design approach that involves considerable trial and error to carry out even for the cases where the sample size is small. In this paper, we first suggest an IPPS sampling scheme for nonnegative and stable variance estimation. The method is simple to implement because it is structured as a nonlinear programming problem consisting of a nonlinear objective function and some linear constraints having flexible features. Second, we introduce two other IPPS sampling schemes that are originally developed by Kim, Heeringa and Solenberger (2003). They also adopt constraints similar to those of the first approach to guarantee the non-negativity and stability of the variance estimator. Third, we presents several strict constraints in nonlinear programming approaches that always yield the smaller variance estimator compared to methods such as PPS sampling with replacement, Murthy (1957)’s method and Brewer (1963)’s method, although they are restricted to the cases where the sample size is two. Finally, we illustrate statistical efficiency of the variance estimator as well as variance of the H-T estimator for our methods by applying them to an example problem in the literature. 2. Variance Estimator of Horvitz-Thompson Estimator
Increasing Accuracy of Classification Using CNN and Random Forest Classifier for Classification of X-RAY Image of Pneumonia,Covid,Lung Opacity Patients

: In this paper we have successfully implemented the ensemble method using CNN and Random Forest for increasing the accuracy and decreasing the loss when we only use CNN. We have created this model to classify the X ray image of patient diagnosed with Pneumonia vs patient diagnosed with Covid Or patients Diagnosed with Lung Opacity and Normal Lungs we have used the data set of X rays images of patients which are labeled Pneumonia, Covid, Lung Opacity and Normal. In the traditional CNN is used for both feature extraction and classification but in this ensemble method we are using the CNN for feature extraction and Random Forest Classifier for classification. Using these in this order we have successfully increased the accuracy of model by 23.1281% and decreased the loss while training. The model created with high accuracy can be used for further detection and classification of Pneumonia, Covid, Lung Opacity via X ray images of that patient which can be easily identified by the classifier.
Eﬃcient and Accurate Spatial-Temporal Denoising Network for Low-dose CT Scans

While deep-learning-based imaging denoising techniques can improve the quality of low-dose computed tomography (CT) scans, repetitive 3D convolution operations cost signiﬁ-cant computation resources and time. We present an eﬃcient and accurate spatial-temporal convolution method to accelerate an existing denoising network based on the SRResNet. We trained and evaluated our model on our dataset containing 184 low-dose chest CT scans. We compared the performance of the proposed spatial-temporal convolution network to the SRResNet with full 3D convolutional layers. Using 8-bit quantization, we demonstrated a 7-fold speed-up during inference. Using lung nodule characterization as a driving task, we analyzed the impact on image quality and radiomic features. Our results show that our method achieves better perceptual quality, and the outputs are consistent with the SRResNet baseline outputs for some radiomics features (31 out of 57 total features). These observations together demonstrate that the proposed spatial-temporal method can be potentially useful for clinical applications where the computational resource is limited.
Ultra-Wide Band Sensor Based Ground Monitoring

One of the most important aspects of early warning systems and risk reduction techniques is early detection of events such as landslides or subsidence is ground displacement monitoring. Landslide is considered as one of the most dangerous natural disaster which occurs mainly in hilly areas. There are already many types of instrumentation, but those capable of delivering real-time alerts on multiple time series are usually based on expensive hardware, illustrating the need to develop a low-cost, easy-to-install device suitable for emergency surveillance. Consequently, a wireless network based on ultra-wideband radiofrequency impulse technology was realized. This network's uniqueness is its ability to determine the distance between nodes using the same signals that are used for transmission without the need for an actual measuring sensor. The system was tested in Central Italy by tracking a mudflow, and revealed its suitability as an early warning device. Further work on potential low-cost hardware integration and eventual industrialization will boost this promising technology further.
Thermal transformations of manufactured nanomaterials as a proposed proxy for ageing

Ageing is an important part of a manufactured nanomaterial's life cycle and can be considered as a transformation over time. It is particularly relevant to nanomaterials (NMs) because they are more reactive than their bulk counterparts and therefore are likely to undergo more significant or faster transformations with time. The conditions upon exposure of a NM to the environment, e.g. temperature, humidity and redox, will all individually affect ageing, as well as time. In experimental simulations, time has to be substituted by a proxy that makes timescales more realistic. Thermal ageing accelerates the normal ageing processes of NMs and thus elevated temperatures can be used to simulate prolonged ageing, allowing access to information on the long-term effects of NM ageing within a shorter time. Similar approaches are utilised in experimental simulation of protein fibrillation, for example, where processes that naturally occur over decades are accelerated to days or hours. In this work, time and temperature dependent studies were carried out on a fully characterised library of laboratory synthesised comparable polyvinylpyrrolidone (PVP) capped NMs (with core compositions of ceria, copper oxide and zinc oxide) and a commercially available uncoated cerium dioxide NM, to assess their transformations. Specifically, physical and chemical changes were studied on NMs exposed to various temperatures (25, 45, 65 and 80 °C) for a period of 4 weeks. The size, zeta potential, agglomeration/aggregation and valence state of the NMs were studied through dynamic light scattering (DLS), zeta potential, ultra-violet visible light spectroscopy (UV-VIS), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS), as a function of time. Results generally show a decrease in NM stability with increasing temperature and time. Changes in the NM size and core oxidation state were noted with increasing temperature/time. These changes varied depending on the NM core composition. Additionally the PVP capping, despite stabilising the NM dispersion, still allowed the NM core to be influenced by external factors, thus indicating likely ageing-related reduction in efficiency, though to a lesser extent than the uncapped particles. Overall the experiments recorded a complex picture of transformations as a function of time/temperature highlighting the complexity of NMs' ageing.
C ROSS - LINGUAL T RANSFER L EARNING FOR P RE - TRAINED C ONTEXTUALIZED L ANGUAGE M ODELS

Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant. In this work, building upon the recent works connecting cross-lingual transfer learning and neural machine translation, we thus propose a novel crosslingual transfer learning framework for PrLMs: TRELM. To handle the symbol order and sequence length differences between languages, we propose an intermediate “TRILayer” structure that learns from these differences and creates a better transfer in our primary translation direction, as well as a new cross-lingual language modeling objective for transfer training. Additionally, we showcase an embedding aligning that adversarially adapts a PrLM’s non-contextualized embedding space and the TRILayer structure to learn a text transformation network across languages, which addresses the vocabulary difference between languages. Experiments on both language understanding and structure parsing tasks show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency. Moreover, despite an insignificant performance loss compared to pre-training from scratch in resourcerich scenarios, our transfer learning framework is significantly more economical.
Accounting for the Effect of Schooling and Abilities in the Analysis of Racial and Ethnic Disparities in Achievement Test Scores

This paper examines the sources of racial and ethnic disparity in achievement test scores. We estimate the effect of schooling on achievement test scores and question the importance of genetic differences. In addition, we estimate the effect of family background and school inputs and other environments on test scores. Using NLSY data on schooling and test scores, we estimate the effect of schooling on test scores using the two methods developed by Hansen et al. (2004). These methods control for the endogeneity of schooling by postulating that both schooling and test scores are generated by a common latent ability. The identification strategy uses scores from tests taken at different schooling levels where individuals attain different levels of final schooling. Our findings show that schooling affects measured ability in a significant way. Larger causal effects of schooling on test scores are found among whites than among minorities. Although schooling raises test scores, it does not reduce test score gaps between blacks, whites and Hispanics. Initial test scores gaps persist regardless of schooling level. These results persist after controlling for family characteristics and school quality. This indicates that policies targeted solely in raising schooling levels to reduce racial-ethnic test score gaps might prove ineffective.
PHOENIX METROPOLITAN AREA: STABILITY IN URBAN PRESERVES

—Currently, there is considerable interest in the response of reptile populations to habitat fragmentation and degradation due to urbanization. We surveyed populations of the Common Chuckwalla (Sauromalus ater) in preserves of the Phoenix Mountains and adjacent areas near the Phoenix Metropolitan region during the spring of 2008. Fecal dropping counts were used to assess the current populations in relation to those sampled in 1995. Our results revealed a strong correlation between estimates gathered in 1995 and 2008. Additionally, one intensively sampled site established that, although estimates could vary somewhat in relation to the number of individuals conducting the survey, they were stable over a 13 year period. Overall, survey results suggest that Common Chuckwalla populations are stable in these island preserves in a sea of urban development.
ON BRIEF INTRODUCTION OF PHARMACEUTICAL COCRYSTALS AND ITS EVALUATION PARAMETERS

REVIEW ON BRIEF INTRODUCTION OF PHARMACEUTICAL COCRYSTALS AND ITS EVALUATION PARAMETERS DIKSHA.B. KOLI* , SAPANA.P.AHIRRAO , SANJAY.J.KSHIRSAGAR Department of Pharmaceutics, MET’s Institute of Pharmacy, Adgaon, Nashik, 422003. Savitribai Phule Pune University, Pune (Maharashtra), India. *For Correspondence: Department of Pharmaceutics, MET’S Institute of Pharmacy, Bhujbal Knowledge City, Adagaon Nashik-422003, Savitribai Phule Pune university Maharashtra, India. ABSTRACT Pharmaceutical cocrystals are promising technology which can be used to improve solubility of poorly aqueous compounds. The role of crystal engineering principles in selection of suitable coformers and nature of supramolecular synthons present within the crystals are described. In this review the success of numerous pharmaceutical cocrystals for improvement of the solubility and dissolution rates of poorly soluble drugs indicated using various examples taken from the literature. Furthermore, cocrystals of direct pharmaceutical interest, along with their in vitro properties and evaluation techniques are discussed highlighting the potential of cocrystals as an attractive route for drug development.
MARKETING PERFORMANCE AND STRATEGY: CASE STUDY ON SMES OF SALTWATER FISH PROCESSING IN INDONESIA

A low level of ability in managing internal and external factors of the business will result in the less optimal of marketing strategies implementation and achieved an outstanding marketing performance. Therefore, a partial study of marketing strategy and marketing performance is needed which aims to determine the marketing strategy and performance. As many as 120 SMEs were chosen as respondents for saltwater fish processing in East Java, Indonesia. The multistage sampling technique used in this study where the Likert scale employed as a measuring tool in the questionnaire was distributed, finally, the data was processed by SEM, the Lisrel program. This study found that the internal and external factors partially influence on marketing strategies; the internal and external business factors partially influence marketing performance; and marketing strategies have no effect on marketing performance. The dominant path that arises is the internalstrategy-performance pathway, which means that the company's ability to manage internal factors is more dominant in influencing marketing strategy and performance.
Surface-Enhanced Dual-Frequency Two-Dimensional Vibrational Spectroscopy of Thin Layers at an Interface

The development of spectroscopic approaches to study molecules at interfaces is important as the molecular properties often differ from those in the bulk. Implementation of surface-enhanced two-dimensional infrared (SE 2DIR) spectroscopy using lithographically fabricated plasmonic nanoarrays is demonstrated for nanometer thick films. The sample, 4-azidobutyrate-N-hydroxysuccinimide ester (azNHS), dispersed in polystyrene was deposited onto the nanoarray. Raw enhancements in the SE 2DIR spectra exceeding 5 × 104 and 1.3 × 103 fold were achieved for the CO and NN peaks, respectively. The field enhancement provided by the nanoarray was sufficient to record cross-peaks in 1 nm thick samples under dilute conditions for azNHS (∼0.1 M). Note that the cross-peaks were recorded for vibrational modes frequency separated by ∼350 cm–1 with the enhancement factor of 4.1 × 104. The effective electric field enhancement factors, measured for NN and CO modes via linear and two nonlinear IR techniques, have similar sample-.
CAREGIVER STRESS AMONG THE ELDERLY WITH DEMENTIA IN MALAYSIA

There is a growing number of dementia patients in Malaysia. Caregivers play a vital role and their health is of utmost importance as it would influence their role. This study assessed the relationship between the caregivers’ stress levels and the demographics as well as the relationship between the caregivers’ stress levels and the severity of neuropsychiatric behaviors of the elderly with dementia. This study used a descriptive study design and quantitative method in whichthequestionnairewasgivento65 participants in the Alzheimer’s Disease Foundation Malaysia (ADFM). The Chi-Square test between caregivers’ stress levels and demographics revealed that gender of elderly (p = 0.013), years ofcaregiving(p = 0.046) and relationship of caregivers with elderly with Dementia (p = 0.014) as well as between caregivers’ stress levels and severity of behavioral problems portrayed by elderly with dementia revealed that agitation/aggression (p = 0.004), dysphoria/depression (p = 0.028), anxiety(p=0.015)andeuphoria/elation (p = 0.007) were significant in indicating high levels of stress among the caregivers of elderly with dementia. Agitation or aggression, dysphoria or depression, anxiety and euphoria or elation are the neuropsychiatric behaviors portrayed by the elderly with dementia which are associatedwithhighstresslevelsamong caregivers in this study.
The Use of Substances at an Early Age: A Qualitative Study among Young Men Living with Substance Use Disorders in Punjab, India

Substance Use Disorders (SUDs) among young people are a major health problem. The magnitude of the problem is increasing in India, especially in Punjab, where the menace of SUDs is debilitating the physical and mental welfare of young people. This qualitative study therefore aims to explore the reasons for initiating substance use at an early age among young people with SUDs. Individual interviews were conducted with young people. A thematic analysis of the data was performed. The analysis yielded four main themes, including adolescent behaviour, peer group influence, family influence, and social and cultural contextual factors. Peer pressure was reported to be the key factor in early substance use leading to SUDs. This study provides an enhanced understanding of the experiences of young people with SUDs, including reasons for early substance use. Further, these experiences may enhance the knowledge required for an integrated approach to prevent early substance use.
A new perspective of permafrost boundaries in France during the Last Glacial Maximum

Abstract. During the Last Glacial Maximum (LGM), a very cold and dry period around 26.5 to 19 thousand years ago, permafrost was widespread across Europe. In this work, we evaluate the potential of regional climate model simulations to reconstruct the permafrost distribution in western Europe during the LGM. With this aim, criteria for possible thermal contraction cracking of the ground are applied to climate model data for the first time. These criteria serve as a precondition for the development of ice and sand wedges, which are a common proxy for past permafrost. Our results show that the permafrost and ground cracking distribution in Europe during the LGM are not consistent with a large-scale circulation with prevailing westerly winds. However, a colder and with regard to proxy data more realistic version of the LGM climate is achieved given more frequent easterly winds conditions. Whereas the permafrost extent and ground cracking regions in the global climate model simulation deviate from proxy evidence, they are in good agreement in the regional counterpart. Given the appropriate forcing, an added value of the regional climate model simulation can thus be achieved. Furthermore, the model data provide evidence that thermal contraction cracking occurred in Europe during the LGM also south of the probable permafrost border. This enables the reconsideration of the significance of ice wedge pseudomorphs and sand wedge casts to understand past climate variations.
C OMPRESSING GRADIENTS IN DISTRIBUTED SGD BY EXPLOITING THEIR TEMPORAL CORRELATION

We propose SignXOR, a novel compression scheme that exploits temporal correlation of gradients for the purpose of gradient compression. Sign-based schemes such as Scaled-sign and SignSGD (Bernstein et al., 2018; Karimireddy et al., 2019) compress gradients by storing only the sign of gradient entries. These methods, however, ignore temporal correlations between gradients. The equality or non-equality of signs of gradients in two consecutive iterations can be represented by a binary vector, which can be further compressed depending on its entropy. By implementing a rate-distortion encoder we increase the temporal correlation of gradients, lowering entropy and improving compression. We achieve theoretical convergence of SignXOR by employing the two-way error-feedback approach introduced by Zheng et al. (2019). Zheng et al. (2019) show that two-way compression with error-feedback achieves the same asymptotic convergence rate as SGD, although convergence is slower by a constant factor. We strengthen their analysis to show that the rate of convergence of two-way compression with errorfeedback asymptotically is the same as that of SGD. As a corollary we prove that two-way SignXOR compression with error-feedback achieves the same asymptotic rate of convergence as SGD. We numerically evaluate our proposed method on the CIFAR-100 and ImageNet datasets and show that SignXOR requires less than 50% of communication traffic compared to sending sign of gradients. To the best of our knowledge we are the first to present a gradient compression scheme that exploits temporal correlation of gradients.
S TABILITY ANALYSIS OF SGD THROUGH THE NORMALIZED LOSS FUNCTION

We prove new generalization bounds for stochastic gradient descent for both the convex and non-convex case. Our analysis is based on the stability framework. We analyze stability with respect to the normalized version of the loss function used for training. This leads to investigating a form of angle-wise stability instead of euclidean stability in weights. For neural networks, the measure of distance we consider is invariant to rescaling the weights of each layer. Furthermore, we exploit the notion of on-average stability in order to obtain a data-dependent quantity in the bound. This data dependent quantity is seen to be more favorable when training with larger learning rates in our numerical experiments. This might help to shed some light on why larger learning rates can lead to better generalization in some practical scenarios
HISTOANATOMY OF ANDROGENIC GLAND OF A BRACHYURAN CRAB, PARASESARMA PLICATUM (LATRIELLE, 1803)

The present study was executed on the androgenic gland of mangrove crab Parasesarma plicatum to study the morphology, histology and its function in male reproduction. Morphologically, the androgenic gland is attached to wall of the ejaculatory duct. The light microscopic study showed two cell types. Type 1 cells are small with large nucleus and small amount of cytoplasm. Type 2 cells occupy more space in the glandular region. These are larger cells with basophilic nuclei and involve in secretary activity. This suggests the function of androgenic gland in male reproduction of crabs.