Loan Loss Provisioning Pratices in Asian Financial Systems

As Basel II aims to increase the sensitivity of bank's capital requirements to the underlying risk of the assets, it may also introduce procyclical effects on the financial system. Specific attention has been paid to the role of bank's loan loss provisioning, which plays an essential part of the overall minimum capital regulatory framework. This paper aims to investigate the determinants of loan loss provisioning practices over a sample of 40 large Asian banks from Hong Kong, Japan, Philippines and Thailand during 2005 to 2012. It is motivated by the hypothesis that both macroeconomic and bank-specific factors have an effect on the provisions to cover risks. The results showed that instead of having procyclical loan loss provisioning practices like most OECD countries, provisions turns to be substantially higher when GDP growth is higher in Asian countries, reflecting increased riskiness of the credit portfolio when the business cycle turns upwards. In addition, there is no evidence of income-smoothing among the Asian jurisdictions except Philippines, in which earnings to assets ratio is positively correlated. The procyclical effect can be mitigated somewhat as provisions rise in times when earnings are higher, suggesting bank managers do save earnings through loan loss provisions in good times and borrow earnings using loan loss provisions in bad times. Keywords: Loan loss provision, financial system procyclicality, income-smoothing, Basel capital regulations

Somatic SF3B1 hotspot mutation in prolactinomas.

The genetic basis and corresponding clinical relevance of prolactinomas remain poorly understood. Here, we perform whole genome sequencing (WGS) on 21 patients with prolactinomas to detect somatic mutations and then validate the mutations with digital polymerase chain reaction (PCR) analysis of tissue samples from 227 prolactinomas. We identify the same hotspot somatic mutation in splicing factor 3 subunit B1 (SF3B1R625H) in 19.8% of prolactinomas. These patients with mutant prolactinomas display higher prolactin (PRL) levels (p = 0.02) and shorter progression-free survival (PFS) (p = 0.02) compared to patients without the mutation. Moreover, we identify that the SF3B1R625H mutation causes aberrant splicing of estrogen related receptor gamma (ESRRG), which results in stronger binding of pituitary-specific positive transcription factor 1 (Pit-1), leading to excessive PRL secretion. Thus our study validates an important mutation and elucidates a potential mechanism underlying the pathogenesis of prolactinomas that may lead to the development of targeted therapeutics

Live births after simultaneous avoidance of monogenic diseases and chromosome abnormality by next-generation sequencing with linkage analyses

In vitro fertilization (IVF), preimplantation genetic diagnosis (PGD), and preimplantation genetic screening (PGS) help patients to select embryos free of monogenic diseases and aneuploidy (chromosome abnormality). Next-generation sequencing (NGS) methods, while experiencing a rapid cost reduction, have improved the precision of PGD/PGS. However, the precision of PGD has been limited by the false-positive and false-negative single-nucleotide variations (SNVs), which are not acceptable in IVF and can be circumvented by linkage analyses, such as short tandem repeats or karyomapping. It is noteworthy that existing methods of detecting SNV/copy number variation (CNV) and linkage analysis often require separate procedures for the same embryo. Here we report an NGS-based PGD/PGS procedure that can simultaneously detect a single-gene disorder and aneuploidy and is capable of linkage analysis in a cost-effective way. This method, called "mutated allele revealed by sequencing with aneuploidy and linkage analyses" (MARSALA), involves multiple annealing and looping-based amplification cycles (MALBAC) for single-cell whole-genome amplification. Aneuploidy is determined by CNVs, whereas SNVs associated with the monogenic diseases are detected by PCR amplification of the MALBAC product. The false-positive and -negative SNVs are avoided by an NGS-based linkage analysis. Two healthy babies, free of the monogenic diseases of their parents, were born after such embryo selection. The monogenic diseases originated from a single base mutation on the autosome and the X-chromosome of the disease-carrying father and mother, respectively.Chemistry and Chemical Biolog

Tirofiban for Stroke without Large or Medium-Sized Vessel Occlusion

The effects of the glycoprotein IIb/IIIa receptor inhibitor tirofiban in patients with acute ischemic stroke but who have no evidence of complete occlusion of large or medium-sized vessels have not been extensively studied. In a multicenter trial in China, we enrolled patients with ischemic stroke without occlusion of large or medium-sized vessels and with a National Institutes of Health Stroke Scale score of 5 or more and at least one moderately to severely weak limb. Eligible patients had any of four clinical presentations: ineligible for thrombolysis or thrombectomy and within 24 hours after the patient was last known to be well; progression of stroke symptoms 24 to 96 hours after onset; early neurologic deterioration after thrombolysis; or thrombolysis with no improvement at 4 to 24 hours. Patients were assigned to receive intravenous tirofiban (plus oral placebo) or oral aspirin (100 mg per day, plus intravenous placebo) for 2 days; all patients then received oral aspirin until day 90. The primary efficacy end point was an excellent outcome, defined as a score of 0 or 1 on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days. Secondary end points included functional independence at 90 days and a quality-of-life score. The primary safety end points were death and symptomatic intracranial hemorrhage. A total of 606 patients were assigned to the tirofiban group and 571 to the aspirin group. Most patients had small infarctions that were presumed to be atherosclerotic. The percentage of patients with a score of 0 or 1 on the modified Rankin scale at 90 days was 29.1% with tirofiban and 22.2% with aspirin (adjusted risk ratio, 1.26; 95% confidence interval, 1.04 to 1.53, P = 0.02). Results for secondary end points were generally not consistent with the results of the primary analysis. Mortality was similar in the two groups. The incidence of symptomatic intracranial hemorrhage was 1.0% in the tirofiban group and 0% in the aspirin group. In this trial involving heterogeneous groups of patients with stroke of recent onset or progression of stroke symptoms and nonoccluded large and medium-sized cerebral vessels, intravenous tirofiban was associated with a greater likelihood of an excellent outcome than low-dose aspirin. Incidences of intracranial hemorrhages were low but slightly higher with tirofiban

Loan Loss Provisioning Pratices in Asian Financial Systems

As Basel II aims to increase the sensitivity of bank's capital requirements to the underlying risk of the assets, it may also introduce procyclical effects on the financial system. Specific attention has been paid to the role of bank's loan loss provisioning, which plays an essential part of the overall minimum capital regulatory framework. This paper aims to investigate the determinants of loan loss provisioning practices over a sample of 40 large Asian banks from Hong Kong, Japan, Philippines and Thailand during 2005 to 2012. It is motivated by the hypothesis that both macroeconomic and bank-specific factors have an effect on the provisions to cover risks. The results showed that instead of having procyclical loan loss provisioning practices like most OECD countries, provisions turns to be substantially higher when GDP growth is higher in Asian countries, reflecting increased riskiness of the credit portfolio when the business cycle turns upwards. In addition, there is no evidence of income-smoothing among the Asian jurisdictions except Philippines, in which earnings to assets ratio is positively correlated. The procyclical effect can be mitigated somewhat as provisions rise in times when earnings are higher, suggesting bank managers do save earnings through loan loss provisions in good times and borrow earnings using loan loss provisions in bad times. Keywords: Loan loss provision, financial system procyclicality, income-smoothing, Basel capital regulations

Real-Time Recognition and Localization Based on Improved YOLOv5s for Robot’s Picking Clustered Fruits of Chilies

Chili recognition is one of the critical technologies for robots to pick chilies. The robots need locate the fruit. Furthermore, chilies are always planted intensively and their fruits are always clustered. It is a challenge to recognize and locate the chilies that are blocked by branches and leaves, or other chilies. However, little is known about the recognition algorithms considering this situation. Failure to solve this problem will mean that the robot cannot accurately locate and collect chilies, which may even damage the picking robot’s mechanical arm and end effector. Additionally, most of the existing ground target recognition algorithms are relatively complex, and there are many problems, such as numerous parameters and calculations. Many of the existing models have high requirements for hardware and poor portability. It is very difficult to perform these algorithms if the picking robots have limited computing and battery power. In view of these practical issues, we propose a target recognition-location scheme GNPD-YOLOv5s based on improved YOLOv5s in order to automatically identify the occluded and non-occluded chilies. Firstly, the lightweight optimization for Ghost module is introduced into our scheme. Secondly, pruning and distilling the model is designed to further reduce the number of parameters. Finally, the experimental data show that compared with the YOLOv5s model, the floating point operation number of the GNPD-YOLOv5s scheme is reduced by 40.9%, the model size is reduced by 46.6%, and the reasoning speed is accelerated from 29 ms/frame to 14 ms/frame. At the same time, the Mean Accuracy Precision (MAP) is reduced by 1.3%. Our model implements a lightweight network model and target recognition in the dense environment at a small cost. In our locating experiments, the maximum depth locating chili error is 1.84 mm, which meets the needs of a chili picking robot for chili recognition

An Efficient License Plate Detection Approach With Deep Convolutional Neural Networks in Unconstrained Scenarios

License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This is because these detectors can only detect the region where the LP is located, and even the most advanced object detectors struggle in unconstrained scenarios. To address this problem, we propose a lightweight Deformation Planar Object Detection Network (DPOD-NET), which can correct the deformed LPs of various vehicles (e.g., car, truck, electric motorcycle, bus) by detecting the LP corner points. Accordingly, the distortion associated with perspective is mitigated when we adjust the LP to a frontal parallel view through the LP corners. To optimize small errors between the predicted and true values of the LP corner points, we propose an LPWing loss function. Compared with the commonly used L1 function, the LPWing loss is derivable at the zero position, and the gradient will be bigger when errors are smaller. This enables the model to converge faster at the position where the error is close to zero, resulting in better convergence when the error between the true values and predicted values is small. In addition, the paper presents a stochastic multi-scale image detail boosting strategy, which effectively augments the dataset. Finally, to objectively evaluate the effectiveness of LP corner detection approaches, we present a dataset (LPDE-4K) including various LP types (e.g., color, country, illumination, distortion). We test the performance on various datasets, and our approach outperforms other existing state-of-the-art approaches in terms of higher accuracy and lower computational cost

The Anonymization Protection Algorithm Based on Fuzzy Clustering for the Ego of Data in the Internet of Things

In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT) in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange

Equivalence between belief propagation instability and transition to replica symmetry breaking in perceptron learning systems

The binary perceptron is a fundamental model of supervised learning for nonconvex optimization, which is a root of the popular deep learning. The binary perceptron is able to achieve a classification of random high-dimensional data based on the marginal probabilities of binary synapses. The relationship between the belief propagation instability and the equilibrium analysis of the model remains elusive. Here, we establish the relationship by showing that the instability condition around the belief propagation fixed point is identical to the instability for breaking the replica symmetric saddle-point solution of the free-energy function. Therefore our analysis will hopefully provide insight towards other learning systems in bridging the gap between nonconvex learning dynamics and statistical mechanics properties of more complex neural networks

Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks

Privacy-preserving in wireless sensor networks is one of the key problems to be solved in practical applications. It is of great significance to solve the problem of data privacy protection for large-scale applications of wireless sensor networks. The characteristics of wireless sensor networks make data privacy protection technology face serious challenges. At present, the technology of data privacy protection in wireless sensor networks has become a hot research topic, mainly for data aggregation, data query, and access control of data privacy protection. In this paper, multiorder fusion data privacy-preserving scheme (MOFDAP) is proposed. Random interference code, random decomposition of function library, and cryptographic vector are introduced for our proposed scheme. In multiple stages and multiple aspects, the difficulty of cracking and crack costs are increased. The simulation results demonstrate that, compared with the typical Slice-Mix-AggRegaTe (SMART) algorithm, the algorithm proposed in this paper has a better data privacy-preserving ability when the traffic load is not very heavy