Semi-nonparametric Estimation of Operational Risk Capital with Extreme Loss Events

Bank operational risk capital modeling using the Basel II advanced measurement approach (AMA) often lead to a counter-intuitive capital estimate of value at risk at 99.9% due to extreme loss events. To address this issue, a flexible semi-nonparametric (SNP) model is introduced using the change of variables technique to enrich the family of distributions to handle extreme loss events. The SNP models are proved to have the same maximum domain of attraction (MDA) as the parametric kernels, and it follows that the SNP models are consistent with the extreme value theory peaks over threshold method but with different shape and scale parameters from the kernels. By using the simulation dataset generated from a mixture of distributions with both light and heavy tails, the SNP models in the Frechet and Gumbel MDAs are shown to fit the tail dataset satisfactorily through increasing the number of model parameters. The SNP model quantile estimates at 99.9 percent are not overly sensitive towards the body-tail threshold change, which is in sharp contrast to the parametric models. When applied to a bank operational risk dataset with three Basel event types, the SNP model provides a significant improvement in the goodness of fit to the two event types with heavy tails, yielding an intuitive capital estimate that is in the same magnitude as the event type total loss. Since the third event type does not have a heavy tail, the parametric model yields an intuitive capital estimate, and the SNP model cannot provide additional improvement. This research suggests that the SNP model may enable banks to continue with the AMA or its partial use to obtain an intuitive operational risk capital estimate when the simple non-model based Basic Indicator Approach or Standardized Approach are not suitable per Basel Committee Banking Supervision OPE10 (2019).Comment: There are 32 pages, including tables, figures, appendix and reference. The research was presented at the MATLAB Annual Computational Finance Conference, September 27-30, 202

Site-directed mutagenesis of structural hot spots for enhanced solubility of deoxyxylulose phosphate pathway enzymes

Increasing the metabolic flux through a biochemical pathway is highly desirable for metabolic engineering. One strategy is to enhance the solubility of overexpressed pace-making enzymes. Accurate theoretical prediction of target mutation sites is instrumental to reduce the experimental efforts and speed up the optimization process. In this study, the rate-limiting steps along the non-mevalonate (DXP) pathway, namely E. coli Dxs and IspG, were used as the model enzymes to learn and develop a set of bioinformatics tools that would enable rational optimization of enzyme solubility. TANGO prediction was first used to identify the aggregation-prone regions (APRs), and then SIFT analysis was carried out to eliminate the non-tolerable amino acids in the APRs. Preliminary results have shown that 5 out of 8 tested mutations have resulted in an increase in Dxs solubility. Similarly, 7 out of 12 IspG mutants have displayed enhanced solubility. Importantly, the in vivo activities of the more soluble mutants were improved. Taken together, the solubility of both Dxs and IspG were enhanced by ~2-fold, by targeted single amino acid mutation. The study demonstrated rapid improvement of enzyme solubility by combinations of computational tools. The information gained would be useful for rational engineering of over-expressed pathway enzymes and improve pathway efficiencies

Private Labels and Product Quality under Asymmetric Information

Contrary to the existing theories of private label products, we demonstrate that the introduction of a private label product by a retailer may improve the profits of the supplier of a competing national brand product. Our theory is built on two main elements. First, the introduction of a private label product may expand the total demand for the products carried by the retailer and thus enlarge the joint profit to be split between the retailer and the supplier of the national brand product. Second, in an environment where consumers do not know the quality of the private label product, the national brand serves as a bond to assure consumers that the retailer sells high-quality products only. This quality assurance enhances the joint profit generated by the introduction of the private label product, which, in conjunction with the weakening of the retailer’s bargaining position caused by asymmetric information, may enable the national brand supplier to earn a larger profit than in the absence of the private label product

Thermoelectric property studies on thallium-doped lead telluride prepared by ball milling and hot pressing

Thallium doping into lead telluride has been demonstrated to increase the dimensionless thermoelectric figure-of-merit (ZT) by enhancing Seebeck coefficient due to the creation of resonant states close to Fermi level without affecting the thermal conductivity. However, the process is tedious, energy consuming, and small in quantities since it involves melting, slow cooling for crystal growth, long time annealing, post-crushing and hot pressing. Here we show that a similar ZT value about 1.3 at 400 °C is achieved on bulk samples with grain sizes of 3–7 μm by ball milling a mixture of elemental thallium, lead, and tellurium and then hot pressing the ball milled nanopowders

A Survey on Generative Diffusion Model

Deep learning shows excellent potential in generation tasks thanks to deep latent representation. Generative models are classes of models that can generate observations randomly concerning certain implied parameters. Recently, the diffusion Model has become a rising class of generative models by its power-generating ability. Nowadays, great achievements have been reached. More applications except for computer vision, speech generation, bioinformatics, and natural language processing are to be explored in this field. However, the diffusion model has its genuine drawback of a slow generation process, single data types, low likelihood, and the inability for dimension reduction. They are leading to many enhanced works. This survey makes a summary of the field of the diffusion model. We first state the main problem with two landmark works -- DDPM and DSM, and a unified landmark work -- Score SDE. Then, we present improved techniques for existing problems in the diffusion-based model field, including speed-up improvement For model speed-up improvement, data structure diversification, likelihood optimization, and dimension reduction. Regarding existing models, we also provide a benchmark of FID score, IS, and NLL according to specific NFE. Moreover, applications with diffusion models are introduced including computer vision, sequence modeling, audio, and AI for science. Finally, there is a summarization of this field together with limitations \& further directions. The summation of existing well-classified methods is in our Github:https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model

Silica nanospheres entrapped with ultra-small luminescent crystals for protein delivery

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Constructing smart nano-systems for intracellular delivery of functional proteins has been endeavored for diverse biomedical applications, but suffered daunting challenges. Herein silica nanospheres entrapped with photoluminescent CaF2:Tm,Yb nanocrystals were synthesized and decorated with amino molecules for protein delivery. Amino-modified nanospheres presented high protein loading capacity and sustained release phenomenon. The photoluminescence of particles highly corresponded to protein release progress. The preliminary in-vitro study confirmed markedly enhanced cell up-taking efficiency of protein molecules with the nanocomposite developed

VOFilter, Bridging Virtual Observatory and Industrial Office Applications

VOFilter is an XML based filter developed by the Chinese Virtual Observatory project to transform tabular data files from VOTable format into OpenDocument format. VOTable is an XML format defined for the exchange of tabular data in the context of the Virtual Observatory (VO). It is the first Proposed Recommendation defined by International Virtual Observatory Alliance, and has obtained wide support from both the VO community and many Astronomy projects. OpenOffice.org is a mature, open source, front office applications suite with the advantage of native support of industrial standard OpenDocument XML file format. Using the VOFilter, VOTable files can be loaded in OpenOffice.org Calc, a spreadsheet application, and then displayed and analyzed as other spreadsheet files. Here, the VOFilter acts as a connector, bridging the coming VO with current industrial office applications. Virtual Observatory and technical background of the VOFilter are introduced. Its workflow, installation and usage are presented. Existing problems and limitations are also discussed together with the future development plans.Comment: Accepted for publication in ChJAA (9 pages, 2 figures, 185KB