On the Expected Discounted Penalty Function for the Classical Risk Model with Potentially Delayed Claims and Random Incomes

We focus on the expected discounted penalty function of a compound Poisson risk model with random incomes and potentially delayed claims. It is assumed that each main claim will produce a byclaim with a certain probability and the occurrence of the byclaim may be delayed depending on associated main claim amount. In addition, the premium number process is assumed as a Poisson process. We derive the integral equation satisfied by the expected discounted penalty function. Given that the premium size is exponentially distributed, the explicit expression for the Laplace transform of the expected discounted penalty function is derived. Finally, for the exponential claim sizes, we present the explicit formula for the expected discounted penalty function

Scaling Attributed Network Embedding to Massive Graphs

Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node vin G to a compact vector Xv, which can be used in downstream machine learning tasks. Ideally, Xv should capture node v's affinity to each attribute, which considers not only v's own attribute associations, but also those of its connected nodes along edges in G. It is challenging to obtain high-utility embeddings that enable accurate predictions; scaling effective ANE computation to massive graphs with millions of nodes pushes the difficulty of the problem to a whole new level. Existing solutions largely fail on such graphs, leading to prohibitive costs, low-quality embeddings, or both. This paper proposes PANE, an effective and scalable approach to ANE computation for massive graphs that achieves state-of-the-art result quality on multiple benchmark datasets, measured by the accuracy of three common prediction tasks: attribute inference, link prediction, and node classification. PANE obtains high scalability and effectiveness through three main algorithmic designs. First, it formulates the learning objective based on a novel random walk model for attributed networks. The resulting optimization task is still challenging on large graphs. Second, PANE includes a highly efficient solver for the above optimization problem, whose key module is a carefully designed initialization of the embeddings, which drastically reduces the number of iterations required to converge. Finally, PANE utilizes multi-core CPUs through non-trivial parallelization of the above solver, which achieves scalability while retaining the high quality of the resulting embeddings. Extensive experiments, comparing 10 existing approaches on 8 real datasets, demonstrate that PANE consistently outperforms all existing methods in terms of result quality, while being orders of magnitude faster.Comment: 16 pages. PVLDB 2021. Volume 14, Issue

Nonisolated switching-capacitor-integrated three- port converters with seamless PWM/PFM modulation

Efficiency and power density of power converters for interfacing photovoltaic panels, energy storage components such as batteries, and loads in photovoltaic (PV) systems become more and more important. Compared with individual converter design for different terminals, power-integrated multiport converters shows obvious advantages in simplifying the system structure, reducing the component count, and improving the operation reliability. Originated from the high power-density switched capacitor topology, a nonisolated switching-capacitor-integrated three-port converter (SCI-TPC) is presented to achieve single-stage direct power conversion among three ports. In order to minimize the cross-regulation effect, pulse-width-modulation (PWM) and pulse-frequency-modulation (PFM) are adopted to realize the flexible power regulation and achieve power balance among three ports. Main operation modes, power flow distribution, and power transfer characteristic are analyzed. With the seamless PWM and PFM hybrid modulation, the current stress can be reduced and the overall conversion efficiency over a full operating range can be improved. Main experimental results are provided to validate the effectiveness of the proposed concept

Genome Assembly for a Yunnan-Guizhou Plateau “3E” Fish, Anabarilius grahami (Regan), and Its Evolutionary and Genetic Applications

A Yunnan-Guizhou Plateau fish, the Kanglang white minnow (Anabarilius grahami), is a typical “3E” (Endangered, Endemic, and Economic) species in China. Its distribution is limited to Fuxian Lake, the nation’s second deepest lake, with a significant local economic value but a drastically declining wild population. This species has been evaluated as VU (Vulnerable) in the China Species Red List. As one of the “Four Famous Fish” in Yunnan province, the artificial breeding has been achieved since 2003. It has not only re-established its wild natural populations by reintroduction of the artificial breeding stocks, but also brought a wide and popular utilization of this species to the local fish farms. A. grahami has become one of the main native aquaculture species in Yunnan province, and the artificial production has been emerging in steady growth each year. To promote the conservation and sustainable utilization of this fish, we initiated its whole genome sequencing project using an Illumina Hiseq2500 platform. The assembled genome size of A. grahami is 1.006 Gb, accounting for 98.63% of the estimated genome size (1.020 Gb), with contig N50 and scaffold N50 values of 26.4 kb and 4.41 Mb, respectively. Approximately about 50.38% of the genome was repetitive. A total of 25,520 protein-coding genes were subsequently predicted. A phylogenetic tree based on 4,580 single-copy genes from A. grahami and 18 other cyprinids revealed three well-supported subclades within the Cyprinidae. This is the first inter-subfamily relationship of cyprinids at genome level, providing a simple yet useful framework for understanding the traditional but popular subfamily classification systems. Interestingly, a further population demography of A. grahami uncovered a historical relationship between this fish and Fuxian Lake, suggesting that range expansion or shrinkage of the habitat has had a remarkable impact on the population size of endemic plateau fishes. Additionally, a total of 33,836 simple sequence repeats (SSR) markers were identified, and 11 loci were evaluated for a preliminary genetic diversity analysis in this study, thus providing another useful genetic resource for studying this “3E” species

Investigating the potential causal association between consumption of green tea and risk of lung cancer: a study utilizing Mendelian randomization

BackgroundLung cancer is the most common global cancer in terms of incidence and mortality. Its main driver is tobacco smoking. The identification of modifiable risk factors isa public health priority. Green tea consumption has been examined in epidemiological studies, with inconsistent findings. Thus, we aimed to apply Mendelian randomization to clarify any causal link between green tea consumption and the risk of lung cancer.MethodsWe utilized a two-sample Mendelian randomization (MR) approach. Genetic variants served as instrumental variables. The goal was to explore a causal link between green tea consumption and different lung cancer types. Green tea consumption data was sourced from the UK Biobank dataset, and the genetic association data for various types of lung cancer were sourced from multiple databases. Our analysis included primary inverse-variance weighted (IVW) analyses and various sensitivity test.ResultsNo significant associations were found between green tea intake and any lung cancer subtypes, including non-small cell lung cancer (adenocarcinoma and squamous cell carcinoma) and small cell lung cancer. These findings were consistent when applying multiple Mendelian randomization methods.ConclusionGreen tea does not appear to offer protective benefits against lung cancer at a population level. However, lung cancer's complex etiology and green tea's potential health benefitssuggest more research is needed. Further studies should include diverse populations, improved exposure measurements and randomized controlled trials, are warranted

SN 2022vqz: A Peculiar SN 2002es-like Type Ia Supernova with Prominent Early Excess Emission

We present extensive photometric and spectroscopic observations of a peculiar type Ia supernova (SN Ia) 2022vqz. It shares many similarities with the SN 2002es-like SNe Ia, such as low luminosity (i.e., $M_{B,\rm max}=-18.11\pm0.16$ mag) and moderate post-peak decline rate (i.e., $\Delta m_{15,B}=1.33\pm0.11$ mag). The nickel mass synthesized in the explosion is estimated as $0.20\pm0.04~{\rm M}_\odot$ from the bolometric light curve, which is obviously lower than normal SNe Ia. SN 2022vqz is also characterized by a slow expanding ejecta, with Si II velocities persisting around 7000 km s$^{-1}$ since 16 days before the peak, which is unique among all known SNe Ia. While all these properties imply a less energetic thermonuclear explosion that should leave considerable amount of unburnt materials, however, absent signature of unburnt carbon in the spectra of SN 2022vqz is puzzling. A prominent early peak is clearly detected in the $c$- and $o$-band light curves of ATLAS and in the $gr$-band data of ZTF within days after the explosion. Possible mechanisms for the early peak are discussed, including sub-Chandrasekhar mass double detonation model and interaction of SN ejecta with circumstellar material (CSM). We found both models face some difficulties in replicating all aspects of the observed data. As an alternative, we propose a hybrid CONe white dwarf as progenitor of SN 2022vqz which can simultaneously reconcile the tension between low ejecta velocity and absence of carbon. We further discuss the diversity of 02es-like objects and possible origins of different scenarios.Comment: 24 pages, 12 figures, submitted to MNRA