Modeling multivariate ultra-high-frequency financial data by Monte Carlo simulation methods

In questa tesi si propone una nuova classe di modelli probabilistici per dati multivariati ad altissima frequenza. Questi dati si incontrano oggigiorno in molti ambiti applicativi e in particolare in finanza quando si considerano contemporaneamente le transazioni di pi\uf9 di un\u2019azione. Le serie temporali di queste transazioni sono caratterizzate da tempi non equispaziati e non sincronizzati e per queste un naturale modello probabilistico di riferimento sono i processi puntuali marcati. In questo lavoro, per modellizzare questo tipo di dati abbiamo considerato una particolare sottoclasse di questi processi, in particolare la classe dei processi di Poisson doppio stocastici con marchi. Nello specifico del caso multivariato, si \ue8 assunto, per ogni azione, che i tempi di arrivo delle transazioni fossero descrivibili da un processo di Poisson doppio stocastico e che le relative intensit\ue0 latenti fossero funzione di alcune componenti dinamiche specifiche e di una componente dinamica comune, tutte di forma \u201cshot noise\u201d. Quest\u2019ultima componente dovrebbe essere responsabile del comportamento osservato sul mercato di alcuni panieri di azioni. Il problema principale posto da questa classe di modelli \ue8 il filtraggio delle intensit\ue0 latenti non osservabili sulla base delle transazioni osservate. Nella tesi si \ue8 proposto di affrontare questo problema di filtraggio non lineare ideando ed implementando una procedura stocastica basata sull\u2019algoritmo \u201creversibile jump Markov chain Monte Carlo\u201d. Per mezzo di questo algoritmo, si \ue8 riusciti a ricostruire a posteriori, non solo le intensit\ue0 latenti, ma anche le loro componenti, in particolare quella comune. Da un punto di vista empirico, sulla base di innumerevoli confronti tra le propriet\ue0 statistiche, relative principalmente alle correlazioni e alle cross-correlazioni tra coppie di azioni, di dati reali provenienti dalla Borsa di Milano e dati simulati, ottenuti sulla base di diverse ipotesi per i tempi di arrivo delle transazioni e per i logreturns, il modello proposto \ue8 risultato essere il pi\uf9 plausibile fornendo quindi un\u2019evidenza empirica per l\u2019esistenza di una componente comune sottostante i tempi di arrivo delle transazioni di panieri di azioni.In this thesis, we propose a modeling framework for multivariate ultra-high-frequency financial data. The proposed models belong to the class of the doubly stochastic Poisson processes with marks which are characterized by the number of events in any time interval to be conditionally Poisson distributed, given another positive stochastic process called intensity. The key assumption of these models is that the intensities are specified through a latent common dynamic factor that jointly drives their common behavior. Assuming the intensities are unobservable, we propose a signal extraction (filtering) method based on the reversible jump Markov chain Monte Carlo algorithm. Our proposed filtering method allows to filter not only the intensities but also their specific and common components. From an empirical stand point, on the basis of a comparison of real data with Monte Carlo simulated data, obtained under different assumptions for ticks (times and logreturns), based mainly on the behavior of the correlation between pairs of assets as a function of the sampling period (Epps effect), we found evidence for the existence of a single latent common factor responsible for the behavior observed in a set of assets from the Borsa di Milano

Effect of oteracil in combination with gimeracil on longterm survival and postoperative complications in elderly patients undergoing radical surgery for biliary tract cancer

Purpose: To investigate the effect of oteracil (Oxo) in combination with gimeracil (CDHP) on long-term survival and postoperative complications in elderly patients undergoing radical surgery for biliary tract cancer (BTC).Methods: Clinical data for 70 patients who underwent radical surgery for BTC in the Oncology Department of the Changle People’s Hospital, Weifang, China from April 2017 to April 2018 were collected. The patients were equally assigned to group A and group B, based on odd or even hospitalization number. After surgery, patients in group A received the combination of Oxo and CDHP, while group B patients received gemcitabine only. Long-term survival and incidence of adverse reactions were compared.Results: Compared with group B, group A had higher total treatment effectiveness (p < 0.05), lower clinical indices (p < 0.05), lower BPI score (p < 0.001) and higher 3-year overall survival (p < 0.05).Conclusion: Combined use of oteracil and gimeracil significantly prolongs the survival time and reduce cancer pain in BTC patients, with minimal toxic and side effects. However, further clinical trials are required prior to application in clinical practice.&nbsp

Architecture Information Communication in Two OSS Projects: the Why, Who, When, and What

Architecture information is vital for Open Source Software (OSS) development, and mailing list is one of the widely used channels for developers to share and communicate architecture information. This work investigates the nature of architecture information communication (i.e., why, who, when, and what) by OSS developers via developer mailing lists. We employed a multiple case study approach to extract and analyze the architecture information communication from the developer mailing lists of two OSS projects, ArgoUML and Hibernate, during their development life-cycle of over 18 years. Our main findings are: (a) architecture negotiation and interpretation are the two main reasons (i.e., why) of architecture communication; (b) the amount of architecture information communicated in developer mailing lists decreases after the first stable release (i.e., when); (c) architecture communications centered around a few core developers (i.e., who); (d) and the most frequently communicated architecture elements (i.e., what) are Architecture Rationale and Architecture Model. There are a few similarities of architecture communication between the two OSS projects. Such similarities point to how OSS developers naturally gravitate towards the four aspects of architecture communication in OSS development.Comment: Preprint accepted for publication in Journal of Systems and Software, 202

A Note on the Security Framework of Two-key DbHtS MACs

Double-block Hash-then-Sum (DbHtS) MACs are a class of MACs achieve beyond-birthday-bound (BBB) security, including SUM-ECBC, PMAC_Plus, 3kf9 and LightMAC_Plus etc. Recently, Shen et al. (Crypto 2021) proposed a security framework for two-key DbHtS MACs in the multi-user setting, stating that when the underlying blockcipher is ideal and the universal hash function is regular and almost universal, the two-key DbHtS MACs achieve 2n/3-bit security. Unfortunately, the regular and universal properties can not guarantee the BBB security of two-key DbHtS MACs. We propose three counter-examples which are proved to be 2n/3-bit secure in the multi-user setting by the framework, but can be broken with probability 1 using only O(2^{n/2}) queries even in the single-user setting. We also point out the miscalculation in their proof leading to such a flaw. However, we haven’t found attacks against 2k-SUM-ECBC, 2k-PMAC_Plus and 2k-LightMAC_Plus proved 2n/3-bit security in their paper

Dietary choline supplementation attenuated high-fat diet-induced inflammation through regulation of lipid metabolism and suppression of NFKB activation in juvenile black seabream (Acanthopagrus schlegelii)

The present study aimed to investigate whether dietary choline can regulate lipid metabolism and suppress NFκB activation and, consequently, attenuate inflammation induced by a high-fat diet in black sea bream (Acanthopagrus schlegelii). An 8-week feeding trial was conducted on fish with an initial weight of 8·16 ± 0·01 g. Five diets were formulated: control, low-fat diet (11 %); HFD, high-fat diet (17 %); and HFD supplemented with graded levels of choline (3, 6 or 12 g/kg) termed HFD + C1, HFD + C2 and HFD + C3, respectively. Dietary choline decreased lipid content in whole body and tissues. Highest TAG and cholesterol concentrations in serum and liver were recorded in fish fed the HFD. Similarly, compared with fish fed the HFD, dietary choline reduced vacuolar fat drops and ameliorated HFD-induced pathological changes in liver. Expression of genes of lipolysis pathways were up-regulated, and genes of lipogenesis down-regulated, by dietary choline compared with fish fed the HFD. Expression of nfκb and pro-inflammatory cytokines in liver and intestine was suppressed by choline supplementation, whereas expression of anti-inflammatory cytokines was promoted in fish fed choline-supplemented diets. In fish that received lipopolysaccharide to stimulate inflammatory responses, the expression of nfκb and pro-inflammatory cytokines in liver, intestine and kidney were all down-regulated by dietary choline compared with the HFD. Overall, the present study indicated that dietary choline had a lipid-lowering effect, which could protect the liver by regulating intrahepatic lipid metabolism, reducing lipid droplet accumulation and suppressing NFκB activation, consequently attenuating HFD-induced inflammation in A. schlegelii

Spectral Adversarial Training for Robust Graph Neural Network

Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but adversarially designed perturbations, known as adversarial examples. To address this issue, robust training methods against adversarial examples have received considerable attention in the literature. \emph{Adversarial Training (AT)} is a successful approach to learning a robust model using adversarially perturbed training samples. Existing AT methods on GNNs typically construct adversarial perturbations in terms of graph structures or node features. However, they are less effective and fraught with challenges on graph data due to the discreteness of graph structure and the relationships between connected examples. In this work, we seek to address these challenges and propose Spectral Adversarial Training (SAT), a simple yet effective adversarial training approach for GNNs. SAT first adopts a low-rank approximation of the graph structure based on spectral decomposition, and then constructs adversarial perturbations in the spectral domain rather than directly manipulating the original graph structure. To investigate its effectiveness, we employ SAT on three widely used GNNs. Experimental results on four public graph datasets demonstrate that SAT significantly improves the robustness of GNNs against adversarial attacks without sacrificing classification accuracy and training efficiency.Comment: Accepted by TKDE. Code availiable at https://github.com/EdisonLeeeee/SA