Nombres transcendents

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Santiago Zarzuela[en] This project is a chronologic summary of several methods to find if a number is transcendental or not. Some of these results will guide us to very interesting others, such as, that $\pi+e$ or $e \cdot \pi$ is a transcendental number, but never both at the same time. And other methods will provide us the existence of at least a transcendental number in a specific set of numbers

A dynamic model of house price

In this paper, we build the rationale of the financial intermediate's decision of making loans to potential home buyers over an infinite time horizon. In the first period "good" borrowers with stable future income flows receive loans and buy homes. In later periods, the intermediate securitizes the loans to raise new capital and makes loans to some of the "bad" borrowers with uncertain future income flows. Currently, we simplify the securitization as a tool to raise capital without cost over time. This unrealistic simplification should be improved in later work. The financial intermediate calculates the expected payoffs in different scenarios under the realizations of uncertainty to decide whether to make loans to a new borrower and whether to liquidate a house if the owner is short of liquidity in the short run. After clarifying the sequence of moves of different agents within each period, we compute the financial intermediate's decision rule described by a Bellman equation. Then we simulate borrowers' income realization and produce a figure of house price as well as value function over time

A dynamic model of house price

In this paper, we build the rationale of the financial intermediate's decision of making loans to potential home buyers over an infinite time horizon. In the first period "good" borrowers with stable future income flows receive loans and buy homes. In later periods, the intermediate securitizes the loans to raise new capital and makes loans to some of the "bad" borrowers with uncertain future income flows. Currently, we simplify the securitization as a tool to raise capital without cost over time. This unrealistic simplification should be improved in later work. The financial intermediate calculates the expected payoffs in different scenarios under the realizations of uncertainty to decide whether to make loans to a new borrower and whether to liquidate a house if the owner is short of liquidity in the short run. After clarifying the sequence of moves of different agents within each period, we compute the financial intermediate's decision rule described by a Bellman equation. Then we simulate borrowers' income realization and produce a figure of house price as well as value function over time

ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection

Recent camouflaged object detection (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from the high intrinsic similarity between camouflaged objects and their background, objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To this end, we propose an effective unified collaborative pyramid network which mimics human behavior when observing vague images and videos, \textit{i.e.}, zooming in and out. Specifically, our approach employs the zooming strategy to learn discriminative mixed-scale semantics by the multi-head scale integration and rich granularity perception units, which are designed to fully explore imperceptible clues between candidate objects and background surroundings. The former's intrinsic multi-head aggregation provides more diverse visual patterns. The latter's routing mechanism can effectively propagate inter-frame difference in spatiotemporal scenarios and adaptively ignore static representations. They provides a solid foundation for realizing a unified architecture for static and dynamic COD. Moreover, considering the uncertainty and ambiguity derived from indistinguishable textures, we construct a simple yet effective regularization, uncertainty awareness loss, to encourage predictions with higher confidence in candidate regions. Our highly task-friendly framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks. The code will be available at \url{https://github.com/lartpang/ZoomNeXt}.Comment: Extensions to the conference version: arXiv:2203.02688; Fixed some word error

SoK: MEV Countermeasures: Theory and Practice

Blockchains offer strong security guarantees, but they cannot protect the ordering of transactions. Powerful players, such as miners, sequencers, and sophisticated bots, can reap significant profits by selectively including, excluding, or re-ordering user transactions. Such profits are called Miner/Maximal Extractable Value or MEV. MEV bears profound implications for blockchain security and decentralization. While numerous countermeasures have been proposed, there is no agreement on the best solution. Moreover, solutions developed in academic literature differ quite drastically from what is widely adopted by practitioners. For these reasons, this paper systematizes the knowledge of the theory and practice of MEV countermeasures. The contribution is twofold. First, we present a comprehensive taxonomy of 28 proposed MEV countermeasures, covering four different technical directions. Secondly, we empirically studied the most popular MEV- auction-based solution with rich blockchain and mempool data. In addition to gaining insights into MEV auction platforms' real-world operations, our study shed light on the prevalent censorship by MEV auction platforms as a result of the recent OFAC sanction, and its implication on blockchain properties

Effect of low frequency magnetic fields on melanoma: tumor inhibition and immune modulation

BACKGROUND: We previously found that the low frequency magnetic fields (LF-MF) inhibited gastric and lung cancer cell growth. We suppose that exposure to LF-MF may modulate immune function so as to inhibit tumor. We here investigated whether LF-MF can inhibit the proliferation and metastasis of melanoma and influence immune function. METHODS: The effect of MF on the proliferation, cell cycle and ultrastracture of B16-F10 in vitro was detected by cell counting Kit-8 assay, flow cytometry, and transmission electron microscopy. Lung metastasis mice were prepared by injection of 2 × 10(5) B16-F10 melanoma cells into the tail vein in C57BL/6 mice. The mice were then exposed to an LF-MF (0.4 T, 7.5 Hz) for 43 days. Survival rate, tumor markers and the innate and adaptive immune parameters were measured. RESULTS: The growth of B16-F10 cells was inhibited after exposure to the LF-MF. The inhibition was related to induction of cell cycle arrest and decomposition of chromatins. Moreover, the LF-MF prolonged the mouse survival rate and inhibited the proliferation of B16-F10 in melanoma metastasis mice model. Furthermore, the LF-MF modulated the immune response via regulation of immune cells and cytokine production. In addition, the number of Treg cells was decreased in mice with the LF-MF exposure, while the numbers of T cells as well as dendritic cells were significantly increased. CONCLUSION: LF-MF inhibited the growth and metastasis of melanoma cancer cells and improved immune function of tumor-bearing mice. This suggests that the inhibition may be attributed to modulation of LF-MF on immune function and LF-MF may be a potential therapy for treatment of melanoma

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure

A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: a case study in the Yangtze Delta, China

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and five integrated industry types by five different machine learning approaches. Multinomial naive Bayesian methods achieved an accuracy of 86.5% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 250 000 enterprises. The relationship between the different industry classes and measurements of soil cadmium and mercury concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of cadmium, elevated concentrations also occurred in some areas because of natural sources. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites