Twilight revelation

The first inspiration came from the thrilling phenomenal nuance caused by the natural light, especially the diffused and cool light — that’s why I am so fascinated with the dawn. Also, the “conditional” idea proposed by Robert Irwin in his book “Being and Circumstance — Notes Toward a Conditional Art” influenced my attitude towards landscape and public art: the design could be a tool to “reveal” the phenomenal changes and make people become more aware of them, instead of changing the existing condition arbitrarily. Then I chose “threshold” — the space between private and public condition, interior and exterior, which might be front or back yard, the street next to residential entrance, or semi-private park as my targeted sites, because they have great potential of physical, phenomenal, mental and programmatic changes. I started my investigation from the residential zone in Providence and developed the prototypical strategy to play with dawn light, and then adapt it to New York city to achieve a wider practice. The outcomes are both a theoretical strategy and a design. The overall objectives are to bring a new thought and attitude towards landscape design and public art, which makes people re-aware of the phenomena in the circumstance, reveal the fascination of dawn light, and create a prototypical design for residential area. During the whole process, the written article needs to be re-thought and revised along with the design

An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of onelarge retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namelyQ, based on demand forecast ahead of the selling season. The suppliers manufacture products accordingto the predicted quantity. When the actual demand has been observed, the retailer conducts demandlearning and determines the actual order quantity. Under the assumption that the products of the twosuppliers are one-way substitutable, an integrated decision-making model for dynamic pricing andinventory control is established.Design/methodology/approach: This paper proposes a mathematical model where a large domestichousehold appliance retailer decides the optimal original ordering quantity before the selling season and theoptimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.Findings:By applying this model to a large domestic household appliance retail terminal, the authors canconclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis showthat when the product prices of two manufacturers both reduce gradually, one manufacturer will often waittill the other manufacturer reduces their price to a crucial inflection point, then their profit will show aqualitative change instead of a real-time profit-price change.Practical implications: This model can be adopted to a supply chain system composed of one largeretailer and two manufacturers, helping manufacturers better make a pricing and inventory controldecision.Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited workhas considered the actual ordering quantity based on demand learning. However, this paper considers boththe optimal original ordering quantity before the selling season and the optimal actual ordering quantityfrom the perspective of the retailerPeer Reviewe

The Research on Shadow Banking System in China

The shadow banking system has grown stronger in the process of evading supervision. Together with traditional commercial banks, it has become an important participant in the financial system, which has caused a fundamental change in the structure of the global financial system. As an exogenous reform force in China’s special period, Shadow Bank has become an important channel for financial resources to “disconnect from reality”. Despite the lack of substantial securitization, China’s shadow banking system has developed rapidly. This paper analyzes the development motivation.This paper believes that the scope of China’s shadow banking system can be defined according to the nature of the fund supply side

Causality adversarial attack generation algorithm for intelligent unmanned communication system

A causality adversarial attack generation algorithm was proposed in response to the causality issue of gradient-based adversarial attack generation algorithms in practical communication system.The sequential input-output features and temporal memory capability of long short-term memory networks were utilized to extract the temporal correlation of communication signals while satisfying practical causality constraints, and enhance the adversarial attack performance against unmanned communication systems.Simulation results demonstrate that the proposed algorithm outperforms existing causality adversarial attack algorithms, such as universal adversarial perturbation, under identical conditions

A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions

Oncodriver inhibition and CD4+ Th1 cytokines cooperate through Stat1 activation to induce tumor senescence and apoptosis in HER2+ and triple negative breast cancer: implications for combining immune and targeted therapies

In patients with HER2-expressing breast cancer many develop resistance to HER2 targeted therapies. We show that high and intermediate HER2-expressing cancer cell lines are driven toward apoptosis and tumor senescence when treated with either CD4+ Th1 cells, or Th1 cytokines TNF-α and IFN-γ, in a dose dependent manner. Depletion of HER2 activity by either siRNA or trastuzumab and pertuzumab, and subsequent treatment with either anti-HER2 Th1 cells or TNF-α and IFN-γ resulted in synergistic increased tumor senescence and apoptosis in cells both sensitive and cells resistant to trastuzumab which was inhibited by neutralizing anti-TNF-α and IFN-γ. Th1 cytokines induced minimal senescence or apoptosis in triple negative breast cancer cells (TNBC); however, inhibition of EGFR in combination with Th1 cytokines sensitized those cells causing both senescence and apoptosis. TNF-α and IFN-γ led to increased Stat1 phosphorylation through serine and tyrosine sites and a compensatory reduction in Stat3 activation. Single agent IFN-γ enhanced Stat1 phosphorylation on tyrosine 701 and similar effects were observed in combination with TNF-α and EGFR inhibition. These results demonstrate Th1 cytokines and antioncodriver blockade cooperate in causing tumor senescence and apoptosis in TNBC and HER2-expressing breast cancer, suggesting these combinations could be explored as non-cross-reactive therapy preventing recurrence in breast cancer.Fil: Rosemblit, Cinthia. H. Lee Moffitt Cancer Center; Estados Unidos. University of Pennsylvania; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Datta, Jashodeep. University of Pennsylvania; Estados UnidosFil: Lowenfeld, Lea. University of Pennsylvania; Estados UnidosFil: Xu, Shuwen. University of Pennsylvania; Estados UnidosFil: Basu, Amrita. H. Lee Moffitt Cancer Center; Estados UnidosFil: Kodumudi, Krithika. H. Lee Moffitt Cancer Center; Estados UnidosFil: Wiener, Doris. H. Lee Moffitt Cancer Center; Estados UnidosFil: Czerniecki, Brian J.. H. Lee Moffitt Cancer Center; Estados Unidos. University of Pennsylvania; Estados Unido

DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich documents to address a variety of questions that necessitate complex reasoning and common-sense responses. Despite the growing number of datasets that aim to answer questions over charts, most only address this task in isolation, without considering the broader context of document-level question answering. Moreover, such datasets lack adequate common-sense reasoning information in their questions. In this work, we introduce a novel task named document-level chart question answering (DCQA). The goal of this task is to conduct document-level question answering, extracting charts or plots in the document via document layout analysis (DLA) first and subsequently performing chart question answering (CQA). The newly developed benchmark dataset comprises 50,010 synthetic documents integrating charts in a wide range of styles (6 styles in contrast to 3 for PlotQA and ChartQA) and includes 699,051 questions that demand a high degree of reasoning ability and common-sense understanding. Besides, we present the development of a potent question-answer generation engine that employs table data, a rich color set, and basic question templates to produce a vast array of reasoning question-answer pairs automatically. Based on DCQA, we devise an OCR-free transformer for document-level chart-oriented understanding, capable of DLA and answering complex reasoning and common-sense questions over charts in an OCR-free manner. Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document. We implement and evaluate a set of baselines, and our proposed method achieves comparable results