The Successful Construction of a High Gravity Dam on Complex Rock Formation

This paper presents an analysis of the stability against sliding of a gravity dam built on a layered rock formation. During the design of this dam, detailed studies of the dam foundation were carried out from a viewpoint of rock mechanics, including laboratory and in-situ tests of the mechanical properties of rocks, calculations by the theory of limit equilibrium and FEM, as well as model tests. Based on these studies, the dam type was selected

Evolutionary Game Dynamics with Non-Uniform Interaction Rates

The classical setting of evolutionary game theory, the replicator equation, assumes uniform interaction rates. The rate at which individuals meet and interact is independent of their strategies. Here we extend this framework by allowing the interaction rates to depend on the strategies. This extension leads to non-linear fitness functions. We show that a strict Nash equilibrium remains uninvadable for non-uniform interaction rates, but the conditions for evolutionary stability need to be modified. We analyze all games between two strategies. If the two strategies coexist or exclude each other, then the evolutionary dynamics do not change qualitatively, only the location of the equilibrium point changes. If, however, one strategy dominates the other in the classical setting, then the introduction of non-uniform interaction rates can lead to a pair of interior equilibria. For the Prisoner's Dilemma, non-uniform interaction rates allow the coexistence between cooperators and defectors. For the snowdrift game, non-uniform interaction rates change the equilibrium frequency of cooperators.Human Evolutionary BiologyMathematic

A Symmetry of Fixation Times in Evoultionary Dynamics

In this paper, we show that for evolutionary dynamics between two types that can be described by a Moran process, the conditional fixation time of either type is the same irrespective of the selective scenario. With frequency dependent selection between two strategies A and B of an evolutionary game, regardless of whether A dominates B, A and B are best replies to themselves, or A and B are best replies to each other, the conditional fixation times of a single A and a single B mutant are identical. This does not hold for Wright–Fisher models, nor when the mutants start from multiple copies.Human Evolutionary BiologyMathematic

Evolutionary Game Dynamics in Finite Populations

We introduce a model of stochastic evolutionary game dynamics in finite populations which is similar to the familiar replicator dynamics for infinite populations. Our focus is on the conditions for selection favoring the invasion and/or fixation of new phenotypes. For infinite populations, there are three generic selection scenarios describing evolutionary game dynamics among two strategies. For finite populations, there are eight selection scenarios. For a fixed payoff matrix a number of these scenarios can occur for different population sizes. We discuss several examples with unexpected behavior.Human Evolutionary BiologyMathematic

Characterization of silica gel-water vapor adsorption and its measurement facility

Master'sMASTER OF ENGINEERIN

An Efficiency-Improved Tightly Coupled Dipole Reflectarray Antenna Using Variant-Coupling-Capacitance Method

In this paper, a tightly coupled dipole reflectarray antenna as well as a variant-coupling-capacitance method to improve the antenna aperture efficiency is presented. Tightly coupled elements and true-time-delay lines are employed in the design of a wideband reflectarray. The proposed reflectarray can operate from 2 GHz to 5 GHz with the gain varying from 11.3 dBi to 21 dBi. Moreover, we propose a variant-coupling-capacitance method to improve the reflectarray aperture efficiency at lower frequency. By changing the coupling capacitance between neighboring elements according to their positions in the reflecting surface, a more linear equivalent distance delay line is achieved. Hence, phase error is reduced. According to measurement, the reflectarray gain in 2 GHz using the proposed method is increased by 3 dBi compared with the previous design. Aperture efficiency in 2 GHz is improved by 21.6%

From Terahertz Imaging to Terahertz Wireless Communications

Terahertz (THz) technology is probably best known to the public as a powerful tool for imaging, since it has been applied in security and medical scanning, resulting in numerous impressive images that would be unobtainable using other technologies. With the roll-out of 5G mobile networks, research into 6G wireless communications is heating up. It is envisioned that THz technology will be used for 6G and future wireless communications. In this paper, we review how THz technology has been employed for imaging and wireless communications, identify state-of-the-art developments in the field, and then examine and compare common devices and issues in both applications. The possibility of integrating THz imaging/sensing and wireless communications is considered, and challenges and future perspectives are presented and discussed. It is shown that THz technology is indeed a key enabling technology for both imaging and wireless communications in the future

GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates

Biomedical entity normalization unifies the language across biomedical experiments and studies, and further enables us to obtain a holistic view of life sciences. Current approaches mainly study the normalization of more standardized entities such as diseases and drugs, while disregarding the more ambiguous but crucial entities such as pathways, functions and cell types, hindering their real-world applications. To achieve biomedical entity normalization on these under-explored entities, we first introduce an expert-curated dataset OBO-syn encompassing 70 different types of entities and 2 million curated entity-synonym pairs. To utilize the unique graph structure in this dataset, we propose GraphPrompt, a prompt-based learning approach that creates prompt templates according to the graphs. GraphPrompt obtained 41.0% and 29.9% improvement on zero-shot and few-shot settings respectively, indicating the effectiveness of these graph-based prompt templates. We envision that our method GraphPrompt and OBO-syn dataset can be broadly applied to graph-based NLP tasks, and serve as the basis for analyzing diverse and accumulating biomedical data.Comment: 12 page

PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task. However, automating the generation of such expert-level prompts remains elusive. Existing prompt optimization methods tend to overlook the depth of domain knowledge and struggle to efficiently explore the vast space of expert-level prompts. Addressing this, we present PromptAgent, an optimization method that autonomously crafts prompts equivalent in quality to those handcrafted by experts. At its core, PromptAgent views prompt optimization as a strategic planning problem and employs a principled planning algorithm, rooted in Monte Carlo tree search, to strategically navigate the expert-level prompt space. Inspired by human-like trial-and-error exploration, PromptAgent induces precise expert-level insights and in-depth instructions by reflecting on model errors and generating constructive error feedback. Such a novel framework allows the agent to iteratively examine intermediate prompts (states), refine them based on error feedbacks (actions), simulate future rewards, and search for high-reward paths leading to expert prompts. We apply PromptAgent to 12 tasks spanning three practical domains: BIG-Bench Hard (BBH), as well as domain-specific and general NLP tasks, showing it significantly outperforms strong Chain-of-Thought and recent prompt optimization baselines. Extensive analyses emphasize its capability to craft expert-level, detailed, and domain-insightful prompts with great efficiency and generalizability.Comment: 34 pages, 10 figure

Actuarial Model Assumptions for Australian Inflation, Equity Returns, and Interest Rates

Though actuaries have developed several types of stochastic investment models for inflation, stock market returns, and interest rates, there are two commonly used in practice: autoregressive time series models with normally distributed errors, and autoregressive conditional heteroscedasticity (ARCH) models. ARCH models are particularly suited when there is heteroscedasticity in inflation and interest rate series. In such cases nonnormal residuals are found in the empirical data. This paper examines whether Australian univariate inflation and interest rate data are consistent with autoregressive time series and ARCH model assumptions