Dam Retirement and Decision-Making

Reservoir is an important part of water conservancy engineering system and an important infrastructure for economic and social development. However, with the increase of operating time, as well as the change of social demand and operating environment, the safety, function, benefit, cost, and other characteristics of the reservoir are also changing. Like living things, reservoirs also have a life cycle of “birth, old age, illness, and death.” The retirement of a dam is an inevitable stage in the life cycle management, as well as a means of resource readjustment and rational utilization. Combined with dam retirement cases that caused severe impacts in history, generalized dam removal eco-environment influence factors are obtained from aspects of materializing, ecology, society, and economy. Based on economic rationality theory and flood consequence assessment, two decision-making methods of dam retirement are put forward. The flood consequence method is applied on the case of Heiwa reservoir; key evaluation indexes are compiled from the aspects of ecology, economy, and society; and the evaluation system based on single index is constructed

Finite-size analysis of continuous-variable measurement-device-independent quantum key distribution

We study the impact of the finite-size effect on the continuous-variable measurement-device-independent quantum key distribution (CV-MDI QKD) protocol, mainly considering the finite-size effect on the parameter estimation procedure. The central-limit theorem and maximum likelihood estimation theorem are used to estimate the parameters. We also analyze the relationship between the number of exchanged signals and the optimal modulation variance in the protocol. It is proved that when Charlie's position is close to Bob, the CV-MDI QKD protocol has the farthest transmission distance in the finite-size scenario. Finally, we discuss the impact of finite-size effects related to the practical detection in the CV-MDI QKD protocol. The overall results indicate that the finite-size effect has a great influence on the secret key rate of the CV-MDI QKD protocol and should not be ignored.Comment: 9 pages, 9 figure

Fast Unsupervised Deep Outlier Model Selection with Hypernetworks

Outlier detection (OD) finds many applications with a rich literature of numerous techniques. Deep neural network based OD (DOD) has seen a recent surge of attention thanks to the many advances in deep learning. In this paper, we consider a critical-yet-understudied challenge with unsupervised DOD, that is, effective hyperparameter (HP) tuning/model selection. While several prior work report the sensitivity of OD models to HPs, it becomes ever so critical for the modern DOD models that exhibit a long list of HPs. We introduce HYPER for tuning DOD models, tackling two fundamental challenges: (1) validation without supervision (due to lack of labeled anomalies), and (2) efficient search of the HP/model space (due to exponential growth in the number of HPs). A key idea is to design and train a novel hypernetwork (HN) that maps HPs onto optimal weights of the main DOD model. In turn, HYPER capitalizes on a single HN that can dynamically generate weights for many DOD models (corresponding to varying HPs), which offers significant speed-up. In addition, it employs meta-learning on historical OD tasks with labels to train a proxy validation function, likewise trained with our proposed HN efficiently. Extensive experiments on 35 OD tasks show that HYPER achieves high performance against 8 baselines with significant efficiency gains.Comment: 10 pages, 6 figure

Coalitions and Cliques in the School Choice Problem

The school choice mechanism design problem focuses on assignment mechanisms matching students to public schools in a given school district. The well-known Gale Shapley Student Optimal Stable Matching Mechanism (SOSM) is the most efficient stable mechanism proposed so far as a solution to this problem. However its inefficiency is well-documented, and recently the Efficiency Adjusted Deferred Acceptance Mechanism (EADAM) was proposed as a remedy for this weakness. In this note we describe two related adjustments to SOSM with the intention to address the same inefficiency issue. In one we create possibly artificial coalitions among students where some students modify their preference profiles in order to improve the outcome for some other students. Our second approach involves trading cliques among students where those involved improve their assignments by waiving some of their priorities. The coalition method yields the EADAM outcome among other Pareto dominations of the SOSM outcome, while the clique method yields all possible Pareto optimal Pareto dominations of SOSM. The clique method furthermore incorporates a natural solution to the problem of breaking possible ties within preference and priority profiles. We discuss the practical implications and limitations of our approach in the final section of the article

Magnetoresistive sensors based on the elasticity of domain walls

Magnetic sensors based on the magnetoresistance effects have a promising application prospect due to their excellent sensitivity and advantages in terms of the integration. However, competition between higher sensitivity and larger measuring range remains a problem. Here, we propose a novel mechanism for the design of magnetoresistive sensors: probing the perpendicular field by detecting the expansion of the elastic magnetic Domain Wall (DW) in the free layer of a spin valve or a magnetic tunnel junction. Performances of devices based on this mechanism, such as the sensitivity and the measuring range can be tuned by manipulating the geometry of the device, without changing the intrinsic properties of the material, thus promising a higher integration level and a better performance. The mechanism is theoretically explained based on the experimental results. Two examples are proposed and their functionality and performances are verified via micromagnetic simulation.Comment: 4 figures, 13 page

PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning framework for approximating numerical solutions for partial differential equations (PDEs). While conventional PINNs and most related studies adopt fully-connected multilayer perceptrons (MLP) as the backbone structure, they have neglected the temporal relations in PDEs and failed to approximate the true solution. In this paper, we propose a novel Transformer-based framework, namely PINNsFormer, that accurately approximates PDEs' solutions by capturing the temporal dependencies with multi-head attention mechanisms in Transformer-based models. Instead of approximating point predictions, PINNsFormer adapts input vectors to pseudo sequences and point-wise PINNs loss to a sequential PINNs loss. In addition, PINNsFormer is equipped with a novel activation function, namely Wavelet, which anticipates the Fourier decomposition through deep neural networks. We empirically demonstrate PINNsFormer's ability to capture the PDE solutions for various scenarios, in which conventional PINNs have failed to learn. We also show that PINNsFormer achieves superior approximation accuracy on such problems than conventional PINNs with non-sensitive hyperparameters, in trade of marginal computational and memory costs, with extensive experiments.Comment: 15 pages (including 9 pages of main text, 3 pages of references, and 3 pages of appendix), 4 figures, 5 table

Combining Machine Learning Models using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.Comment: In Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020

On Strichartz estimates for many-body Schr\"odinger equation in the periodic setting

In this paper, we prove Strichartz estimates for many body Schr\"odinger equations in the periodic setting, specifically on tori $\mathbb{T}^d$, where $d\geq 3$. The results hold for both rational and irrational tori, and for small interacting potentials in a certain sense. Our work is based on the standard Strichartz estimate for Schr\"odinger operators on periodic domains, as developed in Bourgain-Demeter \cite{BD}. As a comparison, this result can be regarded as a periodic analogue of Hong \cite{hong2017strichartz} though we do not use the same perturbation method. We also note that the perturbation method fails due to the derivative loss property of the periodic Strichartz estimate.Comment: 14 pages. Comments are welcom