Bias correction and confidence intervals following sequential tests

An important statistical inference problem in sequential analysis is the construction of confidence intervals following sequential tests, to which Michael Woodroofe has made fundamental contributions. This paper reviews Woodroofe's method and other approaches in the literature. In particular it shows how a bias-corrected pivot originally introduced by Woodroofe can be used as an improved root for sequential bootstrap confidence intervals.Comment: Published at http://dx.doi.org/10.1214/074921706000000590 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Internet Advertising: A Comparison of Pricing Strategy

The primary objective of this study is to explore the various ways that Internet advertising provider (IAP) can charge firms to advertise. There are two pricing strategies that can be adopted. The first is called uniform pricing: Firms pay a fixed fee, depending on the size and location of the advertisement. The second strategy is known as two-part tariff: Firms pay a fixed charge and an additional per-click fee. IAPs may adopt one or both of these pricing strategies. Our model hypothesizes that there are two IAPs offering advertising space. The modeling shows that in cases where a uniform pricing strategy is adopted, the fee that each IAP can attain is monotonically decreasing over their substitutability. That is, IAPs that wish to maximize their revenue have to take measures to distinguish themselves from the competition in order to achieve zero substitutability. In cases where a two-part tariff pricing strategy is adopted, the revenue curve becomes convex. In other words, IAPs may choose to have either full substitutability or zero substitutability in order to maximize their profits

Influence maximization in multilayer networks based on adaptive coupling degree

Influence Maximization(IM) aims to identify highly influential nodes to maximize influence spread in a network. Previous research on the IM problem has mainly concentrated on single-layer networks, disregarding the comprehension of the coupling structure that is inherent in multilayer networks. To solve the IM problem in multilayer networks, we first propose an independent cascade model (MIC) in a multilayer network where propagation occurs simultaneously across different layers. Consequently, a heuristic algorithm, i.e., Adaptive Coupling Degree (ACD), which selects seed nodes with high spread influence and a low degree of overlap of influence, is proposed to identify seed nodes for IM in a multilayer network. By conducting experiments based on MIC, we have demonstrated that our proposed method is superior to the baselines in terms of influence spread and time cost in 6 synthetic and 4 real-world multilayer networks

Quantitative evaluation of motor function before and after engraftment of dopaminergic neurons in a rat model of Parkinson's disease

Although gait change is considered a useful indicator of severity in animal models of Parkinson's disease, systematic and extensive gait analysis in animal models of neurological deficits is not well established. The CatWalk-assisted automated gait analysis system provides a comprehensive way to assess a number of dynamic and static gait parameters simultaneously. In this study, we used the Catwalk system to investigate changes in gait parameters in adult rats with unilateral 6-OHDA-induced lesions and the rescue effect of dopaminergic neuron transplantation on gait function. Four weeks after 6-OHDA injection, the intensity and maximal area of contact were significantly decreased in the affected paws and the swing speed significantly decreased in all four paws. The relative distance between the hind paws also increased, suggesting that animals with unilateral 6-OHDA-induced lesions required all four paws to compensate for loss of balance function. At 8 weeks post-transplantation, engrafted dopaminergic neurons expressed tyrosine hydroxylase. In addition, the intensity, contact area, and swing speed of the four limbs increased and the distance between the hind paws decreased. Partial recovery of methamphetamine-induced rotational response was also noted

MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning

Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL). However, non-stationarity remains a significant challenge in decentralized learning. In the paper, we tackle the non-stationarity problem in the simplest and fundamental way and propose \textit{multi-agent alternate Q-learning} (MA2QL), where agents take turns to update their Q-functions by Q-learning. MA2QL is a \textit{minimalist} approach to fully decentralized cooperative MARL but is theoretically grounded. We prove that when each agent guarantees a $\varepsilon$-convergence at each turn, their joint policy converges to a Nash equilibrium. In practice, MA2QL only requires minimal changes to independent Q-learning (IQL). We empirically evaluate MA2QL on a variety of cooperative multi-agent tasks. Results show MA2QL consistently outperforms IQL, which verifies the effectiveness of MA2QL, despite such minimal changes

Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos

Modeling sounds emitted from physical object interactions is critical for immersive perceptual experiences in real and virtual worlds. Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound. However, they require fine details of both the object geometries and impact locations, which are rarely available in the real world and can not be applied to synthesize impact sounds from common videos. On the other hand, existing video-driven deep learning-based approaches could only capture the weak correspondence between visual content and impact sounds since they lack of physics knowledge. In this work, we propose a physics-driven diffusion model that can synthesize high-fidelity impact sound for a silent video clip. In addition to the video content, we propose to use additional physics priors to guide the impact sound synthesis procedure. The physics priors include both physics parameters that are directly estimated from noisy real-world impact sound examples without sophisticated setup and learned residual parameters that interpret the sound environment via neural networks. We further implement a novel diffusion model with specific training and inference strategies to combine physics priors and visual information for impact sound synthesis. Experimental results show that our model outperforms several existing systems in generating realistic impact sounds. More importantly, the physics-based representations are fully interpretable and transparent, thus enabling us to perform sound editing flexibly.Comment: CVPR 2023. Project page: https://sukun1045.github.io/video-physics-sound-diffusion