Mixed strategy approach destabilizes cooperation in finite populations with clustering coefficient

Evolutionary game theory, encompassing discrete, continuous, and mixed strategies, is pivotal for understanding cooperation dynamics. Discrete strategies involve deterministic actions with a fixed probability of one, whereas continuous strategies employ intermediate probabilities to convey the extent of cooperation and emphasize expected payoffs. Mixed strategies, though akin to continuous ones, calculate immediate payoffs based on the action chosen at a given moment within intermediate probabilities. Although previous research has highlighted the distinct impacts of these strategic approaches on fostering cooperation, the reasons behind the differing levels of cooperation among these approaches have remained somewhat unclear. This study explores how these strategic approaches influence cooperation in the context of the prisoner's dilemma game, particularly in networked populations with varying clustering coefficients. Our research goes beyond existing studies by revealing that the differences in cooperation levels between these strategic approaches are not confined to finite populations; they also depend on the clustering coefficients of these populations. In populations with nonzero clustering coefficients, we observed varying degrees of stable cooperation for each strategic approach across multiple simulations, with mixed strategies showing the most variability, followed by continuous and discrete strategies. However, this variability in cooperation evolution decreased in populations with a clustering coefficient of zero, narrowing the differences in cooperation levels among the strategies. These findings suggest that in more realistic settings, the robustness of cooperation systems may be compromised, as the evolution of cooperation through mixed and continuous strategies introduces a degree of unpredictability

Full Bayesian Significance Testing for Neural Networks

Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations. However, traditional significance testing often needs to derive the distribution of the testing statistic, failing to deal with complex nonlinear relationships. In this paper, we propose to conduct Full Bayesian Significance Testing for neural networks, called \textit{n}FBST, to overcome the limitation in relationship characterization of traditional approaches. A Bayesian neural network is utilized to fit the nonlinear and multi-dimensional relationships with small errors and avoid hard theoretical derivation by computing the evidence value. Besides, \textit{n}FBST can test not only global significance but also local and instance-wise significance, which previous testing methods don't focus on. Moreover, \textit{n}FBST is a general framework that can be extended based on the measures selected, such as Grad-\textit{n}FBST, LRP-\textit{n}FBST, DeepLIFT-\textit{n}FBST, LIME-\textit{n}FBST. A range of experiments on both simulated and real data are conducted to show the advantages of our method.Comment: Published as a conference paper at AAAI 202

Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods

This paper investigates the problem of online statistical inference of model parameters in stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search directions. We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the function-value query complexity and the distribution of search directions. The distributional result reflects the trade-off between statistical efficiency and function query complexity. We further analyze the choices of random search directions to minimize the asymptotic covariance matrix, and conclude that the optimal search direction depends on the optimality criteria with respect to different summary statistics of the Fisher information matrix. Based on the asymptotic distribution result, we conduct online statistical inference by providing two construction procedures of valid confidence intervals. We provide numerical experiments verifying our theoretical results with the practical effectiveness of the procedures

Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis

In text-to-speech (TTS) synthesis, diffusion models have achieved promising generation quality. However, because of the pre-defined data-to-noise diffusion process, their prior distribution is restricted to a noisy representation, which provides little information of the generation target. In this work, we present a novel TTS system, Bridge-TTS, making the first attempt to substitute the noisy Gaussian prior in established diffusion-based TTS methods with a clean and deterministic one, which provides strong structural information of the target. Specifically, we leverage the latent representation obtained from text input as our prior, and build a fully tractable Schrodinger bridge between it and the ground-truth mel-spectrogram, leading to a data-to-data process. Moreover, the tractability and flexibility of our formulation allow us to empirically study the design spaces such as noise schedules, as well as to develop stochastic and deterministic samplers. Experimental results on the LJ-Speech dataset illustrate the effectiveness of our method in terms of both synthesis quality and sampling efficiency, significantly outperforming our diffusion counterpart Grad-TTS in 50-step/1000-step synthesis and strong fast TTS models in few-step scenarios. Project page: https://bridge-tts.github.io

Many-Objective Optimization Using Adaptive Differential Evolution with a New Ranking Method

Pareto dominance is an important concept and is usually used in multiobjective evolutionary algorithms (MOEAs) to determine the nondominated solutions. However, for many-objective problems, using Pareto dominance to rank the solutions even in the early generation, most obtained solutions are often the nondominated solutions, which results in a little selection pressure of MOEAs toward the optimal solutions. In this paper, a new ranking method is proposed for many-objective optimization problems to verify a relatively smaller number of representative nondominated solutions with a uniform and wide distribution and improve the selection pressure of MOEAs. After that, a many-objective differential evolution with the new ranking method (MODER) for handling many-objective optimization problems is designed. At last, the experiments are conducted and the proposed algorithm is compared with several well-known algorithms. The experimental results show that the proposed algorithm can guide the search to converge to the true PF and maintain the diversity of solutions for many-objective problems

Simultaneous Single-Position Oblique Lateral Interbody Fusion Combined With Unilateral Percutaneous Pedicle Screw Fixation for Single-Level Lumbar Tuberculosis: A 3-Year Retrospective Comparative Study

Objective To illustrate a simultaneous single-position oblique lateral interbody fusion (SP-OLIF) combined with unilateral percutaneous pedicle screw fixation in treating single-level lumbar tuberculosis, compared with posterior-only approach in clinical and radiographic evaluations. Methods Consecutive patients who had undergone surgeries for single-level lumbar tuberculosis from January 2018 to December 2020 were retrospectively reviewed. The patients included were divided into SP-OLIF and posterior-only groups according to surgical methods applied, with follow-up for at least 36 months. Outcomes included estimated blood loss, operative time, and complications for safety evaluation; visual analogue scale (VAS), Oswestry Disability Index (ODI) for efficacy evaluation; erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) for evaluating tuberculosis activity; x-ray and computed tomography scan were used for radiographic evaluation. Results A total of 136 patients had been enrolled in the study (60 for SP-OLIF and 76 for Posterior-only). The median operative time, blood loss, and hospital stay in SP-OLIF group were significantly less, with a lower complication rate. Meanwhile, the SP-OLIF group showed substantially lower VAS in 1 and 7 days and decreased ODI in the first month postoperatively, without significant difference afterward. Similarly, the median CRP and ESR in SP-OLIF group were significantly lower in 3 and 7 days postoperatively. All indicators had reduced to normal after 3 months. No recurrence had been reported throughout the whole follow-up. Conclusion SP-OLIF was an efficient minimally invasive protocol for single-level lumbar tuberculosis, facilitating earlier clinical improvement, with decreased blood loss, operative time and hospital stay compared with posterior-only approach

BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis

Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attracted increasing attention. This synthesis process involves not only the basic physical warping of the mono audio, but also room reverberations and head/ear related filtrations, which, however, are difficult to accurately simulate in traditional digital signal processing. In this paper, we formulate the synthesis process from a different perspective by decomposing the binaural audio into a common part that shared by the left and right channels as well as a specific part that differs in each channel. Accordingly, we propose BinauralGrad, a novel two-stage framework equipped with diffusion models to synthesize them respectively. Specifically, in the first stage, the common information of the binaural audio is generated with a single-channel diffusion model conditioned on the mono audio, based on which the binaural audio is generated by a two-channel diffusion model in the second stage. Combining this novel perspective of two-stage synthesis with advanced generative models (i.e., the diffusion models),the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples. Experiment results show that on a benchmark dataset, BinauralGrad outperforms the existing baselines by a large margin in terms of both object and subject evaluation metrics (Wave L2: 0.128 vs. 0.157, MOS: 3.80 vs. 3.61). The generated audio samples (https://speechresearch.github.io/binauralgrad) and code (https://github.com/microsoft/NeuralSpeech/tree/master/BinauralGrad) are available online.Comment: NeurIPS 2022 camera versio

Anticonvulsant activities of α-asaronol ((E)-3'-hydroxyasarone), an active constituent derived from α-asarone.

BACKGROUND: Epilepsy is one of chronic neurological disorders that affects 0.5-1.0% of the world's population during their lifetime. There is a still significant need to develop novel anticonvulsant drugs that possess superior efficacy, broad spectrum of activities and good safety profile. METHODS: α-Asaronol and two current antiseizure drugs (α-asarone and carbamazepine (CBZ)) were assessed by in vivo anticonvulsant screening with the three most employed standard animal seizure models, including maximal electroshock seizure (MES), subcutaneous injection-pentylenetetrazole (PTZ)-induced seizures and 3-mercaptopropionic acid (3-MP)-induced seizures in mice. Considering drug safety evaluation, acute neurotoxicity was assessed with minimal motor impairment screening determined in the rotarod test, and acute toxicity was also detected in mice. RESULTS: In our results, α-asaronol displayed a broad spectrum of anticonvulsant activity (ACA) and showed better protective indexes (PI = 11.11 in MES, PI = 8.68 in PTZ) and lower acute toxicity (LD50 = 2940 mg/kg) than its metabolic parent compound (α-asarone). Additionally, α-asaronol displayed a prominent anticonvulsant profile with ED50 values of 62.02 mg/kg in the MES and 79.45 mg/kg in the sc-PTZ screen as compared with stiripentol of ED50 of 240 mg/kg and 115 mg/kg in the relevant test, respectively. CONCLUSION: The results of the present study revealed α-asaronol can be developed as a novel molecular in the search for safer and efficient anticonvulsants having neuroprotective effects as well as low toxicity. Meanwhile, the results also suggested that α-asaronol has great potential to develop into another new aromatic allylic alcohols type anticonvulsant drug for add-on therapy of Dravet's syndrome