Living and Learning as Responsive Authoring: Reflections on the Feminist Critiques of Merleau-Ponty’s Anonymous Body

Merleau-Ponty’s idea of lived body has played a significant role in understanding selfconstruction and has raised issues about the relationships between the private sense and the public world. Merleau-Ponty argues that the lived body and the world are constructed reciprocally. This notion is acknowledged to be a rich source for feminist thought. Yet there is as much criticism as support of Merleau-Ponty’s philosophy from feminists such as Grosz (1994, 1995), Sullivan (1997, 2000, 2001, 2002) and Young (1989). Shannon Sullivan vigorously criticises Merleau-Ponty’s lived body as an anonymous body which erases particularities and results in domination. This paper defends Merleau-Ponty’s notion by clarifying the meaning of anonymity in terms of the understanding of Merleau-Ponty’s lived body as an “author”, and as such as incorporating the capacity to resist anonymity, and sustain particularity and difference, through an ongoing process of authoring his/her own lived experience. Ken Plummer’s notion of sexual story-telling is used to elaborate the elucidation. In conclusion, the educational implications of resisting anonymity are considered and envisaged in terms of promoting tolerance of difference and assertion of particularity by encouraging and developing the capacity to construct the self through an ongoing process of both responsive and responsible self-authoring. Indo-Pacific Journal of Phenomenology, May 2010, Volume 10, Edition

Roles of Social Media in Disseminating Health Information: An Exploratory Study in China

Social media have largely transformed the way how health information is disseminated. However, the literature is limited in understanding the applications and implications of social media in health information dissemination. In this exploratory research, we interview Chinese social media users with diverse demographics by asking a set of open-ended questions regarding their use of social media in gaining and sharing health information. This research-in-progress paper reports the results of a preliminary analysis of the qualitative data that we were able to collect from 27 respondents by the time of submission. We find social media to be a major or even the only channel of seeking and sharing health information. Despite a number of relative advantages, the uncertainty about credibility is a major concern of many respondents in practicing and sharing the information gained through social media. These findings provide valuable insights for both research and practice

Stochastic smoothing accelerated gradient method for nonsmooth convex composite optimization

We propose a novel stochastic smoothing accelerated gradient (SSAG) method for general constrained nonsmooth convex composite optimization, and analyze the convergence rates. The SSAG method allows various smoothing techniques, and can deal with the nonsmooth term that is not easy to compute its proximal term, or that does not own the linear max structure. To the best of our knowledge, it is the first stochastic approximation type method with solid convergence result to solve the convex composite optimization problem whose nonsmooth term is the maximization of numerous nonlinear convex functions. We prove that the SSAG method achieves the best-known complexity bounds in terms of the stochastic first-order oracle ($\mathcal{SFO}$), using either diminishing smoothing parameters or a fixed smoothing parameter. We give two applications of our results to distributionally robust optimization problems. Numerical results on the two applications demonstrate the effectiveness and efficiency of the proposed SSAG method

Extending synthetic control method for multiple treated units: an application to environmental intervention

Taking the environmental interventions on air quality at G20 Hangzhou Summit as a natural experiment, this paper innovatively establishes an extended synthetic control method with multiple units to evaluate the dynamic treatment effects on air quality improvement at the Summit. The method constructs datadriven weights according to the fluctuation of urban air quality to obtain a more robust and stable estimation with smaller root mean squared prediction error (RMSPE). By minimising RMSPE for pre-intervention model fitting, the study takes nine cities under policy intervention in Zhejiang as treatment cities, and 45 key cities without policy intervention as control cities during 201501–201706 as the final improved experimental scheme. The policy effect of environmental regulations on the average monthly air quality composite index of treated cities in Zhejiang is -0.84 during 201607–201702; while no significant treatment effect is observed since 201702. The results indicate that the environmental policy for the G20 Hangzhou Summit lasted a relatively short period, and it had a significant short-term improvement effect while losing its long-term improving effect on air quality in treated cities. The identification validates the extended synthetic control method with multiple units could also be applied to the policy effect evaluation in other areas

Scalable Algorithms for Power Function Calculations of quantum states in NISQ Era

Quantum computing stands at the vanguard of science, focused on exploiting quantum mechanical phenomena like superposition and entanglement. Its goal is to create innovative computational models that address intricate problems beyond classical computers' capabilities. In the Noisy Intermediate-Scale Quantum (NISQ) era, developing algorithms for nonlinear function calculations on density matrices is of paramount importance. This project endeavors to design scalable algorithms for calculating power functions of mixed quantum states. This study introduces two algorithms based on the Hadamard Test and Gate Set Tomography. Additionally, a comparison of their computational outcomes is offered, accompanied by a meticulous assessment of errors inherent in the Gate Set Tomography based approac

An inexact qq-order regularized proximal Newton method for nonconvex composite optimization

This paper concerns the composite problem of minimizing the sum of a twice continuously differentiable function $f$ and a nonsmooth convex function. For this class of nonconvex and nonsmooth problems, by leveraging a practical inexactness criterion and a novel selection strategy for iterates, we propose an inexact $q\in[2,3]$-order regularized proximal Newton method, which becomes an inexact cubic regularization (CR) method for $q=3$. We justify that its iterate sequence converges to a stationary point for the KL objective function, and if the objective function has the KL property of exponent $\theta\in(0,\frac{q-1}{q})$, the convergence has a local $Q$-superlinear rate of order $\frac{q-1}{\theta q}$. In particular, under a locally H\"{o}lderian error bound of order $\gamma\in(\frac{1}{q-1},1]$ on a second-order stationary point set, the iterate sequence converges to a second-order stationary point with a local $Q$-superlinear rate of order $\gamma(q\!-\!1)$, which is specified as $Q$-quadratic rate for $q=3$ and $\gamma=1$. This is the first practical inexact CR method with $Q$-quadratic convergence rate for nonconvex composite optimization. We validate the efficiency of the proposed method with ZeroFPR as the solver of subproblems by applying it to convex and nonconvex composite problems with a highly nonlinear $f$

A distributionally robust index tracking model with the CVaR penalty: tractable reformulation

We propose a distributionally robust index tracking model with the conditional value-at-risk (CVaR) penalty. The model combines the idea of distributionally robust optimization for data uncertainty and the CVaR penalty to avoid large tracking errors. The probability ambiguity is described through a confidence region based on the first-order and second-order moments of the random vector involved. We reformulate the model in the form of a min-max-min optimization into an equivalent nonsmooth minimization problem. We further give an approximate discretization scheme of the possible continuous random vector of the nonsmooth minimization problem, whose objective function involves the maximum of numerous but finite nonsmooth functions. The convergence of the discretization scheme to the equivalent nonsmooth reformulation is shown under mild conditions. A smoothing projected gradient (SPG) method is employed to solve the discretization scheme. Any accumulation point is shown to be a global minimizer of the discretization scheme. Numerical results on the NASDAQ index dataset from January 2008 to July 2023 demonstrate the effectiveness of our proposed model and the efficiency of the SPG method, compared with several state-of-the-art models and corresponding methods for solving them

Methods for Change Points Detection and Model Selection in State-Space Models

This dissertation first proposes a novel online algorithm for the sequential detection of change points in state-space models. The algorithm is designed to take advantage of the theoretical distribution of the ratio of observation and evolution variance estimators. The algorithm is computationally fast, and can be used for large and streaming data, and is sensitive to changes in model parameters (including observation and evolution variances), as well as model structure. We consider change point detection in a sequential way, when observations are received one by one, or in batches, with a (possibly soft) restart after each detected change point. We provide the theoretical foundation of the algorithm and study its performance in two concrete state-space models which are frequently used to model the growth of epidemics over time: an auto-regressive AR(1) process, and a non-linear dynamical system (SEIR). We illustrate the performance of the algorithm with simulation studies and with an analysis on real Covid-19 datasets. We then extend the study to the theoretical results on the quotient of the variance ratios between two models and design a novel algorithm for state-space model comparison. The algorithm is reliable, and efficient and can help to reduce the computational resources required. Its performance is evaluated through simulation studies on three state-space models: AR(1), AR(3), and SEIR model. The algorithm is also used for model selection using Covid-19 data, and also takes a step further by combining the detected change point with the model selection process. Overall, this dissertation contributes to the field of change point detection and model selection in state-space models and provides useful tools for analyzing and modeling epidemic growth.</p

CRISPR accelerates the cancer drug discovery

Emerging cohorts and basic studies have associated certain genetic modifications in cancer patients, such as gene mutation, amplification, or deletion, with the overall survival prognosis, underscoring patients??? genetic background may directly regulate drug sensitivity/resistance during chemotherapies. Understanding the molecular mechanism underpinning drug sensitivity/resistance and further uncovering the effective drugs have been the major ambition in the cancer drug discovery. The emergence and popularity of CRISPR/Cas9 technology have reformed the entire life science research, providing a precise and simplified genome editing tool with unlimited editing possibilities. Furthermore, it presents a powerful tool in cancer drug discovery, which hopefully facilitates us with a rapid and reliable manner in developing novel therapies and understanding the molecular mechanisms of drug sensitivity/resistance. Herein, we summarized the application of CRISPR/Cas9 in drug screening, with the focus on CRISPR/Cas9 mediated gene knockout, gene knock-in, as well as transcriptional modification. Additionally, this review provides the concerns, cautions, and ethnic considerations that need to be taken when applying CRISPR in the drug discovery.Peer reviewe

An authoring view of education through the exploration of conceptions of nature

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