During a stopover in Germany in the middle of a carefree road trip through Europe, two American girls find themselves alone at night when their car breaks down in the woods. Searching for help at a nearby villa, they are wooed into the clutches of a deranged retired surgeon who explains his mad scientific vision to his captives' utter horror. They are to be the subjects of his sick lifetime fantasy: to be the first to connect people, one to the next, and in doing so bring to life "the human centipede."
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Woman, Demon, Human is also very much a product of the mid- to late 1980s, a period in which many younger Chinese artists and intellectuals were being influenced by and experimenting with newly accessible Western ideas, especially psychoanalysis and feminism, and forms part of the searching interrogation of traditional Chinese culture and values, as well as the political legacies of the recent past, undertaken by many writers, artists and film-makers at this time. However, the film's critique of the refusal of traditional patriarchal Chinese culture to allow women full stature as self-determining human beings remains as valid today as in the 1980s, as does its vindication of the demand of exceptional female artists to be taken seriously by an overwhelmingly male cultural establishment, themes which Huang Shuqin was to revisit some years later in The Soul of the Painter (1994).
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Abstract:Human motion tracking is a powerful tool used in a large range of applications that require human movement analysis. Although it is a well-established technique, its main limitation is the lack of estimation of real-time kinetics information such as forces and torques during the motion capture. In this paper, we present a novel approach for a human soft wearable force tracking for the simultaneous estimation of whole-body forces along with the motion. The early stage of our framework encompasses traditional passive marker based methods, inertial and contact force sensor modalities and harnesses a probabilistic computational technique for estimating dynamic quantities, originally proposed in the domain of humanoid robot control. We present experimental analysis on subjects performing a two degrees-of-freedom bowing task, and we estimate the motion and kinetics quantities. The results demonstrate the validity of the proposed method. We discuss the possible use of this technique in the design of a novel soft wearable force tracking device and its potential applications.Keywords: whole-body force tracking; human wearable dynamics; human inverse dynamics
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Although previous studies have yielded remarkable results, in our opinion, three problems are required to be revisited in current encoding studies. The first one is that the quantified external stimuli used in previous works are limited. In most fMRI studies [e.g. (Shirer et al. 2012; Haxby et al. 2001; Sterzer et al. 2008; Peelen et al. 2009; Mitchell et al. 2008; Nishimoto et al. 2011)], visual features were used to represent external stimuli of image/video, which includes image grid intensity, color (Naselaris et al. 2011; Nishimoto et al. 2011; Miyawaki et al. 2008), semantic category labels [e.g., (Mitchell et al. 2008)], and participants rated scores about the external stimuli such as face and human body in a naturalistic video stream (Bartels and Zeki 2004). However, those representations are generally qualitative and subjective, and thus substantially limit the power of encoding models. To alleviate this problem, researchers tried to model the external stimuli via computational image/video descriptors. For example, Kay (Kay et al. 2008) and Naselaris (Naselaris et al. 2009) adopted Wavelet Gabor filters to model the texture feature of input image. Bartels (Bartels et al. 2008) used a motion energy model to describe visual stimuli of free viewing of movie segments. The computer vision community has developed a large amount of visual feature descriptors to represent image/video from different perspectives, for example, color, shape and motion. These features are typically objective and can be automatically derived by computer vision algorithms. However, whether those computer vision based features are feasible for fMRI encoding models has not been fully examined yet. Furthermore, in the computer vision field, the visual features are typically evaluated by conducting recognition or classification experiments based on image/video benchmarks with their human-labeled ground truth. However, this evaluation mechanism is from an engineering view without taking human brain cognition into full consideration. It is of great interest to explore the feasibility of applying brain encoding models to evaluate and compare various visual features. 2ff7e9595c
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