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Destination puissance - Cohorte 1

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Trofim Rams
Trofim Rams

Art Modeling Liliana Model Sets 01 89



Specific RNN model calculations (blue triangles) and experimental results (red circles) for the large scale process data sets in terms of randomly chosen process runs including TCD (top left), VCD (top right), viability (middle left), titer (middle right) as well as glucose (bottom left) and lactate concentration (bottom right). The blue lines correspond to cubic spline functions as guides for the eyes. The errorbars denote the global root-mean-squared errors of predictions for the RNN in terms of the test data set and the corresponding target variable (see text for more details). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)




Art Modeling Liliana Model Sets 01 89



After the end of her marriage to Rourke, Otis gradually resumed modeling. In 2000, she became one of the oldest models to appear in the Sports Illustrated swimsuit issue, although Christie Brinkley at 34 and Cheryl Tiegs at 42 were featured prominently in the 25th Anniversary edition of Sports Illustrated, released in 1989. Shortly thereafter, Otis began recovering from anorexia nervosa. As part of her recovery, she experienced a weight gain that offered her the opportunity to work as a plus-size model, most notably as the face for Marina Rinaldi and editorials for Mode Magazine; in 2003, Otis became a spokesperson for National Eating Disorders Awareness Week. She also appeared periodically as a correspondent on Channel 4 News in San Francisco.[2]


Abstract:Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement.Keywords: openpose; 2D pose estimation; recurrent neural network; long short-term memory; gated recurrent units; fall detection; action recognition


Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. Language models lack the ability to understand the context and sensitivity of text, and tend to memorize phrases present in their training sets. An adversary can exploit this tendency to extract training data. Depending on the nature of the content and the context in which this data was collected, this could violate expectations of privacy. Thus, there is a growing interest in techniques for training language models that preserve privacy. In this paper, we discuss the mismatch between the narrow assumptions made by popular data protection techniques (data sanitization and differential privacy), and the broadness of natural language and of privacy as a social norm. We argue that existing protection methods cannot guarantee a generic and meaningful notion of privacy for language models. We conclude that language models should be trained on text data which was explicitly produced for public use.


The current trends of language modeling also shows that aggressive data collection and training enormous models are crucial for improving the performance of LMs. State of the art algorithms based on large neural networks enable effective extraction and encoding of a vast number of statistics about the training corpus, and have achieved unprecedented performance on a wide range of applications. The pervasive application of LMs and the ever-larger datasets needed to train them pose serious privacy concerns.


2.0.3 Trends in Language Modeling. Algorithms for learning language models (notably transformer LMs) show an unprecedented performance on extremely large models with hundreds of billions of parameters trained on extremely large datasets [7, 20, 37, 45, 100]. Figure 1 illustrates this trend. What is very important to note is that using large models, large datasets, and high amounts of compute time are all essential for achieving a high performance [45]. Empirical results show that the error (test loss) of a transformer-based language model has a power-law relationship to its model size, dataset size, and the amount of compute used for training (see, for example, Figure 1c). Thus, an order of magnitude scale-up is needed to observe tangible improvements in model performance.


Machine learning models learn by extracting generalizable patterns from their training dataset. Yet it has also been posited that memorizing some of the training data may be necessary to optimally generalize to long-tailed data distributions [30]. For example, nearest neighbor language models [46] which retrieve samples directly from their training dataset are shown to outperform their conventional counterparts. Data memorization can directly lead to leakage of private information from a model's training set, where behavior of the model on samples that were present in the training set is distinguishable from samples that weren't. Such leakage has been demonstrated in high-dimensional machine learning models [85], and recent large LMs [13]. The trend appears to get worse as both the size of LMs and their training sets increase (Figure 1). Below we discuss concrete examples of such privacy risks and their consequences.


While languages and secrets naturally evolve, language models are typically trained once on a static dataset. Over time, these datasets, and thus the language models trained on them, become less useful for understanding current language. In Section 5, we further explore how the use of static datasets can present a challenge for privacy enhancing techniques such as data sanitization (Section 5.1).


6.0.5 Publicly accessible data is not public-intended. Data that is publicly accessible (e.g., on the Web) is not necessarily intended for unfettered public dissemination, and its use in LMs could still pose privacy risks. For example, publicly available data might not be released by the data subject, such as leaked or subpoenaed email datasets [33, 48], copy/pasting conversations to distribute, or doxing an individual. Posts on social media can also sometimes be made public inadvertently [63, 93]. Furthermore, online text can be deleted or modified. A language model trained on earlier versions of such data would thus inadvertently serve as a data archive. Finally, models trained on Web data might also surface new unintended ways for this public data to be searchable. The example given in Table 1 where an individual posted their contact information on their Github is an actual example of training data extracted from a LM [13].


These learner-centered practices include teachers showing students how to make learning choices and monitor the positive and negative consequences of their choices. This is a trial-and-error process that requires teacher support, modeling and encouragement. For example, if a student expresses interest in reading a particular novel as an English assignment, but then finds that he or she is having trouble understanding it because of unfamiliar words, the teacher can recommend a similar novel that has lower level vocabulary. The teacher can also have the student make a list of the unfamiliar words and look up their meanings.


To help students develop the capacity to make choices for themselves, teachers need to help students understand their learning interests, dispositions to be active and autonomous learners, and capacities or strengths in various content or skill areas (Deakin-Crick, McCombs et al., 2007; Deakin-Crick, Stringher, & Ren, 2014; McCombs, 2011; MCombs, 2014a, 2014b). These learner-centered practices include teachers showing students how to make learning choices and monitor the positive and negative consequences of their choices. This is a trial-and-error process that requires teacher support, modeling, and encouragement.


Notably, in general, this framework is in accordance with genetic epistemology (Piaget, 1970/1983) and with a schema-based action-oriented approach to action, music, and language (Arbib, 2003, 2013). Furthermore, Kim proposes developing an approach to experimental research in neurophenomenology that deals with aesthetic experience from a first-person perspective. The conception of aesthetic experience based on shaping and co-shaping is a promising basis for the development of a feasible empirical research strategy as well as ideas for corresponding experimental designs. In total, Kim's proposal entails research on interaction, processes, and phenomenological experience. To this end, a theory of processing and interaction in connection with a theory of consciousness is necessary, which, to my mind, presents three further requirements for empirical aesthetics: 1) Integration of computational cognitive modeling in addition to the development of experimental methods for studying mental processes (Bower & Clapper, 1989); 2) computational models of emotional processes related to music and aesthetics; and 3) a methodology for phenomenology in empirical research and experiments.


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