Hasil untuk "Art"

A. Tan, D. Ashley, G. López et al.

Glioblastoma is the most common malignant primary brain tumor. Overall, the prognosis for patients with this disease is poor, with a median survival of <2 years. There is a slight predominance in males, and incidence increases with age. The standard approach to therapy in the newly diagnosed setting includes surgery followed by concurrent radiotherapy with temozolomide and further adjuvant temozolomide. Tumor-treating fields, delivering low-intensity alternating electric fields, can also be given concurrently with adjuvant temozolomide. At recurrence, there is no standard of care; however, surgery, radiotherapy, and systemic therapy with chemotherapy or bevacizumab are all potential options, depending on the patient's circumstances. Supportive and palliative care remain important considerations throughout the disease course in the multimodality approach to management. The recently revised classification of glioblastoma based on molecular profiling, notably isocitrate dehydrogenase (IDH) mutation status, is a result of enhanced understanding of the underlying pathogenesis of disease. There is a clear need for better therapeutic options, and there have been substantial efforts exploring immunotherapy and precision oncology approaches. In contrast to other solid tumors, however, biological factors, such as the blood-brain barrier and the unique tumor and immune microenvironment, represent significant challenges in the development of novel therapies. Innovative clinical trial designs with biomarker-enrichment strategies are needed to ultimately improve the outcome of patients with glioblastoma.

Bo Hu, Liping Zhong, Yuhua Weng et al.

RNA interference (RNAi) is an ancient biological mechanism used to defend against external invasion. It theoretically can silence any disease-related genes in a sequence-specific manner, making small interfering RNA (siRNA) a promising therapeutic modality. After a two-decade journey from its discovery, two approvals of siRNA therapeutics, ONPATTRO ® (patisiran) and GIVLAARI™ (givosiran), have been achieved by Alnylam Pharmaceuticals. Reviewing the long-term pharmaceutical history of human beings, siRNA therapy currently has set up an extraordinary milestone, as it has already changed and will continue to change the treatment and management of human diseases. It can be administered quarterly, even twice-yearly, to achieve therapeutic effects, which is not the case for small molecules and antibodies. The drug development process was extremely hard, aiming to surmount complex obstacles, such as how to efficiently and safely deliver siRNAs to desired tissues and cells and how to enhance the performance of siRNAs with respect to their activity, stability, specificity and potential off-target effects. In this review, the evolution of siRNA chemical modifications and their biomedical performance are comprehensively reviewed. All clinically explored and commercialized siRNA delivery platforms, including the GalNAc ( N -acetylgalactosamine)–siRNA conjugate, and their fundamental design principles are thoroughly discussed. The latest progress in siRNA therapeutic development is also summarized. This review provides a comprehensive view and roadmap for general readers working in the field.

Md. Zahangir Alom, T. Taha, C. Yakopcic et al.

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

Byu Scholarsarchive, A. Manning, Scott McCloud

Ghassan Zubi, R. Dufo-López, M. Carvalho et al.

Lithium-ion batteries play an important role in the life quality of modern society as the dominant technology for use in portable electronic devices such as mobile phones, tablets and laptops. Beyond this application lithium-ion batteries are the preferred option for the emerging electric vehicle sector, while still underexploited in power supply systems, especially in combination with photovoltaics and wind power. As a technological component, lithium-ion batteries present huge global potential towards energy sustainability and substantial reductions in carbon emissions. A detailed review is presented herein on the state of the art and future perspectives of Li-ion batteries with emphasis on this potential.

Oludare Isaac Abiodun, A. Jantan, Abiodun Esther Omolara et al.

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

Shurong Wang, Gongxin Dai, Haiping Yang et al.

Dmitry Ershov, Minh-Son Phan, Joanna W. Pylvänäinen et al.

A. Akbik, Tanja Bergmann, Duncan A. J. Blythe et al.

We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at https://github.com/zalandoresearch/flair .

In many places, inserted text is highlighted in red. Preface p. xx " all of which are useful in structuring concurrent applications. " Chapter 1 p. 4 In Fig. 1.1, line 4 should say for (int j = (i * block) + 1; j <= (i + 1)* block; j++) { p. 11 p. 18 Second paragraph of Mutual Exclusion bullet: " Initially the can is either up or down. Let us say it was down. Then only the pets can go in, and mutual exclusion holds. " Replace question 6 bullet 2 with:  Suppose the method M accounts for 30% of the program's computation time. Let sn be the program's speedup on n processes, assuming the rest of the program is perfectly parallelizable. Your boss tells you to double this speedup: the revised program should have speedup s'n ≥ sn/2. You advertize for a programmer to replace M with an improved version, k times faster. What value of k should you require? Chapter 2 p. 23 Figure 2.3 should be amended as shown: public long getAndIncrement() { lock.lock(); try { long temp = value; value = temp + 1; return temp; } finally { lock.unlock(); } } p. 25 Pragma 2.3.1: " We explain the reasons in Chapter 3 and Appendix B. " p. 26 In Fig. 2.5, remove all declarations of variables as volatile. Indeed as stated in Pragma 2.3.1 on page 25, one should use memory barriers when implementing these algorithms. However, in this chapter (in all code) we avoid such issues to keep the algorithms simple. Adding volatile to these variables would require more complex coding that would distract readers from the core issues at hand.

J. Elith, M. Kearney, Steven J. Phillips

Ziv Epstein, Aaron Hertzmann, L. Herman et al.

Understanding shifts in creative work will help guide AI’s impact on the media ecosystem The capabilities of a new class of tools, colloquially known as generative artificial intelligence (AI), is a topic of much debate. One prominent application thus far is the production of high-quality artistic media for visual arts, concept art, music, and literature, as well as video and animation. For example, diffusion models can synthesize high-quality images (1), and large language models (LLMs) can produce sensible-sounding and impressive prose and verse in a wide range of contexts (2). The generative capabilities of these tools are likely to fundamentally alter the creative processes by which creators formulate ideas and put them into production. As creativity is reimagined, so too may be many sectors of society. Understanding the impact of generative AI—and making policy decisions around it—requires new interdisciplinary scientific inquiry into culture, economics, law, algorithms, and the interaction of technology and creativity.

P. Duxson, A. Fernández-Jiménez, J. Provis et al.

W. Press, B. Flannery, S. Teukolsky et al.

J. Vlaeyen, S. Linton

P. Senge

R. Müller, K. Mäder, S. Gohla

H. Rubin, I. Rubin

R. Arnheim

Thousands of readers who have profited from engagement with the lively mind of Rudolf Arnheim over the decades will receive news of this new collection of essays expectantly. In the essays collected here, as in his earlier work on a large variety of art forms, Arnheim explores concrete poetry and the metaphors of Dante, photography and the meaning of music. There are essays on color composition, forgeries, and the problems of perspective, on art in education and therapy, on the style of artists' late works, and the reading of maps. Also, in a triplet of essays on pioneers in the psychology of art (Max Wertheimer, Gustav Theodor Fechner, and Wilhelm Worringer) Arnheim goes back to the roots of modern thinking about the mechanisms of artistic perception.

R. Wollheim

In "Painting as an Art," which began as the 1984 Andrew Mellon Lectures at the National Gallery of Art in Washington, D.C., philosopher Richard Wollheim transcended the boundaries and habits of both philosophy and art history to produce a large, encompassing vision of viewing art. Wollheim had three great passions--philosophy, psychology, art--and his work attempted to unify them into a theory of the experience of art. He believed that unlocking the meaning of a painting involved retrieving, almost reenacting, the creative activity that produced it. In order to fully appreciate a work of art, Wollheim argued, critics must bring to the understanding of a work of art a much richer conception of human psychology than they have in the past: "Many [critics] . . . make do with a psychology that, if they tried to live their lives by it, would leave them at the end of an ordinary day without lovers, friends, or any insight into how this came about." Many reviewers have remarked on the insightfulness of the book's final chapter, in which Wollheim contended that certain paintings by Titian, Bellini, de Kooning, and others represent the painters' attempts to project fantasies about the human body onto the canvas. Reviewing the book in the "Los Angeles Times," Daniel A. Herwitz asserted that Wollheim had "done no less than recover for psychology its obvious and irresistible place in the explanation of what is most profound and subtle about paintings."