Computational social science: Obstacles and opportunities Lazer, David M J; Pentland, Alex; Watts, Duncan J ...
Science (American Association for the Advancement of Science),
2020-Aug-28, 2020-08-28, 20200828, Volume:
369, Issue:
6507
Journal Article
Peer reviewed
Open access
Data sharing, research ethics, and incentives must improve
The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using ...observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of infectious diseases. The institutions supporting CSS in the academy have also grown substantially, as evidenced by the proliferation of conferences, workshops, and summer schools across the globe, across disciplines, and across sources of data. But the field has also fallen short in important ways. Many institutional structures around the field—including research ethics, pedagogy, and data infrastructure—are still nascent. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.
A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering ...programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines
were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing
, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling
(GBS) on 216 squeezed modes entangled with three-dimensional connectivity
, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 μs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.
Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we ...address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials.
Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great ...interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other
in silico
applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at
https://coconut.naturalproducts.net
.
The development of silicon semiconductor technology has produced breakthroughs in electronics-from the microprocessor in the late 1960s to early 1970s, to automation, computers and smartphones-by ...downscaling the physical size of devices and wires to the nanometre regime. Now, graphene and related two-dimensional (2D) materials offer prospects of unprecedented advances in device performance at the atomic limit, and a synergistic combination of 2D materials with silicon chips promises a heterogeneous platform to deliver massively enhanced potential based on silicon technology. Integration is achieved via three-dimensional monolithic construction of multifunctional high-rise 2D silicon chips, enabling enhanced performance by exploiting the vertical direction and the functional diversification of the silicon platform for applications in opto-electronics and sensing. Here we review the opportunities, progress and challenges of integrating atomically thin materials with silicon-based nanosystems, and also consider the prospects for computational and non-computational applications.
In this paper gradient based topology optimization (TO) is used to discover 3-D phononic structures that exhibit ultra-wide normalized all-angle all-mode band gaps. The challenging computational task ...of repeated 3-D phononic band-structure evaluations is accomplished by a combination of a fast mixed variational eigenvalue solver and distributed Graphic Processing Unit (GPU) parallel computations. The TO algorithm utilizes the material distribution-based approach and a gradient-based optimizer. The design sensitivity for the mixed variational eigenvalue problem is derived using the adjoint method and is implemented through highly efficient vectorization techniques. We present optimized results for two-material simple cubic (SC), body centered cubic (BCC), and face centered cubic (FCC) crystal structures and show that in each of these cases different initial designs converge to single inclusion network topologies within their corresponding primitive cells. The optimized results show that large phononic stop bands for bulk wave propagation can be achieved at lower than close packed spherical configurations leading to lighter unit cells. For tungsten carbide - epoxy crystals we identify all angle all mode normalized stop bands exceeding 100%, which is larger than what is possible with only spherical inclusions.
IEEE Intelligent Systems is promoting young and aspiring artificial intelligence (AI) scientists and recognizing the rising stars as “AI‘s 10 Watch.” This biennial 2022 edition is slightly different ...from the previous editions: We solicited submissions from individuals who had obtained their Ph.D. up to 10 years prior (as opposed to 5 years in all of the previous editions). This led to more applications of the highest quality. The selection committee finally had to select 10 outstanding contributors from a pool of 30+ highly competitive and strong nominations, which made the selection decisions rather difficult. After a careful and detailed selection process through many rounds of discussions via e-mails and live meetings, the committee voted unanimously on a short list of 10 top candidates who have all demonstrated outstanding achievements in different areas of AI. The selection was based solely on scientific quality, reputation, impact, and expert endorsements accumulated since their Ph.D. It is our honor and privilege to announce the following 2022 class of “AI’s 10 to Watch.”• Bo Li. She is working on trustworthy machine learning (ML) at the intersection of ML, security and privacy, and game theory. She was able to integrate domain knowledge and logical reasoning abilities into data-driven statistical ML models to improve learning robustness with guarantees, and she has designed scalable privacy-preserving data-publishing frameworks for high-dimensional data. Her work has provided rigorous guarantees for the trustworthiness of learning systems and been deployed in industrial applications. She is an assistant professor with the University of Illinois at Urbana-Champaign.• Tongliang Liu. He is working in the fields of trustworthy ML. His work in theories and algorithms of ML with noisy labels has led to significant contributions and influence in the fields of ML, computer vision, natural language processing (NLP), and data mining, as large-scale datasets in those fields are prone to suffering severe label errors. He is a senior lecturer at the School of Computer Science, University of Sydney, and a visiting associate professor at the Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence.• Liqiang Nie. He is the dean of and a professor with the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). He works on multimedia content analysis and search, with a particular emphasis on data-driven multimodal learning and knowledge-guided multimodal reasoning. He pioneered the explicit modeling of consistent, complementary, and partial alignment relationships among modalities.• Soujanya Poria. He is an assistant professor at Singapore University of Technology and Design (SUTD). His seminal research on fusing information from textual, audio, and visual modalities for diverse behavioral and affective tasks significantly improved systems reliant on multimodal data, paving the way to various novel research avenues. His latest works are on information extraction, vision–language reasoning, and understanding human conversations in terms of common sense-based, context-grounded causal explanations.• Deqing Sun. He is a staff research scientist at Google. He has made significant contributions to computer vision, in particular in motion estimation. His work on optical flow (“Classic+NL” and “PWC-Net”) has been very influential and has been powering commercial applications such as Super SloMo in NVIDIA’s RTX platform, Face Unblur, and Fusion Zoom on Google’s Pixel phone.• Yizhou Sun. She is a pioneer in heterogeneous information network (HIN) mining, with a recent focus on deep graph learning, neural symbolic reasoning, and providing neural solutions to multiagent dynamical systems. Her work has a wide spectrum of applications, ranging from e-commerce, health care, and material science to hardware design. She is currently an associate professor at the University of California, Los Angeles (UCLA).• Jiliang Tang. He is a University Foundation Professor at Michigan State University. He works on graph ML and trustworthy AI and their applications in education and biology. His contributions to these fields include highly cited algorithms, well-received systems, and popular books.• Zhangyang “Atlas” Wang. He works on efficient and reliable ML. Recently, his core research theme is to leverage, understand, and expand the role of sparsity, from classical optimization to modern neural networks (NNs), whose impacts span the efficient training/inference of large-foundation models, robustness and trustworthiness, generative AI, graph learning, and more.• Hongzhi Yin. He has worked on trustworthy data intelligence to turn data into privacy-preserving, robust, explainable, and fair intelligent services in various industries and scenarios. He is also a leading expert researching and developing next-generation intelligent systems and algorithms for lightweight on-device predictive analytics as well as recommendation and decentralized ML on massive and heterogeneous data. He is an associate professor and ARC Future Fellow at the University of Queensland.• Liang Zheng. He is a senior lecturer at the Australian National University and works on data-centric computer vision, where he seeks to improve the quality of training and validation data, predict test data difficulty without labels, and more. These efforts provide a complementary perspective to model-centric developments. He has also made significant contributions to object re-identification and the broader smart city initiative through the introduction of widely used benchmarks and baseline methods.
The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics. These advances related to the engineering of materials with new ...functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine-learning tasks through light-matter interactions and diffraction. Here, we analyze the information-processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output field-of-view. We show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network, up to a limit that is dictated by the extent of the input and output fields-of-view. Deeper diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher-dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view and exhibit depth advantages in terms of their statistical inference, learning, and generalization capabilities for different image classification tasks when compared with a single trainable diffractive surface. These analyses and conclusions are broadly applicable to various forms of diffractive surfaces, including, e.g., plasmonic and/or dielectric-based metasurfaces and flat optics, which can be used to form all-optical processors.
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