The primary mechanism of operation of almost all transistors today relies on the electric-field effect in a semiconducting channel to tune its conductivity from the conducting ‘on’ state to a ...non-conducting ‘off’ state. As transistors continue to scale down to increase computational performance, physical limitations from nanoscale field-effect operation begin to cause undesirable current leakage, which is detrimental to the continued advancement of computing1,2. Using a fundamentally different mechanism of operation, we show that through nanoscale strain engineering with thin films and ferroelectrics the transition metal dichalcogenide MoTe2 can be reversibly switched with electric-field-induced strain between the 1T′-MoTe2 (semimetallic) phase to a semiconducting MoTe2 phase in a field-effect transistor geometry. This alternative mechanism for transistor switching sidesteps all the static and dynamic power consumption problems in conventional field-effect transistors3,4. Using strain, we achieve large non-volatile changes in channel conductivity (Gon/Goff ≈ 107 versus Gon/Goff ≈ 0.04 in the control device) at room temperature. Ferroelectric devices offer the potential to reach sub-nanosecond non-volatile strain switching at the attojoule/bit level5–7, with immediate applications in ultrafast low-power non-volatile logic and memory8 while also transforming the landscape of computational architectures because conventional power, speed and volatility considerations for microelectronics may no longer exist.Strain-induced phase change in MoTe2 enables reversible channel conductivity switching in a field-effect transistor geometry.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A huge research literature, across the behavioral and social sciences, uses information on individuals' subjective well-being. These are responses to questions--asked by survey interviewers or ...medical personnel--such as, "How happy do you feel on a scale from 1 to 4?" Yet there is little scientific evidence that such data are meaningful. This study examines a 2005-2008 Behavioral Risk Factor Surveillance System random sample of 1.3 million U.S. citizens. Life satisfaction in each U.S. state is measured. Across America, people's answers trace out the same pattern of quality of life as previously estimated, from solely nonsubjective data, in one branch of economics (so-called "compensating differentials" neoclassical theory, originally from Adam Smith). There is a state-by-state match (r = 0.6, P < 0.001) between subjective and objective well-being. This result has some potential to help to unify disciplines.
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Paramagnetic spin seebeck effect Wu, Stephen M; Pearson, John E; Bhattacharya, Anand
Physical review letters,
2015-May-08, Volume:
114, Issue:
18
Journal Article
Peer reviewed
Open access
We report the observation of the longitudinal spin Seebeck effect in paramagnetic insulators. By using a microscale on-chip local heater, we generate a large thermal gradient confined to the chip ...surface without a large increase in the total sample temperature. Using this technique at low temperatures (<20 K), we resolve the paramagnetic spin Seebeck effect in the insulating paramagnets Gd3Ga5O12 (gadolinium gallium garnet) and DyScO3 (DSO), using either W or Pt as the spin detector layer. By taking advantage of the strong magnetocrystalline anisotropy of DSO, we eliminate contributions from the Nernst effect in W or Pt, which produces a phenomenologically similar signal.
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4.
Antiferromagnetic Spin Seebeck Effect Wu, Stephen M; Zhang, Wei; Kc, Amit ...
Physical review letters,
2016-Mar-04, Volume:
116, Issue:
9
Journal Article
Peer reviewed
Open access
We report on the observation of the spin Seebeck effect in antiferromagnetic MnF_{2}. A device scale on-chip heater is deposited on a bilayer of MnF_{2} (110) (30 nm)/Pt (4 nm) grown by molecular ...beam epitaxy on a MgF_{2} (110) substrate. Using Pt as a spin detector layer, it is possible to measure the thermally generated spin current from MnF_{2} through the inverse spin Hall effect. The low temperature (2-80 K) and high magnetic field (up to 140 kOe) regime is explored. A clear spin-flop transition corresponding to the sudden rotation of antiferromagnetic spins out of the easy axis is observed in the spin Seebeck signal when large magnetic fields (>9 T) are applied parallel to the easy axis of the MnF_{2} thin film. When the magnetic field is applied perpendicular to the easy axis, the spin-flop transition is absent, as expected.
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This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions ...concerning methods, scope, and context of current research.
We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers.
DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a "long tail" of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific.
Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning).
Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.
AbstractIn geotechnical engineering, it is challenging to construct a site-specific multivariate probability distribution model for soil/rock properties because the site-specific data are usually ...sparse and incomplete. In contrast, there are abundant generic soil/rock data in the literature for the construction of a generic multivariate probability distribution model, but this model is typically biased and/or imprecise for a specific site. A hybridization method has been proposed to combine these two sources of soil/rock data (site-specific data and a generic database) to produce a quasi-site-specific model, but this method is essentially heuristic. In the current paper, a more rational method that exploits the geologic origin of soil/rock data is proposed. There is a tendency for data to be more similar within a single site and less similar between sites. This is called site uniqueness in geotechnical engineering practice, but no data-driven methods exist to quantify this data feature currently. The hierarchical Bayesian model (HBM) is a natural model to exploit this group information. The grouping criterion can be site localization, soil/rock types, or others. This paper only studies the group criterion based on site localization. This means that a generic database is now viewed as a collection of data groups labeled by qualitative site labels. This site label does not contain any quantitative information such as GPS location, it merely demarcates each group as distinct. The novel contribution is the development of an efficient HBM with closed-form conditional probabilities based on suitably chosen conjugate priors that can handle multivariate, uncertain and unique, sparse, incomplete, and potentially corrupted (MUSIC) data containing site labels. Numerical comparisons between the hybridization method (which cannot incorporate group information) and HBM show that even the simple qualitative knowledge that data belong to a geographically constrained site can improve the estimation of soil/rock properties. The GPS location of each site is not needed.
Autonomous vehicles (AVs) have the potential to save tens of thousands of lives, but legal and social barriers may delay or even deter manufacturers from offering fully automated vehicles and thereby ...cost lives that otherwise could be saved. Moral philosophers use “thought experiments” to teach us about what ethics might say about the ethical behavior of AVs. If a manufacturer designing an AV decided to make what it believes is an ethical choice to save a large group of lives by steering away from the large group towards a single individual, could the manufacturer be liable to that single individual? I believe the answer is yes. This article discusses famous “trolley problem” thought experiments in moral philosophy, which news articles have recently applied to AVs. After establishing a hypothetical case, applying product liability law to AV behavior may show a way, in some jurisdictions, that will permit a manufacturer to implement an ethical choice without triggering liability, but liability risk remains. I then turn to possible traditional defenses to see if they prevent liability for a manufacturer making what it believes is an ethical design choice—necessity, defense of self, and the sudden emergency doctrine. None of them provides an effective defense. Under current law, maximizing collision avoidance appears to minimize legal risk. I conclude that the only way to provide effective protection to a manufacturer seeking to implement ethical choices about AV behavior is through special legislation or regulation.
The layer stacking order in two-dimensional heterostructures, like graphene, affects their physical properties and potential applications. Trilayer graphene, specifically ABC-trilayer graphene, has ...captured significant interest due to its potential for correlated electronic states. However, achieving a stable ABC arrangement is challenging due to its lower thermodynamic stability compared to the more stable ABA stacking. Despite recent advancements in obtaining ABC graphene through external perturbations, such as strain, the stacking transition mechanism remains insufficiently explored. In this study, we unveil a universal mechanism to achieve ABC stacking, applicable for understanding ABA to ABC stacking changes induced by any mechanical perturbations. Our approach is based on a novel strain engineering technique that induces interlayer slippage and results in the formation of stable ABC domains. We investigate the underlying interfacial mechanisms of this stacking change through computational simulations and experiments. Our findings demonstrate a highly anisotropic and significant transformation of ABA stacking to large and stable ABC domains facilitated by interlayer slippage. Through atomistic simulations and local energy analysis, we systematically demonstrate the mechanism for this stacking transition, that is dependent on specific loading orientation. Understanding such a mechanism allows this material system to be engineered by design compatible with industrial techniques on a device-by-device level. We conduct Raman studies to validate and characterize the formed ABC stacking, highlighting its distinct features compared to the ABA region. Our results contribute to a clearer understanding of the stacking change mechanism and provide a robust and controllable method for achieving stable ABC domains, facilitating their use in developing advanced optoelectronic devices.
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•Improve compressive sensing used for crack segmentation in practical applications.•Replace sparsity constraint in compressive sensing by deep generative models.•Reduce size of training data with ...imperfect generative model capturing cracks well.•Achieve high segmentation accuracy and robustness to noise with the proposed method.
In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy-efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. Different from the popular approach of simultaneously training encoder and decoder using neural network models, the CS theory ensures a high probability of accurate signal reconstruction based on random measurements that is shorter than the length of the original signal under a sparsity constraint. Such method is particularly useful when measurements are expensive, such as wireless sensing of civil structures, because its hardware implementation allows down sampling of signals during the sensing process. Hence, CS methods can achieve significant energy saving for the sensing devices. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard to guarantee for many real images, such as image of cracks. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method. We demonstrate the remarkable performance of our method that takes advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparisons to three existing CS algorithms. Furthermore, we show that our framework is potentially extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and occlusion.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have ...emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called “transfer learning” has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140 000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry.
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