We formulate a version of the information paradox in de Sitter spacetime and show that it is solved by the emergence of entanglement islands in the context of the De Sitter/de Sitter correspondence; ...in particular, the entanglement entropy of a subregion obeys a time-dependent Page curve. Our construction works in general spacetime dimensions and keeps the graviton massless. We interpret the resulting behavior of the entanglement entropy using double holography. It suggests that the spatial distribution of microscopic degrees of freedom depends on descriptions, as in the case of a black hole. In the static (distant) description of de Sitter (black hole) spacetime, these degrees of freedom represent microstates associated with the Gibbons-Hawking (Bekenstein-Hawking) entropy and are localized toward the horizon. On the other hand, in a global (effective two-sided) description, which is obtained by the quantum analog of analytic extension and is intrinsically semiclassical, they are distributed uniformly and in a unique semiclassical de Sitter (black hole) vacuum state.
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In this work we study the constraints on the anomalous tqγ (q=u,c) couplings by photon-produced leading single top production and single top jet associated production through the main reactions ...pp→pγp→pt→pW(→ℓνℓ)b+X and pp→pγp→ptj→pW(→ℓνℓ)bj+X assuming a typical LHC multipurpose forward detectors in a model independent effective lagrangian approach. Our results show that: for the typical detector acceptance 0.0015<ξ1<0.5, 0.1<ξ2<0.5 and 0.0015<ξ3<0.15 with a luminosity of 2 fb−1, the lower bounds of κtqγ through leading single top channel (single top jet channel) are 0.0130 (0.0156), 0.0218 (0.0206) and 0.0133 (0.01655), respectively, corresponding to Br(t→qγ)∼3×10−5. With a luminosity of 200 fb−1, the lower bounds of κtqγ are 0.0041 (0.0048), 0.0069 (0.0064) and 0.0042 (0.0051), respectively, corresponding to Br(t→qγ)∼4×10−6. We conclude that both channels can be used to detect such anomalous tqγ couplings and the detection sensitivity on κtqγ is obtained.
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Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of ...such systems in various science and engineering disciplines. This work introduces a novel approach called physics-informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this discovery approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to approximate the solution of system variables, compute essential derivatives, as well as identify the key derivative terms and parameters that form the structure and explicit expression of the equations. The efficacy and robustness of this method are demonstrated, both numerically and experimentally, on discovering a variety of partial differential equation systems with different levels of data scarcity and noise accounting for different initial/boundary conditions. The resulting computational framework shows the potential for closed-form model discovery in practical applications where large and accurate datasets are intractable to capture.
This tutorial review summarizes strategies elaborated for the discovery and prediction of programmed primary structures derived from quasi-equivalent constitutional isomeric libraries of ...self-assembling dendrons, dendrimers and dendronized polymers. These libraries demonstrate an 82% predictability, defined as the percentage of similar primary structures resulting in at least one conserved supramolecular shape with internal order. A combination of structural and retrostructural analysis that employs methodologies transplanted from structural biology, adapted to giant supramolecular assemblies was used for this process. A periodic table database of programmed primary structures was elaborated and used to facilitate the emergence of a diversity of functions in complex dendrimer systems
via
first principles. Assemblies generated by supramolecular and covalent polymer backbones were critically compared. Although by definition complex functional systems cannot be designed, this tutorial hints to a methodology based on database analysis principles to facilitate design principles that may help to mediate an accelerated emergence of chemical, physical and most probably also societal, political and economic complex systems on a shorter time scale and lower cost than by the current methods. This tutorial review is limited to the simplest, synthetically most accessible self-assembling minidendrons, minidendrimers and polymers dendronized with minidendrons that are best analyzed and elucidated at molecular, supramolecular and theoretical levels, and most used in other laboratories. These structures are all interrelated, and their principles expand in a simple way to their higher generations.
Self-assembly of quasi-equivalent amphiphilic dendrons into secondary and tertiary structures and their self-organization into periodic arrays.
Hyperspectral image (HSI) classification aims to assign each hyperspectral pixel with a proper land-cover label. Recently, convolutional neural networks (CNNs) have shown superior performance. To ...identify the land-cover label, CNN-based methods exploit the adjacent pixels as an input HSI cube, which simultaneously contains spectral signatures and spatial information. However, at the edge of each land-cover area, an HSI cube often contains several pixels whose land-cover labels are different from that of the center pixel. These pixels, named interfering pixels, will weaken the discrimination of spectral-spatial features and reduce classification accuracy. In this article, a spectral-spatial attention network (SSAN) is proposed to capture discriminative spectral-spatial features from attention areas of HSI cubes. First, a simple spectral-spatial network (SSN) is built to extract spectral-spatial features from HSI cubes. The SSN is composed of a spectral module and a spatial module. Each module consists of only a few 3-D convolution and activation operations, which make the proposed method easy to converge with a small number of training samples. Second, an attention module is introduced to suppress the effects of interfering pixels. The attention module is embedded into the SSN to obtain the SSAN. The experiments on several public HSI databases demonstrate that the proposed SSAN outperforms several state-of-the-art methods.
This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic concept is to incorporate available, yet ...incomplete, physics knowledge (e.g., laws of physics, scientific principles) into deep long short-term memory (LSTM) networks, which constrains and boosts the learning within a feasible solution space. The physics constraints are embedded in the loss function to enforce the model training which can accurately capture latent system nonlinearity even with very limited available training datasets. Specifically for dynamic structures, physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. In particular, two physics-informed multi-LSTM network architectures are proposed for structural metamodeling. The satisfactory performance of the proposed framework is successfully demonstrated through two illustrative examples (e.g., nonlinear structures subjected to ground motion excitation). It turns out that the embedded physics can alleviate overfitting issues, reduce the need of big training datasets, and improve the robustness of the trained model for more reliable prediction with extrapolation ability. As a result, the physics-informed deep learning paradigm outperforms classical non-physics-guided data-driven neural networks.
•Presented a novel physics-informed deep learning paradigm for metamodeling of nonlinear structures.•Incorporated physics knowledge into deep LSTM networks boosting the learning within a feasible solution space.•Developed two physics-informed multi-LSTM network architectures.•Demonstrated satisfactory performance of the proposed framework on two nonlinear systems.
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We define the moduli problem of Hitchin pairs over Deligne- Mumford stacks and prove this moduli problem is represented by a separated and locally finitely-presented algebraic space, which is ...considered as the moduli space of Hitchin pairs over a Deligne-Mumford stack.
•A deep physics-guided convolutional neural network (PhyCNN) is developed for structural seismic response estimation.•Available physics can provide constraints to the network outputs, alleviate ...overfitting issues, reduce the need of big training datasets, and thus improve the robustness of the trained model for more reliable prediction.•The proposed approach was successfully demonstrated by both numerical and experimental case studies.
Accurate prediction of building’s response subjected to earthquakes makes possible to evaluate building performance. To this end, we leverage the recent advances in deep learning and develop a physics-guided convolutional neural network (PhyCNN) for data-driven structural seismic response modeling. The concept is to train a deep PhyCNN model based on limited seismic input–output datasets (e.g., from simulation or sensing) and physics constraints, and thus establish a surrogate model for structural response prediction. Available physics (e.g., the law of dynamics) can provide constraints to the network outputs, alleviate overfitting issues, reduce the need of big training datasets, and thus improve the robustness of the trained model for more reliable prediction. The surrogate model is then utilized for fragility analysis given certain limit state criteria. In addition, an unsupervised learning algorithm based on K-means clustering is also proposed to partition the datasets to training, validation and prediction categories, so as to maximize the use of limited datasets. The performance of PhyCNN is demonstrated through both numerical and experimental examples. Convincing results illustrate that PhyCNN is capable of accurately predicting building’s seismic response in a data-driven fashion without the need of a physics-based analytical/numerical model. The PhyCNN paradigm also outperforms non-physics-guided neural networks.
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We investigate the tilt-stability of stable sheaves on projective varieties with respect to certain tilt-stability conditions depends on two parameters constructed by Bridgeland 11 (see also 1,6,5). ...For a stable sheaf, we give effective bounds of these parameters such that the stable sheaf is tilt-stable. These allow us to prove new vanishing theorems for stable sheaves and an effective Serre vanishing theorem for torsion free sheaves. Using these results, we also prove Bogomolov-Gieseker type inequalities for the third Chern character of a stable sheaf on P3.
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The formation of composite materials represents an efficient route to improve the performances of polymers and expand their application scopes. Due to the unique structure and remarkable mechanical, ...electrical, thermal, optical and catalytic properties, carbon nanotube and graphene have been mostly studied as a second phase to produce high performance polymer composites. Although carbon nanotube and graphene share some advantages in both structure and property, they are also different in many aspects including synthesis of composite material, control in composite structure and interaction with polymer molecule. The resulting composite materials are distinguished in property to meet different applications. This review article mainly describes the preparation, structure, property and application of the two families of composite materials with an emphasis on the difference between them. Some general and effective strategies are summarized for the development of polymer composite materials based on carbon nanotube and graphene.
Carbon nanotubes and graphene have been widely incorporated into polymers to synthesize high performance composite materials. This review article describes the preparation, structure, property and application of the two families of composite materials with an emphasis on the difference between them.
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