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Clozapine-Induced Chemogenetic Neuromodulation Rescues Post-Stroke Deficits After Chronic Capsular Infarct
No full textLong-term disabilities induced by stroke impose a heavy burden on patients, families, caregivers, and public health systems. Extensive studies have demonstrated the therapeutic value of neuromodulation in enhancing post-stroke recovery. Among them, chemogenetic neuromodulation activated by clozapine-N-oxide (CNO) has been proposed as the potential tool of neuromodulation. However, recent evidence showed that CNO does not cross the blood − brain barrier and may in fact have low binding affinity for chemogenetic tool. Thus, clozapine (CLZ) has been suggested for use in chemogenetic neuromodulation, in place of CNO, because it readily crosses the blood–brain barrier. Previously we reported that low doses of CLZ (0.1 mg/kg) successfully induced neural responses without off-target effects. Here, we show that low-dose clozapine (0.1 mg/kg) can induce prolonged chemogenetic activation while avoiding permeability issues and minimizing off-target effects. In addition, clozapine-induced excitatory chemogenetic neuromodulation (CLZ-ChemoNM) of sensory-parietal cortex with hsyn-hM3Dq-YFP-enhanced motor recovery in a chronic capsular infarct model of stroke in rats, improving post-stroke behavioral scores to 56% of pre-infarct levels. Longitudinal 2-deoxy-2-[18F]-fluoro-D-glucose microPET (FDG-microPET) scans showed that a reduction in diaschisis volume and activation of corticostriatal circuits were both correlated with post-stroke recovery. We also found c-Fos increases in bilateral cortices and BDNF increases in the cortices and striatum after CLZ-ChemoNM, indicating an increase in neural plasticity. These findings suggest the translational feasibility of CLZ-ChemoNM for augmenting recovery in chronic stroke
A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging
No full textHyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging
Bitcoin: A Natural Oligopoly
No full textWe argue that the concentrated production and ownership of Bitcoin mining hardware arise naturally from the economic incentives of Bitcoin mining. We model Bitcoin mining as a two-stage competition; miners compete in prices to sell hardware while competing in quantities for mining rewards. We characterize equilibria in our model and show that small asymmetries in operational costs result in highly concentrated ownership of mining equipment. We further show that production of mining equipment will be dominated by the miner with the most efficient hardware, who will sell hardware to competitors while possibly also using it to mine
Uncertainty Quantification for Nonconvex Tensor Completion: Confidence Intervals, Heteroscedasticity and Optimality
No full textWe study the distribution and uncertainty of nonconvex optimization for noisy tensor completion—the problem of estimating a low-rank tensor given incomplete and corrupted observations of its entries. Focusing on a two-stage estimation algorithm proposed by Cai et al. , we characterize the distribution of this nonconvex estimator down to fine scales. This distributional theory in turn allows one to construct valid and short confidence intervals for both the unseen tensor entries and the unknown tensor factors. The proposed inferential procedure enjoys several important features: (1) it is fully adaptive to noise heteroscedasticity, and (2) it is data-driven and automatically adapts to unknown noise distributions. Furthermore, our findings unveil the statistical optimality of nonconvex tensor completion: it attains un-improvable ℓ2 accuracy—including both the rates and the pre-constants—when estimating both the unknown tensor and the underlying tensor factors
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
No full textIn this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to VR users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for them using VLC. Here, VR users move in real time and their movement patterns change over time according to their applications, where both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and establish THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the average number of successfully served VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm that adopts a meta-learning approach is proposed. The proposed meta policy gradient (MPG) algorithm enables the trained policy to quickly adapt to new user movement patterns. In order to solve the problem of maximizing the average number of successfully served users for VR scenarios with large numbers of users, a low-complexity dual method based MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 26.8% and 21.9% improvement in the average number of successfully served users as well as 81.2% and 87.5% gains in the convergence speed, respectively
Catalogue of flat-band stoichiometric materials
No full textTopological electronic flattened bands near or at the Fermi level are a promising route towards unconventional superconductivity and correlated insulating states. However, the related experiments are mostly limited to engineered materials, such as moiré systems1,2,3. Here we present a catalogue of the naturally occuring three-dimensional stoichiometric materials with flat bands around the Fermi level. We consider 55,206 materials from the Inorganic Crystal Structure Database catalogued using the Topological Quantum Chemistry website4,5, which provides their structural parameters, space group, band structure, density of states and topological characterization. We combine several direct signatures and properties of band flatness with a high-throughput analysis of all crystal structures. In particular, we identify materials hosting line-graph or bipartite sublattices—in either two or three dimensions—that probably lead to flat bands. From this trove of information, we create the Materials Flatband Database website, a powerful search engine for future theoretical and experimental studies. We use the database to extract a curated list of 2,379 high-quality flat-band materials, from which we identify 345 promising candidates that potentially host flat bands with charge centres that are not strongly localized on the atomic sites. We showcase five representative materials and provide a theoretical explanation for the origin of their flat bands close to the Fermi energy using the S-matrix method introduced in a parallel work
Many-body effects in the X-ray absorption spectra of liquid water
No full textX-ray absorption spectroscopy (XAS) is a powerful experimental technique to probe the local order in materials with core electron excitations. Experimental interpretation requires supporting theoretical calculations. For water, these calculations are very demanding and, to date, could only be done with major approximations that limited the accuracy of the calculated spectra. This prompted an intense debate on whether a substantial revision of the standard picture of tetrahedrally bonded water was necessary to improve the agreement of theory and experiment. Here, we report a first-principles calculation of the XAS of water that avoids the approximations of prior work, thanks to recent advances in electron excitation theory. The calculated XAS spectra, and their variation with changes of temperature and/or with isotope substitution, are in good quantitative agreement with experiments. The approach requires accurate quasiparticle wave functions beyond density functional theory approximations, accounts for the dynamics of quasiparticles, and includes dynamic screening as well as renormalization effects due to the continuum of valence-level excitations. The three features observed in the experimental spectra are unambiguously attributed to excitonic effects. The preedge feature is associated with a bound intramolecular exciton, the main-edge feature is associated with an exciton localized within the coordination shell of the excited molecule, and the postedge feature is delocalized over more distant neighbors, as expected for a resonant state. The three features probe the local order at short, intermediate, and longer range relative to the excited molecule. The calculated spectra are fully consistent with a standard tetrahedral picture of water
Superfluid Weight Bounds from Symmetry and Quantum Geometry in Flat Bands
No full textFlat-band superconductivity has theoretically demonstrated the importance of band topology to correlated phases. In two dimensions, the superfluid weight, which determines the critical temperature through the Berezinksii-Kosterlitz-Thouless criteria, is bounded by the Fubini-Study metric at zero temperature. We show this bound is nonzero within flat bands whose Wannier centers are obstructed from the atoms—even when they have identically zero Berry curvature. Next, we derive general lower bounds for the superfluid weight in terms of momentum space irreps in all 2D space groups, extending the reach of topological quantum chemistry to superconducting states. We find that the bounds can be naturally expressed using the formalism of real space invariants (RSIs) that highlight the separation between electronic and atomic degrees of freedom. Finally, using exact Monte Carlo simulations on a model with perfectly flat bands and strictly local obstructed Wannier functions, we find that an attractive Hubbard interaction results in superconductivity as predicted by the RSI bound beyond mean field. Hence, obstructed bands are distinguished from trivial bands in the presence of interactions by the nonzero lower bound imposed on their superfluid weight
Critical Field Theories with OSp(1|2M ) Symmetry
No full textIn the paper [L. Fei et al., JHEP 09 (2015) 076] a cubic field theory of a scalar field σ and
two anticommuting scalar fields, θ and ¯θ, was formulated. In 6− dimensions it has a weakly
coupled fixed point with imaginary cubic couplings where the symmetry is enhanced to the
supergroup OSp(1|2). This theory may be viewed as a “UV completion” in 2 < d < 6 of
the non-linear sigma model with hyperbolic target space H0|2 described by a pair of intrinsic
anticommuting coordinates. It also describes the q → 0 limit of the critical q-state Potts
model, which is equivalent to the statistical mechanics of spanning forests on a graph. In
this letter we generalize these results to a class of OSp(1|2M ) symmetric field theories whose
upper critical dimensions are dc(M ) = 22M +1
2M −1 . They contain 2M anticommuting scalar
fields, θi, ¯θi, and one commuting one, with interaction g (σ2 + 2θi ¯θi)(2M +1)/2. In dc(M ) −
dimensions, we find a weakly coupled IR fixed point at an imaginary value of g. We propose
that these critical theories are the UV completions of the sigma models with fermionic
hyperbolic target spaces H0|2M . Of particular interest is the quintic field theory with OSp(1|4)
symmetry, whose upper critical dimension is 10/3. Using this theory, we make a prediction
for the critical behavior of the OSp(1|4) lattice system in three dimensions
A Simulation-based Method for Correcting Mode Coupling in CMB Angular Power Spectra
No full textModern cosmic microwave background (CMB) analysis pipelines regularly employ complex time-domain filters, beam models, masking, and other techniques during the production of sky maps and their corresponding angular power spectra. However, these processes can generate couplings between multipoles from the same spectrum and from different spectra, in addition to the typical power attenuation. Within the context of pseudo-Cℓ based, MASTER-style analyses, the net effect of the time-domain filtering is commonly approximated by a multiplicative transfer function, Fℓ, that can fail to capture mode mixing and is dependent on the spectrum of the signal. To address these shortcomings, we have developed a simulation-based spectral correction approach that constructs a two-dimensional transfer matrix, Jℓℓ¢, which contains information about mode mixing in addition to mode attenuation. We demonstrate the application of this approach on data from the first flight of the SPIDER balloon-borne CMB experiment