Chirality-Assisted Electronic Cloaking in Bilayer Graphene Nanostructures

We show that the strong coupling of pseudospin orientation and charge carrier motion in bilayer graphene has a drastic effect on transport properties of ballistic p-n-p junctions. Electronic states with zero momentum parallel to the barrier are confined under it for one pseudospin orientation, whereas states with the opposite pseudospin tunnel through the junction totally uninfluenced by the presence of confined states. We demonstrate that the junction acts as a cloak for confined states, making them nearly invisible to electrons in the outer regions over a range of incidence angles. This behavior is manifested in the two-terminal conductance as transmission resonances with non-Lorentzian, singular peak shapes. The response of these phenomena to a weak magnetic field or electric-field-induced interlayer gap can serve as an experimental fingerprint of electronic cloaking.Comment: 5 pgs, 5 fg

Energy-based Structure Prediction for d(Al70Co20Ni10)

We use energy minimization principles to predict the structure of a decagonal quasicrystal - d(AlCoNi) - in the Cobalt-rich phase. Monte Carlo methods are then used to explore configurations while relaxation and molecular dynamics are used to obtain a more realistic structure once a low energy configuration has been found. We find five-fold symmetric decagons 12.8 A in diameter as the characteristic formation of this composition, along with smaller pseudo-five-fold symmetric clusters filling the spaces between the decagons. We use our method to make comparisons with a recent experimental approximant structure model from Sugiyama et al (2002).Comment: 10pp, 2 figure

Study on the establishment of corneal alkali chemical injury on rats

AIM:To investigate the appropriate methods to establish corneal alkali chemical injury on rats. METHODS:The rats(n=87)were randomly divided into three groups. Corneal alkali injury was induced by placing 1mol/L NaOH soaked filter paper on the limbus of right cornea for 20 seconds(group A, n=34)or 40 seconds(group B, n=23), and on the central axis of the right cornea for 40 seconds(group C, n=30)respectively. Corneal transparency, corneal ulceration, and corneal neovascularization were observed and recorded under slit- lamp biomicroscope on day 7 post-operation. RESULTS: Incidence of corneal ulceration, corneal perforation and positive rate of corneal fluorescein staining in limbal corneal injury groups(group A and B)were significantly higher than that of central corneal injury group(group C)(P<0.05). Incidence of corneal ulceration and corneal perforation in group B was significantly higher than group A(P<0.05). Corneal neovascularization was observed in all three groups. CONCLUSION: Corneal alkali burns induced by 3mm diameter central cornea injury are fit for the study of corneal neovascularization, while those induced by limbus injury for 20 seconds are fit for the study on limbal stem cells deficiency

Landau Level Collapse in Gated Graphene Structures

We describe a new regime of magnetotransport in two dimensional electron systems in the presence of a narrow potential barrier imposed by external gates. In such systems, the Landau level states, confined to the barrier region in strong magnetic fields, undergo a deconfinement transition as the field is lowered. We present transport measurements showing Shubnikov-de Haas (SdH) oscillations which, in the unipolar regime, abruptly disappear when the strength of the magnetic field is reduced below a certain critical value. This behavior is explained by a semiclassical analysis of the transformation of closed cyclotron orbits into open, deconfined trajectories. Comparison to SdH-type resonances in the local density of states is presented.Comment: 4 pages, 2 figure

Learning Markov Random Fields for Combinatorial Structures via Sampling through Lov\'asz Local Lemma

Learning to generate complex combinatorial structures satisfying constraints will have transformative impacts in many application domains. However, it is beyond the capabilities of existing approaches due to the highly intractable nature of the embedded probabilistic inference. Prior works spend most of the training time learning to separate valid from invalid structures but do not learn the inductive biases of valid structures. We develop NEural Lov\'asz Sampler (Nelson), which embeds the sampler through Lov\'asz Local Lemma (LLL) as a fully differentiable neural network layer. Our Nelson-CD embeds this sampler into the contrastive divergence learning process of Markov random fields. Nelson allows us to obtain valid samples from the current model distribution. Contrastive divergence is then applied to separate these samples from those in the training set. Nelson is implemented as a fully differentiable neural net, taking advantage of the parallelism of GPUs. Experimental results on several real-world domains reveal that Nelson learns to generate 100\% valid structures, while baselines either time out or cannot ensure validity. Nelson also outperforms other approaches in running time, log-likelihood, and MAP scores.Comment: accepted by AAAI 2023. The first two authors contribute equall