Quantum-Confined Electronic States arising from Moir\'e Pattern of MoS2-WSe2 Hetero-bilayers

A two-dimensional (2D) hetero-bilayer system consisting of MoS2 on WSe2, deposited on epitaxial graphene, is studied by scanning tunneling microscopy and spectroscopy at temperatures of 5 and 80 K. A moir\'e pattern is observed, arising from lattice mismatch of 3.7% between the MoS2 and WSe2. Significant energy shifts are observed in tunneling spectra observed at the maxima of the moir\'e corrugation, as compared with spectra obtained at corrugation minima, consistent with prior work. Furthermore, at the minima of the moir\'e corrugation, sharp peaks in the spectra at energies near the band edges are observed, for spectra acquired at 5 K. The peaks correspond to discrete states that are confined within the moir\'e unit cells. Conductance mapping is employed to reveal the detailed structure of the wave functions of the states. For measurements at 80 K, the sharp peaks in the spectra are absent, and conductance maps of the band edges reveal little structure.Comment: 10 page (5 figures) in main manuscript, with 9 pages (7 figures) of supplementary information; in v2, refs 8,9,24,25,34,40,41 are added, refs 27-30 are moved from supplementary info to main manuscript, and minor revisions to the text are made in connection with these change

Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, INSPIRE, IOSTAR, VICAVR, DRIVE and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community

Provably Feasible and Stable White-Box Trajectory Optimization

We study the problem of Trajectory Optimization (TO) for a general class of stiff and constrained dynamic systems. We establish a set of mild assumptions, under which we show that TO converges numerically stably to a locally optimal and feasible solution up to arbitrary user-specified error tolerance. Our key observation is that all prior works use SQP as a black-box solver, where a TO problem is formulated as a Nonlinear Program (NLP) and the underlying SQP solver is not allowed to modify the NLP. Instead, we propose a white-box TO solver, where the SQP solver is informed with characteristics of the objective function and the dynamic system. It then uses these characteristics to derive approximate dynamic systems and customize the discretization schemes

A Unified Model for the Two-stage Offline-then-Online Resource Allocation

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of $1/4$ from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.Comment: Accepted by IJCAI 2020 (http://static.ijcai.org/2020-accepted_papers.html) and SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

impact of derivative hedging on risk: evidence from China

The use of derivatives by companies is now increasing and there is a lot of research on the use of derivatives to hedge risk by European and American companies, but relatively little research on Asian companies. This paper selects company data for 2020 and 2021 from 327 companies listed on the Hong Kong Stock Exchange, collects information on the use of derivatives by reading the companies' annual reports, and performs regression analysis in conjunction with the companies' risk and other data. The study finds that the use of derivatives can both reduce and increase company risk. Through further sub-group studies, it was found that the use of derivative hedging was more effective in reducing risk when the company's risk was higher and that the effect of hedging risk became less pronounced when the statistical time lengthened. The findings of this paper are based on a study of Asian companies. A new research perspective is provided to study the impact of derivatives on corporate risk

Distributionally Robust Circuit Design Optimization under Variation Shifts

Due to the significant process variations, designers have to optimize the statistical performance distribution of nano-scale IC design in most cases. This problem has been investigated for decades under the formulation of stochastic optimization, which minimizes the expected value of a performance metric while assuming that the distribution of process variation is exactly given. This paper rethinks the variation-aware circuit design optimization from a new perspective. First, we discuss the variation shift problem, which means that the actual density function of process variations almost always differs from the given model and is often unknown. Consequently, we propose to formulate the variation-aware circuit design optimization as a distributionally robust optimization problem, which does not require the exact distribution of process variations. By selecting an appropriate uncertainty set for the probability density function of process variations, we solve the shift-aware circuit optimization problem using distributionally robust Bayesian optimization. This method is validated with both a photonic IC and an electronics IC. Our optimized circuits show excellent robustness against variation shifts: the optimized circuit has excellent performance under many possible distributions of process variations that differ from the given statistical model. This work has the potential to enable a new research direction and inspire subsequent research at different levels of the EDA flow under the setting of variation shift.Comment: accepted by ICCAD 2023, 8 page

DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing

Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However, a notable limitation of diffusion models, in comparison to GANs, is their difficulty in smoothly interpolating between two image samples, due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work, we present DiffMorpher, the first approach enabling smooth and natural image interpolation using diffusion models. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation. In addition, we propose an attention interpolation and injection technique and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories, bridging a critical functional gap that distinguished diffusion models from GANs