Towards Verified Price Oracles for Decentralized Exchange Protocols

Various smart contracts have been designed and deployed on blockchain platforms to enable cryptocurrency trading, leading to an ever expanding user base of decentralized exchange platforms (DEXs). Automated Market Maker contracts enable token exchange without the need of third party book-keeping. These contracts also serve as price oracles for other contracts, by using a mathematical formula to calculate token exchange rates based on token reserves. However, the price oracle mechanism is vulnerable to attacks both from programming errors and from mistakes in the financial model, and so far their complexity makes it difficult to formally verify them. We present a verified AMM contract and validate its financial model by proving a theorem about a lower bound on the cost of manipulation of the token prices to the attacker. The contract is implemented using the DeepSEA system, which ensures that the theorem applies to the actual EVM bytecode of the contract. This theorem could be used as proof of correctness for other contracts using the AMM, so this is a step towards a verified DeFi landscape

An Accelerated Proximal Alternating Direction Method of Multipliers for Optimal Decentralized Control of Uncertain Systems

To ensure the system stability of the $\bf{\mathcal{H}_{2}}$-guaranteed cost optimal decentralized control problem (ODC), an approximate semidefinite programming (SDP) problem is formulated based on the sparsity of the gain matrix of the decentralized controller. To reduce data storage and improve computational efficiency, the SDP problem is vectorized into a conic programming (CP) problem using the Kronecker product. Then, a proximal alternating direction method of multipliers (PADMM) is proposed to solve the dual of the resulted CP. By linking the (generalized) PADMM with the (relaxed) proximal point algorithm, we are able to accelerate the proposed PADMM via the Halpern fixed-point iterative scheme. This results in a fast convergence rate for the Karush-Kuhn-Tucker (KKT) residual along the sequence generated by the accelerated algorithm. Numerical experiments further demonstrate that the accelerated PADMM outperforms both the well-known CVXOPT and SCS algorithms for solving the large-scale CP problems arising from $\bf{\mathcal{H}_{2}}$-guaranteed cost ODC problems

Metasurface array for single-shot spectroscopic ellipsometry

Spectroscopic ellipsometry is a potent method that is widely adopted for the measurement of thin film thickness and refractive index. However, a conventional ellipsometer, which utilizes a mechanically rotating polarizer and grating-based spectrometer for spectropolarimetric detection, is bulky, complex, and does not allow real-time measurements. Here, we demonstrated a compact metasurface array-based spectroscopic ellipsometry system that allows single-shot spectropolarimetric detection and accurate determination of thin film properties without any mechanical movement. The silicon-based metasurface array with a highly anisotropic and diverse spectral response is combined with iterative optimization to reconstruct the full Stokes polarization spectrum of the light reflected by the thin film with high fidelity. Subsequently, the film thickness and refractive index can be determined by fitting the measurement results to a proper material model with high accuracy. Our approach opens up a new pathway towards a compact and robust spectroscopic ellipsometry system for the high throughput measurement of thin film properties

FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation

Fast video recognition is essential for real-time scenarios, e.g., autonomous driving. However, applying existing Deep Neural Networks (DNNs) to individual high-resolution images is expensive due to large model sizes. Spiking Neural Networks (SNNs) are developed as a promising alternative to DNNs due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus they are useful to enable energy-efficient computation. However, exploiting the spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading energy efficiency. In this work, we, therefore, propose an SNN accelerator called FrameFire for efficient video processing. We introduce a Keyframe-dominated Workload Balance Schedule (KWBS) method. It accelerates the image recognition network with sparse keyframes, then records and analyzes the current workload distribution on hardware to facilitate scheduling workloads in subsequent frames. FrameFire is implemented on a Xilinx XC7Z035 FPGA and verified by video segmentation tasks. The results show that the throughput is improved by 1.7× with the KWBS method. FrameFire achieved 1.04 KFPS throughput and 1.15 mJ/frame recognition energy

A Study of the Impact of Tourism Economic and Non-economic Benefits on Residents\u27 Pro-Environmental Behaviors in Community-based Ecotourism

A survey was conducted on 362 residents of a classical ecotourism destination in China to explore the impacts of both tourism economic and non-economic benefits on residents’ pro-environmental behaviors. The results of Structural Equation Modeling (SEM) indicated that tourism economic and non-economic benefits impacted on residents’ pro-environmental behaviors through perceived positive tourism impact as a mediator. These findings enriched literatures in ecotourism and had managerial value for the practitioners in domestic ecotourism community

Hurricanes Substantially Reduce the Nutrients in Tropical Forested Watersheds in Puerto Rico

Because nutrients including nitrogen and phosphorus are generally limited in tropical forest ecosystems in Puerto Rico, a quantitative understanding of the nutrient budget at a watershed scale is required to assess vegetation growth and predict forest carbon dynamics. Hurricanes are the most frequent disturbance in Puerto Rico and play an important role in regulating lateral nitrogen and phosphorus exports from the forested watershed. In this study, we selected seven watersheds in Puerto Rico to examine the immediate and lagged effects of hurricanes on nitrogen and phosphorous exports. Our results suggest that immediate surges of heavy precipitation associated with hurricanes accelerate nitrogen and phosphorus exports as much as 297 ± 113 and 306 ± 70 times than the long-term average, respectively. In addition, we estimated that it requires approximately one year for post-hurricane riverine nitrogen and phosphorus concentrations to recover to pre-hurricane levels. During the recovery period, the riverine nitrogen and phosphorus concentrations are 30 ± 6% and 28 ± 5% higher than the pre-hurricane concentrations on average

Long-Term Rhythmic Video Soundtracker

We consider the problem of generating musical soundtracks in sync with rhythmic visual cues. Most existing works rely on pre-defined music representations, leading to the incompetence of generative flexibility and complexity. Other methods directly generating video-conditioned waveforms suffer from limited scenarios, short lengths, and unstable generation quality. To this end, we present Long-Term Rhythmic Video Soundtracker (LORIS), a novel framework to synthesize long-term conditional waveforms. Specifically, our framework consists of a latent conditional diffusion probabilistic model to perform waveform synthesis. Furthermore, a series of context-aware conditioning encoders are proposed to take temporal information into consideration for a long-term generation. Notably, we extend our model's applicability from dances to multiple sports scenarios such as floor exercise and figure skating. To perform comprehensive evaluations, we establish a benchmark for rhythmic video soundtracks including the pre-processed dataset, improved evaluation metrics, and robust generative baselines. Extensive experiments show that our model generates long-term soundtracks with state-of-the-art musical quality and rhythmic correspondence. Codes are available at \url{https://github.com/OpenGVLab/LORIS}.Comment: ICML202

Interfacial regulation of aqueous synthesized metal-semiconductor hetero-nanocrystals

Integrating metal and semiconductor components to form metal-semiconductor heterostructures is an attractive strategy to develop nanomaterials for optoelectronic applications, and the rational regulation of their heterointerfaces could effectively influence their charge transfer properties and further determine their performance. Considering the natural large lattice mismatch between metal and semiconductor components, defects and low crystalline heterointerfaces could be easily generated especially for heterostructures with large contacting areas such as core-shell and over quantum-sized nanostructures. The defective interfaces of heterostructures could lead to the undesirable recombination of photo-induced electrons and holes, which would decrease their performances. Based on these issues, the perspective focusing on the most recent progress in the aqueous synthesis of metal-semiconductor heterostructures with emphasis on heterointerface regulation is proposed, especially in the aspect of non-epitaxial growth strategies initiated by cation exchange reaction (CER). The enhanced optoelectronic performance enabled by precise interfacial regulations is also illustrated. We hope this perspective could provide meaningful insights for researchers on nano synthesis and optoelectronic applications