7 research outputs found
Symmetry transformation of nonlinear optical current of tilted Weyl nodes and application to ferromagnetic MnBi2Te4
A Weyl node is characterized by its chirality and tilt. We develop a theory
of how th order nonlinear optical conductivity behaves under transformations
of anisotropic tensor and tilt, which clarify how chirality-dependent and
-independent parts of optical conductivity transform under the reversal of tilt
and chirality. Built on this theory, we propose ferromagnetic MnBi2Te4 as a
magnetoelectrically regulated, terahertz optical device, by magnetoelectrically
switching the chirality-dependent and -independent dc photocurrents. These
results are useful for creating nonlinear optical devices based on topological
Weyl semimetals
Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction
Many state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that canāt be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocksā news sentiment, the focal stockās news sentiment and the focal stockās quantitative features to predict the focal stockās price movement. Results show that our method has an improvement of 8.1% compared with the traditional method
Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression
Echo cancellation and noise reduction are essential for full-duplex
communication, yet most existing neural networks have high computational costs
and are inflexible in tuning model complexity. In this paper, we introduce
time-frequency dual-path compression to achieve a wide range of compression
ratios on computational cost. Specifically, for frequency compression,
trainable filters are used to replace manually designed filters for dimension
reduction. For time compression, only using frame skipped prediction causes
large performance degradation, which can be alleviated by a post-processing
network with full sequence modeling. We have found that under fixed compression
ratios, dual-path compression combining both the time and frequency methods
will give further performance improvement, covering compression ratios from 4x
to 32x with little model size change. Moreover, the proposed models show
competitive performance compared with fast FullSubNet and DeepFilterNet. A demo
page can be found at
hangtingchen.github.io/ultra_dual_path_compression.github.io/.Comment: Accepted by Interspeech 202
Kronos: A Secure and Generic Sharding Blockchain Consensus with Optimized Overhead
Sharding enhances blockchain scalability by dividing the network into shards, each managing specific unspent transaction outputs or accounts. As an introduced new transaction type, cross-shard transactions pose a critical challenge to the security and efficiency of sharding blockchains. Currently, there is a lack of a generic sharding consensus pattern that achieves both security and low overhead.
In this paper, we present Kronos, a secure sharding blockchain consensus achieving optimized overhead. In particular, we propose a new secure sharding consensus pattern, based on a buffer managed jointly by shard members. Valid transactions are transferred to the payee via the buffer, while invalid ones are rejected through happy or unhappy paths. Kronos is proved to achieve security with atomicity under malicious clients with optimal intra-shard overhead ( for involved shard number and for a Byzantine fault tolerance (BFT) cost). Efficient rejection even requires no BFT execution in happy paths, and the cost in unhappy paths is still lower than a two-phase commit. Besides, we propose secure cross-shard certification methods based on batch certification and reliable cross-shard transfer. The former combines hybrid trees or vector commitments, while the latter integrates erasure coding. Handling transactions, Kronos is proved to achieve reliability with low cross-shard overhead ( for shard size and for the security parameter). Notably, Kronos imposes no restrictions on BFT and does not rely on time assumptions, offering optional constructions in various modules. Kronos could serve as a universal framework for enhancing the performance and scalability of existing BFT protocols, supporting generic models, including asynchronous networks, increasing the throughput by several orders of magnitude.
We implement Kronos using two prominent BFT protocols: asynchronous Speeding Dumbo (NDSS\u2722) and partial synchronous Hotstuff (PODC\u2719). Extensive experiments (over up to 1000 AWS EC2 nodes across 4 AWS regions) demonstrate Kronos scales the consensus nodes to thousands, achieving a substantial throughput of 320 ktx/sec with 2.0 sec latency. Compared with the past solutions, Kronos outperforms, achieving up to a 12 improvement in throughput and a 50% reduction in latency when cross-shard transactions dominate the workload