Domain Agnostic Real-Valued Specificity Prediction

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced real-valued specificity ratings. We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels. To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution. We show that our framework generalizes well to three different domains with 50%~68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity. We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.Comment: AAAI 2019 camera read

Provable Guarantees for Neural Networks via Gradient Feature Learning

Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Kernel approach fails to capture their key feature learning ability, while recent analyses on feature learning are typically problem-specific. This work proposes a unified analysis framework for two-layer networks trained by gradient descent. The framework is centered around the principle of feature learning from gradients, and its effectiveness is demonstrated by applications in several prototypical problems, such as mixtures of Gaussians and parity functions. The framework also sheds light on interesting network learning phenomena such as feature learning beyond kernels and the lottery ticket hypothesis.Comment: NeurIPS 2023, 71 page

Integrable deformations of the Bogoyavlenskij-Itoh Lotka-Volterra systems

We construct a family of integrable deformations of the Bogoyavlenskij-Itoh systems and construct a Lax operator with spectral parameter for it. Our approach is based on the construction of a family of compatible Poisson structures for the undeformed system, whose Casimirs are shown to yield a generating function for the integrals in involution of the deformed systems. We show how these deformations are related to the Veselov-Shabat systems.Comment: 23 pages, 14 reference

Applicability of magnetic force models for multi-stable energy harvesters

Multi-stable piezoelectric energy harvesters have been exploited to enhance performance for extracting ambient vibrational energy from a broadband energy source. Since magnetic force plays a significant role in enhancing the dynamic behavior of harvesters, it is necessary to model and understand the significant influencing of structural parameters on magnetic force. Recently, several theoretical modeling methods, including magnetic dipole, improved dipole, magnetic current, and magnetic charge models, have been developed to calculate the magnetic force in multi-stable energy harvesters. However, the influence of structural parameters and magnet dimensions on the accuracy of magnetic force calculation for these methods has not been analyzed. Therefore, it is necessary to investigate the applicability of these methods under a range of operating conditions. New insights into the accuracy and application constraints of these methods are presented in this paper to calculate the impact of magnetic force on multi-stable energy harvesters. From the theoretical derivation of models and numerical results obtained, a quantitative assessment of errors under different structural parameters and magnet sizes is presented and compared to evaluate the application constraints. Moreover, experimental measurements are performed to verify the applicability of these modeling methods for bi-stable and tri-stable energy harvesters with different structural parameters.</p

A Survey on Consortium Blockchain Consensus Mechanisms

Blockchain is a distributed ledger that is decentralized, immutable, and transparent, which maintains a continuously growing list of transaction records ordered into blocks. As the core of blockchain, the consensus algorithm is an agreement to validate the correctness of blockchain transactions. For example, Bitcoin is a public blockchain where each node in Bitcoin uses the Proof of Work (PoW) algorithm to reach a consensus by competing to solve a puzzle. Unlike a public blockchain, a consortium blockchain is an enterprise-level blockchain that does not contend with the issues of creating a resource-saving global consensus protocol. This paper highilights several state-of-the art solutions in consensus algorithms for enterprise blockchain. For example, the HyperLedger by Linux Foundation includes implementing Practical Byzantine Fault Tolerance (PBFT) as the consensus algorithm. PBFT can tolerate a range of malicious nodes and reach consensus with quadratic complexity. Another consensus algorithm, HotStuff, implemented by Facebook Libra project, has achieved linear complexity of the authenticator. This paper presents the operational mechanisms of these and other consensus protocols, and analyzes and compares their advantages and drawbacks.Comment: under submissio

Multiple solutions of asymmetric potential bistable energy harvesters: numerical simulation and experimental validation

In this paper, we investigate the multiple solutions of nonlinear asymmetric potential bistable energy harvesters (BEHs) under harmonic excitations. Basins of attraction under certain excitations explain the existence of multiple solutions in the asymmetric potential BEHs and indicate that the asymmetric system has a higher probability to oscillate in the deeper potential well under low and moderate excitation levels. Thus, the appearance of asymmetric potentials in BEHs has a negative influence on the output performance. Average output powers under different excitation frequencies and initial conditions illustrate that the asymmetric potential BEHs are more likely to achieve high-energy branch (HEB) with initial conditions in the shallower potential well, and the probability is influenced by the degree of asymmetry of the BEHs. Finally, experiments are carried out, and results under constant and sweep frequency excitations demonstrate that the output performance will be actually improved for the asymmetric potential BEHs if the initial oscillations are from the shallower potential well