The Amplitude Nth-Power Squeezing of Radiation Fields in the Degenerate Raman Process

In this paper we study the amplitude Nth-power squeezing of radiation fields in the degenerate Raman process by using the modified effective Hamiltonian approach recently suggested by us. We found that if the field is initially in a coherent state it will not get squeezing for any Nth-power; if the field is initially in a squeezed vacuum, it may get Nth-power squeezing. The time evolution of the field fluctuation was discussed. Its dependences on power-order N, mean photon number bar-n, and squeezing angle xi are analyzed

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction. While recent methods demonstrate good forecasting abilities, they have three fundamental limitations. (i) Discrete neural architectures: Interlacing individually parameterized spatial and temporal blocks to encode rich underlying patterns leads to discontinuous latent state trajectories and higher forecasting numerical errors. (ii) High complexity: Discrete approaches complicate models with dedicated designs and redundant parameters, leading to higher computational and memory overheads. (iii) Reliance on graph priors: Relying on predefined static graph structures limits their effectiveness and practicability in real-world applications. In this paper, we address all the above limitations by proposing a continuous model to forecast $\textbf{M}$ultivariate $\textbf{T}$ime series with dynamic $\textbf{G}$raph neural $\textbf{O}$rdinary $\textbf{D}$ifferential $\textbf{E}$quations ($\texttt{MTGODE}$). Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing, allowing deeper graph propagation and fine-grained temporal information aggregation to characterize stable and precise latent spatial-temporal dynamics. Our experiments demonstrate the superiorities of $\texttt{MTGODE}$ from various perspectives on five time series benchmark datasets.Comment: 14 pages, 6 figures, 5 table

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.Comment: Submitted to AAAI 202

Feasible pickup from intact ossicular chain with floating piezoelectric microphone

<p>Abstract</p> <p>Objectives</p> <p>Many microphones have been developed to meet with the implantable requirement of totally implantable cochlear implant (TICI). However, a biocompatible one without destroying the intactness of the ossicular chain still remains under investigation. Such an implantable floating piezoelectric microphone (FPM) has been manufactured and shows an efficient electroacoustic performance in vitro test at our lab. We examined whether it pick up sensitively from the intact ossicular chain and postulated whether it be an optimal implantable one.</p> <p>Methods</p> <p>Animal controlled experiment: five adult cats (eight ears) were sacrificed as the model to test the electroacoustic performance of the FPM. Three groups were studied: (1) the experiment group (on malleus): the FPM glued onto the handle of the malleus of the intact ossicular chains; (2) negative control group (in vivo): the FPM only hung into the tympanic cavity; (3) positive control group (Hy-M30): a HiFi commercial microphone placed close to the site of the experiment ear. The testing speaker played pure tones orderly ranged from 0.25 to 8.0 kHz. The FPM inside the ear and the HiFi microphone simultaneously picked up acoustic vibration which recorded as .wav files to analyze.</p> <p>Results</p> <p>The FPM transformed acoustic vibration sensitively and flatly as did the in vitro test across the frequencies above 2.0 kHz, whereas inefficiently below 1.0 kHz for its overloading mass. Although the HiFi microphone presented more efficiently than the FPM did, there was no significant difference at 3.0 kHz and 8.0 kHz.</p> <p>Conclusions</p> <p>It is feasible to develop such an implantable FPM for future TICIs and TIHAs system on condition that the improvement of Micro Electromechanical System and piezoelectric ceramic material technology would be applied to reduce its weight and minimize its size.</p

Corporate innovation and environmental investment:The moderating role of institutional environment

Corporate environmental investment helps improve corporate environmental performance, which, therefore, is an effective micro-level solution to mitigate environmental concerns generated by corporate excessive resource exploitation and energy use. Using Chinese listed firms within environment-related industries over the period 2011–2018 as the research setting, this study applies the panel data model to investigate the impact of corporate innovation on environmental investment, as well as the moderating effects of institutional factors. The results show that corporate innovation significantly improves firms' environmental investment with 1% Research & Development (R&D) investment ratio increase generating 2326 CNY (around 351 USD at 2018 exchange rate) increase in environmental investment; the moderating effect of environment policy is positive and significant while the moderating effect of internationalisation level is not significant, indicating that current environment policy implementation helps to strengthen the positive impact of corporate innovation on environmental investment while the role of internationalisation level in this nexus is not observed. From a micro-level perspective, the findings of this study shed light on mitigating environmental concerns through enhancing corporate innovation, and provide evidence that China's corporate internationalisation process awaits more regulatory controls