Spectrally resolved two-photon interference in a modified Hong-Ou-Mandel interferometer

A modified Hong-Ou-Mandel(HOM) interference reveals that the two-photon interference phenomenon can be explained only by the concept of a two-photon wave packet rather than the single-photon one. Previously, the measurements for such interference were usually performed in the time domain where the spectral information of the involved photons was integrated and lost during the measurement. Here, we theoretically explore the spectrally resolved two-photon interference for the modified HOM interferometer both in the cases of CW pump and pulse pump. It is found that, in the CW-pumped case, a one-dimensional (1D) temporal interferogram can be directly recovered by projecting a 2D spectrally resolved interferogram at different phases, without a standard delay-scanning. In the pulse-pumped case, the joint spectral intensity is phase-dependent and can be modulated by the time delay along the directions of both frequency sum and frequency difference between signal and idler photons, which may provide a versatile way to generate high-dimensional frequency entanglement and engineer high-dimensional quantum states. These results not only show more rich spectral information that cannot be extracted from the time domain, but also shed new light on a comprehensive understanding of the two-photon interference phenomenon in the frequency domain.Comment: 13 pages, 6 figure

Analysis of adverse drug reactions of Denosumab (Prolia) in osteoporosis based on FDA adverse event reporting system (FAERS)

ObjectiveTo comprehensively analyze the ADRs associated with Denosumab (Prolia) in the treatment of osteoporosis using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Denosumab (Prolia) therapy.MethodsData of Denosumab (Prolia) were collected from the FAERS database covering the period from first quarter of 2010 to the third quarter of 2023. Disproportionality analysis was performed by calculating the reporting odds ratios (ROR), proportional reporting ratio (PRR), and Bayesian analysis confidence propagation neural network (BCPNN) to detect positive signals.ResultsTotally, 17,985,365 reports were collected from the FAERS database, 1,97,807 reports of Denosumab (Prolia) were identified as the “primary suspected (PS)” ADRs. Denosumab (Prolia) induced ADRs occurred in 27 organ systems. 38 significant disproportionality PTs satisfying with the three algorithms were retained at the same time. Unexpected significant ADRs such as bone density abnormal and immobile also occur. The majority of the ADRs occurred within the first 30 days after Denosumab (Prolia) initiation.ConclusionBased on the American FAERS database, the high frequency ADRs of Denosumab (Prolia) were hypocalcaemia, bone density abnormal, eczema, rebound effect, spinal deformity, etc. Clinical use of this drug should focus on this part of ADRs. Attention should also be paid to newly discovered ADRs, such as immobile, menopausal symptoms, etc., to avoid more serious consequences. Cohort studies, more detailed and comprehensive case information, and long-term clinical investigations are needed to confirm these results and to further understand the safety profile of Denosumab (Prolia)

Chinese Open Instruction Generalist: A Preliminary Release

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs), it is still under-explored whether English-based foundation LLMs can perform similarly on multilingual tasks compared to English tasks with well-designed instruction tuning and how we can construct the corpora needed for the tuning. To remedy this gap, we propose the project as an attempt to create a Chinese instruction dataset by various methods adapted to the intrinsic characteristics of 4 sub-tasks. We collect around 200k Chinese instruction tuning samples, which have been manually checked to guarantee high quality. We also summarize the existing English and Chinese instruction corpora and briefly describe some potential applications of the newly constructed Chinese instruction corpora. The resulting \textbf{C}hinese \textbf{O}pen \textbf{I}nstruction \textbf{G}eneralist (\textbf{COIG}) corpora are available in Huggingface\footnote{\url{https://huggingface.co/datasets/BAAI/COIG}} and Github\footnote{\url{https://github.com/FlagOpen/FlagInstruct}}, and will be continuously updated

On the Effectiveness of Speech Self-supervised Learning for Music

Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have shown promise in music modelling. Nevertheless, research exploring the effectiveness of applying speech SSL models to music recordings has been limited. We explore the music adaption of SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and refer to them as music2vec and musicHuBERT, respectively. We train $12$ SSL models with 95M parameters under various pre-training configurations and systematically evaluate the MIR task performances with 13 different MIR tasks. Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech. However, we identify the limitations of such existing speech-oriented designs, especially in modelling polyphonic information. Based on the experimental results, empirical suggestions are also given for designing future musical SSL strategies and paradigms

MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due to the distinctive challenges associated with modelling musical knowledge, particularly its tonal and pitched characteristics of music. To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. In our exploration, we identified a superior combination of teacher models, which outperforms conventional speech and audio approaches in terms of performance. This combination includes an acoustic teacher based on Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical teacher based on the Constant-Q Transform (CQT). These teachers effectively guide our student model, a BERT-style transformer encoder, to better model music audio. In addition, we introduce an in-batch noise mixture augmentation to enhance the representation robustness. Furthermore, we explore a wide range of settings to overcome the instability in acoustic language model pre-training, which allows our designed paradigm to scale from 95M to 330M parameters. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attains state-of-the-art (SOTA) overall scores. The code and models are online: https://github.com/yizhilll/MERT

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks

Real-Time Detection of Mango Based on Improved YOLOv4

Agricultural mechanization occupies a key position in modern agriculture. Aiming at the fruit recognition target detection part of the picking robot, a mango recognition method based on an improved YOLOv4 network structure is proposed, which can quickly and accurately identify and locate mangoes. The method improves the recognition accuracy of the width adjustment network, then reduces the ResNet (Residual Networks) module to adjust the neck network to improve the prediction speed, and finally adds CBAM (Convolutional Block Attention Module) to improve the prediction accuracy of the network. The newly improved network model is YOLOv4-LightC-CBAM. The training results show that the mAP (mean Average Precision) obtained by YOLOV4-LightC-CBAM is 95.12%, which is 3.93% higher than YOLOv4. Regarding detection speed, YOLOV4-LightC-CBAM is up to 45.4 frames, which is 85.3% higher than YOLOv4. The results show that the modified network can recognize mangoes better, faster, and more accurately

Application of electro-hydraulic proportional control in cathode rod pulling out system of lead electrolysis

In the process of lead electrolysis, due to its production characteristics, it is necessary to extract and collect the cathode conductive rod on the cathode plate after electrolysis, so as to make it reusable. At present, most of the lead electrolytic manufacturers in China still rely on manual extraction in this process. In this study, a rapid, stable and effective cathode rod extraction equipment for lead electrolysis is designed by means of electro-hydraulic proportional technology. AMESim simulation and experimental research on the equipment are also carried out

Performance prediction of tobacco flavouring using response surface methodology and artificial neural network

This study was to predict the optimum condition for leaf flavouring in cigarette manufacturing. To this purpose, an integrated research was used by using response surface and artificial neural network. A series of tobacco flavouring experiment's factors were designed by Experimental Design software. The MATLAB software's Neural Network function was used to forecast the responses, and the optimal solution configuration was coming out from the Response Surface Analysis Method. In the optimum condition, moisture removal opening, roller speed and tobacco process flow, pressure and feed liquid gas ejector flow are 18.60%, 10.74 rpm, 5314.11 kg/h, 3.70 bar and 243.63 kg/h, uniformity of the evaluation index and the utilization rate of material liquid distribution are 93.088% and 98.694%. With the corresponding experimental, results are consistent, under the condition of the error to less 7%, the test results show that through a few experimental data of predictive results of the neural network and response surface design has a certain practicability