EMIR: A novel emotion-based music retrieval system

Music is inherently expressive of emotion meaning and affects the mood of people. In this paper, we present a novel EMIR (Emotional Music Information Retrieval) System that uses latent emotion elements both in music and non-descriptive queries (NDQs) to detect implicit emotional association between users and music to enhance Music Information Retrieval (MIR). We try to understand the latent emotional intent of queries via machine learning for emotion classification and compare the performance of emotion detection approaches on different feature sets. For this purpose, we extract music emotion features from lyrics and social tags crawled from the Internet, label some for training and model them in high-dimensional emotion space and recognize latent emotion of users by query emotion analysis. The similarity between queries and music is computed by verified BM25 model

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of \u3c0 \u3e°C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisi

EMIR: a novel music retrieval system for mobile devices incorporating analysis of user emotion

We present an Emotional Music Information Retrieval system for mobile devices that utilizes a machine learning approach to detect latent emotion from within both user queries (non-descriptive queries) and the lyrics of songs and uses both elements to develop an effective Music Information Retrieval system. Emotion is extracted from the songs and queries and mapped into a high-dimensional emotion space, which allows for the employment of conventional text retrieval techniques to calculate the similarity between a user query and the latent emotion in song lyrics, thereby producing a ranked list of songs for playback

Bivariate Random Effects Meta-Analysis of Diagnostic Studies Using Generalized Linear Mixed Models

Bivariate random effect models are currently one of the main methods recommended to synthesize diagnostic test accuracy studies. However, only the logit-transformation on sensitivity and specificity has been previously considered in the literature. In this paper, we consider a bivariate generalized linear mixed model to jointly model the sensitivities and specificities, and discuss the estimation of the summary receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC). As the special cases of this model, we discuss the commonly used logit, probit and complementary log-log transformations. To evaluate the impact of misspecification of the link functions on the estimation, we present two case studies and a set of simulation studies. Our study suggests that point estimation of the median sensitivity and specificity, and AUC is relatively robust to the misspecification of the link functions. However, the misspecification of link functions has a noticeable impact on the standard error estimation and the 95% confidence interval coverage, which emphasizes the importance of choosing an appropriate link function to make statistical inference

Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue

Recent advances in Large Language Models (LLMs) have achieved remarkable breakthroughs in understanding and responding to user intents. However, their performance lag behind general use cases in some expertise domains, such as Chinese medicine. Existing efforts to incorporate Chinese medicine into LLMs rely on Supervised Fine-Tuning (SFT) with single-turn and distilled dialogue data. These models lack the ability for doctor-like proactive inquiry and multi-turn comprehension and cannot always align responses with safety and professionalism experts. In this work, we introduce Zhongjing, the first Chinese medical LLaMA-based LLM that implements an entire training pipeline from pre-training to reinforcement learning with human feedback (RLHF). Additionally, we introduce a Chinese multi-turn medical dialogue dataset of 70,000 authentic doctor-patient dialogues, CMtMedQA, which significantly enhances the model's capability for complex dialogue and proactive inquiry initiation. We define a refined annotation rule and evaluation criteria given the biomedical domain's unique characteristics. Results show that our model outperforms baselines in various capacities and matches the performance of ChatGPT in a few abilities, despite having 50x training data with previous best model and 100x parameters with ChatGPT. RLHF further improves the model's instruction-following ability and safety.We also release our code, datasets and model for further research

Elimination of the confrontation between theory and experiment in flexoelectric Bi2GeO5

In this paper, we have investigated the flexoelectric effect of Bi2GeO5(BGO), successfully predicted the maximum flexoelectric coefficient of BGO, and tried to explore the difference between experimental and simulated flexoelectric coefficients.Comment: 16 pages,6 figure

Tuning the nonlinear optical absorption of reduced graphene oxide by chemical reduction

Reduced graphene oxides with varying degrees of reduction have been produced by hydrazine reduction of graphene oxide. The linear and nonlinear optical properties of both graphene oxide as well as the reduced graphene oxides have been measured by single beam Z-scan measurement in the picosecond region. The results reveal both saturable absorption and two-photon absorption, strongly dependent on the intensity of the pump pulse: saturable absorption occurs at lower pump pulse intensity (~1.5 GW/cm2 saturation intensity) whereas two-photon absorption dominates at higher intensities (≥5.7 GW/cm2). Intriguingly, we find that the two-photon absorption coefficient (from 1.5 cm/GW to 4.5cm/GW) and the saturation intensity (from 1 GW/cm2 to 2 GW/cm2) vary with chemical reduction, which is ascribed to the varying concentrations of sp2 domains and sp2 clusters in the reduced graphene oxides. Our results not only provide an insight into the evolution of the nonlinear optical coefficient in reduced graphene oxide, but also suggest that chemical engineering techniques may usefully be applied to tune the nonlinear optical properties of various nano-materials, including atomically thick graphene sheets