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

Antecedents of User Stickiness and Loyalty and Their Effects on Users’ Group-Buying Repurchase Intention

Intense competition among a vast number of group-buying websites leads to higher product homogeneity, which allows customers to switch to alternative websites easily and reduce their website stickiness and loyalty. This study explores the antecedents of user stickiness and loyalty and their effects on consumers’ group-buying repurchase intention. Results indicate that systems quality, information quality, service quality, and alternative system quality each has a positive relationship with user loyalty through user stickiness. Meanwhile, information quality directly impacts user loyalty. Thereafter, user stickiness and loyalty each has a positive relationship with consumers’ repurchase intention. Theoretical and managerial implications are also discussed

MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction

We present Mesh Pre-Training (MPT), a new pre-training framework that leverages 3D mesh data such as MoCap data for human pose and mesh reconstruction from a single image. Existing work in 3D pose and mesh reconstruction typically requires image-mesh pairs as the training data, but the acquisition of 2D-to-3D annotations is difficult. In this paper, we explore how to leverage 3D mesh data such as MoCap data, that does not have RGB images, for pre-training. The key idea is that even though 3D mesh data cannot be used for end-to-end training due to a lack of the corresponding RGB images, it can be used to pre-train the mesh regression transformer subnetwork. We observe that such pre-training not only improves the accuracy of mesh reconstruction from a single image, but also enables zero-shot capability. We conduct mesh pre-training using 2 million meshes. Experimental results show that MPT advances the state-of-the-art results on Human3.6M and 3DPW datasets. We also show that MPT enables transformer models to have zero-shot capability of human mesh reconstruction from real images. In addition, we demonstrate the generalizability of MPT to 3D hand reconstruction, achieving state-of-the-art results on FreiHAND dataset

Bacillus velezensis LG37: Functional verification of GlnL and analysis of the ammonia assimilation metabolic pathway

High concentrations of ammonia have toxic effects on bred animals. In aquaculture, the possibility of using Bacillus preparations to remove ammonia nitrogen in aquaculture water through assimilation has been generally recognized. In this study, to analyze the metabolic pathways of ammonia assimilation in Bacillus, the characteristics and pathways of ammonia assimilation of Bacillus velezensis LG37 stored in the laboratory were analyzed. The results showed that the rate of ammonia assimilation by LG37 in a minimal medium could reach 94.2% at 20 h, and the highest assimilation rate was 95.4% within 60 h. In a minimal medium, the growth rate of LG37 in the glutamine group was significantly faster than that in the ammonia group, but the expression of glnL showed opposite results. The gene expression level of glnL in the ammonia group was significantly higher than in the glutamine group. glnL overexpression (OEglnL) and deletion (ΔglnL) strains were constructed by CRISPR/Cas9 technology and using the pHT1K vector, respectively. The expression levels of glnL in LG37, OEglnL, and ΔglnL strains were determined by RT-qPCR. The glnL gene expression levels were ordered as follows: OEglnL > LG37 > ΔglnL. In all three strains (LG37, ΔglnL, and OEglnL) and at different ammonia concentrations, the expression levels of glnL were consistent with glnA and glnB levels, showing a positive correlation. However, the expression levels of glnK and glnR in different strains hardly changed significantly

Beneficial effect of fluid warming in elderly patients with bladder cancer undergoing Da Vinci roboticassisted laparoscopic radical cystectomy

OBJECTIVES: The enhanced recovery after surgery (ERAS) protocol recommends prevention of intraoperative hypothermia. However, the beneficial effect of maintaining normothermia after radical cystectomy has not been evaluated. This study aimed to investigate the efficacy of fluid warming nursing in elderly patients undergoing Da Vinci robotic-assisted laparoscopic radical cystectomy. METHODS: A total of 108 patients with bladder cancer scheduled to undergo DaVinci robotic-assisted laparoscopic radical cystectomy were recruited and randomly divided into the control group (n=55), which received a warming blanket (43o C) during the intraoperative period and the warming group (n=53), in which all intraoperative fluids were administered via a fluid warmer (41o C). The surgical data, body temperature, coagulation function indexes, and postoperative complications were compared between the two groups. RESULTS: Compared to the control group, the warming group had significantly less intraoperative transfusion (p=0.028) and shorter hospitalization days (po0.05). During the entire intraoperative period (from 1 to 6h), body temperature was significantly higher in the warming group than in the control group. There were significant differences in preoperative fibrinogen level, white blood cell count, total bilirubin level, intraoperative lactose level, postoperative thrombin time (TT), and platelet count between the control and warming groups. Multivariate linear regression analysis demonstrated that TT was the only significant factor, suggesting that the warming group had a lower TT than the control group. CONCLUSION: Fluid warming nursing can effectively reduce transfusion requirement and hospitalization days, maintain intraoperative normothermia, and promote postoperative coagulation function in elderly patients undergoing Da Vinci robotic-assisted laparoscopic radical cystectomy

Impacts of salinity parameterizations on temperature simulation over and in a hypersaline lake

In this paper, we introduced parameterizations of the salinity effects (on heat capacity, thermal conductivity, freezing point and saturated vapor pressure) in a lake scheme integrated in the Weather Research and Forecasting model coupled with the Community Land Model (WRF-CLM). This was done to improve temperature simulation over and in a saline lake and to test the contributions of salinity effects on various water properties via sensitivity experiments. The modified lake scheme consists of the lake module in the CLM model, which is the land component of the WRF-CLM model. The Great Salt Lake (GSL) in the USA was selected as the study area. The simulation was performed from September 3, 2001 to September 30, 2002. Our results show that the modified WRF-CLM model that includes the lake scheme considering salinity effects can reasonably simulate temperature over and in the GSL. This model had much greater accuracy than neglecting salinity effects, particularly in a very cold event when that effect alters the freezing point. The salinity effect on saturated vapor pressure can reduce latent heat flux over the lake and make it slightly warmer. The salinity effect on heat capacity can also make lake temperature prone to changes. However, the salinity effect on thermal conductivity was found insignificant in our simulations. © 2015, Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag Berlin Heidelberg

An Empirical Study of Multimodal Model Merging

Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven successful in previous studies, where the models are trained on similar tasks and with the same initialization. In this paper, we expand on this concept to a multimodal setup by merging transformers trained on different modalities. Furthermore, we conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture to create a parameter-efficient modality-agnostic architecture. Through comprehensive experiments, we systematically investigate the key factors impacting model performance after merging, including initialization, merging mechanisms, and model architectures. Our analysis leads to an effective training recipe for matching the performance of the modality-agnostic baseline (i.e. pre-trained from scratch) via model merging. Our code is available at: https://github.com/ylsung/vl-mergin

Patterned nanofiber air filters with high optical transparency, robust mechanical strength, and effective PM_(2.5) capture capability

PM_(2.5), due to its small particle size, strong activity, ease of the attachment of toxic substances and long residence time in the atmosphere, has a great impact on human health and daily production. In this work, we have presented patterned nanofiber air filters with high optical transparency, robust mechanical strength and effective PM_(2.5) capture capability. Here, to fabricate a transparency air filter by a facile electrospinning method, we chose three kinds of patterned wire meshes with micro-structures as negative receiver substrates and directly electrospun polymer fibers onto the supporting meshes. Compared with randomly oriented nanofibers (named “RO NFs” in this paper) and commercially available facemasks, the patterned air filters showed great mechanical properties, and the water contact angles on their surfaces were about 122–143° (the water contact angle for RO NFs was 81°). In addition, the patterned nanofibers exhibited high porosity (>80%), and their mean pore size was about 0.5838–0.8686 μm (the mean pore size of RO NFs was 0.4374 μm). The results indicate that the transparent patterned air filters have the best PM_(2.5) filtration efficiency of 99.99% at a high transmittance of ∼69% under simulated haze pollution