SingNet: A Real-time Singing Voice Beat and Downbeat Tracking System

Singing voice beat and downbeat tracking posses several applications in automatic music production, analysis and manipulation. Among them, some require real-time processing, such as live performance processing and auto-accompaniment for singing inputs. This task is challenging owing to the non-trivial rhythmic and harmonic patterns in singing signals. For real-time processing, it introduces further constraints such as inaccessibility to future data and the impossibility to correct the previous results that are inconsistent with the latter ones. In this paper, we introduce the first system that tracks the beats and downbeats of singing voices in real-time. Specifically, we propose a novel dynamic particle filtering approach that incorporates offline historical data to correct the online inference by using a variable number of particles. We evaluate the performance on two datasets: GTZAN with the separated vocal tracks, and an in-house dataset with the original vocal stems. Experimental result demonstrates that our proposed approach outperforms the baseline by 3-5%.Comment: Accepted for 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2023

Predicting settling performance of ANAMMOX granular sludge based on fractal dimensions

The settling performance of ANAMMOX granular sludge determines the biomass retention in reactors, and finally determines the potential reaction capacity. In this paper, Stokes equation was modified by fractal dimensions to describe the settling performance of ANAMMOX granular sludge. A new method was developed to obtain fractal dimensions, and a fractal settling model was established for ANAMMOX granular sludge. The fractal settling model was excellent with only a small deviation of 0.8% from the experimental data. Assuming normal distribution of all Feret diameters, 88% experimental data fell into the 90% confidence interval of settling velocities. Further assuming logarithmic normal distribution, 95% experimental data fell into the 90% confidence interval. The fractal settling model is helpful for the prediction of settling velocities of granular sludge and the optimization of bioreactor performance

Genetic polymorphisms in plasminogen activator inhibitor-1 predict susceptibility to steroid-induced osteonecrosis of the femoral head in Chinese population

BACKGROUND: Steroid usage has been considered as a leading cause of non-traumatic osteonecrosis of the femoral head (ONFH), which is involved in hypo-fibrinolysis and blood supply interruption. Genetic polymorphisms in plasminogen activator inhibitor-1 (PAI-1) have been demonstrated to be associated with ONFH risk in several populations. However, this relationship has not been established in Chinese population. The aim of this study was to investigate the association of PAI-1 gene polymorphisms with steroid-induced ONFH in a large cohort of Chinese population. METHODS: A case–control study was conducted, which included 94 and 106 unrelated patients after steroid administration recruited from 14 provinces in China, respectively. Two SNPs (rs11178 and rs2227631) within PAI-1 were genotyped using Sequenom MassARRAY system. RESULTS: rs2227631 SNP was significantly associated with steroid-induced ONFH group in codominant (P = 0.04) and recessive (P = 0.02) models. However, there were no differences found in genotype frequencies of rs11178 SNP between controls and patients with steroid-induced ONFH (all P > 0.05). CONCLUSIONS: Our data offer the convincing evidence for the first time that rs2227631 SNP of PAI-1 may be associated with the risk of steroid-induced ONFH, suggesting that the genetic variations of this gene may play an important role in the disease development. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1569909986109783

Neural Activity Is Dynamically Modulated by Memory Load During the Maintenance of Spatial Objects

Visuospatial working memory (WM) is a fundamental but severely limited ability to temporarily remember selected stimuli. Several studies have investigated the underlying neural mechanisms of maintaining various visuospatial stimuli simultaneously (i.e., WM load, the number of representations that need to be maintained in WM). However, two confounding factors, namely verbal representation and encoding load (the number of items that need to be encoded into WM), have not been well controlled in previous studies. In this study, we developed a novel delayed-match-to-sample task (DMST) controlling for these two confounding factors and recorded scalp EEG signals during the task. We found that behavioral performance deteriorated severely as memory load increased. Neural activity was modulated by WM load in a dynamic manner. Specifically, higher memory load induced stronger amplitude in occipital and central channel-clusters during the early delay period, while the inverse trend was observed in central and frontal channel-clusters during late delay. In addition, the same inverse memory load effect, that was lower memory load induced stronger amplitude, was observed in occipital channel-cluster alpha power during late delay. Finally, significant correlations between neural activity and individual reaction time showed a role of late-delay central and frontal channel-cluster amplitude in predicting behavioral performance. Because the occipital cortex is important for visual information maintenance, the decrease in alpha oscillation was consistent with the cognitive role that is “gating by inhibition.” Together, our results from a well-controlled DMST suggest that WM load not exerted constant but dynamic effect on neural activity during maintenance of visuospatial objects

Proteasome activator 28A: A clinical biomarker and pharmaceutical target in acute cerebral infarction therapy

Purpose: To determine the dynamic changes in serum levels of PA28α in patients with acute cerebral infarction (ACI), and to investigate its correlation with infarct size and neurological deficit of the disease. Methods: A total of 100 ACI patients and 100 healthy volunteers were recruited from The First Affiliated Hospital of Xinxiang Medical University as case and control groups, respectively. Their serum levels of PA28α were determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The potential of PA28α in predicting the incidence of ACI was assessed by plotting ROC curves. Multivariate logistic regression analysis was conducted to investigate the risk factors of ACI. In addition, an ACI model in rats was established, and ACI rats were classified into 1, 3, 5, 7 and 14 day subgroups based on the duration post-ACI. Rats in the sham group served as control. Results: Serum level of PA28α was significantly higher in ACI patients than in controls. Moreover, the serum level of PA28α at admission was positively correlated to the NIHSS score and infarct volume of ACI patients. The level of PA28α in ACI rats gradually increased post-ACI, reaching a peak on day 7. The number of apoptotic brain cells in ACI rats gradually decreased after ACI. In addition, PA28α level was negatively correlated to the number of apoptotic brain cells in ACI rats (R2 = 0.5148, p < 0.001). Conclusion: The serum level of PA28α is elevated in ACI patients, and is positively correlated to infarct volume and neurological deficit of the disease. The dynamic change in brain cell apoptosis post-ACI is negatively correlated to the serum level of PA28α. These findings may provide theoretical basis for the diagnosis and treatment of ACI

MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits

Variable speed limit (VSL) control is a promising traffic management strategy for enhancing safety and mobility. This work introduces MARVEL, a multi-agent reinforcement learning (MARL) framework for implementing large-scale VSL control on freeway corridors using only commonly available data. The agents learn through a reward structure that incorporates adaptability to traffic conditions, safety, and mobility; enabling coordination among the agents. The proposed framework scales to cover corridors with many gantries thanks to a parameter sharing among all VSL agents. The agents are trained in a microsimulation environment based on a short freeway stretch with 8 gantries spanning 7 miles and tested with 34 gantries spanning 17 miles of I-24 near Nashville, TN. MARVEL improves traffic safety by 63.4% compared to the no control scenario and enhances traffic mobility by 14.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. An explainability analysis is undertaken to explore the learned policy under different traffic conditions and the results provide insights into the decision-making process of agents. Finally, we test the policy learned from the simulation-based experiments on real input data from I-24 to illustrate the potential deployment capability of the learned policy

Emulated nuclear spin gyroscope with 15^{15}NV centers in diamond

Nuclear spins in solid-state platforms are promising for building rotation sensors due to their long coherence times. Among these platforms, nitrogen-vacancy centers have attracted considerable attention with ambient operating conditions. However, the current performance of NV gyroscopes remains limited by the degraded coherence when operating with large spin ensembles. Protecting the coherence of these systems requires a systematic study of the coherence decay mechanism. Here we present the use of nitrogen-15 nuclear spins of NV centers in building gyroscopes, benefiting from its simpler energy structure and vanishing nuclear quadrupole term compared with nitrogen-14 nuclear spins, though suffering from different challenges in coherence protection. We systematically reveal the coherence decay mechanism of the nuclear spin in different NV electronic spin manifolds and further develop a robust coherence protection protocol based on controlling the NV electronic spin only, achieving a 15-fold dephasing time improvement. With the developed coherence protection, we demonstrate an emulated gyroscope by measuring a designed rotation rate pattern, showing an order-of-magnitude sensitivity improvement

Continuously Controllable Facial Expression Editing in Talking Face Videos

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expressions and emotion-related expressions are often highly coupled. Meanwhile, traditional image-to-image translation methods cannot work well in our application due to the coupling of expressions with other attributes such as poses, i.e., translating the expression of the character in each frame may simultaneously change the head pose due to the bias of the training data distribution. In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously. We present a new perspective for this task as a special case of motion information editing, where we use a 3DMM to capture major facial movements and an associated texture map modeled by a StyleGAN to capture appearance details. Both representations (3DMM and texture map) contain emotional information and can be continuously modified by neural networks and easily smoothed by averaging in coefficient/latent spaces, making our method simple yet effective. We also introduce a mouth shape preservation loss to control the trade-off between lip synchronization and the degree of exaggeration of the edited expression. Extensive experiments and a user study show that our method achieves state-of-the-art performance across various evaluation criteria.Comment: Demo video: https://youtu.be/WD-bNVya6k