Affective Music Information Retrieval

Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio

Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/28

Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries

This paper presents presents presents a fractionally cointegrated vector autoregression (FCVAR) (FCVAR) (FCVAR) (FCVAR) model to examine to examine to examine to examine to examine to examine to examine various relations between stock returns and downside risk. Evidence from major advanced markets markets markets markets supports the supports the notion that notion that notion that downside risk measured by value value value-at -risk ( risk (VaRVaRVaR) has significant information content content that reflects that reflects that reflects that reflects that reflects lagged long-run variance and higher moments of risk for for predict redict ing stock returns. stock returns. stock returns. stock returns. The e The e vidence vidence vidence supports the positive tradeoff hypothesis and and the leverage effect leverage effect leverage in the long in the long in the long run and and for markets in the short run. We find that US downside risk accounts for 54.36% of price discovery, whereas the whereas the whereas the whereas the own effect from own effect from the country itself only 27.06%

SCFKMD Controls Cytokinin Signaling by Regulating the Degradation of Type-B Response Regulators

Cytokinins are plant hormones that play critical roles in growth and development. In Arabidopsis, the transcriptional response to cytokinin is regulated by action of type-B Arabidopsis response regulators (ARRs). Although central elements in the cytokinin signal transduction pathway have been identified, mechanisms controlling output remain to be elucidated. Here we demonstrate that a family of F-box proteins, called the kiss me deadly (KMD) family, targets type-B ARR proteins for degradation. KMD proteins form an S-phase kinase-associated PROTEIN1 (SKP1)/Cullin/F-box protein (SCF) E3 ubiquitin ligase complex and directly interact with type-B ARR proteins. Loss-of-function KMD mutants stabilize type-B ARRs and exhibit an enhanced cytokinin response. In contrast, plants with elevated KMD expression destabilize type-B ARR proteins leading to cytokinin insensitivity. Our results support a model in which an SCF(KMD) complex negatively regulates cytokinin responses by controlling levels of a key family of transcription factors

Virtual Guidance as a Mid-level Representation for Navigation

In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments poses challenges, particularly when the navigation information is multimodal. To address this issue, the paper introduces a novel technique termed "Virtual Guidance," which is designed to visually represent non-visual instructional signals. These visual cues, rendered as colored paths or spheres, are overlaid onto the agent's camera view, serving as easily comprehensible navigational instructions. We evaluate our proposed method through experiments in both simulated and real-world settings. In the simulated environments, our virtual guidance outperforms baseline hybrid approaches in several metrics, including adherence to planned routes and obstacle avoidance. Furthermore, we extend the concept of virtual guidance to transform text-prompt-based instructions into a visually intuitive format for real-world experiments. Our results validate the adaptability of virtual guidance and its efficacy in enabling policy transfer from simulated scenarios to real-world ones.Comment: Tsung-Chih Chiang, Ting-Ru Liu, Chun-Wei Huang, and Jou-Min Liu contributed equally to this work; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A HRNet-based Rehabilitation Monitoring System

The rehabilitation treatment helps to heal minor sports and occupational injuries. In a traditional rehabilitation process, a therapist will assign certain actions to a patient to perform in between hospital visits, and it will rely on the patient to remember actions correctly and the schedule to perform them. Unfortunately, many patients forget to perform actions or fail to recall actions in detail. As a consequence, the rehabilitation treatment is hampered or, in the worst case, the patient may suffer from additional injury caused by performing incorrect actions. To resolve these issues, we propose a HRNet-based rehabilitation monitoring system, which can remind a patient when to perform the actions and display the actions for the patient to follow via the patient's smartphone. In addition, it helps the therapist to monitor the progress of the rehabilitation for the patient. Our system consists of an iOS app and several components at the server side. The app is in charge of displaying and collecting action videos. The server computes the similarity score between the therapist's actions and the patient's in the videos to keep track of the number of repetitions of each action. Theses stats will be shown to both of the patient and therapist. The extensive experiments show that the F1-Score of the similarity calculation is as high as 0.9 and the soft accuracy of the number of repetitions is higher than 90%

Identification of Cytokinin-Responsive Genes Using Microarray Meta-Analysis and RNA-Seq in Arabidopsis

Cytokinins are N6-substituted adenine derivatives that play diverse roles in plant growth and development. We sought to define a robust set of genes regulated by cytokinin as well as to query the response of genes not represented on microarrays. To this end, we performed a meta-analysis of microarray data from a variety of cytokinin-treated samples and used RNA-seq to examine cytokinin-regulated gene expression in Arabidopsis (Arabidopsis thaliana). Microarray meta-analysis using 13 microarray experiments combined with empirically defined filtering criteria identified a set of 226 genes differentially regulated by cytokinin, a subset of which has previously been validated by other methods. RNA-seq validated about 73% of the up-regulated genes identified by this meta-analysis. In silico promoter analysis indicated an overrepresentation of type-B Arabidopsis response regulator binding elements, consistent with the role of type-B Arabidopsis response regulators as primary mediators of cytokinin-responsive gene expression. RNA-seq analysis identified 73 cytokinin-regulated genes that were not represented on the ATH1 microarray. Representative genes were verified using quantitative reverse transcription-polymerase chain reaction and NanoString analysis. Analysis of the genes identified reveals a substantial effect of cytokinin on genes encoding proteins involved in secondary metabolism, particularly those acting in flavonoid and phenylpropanoid biosynthesis, as well as in the regulation of redox state of the cell, particularly a set of glutaredoxin genes. Novel splicing events were found in members of some gene families that are known to play a role in cytokinin signaling or metabolism. The genes identified in this analysis represent a robust set of cytokinin-responsive genes that are useful in the analysis of cytokinin function in plants

Low-cell-number, single-tube amplification (STA) of total RNA revealed transcriptome changes from pluripotency to endothelium

Table S1. Summary of the sequencing results. The alignments against the GRCh38 genome assembly (Aligned Reads) were counted for exon reads (exon) and transcript reads based on GENCODE v22. Intronic counts (intron) were defined by transcript counts minus exon ones. Nontranscript reads were used to obtain tRNA counts (tRNA) based on the tRNA database of GENCODE v22. Nontranscript and non-tRNA reads were used for counts on repetitive sequences (repeats) based on RepeatMasker. Those not belonging to any category were defined as unannotated reads (unannotated). The counting of exonic features was based on the “gene_type” attribute in GENCODE v22. The percentages of mature miRNA reads were defined by reads aligned exclusively to the mature “miRNA” feature divided by reads aligned to the “miRNA_primary_transcript” feature of miRBase v21. (DOCX 42 kb