Modeling Emotion Dynamics in Song Lyrics with State Space Models

Most previous work in music emotion recognition assumes a single or a few song-level labels for the whole song. While it is known that different emotions can vary in intensity within a song, annotated data for this setup is scarce and difficult to obtain. In this work, we propose a method to predict emotion dynamics in song lyrics without song-level supervision. We frame each song as a time series and employ a State Space Model (SSM), combining a sentence-level emotion predictor with an Expectation-Maximization (EM) procedure to generate the full emotion dynamics. Our experiments show that applying our method consistently improves the performance of sentence-level baselines without requiring any annotated songs, making it ideal for limited training data scenarios. Further analysis through case studies shows the benefits of our method while also indicating the limitations and pointing to future directions

A survey and classification of software-defined storage systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.This work was financed by the Portuguese funding agency FCT-Fundacao para a Ciencia e a Tecnologia through national funds, the PhD grant SFRH/BD/146059/2019, the project ThreatAdapt (FCT-FNR/0002/2018), the LASIGE Research Unit (UIDB/00408/2020), and cofunded by the FEDER, where applicable

TaqMan MGB probe fluorescence real-time quantitative PCR for rapid detection of Chinese Sacbrood virus.

Sacbrood virus (SBV) is a picorna-like virus that affects honey bees (Apis mellifera) and results in the death of the larvae. Several procedures are available to detect Chinese SBV (CSBV) in clinical samples, but not to estimate the level of CSBV infection. The aim of this study was develop an assay for rapid detection and quantification of this virus. Primers and probes were designed that were specific for CSBV structural protein genes. A TaqMan minor groove binder (MGB) probe-based, fluorescence real-time quantitative PCR was established. The specificity, sensitivity and stability of the assay were assessed; specificity was high and there were no cross-reactivity with healthy larvae or other bee viruses. The assay was applied to detect CSBV in 37 clinical samples and its efficiency was compared with clinical diagnosis, electron microscopy observation, and conventional RT-PCR. The TaqMan MGB-based probe fluorescence real-time quantitative PCR for CSBV was more sensitive than other methods tested. This assay was a reliable, fast, and sensitive method that was used successfully to detect CSBV in clinical samples. The technology can provide a useful tool for rapid detection of CSBV. This study has established a useful protocol for CSBV testing, epidemiological investigation, and development of animal models

Cotyledon-greening analysis on <i>35S</i>:<i>TsRAVs</i> transgenic <i>Arabidopsis</i> seedlings.

<p>(A) Phenotypic comparison of wild-type and <i>35S</i>:<i>TsRAVs</i> transgenic <i>Arabidopsis</i> seedlings after grown on normal 1/2 MS medium (upper panel) or on 1/2 MS medium with 0.5 <i>μ</i>M ABA for 6 days (lower panel). (B) Cotyledon-greening percentages of <i>35S</i>:<i>TsRAVs</i> transgenic <i>Arabidopsis</i> seedlings after grown on 1/2 MS medium with 0.5 <i>μ</i>M ABA for 6 days. Each data bar represents the mean ± SE of three replicates. More than 100 seeds were measured in each replicate. Different letters indicate significant differences among means (<i>P</i><0.05 by Tukey’s test).</p

ABA sensitivity of <i>35S</i>:<i>TsRAVs</i> transgenic <i>Arabidopsis</i> plants.

<p>(A) Germination rates of <i>35S</i>:<i>TsRAVs</i> transgenic <i>Arabidopsis</i> seeds on 1/2 MS media with 1 <i>μ</i>M ABA. Each data bar represents the means ± SE of three replicates. More than 100 seeds were measured in each replicate. (B) Inhibitory effect of 1 <i>μ</i>M ABA on <i>35S</i>:<i>TsRAVs</i> transgenic <i>Arabidopsis</i> seed germination rates. Each data bar represents the mean ± SE of three replicates. More than 50 seedlings were measured in each replicate. Different letters indicate significant differences among means (<i>P</i><0.05 by Tukey’s test). (C) Inhibitory effect of 30 <i>μ</i>M ABA on <i>35S</i>:<i>TsRAVs</i> transgenic <i>Arabidopsis</i> seedling root elongation. Seedlings were grown on normal media for 5 days before being transferred onto 1/2 MS medium with 30 <i>μ</i>M ABA and grown for other 6 days. Each data bar represents the mean ± SE of three replicates. More than 50 seedlings were measured in each replicate. Different letters indicate significant differences among means (<i>P</i><0.05 by Tukey’s test).</p