Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Selection Approaches for Optimising Music Emotion Recognition Methods

The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task.
Smart meter data processing: a showcase for simple and efficient textual processing

The increase in the production and collection of data from devices is an ongoing trend due to the roll-out of more cyber-physical applications. Smart meters, because of their importance in power grids, are a class of such devices whose produced data requires meticulous processing. In this paper, we use Unicage, a data processing system based on classic Unix shell scripting, that delivers excellent performance in a simple package. We use this methodology to process smart meter data in XML format, subjected to the constraints posed by a real use case. We develop a solution that parses, validates and performs a simple aggregation of 27 million XML files in less than 10 minutes. We present a study of the solution as well as the benefits of its adoption.
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence

Creating an essay based on a few given topics is a challenging NLP task. Although several effective methods for this problem, topic-to-essay generation, have appeared recently, there is still much room for improvement, especially in terms of the coverage of the given topics and the coherence of the generated text. In this paper, we propose a novel approach called TegFormer which utilizes the Transformer architecture where the encoder is enriched with domain-specific contexts while the decoder is enhanced by a large-scale pre-trained language model. Specifically, a \emph{Topic-Extension} layer capturing the interaction between the given topics and their domain-specific contexts is plugged into the encoder. Since the given topics are usually concise and sparse, such an additional layer can bring more topic-related semantics in to facilitate the subsequent natural language generation. Moreover, an \emph{Embedding-Fusion} module that combines the domain-specific word embeddings learnt from the given corpus and the general-purpose word embeddings provided by a GPT-2 model pre-trained on massive text data is integrated into the decoder. Since GPT-2 is at a much larger scale, it contains a lot more implicit linguistic knowledge which would help the decoder to produce more grammatical and readable text. Extensive experiments have shown that the pieces of text generated by TegFormer have better topic coverage and higher text coherence than those from SOTA topic-to-essay techniques, according to automatic and human evaluations. As revealed by ablation studies, both the Topic-Extension layer and the Embedding-Fusion module contribute substantially to TegFormer's performance advantage.
On Ambiguity in Linear Inverse Problems: Entrywise Bounds on Nearly Data-Consistent Solutions and Entrywise Condition Numbers

Ill-posed linear inverse problems appear frequently in various signal processing applications. It can be very useful to have theoretical characterizations that quantify the level of ill-posedness for a given inverse problem and the degree of ambiguity that may exist about its solution. Traditional measures of ill-posedness, such as the condition number of a matrix, provide characterizations that are global in nature. While such characterizations can be powerful, they can also fail to provide full insight into situations where certain entries of the solution vector are more or less ambiguous than others. In this work, we derive novel theoretical lower- and upper-bounds that apply to individual entries of the solution vector, and are valid for all potential solution vectors that are nearly data-consistent. These bounds are agnostic to the noise statistics and the specific method used to solve the inverse problem, and are also shown to be tight. In addition, our results also lead us to introduce an entrywise version of the traditional condition number, which provides a substantially more nuanced characterization of scenarios where certain elements of the solution vector are less sensitive to perturbations than others. Our results are illustrated in an application to magnetic resonance imaging reconstruction, and we include discussions of practical computation methods for large-scale inverse problems, connections between our new theory and the traditional Cramér-Rao bound under statistical modeling assumptions, and potential extensions to cases involving constraints beyond just data-consistency.
Magnetization Dynamics in FexCo1−x in Presence of Chemical Disorder

In this paper, we present a theoretical formulation of magnetization dynamics in disordered binary alloys, based on the Kubo linear response theory, interfaced with a seamless combination of three approaches: density functional-based tight-binding linear muffin-tin orbitals, generalized recursion and augmented space formalism. We applied this method to study the magnetization dynamics in chemically disordered FexCo1−x (x = 0.2, 0.5, 0.8) alloys. We found that the magnon energies decreased with an increase in Co concentration. Significant magnon softening was observed in Fe20Co80 at the Brillouin zone boundary. Magnon–electron scattering increased with increasing Co content, which in turn modified the hybridization between the Fe and Co atoms. This reduced the exchange energy between the atoms and softened down the magnon energy. The lowest magnon lifetime was found in Fe50Co50, where disorder was at a maximum. This clearly indicated that the damping of magnon energies in FexCo1−x was governed by hybridization between Fe and Co, whereas the magnon lifetime was controlled by disorder configuration. Our atomistic spin dynamics simulations show reasonable agreement with our theoretical approach in magnon dispersion for different alloy compositions.
Intense laser interactions with sprays of submicron droplets

Picosecond laser interaction experiments conducted at peak intensities of 1.5×1017 W cm−2 using a new target medium consisting of a dense spray of 0.5 micron radius ethanol droplets indicate a strong laser–plasma coupling. The laser absorption exceeds that seen in solid targets of greater Z, and remains high over more than four orders of magnitude of intensity. Invariance in the laser absorption with wavelength and polarization is also reported. Together with x-ray spectroscopy studies, absorption measurements have been used to implement nonlocal thermodynamic equilibrium (NLTE) plasma simulations in order to isolate the important features of the droplet heating and explosion dynamics. These simulations show that the interplay of laser heating and energy transport processes is significantly different from those seen in continuous solid target interactions and that a substantial fast electron fraction must be inferred.
Diagnostic and therapeutic approach to chronic meningitis in Brazil: a narrative review

Abstract Background Chronic meningitis (CM) is characterized by neurological symptoms associated with the evidence of cerebrospinal fluid pleocytosis lasting > 4 weeks. Studies on the management of CM in Brazil are scarce. Objective To critically review the literature on CM and propose a rational approach in the Brazilian scenario. Methods Narrative literature review discussing the epidemiology, clinical evaluation, basic and advanced diagnostic testing, and empirical and targeted therapy for the most relevant causes of CM. The present review was contextualized with the local experience of the authors. In addition, we propose an algorithm for the management of CM in Brazil. Results In Brazil, tuberculosis and cryptococcosis are endemic and should always be considered in CM patients. In addition to these diseases, neurosyphilis and other endemic conditions should be included in the differential diagnosis, including neurocysticercosis, Baggio-Yoshinari syndrome, and endemic mycosis. After infectious etiologies, meningeal carcinomatosis and autoimmune diseases should be considered. Unbiased and targeted methods should be used based on availability and clinical and epidemiological data. Conclusion We propose a rational approach to CM in Brazil, considering the epidemiological scenario, systematizing the etiological investigation, and evaluating the timely use of empirical therapies.
Relationship between thick or greasy tongue-coating microbiota and tongue diagnosis in patients with primary liver cancer

Tongue diagnosis is a unique aspect of traditional Chinese medicine for diagnosing diseases before determining proper means of treatment, but it also has the disadvantage of relying on the subjective experience of medical practitioners and lack objective basis. The purpose of this article is to elucidate tongue-coating microbiota and metabolic differences in primary liver cancer (PLC) patients with thick or greasy tongue coatings. Tongue-coating samples were analyzed in 60 PLC patients (30 PLC with thick or greasy tongue-coating patients and 30 PLC with tongue-coating neither thick nor greasy) and 25 healthy controls (HC) using 16S rRNA gene sequencing technology. As compared to healthy individuals, tongue coatings of patients with PLC had elevated levels of Firmicutes and Actinobacteria. The abundance of Fusobacteria, SR1_Absconditabacteria_, and Spirochaete were higher in tongue coatings of healthy controls compared to samples in patients with PLC. In addition to site-specific differences, higher abundances of Fusobacteria and Actinobacteria were observed in thick or greasy tongue-coating patients as compared to non-thick and greasy tongue-coating patients. The inferred metagenomic pathways enriched in the PLC tongue-coating patients were mainly those involved in replication, recombination, and repair of protein. We also identify a tongue-coating microbiome signature to discriminate HC and PLC, including 15 variables on genus level. The prediction performance of the signature showed well in the training and validation cohorts. This research illustrates specific clinical features and bacterial structures in PLC patients with different tongue coatings, which facilitates understanding of the traditional tongue diagnosis.
The role of mathematical modelling in understanding prokaryotic predation

With increasing levels of antimicrobial resistance impacting both human and animal health, novel means of treating resistant infections are urgently needed. Bacteriophages and predatory bacteria such as Bdellovibrio bacteriovorus have been proposed as suitable candidates for this role. Microbes also play a key environmental role as producers or recyclers of nutrients such as carbon and nitrogen, and predators have the capacity to be keystone species within microbial communities. To date, many studies have looked at the mechanisms of action of prokaryotic predators, their safety in in vivo models and their role and effectiveness under specific conditions. Mathematical models however allow researchers to investigate a wider range of scenarios, including aspects of predation that would be difficult, expensive, or time-consuming to investigate experimentally. We review here a history of modelling in prokaryote predation, from simple Lotka-Volterra models, through increasing levels of complexity, including multiple prey and predator species, and environmental and spatial factors. We consider how models have helped address questions around the mechanisms of action of predators and have allowed researchers to make predictions of the dynamics of predator–prey systems. We examine what models can tell us about qualitative and quantitative commonalities or differences between bacterial predators and bacteriophage or protists. We also highlight how models can address real-world situations such as the likely effectiveness of predators in removing prey species and their potential effects in shaping ecosystems. Finally, we look at research questions that are still to be addressed where models could be of benefit.
Editorial: Molecular characteristics and personalized treatment for pediatric brain tumors

COPYRIGHT © 2022 Feng, Kawauchi and Wan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 29 December 2022 DOI 10.3389/fonc.2022. 1114280
Coupled Model Intercomparison Project phase 5 and 6 representation of peak and end of rainy season over Upper Blue Nile basin

The Upper Blue Nile river basin (uBN) is vital to three countries' water, food, and energy security (Ethiopia, Sudan, and Egypt). It is also a region with substantial interannual precipitation variability and the potential for significant climate change in coming decades. The El Niño–Southern Oscillation (ENSO) affects both variability and trends in uBN precipitation, with El Niño episodes historically connected with below‐average rainfall. In this context, this study seeks to: (a) examine the representation of ENSO–uBN rainfall teleconnections in simulations from the sixth Coupled Model Intercomparison Project (CMIP6), as compared to the previous generation of models (CMIP5); (b) distinguish between teleconnections during the peak of the rainy season (July–August [JA]) and the agriculturally critical end‐of‐season rains (September–October [SO]); (c) investigate evidence for Tropical Easterly Jet (TEJ) mediation of these teleconnections in CMIP6 versus CMIP5 and for both peak rains and end of the rainy season. Using a subset of high performing models, we find that CMIP6 simulations of uBN precipitation show somewhat less bias than CMIP5 for JA total rainfall, and that correlations with ENSO have become more consistent across models, but that differences between CMIP5 and CMIP6 are modest. CMIP6 simulations, like CMIP5, overestimate SO rainfall and suggest a stronger ENSO association in SO than is indicated by observations. Model representation of the TEJ and its association with both ENSO and uBN precipitation show no systematic change between CMIP5 and CMIP6. The mediating influence of the TEJ appears to be more important in JA than SO.
Tracking the Evolution of an Ocean Within Mimas Using the Herschel Impact Basin

Mimas' small size, lack of geologic activity, and high eccentricity suggest a frozen, inactive history. However, Cassini libration measurements are best explained by a present‐day liquid ocean under an ice shell 24–31 km thick, a configuration that tidal heating can support. These unexpected findings have motivated further study of Mimas' surface geology to constrain its interior evolution. Here, we model the formation of Mimas' large impact basin, Herschel, using iSALE‐2D. Our goal is to determine whether the current estimated ice shell thickness is sufficient to withstand the impact and produce the observed basin morphology. We find that, if Mimas had an ocean at the time of the Herschel‐forming impact, the ice shell had to have been 10s of km thicker than today. These findings are consistent with inferences from minimal tectonic activity that Mimas must be a young ocean world, if it indeed has an ocean today.
Appearance-based color face recognition with 3D model

Appearance-based face recognition approaches explore color cues of face images, i.e. grey or color information for recognition task. They first encode color face images, and then extract facial features for classification. Similar to conventional singular value decomposition, hypercomplex matrix also exists singular value decomposition on hypercomplex field. In this paper, a novel color face recognition approach based on hypercomplex singular value decomposition is proposed. The approach employs hypercomplex to encode color face information of different channels simultaneously. Hypercomplex singular value decomposition is utilized then to compute the basis vectors of the color face subspace. To improve learning efficiency of the algorithm, 3D active deformable model is exploited to generate virtual face images. Color face samples are projected onto the subspace and projection coefficients are utilized as facial features. Experimental results on CMU PIE face database verify the effectiveness of the proposed approach.
Funny You Mention It: A Synthesis of Published Research on Learning from Comedic Versus Serious News

ABSTRACT Studies comparing comedic and serious news have found conflicting results as to whether the inclusion of humor benefits learning information from those shows. Other studies examining learning from comedic news without a serious news comparison, based on whether the humor was presented as funny or informational, have also found conflicting results. This review offers some explanations for those conflicting findings, focusing on the methods and theoretical approaches used in those studies, and suggests a dynamic theoretical approach researchers in this area can take to get a better understanding of the impact of humor on acquiring information from comedic news messages.
Representativeness in the Eyes of the Citizen: Impact of Balanced Citizenship on the Perceived Representativeness in Participatory Governance

ABSTRACT Representativeness is one of the keys to the legitimacy of participatory governance. However, such representativeness might be in the eye of the beholder due to various definitions of, and criteria for, representativeness. This study aims to explore how citizens perceive the representativeness of their representatives and the reasons behind those perceptions. Based on a survey of 2,000 citizens in South Korea, the findings indicate: (1) the maturity of citizenship (i.e., balancing tolerance and participation) significantly influences citizens’ perceptions of the representativeness of public affairs participants, and (2) this pattern of perceived representativeness does not vary according to the representativeness type (i.e., stake, stance, service, specialty, sovereign, and socio-econ) and domain (i.e., community, corporates, and government). The findings imply (1) the existence of four distinct groups of citizens – considerate reformer, reserved endurer, silent groaner, and active grumbler – and (2) the four groups of citizens are predictors of perceived representativeness.
Blood meals from ‘dead-end’ vertebrate hosts enhance transmission potential of malaria-infected mosquitoes

Ingestion of an additional blood meal(s) by mosquitoes can accelerate parasite migration to the salivary glands in infected mosquitoes. Most studies, however, offer blood from the same vertebrate host species as the original challenge (for e.g., human for primary and additional blood meals). Here, we show a second blood meal from bovine and canine hosts can also enhance sporozoite migration in Anopheles stephensi mosquitoes infected with the human- and rodent-restricted Plasmodium falciparum and P. berghei respectively. The extrinsic incubation period (time to sporozoite appearance in salivary glands) showed more consistent reductions with blood from human and bovine donors than canine blood, although the latter’s effect may be confounded by the toxicity, albeit non-specific, associated with the anticoagulant used to collect whole blood from donors. The complex patterns of enhancement highlight the limitations of a laboratory system but are nonetheless reminiscent of parasite host-specificity and mosquito adaptations, and the genetic predisposition of An. stephensi for bovine blood. We suggest that in natural settings, a blood meal from any vertebrate host could accentuate the risk of human infections by P. falciparum, although our observations should also be applicable to other species of Plasmodium and indeed other vector-borne diseases such as arboviruses for instance. Graphical Abstract HIGHLIGHTS Plasmodium falciparum and P. berghei cause malaria in humans and rodents, respectively. They are vectored by Anopheles species that can also imbibe blood from other vertebrate species. Parasite development is enhanced after another blood meal, usually from the same host (e.g., human). Here, bovine, and canine blood also enhanced development of Plasmodium species in An. stephensi. In natural settings, blood from any host could enhance development of vector transmitted parasites.
Refinement of AlphaFold-Multimer structures with single sequence input

AlphaFold2, introduced by DeepMind in CASP14, demonstrated outstanding performance in predicting protein monomer structures. It could model more than 90% of targets with high accuracy, and so the next step would surely be multimer predictions, since many proteins do not act by themselves but with their binding partners. After the publication of After AlphaFold2, DeepMind published AlphaFold-Multimer, which showed excellent performance in predicting multimeric structures. However, its accuracy still has room for improvement compared to that of monomer predictions by AlphaFold2. In this paper, we introduce a fine-tuned version of AlphaFold-Multimer, named AFM-Refine-G, which uses structures predicted by AlphaFold-Multimer as inputs and produces more refined structures without the helps of multiple sequence alignments or templates. The performance of AFM-Refine-G was assessed using two datasets, Ghani_et_al_Benchmark2 and Yin_et_al_Hard, adapted from previous studies by Ghani et al. and Yin et al., respectively. The Ghani_et_al_Benchmark2 dataset consists of 17 recently published heteromers and the Yin_et_al_Hard dataset consists of 133 multimers, including immune-related complexes and repebody-antigen complexes, with several whose correct structure AlphaFold-Multimer could not predict. We predicted five models per target (750 models in total) and analyzed the improvement in the DockQ of each model. Of 750 models, 115 had DockQ improvement > 0.05 after refinement, demonstrating that our model is useful for the refinement of multimer structures. However, 14 structures had degraded DockQ < −0.05 after refinement, and the overall prediction quality for targets in Yin_et_al_Hard was quite low; 97 out of 133 were classified as ‘Incorrect’ with CAPRI criteria, revealing that there is still room for improving multimer predictions.