bluuebunny/crossref_metadata_2025 huggingface dataset:
Teachers’ talk about young students’ writing of narrative and informational texts
Authors: Liberg Caroline , Wiksten Folkeryd Jenny , af Geijerstam Åsa , Nordlund Anna
Published: July 2022

Previous research has shown that teachers’ knowledge of a functional metalanguage plays a central role in supporting students’ writing development. However, only a few of these studies have focused on primary school teachers and their use of metalanguage in various text types. The aim of this study was to investigate how primary school teachers talk about young students’ (ages 7–9) narrative and informational texts before and after taking part in professional development workshops presenting different language resources and accompanying metalanguage. These resources represent a broader view of language than the more formal tradition offered to primary school teachers in Sweden. The results showed that after participating in the workshops, the teachers had broadened their repertoires concerning what aspects they talk about and how they talk about them; that is, their talks became more text-specific and extensive, and they used a formal metalanguage to a greater extent. These results are discussed in relation to the tradition of writing instruction used in primary grades in Sweden and the teachers’ pathways to broadening their repertoire of metalanguage. Also discussed is the potential a broader language view in early grades may have in supporting students’ writing development throughout their school years.
On the Peculiarities of Political Media Communications in the Digital Age
Authors:   Ural Federal University, Kerimov Alexander A.
Published: July 2022

Political processes in the modern world cannot be considered in isolation from the mass media, the role and importance of which are indisputable. In this regard, the topic of mediatization of the political sphere has taken an important place in scientific research, which analyzes the problems of interaction of politics with information and communication channels. The expansion of the networks and the improvement of information and communication technologies open up prospects for a higher level of integration of politics and mass media. The arsenal of this integration is being replenished with new ways and forms of interaction that strengthen the mutual influence of mass media and politics. This process is further enhanced by new media, such as social networks, the blogosphere, podcasts, which are beginning to play an important communicative role in society. The mediatization of politics in the digital age leads to the ordering of information by group interests, acts as a filter on the way of spreading unacceptable information. It creates a space where the audience receives information about politics in an adapted form, with symbols, etc. Thus, political media communications in the digital age do not only record and analyze political processes, but also directly affect them. These channels play an important role in the process of political socialization of the individual, in the formation of public opinion regarding the events of the political life of society, which actualizes the study of problems in the field of interaction between politics and mass media.
On the History of Russian-French Cultural Ties: A French Mark on the Life and Artwork of the Composer Edison Denisov
Authors:   Université de Sorbonne-Paris IV, Kouprovskaia-Bruggeman Ekaterina O.
Published: July 2022

The subject of research in the article is the life and work of the composer Edison Denisov in the context of connections with French culture. The study of these over-the-half-century-long ties makes it possible to fill «white spots», first, in the broader subject of Russian-French cultural relations: Soviet and post Soviet contacts and influences studied on the representative material of musical art, second, in the topic of local but no less important: the role of France and its culture throughout E. Denisov’s life. The life and work of E. Denisov is a largely successful and multifaceted presentation of the French «footprint» in Russian culture in the second half of the 20th century. The material to be analyzed is multidimensional: biographical references to French art (music, literature, painting) and language; various contacts with French culture: writers, composers, musicians, ensembles and orchestras; Literary texts (B. Vian, G. Bataille) when writing music; the immersion in philosophical and humanitarian texts (R. Bart, M. Foucault, J.-P. Sartre); influence of mathematics and the structuralist method on the composer’s mastery (H. Poincaré, C. Lévi-Strauss) ; creative dialogue with works of a composer and theorist P. Bulez; the role of France in promoting Denisov’ music in the West; active educational activity of Denisov in promoting French music in Russia. A separate section of the article analyzes the influence of French composers on Denisov, and also highlights his participation in the restoration and orchestration of the unfinished opera of Claude Debussy «Rodrigo and Ximena». The article focuses only on the composer’s vocal works. The author thoroughly exqamines the works on poetry of French poets (Ch. Baudelaire, G. de Nerval, B. Vian, R. Char, H. Michaud, G. Bataille), the opera “Froth on the Daydream” based on the novel by B. Viana, the opera “Four Girls” on the play by Pablo Picasso.
Estratégia e estrutura para um novo Estado
Authors: BRESSER-PEREIRA LUIZ CARLOS
Published: July 2022

RESUMO Uma das grandes reformas do governo Cardoso é a administrativa. A proposta básica é transformar a administração pública brasileira de uma administração burocrática em gerencial. O surgimento, no século XIX, de uma administração pública burocrática em substituição às formas patrimonialistas de gestão do Estado representou um grande avanço. Mas não fazia mais sentido quando o estado havia adicionado ao seu papel a oferta de educação pública, saúde pública, cultura pública, seguridade social, incentivos em ciência e tecnologia, investimentos em infraestrutura, proteção ao meio ambiente. A administração pública gerencial surgiu como resposta à crise do Estado, como forma de enfrentamento da crise fiscal, como estratégia de tornar menos onerosa e eficiente a administração dos enormes serviços que o Estado assumia, além de dispositivo para proteger o patrimônio público contra o rentismo ou a corrupção. A nova administração pública é orientada para resultados e para o cidadão; pressupõe que os políticos e funcionários públicos têm direito a um grau limitado de confiança; utiliza como estratégia a descentralização e o incentivo à criatividade e inovação; controla os gestores públicos por meio de contratos de gestão.
Corpos descartáveis: neosoberania e exclusão na era digital
Authors: Sabino César
Published: July 2022

Resumo A pesquisa tem por objetivo estudar a formação de um regime neosoberano de poder na contemporaneidade influenciado pelas tecnologias digitais, as quais fortalecem novas configurações socioeconômicas em um processo geral de construção de relações virtuais de trabalho. Utilizando alguns aspectos do pensamento foucaultiano, busca analisar as atuais dinâmicas das relações de poder permeadas pela criação de novas subjetividades administradas por manobras necropolíticas de enfraquecimento psíquico, e, mesmo, eliminação física daqueles que não se encaixam na extração cotidiana de riquezas.◊
Kumida di tera: relações de cuidado e alimentação em Cabo Verde
Authors: Velloso Natalia , Ferreira Vladmir
Published: July 2022

Resumo A reflexão proposta toma o caso de Cabo Verde para ilustrar a hipótese de que nem mesmo nas nações africanas pós-coloniais se desmantelou o racismo colonialista do quotidiano e, sobretudo, não se conseguiu demolir a antinegritude como afeto predominante na configuração do socius pós-colonial. O texto compreende três momentos analíticos, tomando como objetos: (i) comentários de internautas leitores de um importante jornal do país a respeito de um dos traços mais racializados do carnaval cabo-verdiano; (ii) reflexões de imigrantes a respeito da relação entre cabo-verdianos e os imigrantes africanos; (iii) a história de vida de um imigrante, para descortinar nela traços de antinegritude tramando as relações dos próprios imigrantes entre si.
Batuko: alma dum povo! Vivências do batuko cabo-verdiano no período pós-independência
Authors: Semedo Carla Indira
Published: July 2022

Resumo Batuko, gênero músico-coreográfico de Cabo Verde – país localizado na costa ocidental africana, criado pelos africanos negros escravizados, após a independência do país em 1975 – passou por um processo de revalorização e vem sendo visto pelas batukadeiras como possibilidade de se tornar um projeto profissional. Neste artigo, viso reconstruir as múltiplas dinâmicas sociopolíticas do batuko entrecruzadas com dois momentos socio-históricos e políticos da historiografia oficial de Cabo Verde, tendo como material as narrativas e trechos de vivências das batukadeiras do coletivo de São Martinho Grande, resultantes de pesquisa etnográfica realizada em 2008. O primeiro momento se centra no período pós-colonial, após a independência de Cabo Verde em 1975. E, o segundo, no período pós-abertura política com a democratização do sistema político-partidário a partir de 1991 e, com isso, a criação de bases para a revalorização e circulação das artes musicais tidas como tradicionais. Sinalizo que as narrativas das minhas interlocutoras permitem perceber não só o efeito das narrativas hegemônicas de conformação da identidade da nação nas suas vivências do batuko, mas também os efeitos nos modos como se inscrevem como mulheres batukadeiras e almejam o projeto profissional: vir a ser artistas profissionais.
Nutrient requirements of paricá (Schizolobium parahyba var. amazonicum): optimizing seedling quality for reforestation programs
Authors: RAMOS Silvio Junio , TEIXEIRA Renato Alves , GUEDES Rafael Silva , GASTAUER Markus , NUNES Sâmia do Socorro , CALDEIRA Cecílio Frois , SILVA JUNIOR Ediu Carlos da , SOUZA-FILHO Pedro Walfir Martins
Published: July 2022

ABSTRACT Amazonian reforestation programs emphasize the use of species native to the region. However, reforestation using native species requires the production of high-quality seedlings. The present study aimed to evaluate the development and quality of seedlings of paricá (Schizolobium parahyba var. amazonicum), a species native to the Amazon with high potential for reforestation activities. We carried out a greenhouse experiment in which seedlings were subjected to treatments with varying presence of major and micronutrients in the substrate, and evaluated the effect on functional attributes (plant height, stem diameter, specific leaf area, shoot and root dry matter) and physiological response (chlorophyll a, b and carotenoid concentrations). Our results showed that nutrient omission significantly affects paricá seedling growth. Considering the assessed biometric and physiological variables, the highest nutritional requirements of seedlings were for N, P, K, micronutrients, Mg and S, while seedling quality was maintained in the absence of Ca. Overall, this species shows efficient use of available nutrients and potential for growth in soils with low concentrations of bases. Therefore, for the production of high-quality paricá seedlings, major and micronutrients should be added and lime is essential, but the good seedling performance under Ca omission suggested that this element does not need to be added to the soil.
Filling a gap in the distribution of Taulidesmella tabatinga (Polydesmida, Pyrgodesmidae): a new record from the upper Amazon River, Brazil
Authors: INIESTA Luiz F. Moretti , BOUZAN Rodrigo S. , SOUZA Claudio A. R. , BRESCOVIT Antonio D.
Published: July 2022

ABSTRACT The millipede species Taulidesmella tabatinga is known from the border of Brazil with Peru and Colombia, Iquitos (Peru), and environs of Manaus (Brazil). Based on freshly collected material during the rainy season in 2014 along the Amazon River upriver from its confluence with the Negro River, we report a new record of T. tabatinga in floodplain forests in the municipality of Alvarães, Amazonas state (Brazil), filling a gap of 1,000 km between its previously known occurrence points. This record indicates that T. tabatinga is likely widespread along both margins of the upper Amazon River, mostly associated with tree trunks and litter of riparian and floodplain forests.
Territorial expansion of Zenaida auriculata (Aves: Columbidae) in the Brazilian Amazon
Authors: ORTÚZAR-FERREIRA Carlos Nei , TAVARES Gabriel Magalhães , FRANCHIN Alexandre Gabriel
Published: July 2022

ABSTRACT Birds are considered bioindicators of environmental quality because they are sensitive to environmental changes. The eared dove (Zenaida auriculata) is associated with open areas, has low sensitivity to environmental changes and is widely distributed in Brazil, although it is not typically found in the Amazon region. We analyzed the biogeographic expansion of this species in the Brazilian Amazon through searches carried out on digital citizen science platforms. The records of Z. auriculata were related with the area of accumulated deforestation in the biome. We identified 804 records of Z. auriculata in the Brazilian Amazon, of which 259 were in areas where its occurrence had been previously unknown. We show that the species has been expanding its geographic distribution, clearly overlapping with deforested areas.
Phenolic and flavonoid content and in vitro inhibitory effect of some Amazonian fruit juices on CYP3A4 activity
Authors: COSTA Edna Márcia Almeida , DE ANDRADE FIGUEIRA Mariana Brasil , CARRÃO Daniel Blascke , HABENSCHUS Maísa Daniela , OLIVEIRA Anderson Rodrigo Moraes DE , ACHO Leonard Domingo Rosales , LIMA Emerson Silva , DOS SANTOS MAGALHÃES Igor Rafael
Published: July 2022

ABSTRACT Many foods interact with drugs and may cause changes in the pharmacological effects of the co-administered therapeutic agent. The enzyme CYP3A4, which belongs to the cytochrome P450 enzyme complex, is responsible for the metabolism of most drugs currently on the market and is involved in many drug interactions. Hence, the interaction of this enzyme with juices of some fruits, such as grapefruit, can affect the pharmacokinetics of various drugs. However, native fruits from the Amazon region have not yet been the target of this type of research. We determined total polyphenols and flavonoids of the Amazonian fruits açaí (Euterpe precatoria), buriti (Mauritia flexuosa), camu-camu (Myrciaria dubia), cubiu (Solanum sessiliflorum), cupuaçu (Theobroma grandiflorum), jenipapo (Genipa americana), and taperebá (Spondias mombin) and evaluated the effects of each fruit juice on CYP3A4 activity, using the star fruit (Averrhoa carambola) juice as positive control. Açaí juice presented the highest content of total polyphenols and flavonoids (102.6 ± 7.2 µg gallic acid equivalent (GAE) per mL and 7.2 ± 0.6 µg quercetin equivalent (QE) per mL, respectively). All juices were able to inhibit the activity of CYP3A4. There was no residual activity of the drug-metabolizing enzyme for açai, buriti, cubiu, camu-camu, and taperebá juice, while for cupuaçu, jenipapo and the positive control, the residual activity was 44.3, 54.3 and 20.2%, respectively. Additional studies should identify the phytocompound(s) responsible for this inhibition activity, to clarify the mechanisms involved in this phenomenon.
Contrast-induced acute kidney injury in patients submitted to coronary angioplasty: prospective cohort
Authors: Moitinho Matheus Santos , Silva Junior Jumar Reis Da , Cunha Maximina De Barros , Barbosa Dulce Aparecida , Caixeta Adriano Mendes , Pimpinato Attilio Galhardo , Junglos Alana Francine , Belasco Angélica Gonçalves Da Silva , Fonseca Cassiane Dezoti da
Published: July 2022

ABSTRACT Objective: To analyze the incidence, risk factors, and associations of clinical outcomes for contrast-induced acute kidney injury (CI-AKI) in patients with acute coronary syndrome (ACS) after coronary angioplasty. Method: Prospective cohort of 182 patients followed for three months after undergoing angioplasty, from July 2020 to June 2021. The analyzed variables were sociodemographic, clinical, and those related to the procedure. Results: The incidence of CI-AKI was 35.7% (n = 65) and was associated with old age, diabetes mellitus, and chronic kidney disease (p = 0.004, p < 0.001, and p = 0.009, respectively). Out of the 17 patients who died within 90 days, 76.5% had CI-AKI (n = 13), the odds ratio between death and CI-AKI was approximately 7.2 times (95% confidence interval (CI), [2.41;26.36]; p = 0.001). The decrease of one unit in the patient’s baseline hemoglobin showed a 6.5% increase for CI-AKI (95% CI, [−0.089; −0.040]; p < 0.0001). Conclusion: CI-AKI is prevalent in patients with ACS after angioplasty and is related to diabetes mellitus and chronic kidney disease, showing high mortality rates.
Effectiveness of cardiopulmonary resuscitation training in the teaching of family members of cardiac patients
Authors: Citolino Filho Clairton Marcos , Nogueira Lilia de Souza , Gomes Vanessa Miranda , Polastri Thatiane Facholi , Timerman Sergio
Published: July 2022

ABSTRACT Objective: To evaluate the effectiveness of a cardiopulmonary resuscitation training in the skill acquisition of family members of heart disease patients. Method: A quasi-experimental study, conducted in a hospital in São Paulo, Brazil. The study participants were one or more relatives of patients with heart disease that were hospitalized at the institution. In the first phase, the participant’s skills and theoretical knowledge on cardiopulmonary resuscitation were evaluated before and immediately after the training. The second phase took place one month after the training, in which the same evaluations were applied. The McNemar’s and Stuart-Maxwell tests were adopted (5% significance level). Results: The theoretical knowledge of family members before and after training increased and a great retention of this knowledge after 30 days of training was observed. Immediately after training, the family members showed significant improvement of skills in the 15 analyzed actions and, after one month of training, they maintained most of the acquired practices on cardiopulmonary resuscitation, except for chest compressions frequency and the time between turning on the defibrillator and delivering the shock. Conclusion: Cardiopulmonary resuscitation training was effective in the acquisition of theoretical and practical knowledge of the family members.
Efetividade de treinamento sobre ressuscitação cardiopulmonar na aprendizagem de familiares de pacientes cardiopatas
Authors: Citolino Filho Clairton Marcos , Nogueira Lilia de Souza , Gomes Vanessa Miranda , Polastri Thatiane Facholi , Timerman Sergio
Published: July 2022

RESUMO Objetivo: Avaliar a efetividade de um treinamento sobre ressuscitação cardiopulmonar na aprendizagem de familiares de pacientes cardiopatas. Método: Estudo quase experimental, realizado em hospital de São Paulo, Brasil. Participaram da pesquisa um ou mais familiares de pacientes cardiopatas internados na instituição. Na primeira fase foram avaliados o conhecimento teórico e as habilidades dos participantes sobre a ressuscitação cardiopulmonar antes e imediatamente após o treinamento. A segunda fase aconteceu um mês após o treinamento, com a aplicação das mesmas avaliações. Os testes McNemar e Stuart-Maxwell foram adotados (nível de significância de 5%). Resultados: Houve aumento do conhecimento teórico dos familiares antes e após o treinamento e elevada retenção desse conhecimento após 30 dias da capacitação. Os familiares apresentaram, após o treinamento, melhora significativa das habilidades nas 15 ações analisadas e, após um mês da capacitação, mantiveram a maioria das práticas assimiladas sobre ressuscitação cardiopulmonar, com exceção da frequência das compressões torácicas e do tempo entre ligar o desfibrilador e aplicar o choque. Conclusão: O treinamento sobre ressuscitação cardiopulmonar foi efetivo na aprendizagem teórica e prática dos familiares.
Advanced nursing practice training: the reality of the United States and the first steps of Brazil
Authors: Love Rene , Santana Rosimere Ferreira
Published: July 2022

ABSTRACT This is a reflective theoretical essay, with the objective of reporting the experience of the United States and the first steps of Brazil in the training process of advanced practice nurses. The historical trajectory of two traditional graduate programs in the professional modality is described, one from Brazil and one from the United States. Both curriculum models highlight specific training disciplines of nursing practice, the thesis and DNP project, consisting of implementation studies. We concluded that both countries are still making progress in nursing education and practice and are working to collaborate to form the highest level of nursing education and practice.
Enfermería de práctica avanzada: la realidad de los Estados Unidos y los primeros pasos de Brasil
Authors: Love Rene , Santana Rosimere Ferreira
Published: July 2022

RESUMEN Este es un ensayo teórico y reflexivo que pretende reportar la experiencia de los Estados Unidos y los primeros pasos dados por Brasil en el proceso de formación de enfermeros de práctica avanzada. Se describió la trayectoria histórica de dos programas tradicionales de posgrado en la modalidad profesional, uno en Brasil y otro en Estados Unidos. Ambos modelos curriculares destacan las asignaturas de formación específicas de la práctica de enfermería, la tesis y el proyecto DNP, compuesto por estudios de implementación. Se concluyó que los dos países presentan avances y colaboraciones para ofertar un alto nivel de educación y práctica de enfermería.
Experimental composite containing silicon dioxide-coated silver nanoparticles for orthodontic bonding: Antimicrobial activity and shear bond strength
Authors: AGUIAR Rogéria Christina de Oliveira , NUNES Larissa Pereira , BATISTA Eduardo Silva , VIANA Marina Mariante , RODRIGUES Marcela Charantola , BUENO-SILVA Bruno , ROSCOE Marina Guimarães
Published: July 2022

ABSTRACT Objective: This study aimed to investigate the antimicrobial activity and shear bond strength (SBS) of orthodontic brackets to bovine enamel using experimental composites with different concentrations of silicon dioxide-coated silver nanoparticles (Ag@SiO2 NPs). Methods: Fifty bovine incisors were divided into five groups according to the composite (n = 10): G1 - Control Group (Transbond XT Resin), G2 - Experimental composite without Ag@SiO2 NPs; G3 - Experimental composite with 0.5% of Ag@SiO2 NPs; G4 - Experimental composite with 1% of Ag@SiO2 NPs; G5 - Experimental composite with 3% of Ag@SiO2 NPs. The SBS test was performed using a universal mechanical testing machine, and the adhesive remnant index (ARI) was analyzed by optical microscopy. For the antimicrobial activity evaluation, Streptococcus mutans (S. mutans) biofilm was formed for three days in hydroxyapatite discs. Posteriorly, S. mutans colony forming units (CFU) were evaluated. For SBS analysis, Analysis of Variance was used, followed by the Tukey test, at a 5% statistical significance level. The CFU data were analyzed by Kruskal-Wallis, followed by Dunn as a post-hoc test. The ARI results were analyzed descriptively. Results: There was no statistically significant difference in SBS values between the experimental and control groups (p>0.05). A 3% incorporation of Ag@SiO2 NPs statistically reduced the SBS values (p<0.05) compared to the 1% group. The addition of 3% of Ag@SiO2 NPs to the composites significantly reduced S. mutans biofilm formation, compared to group G2 (p<0.05). Conclusion: Composites incorporating 3% of Ag@SiO2 NPs presented similar SBS values compared to the control group, and showed significant antimicrobial activity.
Histórias de quem quer fugir e de quem quer ficar: laços comunitários nas cambiantes periferias de São Paulo
Authors: Fontes Leonardo de Oliveira
Published: July 2022

Resumo: Este texto analisa a possibilidade de formação de relações comunitárias, entendidas como experiências pessoais significativas associadas ao local de moradia, entre moradores das periferias de São Paulo em um contexto de aumento das oportunidades de mobilidade social. Para isso, foi realizada uma pesquisa multimetodológica em dois bairros periféricos da cidade. Para cada uma das três gerações são analisadas as experiências em torno dos espaços de sociabilidade, a representação da violência urbana, as dinâmicas de religiosidade e as perspectivas de mobilidade social como elementos explicativos para a formação ou não dessas relações. O artigo emprega um olhar empírico para o debate teórico a respeito das relações entre vizinhança e comunidade e para as transformações históricas ocorridas nas periferias urbanas brasileiras nas últimas décadas. Argumenta-se que a construção de discursos normalizadores, distintivos ou identitários com relação à estigmatização de territórios periféricos irá depender das experiências locais desenvolvidas em cada grupo geracional. O desejo de ficar ou fugir da periferia é lido como expressão máxima da existência ou não desse tipo de relação comunitária na vizinhança.
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network
Authors: Ren Yuqi , Xiong Deyi
Published: July 2022

Cognitive processing signals can be used to improve natural language processing (NLP) tasks. However, it is not clear how these signals correlate with linguistic information. Bridging between human language processing and linguistic features has been widely studied in neurolinguistics, usually via single-variable controlled experiments with highly-controlled stimuli. Such methods not only compromises the authenticity of natural reading, but also are time-consuming and expensive. In this paper, we propose a data-driven method to investigate the relationship between cognitive processing signals and linguistic features. Specifically, we present a unified attentional framework that is composed of embedding, attention, encoding and predicting layers to selectively map cognitive processing signals to linguistic features. We define the mapping procedure as a bridging task and develop 12 bridging tasks for lexical, syntactic and semantic features. The proposed framework only requires cognitive processing signals recorded under natural reading as inputs, and can be used to detect a wide range of linguistic features with a single cognitive dataset. Observations from experiment results resonate with previous neuroscience findings. In addition to this, our experiments also reveal a number of interesting findings, such as the correlation between contextual eye-tracking features and tense of sentence.
Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning
Authors: Zhang Duzhen , Zhang Tielin , Jia Shuncheng , Xu Bo
Published: July 2022

With the help of deep neural networks (DNNs), deep reinforcement learning (DRL) has achieved great success on many complex tasks, from games to robotic control. Compared to DNNs with partial brain-inspired structures and functions, spiking neural networks (SNNs) consider more biological features, including spiking neurons with complex dynamics and learning paradigms with biologically plausible plasticity principles. Inspired by the efficient computation of cell assembly in the biological brain, whereby memory-based coding is much more complex than readout, we propose a multiscale dynamic coding improved spiking actor network (MDC-SAN) for reinforcement learning to achieve effective decision-making. The population coding at the network scale is integrated with the dynamic neurons coding (containing 2nd-order neuronal dynamics) at the neuron scale towards a powerful spatial-temporal state representation. Extensive experimental results show that our MDC-SAN performs better than its counterpart deep actor network (based on DNNs) on four continuous control tasks from OpenAI gym. We think this is a significant attempt to improve SNNs from the perspective of efficient coding towards effective decision-making, just like that in biological networks.
Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg
Authors: Ahmad Niaz , Khan Jawad , Kim Jeremy Yuhyun , Lee Youngmoon
Published: July 2022

Despite the advances in multi-person pose estimation, state-of-the-art techniques only deliver the human pose structure.Yet, they do not leverage the keypoints of human pose to deliver whole-body shape information for human instance segmentation. This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. For pose estimation, PosePlusSeg first takes a bottom-up approach to detect the soft and hard keypoints of individuals by producing a strong keypoint heat map, then improves the keypoint detection confidence score by producing a body heat map. For instance segmentation, PosePlusSeg generates a mask offset where keypoint is defined as a centroid for the pixels in the embedding space, enabling instance-level segmentation for the human class. Finally, we propose a new pose and instance segmentation algorithm that enables PosePlusSeg to determine the joint structure of the human pose and instance segmentation. Experiments using the COCO challenging dataset demonstrate that PosePlusSeg copes better with challenging scenarios, like occlusions, en-tangled limbs, and overlapped people. PosePlusSeg outperforms state-of-the-art detection-based approaches achieving a 0.728 mAP for human pose estimation and a 0.445 mAP for instance segmentation. Code has been made available at:
https://github.com/RaiseLab/PosePlusSeg.
Deformable Part Region Learning for Object Detection
Authors: Bae Seung-Hwan
Published: July 2022

In a convolutional object detector, the detection accuracy can be degraded often due to the low feature discriminability caused by geometric variation or transformation of an object. In this paper, we propose a deformable part region learning in order to allow decomposed part regions to be deformable according to geometric transformation of an object. To this end, we introduce trainable geometric parameters for the location of each part model. Because the ground truth of the part models is not available, we design classification and mask losses for part models, and learn the geometric parameters by minimizing an integral loss including those part losses. As a result, we can train a deformable part region network without extra super-vision and make each part model deformable according to object scale variation. Furthermore, for improving cascade object detection and instance segmentation, we present a Cascade deformable part region architecture which can refine whole and part detections iteratively in the cascade manner. Without bells and whistles, our implementation of a Cascade deformable part region detector achieves better detection and segmentation mAPs on COCO and VOC datasets, compared to the recent cascade and other state-of-the-art detectors.
Towards End-to-End Image Compression and Analysis with Transformers
Authors: Bai Yuanchao , Yang Xu , Liu Xianming , Jiang Junjun , Wang Yaowei , Ji Xiangyang , Gao Wen
Published: July 2022

We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. Instead of placing an existing Transformer-based image classification model directly after an image codec, we aim to redesign the Vision Transformer (ViT) model to perform image classification from the compressed features and facilitate image compression with the long-term information from the Transformer. Specifically, we first replace the patchify stem (i.e., image splitting and embedding) of the ViT model with a lightweight image encoder modelled by a convolutional neural network. The compressed features generated by the image encoder are injected convolutional inductive bias and are fed to the Transformer for image classification bypassing image reconstruction. Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction. The aggregated features can obtain the long-term information from the self-attention mechanism of the Transformer and improve the compression performance. The rate-distortion-accuracy optimization problem is finally solved by a two-step training strategy. Experimental results demonstrate the effectiveness of the proposed model in both the image compression and the classification tasks.
Visual Consensus Modeling for Video-Text Retrieval
Authors: Cao Shuqiang , Wang Bairui , Zhang Wei , Ma Lin
Published: July 2022

In this paper, we propose a novel method to mine the commonsense knowledge shared between the video and text modalities for video-text retrieval, namely visual consensus modeling. Different from the existing works, which learn the video and text representations and their complicated relationships solely based on the pairwise video-text data, we make the first attempt to model the visual consensus by mining the visual concepts from videos and exploiting their co-occurrence patterns within the video and text modalities with no reliance on any additional concept annotations. Specifically, we build a shareable and learnable graph as the visual consensus, where the nodes denoting the mined visual concepts and the edges connecting the nodes representing the co-occurrence relationships between the visual concepts. Extensive experimental results on the public benchmark datasets demonstrate that our proposed method, with the ability to effectively model the visual consensus, achieves state-of-the-art performances on the bidirectional video-text retrieval task. Our code is available at https://github.com/sqiangcao99/VCM.
Geometry-Contrastive Transformer for Generalized 3D Pose Transfer
Authors: Chen Haoyu , Tang Hao , Yu Zitong , Sebe Nicu , Zhao Guoying
Published: July 2022

We present a customized 3D mesh Transformer model for the pose transfer task. As the 3D pose transfer essentially is a deformation procedure dependent on the given meshes, the intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes. Moreover, locally, a simple yet efficient central geodesic contrastive loss is further proposed to improve the regional geometric-inconsistency learning. At last, we present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task towards unknown spaces. The massive experimental results prove the efficacy of our approach by showing state-of-the-art quantitative performances on SMPL-NPT, FAUST and our new proposed dataset SMG-3D datasets, as well as promising qualitative results on MG-cloth and SMAL datasets. It's demonstrated that our method can achieve robust 3D pose transfer and be generalized to challenging meshes from unknown spaces on cross-dataset tasks. The code and dataset are made available. Code is available: https://github.com/mikecheninoulu/CGT.
Text Gestalt: Stroke-Aware Scene Text Image Super-resolution
Authors: Chen Jingye , Yu Haiyang , Ma Jianqi , Li Bin , Xue Xiangyang
Published: July 2022

In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.
SJDL-Vehicle: Semi-supervised Joint Defogging Learning for Foggy Vehicle Re-identification
Authors: Chen Wei-Ting , Chen I-Hsiang , Yeh Chih-Yuan , Yang Hao-Hsiang , Ding Jian-Jiun , Kuo Sy-Yen
Published: July 2022

Vehicle re-identification (ReID) has attracted considerable attention in computer vision. Although several methods have been proposed to achieve state-of-the-art performance on this topic, re-identifying vehicle in foggy scenes remains a great challenge due to the degradation of visibility. To our knowledge, this problem is still not well-addressed so far. In this paper, to address this problem, we propose a novel training framework called Semi-supervised Joint Defogging Learning (SJDL) framework. First, the fog removal branch and the re-identification branch are integrated to perform simultaneous training. With the collaborative training scheme, defogged features generated by the defogging branch from input images can be shared to learn better representation for the re-identification branch. However, since the fog-free image of real-world data is intractable, this architecture can only be trained on the synthetic data, which may cause the domain gap problem between real-world and synthetic scenarios. To solve this problem, we design a semi-supervised defogging training scheme that can train two kinds of data alternatively in each iteration. Due to the lack of a dataset specialized for vehicle ReID in the foggy weather, we construct a dataset called FVRID which consists of real-world and synthetic foggy images to train and evaluate the performance. Experimental results show that the proposed method is effective and outperforms other existing vehicle ReID methods in the foggy weather. The code and dataset are available in https://github.com/Cihsaing/SJDL-Foggy-Vehicle-Re-Identification--AAAI2022.
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
Authors: Chen Zhe , Wang Wenhai , Xie Enze , Lu Tong , Luo Ping
Published: July 2022

We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfer for the first time. Most of the existing state-of-the-art methods would fall short due to massive memory cost and small stroke size when processing ultra-high resolution images. URST completely avoids the memory problem caused by ultra-high resolution images by (1) dividing the image into small patches and (2) performing patch-wise style transfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN can extract thumbnail features' normalization statistics and apply them to small patches, ensuring the style consistency among different patches.
Overall, the URST framework has three merits compared to prior arts. (1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution. (2) Experiments show that our URST surpasses existing SOTA methods on ultra-high resolution images benefiting from the effectiveness of the proposed stroke perceptual loss in enlarging the stroke size. (3) Our URST can be easily plugged into most existing style transfer methods and directly improve their performance even without training. Code is available at https://git.io/URST.
Model-Based Image Signal Processors via Learnable Dictionaries
Authors: Conde Marcos V. , McDonagh Steven , Maggioni Matteo , Leonardis Ales , Pérez-Pellitero Eduardo
Published: July 2022

Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of noise statistics that result from the direct sensor readings. Despite this, the availability of RAW images is limited in comparison with the abundance and diversity of available RGB data. Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping: handcrafted model-based methods that are interpretable and controllable usually require manual parameter fine-tuning, while end-to-end learnable neural networks require large amounts of training data, at times with complex training procedures, and generally lack interpretability and parametric control. Towards addressing these existing limitations, we present a novel hybrid model-based and data-driven ISP that builds on canonical ISP operations and is both learnable and interpretable. Our proposed invertible model, capable of bidirectional mapping between RAW and RGB domains, employs end-to-end learning of rich parameter representations, i.e. dictionaries, that are free from direct parametric supervision and additionally enable simple and plausible data augmentation. We evidence the value of our data generation process by extensive experiments under both RAW image reconstruction and RAW image denoising tasks, obtaining state-of-the-art performance in both. Additionally, we show that our ISP can learn meaningful mappings from few data samples, and that denoising models trained with our dictionary-based data augmentation are competitive despite having only few or zero ground-truth labels.
Learning to Learn Transferable Attack
Authors: Fang Shuman , Li Jie , Lin Xianming , Ji Rongrong
Published: July 2022

Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversarial perturbations are easily overﬁtting with a single surrogate model and speciﬁc data pattern. In this paper, we propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. For data augmentation, we adopt simple random resizing and padding. For model augmentation, we randomly alter the back propagation instead of the forward propagation to eliminate the effect on the model prediction. By treating the attack of both speciﬁc data and a modiﬁed model as a task, we expect the adversarial perturbations to adopt enough tasks for generalization. To this end, the meta-learning algorithm is further introduced during the iteration of perturbation generation. Empirical results on the widely-used dataset demonstrate the effectiveness of our attack method with a 12.85% higher success rate of transfer attack compared with the state-of-the-art methods. We also evaluate our method on the real-world online system, i.e., Google Cloud Vision API, to further show the practical potentials of our method.
OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
Authors: Fu Chunyang , Li Ge , Song Rui , Gao Wei , Liu Shan
Published: July 2022

In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.
Adversarial Robustness in Multi-Task Learning: Promises and Illusions
Authors: Ghamizi Salah , Cordy Maxime , Papadakis Mike , Traon Yves Le
Published: July 2022

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research has considered complex multi-task models that are common in real applications. In this paper, we evaluate the design choices that impact the robustness of multi-task deep learning networks. We provide evidence that blindly adding auxiliary tasks, or weighing the tasks provides a false sense of robustness. Thereby, we tone down the claim made by previous research and study the different factors which may affect robustness. In particular, we show that the choice of the task to incorporate in the loss function are important factors that can be leveraged to yield more robust models. We provide the appendix, all our algorithms, models, and open source-code at https://github.com/yamizi/taskaugment
Deep Confidence Guided Distance for 3D Partial Shape Registration
Authors: Ginzburg Dvir , Raviv Dan
Published: July 2022

We present a novel non-iterative learnable method for partial-to-partial 3D shape registration.
The partial alignment task is extremely complex, as it jointly tries to match between points, and identify which points do not appear in the corresponding shape, causing the solution to be non-unique and ill-posed in most cases.

Until now, two main methodologies have been suggested to solve this problem: sample a subset of points that are likely to have correspondences, or perform soft alignment between the point clouds and try to avoid a match to an occluded part. These heuristics work when the partiality is mild or when the transformation is small but fails for severe occlusions, or when outliers are present.
We present a unique approach named Confidence Guided Distance Network (CGD-net), where we fuse learnable similarity between point embeddings and spatial distance between point clouds, inducing an optimized solution for the overlapping points while ignoring parts that only appear in one of the shapes.
The point feature generation is done by a self-supervised architecture that repels far points to have different embeddings, therefore succeeds to align partial views of shapes, even with excessive internal symmetries, or acute rotations.
We compare our network to recently presented learning-based and axiomatic methods and report a fundamental boost in performance.