bluuebunny/crossref_metadata_2025 huggingface dataset:
Priority Support Areas and Learning Domains for Students with Severe and Profound Multiple Disabilities According to the Views of Pedagogical Specialists in Bulgaria And Flanders (Belgium)
Authors: Hadzhieva Magdalena
Published: March 2024

The article presents the views of pedagogical specialists supporting students with severe and profound multiple disabilities in the Bulgarian and Flemish education systems about the priorities in education and support of the target group of children and youth. The analysis of the results obtained from the empirical research provides valuable information about the direction in which the greatest efforts are directed in the process of inclusive education in Bulgaria and Belgium, brings out some differences in the prioritization of support and training in the two groups of respondents and opens opportunities for discussion in a comparative plan and taking into account the point of view of the specialists as a key factor for the implementation of an effective process of support and education for students with severe and profound multiple disabilities.
Development and validation of an analytical method for acetamiprid determination in plant protection products using HPLC-DAD
Authors: Marczewska Patrycja , Rolnik Joanna , Stobiecki Tomasz , Sajewicz Mieczysław
Published: March 2024

Plant protection products (PPP), crucial for agricultural production, are experiencing increased global demand, particularly with the growing need for food production. To meet this demand, robust analytical methods are essential for confirming the presence and determining active substance concentrations in PPP. This study introduces an analytical method utilizing high-performance liquid chromatography with a diode array detector (HPLC-DAD) for determination of acetamiprid in water-soluble powder formulations. The method, validated according to SANCO/3030/99 rev.5, demonstrated exhibited adequate accuracy and precision, with repeatability expressed as the ratio of the standard deviation (% RSD) to the relative standard deviation (% RSDr) being lower than 1. Recoveries for the active substance at concentrations above 10% ranged from 97% to 103%. The developed method is also characterized by suitable linearity, confirmed by a correlation coefficient >0.99. Specific chromatographic profiles were generated, and acetamiprid content in 180 formulations was analyzed, including reference products. The developed method aligns with “green chemistry” principles, minimizing solvent use and emphasizing energy efficiency. Overall, it offers a comprehensive approach for qualitative and quantitative analysis, ensuring the reliability of PPP quality control.
The first records of eleven species of the genus Megaselia Rondani, 1856 from Morocco (Diptera, Phoridae)
Authors: Sliman Fatima Zahra , Disney R. Henry L. , Kettani Kawtar
Published: March 2024

We provide new records of scuttle ﬂies, family Phoridae (Diptera), from Morocco, which has been very little studied. Eleven phorid species belonging to the immense genus Megaselia Rondani, 1856 are newly reported from the country, increasing the number of known species in Morocco to 12, and the number of Phoridae to 14. Information on the distribution and ecology of each species is provided.
The Existence of Undergrowth at Forest Garden Stands in Grand Forest Park, Lampung Province
Authors: Indriyanto Indriyanto , Indriyanto Nurani Hardikananda
Published: March 2024

The existence of undergrowth has an important role as an ecosystem both from an ecological and economic perspective. However, the composition and diversity of undergrowth in an area depends on the land use system. This research aimed to know the species composition and diversity of undergrowth at forest garden stands. Data collection was carried out with a vegetation survey using nested rectangular plots with a sampling intensity of 1%. The data collected included undergrowth species, the number of individuals for each species, and tree species as components of forest garden stands. Data were analyzed by calculating density, frequency, important value index, and Shannon diversity index. The results showed that the undergrowth that exists under forest garden stands were 38 species dominated by 12 species, namely Acalypha australis, Asystasia gangetica, Clidemia hirta, Starchytarpeta jamaicensis, Mikania micrantha, Peperomia pellucidia, Setaria barbata, Andropogon aciculatus, Alpinia galanga, Diplazium esculentum, Paederia foetida, and Crassocephalum crepidioides. The forest garden stands were composed of 43 tree species dominated by 4 tree species, namely Durio zibethinus, Aleurites moluccana, Myristica fragrans, and Gnetum gnemon. The level of undergrowth diversity was in the medium category with an H value of 1.540, indicating the diverse of undergrowth species.
Yield of Sorghum bicolor (L.) Moenh Genotypes in A Plastic Mulch Planting System in Gunungkidul
Authors: Muazam Arif , Widyayanti Setyorini , Daryono Budi Setiadi
Published: March 2024

Sorghum is a strategic commodity with good nutritional and high antioxidant content that can be used as a source of food, feed, and industrial raw materials. Sorghum is a relatively low and less intensive crop than other staple food crops. Sorghum yield can be increased by establishing plant population through a mulching planting system. The purpose of this study was to determine the yield power of several sorghum genotypes, namely three new superior varieties (Bioguma, Kawali, and Samurai) and three local varieties (Plonco, Hitam Wareng, and Ketan Merah), planted with a plastic mulch planting system. The research was conducted on farmers' land in Karangmojo, Gunungkidul, Daerah Istimewa Yogyakarta, which is a dryland soil type. The experimental design used a randomized complete block design (RCBD) with 5 replications. Observation parameters included growth components and sorghum production. The results showed that sorghum planting with a mulching planting system was able to provide yields for the Bioguma variety of 7.70 tons/ha, Plonco variety of 8.41 tons/ha, Samurai variety of 5.38 tons/ha, Kawali variety of 8.21 tons/ha, Hitam Wareng variety of 3.91 tons/ha, and Ketan Merah variety of 4.00 tons/ha. Based on the results of the study, it was found that the genotypes of local varieties were able to compete with new superior varieties and had the best growth response and yield power in the rainy season planting.
Transferência e contratransferência: os efeitos clínicos do observador no trabalho em grupos terapêuticos
Authors: Bezerra Maysa Marianne Silva
Published: March 2024

Resumo O propósito deste artigo é refletir sobre os conceitos de transferência e contratransferência, articulando-os com a dimensão clínica do lugar do observador no trabalho em grupos terapêuticos. Inicialmente, será feito um percurso histórico sobre tais conceitos na psicanálise. Posteriormente, a noção de transferência será discutida como um vínculo que pode se manifestar pela via da expressão corporal e sensorialidade, enquanto a contratransferência será analisada a partir do campo da intersubjetividade. Esses aspectos serão ilustrados por duas vinhetas clínicas.
Hamlet e Macbeth nas teorias de Freud e Lacan
Authors: Andrade Cleyton Sidney de , Correia Emmerson Vinicius Duarte Barros
Published: March 2024

Resumo A arte e a literatura estão presentes de maneira significativa, tanto em Freud quanto em Lacan, entretanto elas se inscrevem de diferentes formas nas duas teorias, isso pode ser melhor observado quando os autores se debruçam sobre uma mesma obra, como Hamlet de William Shakespeare. Além desta, Macbeth é outro drama shakespeariano que interessará a Freud por conta da personagem de Lady Macbeth. Diante disso, o artigo analisou as interpretações de Freud acerca dessas duas tragédias shakespearianas, assim como as de Lacan sobre Hamlet, investigando algumas particularidades acerca de relações com a arte e com a literatura. Embora impliquem em interpretações diversas, essas obras reúnem elementos importantes e por vezes com um valor de exemplo paradigmático para reflexões no campo da psicanálise.
Coabitação durante a pandemia de covid-19: a experiência de casais jovens adultos
Authors: Maesima Giovania Mitie , Neumann Angélica Paula , Rocha Fabrício de Andrade , Wagner Adriana
Published: March 2024

Resumo Este estudo qualitativo visou compreender a experiência de oito casais de adultos jovens em coabitação durante a pandemia de covid-19 e as repercussões do período na vida conjugal. Foram realizadas entrevistas semiestruturadas, seis presenciais e duas online, com casais heterossexuais, sem filhos, que coabitavam há menos de três anos. A análise temática reflexiva gerou três temas: desafios e dificuldades na pandemia; aproximação entre o casal; ganhos e aprendizados. Níveis aumentados de estresse, insegurança e ansiedade, sensação de sobrecarga e de privação devido às medidas de distanciamento social, dificuldades na delimitação dos espaços individuais dentro de casa e aumento nos conflitos conjugais foram os principais desafios relatados. Apesar disso, o enfrentamento colaborativo resultou em maior intimidade e no fortalecimento do vínculo conjugal, aumentando o compromisso com a relação. Tais resultados ilustram como um contexto pandêmico pode impactar relacionamentos durante o início da coabitação.
Evidências de validade e fidedignidade do Teste de Fala Comprimida com Figuras
Authors: Sanguebuche Taissane Rodrigues , Pagliarin Karina Carlesso , Peixe Bruna Pias , Moraes Denis Altieri de Oliveira , Garcia Michele Vargas
Published: March 2024

RESUMO Objetivo Buscar evidências de validade e fidedignidade para o Teste de Fala Comprimida com Figuras. Método O estudo foi subdividido em três etapas: validação de construto, critério e fidedignidade. Todos os participantes tinham idade entre 6:00 e 8:11. Para o construto, aplicou-se o Fala Comprimida com Figuras e o teste padrão ouro Fala Comprimida Adaptado em crianças com desenvolvimento fonológico típico. Para análise de critério, aplicou-se o Fala Comprimida com Figuras em dois grupos, com desenvolvimento fonológico típico (G1) e atípico (G2). Por fim, os protocolos de aplicação passaram pela análise de duas Fonoaudiólogas, com experiência na área do Processamento Auditivo Central, buscando obter uma análise de fidedignidade interavaliadores. Resultados O teste de correlação indicou um construto quase perfeito (Rho=0,843 para orelha direita e Rho=0,823 para orelha esquerda). Na análise de critério, percebeu-se que ambos os grupos apresentaram resultados satisfatórios (G1 = 99,6 a 100%; G2 = 96 a 96,5%). Já a análise de fidedignidade demonstrou que o protocolo é de fácil análise, pois ambos os profissionais apresentaram respostas unânimes. Conclusão Foi possível obter evidências de validade e fidedignidade para o instrumento de Fala Comprimida com Figuras. A análise de construto evidenciou que o instrumento mede a mesma variável que o teste padrão outro, com correlação quase perfeita. Na análise de critério, ambos os grupos apresentaram desempenho semelhante, demonstrando que o instrumento não parece diferenciar populações com e sem transtorno fonológico leve. A análise de fidedignidade interavaliador demonstrou que o protocolo é de fácil análise e pontuação.
From academic doctorates to professional doctorates: Comparative analysis of experiences in Ibero-America
Authors: Fruchi André José , Calderón Adolfo-Ignacio , Salceda Josefina Patiño , Bustos Margarita Figueroa
Published: March 2024

Abstract Professional Doctorates (PD) as a model of doctoral training, parallel to and independent from the traditional Academic Doctorates, began to expand throughout the 1990s in countries of the Anglo-Saxon world, echoing timidly in Ibero-American countries. This article carried out a comparative study between the PDs of Brazil and Mexico, the only countries in the Ibero-American region that have created PDs in their educational systems, aiming to analyze their convergences, divergences, and specificities. This is an exploratory, analytic-descriptive, bibliographic, and documental study. Among other important findings, it is noteworthy that unlike the Mexican norms that relate PD specifically to the professional development of doctoral students, in Brazil there is a broader, prevailing conception aimed at increasing the productivity of companies and public and private organizations, technology transfer and the production of innovative knowledge.
Interventores na transição pós-Estado Novo (1945-1947): o elo perdido da descentralização limitada?
Authors: Schlegel Rogerio
Published: March 2024

Resumo O estudo dos interventores nos estados no período de 1945 a 1947 tem sido negligenciado pela historiografia, sobretudo acerca da redistribuição de poder na Federação. Este artigo emprega análise contextual e prosopografia para discutir a influência da transição pós-Estado Novo na descentralização limitada de autoridade administrativa e fiscal verificada naquele período. Destacam-se três fatores que contribuíram para esse resultado: a sequência do processo de descentralização, com a eleição dos governadores após o redesenho da Federação pela Constituinte de 1946; a imposição de neutralidade aos interventores, com fiscalização da imprensa e dos responsáveis pelas indicações; e o perfil dos interventores, uma elite posicional com relativa desconexão das comunidades estaduais.
O poder das famílias na política: das parentelas às redes de base familiar
Authors: Petrarca Fernanda Rios , Oliveira Wilson José Ferreira de
Published: March 2024

Resumo Este artigo analisa os problemas teóricos e metodológicos da utilização do conceito de parentela no estudo dos grupos dirigentes. Trata-se, primeiro, de fazer um contraponto entre as análises que têm como base a noção de grandes famílias e as que se centram nas redes de base familiar, fundadas no conceito de família extensa e parentela. Depois, descreve-se o peso do sistema de alianças para a compreensão das condições de emergência e de transformação das redes de base familiar da elite política de Sergipe. Os resultados evidenciam o valor heurístico do conceito de parentela para a apreensão das condições e das dinâmicas de formação dos grupos dirigentes.
Elites empresariales de Uruguay en pandemia: ¿fisuras en los techos de cristal?
Authors: Serna Miguel , Bottinelli Eduardo
Published: March 2024

Resumen Uruguay tiene grandes empresas transnacionales en sectores estratégicos, donde la participación femenina en posiciones directivas ha sido muy baja en comparación con otros países latinoamericanos. Este estudio indaga sobre percepciones de dirigentes de empresas respecto a inequidades de género en puestos ejecutivos. La investigación muestra que las percepciones de “techos de cristal” son más críticas entre mujeres que hombres, así como identifica barreras atribuidas a determinantes socioculturales y segregación económica, tanto como variables organizacionales de las empresas. Este estudio consistió en una encuesta de actitudes y carreras realizada a 158 dirigentes de un listado de 530 grandes empresas en Uruguay.
O ISEB e os anos 1950 e 1960: reflexões em homenagem a Alzira Alves de Abreu
Authors: Oliveira Lucia Lippi
Published: March 2024

Resumo O artigo teve como objetivo retirar do esquecimento, analisar e valorizar a tese de doutorado de Alzira Alves de Abreu sobre o ISEB, defendida na França em 1975. Procurou-se também comparar a sua análise com a de outros autores que se dedicaram a estudar o mesmo instituto e inserir o debate daquela época no contexto da sociologia emergente.
NUANCES FORMATIVAS NO CONTEXTO DO PROGRAMA RESIDÊNCIA PEDAGÓGICA
Authors: Radetzke Franciele Siqueira , Frison Marli Dallagnol
Published: March 2024

O trabalho apresentado tem como foco situar interlocuções formativas sobre a Teoria da Atividade e a Investigação-Formação-Ação (IFA) como contribuições inerentes ao processo de formação de professores. O contexto de discussão é o Programa Residência Pedagógica (PRP) na área de Ciências da Natureza e suas Tecnologias (CNT), do qual participam estudantes da Graduação, professores de Educação Básica e professores do Ensino Superior. Trata-se de um estudo de análise de compreensões dos participantes do PRP situados em blocos de interlocuções, sendo: i) Contexto de atuação no Programa Residência Pedagógica (PRP), ii) Estratégias metodológicas no PRP, iii) Contribuições Formativas do PRP e iv) Desafios, Avaliação e Modificação. A análise foi realizada com aporte metodológico da ATD. Entre as discussões destaca-se as potencialidades da interação entre os sujeitos que participam do PRP e ressalta-se a importância do programa como um espaço formativo intermediário em que é possível a reflexão sobre as interações decorrentes da Atividade de Estudo e de Ensino. Nos alcances do artigo está a proposição do entrelaçamento metodológico de organização do PRP em considerar a IFA e a Teoria da Atividade, e, nesse processo, emerge a Atividade de Pesquisa
A função social da escola: dimensões a considerar no trabalho para a convivência ética
Authors: Vivaldi Flávia , Vinha Telma Pileggi
Published: March 2024

O presente artigo traz o recorte de um estudo quanti-quali, caracterizado como uma pesquisa-ação desenvolvida em uma Escola Pública Municipal, do interior de São Paulo, aprovada pelo Comitê de Ética em Pesquisa/Unicamp (CEP ) CAAE:32973114.2.0000.5404, que teve como objetivos implantar e avaliar o programa de formação de professores; e identificar quais transformações ocorreram, trazendo os principais avanços e dificuldades da escola, na área de convivência. A coleta de dados envolve diferentes procedimentos: sessões de observações na escola; questionários para avaliação das diferentes etapas da intervenção; questionários para avaliação do clima da escola (antes e depois de um ano de intervenção) em professores, alunos (a partir do 7º ano) e gestores; questionários para avaliação da incidência e tipos de intimidação presentes entre os alunos do Fundamental II (bullying); entrevistas com professores, gestores e alunos; e relatos e depoimentos durante os encontros de formação. A análise de conteúdo foi a metodologia adotada para o tratamento dos dados. Foram elaboradas quatro categorias de análise: as relações sociais e os conflitos na escola; regras, avaliações e segurança na escola; como situações de intimidação entre alunos; transformações pessoais: as habilidades sociais em processo de mudança. Os resultados apontam avanços expressivos no que se refere à visão crítica e consciente de alunos, professores e gestores, sobre a qualidade das interações na escola. As regras da instituição foram revistas pela comunidade e o novo documento dá ênfase aos valores eleitos e defendidos pela escola. As transformações pessoais geradas pelos novos conhecimentos, tanto nos adultos quanto nos alunos da escola, refletiram em habilidades sociais que possibilitaram uma convivência mais harmoniosa. O trabalho desenvolvido quais aspectos do universo escolar devem ser considerados como necessários, no planejamento para a convivência ética.
Esforços Governamentais Alavancam Combate Efetivo à Dengue no Brasil
Authors: Maciel Ethel Leonor Noia , Frutuoso Livia Carla Vinhal , Garcia Marcio Henrique de Oliveira , Werneck Guilherme , Cunha Rivaldo Venâncio da , Lima Nísia Verônica Trindade
Published: March 2024

A dengue evoluiu de uma doença restrita a alguns países para um grave problema global de saúde pública, atingindo mais de 120 países nos últimos anos. No Brasil, após sua reintrodução em 1981, o país enfrentou diversas epidemias, com mais de 16 milhões de casos registrados até o momento. Em 2023, sob a vigência do fenômeno climático El Niño registrou-se uma das maiores epidemias de dengue no país, com mais de 1,6 milhões de casos notificados. Temperaturas e pluviosidade mais elevadas em conjunto com a circulação simultânea dos quatro sorotipos do vírus da dengue aumentaram ainda mais o risco de disseminação da doença em 2024, especialmente em populações sem imunidade para alguns dos sorotipos. Diante deste quadro, o Ministério da Saúde promoveu várias ações para enfrentar a situação, incluindo a instalação de uma Sala Nacional de Arboviroses e um Comitê de Operações de Emergência, repasses financeiros para apoiar estados e municípios em ações contingenciais de vigilância e prevenção de doenças, com ênfase no enfrentamento das arboviroses e investimentos em inovações para o controle da dengue, como vacinação e o método Wolbachia. Ainda assim, o primeiro trimestre de 2024 registrou um número de casos suspeitos de dengue superior ao de 2023, destacando a necessidade de aprimoramentos no monitoramento da situação epidemiológica para detecção precoce de epidemias e da preparação dos serviços de saúde para o cuidado dos casos com sinais de gravidade. Após mais de 40 anos de epidemias recorrentes, o controle efetivo da dengue no país requer ações sustentadas de prevenção por meio de estratégias inovadoras, envolvendo esforços coordenados de todas as esferas da gestão em saúde, juntamente com a participação ativa da população. Ações estruturais para a melhoria na cobertura de saneamento básico e para mitigação dos efeitos das mudanças climá
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation
Authors: Wang Yongkang , Liu Xuan , Huang Feng , Xiong Zhankun , Zhang Wen
Published: March 2024

Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases. Recently, deep generative models have exhibited remarkable potential for generating therapeutic peptides, but they only utilize sequence or structure information alone, which hinders the performance in generation. In this study, we propose a Multi-Modal Contrastive Diffusion model (MMCD), fusing both sequence and structure modalities in a diffusion framework to co-generate novel peptide sequences and structures. Specifically, MMCD constructs the sequence-modal and structure-modal diffusion models, respectively, and devises a multi-modal contrastive learning strategy with inter-contrastive and intra-contrastive in each diffusion timestep, aiming to capture the consistency between two modalities and boost model performance. The inter-contrastive aligns sequences and structures of peptides by maximizing the agreement of their embeddings, while the intra-contrastive differentiates therapeutic and non-therapeutic peptides by maximizing the disagreement of their sequence/structure embeddings simultaneously. The extensive experiments demonstrate that MMCD performs better than other state-of-the-art deep generative methods in generating therapeutic peptides across various metrics, including antimicrobial/anticancer score, diversity, and peptide-docking.
Deep Quantum Error Correction
Authors: Choukroun Yoni , Wolf Lior
Published: March 2024

Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. QECC, as its classical counterpart (ECC), enables the reduction of error rates, by distributing quantum logical information across redundant physical qubits, such that errors can be detected and corrected. In this work, we efficiently train novel end-to-end deep quantum error decoders. We resolve the quantum measurement collapse by augmenting syndrome decoding to predict an initial estimate of the system noise, which is then refined iteratively through a deep neural network. The logical error rates calculated over finite fields are directly optimized via a differentiable objective, enabling efficient decoding under the constraints imposed by the code. Finally, our architecture is extended to support faulty syndrome measurement, by efficient decoding of repeated syndrome sampling. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy, outperforming for small distance topological codes, the existing end-to-end neural and classical decoders, which are often computationally prohibitive.
Text-Guided Molecule Generation with Diffusion Language Model
Authors: Gong Haisong , Liu Qiang , Wu Shu , Wang Liang
Published: March 2024

Text-guided molecule generation is a task where molecules are generated to match specific textual descriptions. Recently, most existing SMILES-based molecule generation methods rely on an autoregressive architecture. In this work, we propose the Text-Guided Molecule Generation with Diffusion Language Model (TGM-DLM), a novel approach that leverages diffusion models to address the limitations of autoregressive methods. TGM-DLM updates token embeddings within the SMILES string collectively and iteratively, using a two-phase diffusion generation process. The first phase optimizes embeddings from random noise, guided by the text description, while the second phase corrects invalid SMILES strings to form valid molecular representations. We demonstrate that TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for additional data resources. Our findings underscore the remarkable effectiveness of TGM-DLM in generating coherent and precise molecules with specific properties, opening new avenues in drug discovery and related scientific domains. Code will be released at: https://github.com/Deno-V/tgm-dlm.
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
Authors: Jin Zhi , Xu Sheng , Zhang Xiang , Ling Tianze , Dong Nanqing , Ouyang Wanli , Gao Zhiqiang , Chang Cheng , Sun Siqi
Published: March 2024

De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing.
SuperJunction: Learning-Based Junction Detection for Retinal Image Registration
Authors: Wang Yu , Wang Xiaoye , Gu Zaiwang , Liu Weide , Ng Wee Siong , Huang Weimin , Cheng Jun
Published: March 2024

Keypoints-based approaches have shown to be promising for retinal image registration, which superimpose two or more images from different views based on keypoint detection and description. However, existing approaches suffer from ineffective keypoint detector and descriptor training. Meanwhile, the non-linear mapping from 3D retinal structure to 2D images is often neglected. In this paper, we propose a novel learning-based junction detection approach for retinal image registration, which enhances both the keypoint detector and descriptor training. To improve the keypoint detection, it uses a multi-task vessel detection to regularize the model training, which helps to learn more representative features and reduce the risk of over-fitting. To achieve effective training for keypoints description, a new constrained negative sampling approach is proposed to compute the descriptor loss. Moreover, we also consider the non-linearity between retinal images from different views during matching. Experimental results on FIRE dataset show that our method achieves mean area under curve of 0.850, which is 12.6% higher than 0.755 by the state-of-the-art method. All the codes are available at https://github.com/samjcheng/SuperJunction.
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
Authors: Wu Tailin , Neiswanger Willie , Zheng Hongtao , Ermon Stefano , Leskovec Jure
Published: March 2024

Deep learning-based surrogate models have demonstrated remarkable advantages over classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over traditional partial differential equation (PDE) solvers. However, a significant challenge hindering their widespread adoption in both scientific and industrial domains is the lack of understanding about their prediction uncertainties, particularly in scenarios that involve critical decision making. To address this limitation, we propose a method that integrates efficient and precise uncertainty quantification into a deep learning-based surrogate model. Our method, termed Latent Evolution of PDEs with Uncertainty Quantification (LE-PDE-UQ), endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems. LE-PDE-UQ leverages latent vectors within a latent space to evolve both the system's state and its corresponding uncertainty estimation. The latent vectors are decoded to provide predictions for the system's state as well as estimates of its uncertainty. In extensive experiments, we demonstrate the accurate uncertainty quantification performance of our approach, surpassing that of strong baselines including deep ensembles, Bayesian neural network layers, and dropout. Our method excels at propagating uncertainty over extended auto-regressive rollouts, making it suitable for scenarios involving long-term predictions. Our code is available at: https://github.com/AI4Science-WestlakeU/le-pde-uq.
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
Authors: Xu Can , Wang Haosen , Wang Weigang , Zheng Pengfei , Chen Hongyang
Published: March 2024

Denoising diffusion models have shown great potential in multiple research areas. Existing diffusion-based generative methods on de novo 3D molecule generation face two major challenges. Since majority heavy atoms in molecules allow connections to multiple atoms through single bonds, solely using pair-wise distance to model molecule geometries is insufficient. Therefore, the first one involves proposing an effective neural network as the denoising kernel that is capable to capture complex multi-body interatomic relationships and learn high-quality features. Due to the discrete nature of graphs, mainstream diffusion-based methods for molecules heavily rely on predefined rules and generate edges in an indirect manner. The second challenge involves accommodating molecule generation to diffusion and accurately predicting the existence of bonds. In our research, we view the iterative way of updating molecule conformations in diffusion process is consistent with molecular dynamics and introduce a novel molecule generation method named Geometric-Facilitated Molecular Diffusion (GFMDiff). For the first challenge, we introduce a Dual-track Transformer Network (DTN) to fully excevate global spatial relationships and learn high quality representations which contribute to accurate predictions of features and geometries. As for the second challenge, we design Geometric-facilitated Loss (GFLoss) which intervenes the formation of bonds during the training period, instead of directly embedding edges into the latent space. Comprehensive experiments on current benchmarks demonstrate the superiority of GFMDiff.
Social Physics Informed Diffusion Model for Crowd Simulation
Authors: Chen Hongyi , Ding Jingtao , Li Yong , Wang Yue , Zhang Xiao-Ping
Published: March 2024

Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have achieved state-of-the-art performance in crowd simulation but fail to model the heterogeneity and multi-modality of human movement comprehensively. In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap. SPDiff takes both the interactive and historical information of crowds in the current timeframe to reverse the diffusion process, thereby generating the distribution of pedestrian movement in the subsequent timeframe. Inspired by the well-known social physics model, i.e., Social Force, regarding crowd dynamics, we design a crowd interaction encoder to guide the denoising process and further enhance this module with the equivariant properties of crowd interactions. To mitigate error accumulation in long-term simulations, we propose a multi-frame rollout training algorithm for diffusion modeling. Experiments conducted on two real-world datasets demonstrate the superior performance of SPDiff in terms of both macroscopic and microscopic evaluation metrics. Code and appendix are available at https://github.com/tsinghua-fib-lab/SPDiff.
Prot2Text: Multimodal Protein’s Function Generation with GNNs and Transformers
Authors: Abdine Hadi , Chatzianastasis Michail , Bouyioukos Costas , Vazirgiannis Michalis
Published: March 2024

In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches.
However, most existing methods formulate the task as a multi-classification problem, i.e. assigning predefined labels to proteins.
In this work, we propose a novel approach, Prot2Text, which predicts a protein's function in a free text style, moving beyond the conventional binary or categorical classifications.
By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including protein sequence, structure, and textual annotation and description.
This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate functional descriptions.
To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Prot2Text.
These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate function prediction of existing as well as first-to-see proteins.
SimCS: Simulation for Domain Incremental Online Continual Segmentation
Authors: Alfarra Motasem , Cai Zhipeng , Bibi Adel , Ghanem Bernard , Müller Matthias
Published: March 2024

Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries. ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they perform poorly in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning. Experiments show that SimCS provides consistent improvements when combined with different CL methods.
Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks
Authors: Arroyo Arevalo Caridad , Noorbakhsh Sayedeh Leila , Dong Yun , Hong Yuan , Wang Binghui
Published: March 2024

Federated learning (FL) has been widely studied recently due to its property to collaboratively train data from different devices without sharing the raw data. Nevertheless, recent studies show that an adversary can still be possible to infer private information about devices' data, e.g., sensitive attributes such as income, race, and sexual orientation. To mitigate the attribute inference attacks, various existing privacy-preserving FL methods can be adopted/adapted. However, all these existing methods have key limitations: they need to know the FL task in advance, or have intolerable computational overheads or utility losses, or do not have provable privacy guarantees.

We address these issues and design a task-agnostic privacy-preserving presentation learning method for FL (TAPPFL) against attribute inference attacks. TAPPFL is formulated via information theory. Specifically,
TAPPFL has two mutual information goals, where one goal learns task-agnostic data representations that contain the least information about the private attribute in each device's data, and the other goal ensures the learnt data representations include as much information as possible about the device data to maintain FL utility. We also derive privacy guarantees of TAPPFL against worst-case attribute inference attacks, as well as the inherent tradeoff between utility preservation and privacy protection. Extensive results on multiple datasets and applications validate the effectiveness of TAPPFL to protect data privacy, maintain the FL utility, and be efficient as well.
Experimental results also show that TAPPFL outperforms the existing defenses.
TREE-G: Decision Trees Contesting Graph Neural Networks
Authors: Bechler-Speicher Maya , Globerson Amir , Gilad-Bachrach Ran
Published: March 2024

When dealing with tabular data, models based on decision
trees are a popular choice due to their high accuracy on these
data types, their ease of application, and explainability properties. However, when it comes to graph-structured data, it
is not clear how to apply them effectively, in a way that in-
corporates the topological information with the tabular data
available on the vertices of the graph. To address this challenge,
we introduce TREE-G. TREE-G modifies standard decision
trees, by introducing a novel split function that is specialized
for graph data. Not only does this split function incorporate
the node features and the topological information, but it also
uses a novel pointer mechanism that allows split nodes to
use information computed in previous splits. Therefore, the
split function adapts to the predictive task and the graph at
hand. We analyze the theoretical properties of TREE-G and
demonstrate its benefits empirically on multiple graph and
vertex prediction benchmarks. In these experiments, TREE-G
consistently outperforms other tree-based models and often
outperforms other graph-learning algorithms such as Graph
Neural Networks (GNNs) and Graph Kernels, sometimes by
large margins. Moreover, TREE-Gs models and their predic
tions can be explained and visualized.
Simplicity Bias in Overparameterized Machine Learning
Authors: Berchenko Yakir
Published: March 2024

A thorough theoretical understanding of the surprising generalization ability of deep networks (and other overparameterized models) is still lacking. Here we demonstrate that simplicity bias is a major phenomenon to be reckoned with in overparameterized machine learning. In addition to explaining the outcome of simplicity bias, we also study its source: following concrete rigorous examples, we argue that (i) simplicity bias can explain generalization in overparameterized learning models such as neural networks; (ii) simplicity bias and excellent generalization are optimizer-independent, as our example shows, and although the optimizer affects training, it is not the driving force behind simplicity bias; (iii) simplicity bias in pre-training models, and subsequent posteriors, is universal and stems from the subtle fact that uniformly-at-random constructed priors are not uniformly-at-random sampled ; and (iv) in neural network models, the biasing mechanism in wide (and shallow) networks is different from the biasing mechanism in deep (and narrow) networks.
EG-NAS: Neural Architecture Search with Fast Evolutionary Exploration
Authors: Cai Zicheng , Chen Lei , Liu Peng , Ling Tongtao , Lai Yutao
Published: March 2024

Differentiable Architecture Search (DARTS) has achieved a rapid search for excellent architectures by optimizing architecture parameters through gradient descent. However, this efficiency comes with a significant challenge: the risk of premature convergence to local optima, resulting in subpar performance that falls short of expectations. To address this issue, we propose a novel and effective method called Evolutionary Gradient-Based Neural Architecture Search (EG-NAS). Our approach combines the strengths of both gradient descent and evolutionary strategy, allowing for the exploration of various optimization directions during the architecture search process. To begin with, we continue to employ gradient descent for updating network parameters to ensure efficiency. Subsequently, to mitigate the risk of premature convergence, we introduce an evolutionary strategy with global search capabilities to optimize the architecture parameters. By leveraging the best of both worlds, our method strikes a balance between efficient exploration and exploitation of the search space. Moreover, we have redefined the fitness function to not only consider accuracy but also account for individual similarity. This inclusion enhances the diversity and accuracy of the optimized directions identified by the evolutionary strategy. Extensive experiments on various datasets and search spaces demonstrate that EG-NAS achieves highly competitive performance at significantly low search costs compared to state-of-the-art methods. The code is available at https://github.com/caicaicheng/EG-NAS.
Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation
Authors: Chen Hui , Jia Yinxu , Wang Guanghui , Zou Changliang
Published: March 2024

Accurately detecting multiple change-points is critical for various applications, but determining the optimal number of change-points remains a challenge. Existing approaches based on information criteria attempt to balance goodness-of-fit and model complexity, but their performance varies depending on the model. Recently, data-driven selection criteria based on cross-validation has been proposed, but these methods can be prone to slight overfitting in finite samples. In this paper, we introduce a method that controls the probability of overestimation and provides uncertainty quantification for learning multiple change-points via cross-validation. We frame this problem as a sequence of model comparison problems and leverage high-dimensional inferential procedures. We demonstrate the effectiveness of our approach through experiments on finite-sample data, showing superior uncertainty quantification for overestimation compared to existing methods. Our approach has broad applicability and can be used in diverse change-point models.
Bridging the Semantic Latent Space between Brain and Machine: Similarity Is All You Need
Authors: Chen Jiaxuan , Qi Yu , Wang Yueming , Pan Gang
Published: March 2024

How our brain encodes complex concepts has been a longstanding mystery in neuroscience. The answer to this problem can lead to new understandings about how the brain retrieves information in large-scale data with high efficiency and robustness. Neuroscience studies suggest the brain represents concepts in a locality-sensitive hashing (LSH) strategy, i.e., similar concepts will be represented by similar responses. This finding has inspired the design of similarity-based algorithms, especially in contrastive learning. Here, we hypothesize that the brain and large neural network models, both using similarity-based learning rules, could contain a similar semantic embedding space. To verify that, this paper proposes a functional Magnetic Resonance Imaging (fMRI) semantic learning network named BrainSem, aimed at seeking a joint semantic latent space that bridges the brain and a Contrastive Language-Image Pre-training (CLIP) model. Given that our perception is inherently cross-modal, we introduce a fuzzy (one-to-many) matching loss function to encourage the models to extract high-level semantic components from neural signals. Our results claimed that using only a small set of fMRI recordings for semantic space alignment, we could obtain shared embedding valid for unseen categories out of the training set, which provided potential evidence for the semantic representation similarity between the brain and large neural networks. In a zero-shot classification task, our BrainSem achieves an 11.6% improvement over the state-of-the-art.
On the Unstable Convergence Regime of Gradient Descent
Authors: Chen Shuo , Peng Jiaying , Li Xiaolong , Zhao Yao
Published: March 2024

Traditional gradient descent (GD) has been fully investigated for convex or L-smoothness functions, and it is widely utilized in current neural network optimization. The classical descent lemma ensures that for a function with L-smoothness, the GD trajectory converges stably towards the minimum when the learning rate is below 2 / L. This convergence is marked by a consistent reduction in the loss function throughout the iterations. However, recent experimental studies have demonstrated that even when the L-smoothness condition is not met, or if the learning rate is increased leading to oscillations in the loss function during iterations, the GD trajectory still exhibits convergence over the long run. This phenomenon is referred to as the unstable convergence regime of GD. In this paper, we present a theoretical perspective to offer a qualitative analysis of this phenomenon. The unstable convergence is in fact an inherent property of GD for general twice differentiable functions. Specifically, the forwardinvariance of GD is established, i.e., it ensures that any point within a local region will always remain within this region under GD iteration. Then, based on the forward-invariance, for the initialization outside an open set containing the local minimum, the loss function will oscillate at the first several iterations and then become monotonely decreasing after the GD trajectory jumped into the open set. This work theoretically clarifies the unstable convergence phenomenon of GD discussed in previous experimental works. The unstable convergence of GD mainly depends on the selection of the initialization, and it is actually inevitable due to the complex nature of loss function.
CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series
Authors: Cheng Yuxiao , Li Lianglong , Xiao Tingxiong , Li Zongren , Suo Jinli , He Kunlun , Dai Qionghai
Published: March 2024

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.