bluuebunny/crossref_metadata_2025 huggingface dataset:
The Devising Approach in Student Training
Authors: Yovcheva Elitsa , Gyurova Viara
Published: March 2024

With the changes brought by the new millennium, educational institutions have been given a " social calling" to prepare young people for the challenges of the 21st century, developing their skills of creative individuals and helping them to continually self-develop and be active agents in their own lives and social expectations towards themselves. Social expectations in the 21st century are high for all professions and the people who perform them. This also applies to creators of works of art, such as theater productions and films. They are creators in the classical sense of the word. Their construction and improvement as such creators is possible as a result of a creative process, the basis of which are the dialogue and cooperation between the participants, characteristic of the very popular today in Bulgaria "pedagogy of cooperation" (also called "collaborative pedagogy") and "collaborative (devising) theater”. The purpose of the article is to show possible projections of the collaborative approach, more specifically, the devising approach in student training. The realization of this goal goes through the presentation of the specific features of the approach laid down in the foundations of the pedagogy of cooperation and devising theater, and the outline of the parameters and conditions for its effective application in the training of students - future actors and theater or cinema directors, as well as in the training of specialists in other humanities majors.
Synergy Between Rhythm Therapy and Escape Room in the Training of Future Teachers
Authors: Mishkova-Yotova Irina , Petrova Borislava
Published: March 2024

The application of the gaming approach combined with rhythm-therapy in the educational process has the potential to immerse learners in an environment where acquiring educational content becomes an exciting experience. It helps them discover and overcome challenges collectively, fosters learning, and develops critical and analytical thinking. The synergy between these two approaches contributes to the development of competencies for future educators and the formation of their comprehensive and harmonious personalities. The article explores the implementation of the "Escape Room" method as part of the gaming approach and rhythm therapy approach in successfully teaching future educators. It describes a game based on the story of Little Red Riding Hood. The impact of the storyline in the "Escape Room" model is enhanced by integrating rhythm therapy. This approach enables students to apply their knowledge to solve specific problems in untypical situations, work under pressure dictated by a specific rhythm and music.
Quercetin Compound of Okra Fruit (Abelmoschus esculentus L.) as Potential of Antioxidant and Antimicrobial
Authors: Astutiningsih Christina , Kristanti Jesi , Ariani Winda Ludfi
Published: March 2024

Respiratory tract infections can be caused by various microbes including Staphylococcus aureus bacteria and Candida albicans fungus. The treatment can use antibiotics or medical plants. Okra plants especially the fruit, are plants with antioxidant and antimicrobial activity caused by one of the flavonoids namely quercetin compounds. The purpose of this study was determine the total flavonoid, identify the isolate, and test the antioxidant and antimicrobial activity of the quercetin compound isolated from okra fruit. The result of the measurement of the total flavonoid content of the diethyl ether fraction obtained an average of 54.8723 ± 0.8059 mg QE/g and the ethyl acetate fraction obtained an average of 99.3155 ±1.4518 mgQE/g. The isolated flavonoid from okra fruit had antioxidant power with IC50 of 31,4 ppm, antibacterial activity against Staphylococcus aureus with inhibition zone diameter of 10,372mm; 13,964 mm; and 15, 392mm (1%:5%:10%), and the antifungal Candida albicans with inhibition zone diameter of 9,104mm; 284mm; and 11,380 mm (1%:5%:10%). Flavonoid isolates from Okra Fruit, namely quercetin compounds had strong antioxidant and antimicrobial activity.
Diet and Dietary Habits of The Mystus gulio from The Cianjur Estuaries, Indonesia
Authors: Paujiah Epa , Dhahiyat Yayat , Herawati Titin , Iskandar Iskandar , Haryono Haryono , Gumilar Iwang
Published: March 2024

Food and feeding habits are important things in bioecological of fish. This study provides an understanding of the feeding habits and intensity of the M. gulio, by analyzing its food composition, relative gut length (RGL) and gastro-somatic index (GaSI). Fish specimens were caught with a gill net and fishing rod in an area of Cianjur estuaries during a period of one year from October 2021 to September 2022. The analysis of 452 fish samples shows that the relative gut length (RGL) values revealed the feeding habit of M. gulio as carni-omnivorous. The GaSI value was used to determine feeding intensity and was found to be highest in November, at Cidamar estuary, and in the second size group (7.1-10 cm). These results provide new knowledge on this fish species’ feeding habit and intensity, which also helps understand the fish adaptation and conservation in the study area.
Metallothionein Protein Modeling from Pseudomonas aeruginosa PAO1 as A Metal Biosorber Candidate
Authors: Ikhsan Fajri , Shulhany Ahmad , Abdullah Syarif
Published: March 2024

Metallothionein is a protein that is well known to play a role in metal metabolism in bacterial cells. Metallothionein is a multifunctional protein that has the potential to be used as a metal adsorbing agent. Pseudomonas aeruginosa is a ubiquitous gram-negative and rapid-growth bacterium. In addition, the complete genome of Pseudomonas aeruginosa has been largely known. Pseudomonas aeruginosa PAO1 is a strain of Pseudomonas aeruginosa that the complete genome of this strain is easily accessible in NCBI. These features make Pseudomonas aeruginosa PAO1 become a common model in bacterial studies. This research aimed to find and model the putative metallothionein of Pseudomonas aeruginosa PAO1. This research was carried out by bioinformatic and protein homology methods. Based on the results, the putative metallothionein of Pseudomonas aeruginosa PAO1 was found in the bacterial genome at base sequence of 2355918 to 2356157. The putative metallothionein-encoding gene of Pseudomonas aeruginosa PAO1 has a size of 240 bp. The translation result of the gene showed that the putative metallothionein of Pseudomonas aeruginosa PAO1 has 79 amino acids. The modeling result showed the 3D structure of the putative metallothionein of Pseudomonas aeruginosa PAO1 is similar to the metallothionein 3D structure of Pseudomonas fluorescens Q2-87. The 3D structure of the putative metallothionein of Pseudomonas aeruginosa PAO1 was dominated by turn and coil, but contained 1 α-helix structure and 2 β-sheet structures. Based on protein analysis, it was found that the putative metallothionein of Pseudomonas aeruginosa PAO1 has 1 metal-binding cluster with 10 amino acids and the most important amino acid residue is Cysteine . Even though, there was 1 Histidine amino acid residue on the metal-binding cluster.
Exclusive breastfeeding duration within a cohort of indigenous Terena living in the urban area of Campo Grande, Central-West Region, Brazil
Authors: Bresan Deise , Leite Maurício Soares , Castro Teresa Gontijo de , Ferreira Aline Alves , Cury Elenir Rose Jardim
Published: March 2024

Abstract In Brazil, current information about breastfeeding indicators among indigenous living in the urban areas is lacking. This article describes the duration of exclusive breastfeeding and its associations with mother and child characteristics in a cohort of Terena infants. The study enrolled infants born between June 2017 to July 2018 (n = 42) and living in villages of the urban area of Campo Grande, Mato Grosso do Sul State, Brazil. Information was collected in four time-points. Variables on maternal sociodemographics and on maternal and child health characteristics were collected, respectively, during the antenatal and the first-month interviews. Variables on breastfeeding practices and bottle use were collected during the first-, six- and 12-month interviews. Associations were examined using Wilcoxon, Kruskal-Wallis, Pearson’s chi-square, and Fisher’s exact tests. The prevalence of exclusive breastfeeding duration to the ages of three and six months were, respectively, 50% and 11.9%. Compared to infants never introduced to bottles during the first three months of life, those bottle-fed had lower median duration of exclusive breastfeeding (15 versus 150 days) and lower prevalence of exclusive breastfeeding duration to the age of three months (22.7% versus 80%). Most Terena infants fell short of meeting the international recommended duration of exclusive breastfeeding until six months of age and suggested the negative impact of bottle use in the duration of exclusive breastfeeding.
Permanences and changes in the production structure of agriculture in Parana, 2006-2017
Authors: Cunha Marina Silva da
Published: March 2024

Abstract The objective of this work is to analyze the dynamics of changes in the productive structure of agriculture in the State of Paraná, based on information from the 2006 and 2017 Agricultural Censuses. Multivariate analyzes, factorial and cluster, were used to characterize temporal changes and spatial, considering twenty indicators of the modernization of agriculture associated with land use, technology and capitalization, labor and condition of the producer and establishments. The research results show significant transformations in agriculture in the State of Paraná, but still favoring the country's traditional trends, with expansion of agriculture for export and reduction of family farming, which still absorbs the largest share of occupation in rural areas.
Hemodiafiltração: uma sinergia ainda não convincente
Authors: Thangarajah Brammah Rajarajeswaran
Published: March 2024

RESUMO A tentativa desesperada de melhorar a mortalidade, morbidade, qualidade de vida e desfechos relatados pelos pacientes em indivíduos em hemodiálise levou a diversas tentativas de aprimorar os diferentes modos, frequências e durações das sessões de diálise nas últimas décadas. Nada foi mais atrativo do que a combinação de difusão e convecção na forma de hemodiafiltração. Apesar das evidências concretas de melhor depuração de moléculas de peso médio e melhor estabilidade hemodinâmica, evidências tangíveis para apoiar a adoção universal ainda estão distantes. Os benefícios de sobrevida observados em grupos selecionados que provavelmente toleram a hemodiafiltração com melhor acesso vascular e com menor carga de comorbidades precisam ser estendidos aos pacientes reais em diálise, que são mais velhos do que a população estudada e apresentam uma carga de comorbidades significativamente maior. As exigências técnicas do início da hemodiafiltração, os custos associados e os benefícios incrementais almejados, juntamente com os desfechos relatados pelos pacientes, precisam ser melhor explorados antes de se recomendar a hemodiafiltração como o modo de escolha.
Judicialização da previdência: o perfil dos segurados e das demandas por incapacidade
Authors: Queiroz Daniela Zarzar Pereira de Melo , Almeida Adriana Conrado de , Porto Gabriela Granja
Published: March 2024

Resumo Benefícios por incapacidade representam o assunto mais frequente no Judiciário Federal, e os altos gastos desses processos ameaçam a manutenção dessas prestações na previdência pública, com possibilidade de o risco passar ao seguro privado. Debruçar-se sobre essas demandas mediante identificação das causas de ajuizamento, além de minorar a litigiosidade, pode assegurá-las no regime geral. O objetivo do presente artigo é traçar o panorama do segurado, das causas de indeferimentos e das doenças incapacitantes, dos processos atinentes a esse assunto, notadamente o tempo de tramitação, a importância da perícia, a concordância da sentença com o exame técnico e deste com a decisão administrativa, o percentual de confirmação do pedido administrativo. Para isso, foi utilizado método científico-indutivo, por meio de técnica de investigação observacional mediante estudo descritivo, transversal e retrospectivo sobre os processos do assunto judicial auxílio-doença e aposentadoria por invalidez entre 2015 e 2019 em cidades que representam cinco regiões dos Juizados Especiais Federais, escolhidas entre varas de interior e de capital, especializadas ou não. A partir dos resultados, foram discutidas as situações que ensejaram demandas dessa ordem, sob o prisma das doenças e do processo em si, concluindo que há meios de conter a massificação de demandas por infortúnios que afetam a capacidade laborativa.
Eleições em dois tempos: o impacto da Lei dos Círculos na representação mineira da Câmara dos Deputados (1852 e 1856)
Authors: Freitas Ana Paula Ribeiro
Published: March 2024

Resumo A Lei Eleitoral de 1855, ou Lei dos Círculos, prometia alterar a representação parlamentar brasileira por meio do voto distrital e das inelegibilidades. Qual o impacto dessa lei na província com a maior bancada do Império? Este estudo identificou e comparou o perfil da representação de Minas Gerais eleita antes e depois da reforma, recorrendo a diversas fontes: almanaques, periódicos, relatórios presidenciais, debates parlamentares e biografias. Concluiu-se que a Lei dos Círculos trouxe diversidade partidária e estreia de elites locais mineiras na Câmara dos Deputados. Diante disso, nova lei foi adotada em 1860 a fim de reduzir sua imprevisibilidade.
Sobre elites e instituições: caminhos de pesquisa
Authors: Seidl Ernesto , Barreiros Bruno Costa
Published: March 2024

Resumo O artigo se apoia em pesquisas sobre elites militares e empresariais para discutir o eventual proveito de esquemas analíticos que integram elementos da sociologia bourdieusiana e da sociologia das instituições no estudo de elites. Destacamos a problemática das condições de existência e dos mecanismos de legitimação de elites institucionais em contextos importadores como o brasileiro. Privilegiamos o exame das dinâmicas dos processos de institucionalização (do Exército e da “sustentabilidade empresarial”), com foco nas práticas concretas e nos dispositivos acionados em lutas pela definição dos critérios de hierarquização e excelência em cada espaço institucional. Defendemos em especial pesquisas atentas aos investimentos individuais na instituição, bem como às diversas formas de engajamento e às batalhas particulares ou coletivas travadas em busca de reconhecimento e de poder. Evidenciamos ainda a riqueza da combinação de diferentes métodos e técnicas de pesquisa social — de produção, de tratamento, de análise e interpretação de dados —, com destaque para a investigação sociogenética das instituições, a prosopografia de elites institucionais e a análise de suas principais estratégias e práticas discursivas.
Towards Automated RISC-V Microarchitecture Design with Reinforcement Learning
Authors: Bai Chen , Zhai Jianwang , Ma Yuzhe , Yu Bei , Wong Martin D. F.
Published: March 2024

Microarchitecture determines the implementation of a microprocessor. Designing a microarchitecture to achieve better performance, power, and area (PPA) trade-off has been increasingly difficult. Previous data-driven methodologies hold inappropriate assumptions and lack more tightly coupling with expert knowledge. This paper proposes a novel reinforcement learning-based (RL) solution that addresses these limitations. With the integration of microarchitecture scaling graph, PPA preference space embedding, and proposed lightweight environment in RL, experiments using commercial electronic design automation (EDA) tools show that our method achieves an average PPA trade-off improvement of 16.03% than previous state-of-the-art approaches with 4.07× higher efficiency. The solution qualities outperform human implementations by at most 2.03× in the PPA trade-off.
Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback
Authors: Brahmavar Shreyas Bhat , Srinivasan Ashwin , Dash Tirtharaj , Krishnan Sowmya Ramaswamy , Vig Lovekesh , Roy Arijit , Aduri Raviprasad
Published: March 2024

Large Language Models (LLMs) can be used as repositories of biological and chemical information to generate pharmacological lead compounds. However, for LLMs to focus on specific drug targets typically requires experimentation with progressively more refined prompts. Results thus become dependent not just on what is known about the target, but also on what is known about the prompt- engineering. In this paper, we separate the prompt into domain-constraints that can be written in a standard logical form and a simple text-based query. We investigate whether LLMs can be guided, not by refining prompts manually, but by refining the logical component automatically, keeping the query unchanged. We describe an iterative procedure LMLF (“Language Model with Logical Feedback”) in which the constraints are progressively refined using a logical notion of generalisation. On any iteration, newly generated instances are verified against the constraint, providing "logical-feedback" for the next iteration's refinement of the constraints. We evaluate LMLF using two well-known targets (inhibition of the Janus Kinase 2; and Dopamine Receptor D2); and two different LLMs (GPT-3 and PaLM). We show that LMLF, starting with the same logical constraints and query text, can be used to guide both LLMs to generate potential leads. We find: (a) Binding affinities of LMLF-generated molecules are skewed towards higher binding affinities than those from existing baselines; (b) LMLF results in generating molecules that are skewed towards higher binding affinities than without logical feedback; (c) Assessment by a computational chemist suggests that LMLF generated compounds may be novel inhibitors. These findings suggest that LLMs with logical feedback may provide a mechanism for generating new leads without requiring the domain-specialist to acquire sophisticated skills in prompt-engineering.
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
Authors: Chen Haoyang , Sun Peiyan , Song Qiyuan , Wang Wanyuan , Wu Weiwei , Zhang Wencan , Gao Guanyu , Lyu Yan
Published: March 2024

Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%.
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting
Authors: Li Yanhong , Xu Jack , Anastasiu David
Published: March 2024

In the hydrology field, time series forecasting is crucial for efficient water resource management, improving flood and drought control and increasing the safety and quality of life for the general population. However, predicting long-term streamflow is a complex task due to the presence of extreme events. It requires the capture of long-range dependencies and the modeling of rare but important extreme values. Existing approaches often struggle to tackle these dual challenges simultaneously. In this paper, we specifically delve into these issues and propose Distance-weighted Auto-regularized Neural network (DAN), a novel extreme-adaptive model for long-range forecasting of stremflow enhanced by polar representation learning. DAN utilizes a distance-weighted multi-loss mechanism and stackable blocks to dynamically refine indicator sequences from exogenous data, while also being able to handle uni-variate time-series by employing Gaussian Mixture probability modeling to improve robustness to severe events. We also introduce Kruskal-Wallis sampling and gate control vectors to handle imbalanced extreme data. On four real-life hydrologic streamflow datasets, we demonstrate that DAN significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.
PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
Authors: She Rui , Wang Sijie , Kang Qiyu , Zhao Kai , Song Yang , Tay Wee Peng , Geng Tianyu , Jian Xingchao
Published: March 2024

Point cloud registration is a crucial technique in 3D computer vision with a wide range of applications. However, this task can be challenging, particularly in large fields of view with dynamic objects, environmental noise, or other perturbations. To address this challenge, we propose a model called PosDiffNet. Our approach performs hierarchical registration based on window-level, patch-level, and point-level correspondence. We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds. We incorporate position embeddings into a Transformer module based on a neural ordinary differential equation (ODE) to efficiently represent patches within points. We employ the multi-level correspondence derived from the high feature similarity scores to facilitate alignment between point clouds. Subsequently, we use registration methods such as SVD-based algorithms to predict the transformation using corresponding point pairs. We evaluate PosDiffNet on several 3D point cloud datasets, verifying that it achieves state-of-the-art (SOTA) performance for point cloud registration in large fields of view with perturbations. The implementation code of experiments is available at https://github.com/AI-IT-AVs/PosDiffNet.
Dual-Channel Learning Framework for Drug-Drug Interaction Prediction via Relation-Aware Heterogeneous Graph Transformer
Authors: Su Xiaorui , Hu Pengwei , You Zhu-Hong , Yu Philip S. , Hu Lun
Published: March 2024

Identifying novel drug-drug interactions (DDIs) is a crucial task in pharmacology, as the interference between pharmacological substances can pose serious medical risks. In recent years, several network-based techniques have emerged for predicting DDIs. However, they primarily focus on local structures within DDI-related networks, often overlooking the significance of indirect connections between pairwise drug nodes from a global perspective. Additionally, effectively handling heterogeneous information present in both biomedical knowledge graphs and drug molecular graphs remains a challenge for improved performance of DDI prediction. To address these limitations, we propose a Transformer-based relatIon-aware Graph rEpresentation leaRning framework (TIGER) for DDI prediction. TIGER leverages the Transformer architecture to effectively exploit the structure of heterogeneous graph, which allows it direct learning of long dependencies and high-order structures. Furthermore, TIGER incorporates a relation-aware self-attention mechanism, capturing a diverse range of semantic relations that exist between pairs of nodes in heterogeneous graph. In addition to these advancements, TIGER enhances predictive accuracy by modeling DDI prediction task using a dual-channel network, where drug molecular graph and biomedical knowledge graph are fed into two respective channels. By incorporating embeddings obtained at graph and node levels, TIGER can benefit from structural properties of drugs as well as rich contextual information provided by biomedical knowledge graph. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of TIGER in DDI prediction. Furthermore, case studies highlight its ability to provide a deeper understanding of underlying mechanisms of DDIs.
Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations
Authors: Wang Zilin , Zhuang Haolin , Li Lu , Zhang Yinmin , Zhong Junjie , Chen Jun , Yang Yu , Tang Boshi , Wu Zhiyong
Published: March 2024

This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models. Current models often generate monotonous and simplistic dance sequences that misalign with human preferences because they lack exploration capabilities.The E3D2 framework involves a reward model trained from automatically-ranked dance demonstrations, which then guides the reinforcement learning process. This approach encourages the agent to explore and generate high quality and diverse dance movement sequences. The soundness of the reward model is both theoretically and experimentally validated. Empirical experiments demonstrate the effectiveness of E3D2 on the AIST++ dataset.
Designing Biological Sequences without Prior Knowledge Using Evolutionary Reinforcement Learning
Authors: Zeng Xi , Hao Xiaotian , Tang Hongyao , Tang Zhentao , Jiao Shaoqing , Lu Dazhi , Peng Jiajie
Published: March 2024

Designing novel biological sequences with desired properties is a significant challenge in biological science because of the extra large search space. The traditional design process usually involves multiple rounds of costly wet lab evaluations. To reduce the need for expensive wet lab experiments, machine learning methods are used to aid in designing biological sequences. However, the limited availability of biological sequences with known properties hinders the training of machine learning models, significantly restricting their applicability and performance. To fill this gap, we present ERLBioSeq, an Evolutionary Reinforcement Learning algorithm for BIOlogical SEQuence design. ERLBioSeq leverages the capability of reinforcement learning to learn without prior knowledge and the potential of evolutionary algorithms to enhance the exploration of reinforcement learning in the large search space of biological sequences. Additionally, to enhance the efficiency of biological sequence design, we developed a predictor for sequence screening in the biological sequence design process, which incorporates both the local and global sequence information. We evaluated the proposed method on three main types of biological sequence design tasks, including the design of DNA, RNA, and protein. The results demonstrate that the proposed method achieves significant improvement compared to the existing state-of-the-art methods.
Generalize for Future: Slow and Fast Trajectory Learning for CTR Prediction
Authors: Zhu Jian , Liu Congcong , Jiang Xue , Peng Changping , Lin Zhangang , Shao Jingping
Published: March 2024

Deep neural networks (DNNs) have achieved significant advancements in click-through rate (CTR) prediction by demonstrating strong generalization on training data. However, in real-world scenarios, the assumption of independent and identically distributed (i.i.d.) conditions, which is fundamental to this problem, is often violated due to temporal distribution shifts. This violation can lead to suboptimal model performance when optimizing empirical risk without access to future data, resulting in overfitting on the training data and convergence to a single sharp minimum. To address this challenge, we propose a novel model updating framework called Slow and Fast Trajectory Learning (SFTL) network. SFTL aims to mitigate the discrepancy between past and future domains while quickly adapting to recent changes in small temporal drifts. This mechanism entails two interactions among three complementary learners: (i) the Working Learner, which updates model parameters using modern optimizers (e.g., Adam, Adagrad) and serves as the primary learner in the recommendation system, (ii) the Slow Learner, which is updated in each temporal domain by directly assigning the model weights of the working learner, and (iii) the Fast Learner, which is updated in each iteration by assigning exponentially moving average weights of the working learner. Additionally, we propose a novel rank-based trajectory loss to facilitate interaction between the working learner and trajectory learner, aiming to adapt to temporal drift and enhance performance in the current domain compared to the past. We provide theoretical understanding and conduct extensive experiments on real-world CTR prediction datasets to validate the effectiveness and efficiency of SFTL in terms of both convergence speed and model performance. The results demonstrate the superiority of SFTL over existing approaches.
Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms
Authors: Ding Jianhao , Yu Zhaofei , Huang Tiejun , Liu Jian K.
Published: March 2024

Spiking neural networks (SNNs) exploit neural spikes to provide solutions for low-power intelligent applications on neuromorphic hardware. Although SNNs have high computational efficiency due to spiking communication, they still lack resistance to adversarial attacks and noise perturbations. In the brain, neuronal responses generally possess stochasticity induced by ion channels and synapses, while the role of stochasticity in computing tasks is poorly understood. Inspired by this, we elaborate a stochastic gating spiking neural model for layer-by-layer spike communication, introducing stochasticity to SNNs. Through theoretical analysis, our gating model can be viewed as a regularizer that prevents error amplification under attacks. Meanwhile, our work can explain the robustness of Poisson coding. Experimental results prove that our method can be used alone or with existing robust enhancement algorithms to improve SNN robustness and reduce SNN energy consumption. We hope our work will shed new light on the role of stochasticity in the computation of SNNs. Our code is available at https://github.com/DingJianhao/StoG-meets-SNN/.
A Brain-Inspired Way of Reducing the Network Complexity via Concept-Regularized Coding for Emotion Recognition
Authors: Lu Han , Zhuang Xiahai , Luo Qiang
Published: March 2024

The human brain can effortlessly and reliably perceive emotions, whereas existing facial emotion recognition (FER) methods suffer from drawbacks such as complex model structures, high storage requirements, and poor interpretability. Inspired by the role of emotion concepts in visual perception coding within the human brain, we propose a dual-pathway framework emulating the neural computation of emotion recognition. Specifically, these two pathways are designed to model the representation of emotion concepts in the brain and the visual perception process, respectively. For the former, we adopt a disentangled approach to extract emotion concepts from complex facial geometric attributes; for the latter, we employ an emotional confidence evaluation strategy to determine which concept is optimal for regularizing the perceptual coding. The proposed concept-regularized coding strategy endows the framework with flexibility and interpretability as well as good performances on several benchmarking FER datasets.
Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization
Authors: Ahmadianshalchi Alaleh , Belakaria Syrine , Doppa Janardhan Rao
Published: March 2024

We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allowed to evaluate a batch of inputs. This problem arises in many real-world applications including penicillin production where diversity of solutions is critical. We solve this problem in the framework of Bayesian optimization (BO) and propose a novel approach referred to as Pareto front-Diverse Batch Multi-Objective BO (PDBO). PDBO tackles two important challenges: 1) How to automatically select the best acquisition function in each BO iteration, and 2) How to select a diverse batch of inputs by considering multiple objectives. We propose principled solutions to address these two challenges. First, PDBO employs a multi-armed bandit approach to select one acquisition function from a given library. We solve a cheap MOO problem by assigning the selected acquisition function for each expensive objective function to obtain a candidate set of inputs for evaluation. Second, it utilizes Determinantal Point Processes (DPPs) to choose a Pareto-front-diverse batch of inputs for evaluation from the candidate set obtained from the first step. The key parameters for the methods behind these two steps are updated after each round of function evaluations. Experiments on multiple MOO benchmarks demonstrate that PDBO outperforms prior methods in terms of both the quality and diversity of Pareto solutions.
Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
Authors: Alharbi Meshal , Roozbehani Mardavij , Dahleh Munther
Published: March 2024

The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this paper, we study the sample complexity of online Q-learning methods when some prior knowledge about the dynamics is available or can be learned efficiently. We focus on systems that evolve according to an additive disturbance model of the form S_{h+1} = ƒ(S_h, A_h) + W_h, where ƒ represents the underlying system dynamics, and W_h are unknown disturbances independent of states and actions. In the setting of finite episodic Markov decision processes with S states, A actions, and episode length H, we present an optimistic Q-learning algorithm that achieves Õ(Poly(H)√T) regret under perfect knowledge of ƒ, where T is the total number of interactions with the system. This is in contrast to the typical Õ(Poly(H)√SAT) regret for existing Q-learning methods. Further, if only a noisy estimate ƒ_hat of ƒ is available, our method can learn an approximately optimal policy in a number of samples that is independent of the cardinalities of state and action spaces. The sub-optimality gap depends on the approximation error ƒ_hat − ƒ, as well as the Lipschitz constant of the corresponding optimal value function. Our approach does not require modeling of the transition probabilities and enjoys the same memory complexity as model-free methods.
Understanding and Improving Optimization in Predictive Coding Networks
Authors: Alonso Nicholas , Krichmar Jeffrey , Neftci Emre
Published: March 2024

Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible. In contrast, the standard learning algorithm for predictive coding (PC) models in neuroscience, known as the inference learning algorithm (IL), is a promising, bio-plausible alternative. However, several challenges and questions hinder IL's application to real-world problems. For example, IL is computationally demanding, and without memory-intensive optimizers like Adam, IL may converge to poor local minima. Moreover, although IL can reduce loss more quickly than BP, the reasons for these speedups or their robustness remains unclear. In this paper, we tackle these challenges by 1) altering the standard implementation of PC circuits to substantially reduce computation, 2) developing a novel optimizer that improves the convergence of IL without increasing memory usage, and 3) establishing theoretical results that help elucidate the conditions under which IL is sensitive to second and higher-order information.
Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging
Authors: AlQuabeh Hilal , De Vazelhes William , Gu Bin
Published: March 2024

Pairwise learning, an important domain within machine learning, addresses loss functions defined on pairs of training examples, including those in metric learning and AUC maximization. Acknowledging the quadratic growth in computation complexity accompanying pairwise loss as the sample size grows, researchers have turned to online gradient descent (OGD) methods for enhanced scalability. Recently, an OGD algorithm emerged, employing gradient computation involving prior and most recent examples, a step that effectively reduces algorithmic complexity to O(T), with T being the number of received examples. This approach, however, confines itself to linear models while assuming the independence of example arrivals. We introduce a lightweight OGD algorithm that does not require the independence of examples and generalizes to kernel pairwise learning. Our algorithm builds the gradient based on a random example and a moving average representing the past data, which results in a sub-linear regret bound with a complexity of O(T). Furthermore, through the integration of O(√T logT) random Fourier features, the complexity of kernel calculations is effectively minimized. Several experiments with real-world datasets show that the proposed technique outperforms kernel and linear algorithms in offline and online scenarios.
Active Learning Guided by Efficient Surrogate Learners
Authors: An Yunpyo , Park Suyeong , Kim Kwang In
Published: March 2024

Re-training a deep learning model each time a single data point receives a new label is impractical due to the inherent complexity of the training process. Consequently, existing active learning (AL) algorithms tend to adopt a batch-based approach where, during each AL iteration, a set of data points is collectively chosen for annotation. However, this strategy frequently leads to redundant sampling, ultimately eroding the efficacy of the labeling procedure. In this paper, we introduce a new AL algorithm that harnesses the power of a Gaussian process surrogate in conjunction with the neural network principal learner. Our proposed model adeptly updates the surrogate learner for every new data instance, enabling it to emulate and capitalize on the continuous learning dynamics of the neural network without necessitating a complete retraining of the principal model for each individual label. Experiments on four benchmark datasets demonstrate that this approach yields significant enhancements, either rivaling or aligning with the performance of state-of-the-art techniques.
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
Authors: Bai Qinbo , Mondal Washim Uddin , Aggarwal Vaneet
Published: March 2024

In this paper, we consider an infinite horizon average reward Markov Decision Process (MDP). Distinguishing itself from existing works within this context, our approach harnesses the power of the general policy gradient-based algorithm, liberating it from the constraints of assuming a linear MDP structure. We propose a vanilla policy gradient-based algorithm and show its global convergence property. We then prove that the proposed algorithm has O(T^3/4) regret. Remarkably, this paper marks a pioneering effort by presenting the first exploration into regret bound computation for the general parameterized policy gradient algorithm in the context of average reward scenarios.
A Theory of Non-acyclic Generative Flow Networks
Authors: Brunswic Leo , Li Yinchuan , Xu Yushun , Feng Yijun , Jui Shangling , Ma Lizhuang
Published: March 2024

GFlowNets is a novel flow-based method for learning a stochastic policy to generate objects via a sequence of actions and with probability proportional to a given positive reward. We contribute to relaxing hypotheses limiting the application range of GFlowNets, in particular: acyclicity (or lack thereof). To this end, we extend the theory of GFlowNets on measurable spaces which includes continuous state spaces without cycle restrictions, and provide a generalization of cycles in this generalized context. We show that losses used so far push flows to get stuck into cycles and we define a family of losses solving this issue. Experiments on graphs and continuous tasks validate those principles.
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision
Authors: Chang Wonjoon , Kwon Dahee , Choi Jaesik
Published: March 2024

Understanding intermediate representations of the concepts learned by deep learning classifiers is indispensable for interpreting general model behaviors. Existing approaches to reveal learned concepts often rely on human supervision, such as pre-defined concept sets or segmentation processes. In this paper, we propose a novel unsupervised method for discovering distributed representations of concepts by selecting a principal subset of neurons. Our empirical findings demonstrate that instances with similar neuron activation states tend to share coherent concepts. Based on the observations, the proposed method selects principal neurons that construct an interpretable region, namely a Relaxed Decision Region (RDR), encompassing instances with coherent concepts in the feature space. It can be utilized to identify unlabeled subclasses within data and to detect the causes of misclassifications. Furthermore, the applicability of our method across various layers discloses distinct distributed representations over the layers, which provides deeper insights into the internal mechanisms of the deep learning model.
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models
Authors: Chen Jinqian , Zhu Jihua , Zheng Qinghai , Li Zhongyu , Tian Zhiqiang
Published: March 2024

Federated learning encounters substantial challenges with heterogeneous data, leading to performance degradation and convergence issues. While considerable progress has been achieved in mitigating such an impact, the reliability aspect of federated models has been largely disregarded. In this study, we conduct extensive experiments to investigate the reliability of both generic and personalized federated models. Our exploration uncovers a significant finding: federated models exhibit unreliability when faced with heterogeneous data, demonstrating poor calibration on in-distribution test data and low uncertainty levels on out-of-distribution data. This unreliability is primarily attributed to the presence of biased projection heads, which introduce miscalibration into the federated models. Inspired by this observation, we propose the "Assembled Projection Heads" (APH) method for enhancing the reliability of federated models. By treating the existing projection head parameters as priors, APH randomly samples multiple initialized parameters of projection heads from the prior and further performs targeted fine-tuning on locally available data under varying learning rates. Such a head ensemble introduces parameter diversity into the deterministic model, eliminating the bias and producing reliable predictions via head averaging. We evaluate the effectiveness of the proposed APH method across three prominent federated benchmarks. Experimental results validate the efficacy of APH in model calibration and uncertainty estimation. Notably, APH can be seamlessly integrated into various federated approaches but only requires less than 30% additional computation cost for 100x inferences within large models.
PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance
Authors: Chen Taicai , Duan Yue , Li Dong , Qi Lei , Shi Yinghuan , Gao Yang
Published: March 2024

Variational Autoencoder based Bayesian Optimization (VAE-BO) has demonstrated its excellent performance in addressing high-dimensional structured optimization problems. However, current mainstream methods overlook the potential of utilizing a pool of unlabeled data to construct the latent space, while only concentrating on designing sophisticated models to leverage the labeled data. Despite their effective usage of labeled data, these methods often require extra network structures, additional procedure, resulting in computational inefficiency. To address this issue, we propose a novel method to effectively utilize unlabeled data with the guidance of labeled data. Specifically, we tailor the pseudo-labeling technique from semi-supervised learning to explicitly reveal the relative magnitudes of optimization objective values hidden within the unlabeled data. Based on this technique, we assign appropriate training weights to unlabeled data to enhance the construction of a discriminative latent space. Furthermore, we treat the VAE encoder and the Gaussian Process (GP) in Bayesian optimization as a unified deep kernel learning process, allowing the direct utilization of labeled data, which we term as Gaussian Process guidance. This directly and effectively integrates the goal of improving GP accuracy into the VAE training, thereby guiding the construction of the latent space. The extensive experiments demonstrate that our proposed method outperforms existing VAE-BO algorithms in various optimization scenarios. Our code will be published at https://github.com/TaicaiChen/PG-LBO.
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
Authors: Chen Wentse , Huang Shiyu , Chiang Yuan , Pearce Tim , Tu Wei-Wei , Chen Ting , Zhu Jun
Published: March 2024

Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance, to make an agent's interaction with users more engaging, or improve the robustness of a policy to an unexpected perturbance. We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task. Unlike prior work, it achieves this with a shared policy network trained over a single run. Specifically, we design an intrinsic reward based on an information-theoretic diversity objective. Our final objective alternately constraints on the diversity of the strategies and on the extrinsic reward. We solve the constrained optimization problem by casting it as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently discovers diverse strategies in a wide variety of reinforcement learning tasks. Compared to baseline methods, DGPO achieves comparable rewards, while discovering more diverse strategies, and often with better sample efficiency.
Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
Authors: Chen Yang , Lin Xiao , Yan Bo , Zhang Libo , Liu Jiamou , Özkan Tan Neset , Witbrock Michael
Published: March 2024

Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations. While promising, the assumption of agent homogeneity limits the capability of existing methods to handle demonstrations with heterogeneous and unknown objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method. Critically, our method can infer rewards from different yet structurally similar tasks without prior knowledge about underlying contexts or modifying the MFG model itself. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs.