bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulus, Hannibal, and why Roman POWs can't go home again
Authors: Stern Gaius
Published: March 2024

In August 216 BC, Hannibal offered Rome a chance to ransom 10,000 POWs (prisoners of war), but the Senate, even though it was desperate for manpower, rejected his offer and instead purchased and freed 8,000 slaves to enlist in the army. The message was that Rome preferred newly freedmen who would fight for Rome over the men who had not fought their way out of the enemy's grasp. Hannibal sold the POWs into slavery. Thereafter, disdain for prisoners became a permanent feature of the Roman war machine. Diodorus, Livy, Plutarch, and Dio acknowledge that the Romans used to ransom and exchange POWs just like everyone else, but after Cannae they stopped. Cannae revived traumatic memories of how Rome had surrendered to Brennus and ransomed the city in 387 BC and surrendered to the Samnites in 321 BC at Caudine Forks and signed an unfavorable peace. Although Romans invented stories of salvation and exacting revenge in both cases, these humiliating events left deep scars in the Roman psyche, which never completely healed.

The defeat and capture of Atilius Regulus in Africa in 255 directly relates to the above-mentioned disasters. Although Romans transformed Regulus into a hero and martyr for integrity, claiming that he returned to Rome in 250 BC (five years after his death!) and denounced a prisoner exchange he had promised to endorse, the legend obscured the fact that Rome did exchange prisoners out of necessity in 249.
Effects of lactic acid bacteria exposed to extreme conditions on yoghurt quality
Authors:   Afyon Kocatepe University, Faculty of Engineering, Food Engineering Department, Afyonkarahisar, Turkey, Akarca Gökhan
Published: March 2024

In this study, two bacterial strains used in the production of yoghurt were subjected to three distinct stress conditions (low pressure, infrared light, and low pressure + infrared light) and then used as starter cultures for yoghurt production. The obtained results revealed that the bacterial stress response mechanisms, especially those of bacteria exposed to low pressure, positively affected the physicochemical, textural, and microbiological quality during storage of yoghurts produced with these bacteria. In addition, it was found that prolonging the exposure time to stress conditions increased this effect even more. The samples subjected to low pressure for 2 hours had the shortest fermentation time among all samples, while the samples subjected to infrared light for 2 hours had the longest fermentation time. Yoghurts produced with bacteria subjected to low pressure conditions had more organic acids and aroma components and less syneresis during storage compared to other samples. Furthermore, it was determined that yoghurt samples produced with bacteria exposed to stress conditions had higher L* and a* values, lower b* values, improved textural values, and higher bacterial counts during the 7-day storage period compared to control samples.
The variability in cow’s recovery potential regarding the milk recording season
Authors:   Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, V. Preloga 1, 31000 Osijek, Croatia, Gantner Vesna
Published: March 2024

The purpose of the study was to investigate how different milk recording seasons affected the prevalence of mastitis and the potential for cows to recover from it. After conducting logical data validation, a total of 3,953,637 test-day records of Holstein cows obtained during the milk recording period from January 1, 2005, to December 31, 2022, were used. The findings revealed that mastitis was most prevalent in the autumn season, and during this period, there was a lower total increase in milk production. On the other hand, the prevalence of healthy cows was maximum in summer and winter, while the winter season showed the highest overall increase in milk production. These results suggest that the prevalence of mastitis and the potential for recovery in cows are significantly influenced by the season, and the values may vary considerably. During the winter season, which is characterized by lower temperatures and humidity levels, the animals have the maximum likelihood of recuperation and regaining their production capacity in a manner that is consistent with their genetic potential.
Relação entre temperatura do ar e incidência de dengue: estudo de séries temporais em Minas Gerais, Brasil (2010-2019)
Authors: Gomes João Pedro Medeiros , Ribas Igor Magaton , Valadares Pedro Augusto Rosa , Jardim Lucas Santos , Nogueira Mário Círio , Ferreira Cássia de Castro Martins , Watanabe Aripuanã Sakurada Aranha , Ferreira Letícia de Castro Martins
Published: March 2024

Resumo A temperatura do ar é um fator climático que afeta a incidência da dengue, com efeitos variando conforme o tempo e o espaço. Investigamos a relação entre a temperatura mínima do ar e a incidência da doença em Minas Gerais, Brasil, e avaliamos a influência de variáveis socioeconômicas e geográficas nessa relação, calculando-se o risco relativo (RR). Este é um estudo de série temporal com análise conduzida em três etapas distintas: modelagem por uso de distributed lag non-linear model (modelos não-lineares distributivos com defasagem), metanálise dos modelos obtidos e metarregressão com dados geográficos e socioeconômicos. A temperatura mínima foi um fator de proteção quando em temperaturas frias extremas (RR = 0,65; IC95%: 0,56-0,76) e moderadas (RR = 0,71; IC95%: 0,64-0,79) e fator de risco em temperaturas de calor moderado (RR = 1,15; IC95%: 1,07-1,24), mas não em extremo (RR = 1,1; IC95%: 0,99-1,22). A heterogeneidade dos modelos foi elevada (I2 = 60%) e essa medida não foi alterada em metarregressão. Temperaturas frias moderadas e extremas causam efeito protetivo, enquanto moderadas quentes aumentam o risco. No entanto, a temperatura mínima do ar não explica nem a variabilidade da região, nem mesmo com as outras variáveis em metarregressão.
Serviço de convivência e fortalecimento de vínculos: reXistências por via do acontecimento
Authors: Sestren Luiza Jaques , Bueno Gabriel
Published: March 2024

Resumo Este texto traz um relato de experiência advindo das atividades de uma orientadora social realizado no Serviço de Convivência e Fortalecimento de Vínculos (SCFV) de um Centro de Referência de Assistência Social (Cras) situado na Grande Florianópolis (SC). O serviço integrou 20 jovens com idade entre 12 e 18 anos no espaço de uma instituição de formação policial. Este trabalho analisará: (1) um acontecimento ocorrido na instituição policial; (2) quais foram os seus desdobramentos; e (3) como a orientadora social atuou na mediação do grupo. A análise da do relato de experiência e a mediação da orientadora social foram embasadas no conceito de acontecimento de Gilles Deleuze. No decorrer dos encontros, a instituição parceira do projeto adotou medidas disciplinares que desqualificavam a cultura proveniente dos cotidiano dos(as) jovens participantes, o que levou o grupo a constituir uma identidade coletiva a partir da resistência à violência simbólica. Assim, diante da situação-problema, tornou-se possível uma experiência de fortalecimentos de laços comunitários.
Entre a psicanálise e a fenomenologia de Merleau-Ponty: traçando caminhos para uma noção de experiência
Authors: Klein Thais , Verztman Júlio Sérgio
Published: March 2024

Resumo Partindo de uma articulação entre a psicanálise e a fenomenologia de Merleau-Ponty, o objetivo deste artigo é traçar um caminho na psicanálise para uma noção de experiência. A experiência, seja pensada a partir de problemáticas levantadas pela psicanálise contemporânea, mais especificamente no que concerne aos estados limites, seja a partir da fenomenologia, não pertence a um sujeito, mas comporta uma abertura para o mundo. Com base em certas noções e interpretações de diferentes autores na psicanálise, buscam-se caminhos que permitam alcançar um deslocamento de uma posição idealista ou objetivista. Seguindo as indicações de Winnicott, três eixos são destacados como hipóteses centrais para definir a experiência: a corporeidade, o tempo e o sentido. Por fim, propõe-se um questionamento sobre a possibilidade de conceber a experiência como um conceito para a psicanálise contemporânea.
Donald Trump e o fascismo: uma análise inspirada na teoria crítica
Authors: Caputo Ubirajara de None , Aragusuku Henrique Araujo
Published: March 2024

Resumo A atuação de Donald Trump durante o período em que esteve na presidência dos Estados Unidos suscita a investigação de possíveis semelhanças entre ele e líderes fascistas do passado. A proposta deste ensaio é apresentar reflexões sobre a atuação política de Trump, inspiradas pelas discussões sobre a psicologia e a propaganda fascista na teoria crítica. Embora pareça impossível tomar Trump por um líder fascista clássico, principalmente em razão de contextos históricos muito diferentes, também é impossível desconsiderar o nexo entre suas estratégias políticas e o modus operandi de agitadores fascistas no século XX. Além disso, é inegável que sua política mobiliza elementos sociopsicológicos que remontam às análises da emergência do fascismo histórico, como a identificação com uma figura idealizada e transcendente, a submissão a uma autoridade ou causa superior e a agressividade direcionada às ameaças do out-group.
Yield increase of corn inoculated with Pseudomonas thivervalensis strain SC5 in Brazil
Authors: Meyer Edenilson , Giachini Admir José
Published: March 2024

ABSTRACT: Previous research has demonstrated the ability of isolate Pseudomonas thivervalensis SC5 to express the enzyme 1-aminocyclopropane-1-carboxylate deaminase (ACC), which regulates ethylene levels, one of the most important phytohormones in the regulation of plant growth and development. Thus, the present study evaluated the agronomic efficiency of a biological conditioner based on P. thivervalensis SC5 in the growth and productivity increases of corn in Brazil. It was found that corn was highly responsive to the inoculation of P. thivervalensis SC5, with increments ranging from 10.1 to 40.6% in the production of dry shoot biomass (DSB) compared to the control, while for grain yield the increments ranged from 9.0 to 27.8%. The increments are related to the levels and accumulations of N and P in the shoots of the plants. This suggested the participation of P. thivervalensis SC5 in mechanisms of soil modulation and nutrient acquisition. The inoculation of P. thivervalensis SC5 provided average increments in FDA hydrolysis ranging from 16.7 to 47.4% compared to the control, confirming the ability of this strain to increase the supply of nutrients to plants. Therefore, it is concluded that Pseudomonas thivervalensis SC5 participates in key mechanisms in the soil-plant system, with a consequent improvement in soil quality and other plant-related parameters.
Microbiological characterization, nutritional value and digestibility of black oat hay with crotalaria or corn as predecessors
Authors: Santos Leslei Caroline , Neres Marcela Abbado , Neumann Mikael , Nath Caroline Daiane , Silva Lurdes Rodrigues da , Ventura André Christofoleti , Souza André Martins de
Published: March 2024

ABSTRACT: The present study assessed the influence of the preceding crop (crotalaria or corn) on the bromatological, digestibility and microbiological aspects of the EMBRAPA 139 black oat hay (Avena strigosa) cultivar. A randomized block design was used, with subplots divided according to time, whereby the preceding crop (crotalaria or corn) was allocated to the main plot and hay storage times (baling, 30, 60 and 90 days of storage) to the subplots, with five repetitions. Crotalaria as predecessor resulted in lower indigestible fiber and higher protein contents in black oat hay, both in acid detergent, as well as higher pH. Corn as the preceding crop provided better hay digestibility due to lower neutral detergent insoluble protein content and higher in vitro neutral detergent fiber digestibility. The preceding crop did not significantly affect the drying time of forage and the microorganism population.
Review: Enzymatic hydrolysates of fish by-products: technological advantages and bioactive properties
Authors: Robalo Silvino Sasso , Silva Leila Picolli Da , Menezes Cristiano Regagnin De , Barin Juliano Smanioto , Campagnol Paulo Cezar Bastianello , Cichoski Alexandre José
Published: March 2024

ABSTRACT: Millions of tons of fish are filleted each year, and a significant portion of unwanted carcasses is discarded worldwide. An alternative approach to use these materials is through hydrolysis reactions, which allow for the production of compounds with modified technological properties such as solubility, emulsification capacity, foam formation ability, and viscosity. In addition, they may exhibit different biological activities with beneficial effects such as antioxidant, antihypertensive, anticarcinogenic, lipid profile-lowering, and neuroprotective properties. Thus, these compounds can be included in food formulations as promising adjuncts in treatments, with health benefits to consumers. This study discussed the different enzymatic hydrolyses used in the treatment of fish waste and evaluate the compounds and their effects.
An insight into seed priming response of Crotalaria ochroleuca and Crotalaria spectabilis during storage
Authors: Silva B. N. P. , Masetto T. E. , Rocha L. G.
Published: March 2024

Abstract The proper establishment of plants is essential for the efficient use of resources such as water and light. Besides, even after seed storage and sowing the uniform establishment of plants is essential for their success. Crotalaria ochroleuca and Crotalaria spectabilis are important medicinal plants with poor seed germination rate, occasionally. The effects of seed priming in both C. ochroleuca and C. spectabilis were evaluated in seed performance even after seeds storage for up 90-days. Experimental assays were performed in a randomized design with gibberellic acid (GA3, 100 ppm), polyethylene glycol (PEG 6000, -0.2 MPa) and PEG (-0.2 MPa) + GA3 (100 ppm) solutions during seed priming in four replicates. Seeds not submitted to priming treatments constituted control. Seeds physiological performance were evaluated immediately and even after 30, 60 and 90-days seed dry-storage. The data obtained in each experiment were submitted to variance analysis (ANOVA) adopting a confidence level of 95%. The effects of seed priming with PEG and GA3 during seed ageing were significant for germination variables of C. ochroleuca and C. spectabilis. During dry storage, seed viability of both species gradually decreased and the first symptoms were delayed seed germination, especially more evident for C. ochroleuca, even in primed or non-primed seeds. Afterwards, C. ochroleuca seeds previously GA3 primed had higher results of root protrusion (86%), hypocotyls elongation (76%) and complete seedlings (75%) than non-primed seeds (control). These findings shown a good potential of hormopriming to attenuate damage during the seed aging of C. ochroleuca.
Impacto fiscal e extrafiscal da descentralização do ITR: uma avaliação para o estado do Rio Grande do Sul
Authors: Quadros Jeferson Freitas , Oliveira Cassius Rocha de , Gonçalves Rodrigo da Rocha , Teixeira Gibran da Silva
Published: March 2024

Resumo Este artigo avalia o impacto da descentralização do Imposto sobre a Propriedade Territorial Rural (ITR) nos municípios do Rio Grande do Sul. O ITR contém instrumentos que agem nas esferas fiscal e extrafiscal, mas, segundo a literatura, a fiscalização ineficiente da União compromete sua efetividade. O convênio de 100% busca enfrentar esse problema transferindo integralmente o imposto para municípios que assumam a administração do tributo. Este estudo analisa o impacto desse acordo tanto sobre a tributação quanto sobre os aspectos extrafiscais, como Valor Adicionado, uso do solo, expansão da fronteira agrícola e concentração fundiária dos municípios conveniados. Usando o método de diferença-em-diferenças para múltiplos períodos de tempo e dados de 2002 a 2019, o estudo encontrou evidências de que os municípios conveniados experimentaram aumentos de até 35% na arrecadação do ITR, um aumento no uso da terra de até 12% e uma participação da produção agrícola no produto total até 24% maior. Entretanto, não houve efeitos estatisticamente significativos na concentração de terras ou no desmatamento. Esses resultados sugerem que a descentralização do ITR está sendo bem-sucedida em aumentar a eficácia fiscal e extrafiscal do imposto. Pesquisas futuras podem expandir a análise para outras regiões ou nível nacional.
University professors: What are the challenges and perspectives from pandemic?
Authors: Binotto Erlaine , Mendonça Jane Corrêa Alves , Araujo João André Amorim , Fountoura Roselyn Rodrigues
Published: March 2024

Abstract The paper analyzed, in two periods, the challenges and perspectives in the working life of professors who carried out telework during the COVID-19 pandemic, with professors from the Federal University of Grande Dourados (UFGD). A questionnaire on the Google platform was used to collect data in two moments of the pandemic. The results showed the possibility of continuing remote work, but alternating between remote and full-time. Since step 1, five months after the start of the pandemic, and step 2, in 2022, teachers readjusted many situations to face the times of pandemic and remote work/teaching. Positive aspects were facilities brought by remote work and the low incidence of contagion. They suggest the institution to improve assistance on virtual platforms and training, because 58% want full or partial continuity of remote activities.
Assessment of Education for peace and ethical learning at school
Authors: Lino Glaucya Maria Lopes , Lins Maria Judith Sucupira da Costa
Published: March 2024

Abstract This article discusses Education for Peace based on Aristotelian virtues with children in situation of risk. The problem of the research is violence at school and the necessity of building peace there. The Hypothesis is the possibility of Education for Peace at areas of risk. The objective is that 4th grader students can learn and practice the virtue of Friendship. The locus of the research is a public Elementary School in an area of risk in the city of Rio de Janeiro, Brazil. The Living Peace project is the core concept for this research on Education for Peace. We concluded that the practice of Ethics and Education for Peace through Virtue is possible if the students learn to practice virtues as the basis of a Culture of Peace.
Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion
Authors: Fan Na , Tian Zeyue , Dubey Amartansh , Deshmukh Samruddhi , Murch Ross , Chen Qifeng
Published: March 2024

Device-free localization (DFL) using easily-obtained Wi-Fi received signal strength (RSS) has wide real-world applications for not requiring people to carry trackable devices.
However, accurate multitarget DFL remains challenging due to the unknown number of targets, multipath interference (MPI), especially between nearby targets, and limited real-world data.
In this study, we pioneeringly propose a transformer-based learning method with Wi-Fi RSS as input, and an attentional prior fusion module, to simultaneously locate an unknown number of people at random positions.
To overcome the multitarget data collection challenges, we contribute a large-scale cross-domain real-simulation-augmentation training dataset with one and two real-world nearby non-person objects at limited positions and up to five simulated and augmented randomly distributed targets.
Experimental results demonstrate our method's improved accuracy, generalization ability, and robustness with fewer Wi-Fi nodes than previous methods.
Adversarial Robust Safeguard for Evading Deep Facial Manipulation
Authors: Guan Jiazhi , Zhao Yi , Xu Zhuoer , Meng Changhua , Xu Ke , Zhao Youjian
Published: March 2024

The non-consensual exploitation of facial manipulation has emerged as a pressing societal concern. In tandem with the identification of such fake content, recent research endeavors have advocated countering manipulation techniques through proactive interventions, specifically the incorporation of adversarial noise to impede the manipulation in advance. Nevertheless, with insufficient consideration of robustness, we show that current methods falter in providing protection after simple perturbations, e.g., blur. In addition, traditional optimization-based methods face limitations in scalability as they struggle to accommodate the substantial expansion of data volume, a consequence of the time-intensive iterative pipeline. To solve these challenges, we propose a learning-based model, Adversarial Robust Safeguard (ARS), to generate desirable protection noise in a single forward process, concurrently exhibiting a heightened resistance against prevalent perturbations.
Specifically, our method involves a two-way protection design, characterized by a basic protection component responsible for generating efficacious noise features, coupled with robust protection for further enhancement. In robust protection, we first fuse image features with spatially duplicated noise embedding, thereby accounting for inherent information redundancy. Subsequently, a combination comprising a differentiable perturbation module and an adversarial network is devised to simulate potential information degradation during the training process. To evaluate it, we conduct experiments on four manipulation methods and compare recent works comprehensively. The results of our method exhibit good visual effects with pronounced robustness against varied perturbations at different levels.
Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs
Authors: Lee Seungjun , Oh TaeiL
Published: March 2024

Solving partial differential equations (PDEs) by learning the solution operators has emerged as an attractive alternative to traditional numerical methods. However, implementing such architectures presents two main challenges: flexibility in handling irregular and arbitrary input and output formats and scalability to large discretizations. Most existing architectures are limited by their desired structure or infeasible to scale large inputs and outputs. To address these issues, we introduce an attention-based model called an inducing point operator transformer (IPOT). Inspired by inducing points methods, IPOT is designed to handle any input function and output query while capturing global interactions in a computationally efficient way. By detaching the inputs/outputs discretizations from the processor with a smaller latent bottleneck, IPOT offers flexibility in processing arbitrary discretizations and scales linearly with the size of inputs/outputs. Our experimental results demonstrate that IPOT achieves strong performances with manageable computational complexity on an extensive range of PDE benchmarks and real-world weather forecasting scenarios, compared to state-of-the-art methods. Our code is publicly available at https://github.com/7tl7qns7ch/IPOT.
Conformal Crystal Graph Transformer with Robust Encoding of Periodic Invariance
Authors: Wang Yingheng , Kong Shufeng , Gregoire John M. , Gomes Carla P.
Published: March 2024

Machine learning techniques, especially in the realm of materials design, hold immense promise in predicting the properties of crystal materials and aiding in the discovery of novel crystals with desirable traits. However, crystals possess unique geometric constraints—namely, E(3) invariance for primitive cell and periodic invariance—which need to be accurately reflected in crystal representations. Though past research has explored various construction techniques to preserve periodic invariance in crystal representations, their robustness remains inadequate. Furthermore, effectively capturing angular information within 3D crystal structures continues to pose a significant challenge for graph-based approaches. This study introduces novel solutions to these challenges. We first present a graph construction method that robustly encodes periodic invariance and a strategy to capture angular information in neural networks without compromising efficiency. We further introduce CrystalFormer, a pioneering graph transformer architecture that emphasizes angle preservation and enhances long-range information. Through comprehensive evaluation, we verify our model's superior performance in 5 crystal prediction tasks, reaffirming the efficiency of our proposed methods.
RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction
Authors: Yu Yemin , Yuan Luotian , Wei Ying , Gao Hanyu , Wu Fei , Wang Zhihua , Ye Xinhai
Published: March 2024

Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions. Despite steady progress on standard benchmarks, our understanding of existing retrosynthesis prediction models under the premise of distribution shifts remains stagnant. To this end, we first formally sort out two types of distribution shifts in retrosynthesis prediction and construct two groups of benchmark datasets. Next, through comprehensive experiments, we systematically compare state-of-the-art retrosynthesis prediction models on the two groups of benchmarks, revealing the limitations of previous in-distribution evaluation and re-examining the advantages of each model. More remarkably, we are motivated by the above empirical insights to propose two model-agnostic techniques that can improve the OOD generalization of arbitrary off-the-shelf retrosynthesis prediction algorithms. Our preliminary experiments show their high potential with an average performance improvement of 4.6%, and the established benchmarks serve as a foothold for further retrosynthesis prediction research towards OOD generalization.
Neural Reasoning about Agents’ Goals, Preferences, and Actions
Authors: Bortoletto Matteo , Shi Lei , Bulling Andreas
Published: March 2024

We propose the Intuitive Reasoning Network (IRENE) - a novel neural model for intuitive psychological reasoning about agents' goals, preferences, and actions that can generalise previous experiences to new situations. IRENE combines a graph neural network for learning agent and world state representations with a transformer to encode the task context. When evaluated on the challenging Baby Intuitions Benchmark, IRENE achieves new state-of-the-art performance on three out of its five tasks - with up to 48.9% improvement. In contrast to existing methods, IRENE is able to bind preferences to specific agents, to better distinguish between rational and irrational agents, and to better understand the role of blocking obstacles. We also investigate, for the first time, the influence of the training tasks on test performance. Our analyses demonstrate the effectiveness of IRENE in combining prior knowledge gained during training for unseen evaluation tasks.
Boosting Neural Cognitive Diagnosis with Student’s Affective State Modeling
Authors: Wang Shanshan , Zeng Zhen , Yang Xun , Xu Ke , Zhang Xingyi
Published: March 2024

Cognitive Diagnosis Modeling aims to infer students' proficiency level on knowledge concepts from their response logs. Existing methods typically model students’ response processes as the interaction between students and exercises or concepts based on hand-crafted or deeply-learned interaction functions. Despite their promising achievements, they fail to consider the relationship between students' cognitive states and affective states in learning, e.g., the feelings of frustration, boredom, or confusion with the learning content, which is insufficient for comprehensive cognitive diagnosis in intelligent education. To fill the research gap, we propose a novel Affect-aware Cognitive Diagnosis (ACD) model which can effectively diagnose the knowledge proficiency levels of students by taking into consideration the affective factors. Specifically, we first design a student affect perception module under the assumption that the affective state is jointly influenced by the student's affect trait and the difficulty of the exercise. Then, our inferred affective distribution is further used to estimate the student's subjective factors, i.e., guessing and slipping, respectively. Finally, we integrate the estimated guessing and slipping parameters with the basic neural cognitive diagnosis framework based on the DINA model, which facilitates the modeling of complex exercising interactions in a more accurate and interpretable fashion. Besides, we also extend our affect perception module in an unsupervised learning setting based on contrastive learning, thus significantly improving the compatibility of our ACD. To the best of our knowledge, we are the first to unify the cognition modeling and affect modeling into the same framework for student cognitive diagnosis. Extensive experiments on real-world datasets clearly demonstrate the effectiveness of our ACD. Our code is available at https://github.com/zeng-zhen/ACD.
Transient Glimpses: Unveiling Occluded Backgrounds through the Spike Camera
Authors: Zhang Jiyuan , Chen Shiyan , Zheng Yajing , Yu Zhaofei , Huang Tiejun
Published: March 2024

The de-occlusion problem, involving extracting clear background images by removing foreground occlusions, holds significant practical importance but poses considerable challenges. Most current research predominantly focuses on generating discrete images from calibrated camera arrays, but this approach often struggles with dense occlusions and fast motions due to limited perspectives and motion blur. To overcome these limitations, an effective solution requires the integration of multi-view visual information. The spike camera, as an innovative neuromorphic sensor, shows promise with its ultra-high temporal resolution and dynamic range. In this study, we propose a novel approach that utilizes a single spike camera for continuous multi-view imaging to address occlusion removal. By rapidly moving the spike camera, we capture a dense stream of spikes from occluded scenes. Our model, SpkOccNet, processes these spikes by integrating multi-view spatial-temporal information via long-short-window feature extractor (LSW) and employs a novel cross-view mutual attention-based module (CVA) for effective fusion and refinement. Additionally, to facilitate research in occlusion removal, we introduce the S-OCC dataset, which consists of real-world spike-based data. Experimental results demonstrate the efficiency and generalization capabilities of our model in effectively removing dense occlusions across diverse scenes. Public project page: https://github.com/Leozhangjiyuan/SpikeDeOcclusion.
Transfer and Alignment Network for Generalized Category Discovery
Authors: An Wenbin , Tian Feng , Shi Wenkai , Chen Yan , Wu Yaqiang , Wang Qianying , Chen Ping
Published: March 2024

Generalized Category Discovery (GCD) is a crucial real-world task that aims to recognize both known and novel categories from an unlabeled dataset by leveraging another labeled dataset with only known categories. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. The former leads to unreliable estimation of learning targets for novel categories and the latter hinders models from learning discriminative features. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features.
Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding of our model in general.
Our code and data are available at https://github.com/Lackel/TAN.
Fluctuation-Based Adaptive Structured Pruning for Large Language Models
Authors: An Yongqi , Zhao Xu , Yu Tao , Tang Ming , Wang Jinqiao
Published: March 2024

Network Pruning is a promising way to address the huge computing resource demands of the deployment and inference of Large Language Models (LLMs). Retraining-free is important for LLMs' pruning methods. However, almost all of the existing retraining-free pruning approaches for LLMs focus on unstructured pruning, which requires specific hardware support for acceleration. In this paper, we propose a novel retraining-free structured pruning framework for LLMs, named FLAP (FLuctuation-based Adaptive
Structured Pruning). It is hardware-friendly by effectively reducing storage and enhancing inference speed. For effective structured pruning of LLMs, we highlight three critical elements that demand the utmost attention: formulating structured importance metrics, adaptively searching the global compressed model, and implementing compensation mechanisms to mitigate performance loss. First, FLAP determines whether the output feature map is easily recoverable when a column of weight is removed, based on the fluctuation pruning metric. Then it standardizes the importance scores to adaptively determine the global compressed model structure. At last, FLAP adds additional bias terms to recover the output feature maps using the baseline values. We thoroughly evaluate our approach on a variety of language benchmarks. Without any retraining, our method significantly outperforms the state-of-the-art methods, including LLM-Pruner and the extension of Wanda in structured pruning. The code is released at https://github.com/CASIA-IVA-Lab/FLAP.
Generator Assisted Mixture of Experts for Feature Acquisition in Batch
Authors: Asgaonkar Vedang , Jain Aditya , De Abir
Published: March 2024

Given a set of observations, feature acquisition is about finding the subset of unobserved features which would enhance accuracy. Such problems has been explored in a sequential setting in prior work. Here, the model receives feedback from every new feature acquireed and chooses to explore more features or to predict. However, sequential acquisition is not feasible in some settings where time is of essence. We consider the problem of feature acquisition in batch, where the subset of features to be queried in batch is chosen based on the currently observed features, and then acquired as a batch, followed by prediction. We solve this problem using several technical innovations. First, we use a feature generator to draw a subset of the synthetic features for some examples, which reduces the cost of oracle queries. Second, to make the feature acquisition problem tractable for the large heterogeneous observed features, we partition the data into buckets, by borrowing tools from locality sensitive hashing and then train a mixture of experts model. Third, we design a tractable lower bound of the original objective.
We use a greedy algorithm combined with model training to solve the underlying problem. Experiments with four datasets shows that our approach outperforms these methods in terms of trade off between accuracy and feature acquisition cost.
Robustness-Guided Image Synthesis for Data-Free Quantization
Authors: Bai Jianhong , Yang Yuchen , Chu Huanpeng , Wang Hualiang , Liu Zuozhu , Chen Ruizhe , He Xiaoxuan , Mu Lianrui , Cai Chengfei , Hu Haoji
Published: March 2024

Quantization has emerged as a promising direction for model compression. Recently, data-free quantization has been widely studied as a promising method to avoid privacy concerns, which synthesizes images as an alternative to real training data. Existing methods use classification loss to ensure the reliability of the synthesized images. Unfortunately, even if these images are well-classified by the pre-trained model, they still suffer from low semantics and homogenization issues. Intuitively, these low-semantic images are sensitive to perturbations, and the pre-trained model tends to have inconsistent output when the generator synthesizes an image with low semantics. To this end, we propose Robustness-Guided Image Synthesis (RIS), a simple but effective method to enrich the semantics of synthetic images and improve image diversity, further boosting the performance of data-free compression tasks. Concretely, we first introduce perturbations on input and model weight, then define the inconsistency metrics at feature and prediction levels before and after perturbations. On the basis of inconsistency on two levels, we design a robustness optimization objective to eliminate low-semantic images. Moreover, we also make our approach diversity-aware by forcing the generator to synthesize images with small correlations. With RIS, we achieve state-of-the-art performance for various settings on data-free quantization and can be extended to other data-free compression tasks.
MEPSI: An MDL-Based Ensemble Pruning Approach with Structural Information
Authors: Bi Xiao-Dong , Zhang Shao-Qun , Jiang Yuan
Published: March 2024

Ensemble pruning that combines a subset of individual learners generated in parallel to make predictions is an important topic in ensemble learning. Past decades have developed a lot of pruning algorithms that focus on the external behavior of learners on samples, which may lead to over-fitting. In this paper, we conjecture that the generalization performance of an ensemble is not only related to its external behavior on samples but also dependent on the internal structure of individual learners. We propose the general MEPSI approach based on Kolmogorov complexity and the Minimum Description Length (MDL) principle, which formulates the ensemble pruning task as the two-objective optimization problem that comprises the empirical error and structural information among individual learners. We also provide a concrete implementation of MEPSI on decision trees. The theoretical results provide generalization bounds for both the general MEPSI approach and tree-based implementation. The comparative experiments conducted on multiple real-world data sets demonstrate the effectiveness of our proposed method.
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization
Authors: Cai Xu , Scarlett Jonathan
Published: March 2024

In this paper, we study the problem of estimating the normalizing constant through queries to the black-box function f, which is the integration of the exponential function of f scaled by a problem parameter lambda. We assume f belongs to a reproducing kernel Hilbert space (RKHS), and show that to estimate the normalizing constant within a small relative error, the level of difficulty depends on the value of lambda: When lambda approaches zero, the problem is similar to Bayesian quadrature (BQ), while when lambda approaches infinity, the problem is similar to Bayesian optimization (BO). More generally, the problem varies between BQ and BO. We find that this pattern holds true even when the function evaluations are noisy, bringing new aspects to this topic. Our findings are supported by both algorithm-independent lower bounds and algorithmic upper bounds, as well as simulation studies conducted on a variety of benchmark functions.
Continuous-Time Graph Representation with Sequential Survival Process
Authors: Çelikkanat Abdulkadir , Nakis Nikolaos , Mørup Morten
Published: March 2024

Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including bioinformatics, chemistry, and the social sciences. However, current dynamic network approaches focus on discrete-time networks or treat links in continuous-time networks as instantaneous events. Therefore, these approaches have limitations in capturing the persistence or absence of links that continuously emerge and disappear over time for particular durations. To address this, we propose a novel stochastic process relying on survival functions to model the durations of links and their absences over time. This forms a generic new likelihood specification explicitly accounting for intermittent edge-persistent networks, namely GraSSP: Graph Representation with Sequential Survival Process. We apply the developed framework to a recent continuous time dynamic latent distance model characterizing network dynamics in terms of a sequence of piecewise linear movements of nodes in latent space. We quantitatively assess the developed framework in various downstream tasks, such as link prediction and network completion, demonstrating that the developed modeling framework accounting for link persistence and absence well tracks the intrinsic trajectories of nodes in a latent space and captures the underlying characteristics of evolving network structure.
Data Shunt: Collaboration of Small and Large Models for Lower Costs and Better Performance
Authors: Chen Dong , Zhuang Yueting , Zhang Shuo , Liu Jinfeng , Dong Su , Tang Siliang
Published: March 2024

Pretrained large models, particularly large language models, have garnered increasing attention, as they have demonstrated remarkable abilities through contextual learning. Pretrained large models are increasingly recognized as fundamental tools for solving various tasks. However, the substantial computational demands of large models have dissuaded most product teams and individuals from running them. In such scenarios, to leverage the exceptional performance of large models, one must solely depend on costly APIs, further burdening product teams and individuals. On the other hand, despite the overall inferior performance of small models compared to large models, there are certain distributions where small models can achieve comparable or even superior results. For instance, during training, small models may become trapped in a local optimum that is unique to certain distributions, leading to superior performance. Hence, we propose Data Shunt (DS), a general paradigm for collaboration of small and large models. DS not only substantially reduces the cost associated with deploying large models but also effectively enhances overall performance. Specifically, DS determines the shunting direction by evaluating the confidence level of small models. When the confidence level falls below a specific threshold, the input data is forwarded to large models. To further leverage the advantages of the small and large models, we introduce Prompt Pruning (PP) and 2-Stage Confidence Distillation (2CD), which facilitate mutual collaboration, leading to better results and less cost.
The remarkable performance across diverse modalities and tasks demonstrates the superiority of the proposed DS over large models. For instance, ChatGPT achieves an accuracy of 94.43% on Amazon Product sentiment analysis, and DS achieves an accuracy of 95.64%, while the cost has been reduced to only 31.18%. The code for the proposed method are provided for research purposes https://github.com/Anfeather/Data-Shunt.
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility
Authors: Chen E , Cao Yang , Ge Yifei
Published: March 2024

The shuffle model of local differential privacy is an advanced method of privacy amplification designed to enhance privacy protection with high utility.
It achieves this by randomly shuffling sensitive data, making linking individual data points to specific individuals more challenging.
However, most existing studies have focused on the shuffle model based on
(ε0,0)-Locally Differentially Private (LDP) randomizers, with limited consideration for complex scenarios such as (ε0,δ0)-LDP or personalized LDP (PLDP).
This hinders a comprehensive understanding of the shuffle model's potential and limits its application in various settings.
To bridge this research gap, we propose a generalized shuffle framework that can be applied to PLDP setting. This generalization allows for a broader exploration of the privacy-utility trade-off and facilitates the design of privacy-preserving analyses in diverse contexts.
We prove that the shuffled PLDP process approximately preserves μ-Gaussian Differential Privacy with
μ = O(1/√n).
This approach allows us to avoid the limitations and potential inaccuracies associated with inequality estimations.
To strengthen the privacy guarantee, we improve the lower bound by utilizing hypothesis testing instead of relying on rough estimations like the Chernoff bound or Hoeffding's inequality.
Furthermore, extensive comparative evaluations clearly show that our approach outperforms existing methods in achieving strong central privacy guarantees while preserving the utility of the global model.
We have also carefully designed corresponding algorithms for average function, frequency estimation, and stochastic gradient descent.
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Authors: Cheng Debo , Xu Ziqi , Li Jiuyong , Liu Lin , Liu Jixue , Gao Wentao , Le Thuc Duy
Published: March 2024

Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders. Instrumental variable (IV) is a powerful tool for addressing the latent confounders issue, but the traditional IV technique cannot deal with latent time-dependent confounders in longitudinal studies. In this work, we propose a novel Time-dependent Instrumental Factor Model (TIFM) for time-varying causal effect estimation from data with latent time-dependent confounders. At each time-step, the proposed TIFM method employs the Recurrent Neural Network (RNN) architecture to infer latent IV, and then uses the inferred latent IV factor for addressing the confounding bias caused by the latent time-dependent confounders. We provide a theoretical analysis for the proposed TIFM method regarding causal effect estimation in longitudinal data. Extensive evaluation with synthetic datasets demonstrates the effectiveness of TIFM in addressing causal effect estimation over time. We further apply TIFM to a climate dataset to showcase the potential of the proposed method in tackling real-world problems.