bluuebunny/crossref_metadata_2025 huggingface dataset:
RZCR: Zero-shot Character Recognition via Radical-based Reasoning
Authors: Diao Xiaolei , Shi Daqian , Tang Hao , Shen Qiang , Li Yanzeng , Wu Lei , Xu Hao
Published: August 2023

The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets. Character image datasets are also affected by such unbalanced data distribution due to differences in character usage frequency. Thus, current character recognition methods are limited when applied in the real world, especially for the categories in the tail that lack training samples, e.g., uncommon characters. In this paper, we propose a zero-shot character recognition framework via radical-based reasoning, called RZCR, to improve the recognition performance of few-sample character categories in the tail. Specifically, we exploit radicals, the graphical units of characters, by decomposing and reconstructing characters according to orthography. RZCR consists of a visual semantic fusion-based radical information extractor (RIE) and a knowledge graph character reasoner (KGR). RIE aims to recognize candidate radicals and their possible structural relations from character images in parallel. The results are then fed into KGR to recognize the target character by reasoning with a knowledge graph. We validate our method on multiple datasets, and RZCR shows promising experimental results, especially on few-sample character datasets.
Anti-unification and Generalization: A Survey
Authors: Cerna David M. , Kutsia Temur
Published: August 2023

Anti-unification (AU) is a fundamental operation for generalization computation used for inductive inference. It is the dual operation to unification, an operation at the foundation of automated theorem proving. Interest in AU from the AI and related communities is growing, but without a systematic study of the concept nor surveys of existing work, investigations often resort to developing application-specific methods that existing approaches may cover. We provide the first survey of AU research and its applications and a general framework for categorizing existing and future developments.
A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
Authors: Deng Yang , Lei Wenqiang , Lam Wai , Chua Tat-Seng
Published: August 2023

Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that this first survey of proactive dialogue systems can provide the community with a quick access and an overall picture to this practical problem, and stimulate more progresses on conversational AI to the next level.
A Survey on Out-of-Distribution Evaluation of Neural NLP Models
Authors: Li Xinzhe , Liu Ming , Gao Shang , Buntine Wray
Published: August 2023

Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models.

However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. This survey will 1) compare the three lines of research under a unifying definition; 2) summarize their data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work.
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
Authors: Osika Zuzanna , Zatarain Salazar Jazmin , Roijers Diederik M. , Oliehoek Frans A. , Murukannaiah Pradeep K.
Published: August 2023

We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by these algorithms are scattered across fields. We provide an overview of the current advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and support on ethical aspects. We synthesize these methods drawing from different fields of research to enable building a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions.
A Systematic Survey of Chemical Pre-trained Models
Authors: Xia Jun , Zhu Yanqiao , Du Yuanqi , Li Stan Z.
Published: August 2023

Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design. However, training Deep Neural Networks (DNNs) from scratch often requires abundant labeled molecules, which are expensive to acquire in the real world. To alleviate this issue, tremendous efforts have been devoted to Chemical Pre-trained Models (CPMs), where DNNs are pre-trained using large-scale unlabeled molecular databases and then fine-tuned over specific downstream tasks. Despite the prosperity, there lacks a systematic review of this fast-growing field. In this paper, we present the first survey that summarizes the current progress of CPMs. We first highlight the limitations of training molecular representation models from scratch to motivate CPM studies. Next, we systematically review recent advances on this topic from several key perspectives, including molecular descriptors, encoder architectures, pre-training strategies, and applications. We also highlight the challenges and promising avenues for future research, providing a useful resource for both machine learning and scientific communities.
On Tackling Explanation Redundancy in Decision Trees (Extended Abstract)
Authors: Izza Yacine , Ignatiev Alexey , Marques-Silva Joao
Published: August 2023

Claims about the interpretability of decision trees can be traced back to the origins of machine learning (ML). Indeed, given some input consistent with a decision tree's path, the explanation for the resulting prediction consists of the features in that path. Moreover, a growing number of works propose the use of decision trees, and of other so-called interpretable models, as a possible solution for deploying ML models in high-risk applications. This paper overviews recent theoretical and practical results which demonstrate that for most decision trees, tree paths exhibit so-called explanation redundancy, in that logically sound explanations can often be significantly more succinct than what the features in the path dictates. More importantly, such decision tree explanations can be computed in polynomial-time, and so can be produced with essentially no effort other than traversing the decision tree. The experimental results, obtained on a large range of publicly available decision trees, support the paper's claims.
Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract)
Authors: Javed Rana Tallal , Nasir Osama , Borit Melania , Vanhée Loïs , Zea Elias , Gupta Shivam , Vinuesa Ricardo , Qadir Junaid
Published: August 2023

This study explores the topics and trends of teaching AI ethics in higher education, using Latent Dirichlet Allocation as the analysis tool. The analyses included 166 courses from 105 universities around the world. Building on the uncovered patterns, we distil a model of current pedagogical practice, the BAG model (Build, Assess, and Govern), that combines cognitive levels, course content, and disciplines. The study critically assesses the implications of this teaching paradigm and challenges practitioners to reflect on their practices and move beyond stereotypes and biases.
Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters (Extended Abstract)
Authors: Jin Kebing , Zhuo Hankz Hankui , Xiao Zhanhao , Wan Hai , Kambhampati Subbarao
Published: August 2023

Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems, especially when the problems contain obstacles and non-linear numeric effects. In this work, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend the syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to the next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem.
A Computational Model of Ostrom's Institutional Analysis and Development Framework (Extended Abstract)
Authors: Montes Nieves , Osman Nardine , Sierra Carles
Published: August 2023

Ostrom's Institutional Analysis and Development (IAD) framework represents a comprehensive theoretical effort to identify and outline the variables that determine the outcome in any social interaction. Taking inspiration from it, we define the Action Situation Language (ASL), a machine-readable logical language to express the components of a multiagent interaction, with a special focus on the rules adopted by the community. The ASL is complemented by a game engine that takes an interaction description as input and automatically grounds its semantics as an Extensive-Form Game (EFG), which can be readily analysed using standard game-theoretical solution concepts. Overall, our model allows a community of agents to perform what-if analysis on a set of rules being considered for adoption, by automatically connecting rule configurations to the outcomes they incentivize.
A Logic-based Explanation Generation Framework for Classical and Hybrid Planning Problems (Extended Abstract)
Authors: Vasileiou Stylianos Loukas , Yeoh William , Tran Son , Kumar Ashwin , Cashmore Michael , Magazzeni Daniele
Published: August 2023

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent's model. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes.
Large Decision Models
Authors: Zhang Weinan
Published: August 2023

Over recent decades, sequential decision-making tasks are mostly tackled with expert systems and reinforcement learning. However, these methods are still incapable of being generalizable enough to solve new tasks at a low cost. In this article, we discuss a novel paradigm that leverages Transformer-based sequence models to tackle decision-making tasks, named large decision models. Starting from offline reinforcement learning scenarios, early attempts demonstrate that sequential modeling methods can be applied to train an effective policy given sufficient expert trajectories. When the sequence model goes large, its generalization ability over a variety of tasks and fast adaptation to new tasks has been observed, which is highly potential to enable the agent to achieve artificial general intelligence for sequential decision-making in the near future.
Interpretability and Fairness in Machine Learning: A Formal Methods Approach
Authors: Ghosh Bishwamittra
Published: August 2023

The last decades have witnessed significant progress in machine learning with a host of applications of algorithmic decision-making in different safety-critical domains, such as medical, law, education, and transportation. In high-stake domains, machine learning predictions have far-reaching consequences on the end-users. With the aim of applying machine learning for societal goods, there have been increasing efforts to regulate machine learning by imposing interpretability, fairness, robustness, etc. in predictions. Towards responsible and trustworthy machine learning, we propose two research themes in our dissertation research: interpretability and fairness of machine learning classifiers. In particular, we design algorithms to learn interpretable rule-based classifiers, formally verify fairness, and explain the sources of unfairness. Prior approaches to these problems are often limited by scalability, accuracy, or both. To overcome these limitations, we closely integrate automated reasoning, formal methods, and statistics with fairness and interpretability to develop scalable and accurate solutions.
Sub-Band Based Attention for Robust Polyp Segmentation
Authors: Fang Xianyong , Shi Yuqing , Guo Qingqing , Wang Linbo , Liu Zhengyi
Published: August 2023

This article proposes a novel spectral domain based solution to the challenging polyp segmentation. The main contribution is based on an interesting finding of the significant existence of the middle frequency sub-band during the CNN process. Consequently, a Sub-Band based Attention (SBA) module is proposed, which uniformly adopts either the high or middle sub-bands of the encoder features to boost the decoder features and thus concretely improve the feature discrimination. A strong encoder supplying informative sub-bands is also very important, while we highly value the local-and-global information enriched CNN features. Therefore, a Transformer Attended Convolution (TAC) module as the main encoder block is introduced. It takes the Transformer features to boost the CNN features with stronger long-range object contexts. The combination of SBA and TAC leads to a novel polyp segmentation framework, SBA-Net. It adopts TAC to effectively obtain encoded features which also input to SBA, so that efficient sub-bands based attention maps can be generated for progressively decoding the bottleneck features. Consequently, SBA-Net can achieve the robust polyp segmentation, as the experimental results demonstrate.
Sample Efficient Paradigms for Personalized Assessment of Taskable AI Systems
Authors: Verma Pulkit
Published: August 2023

The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop paradigms that would enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system's capabilities in fully observable settings.
SemFORMS: Automatic Generation of Semantic Transforms By Mining Data Science Code
Authors: Abdelaziz Ibrahim , Dolby Julian , Khurana Udayan , Samulowitz Horst , Srinivas Kavitha
Published: August 2023

Careful choice of feature transformations in a dataset can help predictive model performance, data understanding and data exploration. However, finding useful features is a challenge, and while recent Automated Machine Learning (AutoML) systems provide some limited automation for feature engineering or data exploration, it is still mostly done by humans. We demonstrate a system called SemFORMS (Semantic Transforms), which attempts to mine useful expressions for a dataset from access to a repository of code that may target the same dataset/similar dataset. In many enterprises, numerous data scientists often work on the same or similar datasets, but are largely unaware of each other's work. SemFORMS finds appropriate code from such a repository, and normalizes the code to be an actionable transform that can prepended into any AutoML pipeline. We demonstrate SemFORMS operating over example datasets from the OpenML benchmarks where it sometimes leads to significant improvements in AutoML performance.
SiWare: Contextual Understanding of Industrial Data for Situational Awareness
Authors: Bhamidipaty Anuradha , Khabiri Elham , Agrawal Bhavna , Li Yingjie
Published: August 2023

SiWare is an AI-powered Knowledge Discovery system, that helps unlock new insights and accelerates data-driven decisions with contextualized Industrial data. SiWare links and fuses heterogeneous data sources with an industry semantic model leveraging multiple AI capabilities to provide system-wide visibility into operational characteristics. As part of this demo paper, we describe the requirements for such a system, and deployment aspects, and demonstrate the benefits in two industrial scenarios.
Practical Model Reductions for Verification of Multi-Agent Systems
Authors: Jamroga Wojciech , Kim Yan
Published: August 2023

Formal verification of intelligent agents is often computationally infeasible due to state-space explosion.

We present a tool for reducing the impact of the explosion by means of state abstraction that is (a) easy to use and understand by non-experts, and (b) agent-based in the sense that it operates on a modular representation of the system, rather than on its huge explicit state model.
IMPsys: An Intelligent Mold Processing System for Smart Factory
Authors: Zhou Xueyi , Na Yohan , Bang Minju , Chae Dong-Kyu
Published: August 2023

The explosive popularity of smart manufacturing has caught the attention of researchers in terms of intelligent mold processing and management. Machining mold components is a crucial step in the mold production process for many industries, which creates (e.g., cutting, drilling, and shaping a metal) the individual parts (e.g., core pins, ejector pins, cavities, slides, and lifters) that make up a mold used in manufacturing. We present IMPsys, an AI-based system that automatically explores machining jobs, infers their processing time and schedules them on machines, given numerous 3D modelling files of mold components. Our demo video can be found at: http://bit.ly/3EeKnyL.
Contrastive Learning for Sign Language Recognition and Translation
Authors: Gan Shiwei , Yin Yafeng , Jiang Zhiwei , Xia Kang , Xie Lei , Lu Sanglu
Published: August 2023

There are two problems that widely exist in current end-to-end sign language processing architecture. One is the CTC spike phenomenon which weakens the visual representational ability in Continuous Sign Language Recognition (CSLR). The other one is the exposure bias problem which leads to the accumulation of translation errors during inference in Sign Language Translation (SLT). In this paper, we tackle these issues by introducing contrast learning, aiming to enhance both visual-level feature representation and semantic-level error tolerance. Specifically, to alleviate CTC spike phenomenon and enhance visual-level representation, we design a visual contrastive loss by minimizing visual feature distance between different augmented samples of frames in one sign video, so that the model can further explore features by utilizing numerous unlabeled frames in an unsupervised way. To alleviate exposure bias problem and improve semantic-level error tolerance, we design a semantic contrastive loss by re-inputting the predicted sentence into semantic module and comparing features of ground-truth sequence and predicted sequence, for exposing model to its own mistakes. Besides, we propose two new metrics, i.e., Blank Rate and Consecutive Wrong Word Rate to directly reflect our improvement on the two problems. Extensive experimental results on current sign language datasets demonstrate the effectiveness of our approach, which achieves state-of-the-art performance.
SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity
Authors: Han Young-Joo , Yu Ha-Jin
Published: August 2023

Recently, numerous studies have been conducted on supervised learning-based image denoising methods. However, these methods rely on large-scale noisy-clean image pairs, which are difficult to obtain in practice. Denoising methods with self-supervised training that can be trained with only noisy images have been proposed to address the limitation. These methods are based on the convolutional neural network (CNN) and have shown promising performance. However, CNN-based methods do not consider using nonlocal self-similarities essential in the traditional method, which can cause performance limitations. This paper presents self-similarity attention (SS-Attention), a novel self-attention module that can capture nonlocal self-similarities to solve the problem. We focus on designing a lightweight self-attention module in a pixel-wise manner, which is nearly impossible to implement using the classic self-attention module due to the quadratically increasing complexity with spatial resolution. Furthermore, we integrate SS-Attention into the blind-spot network called self-similarity-based blind-spot network (SS-BSN). We conduct the experiments on real-world image denoising tasks. The proposed method quantitatively and qualitatively outperforms state-of-the-art methods in self-supervised denoising on the Smartphone Image Denoising Dataset (SIDD) and Darmstadt Noise Dataset (DND) benchmark datasets.
Anticipatory Fictitious Play
Authors: Cloud Alex , Wang Albert , Kerr Wesley
Published: August 2023

Fictitious play is an algorithm for computing Nash equilibria of matrix games. Recently, machine learning variants of fictitious play have been successfully applied to complicated real-world games. This paper presents a simple modification of fictitious play which is a strict improvement over the original: it has the same theoretical worst-case convergence rate, is equally applicable in a machine learning context, and enjoys superior empirical performance. We conduct an extensive comparison of our algorithm with fictitious play, proving an optimal O(1/t) convergence rate for certain classes of games, demonstrating superior performance numerically across a variety of games, and concluding with experiments that extend these algorithms to the setting of deep multiagent reinforcement learning.
Dual Video Summarization: From Frames to Captions
Authors: Hu Zhenzhen , Wang Zhenshan , Song Zijie , Hong Richang
Published: August 2023

Video summarization and video captioning both condense the video content from the perspective of visual and text modes, i.e. the keyframe selection and language description generation. Existing video-and-language learning models commonly sample multiple frames for training instead of observing all. These sampled deputies greatly improve computational efficiency, but do they represent the original video content enough with no more redundancy? In this work, we propose a dual video summarization framework and verify it in the context of video captioning. Given the video frames, we firstly extract the visual representation based on the ViT model fine-tuned on the video-text domain. Then we summarize the keyframes according to the frame-lever score. To compress the number of keyframes as much as possible while ensuring the quality of captioning, we learn a cross-modal video summarizer to select the most semantically consistent frames according to the pseudo score label. Top K frames ( K is no more than 3% of the entire video.) are chosen to form the video representation. Moreover, to evaluate the static appearance and temporal information of video, we design the ranking scheme of video representation from two aspects: feature-oriented and sequence-oriented. Finally, we generate the descriptions with a lightweight LSTM decoder. The experiment results on the MSR-VTT and MSVD dataset reveal that, for the generative task as video captioning, a small number of keyframes can convey the same semantic information to perform well on captioning, or even better than the original sampling.
Part Aware Contrastive Learning for Self-Supervised Action Recognition
Authors: Hua Yilei , Wu Wenhan , Zheng Ce , Lu Aidong , Liu Mengyuan , Chen Chen , Wu Shiqian
Published: August 2023

In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the semantic distinction of human action features is often represented by local body parts, such as legs or hands, which are advantageous for skeleton-based action recognition. This paper proposes an attention-based contrastive learning framework for skeleton representation learning, called SkeAttnCLR, which integrates local similarity and global features for skeleton-based action representations. To achieve this, a multi-head attention mask module is employed to learn the soft attention mask features from the skeletons, suppressing non-salient local features while accentuating local salient features, thereby bringing similar local features closer in the feature space. Additionally, ample contrastive pairs are generated by expanding contrastive pairs based on salient and non-salient features with global features, which guide the network to learn the semantic representations of the entire skeleton. Therefore, with the attention mask mechanism, SkeAttnCLR learns local features under different data augmentation views. The experiment results demonstrate that the inclusion of local feature similarity significantly enhances skeleton-based action representation. Our proposed SkeAttnCLR outperforms state-of-the-art methods on NTURGB+D, NTU120-RGB+D, and PKU-MMD datasets. The code and settings are available at this repository: https://github.com/GitHubOfHyl97/SkeAttnCLR.
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning
Authors: Huang Xinyang , Zhu Chuang , Chen Wenkai
Published: August 2023

In semi-supervised domain adaptation (SSDA), a few labeled target samples of each class help the model to transfer knowledge representation from the fully labeled source domain to the target domain. Many existing methods ignore the benefits of making full use of the labeled target samples from multi-level. To make better use of this additional data, we propose a novel Prototype-based Multi-level Learning (ProML) framework to better tap the potential of labeled target samples. To achieve intra-domain adaptation, we first introduce a pseudo-label aggregation based on the intra-domain optimal transport to help the model align the feature distribution of unlabeled target samples and the prototype. At the inter-domain level, we propose a cross-domain alignment loss to help the model use the target prototype for cross-domain knowledge transfer. We further propose a dual consistency based on prototype similarity and linear classifier to promote discriminative learning of compact target feature representation at the batch level. Extensive experiments on three datasets, including DomainNet, VisDA2017, and Office-Home, demonstrate that our proposed method achieves state-of-the-art performance in SSDA. Our code is available at https://github.com/bupt-ai-cz/ProML.
Liquid Oxygen/Gaseous Methane Single-Element Shear Coaxial Flame Dynamics
Authors: Roa Mario , Talley Douglas G. , Munipalli Ramakanth
Published: August 2023

An experimental study was conducted to visualize the dynamics and spectra of single-element shear coaxial liquid oxygen (LOX)/gaseous methane flames at high but still subcritical pressure for the LOX, and at supercritical temperature for the methane. High-speed shadowgraphs were employed to image and track the flame and jet dynamics close to the liquid oxygen’s post region. High-speed [Formula: see text] and [Formula: see text] chemiluminescence were also simultaneously collected along the same line of sight as the shadowgraphs. The results were compared to previous LOX/hydrogen results using the same injector at the same momentum flux ratios. The initial temperature of the methane was varied between 200 and 300 K. The LOX/methane flames were found to share many features in common with the LOX/hydrogen flames previously studied, but there were notable differences. The spreading rate of the LOX/hydrogen flames was larger than that of LOX/methane flames. The amplitude of the spreading rate fluctuations for hydrogen was also larger, although the relative spreading rate fluctuations normalized by the average spreading rates were about the same. Despite the differences, a wave amplification mechanism found previously to be active for shear coaxial LOX/hydrogen flames was found to also be active for LOX/methane flames. Overall, both the LOX/methane flames and the LOX/hydrogen were both found to be spectrally fairly quiet.
Papa Francisco
Authors: Susin Luiz Carlos
Published: August 2023

O presente artigo trata do conjunto do pontificado do Papa Francisco sob a ótica da sua condição latino-americana, especialmente a opção preferencial pelos pobres da tradição eclesial latino-americana, examinando o quanto desta origem está em seu governo de toda a Igreja. Examina seu programa, a prioridade da evangelização e a resistência do clericalismo ad intra. Aprofunda a compreensão do exercício do poder que está na base do clericalismo e dos diversos abusos que vieram afetando a Igreja, e reflete sobre o que pode ser o ministério e exercício do poder em uma Igreja sinodal. Do ponto de vista da relação com o mundo contemporâneo, ad extra, o artigo enfatiza sua colaboração a um mundo de paz e fraternidade, e pontua de modo especial a superação da religião em direção a uma fraternidade de todos, e alguma superação do antropocentrismo por uma visão positiva de todas as criaturas e pelo cuidado ecológico. Finalmente, examina algumas tensões e situações ainda abertas. O método será analítico, com algumas reflexões de caráter fenomenológico.
Perfil clínico e epidemiológico da sífilis gestacional e congênita na região oeste do estado da Bahia
Authors: Batista Magnun Luan , Arsego Beatriz Araújo , Lima Igor de Souza , Barreto Adriana Edwirgens de Albuquerque , Silveira Jéssica Barreto da , Honorato Luana Ramalho , Baptista Maria Clara Maia Dourado , Franca Vinicius Huan Leite Lins , Karsten Juliane , Dobrachinski Leandro
Published: August 2023

Objetivo: Determinar o perfil clínico e epidemiológico da sífilis gestacional e congênita na região oeste do estado da Bahia. Métodos: Estudo transversal retrospectivo, utilizando a análise de prontuários de gestantes e recém-nascidos entre os anos de 2018 e 2022. Os prontuários foram disponibilizados pelo Hospital da Mulher, dedicado ao atendimento especializado e humanizado dos agravos que acometem o público feminino. As variáveis do estudo foram descritas utilizando frequências absolutas e relativas. A associação entre variáveis categóricas foi analisada utilizando o teste Exato de Fisher. Resultados: Com base na análise de 5.349 prontuários disponibilizados pelo Serviço de Controle de Infecção Hospitalar e Núcleo de Epidemiologia do Hospital da Mulher, no período de 2018 a 2022, foram confirmados 60 casos de sífilis gestacional e 42 casos de sífilis congênita. Isso representa uma taxa de detecção de sífilis na gestação de 11,6 casos por 1.000 nascidos vivos e uma incidência de 8,1 casos de sífilis congênita por 1.000 nascidos vivos. Conclusão: Esses dados ressaltam a importância de medidas efetivas de prevenção, diagnóstico e tratamento da sífilis. É fundamental continuar monitorando e analisando os dados para compreender melhor as tendências e adotar estratégias adequadas para o controle da sífilis gestacional e congênita na região.
An often-forgotten cause of perihepatic abscess following cholecystectomy
Authors: Kondoor Vishaal , Le Duc , Kuo Anderson
Published: August 2023

Gallbladder perforation during laparoscopic cholecystectomy with gallstone spillage is common and typically without serious sequela. Rarely, retained gallstones may cause important complications such as focal infection, fistula formation, and adhesions. Here, a case of dropped gallstone related perihepatic abscess and sepsis is presented. Because the retained stone location and timeline of presentation are both highly variable, accurate diagnosis can be difficult. Knowledge of this condition is thus paramount to prompt consideration of this possibility.
Delayed blink R1 latency in a patient with trigeminal neuralgia due to a contralateral vestibular schwannoma: An illustrative case
Authors: Oshida Sotaro , Saura Hiroaki , Akamatsu Yosuke , Yanagihara Wataru , Fujimoto Kentaro , Nagasawa Kazuki , Takahashi Kodai , Ogasawara Kuniaki
Published: August 2023

Background:

Although the blink reflex (BR) is effective in objectively evaluating trigeminal neuropathy, few studies have demonstrated its effect on trigeminal neuralgia (TN). The authors report a patient with TN due to contralateral vestibular schwannoma (VS) functionally diagnosed by delayed R1 latency of the BR.

Case Description:

A 36-year-old man presented with left-sided deafness and paroxysmal facial pain in the right V1-3 area. Magnetic resonance imaging (MRI) showed a solid cystic mass compressing the right pons and left brainstem at the left cerebellopontine angle. Although preoperative BR evoked by right supraorbital nerve stimulation-induced delayed ipsilateral R1 latency and normal ipsilateral and contralateral R2 responses, the BR latency evoked by left supraorbital nerve stimulation was normal, indicating deficits in the principal nucleus of the trigeminal nerve in the right pons. The symptoms of TN disappeared after the removal of the VS. Postoperative MRI showed subtotal removal of the tumor and sufficient decompression of the pons and cerebellopontine cistern. The R1 latency returned to normal 50 days after surgery.

Conclusion:

The perioperative BR test was not only useful for objective evaluation of the localization of trigeminal neuropathy but also correlated with the symptoms of TN.
Calvarial hyperpigmentation
Authors: van Eck Devon , Ndlovu Blessing , Sattar Mohammed Ouwais Abdul , Naicker Denver , Mpanza Morena Nthuse , Ouma John Richard Bwire , Profyris Christos
Published: August 2023

Background:

Osseous hyperpigmentation of the calvarium is an extremely rare finding with only few reported cases in literature.

Case Description:

The case is of a 59-year-old Caucasian male who presented with an acute history of generalized tonic clonic seizures and progressive weakness of the right upper limb. He had a background history of a malignant melanoma which had been resected from his left external acoustic meatus 4 weeks prior. Neuroimaging of the brain showed an intra-axial space-occupying lesion in his left parietal lobe with no associated osseous changes. A left mini parietal craniotomy was performed which revealed black discoloration of the parietal bone. The lesion was successfully resected and the bone flap was secured back in place. The patient was discharged on the 4th day postoperatively with no complications. The unusual finding of black discoloration of the calvarium was found to be secondary to adolescent tetracycline use.

Conclusion:

Calvarial hyperpigmentation is a phenomenon encountered incidentally and will often come as surprise for surgeons. Once encountered, thorough history taking and examination should be done to investigate the cause.
“Hot nose” sign in brain death
Authors: Zubair Adeel Shakil
Published: August 2023

Background:

Brain death testing is a rigorous process in which meticulous examination is crucial. In certain cases, ancillary testing is required.

Case Description:

A 30-year-old male presented to the emergency room after a motor vehicle accident and was found to have subarachnoid hemorrhage and subdural hematoma. The examination was notable for the absence of brainstem responses. A nuclear medicine brain scan was completed which showed carotid arterial activity up to the level of the skull base with no intracranial arterial activity above with a “hot nose” sign consistent with brain death.

Conclusion:

The “hot nose” sign has been described in brain-dead patients and is postulated to occur due to increased flow to the nose through the external carotid artery.
Development of numerical algorithms for solving the direct problem of propagation of ultrasonic waves in thin plates
Authors: Беляев А.С. , Гончарский А.В. , Романов С.Ю.
Published: August 2023

Статья посвящена разработке эффективных численных методов решения прямых задач распространения волн в твердых телах в векторных математических моделях. Итераци онные методы решения обратных задач волновой томографии используют на каждой итерации решение прямой задачи распространения волн как в прямом, так и в обратном времени для вычисления градиента функционала невязки. Поэтому решение прямой задачи распростране ния волн в упругих средах является неотъемлемой частью решения обратных задач волновой томографии. Целью статьи также является определение с помощью методов математического моделирования характеристик волн Лэмба для ультразвуковой диагностики дефектов в тонких пластинах, определение диапазонов значений характерных параметров эксперимента по томо графической диагностике в тонких пластинах на волнах Лэмба. Инструментом для проведения математического моделирования являются разрабатываемые численные методы и программы решения прямых задач. Конечной целью исследований является разработка методов решения обратных задач томографического неразрушающего ультразвукового контроля как на волнах Лэмба, так и на объемных волнах.

This article is devoted to the development of efficient numerical methods for solving direct problems of wave propagation in solids in vector mathematical models. Iterative methods for solving inverse problems of wave tomography use, at each iteration, the solution of the direct problem of wave propagation both in forward and backward time to calculate the gradient of the residual functional. Therefore, the solution of the direct problem of wave propagation in elastic media is an integral part of the solution of inverse problems of wave tomography. The purpose of the article is also to determine, using the methods of mathematical modeling characteristics of Lamb waves for ultrasonic diagnostics of defects in thin plates, determination of the ranges of values of the characteristic parameters of the experiment on tomographic diagnostics in thin plates on Lamb waves. The tools for mathematical modeling are the developed numerical methods and programs for solving direct problems. The ultimate goal of the research is to develop methods for solving inverse problems of tomographic non-destructive ultrasonic testing both on Lamb waves and on bulk waves.
Productivity and quality of different tomato cultivars under intercropping system with maize and dry farming conditions in Southern Italy
Authors: Castronuovo Donato , Cardone Loriana , Cruoglio Mariagrazia , Lela Ludovica , Benedetto Nadia , Carlucci Vittorio , Candido Vincenzo
Published: August 2023

This research, carried out in a typical Mediterranean environment of Southern Italy, reports some quantitative and qualitative traits of three local tomato cultivars (‘Arsicolo’, ‘Crovarese’, and ‘San Marzano’) plus a commercial one (‘Datterino’) cultivated without irrigation and intercropped with maize. For each cultivar, in addition to the determination of fruit production, tomato paste and “conserva” paste were prepared using a traditional technique of the Tanagro Valley (province of Salerno, Southern Italy). Results highlighted that ‘Arsicolo’ showed the best fruit yield (32.2 metric tons ha-1) and tomato paste production (92.9% paste return by tomato fruits weight) due to its ancient peculiar adaptability to cultivation in dry conditions, while ‘Datterino’, ‘Crovarese’, and ‘San Marzano’ had 19, 16.7, 10.5 tons ha-1 of fruit yield and 85.7, 83.9, 76.2% of tomato paste return, respectively. Conversely, for the qualitative traits, such as total solids, soluble solids, titratable acidity, and ascorbic acid, ‘Crovarese’ showed the best results (9.1%, 6.6 °Brix, 0.93%, and 39.7 mg per 100 g of fresh weight, respectively). The two components extracted by the principal components analysis (PCA) explained 84.9% of the total variance in the morphological, quantitative and qualitative traits and the dendrogram obtained by hierarchical cluster analysis allowed to divide the cultivars into three groups. Our findings highlighted that local tomato cultivars, intercropped with maize, can be cultivated adopting only few sustainable field operations and with no irrigation.
Enantioselective Synthesis of C-O Axially Chiral Diaryl Ethers via NHC-Catalyzed Atroposelective Desymmetrization
Authors: Biju A T , Shee Sayan , Ranganathappa Sowmya , Gadhave Mahesh , Gogoi Romin
Published: August 2023

Axially chiral diaryl ethers, a distinguished class of atropisomers possessing unique dual C-O axis, hold immense potential for diverse research domains. In contrast to the catalytic enantioselective synthesis of conventional single axis bearing atropisomers, the atroposelective synthesis of axially chiral ethers containing flexible C-O axis remains a significant challenge. Herein, we demonstrate the first N-heterocyclic carbene (NHC)-catalyzed synthesis of axially chiral diaryl ethers via atroposelective esterification of dialdehyde-containing diaryl ethers. Mechanistically, the reaction proceeds via NHC-catalyzed desymmetrization strategy to afford the corresponding axially chiral diaryl ether atropisomers in good yields and high enantioselectivities under mild conditions. The derivatization of the synthesized product expands the utility of present strategy via access to a library of C-O axially chiral compounds. The temperature dependency and preliminary investigations on the racemization barrier of C-O bonds are also presented.
Factores asociados a la adicción a redes sociales en universitarios: una revisión sistemática y metaanálisis
Authors: Basauri Delgado Miguel Ángel
Published: August 2023

La adicción a las redes sociales se ha incrementado a lo largo del brote del COVID-19, en especial dentro del contexto universitario, debido al uso continuo de estas como medio de socialización, entretenimiento, educación y trabajo. El estudio tuvo como objetivo realizar una revisión sistemática y metaanálisis de los factores asociados a la adicción a redes sociales en universitarios. Se revisaron las bases de datos de Scielo, Eric, PubMed y ScienceDirect, donde se encontraron 31 artículos que cumplieron con los criterios de inclusión y exclusión en una muestra total de 13 028 universitarios. Existieron 91 variables asociadas a la adicción a las redes sociales, las cuales fueron agrupadas dentro de nueve categorías para realizar el metaanálisis. Existieron seis categorías con relación directa (factores en línea = .41, factores cognitivos = .41, sintomatología negativa = .30, adicción y uso de celular = .29, uso de redes = .21, factores sociales = .29), una con relación inversa (emociones positivas = -.14) y dos que no cuentan con valores significativos (factores académicos = .01 y competencias personales = .06). Se concluye que existe mayor fuerza de relación entre los factores en línea y los factores cognitivos con la adicción a las redes sociales.
El contacto con la naturaleza y su relación con la vitalidad y el afecto negativo en deportistas de surf: el rol mediador de las necesidades psicológicas básicas
Authors: Martínez Bruno , Gargurevich Rafael , Matos Lennia
Published: August 2023

La literatura científica muestra consistentemente los beneficios para la salud física y mental que se obtienen de las actividades en entornos naturales, a diferencia de entornos urbanos. Tal es el caso del surf, un deporte que se realiza en contacto con la naturaleza. Para explicar por qué resulta beneficioso practicar surf, se utilizó la teoría de necesidades psicológicas planteada por la teoría de la autodeterminación (Ryan & Deci, 2017). Así se estudió la relación entre el contacto con la naturaleza, las necesidades psicológicas básicas (satisfacción y frustración), la vitalidad subjetiva y el afecto negativo en una muestra que estuvo conformada por 236 deportistas aficionados a diversas modalidades de surf (bodyboard, longboard, surfing, paddleboard, bodysurf). Para ello, primero se examinaron las propiedades psicométricas de los instrumentos aplicados y se encontraron adecuadas evidencias de validez y confiabilidad. Los resultados del análisis de senderos mostraron que el aumento en el contacto con la naturaleza por parte de los deportistas de surf se relacionaba positivamente con un aumento en la vitalidad y con la disminución del afecto negativo, y que estas relaciones estaban mediadas por la satisfacción de las necesidades psicológicas básicas. Los resultados se discuten en términos de la teoría de la autodeterminación y su relación con el bienestar y la vitalidad.
The spatial logic of the conflict of convex sets
Authors: Поморцев Леонид Александрович
Published: August 2023

В статье апробируются методы логического ветвления высказываний теории выпуклых множеств. Представленная работа опирается на концепцию Аристотелевского Категорического Силлогизма (АКС), выполняющего многократный вход в результат логического вывода с помощью конъюнкции -И- . Формальное инвертирование импликаций, составляющих АКС, позволяет интерпретировать его для выполнения дизъюнктивного ветвления предиката на выходе из него. В результате имеем логическое разложение предиката на следующие из него утверждения, группирующиеся вокруг союза -ИЛИ- . Для полноты картины добавим ещё два вида ветвления: дизъюнктивный вход и конъюнктивный выход, которые трудностей в понимании и применении не представляют. В математике проблемы концентрируются вокруг АКС-ветвлений. Четыре вида логического ветвления в сочетании со связями Передачи Управления (ПеУ – то же, что GoTo) демонстрируются решением задачи разбиения векторного пространства на два непересекающихся выпуклых множества. Показано, что пересечение их замыканий, далее называемого трещиной векторного пространства, представляет собой гиперплоскость. ВМ не обязано быть замкнутым или открытым.

The article tests methods of logical branching of propositions of the theory of convex sets. The presented work is based on the concept of the Aristotelian Categorical Syllogism (ACS), which performs multiple entry into the result of logical inference using the conjunction -AND- . Formal inverting of the implications that make up ACS allows interpreting it to perform disjunctive branching of the predicate at the exit from it. As a result, we have a logical decomposition of the predicate into the following statements from it, grouped around the union -OR- . To complete the picture, we add two more types of branching: disjunctive entry and conjunctive exit, which are not difficult for understanding and applying. In mathematics, problems concentrate around ACS branches. Four types of logical branching in combination with Transfer of Control (ToC – same as GoTo) are demonstrated by solving the problem of splitting a vector space into two disjoint Convex Sets. It is shown that the intersection of their closures, hereinafter referred to as the «crack» of the vector space, is a hyperplane.