bluuebunny/crossref_metadata_2025 huggingface dataset:
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
Authors: Wang Mingyang , Bing Zhenshan , Yao Xiangtong , Wang Shuai , Kai Huang , Su Hang , Yang Chenguang , Knoll Alois
Published: June 2023

Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. We extend meta-RL to broad non-parametric task distributions which have never been explored before, and also achieve state-of-the-art results in non-stationary and out-of-distribution tasks. Specifically, MoSS consists of a task inference module and a policy module. We utilize the Gaussian mixture model for task representation to imitate the parametric and non-parametric task variations. Additionally, our online adaptation strategy enables the agent to react at the first sight of a task change, thus being applicable in non-stationary tasks. MoSS also exhibits strong generalization robustness in out-of-distributions tasks which benefits from the reliable and robust task representation. The policy is built on top of an off-policy RL algorithm and the entire network is trained completely off-policy to ensure high sample efficiency. On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.
USER: Unsupervised Structural Entropy-Based Robust Graph Neural Network
Authors: Wang Yifei , Wang Yupan , Zhang Zeyu , Yang Song , Zhao Kaiqi , Liu Jiamou
Published: June 2023

Unsupervised/self-supervised graph neural networks (GNN) are susceptible to the inherent randomness in the input graph data, which adversely affects the model's performance in downstream tasks. In this paper, we propose USER, an unsupervised and robust version of GNN based on structural entropy, to alleviate the interference of graph perturbations and learn appropriate representations of nodes without label information. To mitigate the effects of undesirable perturbations, we analyze the property of intrinsic connectivity and define the intrinsic connectivity graph. We also identify the rank of the adjacency matrix as a crucial factor in revealing a graph that provides the same embeddings as the intrinsic connectivity graph. To capture such a graph, we introduce structural entropy in the objective function. Extensive experiments conducted on clustering and link prediction tasks under random-perturbation and meta-attack over three datasets show that USER outperforms benchmarks and is robust to heavier perturbations.
Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning
Authors: Wu Zifan , Yu Chao , Chen Chen , Hao Jianye , Zhuo Hankz Hankui
Published: June 2023

Research in model-based reinforcement learning has made significant progress in recent years. Compared to single-agent settings, the exponential dimension growth of the joint state-action space in multi-agent systems dramatically increases the complexity of the environment dynamics, which makes it infeasible to learn an accurate global model and thus necessitates the use of agent-wise local models. However, during multi-step model rollouts, the prediction of one local model can affect the predictions of other local models in the next step. As a result, local prediction errors can be propagated to other localities and eventually give rise to considerably large global errors. Furthermore, since the models are generally used to predict for multiple steps, simply minimizing one-step prediction errors regardless of their long-term effect on other models may further aggravate the propagation of local errors. To this end, we propose Models as AGents (MAG), a multi-agent model optimization framework that reversely treats the local models as multi-step decision making agents and the current policies as the dynamics during the model rollout process. In this way, the local models are able to consider the multi-step mutual affect between each other before making predictions. Theoretically, we show that the objective of MAG is approximately equivalent to maximizing a lower bound of the true environment return. Experiments on the challenging StarCraft II benchmark demonstrate the effectiveness of MAG.
A Survey on Model Compression and Acceleration for Pretrained Language Models
Authors: Xu Canwen , McAuley Julian
Published: June 2023

Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long inference delay prevent Transformer-based pretrained language models (PLMs) from seeing broader adoption including for edge and mobile computing. Efficient NLP research aims to comprehensively consider computation, time and carbon emission for the entire life-cycle of NLP, including data preparation, model training and inference. In this survey, we focus on the inference stage and review the current state of model compression and acceleration for pretrained language models, including benchmarks, metrics and methodology.
Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation
Authors: Xu Kang , Ma Yan , Wei Bingsheng , Li Wei
Published: June 2023

While Reinforcement Learning can achieve impressive results for complex tasks, the learned policies are generally prone to fail in downstream tasks with even minor model mismatch or unexpected perturbations. Recent works have demonstrated that a policy population with diverse behavior characteristics can generalize to downstream environments with various discrepancies. However, such policies might result in catastrophic damage during the deployment in practical scenarios like real-world systems due to the unrestricted behaviors of trained policies. Furthermore, training diverse policies without regulation of the behavior can result in inadequate feasible policies for extrapolating to a wide range of test conditions with dynamics shifts. In this work, we aim to train diverse policies under the regularization of the behavior patterns. We motivate our paradigm by observing the inverse dynamics in the environment with partial state information and propose Diversity in Regulation (DiR) training diverse policies with regulated behaviors to discover desired patterns that benefit the generalization. Considerable empirical results on various variations of different environments indicate that our method attains improvements over other diversity-driven counterparts.
Fast and Accurate Binary Neural Networks Based on Depth-Width Reshaping
Authors: Xue Ping , Lu Yang , Chang Jingfei , Wei Xing , Wei Zhen
Published: June 2023

Network binarization (i.e., binary neural networks, BNNs) can efficiently compress deep neural networks and accelerate model inference but cause severe accuracy degradation. Existing BNNs are mainly implemented based on the commonly used full-precision network backbones, and then the accuracy is improved with various techniques. However, there is a question of whether the full-precision network backbone is well adapted to BNNs. We start from the factors of the performance degradation of BNNs and analyze the problems of directly using full-precision network backbones for BNNs: for a given computational budget, the backbone of a BNN may need to be shallower and wider compared to the backbone of a full-precision network. With this in mind, Depth-Width Reshaping (DWR) is proposed to reshape the depth and width of existing full-precision network backbones and further optimize them by incorporating pruning techniques to better fit the BNNs. Extensive experiments demonstrate the analytical result and the effectiveness of the proposed method. Compared with the original backbones, the DWR backbones constructed by the proposed method result in close to O(√s) decrease in activations, while achieving an absolute accuracy increase by up to 1.7% with comparable computational cost. Besides, by using the DWR backbones, existing methods can achieve new state-of-the-art (SOTA) accuracy (e.g., 67.2% on ImageNet with ResNet-18 as the original backbone). We hope this work provides a novel insight into the backbone design of BNNs. The code is available at https://github.com/pingxue-hfut/DWR.
DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness
Authors: Yan Gang , Wang Hao , Yuan Xu , Li Jian
Published: June 2023

Federated learning (FL) is known to be susceptible to model poisoning attacks in which malicious clients hamper the accuracy of the global model by sending manipulated model updates to the central server during the FL training process. Existing defenses mainly focus on Byzantine-robust FL aggregations, and largely ignore the impact of the underlying deep neural network (DNN) that is used to FL training. Inspired by recent findings on critical learning periods (CLP) in DNNs, where small gradient errors have irrecoverable impact on the final model accuracy, we propose a new defense, called a CLP-aware defense against poisoning of FL (DeFL). The key idea of DeFL is to measure fine-grained differences between DNN model updates via an easy-to-compute federated gradient norm vector (FGNV) metric. Using FGNV, DeFL simultaneously detects malicious clients and identifies CLP, which in turn is leveraged to guide the adaptive removal of detected malicious clients from aggregation. As a result, DeFL not only mitigates model poisoning attacks on the global model but also is robust to detection errors. Our extensive experiments on three benchmark datasets demonstrate that DeFL produces significant performance gain over conventional defenses against state-of-the-art model poisoning attacks.
Understanding Representation Learnability of Nonlinear Self-Supervised Learning
Authors: Yang Ruofeng , Li Xiangyuan , Jiang Bo , Li Shuai
Published: June 2023

Self-supervised learning (SSL) has empirically shown its data representation learnability in many downstream tasks. There are only a few theoretical works on data representation learnability, and many of those focus on final data representation, treating the nonlinear neural network as a ``black box". However, the accurate learning results of neural networks are crucial for describing the data distribution features learned by SSL models. Our paper is the first to analyze the learning results of the nonlinear SSL model accurately. We consider a toy data distribution that contains two features: the label-related feature and the hidden feature. Unlike previous linear setting work that depends on closed-form solutions, we use the gradient descent algorithm to train a 1-layer nonlinear SSL model with a certain initialization region and prove that the model converges to a local minimum. Furthermore, different from the complex iterative analysis, we propose a new analysis process which uses the exact version of Inverse Function Theorem to accurately describe the features learned by the local minimum. With this local minimum, we prove that the nonlinear SSL model can capture the label-related feature and hidden feature at the same time. In contrast, the nonlinear supervised learning (SL) model can only learn the label-related feature. We also present the learning processes and results of the nonlinear SSL and SL model via simulation experiments.
Prototypical Partial Optimal Transport for Universal Domain Adaptation
Authors: Yang Yucheng , Gu Xiang , Sun Jian
Published: June 2023

Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain without requiring the same label sets of both domains. The existence of domain and category shift makes the task challenging and requires us to distinguish “known” samples (i.e., samples whose labels exist in both domains) and “unknown” samples (i.e., samples whose labels exist in only one domain) in both domains before reducing the domain gap. In this paper, we consider the problem from the point of view of distribution matching which we only need to align two distributions partially. A novel approach, dubbed mini-batch Prototypical Partial Optimal Transport (m-PPOT), is proposed to conduct partial distribution alignment for UniDA. In training phase, besides minimizing m-PPOT, we also leverage the transport plan of m-PPOT to reweight source prototypes and target samples, and design reweighted entropy loss and reweighted cross-entropy loss to distinguish “known” and “unknown” samples. Experiments on four benchmarks show that our method outperforms the previous state-of-the-art UniDA methods.
Purifier: Defending Data Inference Attacks via Transforming Confidence Scores
Authors: Yang Ziqi , Wang Lijin , Yang Da , Wan Jie , Zhao Ziming , Chang Ee-Chien , Zhang Fan , Ren Kui
Published: June 2023

Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a method, namely PURIFIER, to defend against membership inference attacks. It transforms the confidence score vectors predicted by the target classifier and makes purified confidence scores indistinguishable in individual shape, statistical distribution and prediction label between members and non-members. The experimental results show that PURIFIER helps defend membership inference attacks with high effectiveness and efficiency, outperforming previous defense methods, and also incurs negligible utility loss. Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks. For example, the inversion error is raised about 4+ times on the Facescrub530 classifier, and the attribute inference accuracy drops significantly when PURIFIER is deployed in our experiment.
i-Code: An Integrative and Composable Multimodal Learning Framework
Authors: Yang Ziyi , Fang Yuwei , Zhu Chenguang , Pryzant Reid , Chen DongDong , Shi Yu , Xu Yichong , Qian Yao , Gao Mei , Chen Yi-Ling , Lu Liyang , Xie Yujia , Gmyr Robert , Codella Noel , Kanda Naoyuki , Xiao Bin , Yuan Lu , Yoshioka Takuya , Zeng Michael , Huang Xuedong
Published: June 2023

Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to one or two modalities. We present i-Code, a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and general-purpose vector representations. In this framework, data from each modality are first given to pretrained single-modality encoders. The encoder outputs are then integrated with a multimodal fusion network, which uses novel merge- and co-attention mechanisms to effectively combine information from the different modalities. The entire system is pretrained end-to-end with new objectives including masked modality unit modeling and cross-modality contrastive learning. Unlike previous research using only video for pretraining, the i-Code framework can dynamically process single, dual, and triple-modality data during training and inference, flexibly projecting different combinations of modalities into a single representation space. Experimental results demonstrate how i-Code can outperform state-of-the-art techniques on five multimodal understanding tasks and single-modality benchmarks, improving by as much as 11% and demonstrating the power of integrative multimodal pretraining.
Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch Representation
Authors: Zang Sicong , Tu Shikui , Xu Lei
Published: June 2023

Graphic sketch representations are effective for representing sketches. Existing methods take the patches cropped from sketches as the graph nodes, and construct the edges based on sketch's drawing order or Euclidean distances on the canvas. However, the drawing order of a sketch may not be unique, while the patches from semantically related parts of a sketch may be far away from each other on the canvas. In this paper, we propose an order-invariant, semantics-aware method for graphic sketch representations. The cropped sketch patches are linked according to their global semantics or local geometric shapes, namely the synonymous proximity, by computing the cosine similarity between the captured patch embeddings. Such constructed edges are learnable to adapt to the variation of sketch drawings, which enable the message passing among synonymous patches. Aggregating the messages from synonymous patches by graph convolutional networks plays a role of denoising, which is beneficial to produce robust patch embeddings and accurate sketch representations. Furthermore, we enforce a clustering constraint over the embeddings jointly with the network learning. The synonymous patches are self-organized as compact clusters, and their embeddings are guided to move towards their assigned cluster centroids. It raises the accuracy of the computed synonymous proximity. Experimental results show that our method significantly improves the performance on both controllable sketch synthesis and sketch healing.
Neural Integro-Differential Equations
Authors: Zappala Emanuele , O. Fonseca Antonio H. de , Moberly Andrew H. , Higley Michael J. , Abdallah Chadi , Cardin Jessica A. , Van Dijk David
Published: June 2023

Modeling continuous dynamical systems from discretely sampled observations is a fundamental problem in data science. Often, such dynamics are the result of non-local processes that present an integral over time. As such, these systems are modeled with Integro-Differential Equations (IDEs); generalizations of differential equations that comprise both an integral and a differential component. For example, brain dynamics are not accurately modeled by differential equations since their behavior is non-Markovian, i.e. dynamics are in part dictated by history. Here, we introduce the Neural IDE (NIDE), a novel deep learning framework based on the theory of IDEs where integral operators are learned using neural networks.
We test NIDE on several toy and brain activity datasets and demonstrate that NIDE outperforms other models. These tasks include time extrapolation as well as predicting dynamics from unseen initial conditions, which we test on whole-cortex activity recordings in freely behaving mice. Further, we show that NIDE can decompose dynamics into their Markovian and non-Markovian constituents, via the learned integral operator, which we test on fMRI brain activity recordings of people on ketamine. Finally, the integrand of the integral operator provides a latent space that gives insight into the underlying dynamics, which we demonstrate on wide-field brain imaging recordings. Altogether, NIDE is a novel approach that enables modeling of complex non-local dynamics with neural networks.
Acceleration of Large Transformer Model Training by Sensitivity-Based Layer Dropping
Authors: Zeng Yujie , He Wenlong , Vasyltsov Ihor , Pang Jiali , Chen Lin
Published: June 2023

Transformer models are widely used in AI applications such as Natural Language Processing (NLP), Computer Vision (CV), etc. However, enormous computation workload be-comes an obstacle to train large transformer models efficiently. Recently, some methods focus on reducing the computation workload during the training by skipping some layers. How-ever, these methods use simple probability distribution and coarse-grained probability calculation, which significantly affect the model accuracy. To address the issue, in this paper we propose a novel method to accelerate training—Sensitivity-Based Layer Dropping (SBLD). SBLD uses lay-er-wise sensitivity data to switch on/off transformer layers in proper order to keep high accuracy. Besides, we adjust the probability of skipping transformer layers with a scheduler to accelerate training speed and get faster convergence. Our results show that SBLD solves the accuracy drop issue com-pared with prior layer dropping methods. Our SBLD method can decrease end-to-end training time by 19.67% during training of GPT-3 Medium model, the same time increasing the accuracy by 1.65% w.r.t. baseline. Furthermore, for SwinV2-L model the obtained Top-1 and Top-5 accuracies are also higher vs. the baseline. Thus, the proposed method is efficient and practical to improve the large transformer model training.
DARL: Distance-Aware Uncertainty Estimation for Offline Reinforcement Learning
Authors: Zhang Hongchang , Shao Jianzhun , He Shuncheng , Jiang Yuhang , Ji Xiangyang
Published: June 2023

To facilitate offline reinforcement learning, uncertainty estimation is commonly used to detect out-of-distribution data. By inspecting, we show that current explicit uncertainty estimators such as Monte Carlo Dropout and model ensemble are not competent to provide trustworthy uncertainty estimation in offline reinforcement learning. Accordingly, we propose a non-parametric distance-aware uncertainty estimator which is sensitive to the change in the input space for offline reinforcement learning. Based on our new estimator, adaptive truncated quantile critics are proposed to underestimate the out-of-distribution samples. We show that the proposed distance-aware uncertainty estimator is able to offer better uncertainty estimation compared to previous methods. Experimental results demonstrate that our proposed DARL method is competitive to the state-of-the-art methods in offline evaluation tasks.
DRGCN: Dynamic Evolving Initial Residual for Deep Graph Convolutional Networks
Authors: Zhang Lei , Yan Xiaodong , He Jianshan , Li Ruopeng , Chu Wei
Published: June 2023

Graph convolutional networks (GCNs) have been proved to be very practical to handle various graph-related tasks. It has attracted considerable research interest to study deep GCNs, due to their potential superior performance compared with shallow ones. However, simply increasing network depth will, on the contrary, hurt the performance due to the over-smoothing problem. Adding residual connection is proved to be effective for learning deep convolutional neural networks (deep CNNs), it is not trivial when applied to deep GCNs. Recent works proposed an initial residual mechanism that did alleviate the over-smoothing problem in deep GCNs. However, according to our study, their algorithms are quite sensitive to different datasets. In their setting, the personalization (dynamic) and correlation (evolving) of how residual applies are ignored. To this end, we propose a novel model called Dynamic evolving initial Residual Graph Convolutional Network (DRGCN). Firstly, we use a dynamic block for each node to adaptively fetch information from the initial representation. Secondly, we use an evolving block to model the residual evolving pattern between layers. Our experimental results show that our model effectively relieves the problem of over-smoothing in deep GCNs and outperforms the state-of-the-art (SOTA) methods on various benchmark datasets. Moreover, we develop a mini-batch version of DRGCN which can be applied to large-scale data. Coupling with several fair training techniques, our model reaches new SOTA results on the large-scale ogbn-arxiv dataset of Open Graph Benchmark (OGB). Our reproducible code is available on GitHub.
High-Dimensional Dueling Optimization with Preference Embedding
Authors: Zhang Yangwenhui , Qian Hong , Shu Xiang , Zhou Aimin
Published: June 2023

In many scenarios of black-box optimization, evaluating the objective function values of solutions is expensive, while comparing a pair of solutions is relatively cheap, which yields the dueling black-box optimization. The side effect of dueling optimization is that it doubles the dimension of solution space and exacerbates the dimensionality scalability issue of black-box optimization, e.g., Bayesian optimization. To address this issue, the existing dueling optimization methods fix one solution when dueling throughout the optimization process, but it may reduce their efficacy. Fortunately, it has been observed that, in recommendation systems, the dueling results are mainly determined by the latent human preferences. In this paper, we abstract this phenomenon as the preferential intrinsic dimension and inject it into the dueling Bayesian optimization, resulting in the preferential embedding dueling Bayesian optimization (PE-DBO). PE-DBO decouples optimization and pairwise comparison via the preferential embedding matrix. Optimization is performed in the preferential intrinsic subspace with much lower dimensionality, while pairwise comparison is completed in the original dueling solution space. Theoretically, we disclose that the preference function can be approximately preserved in the lower-dimensional preferential intrinsic subspace. Experiment results verify that, on molecule discovery and web page recommendation dueling optimization tasks, the preferential intrinsic dimension exists and PE-DBO is superior in scalability compared with that of the state-of-the-art (SOTA) methods.
Tensorized Incomplete Multi-View Clustering with Intrinsic Graph Completion
Authors: Zhao Shuping , Wen Jie , Fei Lunke , Zhang Bob
Published: June 2023

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete multi-view clustering with intrinsic graph completion (TIMVC_IGC) is proposed. Firstly, owing to the effectiveness of the low-rank representation in revealing the inherent structure of the data, we exploit it to infer the missing instances and construct the complete graph for each view. Afterwards, inspired by the structural consistency, a between-view consistency constraint is imposed to guarantee the similarity of the graphs from different views. More importantly, the TIMVC_IGC simultaneously learns the low-rank structures of the different views and explores the correlations of the different graphs in a latent manifold sub-space using a low-rank tensor constraint, such that the intrinsic graphs of the different views can be obtained. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. Experimental results on several real-world databases illustrates that the proposed method can outperform the other state-of-the-art related methods for incomplete multi-view clustering.
Multi-Level Confidence Learning for Trustworthy Multimodal Classification
Authors: Zheng Xiao , Tang Chang , Wan Zhiguo , Hu Chengyu , Zhang Wei
Published: June 2023

With the rapid development of various data acquisition technologies, more and more multimodal data come into being. It is important to integrate different modalities which are with high-dimensional features for boosting final multimodal data classification task. However, existing multimodal classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence during information fusion. In this paper, we propose a trustworthy multimodal classification network via multi-level confidence learning, referred to as MLCLNet. Considering that a large number of feature dimensions could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. In order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation and generate modal-specific initial classification labels. Since samples from different modalities should share consistent labels, a cross-modal label fusion module is deployed to capture the label correlations of different modalities. In addition, motivated the ideally orthogonality of final fused label matrix, we design a label confidence loss to supervise the network for learning more separable data representations. To the best of our knowledge, MLCLNet is the first work which integrates both feature and label-level confidence learning for multimodal classification. Extensive experiments on four multimodal medical datasets are conducted to validate superior performance of MLCLNet when compared to other state-of-the-art methods.
QUANTUM-CHEMICAL CALCULATION OF SINGLE CHAIN FOLDING OF POLY(p-PHENYLENE-2,6-BENZOBISOXAZOLE) (ZYLON®)
Authors: Nikitin Sergey M. , Karnet Yulia N.
Published: June 2023

Using computer simulation methods, the geometry of the blocks in a poly(p-phenylene-2,6-benzobisoxazole)
Zylon® polymer chain was established. The computational results obtained by the simulations
showed the rotational barrier of the conformational transition of the benzobisoxazole block and that
the polymer was not strictly linear. An assumption was made about the possibility of the transition of
Zylon into various conformational states when creating high-strength fibers.
Antiparasitic Activity of Compounds Isolated from Ganoderma tuberculosum (Agaricomycetes) from Mexico
Authors: Espinosa-García Victoria , Fernandez Jose J. , Nicolás-Hernández Desirée San , Arberas-Jiménez Iñigo , Rodríguez-Expósito Rubén L. , Souto María L. , Piñero José E. , Mendoza Guillermo , Lorenzo-Morales Jacob , Trigos Ángel
Published: June 2023

The genus Ganoderma has a long history of use in traditional Asiatic medicine due to its different nutritional
and medicinal properties. In Mexico, the species G. tuberculosum is used in indigenous communities, for example, the Wixaritari and mestizos of Villa Guerrero Jalisco for the treatment of diseases that may be related to parasitic infections; however, few chemical studies corroborate its traditional medicinal potential. Thereby, the objective of this study was to isolate and identify anti-parasitic activity compounds from a strain of G. tuberculosum native to Mexico. From the fruiting bodies of G. tuberculosum (GVL-21) a hexane extract was obtained which was subjected to guided fractioning to isolate pure compounds. The in vitro anti-parasitic activity of the pure compound (IC50) was assayed against Leishmania amazonensis, Trypanosoma cruzi, Acanthamoeba castellanii Neff, and Naegleria fowleri. Furthermore, the cytotoxicity (CC50) of the isolated compounds was determined against murine macrophages. The guided fractioning produced 5 compounds: ergosterol (1), ergosta-4,6,8(14),22-tetraen-3-one (2), ergosta-7,22-dien-3β-ol (3), 3,5-dihydroxy-ergosta-7,22-dien-6-one (4), and ganoderic acid DM (5). Compounds 2 and 5 showed the best anti-parasitic activity in an IC50 range of 54.34 ± 8.02 to 12.38 ± 2.72 µM against all the parasites assayed and low cytotoxicity against murine macrophages. The present study showed for the first time the in vitro anti-parasitic activity of compounds 1-5 against L. amazonensis, T. cruzi, A. castellanii Neff, and N. fowleri, corroborating the medicinal potential of Ganoderma and its traditional applications.
Entrevista psicodiagnóstica en niños desde las perspectivas dinámica y cognitivo-conductual
Authors:   Universidad Cooperativa de Colombia, Sánchez Ramos Claudia Patricia , Gómez Marín Dayra Dayana
Published: June 2023

El objetivo de la presente guía práctica es orientar el proceso de la entrevista psicodiagnóstica en niños desde las perspectivas dinámica y cognitivo-conductual. La evaluación psicodiagnóstica en niños es un proceso importante para comprender la dinámica, el funcionamiento, el comportamiento y las emociones a partir del motivo de consulta referido por los padres con el fin de plantear los objetivos y la intervención en el proceso psicoterapéutico. En este documento se puntualizan elementos claves desde dos enfoques: cognitivo-conductual y psicoanalítico, y algunas recomendaciones sobre los aspectos que se deben tener en cuenta desde la psicología clínica.
Middle Management’s Resistance to Digital Change
Authors:   National Research University Higher School of Economics, Bagrationi Konstantin , Thurner Thomas ,   National Research University Higher School of Economics
Published: June 2023

This paper investigates middle managers’ resistance to digital transformation initiatives and suggests strategies for overcoming such resistance using the example of a major Russian transportation company. This study employed a mixed-methods approach to assess middle managers’ values and to identify patterns of resistance behavior. The case studies further illustrate the resistance of middle managers and how the company under study responded to these incidents. The findings reveal a significant relationship between employees’ attitudes toward routine and their resistance to digital transformation. Managers with high scores in tradition, conformity, security, and power values, as well as a strong positive attitude toward routine, were more resistant to change. Conversely, those with high scores in universalism, self-direction, and stimulation values were more open to change. By addressing the values and concerns driving middle managers’ attitudes, organizations can better support them in overcoming resistance to digital transformation. The study also offers practical strategies for aligning digital transformation efforts with middle managers’ values, thereby fostering a more positive attitude toward change and facilitating successful implementation.
Katokkon Pepper (Capsicum chinense Jacq.) Ploidy Determination by Morphological Characteristic and Flow Cytometry Analysis
Authors: Sjahril Rinaldi , Riadi Muh. , Saleh Ifayanti Ridwan , Novitasari  , Galla Ernitha A. , Kasmiati  , Trisnawaty A. R. , Panga Nurhaya J.
Published: June 2023

Katokkon pepper plant is originated from Toraja, South Sulawesi, Indonesia. This endemic pepper variety has unique hot and spicy characters with a distinctive bell pepper aroma, and aesthetic shape similar to paprika, but with smaller size. This research was conducted to identify ploidy level and plant morphology of colchicine induced Katokkon pepper at Laboratory of Plant Reproduction Bioscience and Biotechnology, Department of Agronomy, Faculty of Agriculture, Universitas Hasanuddin and experimental field of Agricultural Extension System Vocational High School Santo Paulus Tana Toraja (S 03°04’177” E 119°51’526”). Two weeks old seedlings were immersed for 4.5 hours in colchicine concentration (0.00%, 0.0125%, 0.025%, 0.05% and 0.10%). Flow cytometry analysis was carried out using Partec Cy-Flow SpaceTM. Result showed that colchicine concentration (0.025%, 0.05% and 0.1%) produced mixoploid plants with two set of chromosomes (2n=24, 4n=48). This study also found morphological differences between mixoploids plants induced by 0.025%, 0.05% and 0.1% colchicine and diploid plants (0% and 0.0125% colchicine) during first two juvenile leaves phase. However, this difference did not occur further, and eventually morphology of adult mixoploid plant was not significantly different from control (diploid), which concurred to grading mixoploid grade 2. The mixoploid plants analyzed consist of higher diploids cells than tetraploid.
Amylose Accumulation Under Water Deficit in Glutinous Rice (Oryza sativa L. Var. glutinosa)
Authors: Yarwati Yuli , Barunawati Nunun , Ariffin
Published: June 2023

The research aims to obtain a genotype of local glutinous rice tolerant to drought stress by investigating yield and physiological responses. The experiment is conducted in May-September 2020 in West Lombok, West Nusa Tenggara. The factorial experiment uses a Randomized Block Design, including the first factor: glutinous rice varieties, namely Me'e, Kala, Samada, and Paketih. The second factor is water supply, consisting of 2,250 ml, 3,375 ml, 4,500 ml, and 5,625 ml. The variables are observed stomata density, the content of proline and chlorophyll, panicle length, number and weight of grains, starch, and amylose content. The result shows that the stomata density has about 39 to 54.74 mm2 caused by water application on all glutinous rice strains. The chlorophyll content decreases to 50% following the declining amount of water application, while proline content on all varieties reaches 40% by water supply at 2,250 ml. In addition, the amylose content reaches 40% with 4,500 ml of water and 5,625 ml of water in all varieties. By contrast, increasing the amount in water supply affect several variables observed, an increase in panicle length of around 10% and the number and weight of the grains at 20% and 40%, respectively.
Dispatches From Eeyou Istchee: Cree Networks, Digital, and Social Inclusion
Authors: Toso Tricia , Foward Scott
Published: June 2023

This article offers a fragmentary, partial history of the successes and challenges the Cree of Eeyou Istchee have encountered as they’ve developed the capacity to offer their region and communities a range of traditional, analogue, and digital services through the development and maintenance of different yet interconnected networks. Using social construction of technology (SCOT) and social shaping of technology (SST) theories as a framework, these dispatches offer a glimpse of the complexity and layeredness of two Cree networks as they come into contact and/or overlap with those of extractive colonialism, Canadian settler policies, and traditional Cree law and policy.
UKRAINISM OF JÓZEF ŁOBODOWSKI
Authors:   Taras Shevchenko national University of Kyiv, Ivanova Anna
Published: June 2023

The figure of J. Łobodowski is extremely important for Polish- Ukrainian history, especially in modern times. The whole life and creative path of the writer was connected with Ukraine. This is evidenced by the pages of his biography, as evidenced by journalistic and literary works. Ukraine was spiritually close to him, he intensively worked on many topics related to it. Łobodowski is called a spokesman for the Polish-Ukrainian dialogue, a herald of an idea that still remains relevant given the current problems and threats. The writer’s work is extremely versatile and extraordinary. His poetry collections „Zolota Hramota” and „Song of Ukraine” are dedicated to Ukraine. He was a well-known author of prose works, his trilogy “Reeds” is dedicated to the Ukrainian Cossacks, and the tetralogy “History of Joseph Zakrevsky” tells about Lublin and the land of Lublin, Volyn of the interwar period, as well as Ukraine in the 20-30s of XX century. Historians of Polish and Ukrainian literature count Józef Łobodowski among the successors of the traditions of the “Ukrainian school” or the Polish-Ukrainian border. The public and publicist activity of the artist falls on a difficult historical period, when in the mass consciousness of the majority of representatives of both societies there was mutual hostility and mistrust. In Łobodowski’s poetry, Ukraine acquires the features of a kind of steppe Greece - the dream of Arcadia. The artist made a significant contribution to the formation of the image of Ukraine in Polish literature, and thus in the world. The concept of Scythian Ukraine-Greece as the heiress of the Hellenic world, has been woven organically into the European cultural tradition.
ROMANTIC VISION OF THE UKRAINIAN PAST IN THE WORK OF TOMASH PADURA
Authors:   Borys Grinchenko Kyiv University, Shcherbak Vitalii
Published: June 2023

The article highlights the romantic image of Cossack Ukraine of the XVI—XVIII c. in the creative heritage of the Polish poet and writer Tomash Padura, a native of the family of Bratslav nobleman. It was noted that in his young years the main source for him was oral folk art - storytelling, legends, Ukrainian songs and historical dumas. A few decades after the fall of the Hetm- anate, they reflected the realities of the Cossack era, the character and activities of its leaders. The complex of sources for Padura also included Polish literary and historical narratives with relevant assessments of the Ukrainian past. In Pa- dura’s works, Ukraine appears before the reader as a mysterious and exotic land that has been associated with freedom and freedom-loving people for centuries. Therefore, the main character of his works was the Cossacks, the chivalrous spirit of the Cossacks, victories over external enemies Moscovites, Tatars and Otto- mans were celebrated. At the same time, Padura proclaimed the idea of Polish- Ukrainian unity, bypassing the conflict pages in their past, sought, first of all, to involve the Ukrainian people into the struggle for the Polish cause - liberation from Russian imperial oppression. The leading figure in the poet’s poem is Kosh Ataman Ivan Sirko, who was distinguished for his courage and fostered a sense of independence and freedom in the hearts of his comrades. At the same time, Padura keeps silent about Sirko’s confrontation with Hetman Petro Doroshenko, who continued Bohdan Khmelnytsky’s policy of uniting Ukraine within ethnic boundaries. The figure of Hetman Ivan Mazepa as a fighter against Russian des- potism is also positively assessed. Padura strongly protests against the anath- ema imposed by the Moscovites on the “native hetman” and its proclamation in Ukrainian churches. Padura’s assessment of the Cossack era takes a completely different tone when it comes to the Ukrainian cause itself, the people’s desire to gain independence. In particular, he gives the most negative traits to Hetman Bohdan Khmelnytsky, who revived the Ukrainian state in the middle of the XVII century. Thus, the ethnic origin of Tomash Padura influenced the objective per- ception and coverage of the past of Ukraine. However, his work had a positive impact on the formation of historical themes as an integral factor of the Ukrai- nian national revival.
A prospective study on correlation of cord blood bilirubin with occurrence of neonatal hyperbilirubinemia
Authors: S Anjanappa , TF Ansari , P Unki , M Krishnegowda
Published: June 2023

Background: Neonatal hyperbilirubinemia is one of the most common problems that can occur in a healthy new-born. This physiological rise in indirect component of bilirubin resolves gradually without any intervention in majority of cases. But few babies require intervention in the form of phototherapy and exchange transfusion when the bilirubin levels exceed the normal physiological range for gestational age. The study was aimed to evaluate the predictive value of cord blood bilirubin level for identifying newborn for development of significant hyperbilirubinemia. Methods: It was a prospective observational study carried out in Adichunchanagiri Institute of Medical Sciences, B.G. Nagara, Karnataka. The cord blood bilirubin at birth and 48 hrs serum bilirubin levels were measured. Also, blood group, Rh status and DCT (direct coomb’s test) were tested to estimate the risk in development of hyperbilirubinemia. Results: A total of 210 were included in the study, out of which 52 babies required phototherapy, with a sensitivity of 94.23% and specificity of 3.97%, with the mean cord blood bilirubin level of 2.6mg/dl, having sensitivity of 63.46% and specificity of 90.50%. The multivariate analysis showed ABO and Rh incompatibility, cord blood bilirubin level and lower gestational age had an increased the risk of hyperbilirubinemia requiring phototherapy. Conclusions: Cord blood bilirubin may be used as a potential tool to determine hyperbilirubinemia in newborns. And initiation of phototherapy early in at-risk neonates may decrease the hyperbilirubinemia related morbidity and mortality. Keywords: hyperbilirubinemia; cord blood bilirubin; phototherapy; newborn
Effect of fenugreek (Trigonella foenum-graecum) seed powder on lipid profile: A single blind placebo controlled study
Authors: A Amit , V Shubhangi , D Ajay , C Neel , B Kasturi
Published: June 2023

Background: Dyslipidemia is one of the most prevalent risk factors contributing to atherosclerotic cardiovascular disease. Clinical trials have demonstrated that treatment of dyslipidemia reduces cardiovascular events. Fenugreek seed powder supplementation has lipid-lowering activity, but has not been studied extensively. In light of this, we undertook the present study at tertiary care hospital in Amravati, Maharashtra, with the aim of studying the effect of fenugreek seed powder on serum lipid profile in patients with dyslipidemia & to determine the adverse effects of it. Methods: The study was a single blind placebo controlled study conducted on 60 patients with dyslipidemia from June 2022 to November 2022, after meeting inclusion and exclusion criteria. Results: It was found significant decrease in serum total cholesterol (237.32 to 204.51mg/dl) p value <0.001, serum LDL cholesterol (154.69 to 133.88 mg/dl) p value < 0.001 & serum triglycerides (196.95 to 165.09 mg/dl) p <0.01 with no effect on serum HDL cholesterol (41.11 to 38.92mg/dl) p >0.05, without any major side effects. Conclusion: Fenugreek seed powder significantly lowers serum total cholesterol, LDL cholesterol and triglyceride levels in dyslipidemia patients, with no effect on serum HDL cholesterol levels. fenugreek seed powder supplementation considerably improves lipid Profile. Hence it could be well-thought-out as an effective lipid lowering nutritional supplement. Further high quality & large scale studies are needed to decisively establish the clinical efficacy of fenugreek seed powder. Keywords: fenugreek seed powder; dyslipidemia; lipid profile; cholesterol; LDL; triglycerides; HDL
Comparison between ultrasound guided and landmark – based technique for superficial cervical plexus block in patients undergoing thyroid surgery
Authors: V Balakrishnan , JN Saral , M Kavitha , K Sureshbabu , R Shankar
Published: June 2023

Background: The major advantage of ultrasound guided technique over the traditional landmark technique in bilateral superficial cervical plexus block is that, it helps to minimize the injury to cervical muscle, fascia and nerves. The study aimed to compare the effectiveness between landmark technique and ultrasound guided technique with respect to the post-operative analgesia, intra-operative hemodynamics and the complication rate of the procedure. Methodology: A total of 100 patients were included as our study sample which was divided into two groups of 50 each. Group A patients received Superficial Cervical Plexus Block (SCPB) using the landmark based technique and Group B patients received SCPB under ultrasound guided technique. Parameters such as success of the procedure performance time, time of onset of action, block pain score, post-operative pain score, intra-operative hemodynamics and complication rate were compared between the two groups. Results: The success rate of the block was found to be 72% in the landmark technique group compared to 90% in the ultrasound guided group. The onsite time of analgesia was found to be much earlier in ultrasound guided group (16.2 vs 11.1 mins). The hemodynamic parameters were found to be slightly higher among the landmark group compared to ultrasound guided group. Post-operative pain score was significantly better and the incidence of complication rate was nil among ultrasound guided group. Conclusion: Ultrasound guided technique is a better alternate for landmark technique for providing bilateral superficial cervical plexus block while performing thyroidectomy surgery. Keywords: cervical plexus block; landmark technique; ultrasound technique; thyroidectomy