bluuebunny/crossref_metadata_2025 huggingface dataset:
Simultaneously Updating All Persistence Values in Reinforcement Learning
Authors: Sabbioni Luca , Al Daire Luca , Bisi Lorenzo , Metelli Alberto Maria , Restelli Marcello
Published: June 2023

In Reinforcement Learning, the performance of learning agents is highly sensitive to the choice of time discretization. Agents acting at high frequencies have the best control opportunities, along with some drawbacks, such as possible inefficient exploration and vanishing of the action advantages. The repetition of the actions, i.e., action persistence, comes into help, as it allows the agent to visit wider regions of the state space and improve the estimation of the action effects. In this work, we derive a novel operator, the All-Persistence Bellman Operator, which allows an effective use of both the low-persistence experience, by decomposition into sub-transition, and the high-persistence experience, thanks to the introduction of a suitable bootstrap procedure. In this way, we employ transitions collected at any time scale to update simultaneously the action values of the considered persistence set. We prove the contraction property of the All-Persistence Bellman Operator and, based on it, we extend classic Q-learning and DQN. After providing a study on the effects of persistence, we experimentally evaluate our approach in both tabular contexts and more challenging frameworks, including some Atari games.
Exploration via Epistemic Value Estimation
Authors: Schmitt Simon , Shawe-Taylor John , Van Hasselt Hado
Published: June 2023

How to efficiently explore in reinforcement learning is an open problem. Many exploration algorithms employ the epistemic uncertainty of their own value predictions -- for instance to compute an exploration bonus or upper confidence bound. Unfortunately the required uncertainty is difficult to estimate in general with function approximation.

We propose epistemic value estimation (EVE): a recipe that is compatible with sequential decision making and with neural network function approximators. It equips agents with a tractable posterior over all their parameters from which epistemic value uncertainty can be computed efficiently.

We use the recipe to derive an epistemic Q-Learning agent and observe competitive performance on a series of benchmarks. Experiments confirm that the EVE recipe facilitates efficient exploration in hard exploration tasks.
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces
Authors: Vahidian Saeed , Morafah Mahdi , Wang Weijia , Kungurtsev Vyacheslav , Chen Chen , Shah Mubarak , Lin Bill
Published: June 2023

Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters.
This is especially effective in scenarios where separate groups of clients have significant differences in the distributions of their local data. Existing clustered FL algorithms are essentially trying to group together clients with similar distributions so that clients in the same cluster can leverage each other's data to better perform federated learning. However, prior clustered FL algorithms attempt to learn these distribution similarities indirectly during training, which can be quite time consuming as many rounds of federated learning may be required until the formation of clusters is stabilized. In this paper, we propose a new approach to federated learning that directly aims to efficiently identify distribution similarities among clients by analyzing the principal angles between the client data subspaces. Each client applies a truncated singular value decomposition (SVD) step on its local data in a single-shot manner to derive a small set of principal vectors, which provides a signature that succinctly captures the main characteristics of the underlying distribution.
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
This is achieved by comparing the similarities of the principal angles between the client data subspaces spanned by those principal vectors. The approach provides a simple, yet effective clustered FL framework that addresses a broad range of data heterogeneity issues beyond simpler forms of Non-IIDness like label skews. Our clustered FL approach also enables convergence guarantees for non-convex objectives.
Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data
Authors: Wang Hongni , Yan Jingxin , Yan Xiaodong
Published: June 2023

Herein, we propose a Spearman rank correlation-based screening procedure for ultrahigh-dimensional data with censored response cases. The proposed method is model-free without specifying any regression forms of predictors or response variables and is robust under the unknown monotone transformations of these response variable and predictors. The sure-screening and rank-consistency properties are established under some mild regularity conditions. Simulation studies demonstrate that the new screening method performs well in the presence
of a heavy-tailed distribution, strongly dependent predictors or outliers, and offers superior performance over the existing nonparametric screening procedures. In particular, the new screening method still works well when a response variable is observed under a high censoring rate. An illustrative example is provided.
Robust Self-Supervised Multi-Instance Learning with Structure Awareness
Authors: Wang Yejiang , Zhao Yuhai , Wang Zhengkui , Wang Meixia
Published: June 2023

Multi-instance learning (MIL) is a supervised learning where each example is a labeled bag with many instances. The typical MIL strategies are to train an instance-level feature extractor followed by aggregating instances features as bag-level representation with labeled information. However, learning such a bag-level representation highly depends on a large number of labeled datasets, which are difficult to get in real-world scenarios. In this paper, we make the first attempt to propose a robust Self-supervised Multi-Instance LEarning architecture with Structure awareness (SMILEs) that learns unsupervised bag representation. Our proposed approach is: 1) permutation invariant to the order of instances in bag; 2) structure-aware to encode the topological structures among the instances; and 3) robust against instances noise or permutation. Specifically, to yield robust MIL model without label information, we augment the multi-instance bag and train the representation encoder to maximize the correspondence between the representations of the same bag in its different augmented forms. Moreover, to capture topological structures from nearby instances in bags, our framework learns optimal graph structures for the bags and these graphs are optimized together with message passing layers and the ordered weighted averaging operator towards contrastive loss. Our main theorem characterizes the permutation invariance of the bag representation. Compared with state-of-the-art supervised MIL baselines, SMILEs achieves average improvement of 4.9%, 4.4% in classification accuracy on 5 benchmark datasets and 20 newsgroups datasets, respectively. In addition, we show that the model is robust to the input corruption.
AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution
Authors: Wang Yu , Drgoňa Ján , Zhang Jiaxin , Nanjangud Suryanarayana Karthik Somayaji , Schram Malachi , Liu Frank , Li Peng
Published: June 2023

Normalizing flows (NF) build upon invertible neural networks and have wide applications in probabilistic modeling. Currently, building a powerful yet computationally efficient flow model relies on empirical fine-tuning over a large design space. While introducing neural architecture search (NAS) to NF is desirable, the invertibility constraint of NF brings new challenges to existing NAS methods whose application is limited to unstructured neural networks. Developing efficient NAS methods specifically for NF remains an open problem. We present AutoNF, the first automated NF architectural optimization framework. First, we present a new mixture distribution formulation that allows efficient differentiable architecture search of flow models without violating the invertibility constraint. Second, under the new formulation, we convert the original NP-hard combinatorial NF architectural optimization problem to an unconstrained continuous relaxation admitting the discrete optimal architectural solution, circumventing the loss of optimality due to binarization in architectural optimization. We evaluate AutoNF with various density estimation datasets and show its superior performance-cost trade-offs over a set of existing hand-crafted baselines.
Code-Aware Cross-Program Transfer Hyperparameter Optimization
Authors: Wang Zijia , He Xiangyu , Chen Kehan , Lin Chen , Su Jinsong
Published: June 2023

Hyperparameter tuning is an essential task in automatic machine learning and big data management.
To accelerate tuning, many recent studies focus on augmenting BO, the primary hyperparameter tuning strategy, by transferring information from other tuning tasks.
However, existing studies ignore program similarities in their transfer mechanism, thus they are sub-optimal in cross-program transfer when tuning tasks involve different programs.
This paper proposes CaTHPO, a code-aware cross-program transfer hyperparameter optimization framework, which makes three improvements.
(1) It learns code-aware program representation in a self-supervised manner to give an off-the-shelf estimate of program similarities.
(2) It adjusts the surrogate and AF in BO based on program similarities, thus the hyperparameter search is guided by accumulated information across similar programs.
(3) It presents a safe controller to dynamically prune undesirable sample points based on tuning experiences of similar programs.
Extensive experiments on tuning various recommendation models and Spark applications have demonstrated that CatHPO can steadily obtain better and more robust hyperparameter performances within fewer samples than state-of-the-art competitors.
Contrastive Open Set Recognition
Authors: Xu Baile , Shen Furao , Zhao Jian
Published: June 2023

In conventional recognition tasks, models are only trained to recognize learned targets, but it is usually difficult to collect training examples of all potential categories. In the testing phase, when models receive test samples from unknown classes, they mistakenly classify the samples into known classes. Open set recognition (OSR) is a more realistic recognition task, which requires the classifier to detect unknown test samples while keeping a high classification accuracy of known classes. In this paper, we study how to improve the OSR performance of deep neural networks from the perspective of representation learning. We employ supervised contrastive learning to improve the quality of feature representations, propose a new supervised contrastive learning method that enables the model to learn from soft training targets, and design an OSR framework on its basis. With the proposed method, we are able to make use of label smoothing and mixup when training deep neural networks contrastively, so as to improve both the robustness of outlier detection in OSR tasks and the accuracy in conventional classification tasks. We validate our method on multiple benchmark datasets and testing scenarios, achieving experimental results that verify the effectiveness of the proposed method.
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing
Authors: Yalçın Baturalp , Ma Ziye , Lavaei Javad , Sojoudi Somayeh
Published: June 2023

Many fundamental low-rank optimization problems, such as matrix completion, phase retrieval, and robust PCA, can be formulated as the matrix sensing problem. Two main approaches for solving matrix sensing are based on semidefinite programming (SDP) and Burer-Monteiro (B-M) factorization. The former suffers from high computational and space complexities, whereas the latter may return a spurious solution due to the non-convexity of the problem. The existing theoretical guarantees for the success of these methods have led to similar conservative conditions, which may wrongly imply that these methods have comparable performances. In this paper, we shed light on some major differences between these two methods. First, we present a class of structured matrix completion problems for which the B-M methods fail with an overwhelming probability, while the SDP method works correctly. Second, we identify a class of highly sparse matrix completion problems for which the B-M method works and the SDP method fails. Third, we prove that although the B-M method exhibits the same performance independent of the rank of the unknown solution, the success of the SDP method is correlated to the rank of the solution and improves as the rank increases. Unlike the existing literature that has mainly focused on those instances of matrix sensing for which both SDP and B-M work, this paper offers the first result on the unique merit of each method over the alternative approach.
Lifelong Compression Mixture Model via Knowledge Relationship Graph
Authors: Ye Fei , Bors Adrian G.
Published: June 2023

Task-Free Continual Learning (TFCL) represents a challenging scenario for lifelong learning because the model, under this paradigm, does not access any task information. The Dynamic Expansion Model (DEM) has shown promising results in this scenario due to its scalability and generalisation power. However, DEM focuses only on addressing forgetting and ignores minimizing the model size, which limits its deployment in practical systems. In this work, we aim to simultaneously address network forgetting and model size optimization by developing the Lifelong Compression Mixture Model (LGMM) equipped with the Maximum Mean Discrepancy (MMD) based expansion criterion for model expansion. A diversity-aware sample selection approach is proposed to selectively store a variety of samples to promote information diversity among the components of the LGMM, which allows more knowledge to be captured with an appropriate model size. In order to avoid having multiple components with similar knowledge in the LGMM, we propose a data-free component discarding mechanism that evaluates a knowledge relation graph matrix describing the relevance between each pair of components. A greedy selection procedure is proposed to identify and remove the redundant components from the LGMM. The proposed discarding mechanism can be performed during or after the training. Experiments on different datasets show that LGMM achieves the best performance for TFCL.
Compressing Transformers: Features Are Low-Rank, but Weights Are Not!
Authors: Yu Hao , Wu Jianxin
Published: June 2023

Transformer and its variants achieve excellent results in various computer vision and natural language processing tasks, but high computational costs and reliance on large training datasets restrict their deployment in resource-constrained settings. Low-rank approximation of model weights has been effective in compressing CNN models, but its application to transformers has been less explored and is less effective. Existing methods require the complete dataset to fine-tune compressed models, which are both time-consuming and data-hungry. This paper reveals that the features (i.e., activations) are low-rank, but model weights are surprisingly not low-rank. Hence, AAFM is proposed, which adaptively determines the compressed model structure and locally compresses each linear layer's output features rather than the model weights. A second stage, GFM, optimizes the entire compressed network holistically. Both AAFM and GFM only use few training samples without labels, that is, they are few-shot, unsupervised, fast and effective. For example, with only 2K images without labels, 33% of the parameters are removed in DeiT-B with 18.8% relative throughput increase, but only a 0.23% accuracy loss for ImageNet recognition. The proposed methods are successfully applied to the language modeling task in NLP, too. Besides, the few-shot compressed models generalize well in downstream tasks.
Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network and Word-Pair Relation Tagging
Authors: Yuan Li , Cai Yi , Wang Jin , Li Qing
Published: June 2023

Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction (JMERE) task .
Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for the JMERE task. Specifically, we first design a word-pair relation tagging to exploit the bidirectional interaction between MNER and MRE and avoid error propagation. Then, we propose an edge-enhanced graph alignment network to enhance the JMERE task by aligning nodes and edges in the cross-graph. Compared with previous methods, the proposed method can leverage the edge information to auxiliary alignment between objects and entities and find the correlations between entity-entity relationships and object-object relationships. Experiments are conducted to show the effectiveness of our model.
Optimal Sparse Regression Trees
Authors: Zhang Rui , Xin Rui , Seltzer Margo , Rudin Cynthia
Published: June 2023

Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within the large literature on regression trees, there has been little effort towards full provable optimization, mainly due to the computational hardness of the problem. This work proposes a dynamic programming-with-bounds approach to the construction of provably-optimal sparse regression trees. We leverage a novel lower bound based on an optimal solution to the k-Means clustering algorithm on one dimensional data. We are often able to find optimal sparse trees in seconds, even for challenging datasets that involve large numbers of samples and highly-correlated features.
Combining Adversaries with Anti-adversaries in Training
Authors: Zhou Xiaoling , Yang Nan , Wu Ou
Published: June 2023

Adversarial training is an effective learning technique to improve the robustness of deep neural networks. In this study, the influence of adversarial training on deep learning models in terms of fairness, robustness, and generalization is theoretically investigated under more general perturbation scope that different samples can have different perturbation directions (the adversarial and anti-adversarial directions) and varied perturbation bounds. Our theoretical explorations suggest that the combination of adversaries and anti-adversaries (samples with anti-adversarial perturbations) in training can be more effective in achieving better fairness between classes and a better tradeoff between robustness and generalization in some typical learning scenarios (e.g., noisy label learning and imbalance learning) compared with standard adversarial training. On the basis of our theoretical findings, a more general learning objective that combines adversaries and anti-adversaries with varied bounds on each training sample is presented. Meta learning is utilized to optimize the combination weights. Experiments on benchmark datasets under different learning scenarios verify our theoretical findings and the effectiveness of the proposed methodology.
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
Authors: Zou Qiran , Yang Yu , Cheung Wing Yin , Liu Chang , Ji Xiangyang
Published: June 2023

Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise. However, without human annotations, they are typically prone to produce foreground and background layers with non-negligible semantic and visual confusion, dubbed "information leakage", resulting in notable degeneration of the generated segmentation mask. To alleviate this issue, we propose a simple-yet-effective explicit layer independence modeling approach, termed Independent Layer Synthesis GAN (ILSGAN), pursuing independent foreground-background layer generation by encouraging their discrepancy. Specifically, it targets minimizing the mutual information between visible and invisible regions of the foreground and background to spur interlayer independence. Through in-depth theoretical and experimental analyses, we justify that explicit layer independence modeling is critical to suppressing information leakage and contributes to impressive segmentation performance gains. Also, our ILSGAN achieves strong state-of-the-art generation quality and segmentation performance on complex real-world data.
དབུ་མ་ཐལ་རང་གི་ཐ་སྙད་ལ་དཔྱད་པ།
Authors: ཕུན་ཚོགས། བཀྲ་ཤིས་
Published: June 2023

འདི་ནི་དབུ་མ་པ་ལ་དབུ་མ་རང་རྒྱུད་པ་དང་ཐལ་འགྱུར་པ་ཞེས་པའི་ཐ་སྙད་དེ་ཇི་ལྟར་བྱུང་ཡོད་མེད་དཔྱད་པའི་རྩོམ་ཡིག་ཞིག་ཡིན། ཐ་སྙད་དེ་གཉིས་ཐོག་མར་གསུང་པ་པོ་སུ་ཡིན་ཁ་གསལ་ཞིག་མེད་པ་ལ་བརྟེན་ནས། ཁོ་བོས་འདི་གཉིས་ཀྱི་ཐ་སྙད་དུས་ཇི་ཙམ་ནས་དར་ཡོད་མེད་གཙོ་བོ་དཔེ་བསྡུར་ཞིབ་འཚོལ་གྱིི་ཐོག་ཁུངས་བཙུན་ཐབས་ལམ་ཐོག་འགྲེལ་བཤད་ཞིབ་འཚོལ་བྱེད་ཡོད་པའི་ཞིབ་འཚོལ་གྲུབ་འབྲས་སྙན་ཞུ་ཡིན། དེ་ལ་གཙོ་བོ་རྒྱ་གར་གྱི་མཁས་པ་དང་། བཀའ་གདམས་པའི་དགེ་བཤེས་རྣམས་ཀྱིས་མཛད་པའི་དབུ་མའི་གཞུང་ལུགས་རྣམས་སུ་དབུ་མ་པའི་ཐ་སྙད་ཇི་ལྟར་གསལ་ཡོད་མེད་དཔེ་བསྡུར་ཐོག་གནས་སྡུད་རྣམས་བསྡུ་བསྒྲིགས་བྱས་པ་དང་། དེ་དག་གི་ཚིག་དོན་ལ་དོགས་པ་ཡོད་པ་དང་གསལ་ཁ་མེད་པ་རྣམས་ཟིིན་བྲིས་བཀོད་དེ་མཁས་དབང་དང་ མཁན་པོ་རྣམས་ལ་རིམ་སྒྲིག་མེད་པའི་དྲི་བ་དྲིས་ལན་བགྱིས་ཏེ་ གནས་སྡུད་བསྐྱར་ཞིབ་བྱས་ཡོད་ཅིང་། གནས་སྡུད་དེ་རྣམས་དཔྱད་ཞིབ་བྱས་ཡོད་པའི་གྲུབ་འབྲས་ལྟར་ན། དེ་ལ་རང་རྒྱུད་པ་ཞེས་པའི་ཐ་སྙད་ནི་ ཁ་ཆེའི་པན་ཌི་ཏ་ཆེན་པོ་ ཛ་ཡ་ཨཱ་ནན་ཏས་དབུ་མ་ལ་འཇུག་པའི་འགྲེལ་བཤད་ནང་ཟུར་ཙམ་བཀོད་འདུག་ལ། ཐལ་འགྱུར་པ་ཞེས་པའི་ཐ་སྙད་ནི་པ་ཚབ་སྔོན་གྱིི་མཁས་པ་ཚོས་གསུངས་པའི་དཔེ་དེབ་གཅིག་ཀྱང་རྙེད་མ་ཐུབ། དེས་ན་པ་ཚབ་ལོ་ཙཱ་བའི་དུས་ཙམ་ལ་ཐ་སྙད་དེ་གཉིས་དར་ཡོད་པར་གསལ་ཐུབ། འོན་ཀྱང་ཐ་སྙད་དེ་གཉིས་འདོགས་པའི་བརྡ་མཁན་པོ་ནི་སུ་ཡིན་ད་དུང་རྙེད་མེད། ཐ་སྙད་འདི་གཉིས་མ་གྲགས་པའི་སྔོན་དུ་དབུ་མ་པ་ལ། མདོ་སྡེ་སྤྱོད་པ་དང་། རྣལ་འབྱོར་སྤྱོད་པའི་དབུ་མ་པ་ཞེས་པ་ལ་སོགས་པའི་ཐ་སྙད་རྣམས་དར་འདུག་ཅིང་། ཕྱིས་སུ་ས་དགེ་བཀའ་རྙིང་ལ་སོགས་པའི་མཁས་པ་སོ་སོས་དབུ་མའི་གཞུང་རྣམས་སུ་ཐལ་རང་ཞེས་ཐ་སྙད་དེ་གཉིས་ཡོངས་སུ་གྲགས་པ་བྱུང་འདུག་ལ། མཁས་པ་རང་རང་སོ་སོས་ ཐལ་འགྱུར་པ་དང་རང་རྒྱུད་པའི་ཐ་སྙད་འདི་འདོགས་པའི་གཞི་ལའང་རྣམ་གཞག་མི་འདྲ་སྣ་ཚོགས་པ་ཅིག་བྱས་པ་ནས་ཐ་སྙད་དེའི་དར་ཆེ་རུ་གྱུར་པ་ཡོད་ཞིག་རེ་འདུག།
Tropospheric ozone (O3) pollution in Johannesburg, South Africa: Exceedances, diurnal cycles, seasonality, Ox chemistry and O3 production rates
Authors: Borduas-Dedekind Nadine , Naidoo Mogesh , Zhu Biqing , Geddes Jeffrey , Garland Rebecca M.
Published: June 2023

Ground-level ozone (O3) is an air pollutant of major health and environmental concern. The Johannesburg-Pretoria megacity in South Africa is the industrial and economical capital of the country with more than 10 million inhabitants experiencing poor air quality. In 2004, the City of Johannesburg (CoJ) began monitoring trace gases to assess ground-level O3 pollution. Here, we use CoJ’s publicly available air quality data, and present the first long-term data analysis of O3, nitric oxide (NO), nitrogen dioxide (NO2), NOx and carbon monoxide (CO) in the City from 2004 to 2011 at three air quality monitoring sites: Buccleuch, Delta Park and Newtown. We quantified CoJ’s South African National Ambient Air Quality Standards (NAAQS) exceedances for O3 and NO2, and demonstrate the City’s substantial O3 and NO2 air pollution problem. O3 mixing ratios peak in the early afternoon as expected due to photochemical production. To estimate O3 production rates, we summed O3 and NO2 diurnal profiles to obtain Ox mixing ratios at each site. This analysis provided insight into missing volatile organic compound (VOC) reactivity as well as primary NO2 emissions information necessary for developing tropospheric O3 pollution mitigation strategies. Furthermore, CoJ experiences high O3 mixing ratios on weekends due to lower NOx traffic emissions titrating the O3, thereby providing evidence of a VOC-limited regime for O3 production. Seasonal peak O3 occurs in the austral spring, a maximum that we link to increases in water (H2O) concentrations which in turn increases radical chemistry leading to O3. In addition, wintertime VOC and aerosol emissions from biomass burning over the winter add important precursors for O3 formation once radical chemistry is initiated during the first rain events in early spring. In all, this study will help inform air quality modelling and policy work on air pollutants in the City of Johannesburg, South Africa.
Z ve X kuşağının bireyci ve kolektivist reklam mesajlarına yönelik tutum ve satın alma niyeti üzerine bir araştırma
Authors: USTAAHMETOĞLU Erol , TOKLU İsmail Tamer
Published: June 2023

Bu araştırmanın amacı, reklamlarda kullanılan bireyci ve kolektivist mesajların tüketicilerin tutum ve satın alma niyetleri üzerindeki etkisini belirlemektir. X ve Z kuşağında yer alan deneklerin yer aldığı bu çalışma, bireyci ve kolektivist kültürel değerlere sahip tüketicilerin, bireyci ve kolektivist mesaj içeren reklamlara karşı tutum, satın alma niyeti etkinliği arasındaki bağlantıları keşfetmek için tasarlanmış deneyin sonuçlarını raporlamaktadır. X kuşağı ve Z kuşağının bireyci ve kolektivist reklam mesajlarına yönelik tepkilerinin ölçülmesi hedeflenmektedir. Hipotezleri test etmek için kolayda örnekleme yöntemi ile toplanmış verilerden yararlanılmış ve bağımsız örneklem t testi ve eşleştirilmiş örneklem t testi ile analizler yapılmıştır. Genel olarak Türkiye hala daha kolektivist kültürel değerlere sahip olmakla birlikte Z kuşağı üzerinde bireyci kültürel etkilerin daha baskın gözlemlenmiştir. Bu durum da Z kuşağı ile X kuşağı arasında reklamlara yönelik farklı tepkilerin geliştirmesine yol açmaktadır. Bulgular, reklam stratejilerini düzenlemede ve geliştirmede ilgili taraflara yardımcı olabilir veya rehberlik edebilir. Ulaşılan sonuçlar, X ve Z kuşağı için daha iyi reklam etkinliği yaratmada bireyci ve kolektivist kültür değişkenlerinin anlaşılmasını zenginleştirir. Türkiye’de Z ve X kuşağına mensup katılımcıların bireycilik/kolektivizm boyutuna dair eğilimlerini değerlendiren ilk çalışmalardan biridir.
Governing Complexity for Sustainable Development
Authors:   Waseda University, Mohd Rom Fara Adilah Firdaus , Soda Osamu ,   Waseda University
Published: June 2023

The concept and principles of good governance are recognized by many countries as a key condition for the effective implementation of large-scale projects, ensuring economic growth, and improving the welfare of the population. However, approaches to its implementation depend on many factors, primarily the cultural context of a particular country. In this article, using the example of the management of the construction sector in Malaysia, the perception of stakeholders of this concept and the practices of its application are analyzed. Of the considered components of this model, the respondents most often singled out the effective organization of managing complex processes and multi-level relationships between the components of the system, strict adherence to the rules, and transparency and conscientiousness in the performance of duties. This study shows where good governance information campaigns need to be improved to reach their full potential.
Миф, ритуал, литература
Authors: Юлия Иванова
Published: June 2023

От Септуагинты до поэзии скальдов, от орфических табличек до трактатов барочных полигисторов, от Пилосского агата до полотен Артемизии Джентилески — сложную динамику смысловых и хронологических связей в триаде «миф — ритуал — литература» на материале разнообразной исследовательской проблематики, относящейся к культурам как европейской, так и восточной архаики, классики и Средневековья прослеживает коллектив из нескольких десятков авторов — классиков, медиевистов, востоковедов, антропологов, историков, фольклористов и лингвистов. Все эти столь многоразличные документы человеческого духа размещены в координатах исследовательских интересов выдающегося антиковеда и историка культуры Н.В. Брагинской. Для специалистов и любителей филологии, литературоведения, истории культуры и религии.
Screening of Red Rice (Oryza sativa L.) Landraces for Drought Tolerance at Early Stages Using PEG 6000
Authors: Fatimah Siti , Amzeri Achmad , Syafii Mohammad , Purwaningsih Yuzy
Published: June 2023

Despite being one of the most important staple food consumed by more than half of the world population, the development of red rice still lacks behind the regular white rice. In Madura, most farmers still use their local landraces with red pericarp. Since most of the growing season is dependent on rainfall, those landraces are expected to provide some useful genes related to drought tolerance. This research aimed to select drought-tolerant accessions candidates of red rice derived from pure lines selection at early stages using PEG 6000. A randomized complete block design was used with two factors and three replications, the first factor was red rice genotype and the second factor was two levels of PEG 6000 concentration (0% and 25%). The results showed that there were different responses in all genotypes used in terms of their responses to PEG 6000 screening. Seedling length, seminal root length, shoot length, seminal root dry weight, and shoot dry weight were reduced by the application of PEG 6000 compared to non-PEG 6000 media. Two genotypes had a better response to PEG 6000 treatment compared to check tolerant (Salumpikit), and these are expected to become a valuable resources for further breeding activities.
Effect of Co-Fertilizer Application and Dolomite Amendments on Yield and Grain Quality of Rice Grown on Post-Active Acid Sulfate Soil
Authors: Sukyankij Saychol , Sukyankij Sopida , Panich-pat Thanawan
Published: June 2023

Improving the grain yield of rice and increasing the availability of nutrients in cultivated soils are important goals of sustainable agriculture. This study was aimed to determine the effects of different fertilizer applications and dolomite amendments on rice yield, quality, and soil fertility after plantation in post-active acid sulfate soil in the Phra Nakhon Si Ayutthaya province, Thailand. A randomized complete block design (RCBD) with six treatments and three replications was employed, i.e. T1 (no fertilizer); T2 (fertilizer at the rate of the farmers’ practice, CFFMP); T3 (fertilizer at the rate of site-specific nutrient management, SSNM); T4 (SSNM+organic fertilizer, OF); T5 (SSNM+dolomite, DM); and T6 (SSNM+OF0.5+DM0.5). Our results indicated that the treatment of SSNM+DM provided the highest grain yield (6,171 kg/ha). For the quality of rice grain, the highest protein level was found in the treatment of CFFMP (80.2 g/kg). Application of fertilizer according to the SSNM+OF treatment resulted in higher soil organic matter, available phosphorus, and exchangeable potassium in the soil after rice cultivation than in other treatments. The results suggested that the application of SSNM+DM and SSNM +OF0.5+DM0.5 was the best options for improving rice yield in this area.
Basic Instructions for Running Cell Cycle Analysis with Propidium Iodide on the Beckman Coulter Cytoflex S or Other Cytometers v1
Authors: C Tijerina Jamie
Published: June 2023

Basic instructions for performing Cell Cycle Analysis on the Beckman Coulter Cytoflex S, or any similar flow cytometry analyzer in tube mode. This protocol does not cover sample prep. For advice on the preparation of samples for cell cycle analysis, a suggested reference is Diamond, R. A., & DeMaggio, S. (2000). In Living Color: Protocols in Flow Cytometry and Cell Sorting. Springer Lab Manual, pg 361
Halo-LC3B processing assay to assess autophagy v1
Authors: Ren Xuefeng
Published: June 2023

This protocol details Halo-LC3B processing assay to assess autophagy.
Nuclei isolation from frozen tissue v1
Authors: Stephenson Emily
Published: June 2023

This protocol describes a method for the isolation of intact nuclei for further downstream methods such as single nuclei RNA-seq, ATAC-seq or 10x Multiome. The protocol is adapted from Nadelmann et al doi:10.1002/cpz1.132
Generation of stable ATG3-mCherry wild-type or mutans HeLa cells with HaloTag-LC3B using lentivirus v1
Authors: Ren Xuefeng
Published: June 2023

This protocol details the generation of stable ATG3-mCherry wild-type or mutant HeLa cells with HaloTag-LC3B using lentivirus.
Técnica de inmunofluorescencia indirecta para detección de dengue virus v1
Authors: Lopez Calderon Juliana
Published: June 2023

La inmunofluorescencia indirecta (IFI) es una técnica para detectar la presencia de anticuerpos en una muestra. Esta se debe a la unión de anticuerpos fluorescentes a los anticuerpos presentes en la muestra, lo que permite la detección visual de estos. Para la visualización, se aplican anticuerpos fluorescentes a un portaobjetos de vidrio que contiene una muestra fijada y se observa con un microscopio de fluorescencia. Si hay anticuerpos en la muestra que se unen a los anticuerpos fluorescentes, se produce una fluorescencia indicando la presencia de anticuerpos. Este protocolo fue desarrollado gracias al apoyo administrativo de la CUE Alexander von Humboldt de Armenia y desarrollado dentro de la convocatoria de Minciencia 917 de estancia en investigación: Desarrollo de una prueba tipo Western blot, Dot blot e Inmunofluorescencia para detección de antígenos virales de dengue serotipos 1 – 4.
THE POETICS OF CORPOREALITY IN GABRIELA ZAPOLSKA’S NOVEL CATHY THE CARYATID
Authors:   Taras Shevchenko national University of Kyiv, Meshkova Oleksandra
Published: June 2023

The article attempts to analyze the novel of a polish writer Gabriela Zapolska “Cathy the Caryatid” from the perspective of corporeality - feminine and masculine. The article indicates the main aspects of the interpretation of the concept of corporeality - gender and sociocultural, which are important for understanding the artistic specifics of the representation of human corporeality in the novel. In the novel, female corporeality is represented by two perspectives: personal bodily experience and the objectification of the female body by men in patriarchal culture. The main character of the novel tried to preserve her innocence, considering it the greatest treasure. At the beginning of the novel, a young, healthy girl of twenty with exaggerated physical characteristics and idealized moral qualities appears before the reader. When the girl came to get a job, her body was viewed as a commodity that could be purchased and exploited at work. The main character dreams of getting married, so she strives to keep herself “pure”, because this is one of the conditions under which a decent man can marry her. G. Zapolska portrays the body of the heroine as a kind of “bargaining chip”: a girl can choose two ways - to preserve her innocence in order to “exchange” chastity for a legal marriage and a happy future, or to take the path of debauchery. The loss of virginity is a turning point in the novel by G. Zapolska. Rape inflicted a great psychological trauma on the girl, because until now virginity was her only chance for a happy future. The article analyzes how the patriarchal apparatus implements the objectification of women, as well as sexual objectification. Male dominance in society includes control over female corporeality, women are interpreted as objects that can be owned and changed at will. The value of an object, as a rule, depends primarily on physical data - beauty, health, fertility, etc., while the spiritual development and education of women are often secondary. The protagonist Jan Viebig is the personification of a typical masculine man who cannot contain his physiological desire and understand the girl’s refusal. A man is guided by instincts, raping a girl, using her body for pleasure, and then rejecting her and his promises. The main characteristics of the naturalistic style of the writer are presented, consisting in the depiction of ugly and sometimes disgusting details of the surrounding world, which were parts of reality, in particular, descriptions of rape, death and posthumous decomposition of the body. Attention is focused on the themes of morality and exemplary morality, female virginity, rape, illegitimate pregnancy, images of the human body in art, nudity for art.
MASK AS A SOCIO-CULTURAL PHENOMENON (BASED ON THE POEM BY V. SHYMBORSKA “PORTRAIT OF A WOMAN” AND A. CHEKHOV`S STORY “MASK” WITH A PROJECTION ON THE PRESENT)
Authors:   Lesya Ukrain-ka Volyn National University, Ostapchuk Viktoriya
Published: June 2023

The article examines the role of the mask in the social and cul- tural space of human life. The problem is revealed on the basis of a comparative analysis of V. Shymborska’s poem “Portrait of a woman” and A. Chekhov’s story “Mask” with a projection on the modern epidemiological situation. Poet- ics reveals the aspect of philosophical understanding of changing human nature in both works, focuses on different types of masks presented in V. Shymborska’s poem “Portrait of a woman” and A. Chekhov’s story “Mask”, studies the impact of masks on society, culture and the individual, the correlation of the concepts of portrait and mask, made in the titles of the analyzed works. In V. Shymborska’s poem we are dealing with an internal social mask that influences the character appearance: wearing a mask, a woman learnt to reshape, even cardinally transform herself, and V. Shymborska showed it. Since the mask of the lyrical character of the Polish poem is internal, hidden, the word mask does not appear in the text itself. The character of V. Shymborska has a mask, but it is so disguised that it is almost non-existent. In A. Chekhov’s story, the word mask is included in the title; the mask serves as a tool for removing the “mask” from those people who did not wear it. V. Shymborska and A. Chekhov use different methods to prove that the inner mask affects the appearance of the characters. But the question arises: can the outer mask affect the inner world of man? This issue has been raised by the current situation of the spread of coronavirus infection, which forced the introduction of the rules of the “new etiquette”, one of which requires covering the nose and mouth with a mask. Psychotherapists argue that the mask has become a tool for managing people, a tool for manipulating consciousness and the human will, because a closed face develops obedience, suppresses freedom and volitional qualities Thus, the observations of medical workers and other specialists clearly show that A. Chekhov and V. Shymborska in their works revealed that the mask really destroys normal relations between people. It can be noted that both writers acted as a kind of soothsayers. At the same time, it should be emphasized that a comparative analysis of these works convinces that V. Shymborska and A. Chekhov significantly enriched the meaning of the concept of the mask, helping to better understand human nature, its behaviour. And this understanding can contribute to the establishment of normal communicative relations between members of society. The article only partially reveals an important humanistic topic – “Mask as a socio-cultural phenomenon...”, which remains relevant and requires further study with the involvement of texts of domestic and world fiction.
THE DECLINE OF THE NOBLE PAST AS A PROBLEM COMPLEX IN THE MEMOIRS OF T. BOBROVSKY
Authors:   Comprehensive school of I-III degrees No36, J. Dąbrowski of Zhytomyr, Bondarchuk Olesya
Published: June 2023

The article presents a study of issues that were often on the periph- ery of the author’s narrative. It was found that the problem of exdivision (collection of debts from movable property) is revealed by the memoirist with the help of factuality and saturation of the text with numbers, which is a manifestation of positivist tendencies in the work of T. Bobrovsky. This process leads to the decline of noble residences, which is often accompanied by the image of ruin ‒ a symbol not only of the decline of the former material greatness of the nobility, but also the moral and ethical values of ancient Poland. The problems of conformism and “right-bank euphoria”, highlighted by the memoirist as a spiritual destruction of society, are depicted in numerous plot inserts of comic content. Particular attention in the work of T. Bobrovsky is paid to the phenomenon of «Balagulshchyna», which is seen as a result of degradation and demoralization of the youth of that time, which, on the one hand, could not be properly realized in the modern world with the help of chivalrous landmarks, and on the other - as a protest against the outdated social order. The problems of social depression and the decline of ancient traditions are expressed in «Memoirs ...» by the motive of longing for the noble past. One of the manifestations of sadness in the old days, orders and customs is the tendentious use of the landscape, such as the landscape sketch of Zhytomyr. In a laconic, dry, and sometimes protocol presentation, memories of the nature of the Zhytomyr area impress with their splendor and imagery, emphasizing the uniqueness of the content. This approach to the artistic reflections of the landscape is the impetus for the development of conclusions about the historical past of the region and about nostalgic reflections, which relate primarily to the noble past. The figurative landscape, in the context of a rather «ascetic» narration, stimulates the recipient of the memoirs to read and read quite carefully, because this is a single description of the landscape in the «Memoirs ...». Issues of social depression, «right-bank euphoria», conformism and the decline of aristocratic traditions are revealed in the descriptions of events not only after the November but also the January Uprising. Such duality, according to the author, demonstrates the need to restore statehood by democratic rather than armed methods. Thus, in the memoirist’s work we find a whole set of issues that reveal the problem of the decline of the aristocratic past and which, on the example of public life of the Right Bank, reflect the national tragedy of Polish statehood as a whole.
CREATIVITY OF T. PADURA IN CRITICAL RECEPTION OF V. HNATIUK
Authors:   Regional  Humanitarian  and  Pedagogical  Academy  of  Taras Shevchenko in Kremenets, Nazarets Vitalii
Published: June 2023

This article states that along with numerous Ukrainian and Polish researchers of the 19th-20th centuries. perhaps the most important among the scientific works of philological scientists was the contribution made by the famous Ukrainian scientist Volodymyr Hnatiuk to the study of the literary and historical aspects of the activity of the «Ukrainian school» of Polish romanticism in general and the work of its prominent representative - T. Padura in particular. The purpose of the proposed investigation is an attempt to highlight the various aspects of T. Padura’s work in the critical receptions of V. Hnatiuk. The tasks detailing the stated goal are the systematization and generalization of: 1) the main stages of V. Hnatiuk’s scientific activity, 2) the scientist’s contribution to the development of Ukrainian Polonistics, 3) the main directions of V. Hnatiuk’s research of T. Padura’s work. The article traces the main biographical milestones of V. Hnatiuk’s life, the tragic circumstances of his death in Stalin’s concentration camps, characterizes the content and main vectors of scientific orientation of the scientist’s historical and literary legacy. It is noted that the historical-literary heritage of V. Hnatiuk is impressive both in its volume and in the variety of the thematic scope of research interests. In addition to numerous works devoted to various issues of the history of Ukrainian literature, the most important part of V. Hnatiuk’s literary heritage is his extremely balanced and thorough Polonistic explorations, devoted mainly to the study of historical stages and works of the most prominent representatives of Polish romanticism and, in particular, his «Ukrainian schools». The corpus of Polonistic investigations by V. Hnatiuk dedicated to the study of the work of one of the most prominent representatives of the Ukrainian school, T. Padura, is characterized. In the general range of vectors of research interest on the part of V. Hnatiuk in the work of T. Padura, the following main thematic areas are distinguished: 1) Biographical sketch of the stages of the life path of T. Padura, 2) Worldview orientations of the writer, 3) Ukrainian national question in the work of T. Padura, 4) Ideological and thematic aspects of creativity, 5) Artistic features of poetry.
Thrombocytosis in relation to severity of lower respiratory tract infection in children aged 2 months to 5 years
Authors: R Begum , S Saurabh , I Das , A Das
Published: June 2023

Background: Primary or essential thrombocytosis in children is very rare but reactive thrombocytosis occurs in children and respiratory tract infection is the commonest cause. The study aimed to determine the association of thrombocytosis with severity of lower respiratory tract infection; and the association of degree of thrombocytosis with severity of pneumonia. Materials and methods: This observational study was conducted in the Department of Paediatrics of Fakhruddin Ali Ahmed Medical College & Hospital, Barpeta in Assam. This study was done on 120 children aged between 2 months and 5 years who were admitted with lower respiratory tract infection and classified according to WHO guidelines. Complete general physical examination and systemic examination with special orientation towards respiratory system was done. Complete blood count was performed with automated system sysmex xs800i cell counter. Thrombocytosis was noted and severity of thrombocytosis was classified. Results: Among 120 cases of lower respiratory tract infections, 48 children had thrombocytosis. 13 (48.1%) children with very severe disease, 29 (46.1%) children with severe pneumonia and 6(20%) children with pneumonia had thrombocytosis. Thus it was seen that there was significant association between platelet count and severity of pneumonia (p value=0.018). Two children had severe thrombocytosis (9.01-10.00L/mm3) and both of them had very severe disease. It indicates that degree of thrombocytosis increases as severity of pneumonia increases. Conclusion: There is a significant relationship between thrombocytosis and severity of pneumonia. Also degree of thrombocytosis increases as severity of pneumonia increases. Keywords: thrombocytosis; lower respiratory tract infection; pneumonia; severe pneumonia