Proč na pravdě záleží? Arendt a Foucault o pravdě v post-pravdivém věku.

Hannah Arendt and Michel Foucault have been controversial with their radical position on their conception of truth. With the rise of post-truth politics, scholars argue that Hannah Arendt and Michel Foucault have contributed to the rise of post-truth politics. In this thesis, the author defends both Hannah Arendt and Michel Foucault. The author argue Arendt's and Foucault's alternative readings on truth have provided significant insights in the post-truth age. Abstrakt Hannah Arendtová a Michela Foucaulta byli s jejich postojem k jejich pojetí pravdy kontroverzní. Se vzestupem postpravdové politiky vědci tvrdí, že právě Hannah Arendtová a Michela Foucaulta přispěli k jejímu vzniku. V této práci autor obhajuje Hannah Arendtovou i Michela Foucaulta. Autor tvrdí, že alternativní čtení Arendta a Foucaulta o pravdě poskytly významné postřehy o postpravdovém období. Keywords Hannah Arendt, Michel Foucault, Truth, Post-truth, Truth-telling, Socrates Klíčová slova Hannah Arendtová, Michela Foucaulta, Pravda, Post-pravdivém, Pravdě, Sókratés Title Why Truth Matters? Arendt and Foucault on Truth-telling in Post-truth Age Název práce Proč na pravdě záleží? Arendt a Foucault o pravdě v post-pravdivém věku.Hannah Arendt and Michel Foucault have been controversial with their radical position on their conception of truth. With the rise of post-truth politics, scholars argue that Hannah Arendt and Michel Foucault have contributed to the rise of post-truth politics. In this thesis, the author defends both Hannah Arendt and Michel Foucault. The author argue Arendt's and Foucault's alternative readings on truth have provided significant insights in the post-truth age. Abstrakt Hannah Arendtová a Michela Foucaulta byli s jejich postojem k jejich pojetí pravdy kontroverzní. Se vzestupem postpravdové politiky vědci tvrdí, že právě Hannah Arendtová a Michela Foucaulta přispěli k jejímu vzniku. V této práci autor obhajuje Hannah Arendtovou i Michela Foucaulta. Autor tvrdí, že alternativní čtení Arendta a Foucaulta o pravdě poskytly významné postřehy o postpravdovém období. Keywords Hannah Arendt, Michel Foucault, Truth, Post-truth, Truth-telling, Socrates Klíčová slova Hannah Arendtová, Michela Foucaulta, Pravda, Post-pravdivém, Pravdě, Sókratés Title Why Truth Matters? Arendt and Foucault on Truth-telling in Post-truth Age Název práce Proč na pravdě záleží? Arendt a Foucault o pravdě v post-pravdivém věku.Department of Political ScienceKatedra politologieFakulta sociálních vědFaculty of Social Science

MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning

Recent approaches have utilized self-supervised auxiliary tasks as representation learning to improve the performance and sample efficiency of vision-based reinforcement learning algorithms in single-agent settings. However, in multi-agent reinforcement learning (MARL), these techniques face challenges because each agent only receives partial observation from an environment influenced by others, resulting in correlated observations in the agent dimension. So it is necessary to consider agent-level information in representation learning for MARL. In this paper, we propose an effective framework called \textbf{M}ulti-\textbf{A}gent \textbf{M}asked \textbf{A}ttentive \textbf{C}ontrastive \textbf{L}earning (MA2CL), which encourages learning representation to be both temporal and agent-level predictive by reconstructing the masked agent observation in latent space. Specifically, we use an attention reconstruction model for recovering and the model is trained via contrastive learning. MA2CL allows better utilization of contextual information at the agent level, facilitating the training of MARL agents for cooperation tasks. Extensive experiments demonstrate that our method significantly improves the performance and sample efficiency of different MARL algorithms and outperforms other methods in various vision-based and state-based scenarios. Our code can be found in \url{https://github.com/ustchlsong/MA2CL

Temporal Interest Network for Click-Through Rate Prediction

The history of user behaviors constitutes one of the most significant characteristics in predicting the click-through rate (CTR), owing to their strong semantic and temporal correlation with the target item. While the literature has individually examined each of these correlations, research has yet to analyze them in combination, that is, the quadruple correlation of (behavior semantics, target semantics, behavior temporal, and target temporal). The effect of this correlation on performance and the extent to which existing methods learn it remain unknown. To address this gap, we empirically measure the quadruple correlation and observe intuitive yet robust quadruple patterns. We measure the learned correlation of several representative user behavior methods, but to our surprise, none of them learn such a pattern, especially the temporal one. In this paper, we propose the Temporal Interest Network (TIN) to capture the quadruple semantic and temporal correlation between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic embedding, to represent behaviors and the target. Furthermore, we deploy target-aware attention, along with target-aware representation, to explicitly conduct the 4-way interaction. We performed comprehensive evaluations on the Amazon and Alibaba datasets. Our proposed TIN outperforms the best-performing baselines by 0.43\% and 0.29\% on two datasets, respectively. Comprehensive analysis and visualization show that TIN is indeed capable of learning the quadruple correlation effectively, while all existing methods fail to do so. We provide our implementation of TIN in Tensorflow

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset

Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, We present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event.Comment: EMNLP 202

CRISPR/Cas9-mediated enhancement of semi-dwarf glutinous traits in elite Xiangdaowan rice (Oryza sativa L.): targeting SD1 and Wx genes for yield and quality improvement

In rice cultivation, the traits of semi-dwarfism and glutinous texture are pivotal for optimizing yield potential and grain quality, respectively. Xiangdaowan (XDW) rice, renowned for its exceptional aromatic properties, has faced challenges due to its tall stature and high amylose content, resulting in poor lodging resistance and suboptimal culinary attributes. To address these issues, we employed CRISPR/Cas9 technology to precisely edit the SD1 and Wx genes in XDW rice, leading to the development of stable genetically homozygous lines with desired semi-dwarf and glutinous characteristics. The sd1-wx mutant lines exhibited reduced gibberellin content, plant height, and amylose content, while maintaining hardly changed germination rate and other key agronomic traits. Importantly, our study demonstrated that exogenous GA3 application effectively promoted growth by compensating for the deficiency of endogenous gibberellin. Based on this, a semi-dwarf glutinous elite rice (Oryza sativa L.) Lines was developed without too much effect on most agronomic traits. Furthermore, a comparative transcriptome analysis unveiled that differentially expressed genes (DEGs) were primarily associated with the anchored component of the membrane, hydrogen peroxide catabolic process, peroxidase activity, terpene synthase activity, and apoplast. Additionally, terpene synthase genes involved in catalyzing the biosynthesis of diterpenoids to gibberellins were enriched and significantly down-regulated. This comprehensive study provides an efficient method for simultaneously enhancing rice plant height and quality, paving the way for the development of lodging-resistant and high-quality rice varieties

Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients

BackgroundGALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations.MethodsThere are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model.ResultThe GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888).ConclusionsThe new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed