Les « droitiers actifs » de 1957 et la postérité d'une réflexion

S’appuyant sur de récentes publications et de nombreux témoignages, Chen Ziming apporte, dans cet article, un éclairage nouveau sur le mouvement anti-droitier de 1957. Dans une première partie, il différencie les « droitiers actifs » (zhudong youpai) des « droitiers passifs » ( beidong) et distingue, parmi les premiers, trois types d’acteurs – les « intellectuels de droite », les « révisionnistes » ainsi que les « défenseurs des droits ». Présentant respectivement ces trois catégories de droitiers, il met en avant leurs spécificités ainsi que leurs divergences. Alors que les « intellectuels de droite » regroupent des personnalités démocrates influentes avant 1949, au premier rang desquelles Zhang Bojun et Luo Longji, et ont pour revendications principales « la modification de la constitution et du mode de gouvernement », les « révisionnistes » rassemblent des intellectuels membres du parti (Li Shenzhi et Liu Binyan) ainsi que des étudiants élevés « sous le drapeau rouge » (Lin Xiling et Tan Tianrong). Influencés par les évolutions récentes au sein du camp communiste, ils dénoncent le culte de la personnalité et les dérives du système et exigent un changement de ligne politique et idéologique en faveur d’une « grande démocratie ». Les « défenseurs des droits » s’appuient, quant à eux, sur la constitution de la République populaire de Chine, pour dénoncer le non–respect des droits politiques (particulièrement pendant les mouvements politiques), des libertés individuelles, des droits économiques et sociaux, ainsi que l’absence de liberté dans les sphères scientifiques, culturelles et artistiques (symbolisée notamment par la suppression de disciplines universitaires comme le droit, les sciences politiques et la sociologie). Dans une dernière partie, Chen Ziming montre enfin l’héritage de ce mouvement mais aussi son dépassement au sein des diverses forces qui oeuvrent actuellement en faveur d’une démocratisation en Chine. Nous avons choisi, pour ce numéro de Perspectives chinoises, de donner la traduction intégrale de la partie où Chen Ziming aborde la première catégorie de droitiers, celle des « intellectuels de droite », ainsi que de larges extraits de la dernière partie de son texte. Le lecteur intéressé trouvera le texte intégral de l’article en chinois sur le site internet du CEFC. (Ndt)

The “Active Rightists” of 1957 and Their Legacy: “Right-wing Intellectuals,” Revisionists, and Rights Defenders

In this article, Chen Ziming makes use of recent publications and many first-hand witness accounts to bring a new perspective to the Anti-Rightist Campaign of 1957. The first part draws a distinction between “active rightists” (zhudong youpai) and “passive rightists” (beidong youpai)and further divides the former category into three groups: “right-wing intellectuals”, “revisionists” and “rights defenders, analysing the specificities of and differences between these groups. While the “right-wing intellectuals” consisted of democratic personalities influential prior to 1949, particularly Zhang Bojun and Luo Longji, who advocated “changing the constitution and the mode of government,” the “revisionists” encompassed intellectuals within the Party (Li Shenzhi and Liu Binyan) as well as students raised “under the red flag” (Lin Xiling and Tan Tianrong). Influenced by recent developments within the communist camp, they denounced the personality cult and the excesses of the system and called for a change of political and ideological line in favour of a “great democracy.” The “rights defenders” referred to the constitution of the People’s Republic of China to denounce violations of political rights (in particular during political campaigns), and of individual freedoms, economic and social rights, as well as the absence of liberty in the scientific, cultural, and artistic spheres (epitomized by the suppression of entire academic fields such as law, political science, and sociology). The last part of the article highlights the legacy of the movement, and describes how its ideas have been taken further by various forces campaigning for a democratisation of China. For this issue of China Perspectives, we have chosen to publish a full translation of the part of Chen Ziming’s essay that deals with the first group of rightists, the “right-wing intellectuals,” as well as substantial extracts from the last part. (Editor’s note

NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning

Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. However, even fine-tuning the PLMs and doing inference are expensive, especially on edge devices with low computing power. Some general approaches (e.g. quantization and distillation) have been widely studied to reduce the compute/memory of PLM fine-tuning, while very few one-shot compression techniques are explored. In this paper, we investigate the neural tangent kernel (NTK)--which reveals the gradient descent dynamics of neural networks--of the multilayer perceptrons (MLP) modules in a PLM and propose to coin a lightweight PLM through NTK-approximating MLP fusion. To achieve this, we reconsider the MLP as a bundle of sub-MLPs, and cluster them into a given number of centroids, which can then be restored as a compressed MLP and surprisingly shown to well approximate the NTK of the original PLM. Extensive experiments of PLM fine-tuning on both natural language understanding (NLU) and generation (NLG) tasks are provided to verify the effectiveness of the proposed method MLP fusion. Our code is available at https://github.com/weitianxin/MLP_Fusion.Comment: ICML 202

Mitigating Label Biases for In-context Learning

Various design settings for in-context learning (ICL), such as the choice and order of the in-context examples, can bias the model's predictions. While many studies discuss these design choices, there have been few systematic investigations into categorizing them and mitigating their impact. In this work, we define a typology for three types of label biases in ICL for text classification: vanilla-label bias, context-label bias, and domain-label bias (which we conceptualize and detect for the first time). Our analysis demonstrates that prior label bias calibration methods fall short of addressing all three types of biases. Specifically, domain-label bias restricts LLMs to random-level performance on many tasks regardless of the choice of in-context examples. To mitigate the effect of these biases, we propose a simple bias calibration method that estimates a language model's label bias using random in-domain words from the task corpus. After controlling for this estimated bias when making predictions, our novel domain-context calibration significantly improves the ICL performance of GPT-J and GPT-3 on a wide range of tasks. The gain is substantial on tasks with large domain-label bias (up to 37% in Macro-F1). Furthermore, our results generalize to models with different scales, pretraining methods, and manually-designed task instructions, showing the prevalence of label biases in ICL.Comment: Accepted to ACL 202

DISCO: Distilling Phrasal Counterfactuals with Large Language Models

Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsourced, such data is typically limited in scale and diversity; when generated using supervised methods, it is computationally expensive to extend to new counterfactual dimensions. In this work, we introduce DISCO (DIStilled COunterfactual Data), a new method for automatically generating high quality counterfactual data at scale. DISCO engineers prompts to generate phrasal perturbations with a large general language model. Then, a task-specific teacher model filters these generations to distill high-quality counterfactual data. While task-agnostic, we apply our pipeline to the task of natural language inference (NLI) and find that on challenging evaluations such as the NLI stress test, comparatively smaller student models trained with DISCO generated counterfactuals are more robust (6% absolute) and generalize better across distributions (2%) compared to models trained without data augmentation. Furthermore, DISCO augmented models are 10% more consistent between counterfactual pairs on three evaluation sets, demonstrating that DISCO augmentation enables models to more reliably learn causal representations. Our repository is available at: https://github.com/eric11eca/discoComment: ACL 2023 camera read

Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks.Comment: This paper has been accepted by the 32st International Joint Conference on Artificial Intelligence (IJCAI-23, Main Track

Comparing the difference of adverse events with HER2 inhibitors: a study of the FDA adverse event reporting system (FAERS)

Aim and background: This study attempted to identify similarities and differences in adverse events (AEs) between human epidermal growth factor receptor 2 (HER2) inhibitors, especially those related to hemorrhagic events and nervous system disorders.Methods: This study summarized the types, frequencies, and system organ classes (SOCs) of AEs of HER2 inhibitors. The US Food and Drug Administration Adverse Event Reporting System (FAERS) data from January 2004 through March 2022 was collected and analyzed. Disproportionality analyses were conducted to detect AEs signals for every HER2 inhibitor. The chi-square test, Wilcoxon test, and descriptive analysis were used to compare the differences of AEs for specific SOCs or drugs.Results: A total of 47,899 AE reports were obtained for eight HER2 inhibitors. Trastuzumab-related AEs were reported in the highest number and combination of regimens. In monotherapy, trastuzumab had the highest reported rate of cardiac disorders-related AEs (24.0%). However, small-molecule drugs exceeded other drugs in the reported rates of AEs related to gastrointestinal disorders, metabolism and nutrition disorders. The highest reported rates of respiratory disorders (47.3%) and hematologic disorders (22.4%) were associated with treatment with trastuzumab deruxtecan (T-DXd). Patients treated with trastuzumab emtansine (TDM-1) had the highest reported rate (7.28%) of hemorrhagic events, especially intracranial haemorrhage events. In addition, patients treated with TDM-1 with concomitant thrombocytopenia were likely to experience hemorrhagic events compared to other HER2 inhibitors (p < 0.001). The median time to onset of intracranial haemorrhage associated with trastuzumab (0.5 months) and TDM-1 (0.75 months) was short. However, there was no significant difference in median time to onset intracranial haemorrhage between patients in different age groups or with different outcomes. Disproportionality analysis results reveal that cerebral haemorrhage is a positive signal associated with T-DXd and TDM-1. In addition, tucatinib was the drug with the highest rate of reported nervous system disorders (31.38%). Memory impairment (83 cases) is a positive signal for tucatinib.Conclusion: The types and reporting rates of AEs associated with different HER2 inhibitors vary across multiple systems. In addition, hemorrhagic events concomitant with TDM-1 treatment and nervous system disorders concomitant with tucatinib treatment may be worthy of attention

Hybrid light-emitting polymer/SiN_x platform for photonic integration

Organic semiconductors have potentials for a broad range of applications; however, it is difficult to be integrated with traditional inorganic material to meet the need of further application. Based on low-temperature silicon nitride (SiNx) deposition technique, here we demonstrate a hybrid structure fabricated by directly depositing high-quality SiNx on organic polymer film Poly[2-(2',5'-bis(2"-ethylhexyloxy)- phenyl) -1,4-phenylene vinylene] (BEHP-PPV). Stacked BEHP-PPV/SiNx hybrid structures with different periods are obtained and their optical properties are systematically characterized. Moreover, a group of BEHP/PPV embedded SiNx micro-disk is fabricated and amplification of spontaneous emission (ASE) is observed under optical pumping, further confirming that the gain properties of BEHP/PPV are well preserved. Our technique offers a platform to fabricate organic/inorganic hybrid optical devices compatible with integrated components.Comment: 6 pages, 4 figure