Le marquage de l’aspect en chinois LM et LE

Au cours de ces vingt-cinq dernières années, les travaux acquisitionnels en langue maternelle et en langue seconde ont montré que la morphologie flexionnelle émerge dans une configuration qui s’écarte de celle de la langue cible (Andersen & Shirai, 1994, 1996 ; Bardovi-Harlig, 1999, 2000 pour un bilan exhaustif de ces études). La découverte de ce type de régularités a conduit à formuler l’Hypothèse de l’Aspect, selon laquelle les apprenants en LM et en LE initialement « influencés par les propriétés temporelles inhérentes du verge ou du prédicat dans leur acquisition du temps et de l’aspect (Andersen & Shirai, 1194 : 133). Cette hypothèse s’est vérifiée en LM et en LE dans des langues indo-europennes, mais les études portant sur d’autres familles de langues sont rares. Cette études tente de combler cette tâche communicative en LM et en Le et par l’explicatio n des différences et des similarités, en tenant compte de la maturité cognitive des deux groupes.Over the last twenty-five years, it has been found that in both L1 and L2 interlanguage verb inflections emerge in an observable restricted pattern that deviates from the target (See Andersen and Shirai 1994, 1996, and Bardovi-Harlig 1999, 2000 for very good reviews). Past tense or perfective marking tends to associate first with only Achievement and Accomplishment predicates whereas progressive marking is used exclusively on Activities. In languages that grammaticalise the distinction between the perfective and the imperfective, perfective past precedes imperfective past, and the latter starts with States and Activities. Progressive marking never generalises to States in L1, but it does in L2 although very rarely, as pointed out by Bardovi-Harlig (1999-2000). This phenomenon has been labelled as the Aspect Hypothesis (AH) because both the L1 and L2 learners seem to be initially “influenced by the inherent semantic aspect of verbs or predicates in the acquisition of tense and aspect markers” ( Andersen and Shirai 1994 ; 133). Although the Aspect Hypothesis has been well-attested in research on the L1 and L2 acquisition of such Indo-European languages as English, French German, Italian and Spanish, studies to test the Aspect Hypothesis in the L1 and L2 acquisition of Chinese have been scarce. This paper attempts to fill in the gap. Moreover, the present study, by using the same elicitation material for both L1 and L2 learner groups, aims at finding out whether the development of aspect marking in L1 and L2 Chinese follow the same pattern and also at providing an explanation for the similarities and differences

Are Onconeural Antibodies a Clinical Phenomenology in Paraneoplastic Limbic Encephalitis?

Paraneoplastic neurological syndromes (PNSs) occur in patients with cancer and can cause clinical symptoms and signs of dysfunction of the nervous system that are not due to a local effect of the tumor or its metastases. Most of these clinical syndromes in adults are associated with lung cancer, especially small cell lung cancer (SCLC), lymphoma, and gynecological tumors. The finding of highly specific antibodies directed against onconeural antigens has revolutionized the diagnosis and promoted the understanding of these syndromes and led to the current hypothesis of an autoimmune pathophysiology. Accumulating data strongly suggested direct pathogenicity of these antibodies. The field of PNS has expanded rapidly in the past few years with the discovery of limbic encephalitis associated with glutamic acid decarboxylase (GAD) 65, the voltage (VGKC-gated potassium channel) complex, the methyl (N-NMDA-D-aspartate), alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and gamma aminobutyric acid (GABA) (B) receptors, and so forth. Despite this, the clinical spectrum of these diseases has not yet been fully investigated. The clinical importance of these conditions lies in their frequent response to immunotherapies and, less commonly, their association with distinctive tumors. This review provides an overview on the pathogenesis and diagnosis of PNS, with emphasis on the role of antibodies in limbic encephalitis

The Relationship between Estimated Glomerular Filtration Rate and Diabetic Retinopathy

Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness in working-aged people. Several studies have suggested that glomerular filtration rate (GFR) was correlated with DR. This is a hospital-based study and the aim of it was to examine the relationship between the GFR and DR in patients with type 2 diabetes mellitus (T2DM). We used CKD-EPI equation to estimate GFR and SPSS 19.0 and EmpowerStats software to assess their relationship. Among the 1613 participants (aged 54.75 ± 12.19 years), 550 (34.1%) patients suffered from DR. The multivariate analysis revealed that the risk factors for DR include age (P<0.001, OR = 0.940), duration of diabetes (P<0.001, OR = 1.163), hemoglobin A1c (P=0.007, OR = 1.224), systolic blood pressure (P<0.001, OR = 1.032), diastolic blood pressure (P=0.007, OR = 0.953), high density lipoprotein cholesterol (P=0.024, OR = 3.884), and eGFR (P=0.010, OR = 0.973). Through stratified analysis and saturation effect analysis, our data suggests that eGFR of 99.4 mL/min or lower might imply the early stage of DR in diabetic patients. Thus, the evaluation of eGFR has clinical significance for the early diagnosis of DR

Pave the Way to Grasp Anything: Transferring Foundation Models for Universal Pick-Place Robots

Improving the generalization capabilities of general-purpose robotic agents has long been a significant challenge actively pursued by research communities. Existing approaches often rely on collecting large-scale real-world robotic data, such as the RT-1 dataset. However, these approaches typically suffer from low efficiency, limiting their capability in open-domain scenarios with new objects, and diverse backgrounds. In this paper, we propose a novel paradigm that effectively leverages language-grounded segmentation masks generated by state-of-the-art foundation models, to address a wide range of pick-and-place robot manipulation tasks in everyday scenarios. By integrating precise semantics and geometries conveyed from masks into our multi-view policy model, our approach can perceive accurate object poses and enable sample-efficient learning. Besides, such design facilitates effective generalization for grasping new objects with similar shapes observed during training. Our approach consists of two distinct steps. First, we introduce a series of foundation models to accurately ground natural language demands across multiple tasks. Second, we develop a Multi-modal Multi-view Policy Model that incorporates inputs such as RGB images, semantic masks, and robot proprioception states to jointly predict precise and executable robot actions. Extensive real-world experiments conducted on a Franka Emika robot arm validate the effectiveness of our proposed paradigm. Real-world demos are shown in YouTube (https://www.youtube.com/watch?v=1m9wNzfp_4E ) and Bilibili (https://www.bilibili.com/video/BV178411Z7H2/ )

Modified Si–Jun–Zi–Tang Attenuates Airway Inflammation in a Murine Model of Chronic Asthma by Inhibiting Teff Cells via the mTORC1 Pathway

Background: Modified Si–Jun–Zi–Tang (MSJZT), a multi-herb formulation, is frequently used in traditional Chinese medicine for patients during the remission stage of asthma. However, the pharmacological basis underlying the effects of MSJZT on asthma has yet to be elucidated. This study aims at evaluating the anti-asthmatic effects of MSJZT and investigating its possible mechanism.Methods: A chronic murine model of asthma was established by sensitization and repeated challenge with ovalbumin (OVA) in female BALB/c mice, followed with oral administration of MSJZT during remission, and then mouse were re-challenged by OVA. The chemical profile of MSJZT was analyzed by high-performance liquid chromatography. The characteristic features of allergic asthma, including airway hyperreactivity, histopathology, cytokine levels (IL-4, -5, -13, -17, and INF-γ), T regulatory (Treg) lymphocytes (Foxp3+CD4+CD25+), and T effector (Teff) lymphocytes (Foxp3-CD25+CD4+) in bronchoalveolar lavage fluid (BALF), and downstream proteins of mTORC1/2 signaling pathway were examined.Results: MSJZT markedly suppressed airway hyper-responsiveness to aerosolized methacholine, and reduced levels of IL-4, IL-5, and IL-13 in the BALF. Histological studies showed that MSJZT significantly reduced inflammatory infiltration in lung tissues. The percentage and absolute number of Teff cells were suppressed to a remarkable level by MSJZT without affecting Treg cells. Furthermore, MSJZT effectively inhibited the mTORC1 activity, but exerted limited effects on mTORC2, as assessed by the phosphorylation of the mTORC1 and mTORC2 substrates, S6 ribosomal protein, p70 S6 kinase, mTOR S2481, and Akt, respectively.Conclusion: MSJZT attenuated chronic airway inflammation in a mouse model of asthma by inhibiting Teff cells, which occurred, at least in part, via modulation of the mTORC1 signaling pathway

Astragaloside IV Ameliorates Airway Inflammation in an Established Murine Model of Asthma by Inhibiting the mTORC1 Signaling Pathway

Astragaloside IV (AS-IV), a main active constituent of Astragalus membranaceus, has been confirmed to have antiasthmatic effects. However, it remained unclear whether the beneficial effects of AS-IV on asthma were attributed to the mTOR inhibition; this issue was the focus of the present work. BALB/c mice were sensitized and challenged with ovalbumin followed with 3 weeks of rest/recovery and then reexposure to ovalbumin. AS-IV was administrated during the time of rest and reexposure. The characteristic features of allergic asthma, including airway hyperreactivity, histopathology, cytokines (IL-4, IL-5, IL-13, IL-17, and INF-γ), and CD4+CD25+Foxp3+Treg cells in bronchoalveolar lavage fluid (BALF), and downstream proteins of mTORC1/2 signaling were examined. AS-IV markedly suppressed airway hyperresponsiveness and reduced IL-4, IL-5, and IL-17 levels and increased INF-γ levels in the BALF. Histological studies showed that AS-IV markedly decreased inflammatory infiltration in the lung tissues. Notably, AS-IV inhibited mTORC1 activity, whereas it had limited effects on mTORC2, as assessed by phosphorylation of mTORC1 and mTORC2 substrates S6 ribosomal protein, p70 S6 Kinase, and Akt, respectively. CD4+CD25+Foxp3+Treg cells in BALF were not significantly changed by AS-IV. Together, these results suggest that the antiasthmatic effects of AS-IV were at least partially from inhibiting the mTORC1 signaling pathway

AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation

We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., making a smiley face) and robot actions (e.g., end-effector movement). Existing approaches relieve this challenge by adopting an open-loop paradigm decomposing high-level instructions into simple sub-task plans, and executing them step-by-step using low-level control models. However, these approaches are short of instant observations in multi-step reasoning, leading to sub-optimal results. To address this issue, we propose to automatically collect a cognitive robot dataset by Large Language Models (LLMs). The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation sequences. To enable efficient data acquisition, we employ elaborated multi-round prompt designs that effectively reduce the burden of extensive human involvement. We further propose a closed-loop multi-modal embodied planning model that autoregressively generates plans by taking image observations as input. To facilitate effective learning, we leverage MiniGPT-4 with a frozen visual encoder and LLM, and finetune additional vision adapter and Q-former to enable fine-grained spatial perception for manipulation tasks. We conduct experiments to verify the superiority over existing open and closed-loop methods, and achieve a significant increase in success rate by 21.4% and 14.5% over ChatGPT and GPT-4 based robot tasks. Real-world demos are shown in https://www.youtube.com/watch?v=ayAzID1_qQk

A construction of strongly regular Cayley graphs and their applications to codebooks

In this paper, we give a kind of strongly regular Cayley graphs and a class of codebooks. Both constructions are based on choosing subsets of finite fields, and the main tools that we employed are Gauss sums. In particular, these obtained codebooks are asymptotically optimal with respect to the Welch bound and they have new parameters

Launching Return-Oriented Programming Attacks against Randomized Relocatable Executables

Abstract—Since the day it was proposed, return-oriented programming has shown to be an effective and powerful attack technique against the write or execute only (W ⊕ X) protection. However, a general belief in the previous research is, systems deployed with address space randomization where the executables are also randomized at run-time are able to defend against return-oriented programming, as the addresses of all instructions are randomized. In this paper, we show that due to the weakness of current address space randomization technique, there are still ways of launching return-oriented programming attacks against those well-protected systems efficiently. We demonstrate and evaluate our attacks with existing typical web server applications and discuss possible methods of mitigating such threats. Keywords-return-oriented programming; address space randomization; position independent executable; I