Interviews with Yang Jiang

Yang Jiang was born, under her real name of Yang Jikang, in 1911. She is the author of a novel, several plays, and a large number of sanwen. Her first writing dates back to 1933, and her latest work, Women sa (We Three), in which she recalls family memories, appeared in July 2003, and has been highly successful, with 180,000 copies sold within two months. However, for thirty years, from 1949 to 1981, for obvious reasons, Yang Jiang preferred to devote herself entirely to teaching, research—she is also an expert on Chinese and foreign literature—, and translation: she is the translator, most notably, of the Chinese version of Don Quixote. She is now devoting herself to the publication of the work of her husband, the scholar Qian Zhongshu (1910-1998). In France she is best known for her narratives of the Cultural Revolution, published by Christian Bourgois.The two interviews that follow were carried out in 2005. Yang Jiang gave written answers to the questions I had sent her, which explains the slightly abrupt nature of our exchanges, given that it was not possible for me, by the nature of the interviews, to respond spontaneously to her words. If we seem to jump from one subject to another, it is because I had asked her to clarify certain details that I planned to use in my research into her work (« La Figure de l’intellectuel chez Yang Jiang » [“The Intellectual in The Work of Yang Jiang”], which became my doctoral thesis in Chinese Studies, under the direction of Isabelle Rabut, Inalco, Paris, December 2005, 404 pp.). Yet, to me, these words of Yang Jiang are of interest just as they are, since she uses words so sparingly and generally refuses to do interviews. In any case, and I am grateful to her for this, she only allowed these words to be published precisely because she had written them herself

Nationality Classification Using Name Embeddings

Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support fine-grained classification. We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems. Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population. In an evaluation against other published systems over 13 common classes, our F1 score (0.795) is substantial better than our closest competitor Ethnea (0.580). To the best of our knowledge, this is the most accurate, fine-grained nationality classifier available. As a social media application, we apply our classifiers to the followers of major Twitter celebrities over six different domains. We demonstrate stark differences in the ethnicities of the followers of Trump and Obama, and in the sports and entertainments favored by different groups. Finally, we identify an anomalous political figure whose presumably inflated following appears largely incapable of reading the language he posts in.Comment: 10 pages, 9 figures, 4 table, accepted by CIKM 2017, Demo and free API: www.name-prism.co

Simultaneous Smoothing and Estimation of DTI via Robust Variational Non-local Means

International audienceRegularized diffusion tensor estimation is an essential step in DTI analysis. There are many methods proposed in literature for this task but most of them are neither statistically robust nor feature preserving denoising techniques that can simultaneously estimate symmetric positive definite (SPD) diffusion tensors from diffusion MRI. One of the most popular techniques in recent times for feature preserving scalar- valued image denoising is the non-local means filtering method that has recently been generalized to the case of diffusion MRI denoising. However, these techniques denoise the multi-gradient volumes first and then estimate the tensors rather than achieving it simultaneously in a unified approach. Moreover, some of them do not guarantee the positive definiteness of the estimated diffusion tensors. In this work, we propose a novel and robust variational framework for the simultaneous smoothing and estimation of diffusion tensors from diffusion MRI. Our variational principle makes use of a recently introduced total Kullback-Leibler (tKL) divergence, which is a statistically robust similarity measure between diffusion tensors, weighted by a non-local factor adapted from the traditional non-local means filters. For the data fidelity, we use the nonlinear least-squares term derived from the Stejskal-Tanner model. We present experimental results depicting the positive performance of our method in comparison to competing methods on synthetic and real data examples

A low-mass line-rich core found in Massive Star-forming Region IRAS 16351-4722

We present ALMA sub-arcsecond-resolution observations of both continuum and molecular lines at 345 GHz towards the massive star-forming region IRAS 16351-4722 (hereafter I16351). A total of 12 dust cores were detected based on high spatial resolution observations of the continuum. Among them, a high-mass core (11.6 Msun) and a low-mass core (1.7 Msun) show abundant molecular line emissions. 164 molecular transitions from 29 species and 104 molecular transitions from 25 species are identified in the high-mass and low-mass cores, respectively. Complex organic molecules (COMs) such as CH3OH, CH3OCHO, CH3OCH3, C2H5OH, and C2H5CN are detected in the two cores. Under the assumption of local thermodynamic equilibrium (LTE), rotational temperatures and column densities of the COMs are derived with the XCLASS software. The maximum rotation temperature values in the low-mass core and the high-mass core were found to be approximately 130 K and 198 K, respectively. Additionally, the line widths in the high-mass core are larger than those in the low-mass one. Abundant complex organic molecular line transitions, high gas temperatures, and smaller line widths indicate the presence of a low-mass line-rich core in the massive star formation region for the first time, while the high-mass line-rich core shows hot core property. When comparing the molecular abundances of CH3OH, CH3OCHO, CH3OCH3 and C2H5OH of the two cores with other hot cores and hot corinos reported in the literature, we further confirm that both a hot core and a low-mass line-rich core are simultaneously detected in I16351.Comment: 22 pages, 5 figures, 5 tables, 70 references, accepted by Ap

Carbon dots-based dual-emission ratiometric fluorescence sensor for dopamine detection

The detection of Dopamine (DA) is significant for disease surveillance and prevention. However, the development of the precise and simple detection techniques is still at a preliminary stage due to their high tester requirements, time-consuming process, and low accuracy. In this work, we present a novel dual-emission ratiometric fluorescence sensing system based on a hybrid of carbon dots (CDs) and 7-amino-4-methylcoumarin (AMC) to quickly monitor the DA concentration. Linked via amide bonds, the CDs and AMC offered dual-emissions with peaks located at 455 and 505 nm, respectively, under a single excitation wavelength of 300 nm. Attributed to the fluorescence of the CDs and AMC in the nanohybrid system can be quenched by DA, the concentration of DA could be quantitatively detected by monitoring the ratiometric ratio change in fluorescent intensity. More importantly, the CDs-AMC-based dual-emission ratiometric fluorescence sensing system demonstrated a remarkable linear relationship in the range of 0–33.6 μM to detection of DA, and a low detection limit of 5.67 nM. Additionally, this sensor successfully applied to the detection of DA in real samples. Therefore, the ratiometric fluorescence sensing system may become promising to find potential applications in biomedical dopamine detection

The ALMA Survey of Star Formation and Evolution in Massive Protoclusters with Blue Profiles (ASSEMBLE): Core Growth, Cluster Contraction, and Primordial Mass Segregation

The ALMA Survey of Star Formation and Evolution in Massive Protoclusters with Blue Profiles (ASSEMBLE) aims to investigate the process of mass assembly and its connection to high-mass star formation theories in protoclusters in a dynamic view. We observed 11 massive (Mclump>1000 Msun), luminous (Lbol>10,000 Lsun), and blue-profile (infall signature) clumps by ALMA with resolution of 2200-5500 au at 350 GHz (870 um) in continuum and line emission. 248 dense cores were identified, including 106 cores showing protostellar signatures and 142 prestellar core candidates. Compared to early-stage infrared dark clouds (IRDCs) by ASHES, the core mass and surface density within the ASSEMBLE clumps exhibited significant increment, suggesting concurrent core accretion during the evolution of the clumps. The maximum mass of prestellar cores was found to be 2 times larger than that in IRDCs, indicating evolved protoclusters have the potential to harbor massive prestellar cores. The mass relation between clumps and their most massive core (MMCs) is observed in ASSEMBLE but not in IRDCs, which is suggested to be regulated by multiscale mass accretion. The mass correlation between the core clusters and their MMCs has a steeper slope compared to that observed in stellar clusters, which can be due to fragmentation of the MMC and stellar multiplicity. We observe a decrease in core separation and an increase in central concentration as protoclusters evolve. We confirm primordial mass segregation in the ASSEMBLE protoclusters, possibly resulting from gravitational concentration and/or gas accretion.Comment: 37 pages, 13 figures, 5 tables; accepted for publication in ApJ