Caught Between the State, the Market, and Civil Society: The Divergent Paths of Chinese Non-Governmental Organizations (NGOs) Seeking to Make Social Change in China

Research on Chinese civil society has tended to focus on the relationship between non-governmental organizations (NGOs) and the state. Such work has underestimated the complexity of the emerging institutional environment where Chinese NGOs are often caught between the state, the market, and a constrained civil society. How do civil society organizations and their respective nonprofit sectors emerge, what forms do they take? More specifically, how do organizational forms and strategies reflect political and market structures at the time? Chinese nonprofit sectors re-emerged in the late 1990s, and their relationships with the state have been contentious. The rapid transformation of the nonprofit sectors provides a unique opportunity to look at the emergence of a new organizational field. Using strategic action fields (SAFs) theory, I examine how organizations within the field of nonprofit organizations attempted to establish and defend their positions vis-à-vis the state and market. I conducted comparative case studies of two leading China\u27s NGOs –Civil Society Center (CSC) in the city of Guangzhou and Excellence Promoter (EP) in the city of Shanghai. I traced the histories and current development of CSC and EP and their connected organizations and used ethnographic, interview, and survey data to triangulate the emerging urban nonprofit sectors in the context of an authoritarian state. I argue that, in different periods, the nonprofit organizational fields of Guangzhou and Shanghai reflected political and market structures at the time.  In the early 2000s, Guangzhou’s nonprofit organizations were grassroots-driven, and a State Avoidance Autonomous field arose as large organizations decentralized into smaller organizations to decrease state scrutiny and intervention. In contrast, in Shanghai, where the state promoted nonprofit organizations as an extension of governmental programs, a State Alliance Social Market Field developed. This State Alliance Social Market Field prioritized business values and practices to guide organizational strategies rather than the ethical commitments that had been the center of the State Avoidance Autonomous field. By partnering with government, EP was able to rapidly expand while its Guangzhou counterpart, CSC, remained small and marginal. Powerful e-commerce companies such as Tencent, however, have been changing the rules and norms that used to govern the field. They entered the nonprofit field through the creation of a new fundraising platform that opened up alternative resources for Chinese NGOs. The involvement of the market through corporate foundations and new technologies has provided alternative funding for grassroots NGOs under the attack from the state. My study contributes to nonprofit studies and China studies by providing insight into how NGOs interact with different state and market players and the consequences of such interactions on organizational strategies

VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene Graph

Semantic Web technology has successfully facilitated many RDF models with rich data representation methods. It also has the potential ability to represent and store multimodal knowledge bases such as multimodal scene graphs. However, most existing query languages, especially SPARQL, barely explore the implicit multimodal relationships like semantic similarity, spatial relations, etc. We first explored this issue by organizing a large-scale scene graph dataset, namely Visual Genome, in the RDF graph database. Based on the proposed RDF-stored multimodal scene graph, we extended SPARQL queries to answer questions containing relational reasoning about color, spatial, etc. Further demo (i.e., VGStore) shows the effectiveness of customized queries and displaying multimodal data.Comment: ISWC 2022 Posters, Demos, and Industry Track

Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field

Prior works on joint Information Extraction (IE) typically model instance (e.g., event triggers, entities, roles, relations) interactions by representation enhancement, type dependencies scoring, or global decoding. We find that the previous models generally consider binary type dependency scoring of a pair of instances, and leverage local search such as beam search to approximate global solutions. To better integrate cross-instance interactions, in this work, we introduce a joint IE framework (CRFIE) that formulates joint IE as a high-order Conditional Random Field. Specifically, we design binary factors and ternary factors to directly model interactions between not only a pair of instances but also triplets. Then, these factors are utilized to jointly predict labels of all instances. To address the intractability problem of exact high-order inference, we incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method, which achieves consistent learning and inference. The experimental results show that our approach achieves consistent improvements on three IE tasks compared with our baseline and prior work

A META-ANALYSIS OF ABNORMAL GLUCOSE METABOLISM IN FIRST-EPISODE DRUG-NAIVE SCHIZOPHRENIA

Background: Patients with schizophrenia exhibit a higher mortality rate compared with the general population. This mortality has been attributed predominantly by the high risk of type 2 diabetes mellitus in the patients. We aimed to assess the inherent risk of glucose metabolism abnormalities in first-episode drug-naïve schizophrenia. Subjects and methods: We searched English database (PubMed, EMBASE, MEDLINE, Cochrane Library databases) and Chinese database (Wan Fang Data, CBM disc, VIP, and CNKI) from their inception until Jul 2018 for case-control studies examining glucose metabolism abnormalities. Measurements, such as fasting plasma glucose levels, fasting plasma insulin levels, insulin resistance and HbA1c levels in first episode antipsychotic-naive patients were used to test for prediabetes. Standardized/weighted mean differences and 95% confidence intervals were calculated and analyzed. Results: 19 studies (13 in English and 6 in Chinese) consisting of 1065 patients and 873 controls were included. Fasting plasma glucose levels (95% CI; 0.02 to 0.29; P=0.03), 2 h plasma glucose levels after an OGTT (95% CI; 0.63 to 1.2; P<0.00001), fasting plasma insulin levels (95% CI; 0.33 to 0.73; P<0.00001), insulin resistance (95% CI; 0.29 to 0.6; P<0.00001) in patients with firstepisode schizophrenia were significant elevated. There was no significant difference in HbA1c level (95% CI; -0.34 to 0.18; P=0.54) in patients with first-episode schizophrenia compared with controls. Conclusions: This meta-analysis showed that glucose metabolism was impaired in patients with first-episode schizophrenia. Higher quality studies with larger samples are warranted to confirm these findings

Submission of Evidence on Online Violence Against Women to the UN Special Rapporteur on Violence Against Women, its Causes and Consequences, Dr Dubravka Šimonović

Figure S1. B3GALNT2 levels determined by W.B. and ROC curve. a–c Relative mRNA expression of B3GALNT2 in HCC tumor tissues and normal liver tissues obtained from GSE76427, GSE36376, and TCGA-LIHC datasets. d Western blot analysis of B3GALNT2 levels in 24 pairs of HCC tissues. T HCC tumor tissue, N adjacent non-tumor tissue. e ROC curve analysis of the sensitivity and specificity for the predictive value of TNM model, B3GALNT2 expression, and the combination model. (TIFF 546 kb

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%

Genome-wide analysis of a avirulent and reveal the strain induces pro-tective immunity against challenge with virulent Streptococcus suis Serotype 2

BACKGROUND: It was previously reported in China that two recent large-scale outbreaks of Streptococcus suis serotype 2 (S. suis 2) infections in human were caused by two highly virulent S. suis 2 strains, from which a novel genomic island (GEI), associated with disease onset and progression and designated 89 K, was identified. Here, an avirulent strain, 05HAS68, was isolated from a clinically healthy pig. RESULTS: By comparing the genomes of this avirulent strain with virulent strains, it was found that massive genomic rearrangements occurred, resulting in alterations in gene expression that caused enormous single gene gain and loss. Important virulent genes were lost, such as extracellular protein factor (ef) and suilysin (sly) and larger mutants, such as muramidase-released protein (mrp). Piglets vaccinated with the avirulent strain, 05HAS68, had increased TNF-α and IFN-γ levels in the peripheral blood and were fully protected from challenge infection with the most virulent S. suis 2 strain, 05ZYH33. Transfusion of T cells and plasma from vaccinated pigs resulted in protection of recipient animals against the 05ZYH33 challenge. CONCLUSION: These results suggest that analysis genome of the avirulent strains are instrumental in the development of vaccines and for the functional characterization of important of genetic determinants

Opportunistic spectrum sharing for D2D-based URLLC

A device-to-device (D2D) ultra-reliable low latency communications network is investigated in this paper. Specifically, a D2D transmitter opportunistically accesses the radio resource provided by a cellular network and directly transmits short packets to its destination. A novel performance metric is adopted for finite block-length code. We quantify the maximum achievable rate for the D2D network, subject to a probabilistic interference power constraint based on imperfect channel state information. First, we perform a convexity analysis that reveals that the finite block-length rate for the D2D pair in short-packet transmission is not always concave. To address this issue, we propose two effective resource allocation schemes using the successive convex approximation based iterative algorithm. To gain more insights, we exploit the monotonicity of the average finite block-length rate. By capitalizing on this property, an optimal power control policy is proposed, followed by closed-form expressions and approximations for the optimal average power and the maximum achievable average rate in the finite block-length regime. Numerical results are provided to confirm the effectiveness of the proposed resource allocation schemes and validate the accuracy of the derived theoretical results

Flexoelectricity-stabilized ferroelectric phase with enhanced reliability in ultrathin La:HfO2 films

Doped HfO2 thin films exhibit robust ferroelectric properties even for nanometric thicknesses, are compatible with current Si technology and thus have great potential for the revival of integrated ferroelectrics. Phase control and reliability are core issues for their applications. Here we show that, in (111)-oriented 5%La:HfO2 (HLO) epitaxial thin films deposited on (La0.3Sr0.7)(Al0.65Ta0.35)O3 substrates, the flexoelectric effect, arising from the strain gradient along the films normal, induces a rhombohedral distortion in the otherwise Pca21 orthorhombic structure. Density functional calculations reveal that the distorted structure is indeed more stable than the pure Pca21 structure, when applying an electric field mimicking the flexoelectric field. This rhombohedral distortion greatly improves the fatigue endurance of HLO thin films by further stabilizing the metastable ferroelectric phase against the transition to the thermodynamically stable non-polar monoclinic phase during repetitive cycling. Our results demonstrate that the flexoelectric effect, though negligibly weak in bulk, is crucial to optimize the structure and properties of doped HfO2 thin films with nanometric thicknesses for integrated ferroelectric applications