K(now) W(here)

“How does one discover solace and belonging within these layered narratives?” In mythology, narratives were once created to answer the incomprehensible questions of an era. These narratives unveil half-truths, customs, and convictions. K(now) W(here) is based on the experience of the artist who is Chinese and immigrated to South Africa at a young age; she elaborates the story about assimilation, authenticity, tales of her ancestral roots, and, most often, myths of identity. The artist used narratives from Chinese mythology, collaged physically and metaphorically using tangible objects from other’s homes in combination with photography, digital media, and domestic items and assemblage and taking control of her narratives, which was also a collective narrative. In this process, the artist invited viewers to recreate and heal. Is this me? Is this you? How would I know? We are nowhere But also now here

To Construct the Interpretation Templates for the Chinese Noun Compounds Based on Semantic Classes and Qualia Structures

This paper focuses on the semantic relations and interpratations of Chinese noun compounds (mostly search terms). In light of the semantic classification from Semantic Knowledge-base of Contemporary Chinese (SKCC) and Qualia Structures introduced by Pustejovsky (1991, 1995), we analyze the combinations of the semantic classes of the noun compounds, and thus, discover the implicit predicates of the noun compounds. Based on these semantic relations of the nouns, we summarize the semantic patterns of the noun compounds and built up an interpretation template database of the paraphrasing verbs for the noun compounds. In conjunction with this database, we further develope an automatic interpreatation program of Chinese noun compounds. ? 2012 The PACLIC.EI

Tryptophan-kynurenine pathway as a novel link between gut microbiota and schizophrenia: A review

Gut microbiota and its metabolite tryptophan play an important role in regulating neurotransmission, immune homeostasis and oxidative stress which are critical for brain development. The kynurenine pathway is the main route of tryptophan catabolism. Kynurenine metabolites regulate many biological processes including host-microbiome communication, immunity and oxidative stress, as well as neuronal excitability. The accumulation of metabolites produced by kynurenine pathway in brain results in the activation of the immune system (increase in the levels of inflammatory factors) and oxidative stress (production of reactive oxygen species, ROS), which are associated with mental disorders, for example schizophrenia. Thus, it was hypothesized that perturbations in kynurenine pathway could cause activation of immunity, and that oxidative stress may be involved in the etiology of schizophrenia. The present work is a review of the latest studies on the possible role of kynurenine pathway in schizophrenia, and mechanism(s) involved

Tris(tetra­methyl­ammonium) tetra-μ2-sulfido-tetra­sulfidocopper(I)dimolyb­denum(VI) N,N-dimethyl­formamide solvate

The title compound, (C4H12N)3[CuMo2S8]·C3H7NO, was obtained from the self-assembly of tetra­thio­molybdate, tetra­methyl­ammonium nitrate and cuprous sulfide in dimethyl­formamide (DMF). The asymmetric unit contains three (NMe4)+ cations, one [Mo2S8Cu]3− anion and one DMF solvent mol­ecule, and no obvious inter­actions are observed between these species. The trinuclear anion can be viewed as fused [MoS4Cu]− units sharing a copper center. The geometric parameters of the trivalent anion are comparable to those reported for other related salts including isomorphous anions, namely (NEt4)2(PPh4)[Mo2S8Cu] (a) and (Ph3P=N=PPh3)2(NEt4)[W2S8Cu]·2CH3CN (b). However, the Mo—Cu—Mo angle is found to be 160.24 (3)° for the title salt, while this angle is 162.97 (2)° in (a) and the W—Cu—W angle is 170.3 (2)° in (b), indicating that the largest deviation from linearity is in the title compound

In vivo trafficking of endogenous opioid receptors

Studies on trafficking of endogenous opioid receptors in vivo are subject of the present review. In many of the in vivo studies, the use of semi-quantitative immuno-electron microscopy is the approach of choice. Endogenous opioid receptors display differential subcellular distributions with μ opioid receptor (MOPR) being mostly present on the plasma membrane and δ- and κ-opioid receptors (DOPR and KOPR, respectively) having a significant intracellular pool. Etorphine and DAMGO cause endocytosis of the MOPR, but morphine does not, except in some dendrites. Interestingly, chronic inflammatory pain and morphine treatment promote trafficking of intracellular DOPR to the cell surface which may account for the enhanced antinociceptive effects of DOPR agonists. KOPR has been reported to be associated with secretory vesicles in the posterior pituitary and translocated to the cell surface upon salt loading along with the release of vasopressin. The study of endogenous opioid receptors using in vivo models has produced some interesting results that could not have been anticipated in vitro. In vivo studies, therefore, are essential to provide insight into the mechanisms underlying opioid receptor regulation

A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions

The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content. The LLM-generated text detection aims to discern if a piece of text was produced by an LLM, which is essentially a binary classification task. The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, zero-shot methods, fine-turning LMs methods, adversarial learning methods, LLMs as detectors, and human-assisted methods. In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research. We also delve into prevalent datasets, elucidating their limitations and developmental requirements. Furthermore, we analyze various LLM-generated text detection paradigms, shedding light on challenges like out-of-distribution problems, potential attacks, and data ambiguity. Conclusively, we highlight interesting directions for future research in LLM-generated text detection to advance the implementation of responsible artificial intelligence (AI). Our aim with this survey is to provide a clear and comprehensive introduction for newcomers while also offering seasoned researchers a valuable update in the field of LLM-generated text detection. The useful resources are publicly available at: https://github.com/NLP2CT/LLM-generated-Text-Detection