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Embedded diaspora diplomacy and inward-facing mobilization: mapping the Yemeni diaspora in Türkiye
No full textTürkiye has become a major crossroads for numerous diaspora communities, serving as a space of transnational mobility, refuge, and activism. Among these groups, the Yemeni community remains one of the least examined, despite its steady growth as a multifaceted group. Due to the civil conflict that began in 2014, many Yemenis have left the country, and temporarily or permanently settled abroad. This study seeks to widen the lens of diaspora scholarship by encouraging approaches that capture the varied and regionally dispersed modes of mobilization that such communities pursue. The article asks: How does the Yemeni diaspora in Türkiye mobilize politically and socially, and what opportunities and challenges doe the host country context present? To address this, the research draws on an extensive review of publicly available material supplemented by semi-structured interviews with Yemeni residents in Türkiye. The discussion unfolds in three parts: (1) an outline of the community’s development, and early organizational efforts; (2) an analysis of their evolving forms of collective action; and (3) a trace of how Yemeni actors engage with Türkiye’s political environment and subtly contribute to policy debates affecting both their country of origin and their place of residence
Productivity Gain, Community Strain: Stack Overflow’s Community Response to the AI Initiatives
No full textWe examine how Stack Overflow’s AI initiatives impact its open-source knowledge community. Motivated by visible community fracture, such as a moderator strike and content vandalism, we use netnography with trace data to follow contributor reactions to AI venture announcements, revealing challenges around attribution, compensation, autonomy, trust, and representation. We then provide a set of recommendations that community developers or moderators may implement to help address or mitigate these challenges
Restricted chain-order polytopes via combinatorial mutations
Get PDFWe study restricted chain-order polytopes associated to Young diagrams using combinatorial mutations. These polytopes are obtained by intersecting chain-order polytopes with certain hyperplanes. The family of chain-order polytopes associated to a poset interpolate between the order and chain polytopes of the poset. Each such polytope retains properties of the order and chain polytope; for example its Ehrhart polynomial. For a fixed Young diagram, we show that all restricted chain-order polytopes are related by a sequence of combinatorial mutations. Since the property of giving rise to the period collapse phenomenon is invariant under combinatorial mutations, we provide a large class of rational polytopes that give rise to period collapse
Interrelationships between childhood trauma, alexithymia, and depressive symptoms: A network analysis and replication
No full textBackgroundChildhood trauma has been found to increase the risk of developing alexithymia and depressive symptoms. However, the complex interplay between childhood trauma, alexithymia, and depressive symptoms remains unclear.ObjectiveTo understand how different facets of childhood trauma, alexithymia across positive and negative emotions, and depressive symptoms interact with each other, this study adopted the network analysis approaches to examine this complex relationship.Participants and settingAn initial sample of 2918 Chinese college students completed a set of psychometric questionnaires measuring childhood trauma, alexithymia and depressive symptoms. Another independent sample (n = 858) was used to investigate the replicability of our results.MethodsUndirected networks were estimated to explore the most relevant connections between the above variables. Bayesian network analysis was further used to explore the potential causal directions between the variables.ResultsFindings from the initial dataset showed that childhood trauma was positively correlated with both alexithymia and depressive symptoms in the undirected networks. Physical abuse was the most central node. The Bayesian network analysis indicated that externally orientated thinking and depressed mood may be key drivers for activating other symptoms. Physical abuse might affect suicide ideation through difficulties in describing negative emotions. The replication dataset showed similar network structures as the initial dataset.ConclusionsThe findings suggest that childhood trauma, especially physical abuse, plays an important role in developing later depressive symptoms via valenced components of alexithymia. This study clarifies how early adversities link to depressive symptoms through emotional functioning and informs clinical interventions targeting influential symptoms in trauma-exposed populations
Measuring R&D activity through 10-K narrative disclosures
No full textThis study develops a textual R&D measure based on narrative disclosures in firms’ 10-K filings. Using a tailored lexicon and a core-contextual word-pairing approach, our measure captures qualitative narratives of firms’ R&D activities. We validate this measure using discriminant, construct, reliability, predictive, and external validity tests. The results show that the measure captures latent R&D activity and significantly improves the prediction of important outcomes, including patents, citations, and firm valuation, particularly for firms with missing or zero reported R&D data. We find that narrative R&D is positively associated with growth opportunities, suggesting a mechanism through which it influences firm valuation. Further, positive changes in narrative R&D are not associated with the R&D puzzle, suggesting that the measure facilitates more immediate market integration of innovation-related information than traditional metrics. The textual R&D measure provides a new perspective on the drivers of future innovation output and firm performance, shedding light on key factors that shape long-term competitiveness. Importantly, the textual measure helps to mitigate the limitations of traditional R&D metrics, particularly in cases where firms under-report R&D expenditures or engage in non-patentable forms of innovation
Atmospheres of Silence
Get PDFIn a study of the affective atmospheres that surround the (non)consumption experiences of sexual health products, we developed the concept of ‘atmospheres of silence’. These are affective dynamics that emerge through the interactions of bodies and non-human bodies in place, where silence is sensed, made sense of, and enacted. Drawing on an decolonial femininist ethnography, we unveil affective tonalities such as shame, fear and anger that are circulated through ‘atmospheres of silence’ and the silent acts of omission and commission that they afford. By doing so, we create space for voices to be heard and for intersectional oppressions—rooted in the coloniality of gender—to be challenged. We contribute to the growing body of work on affective atmospheres in marketing, by showing how they not only envelop consumption but can also be oppressive and work as a barrier to consumption
SNR-Guided Enhancement and Autoregressive Depth Estimation for Single-Photon Camera Imaging
Get PDFRecent advances in deep learning have intensified the need for robust low-light image processing in critical applications like autonomous driving, where single-photon cameras (SPCs) offer high photon sensitivity but produce noisy outputs requiring specialized enhancement. This work addresses this challenge through a unified framework integrating three key components: an SNR-guided adaptive enhancement framework that dynamically processes regions with varying noise levels using spatial-adaptive operations and intelligent feature fusion; a specialized self-attention mechanism optimized for low-light conditions; and a conditional autoregressive generation approach applied to robust depth estimation from enhanced SPC images. Our comprehensive evaluation across multiple datasets demonstrates improved performance over state-of-the-art methods, achieving a PSNR of 24.61 dB on the LOL-v1 dataset and effectively recovering fine-grained textures in depth estimation, particularly in real-world SPC applications, while maintaining computational efficiency. The integrated solution effectively bridges the gap between single-photon sensing and practical computer vision tasks, facilitating more reliable operation in photon-starved environments through its novel combination of adaptive noise processing, attention-based feature enhancement, and generative depth reconstruction
Anthracene Bisurea as a Supramolecular Chloride Receptor for Additive‐Free, Broad‐Scope Gold(I) Catalysis
Get PDFGold catalysis provides access to a remarkable array of complex carbon scaffolds, but the use of silver salts to activate gold(I) chloride precatalysts can be problematic due to Ag(I) light sensitivity, hygroscopicity, redox activity, and interference with the desired catalysis. Although H-bond donors are a promising alternative to silver salts, they still suffer from much lower activity and narrower applicability, as Au–Cl cleavage remains rate limiting. To address these limitations, we have rationally designed a self-activating phosphine Au(I) chloride complex that incorporates a supramolecular chloride receptor in the form of an anthracene bisurea quintuple H-bond donor. In the absence of any additive, this complex promotes multiple intra- and intermolecular reactions, with a catalytic activity rivalling traditional inorganic chloride scavengers. Mechanistic studies for the model reaction show that the exceptional chloride binding ability of the anthracene bisurea unlocks access to a zwitterionic catalyst resting state where the Au─Cl bond has been cleaved, thus significantly reducing barriers for catalysis. The principles uncovered in this work show how supramolecular anion recognition moieties impact catalyst speciation and enhance performance, enabling for the first time H-bond donors to compete with inorganic chloride scavengers in terms of activity and generality
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