Encountering migrant-driven diversity: producing difference in Singapore

Migrant arrival cities, many of which are located outside of European and North American contexts, are experiencing urban growth because of migrants coming from an ever heterogeneous array of backgrounds. The management of migrants at both the level of the state and the everyday is also changing as a response to these shifts. How difference has been conceived, regulated and experienced through encounters in everyday spaces of these arrival cities has been well-documented (Amin, 2012; Watson, 2009; Wilson, 2011). Building upon this body of work, this paper examines the co-production of urban space through managerial practices by the state and the diverse users of the space. I analyse how migrant-driven diversity is produced through pastoral discourses of care and control. Drawing upon qualitative data conducted before regulations at City Plaza, in a neighbourhood in the east side of Singapore, I locate the sites of co-production at the level of policy regulation and at the levels of everyday surveillance in shared spaces where branches of the state such as surveillance technologies, explicit rules on signboards, auxiliary police officers and different groups of new arrivals (i.e. “new migrants”) encounter one another regularly on weekends. I demonstrate that this production of difference from various stakeholders reinforces boundaries of civility through encounters, re-producing the desirable/non-desirable migrant. The arrival city is therefore marked by these diffuse generative forces that both subvert and reinforce dominant modes of belonging

Subwavelength grating devices in silicon photonics

Subwavelength grating (SWG) waveguides in silicon-on-insulator are emerging as an enabling technology for implementing compact, high-performance photonic integrated devices and circuits for signal processing and sensing applications. We provide an overview of our recent work on developing wavelength selective SWG filters based on Bragg gratings and ring resonators, as well as optical delay lines. These components increase the SWG toolbox and can be used to realize more complex photonic integrated circuits with enhanced or new functionality

Bis(2,6-dihy­droxy­benzoato-κ2 O 1,O 1′)(nitrato-κ2 O,O′)bis­(1,10-phenanthroline-κ2 N,N′)neodymium(III)

In the mononuclear title complex, [Nd(C7H5O4)2(NO3)(C12H8N2)2], the NdIII atom is in a distorted bicapped square-anti­prismatic geometry formed by four N atoms from two chelating 1,10-phenanthroline (phen) ligands, four O atoms from two 2,6-dihy­droxy­benzoate (DHB) ligands and two O atoms from a nitrate anion. π–π stacking inter­actions between the phen and DHB ligands of adjacent complexes [centroid–centroid distances = 3.520 (6) and 3.798 (6) Å] stabilize the crystal structure. Intra­molecular O—H⋯O hydrogen bonds are observed in the DHB ligands

Subwavelength grating filters in SOI

We demonstrate subwavelength grating filters in silicon-on-insulator based on Bragg gratings and racetrack resonators. The Bragg grating has a 3 dB bandwidth = 0.5 nm and reflectivity > 90%; the resonator has a 3 dB bandwidth = 1 nm and extinction ratio > 33 dB

Attentive Symmetric Autoencoder for Brain MRI Segmentation

Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding. However, existing methods seldom study the various importance of reconstructed patches and the symmetry of anatomical structures, when they are applied to 3D medical images. In this paper we propose a novel Attentive Symmetric Auto-encoder (ASA) based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. We conjecture that forcing the auto-encoder to recover informative image regions can harvest more discriminative representations, than to recover smooth image patches. Then we adopt a gradient based metric to estimate the importance of each image patch. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Moreover, we resort to the prior of brain structures and develop a Symmetric Position Encoding (SPE) method to better exploit the correlations between long-range but spatially symmetric regions to obtain effective features. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models on three brain MRI segmentation benchmarks.Comment: MICCAI 2022, code:https://github.com/lhaof/AS