The Belt & Road Initiative: New Driving Force for Regionalisation and Globalisation

The Belt and Road Initiative (BRI) is China's idea, but the opportunities it has created belong to the world. Over the past nine years, the BRI has evolved from a concept into tangible actions, from vision to reality, bringing enormous opportunities and benefits to countries worldwide. Due to the COVID-19 pandemic and the Russia-Ukraine crisis, our world has entered a period of turbulence and transformation, but the BRI cooperation did not come to a halt. It continued to move forward, showing remarkable resilience and vitality. Facing an increasingly complex international environment, will China continue to reform and open up or close the door to the outside world? Prof. Chen Xiangming, the Distinguished Professor of Global Urban Studies and Sociology at Trinity College and a guest professor at Fudan University, China, replied to this question in his recent monograph "The Belt and Road Initiative as Epochal Regionalisation". In this book, he illustrated the contribution of the BRI to regional and global connectivity with a regional focus, pointing out that the BRI is evolving from a single initiative to a worldwide synergy

Fermi Bubbles Inflated by Winds Launched from the Hot Accretion Flow in Sgr A*

A pair of giant gamma-ray bubbles have been revealed by the {\it Fermi} LAT. In this paper we investigate their formation mechanism. Observations have indicated that the activity of the supermassive black hole located at the Galactic center, Sgr A*, was much stronger than the present time. Specifically, one possibility is that while Sgr A* was also in the hot accretion regime, the accretion rate should be $10^3-10^4$ times higher during the past $\sim 10^7$ yr. On the other hand, recent MHD numerical simulations of hot accretion flows have unambiguously shown the existence of strong winds and obtained their properties. Based on these knowledge, by performing three-dimensional hydrodynamical simulations, we show in this paper that the Fermi bubbles could be inflated by winds launched from the ``past' hot accretion flow in Sgr A*. In our model, the active phase of Sgr A* is required to last for about 10 million years and it was quenched no more than 0.2 million years ago. The Central Molecular Zone (CMZ) is included and it collimates the wind orientation towards the Galactic poles. Viscosity suppresses the Rayleigh-Taylor and Kelvin-Helmholtz instabilities and results in the smoothness of the bubble edge. The main observational features of the bubbles can be well explained. Specifically, the {\it ROSAT} X-ray features are interpreted by the shocked interstellar medium and the interaction region between winds and CMZ gas. The thermal pressure and temperature obtained in our model are in good consistency with the recent {\it Suzaku} observations.Comment: 12 pages,8 figures, Accepted by Ap

On the Convergence of Deep Learning with Differential Privacy

In deep learning with differential privacy (DP), the neural network achieves the privacy usually at the cost of slower convergence (and thus lower performance) than its non-private counterpart. This work gives the first convergence analysis of the DP deep learning, through the lens of training dynamics and the neural tangent kernel (NTK). Our convergence theory successfully characterizes the effects of two key components in the DP training: the per-sample clipping (flat or layerwise) and the noise addition. Our analysis not only initiates a general principled framework to understand the DP deep learning with any network architecture and loss function, but also motivates a new clipping method -- the global clipping, that significantly improves the convergence while preserving the same privacy guarantee as the existing local clipping. In terms of theoretical results, we establish the precise connection between the per-sample clipping and NTK matrix. We show that in the gradient flow, i.e., with infinitesimal learning rate, the noise level of DP optimizers does not affect the convergence. We prove that DP gradient descent (GD) with global clipping guarantees the monotone convergence to zero loss, which can be violated by the existing DP-GD with local clipping. Notably, our analysis framework easily extends to other optimizers, e.g., DP-Adam. Empirically speaking, DP optimizers equipped with global clipping perform strongly on a wide range of classification and regression tasks. In particular, our global clipping is surprisingly effective at learning calibrated classifiers, in contrast to the existing DP classifiers which are oftentimes over-confident and unreliable. Implementation-wise, the new clipping can be realized by adding one line of code into the Opacus library

The ciliary protein cystin forms a regulatory complex with necdin to modulate Myc expression

Cystin is a novel cilia-associated protein that is disrupted in the cpk mouse, a well-characterized mouse model of autosomal recessive polycystic kidney disease (ARPKD). Interestingly, overexpression of the Myc gene is evident in animal models of ARPKD and is thought to contribute to the renal cystic phenotype. Using a yeast two-hybrid approach, the growth suppressor protein necdin, known to modulate Myc expression, was found as an interacting partner of cystin. Deletion mapping demonstrated that the C-terminus of cystin and both termini of necdin are required for their mutual interaction. Speculating that these two proteins may function to regulate gene expression, we developed a luciferase reporter assay and observed that necdin strongly activated the Myc P1 promoter, and cystin did so more modestly. Interestingly, the necdin effect was significantly abrogated when cystin was co-transfected. Chromatin immunoprecipitation and electrophoretic mobility shift assays revealed a physical interaction with both necdin and cystin and the Myc P1 promoter, as well as between these proteins. The data suggest that these proteins likely function in a regulatory complex. Thus, we speculate that Myc overexpression in the cpk kidney results from the dysregulation of the cystin-necdin regulatory complex and c-Myc, in turn, contributes to cystogenesis in the cpk mouse

4,4′-Bis(benzimidazol-1-yl)biphen­yl

The mol­ecule of the title compound, C26H18N4, resides on a crystallographic inversion centre with a dihedral angle of 44.94 (5)° between the benzimidazole ring system and the benzene ring. The primary hydrogen bond is C—H⋯N and inversion-related pairs of these generate a chain of rings along the c-axis direction; π⋯π stacking involving the benzimidazole groups with inter­planar separations of ca 3.4 Å complete the inter­actions

L dwarfs detection from SDSS images using improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.Comment: 12 pages, 10 figures, accepted to be published in A

Neuromorphic Computing with Deeply Scaled Ferroelectric FinFET in Presence of Process Variation, Device Aging and Flicker Noise

This paper reports a comprehensive study on the applicability of ultra-scaled ferroelectric FinFETs with 6 nm thick hafnium zirconium oxide layer for neuromorphic computing in the presence of process variation, flicker noise, and device aging. An intricate study has been conducted about the impact of such variations on the inference accuracy of pre-trained neural networks consisting of analog, quaternary (2-bit/cell) and binary synapse. A pre-trained neural network with 97.5% inference accuracy on the MNIST dataset has been adopted as the baseline. Process variation, flicker noise, and device aging characterization have been performed and a statistical model has been developed to capture all these effects during neural network simulation. Extrapolated retention above 10 years have been achieved for binary read-out procedure. We have demonstrated that the impact of (1) retention degradation due to the oxide thickness scaling, (2) process variation, and (3) flicker noise can be abated in ferroelectric FinFET based binary neural networks, which exhibits superior performance over quaternary and analog neural network, amidst all variations. The performance of a neural network is the result of coalesced performance of device, architecture and algorithm. This research corroborates the applicability of deeply scaled ferroelectric FinFETs for non-von Neumann computing with proper combination of architecture and algorithm