SANet: Scene agnostic network for camera localization

This thesis presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other. Despite recent advancement in learning based methods with scene coordinate regression, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and robotic navigation, where a model must be built on-the-fly. Our approach learns to build a hierarchical scene representation and predicts a dense scene coordinate map of a query RGB image on-the-fly given an arbitrary scene. The 6 DoF camera pose of the query image can be estimated with the predicted scene coordinate map. Additionally, the dense prediction can be used for other online robotic and AR applications such as obstacle avoidance. We demonstrate the effectiveness and efficiency of our method on both indoor and outdoor benchmarks, achieving state-of-the-art performance among methods working for arbitrary scenes without retraining or adaptation

A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication

This paper has two contributions. First, we propose a novel coded matrix multiplication technique called Generalized PolyDot codes that advances on existing methods for coded matrix multiplication under storage and communication constraints. This technique uses "garbage alignment," i.e., aligning computations in coded computing that are not a part of the desired output. Generalized PolyDot codes bridge between Polynomial codes and MatDot codes, trading off between recovery threshold and communication costs. Second, we demonstrate that Generalized PolyDot can be used for training large Deep Neural Networks (DNNs) on unreliable nodes prone to soft-errors. This requires us to address three additional challenges: (i) prohibitively large overhead of coding the weight matrices in each layer of the DNN at each iteration; (ii) nonlinear operations during training, which are incompatible with linear coding; and (iii) not assuming presence of an error-free master node, requiring us to architect a fully decentralized implementation without any "single point of failure." We allow all primary DNN training steps, namely, matrix multiplication, nonlinear activation, Hadamard product, and update steps as well as the encoding/decoding to be error-prone. We consider the case of mini-batch size $B=1$, as well as $B>1$, leveraging coded matrix-vector products, and matrix-matrix products respectively. The problem of DNN training under soft-errors also motivates an interesting, probabilistic error model under which a real number $(P,Q)$ MDS code is shown to correct $P-Q-1$ errors with probability $1$ as compared to $\lfloor \frac{P-Q}{2} \rfloor$ for the more conventional, adversarial error model. We also demonstrate that our proposed strategy can provide unbounded gains in error tolerance over a competing replication strategy and a preliminary MDS-code-based strategy for both these error models.Comment: Presented in part at the IEEE International Symposium on Information Theory 2018 (Submission Date: Jan 12 2018); Currently under review at the IEEE Transactions on Information Theor

Examining Spatial Inequalities in Public Green Space Accessibility: A Focus on Disadvantaged Groups in England

Green spaces have been recognised for their positive impact on residents’ health and well-being. However, equitable access to these spaces remains a concern as certain social groups face barriers to reaching public green areas (PGS). Existing studies have explored the relationship between green spaces and vulnerable populations but have often overlooked the spatial variations in accessibility experienced by these groups. This research aimed to investigate the spatial association between green space accessibility and five key variables representing vulnerability: age, educational deprivation, health deprivation, crime rates, and housing barriers. Ordinary least squares and multi-scale geographically weighted regression (MGWR) techniques were employed to analyse the relationship between the nearest distance to public green spaces and the challenges experienced by vulnerable groups based on socioeconomic factors in England. The findings highlight disparities in open green space access for vulnerable groups, particularly older adults and individuals with limited education and housing accessibility, who are more likely to face restricted access to green spaces. There was a negative correlation found between health deprivation and the accessibility of green spaces, indicating people who suffer from the disease may live closer to green spaces. Surprisingly, although a positive association was observed between crime risk and distance to public green space in most areas, there were specific areas that exhibit a negative correlation between them. This study emphasises the importance of considering the perspectives of vulnerable groups in addressing PGS inequality and underscores the need for inclusive public green space planning and policy development

Iridium-Catalyzed Enantioselective C(sp 3 )-H Amidation Controlled by Attractive Noncovalent Interactions

While remarkable progress has been made over the past decade, new design strategies for chiral catalysts in enantioselective C(sp 3 )-H functionalization reactions are still highly desirable. In particular, the ability to use attractive noncovalent interactions for rate acceleration and enantiocontrol would significantly expand the current arsenal for asymmetric metal catalysis. Herein, we report the development of a highly enantioselective Ir(III)-catalyzed intramolecular C(sp 3 )-H amidation reaction of dioxazolone substrates for synthesis of optically enriched γ-lactams using a newly designed α-amino-acid-based chiral ligand. This Ir-catalyzed reaction proceeds with excellent efficiency and with outstanding enantioselectivity for both activated and unactivated alkyl C(sp 3 )-H bonds under very mild conditions. It offers the first general route for asymmetric synthesis of γ-alkyl γ-lactams. Water was found to be a unique cosolvent to achieve excellent enantioselectivity for γ-aryl lactam production. Mechanistic studies revealed that the ligands form a well-defined groove-type chiral pocket around the Ir center. The hydrophobic effect of this pocket allows facile stereocontrolled binding of substrates in polar or aqueous media. Instead of capitalizing on steric repulsions as in the conventional approaches, this new Ir catalyst operates through an unprecedented enantiocontrol mechanism for intramolecular nitrenoid C-H insertion featuring multiple attractive noncovalent interactions. Copyright © 2019 American Chemical Society

Nitrene-mediated intermolecular N–N coupling for efficient synthesis of hydrazides

© 2021, The Author(s), under exclusive licence to Springer Nature Limited.N–N linkages are found in many natural compounds and endow fascinating structural and functional properties. In comparison to the myriad methods for the construction of C–N bonds, chemistry for N–N coupling, especially in an intermolecular fashion, remains underdeveloped. Here, we report a nitrene-mediated intermolecular N–N coupling of dioxazolones and arylamines under iridium or iron catalysis. These reactions offer a simple and efficient method for the synthesis of various hydrazides from readily available carboxylic acid and amine precursors. Although the Ir-catalysed conditions usually give higher N–N coupling yield than the Fe-catalysed conditions, the reactions of sterically more demanding dioxazolones derived from α-substituted carboxylic acids work much better under the Fe-catalysed conditions. Mechanistic studies revealed that the nitrogen atom of Ir acyl nitrene intermediates has strong electrophilicity and can undergo nucleophilic attack with arylamines with the assistance of Cl···HN hydrogen bonding to form the N–N bond with high efficiency and chemoselectivity. [Figure not available: see fulltext.]11Nsciescopu

The Calibration of the 35–40 GHz Solar Radio Spectrometer with the New Moon and a Noise Source

Calibrating solar radio flux has always been a concern in the solar community. Previously, fluxes were calibrated by matching load or the new Moon for relative calibration, and at times with the assistance of other stations’ data. Moreover, the frequency coverage seldom exceeded 26 GHz. This paper reports the upgraded and calibrated Chashan Broadband Solar millimeter spectrometer (CBS) working from 35 to 40 GHz at the Chashan Solar Observatory (CSO). Initially, the calibration of the solar radiation brightness temperature is accomplished using the new Moon as the definitive source. Subsequently, the 35–40 GHz standard flux is achieved by establishing the correlation between the solar radio flux, brightness temperature, and frequency. Finally, the calibration of the solar radio flux is implemented by utilizing a constant temperature-controlled noise source as a reference. The calibration in 2023 February and March reveals that the solar brightness temperature is 11,636 K at 37.25 GHz with a standard deviation (STD) of 652 K. The solar radio flux’s intensity is ∼3000–4000 solar flux units (SFU) in the range of 35–40 GHz with a consistency bias of ±5.3%. The system sensitivity is about ∼5–8 SFU by a rough evaluation, a noise factor of about 200 K, and the coefficient of variation of the system transmission slope of 6.5% @ 12 hr at 37.25 GHz. It is expected that the upgraded CBS will capture more activity during the upcoming solar cycle