A Novel Time Lag Method to Measure the Permeation of Vapor-Gas Mixtures

A novel time lag method was proposed to study the permeation of gas mixtures or vapor-gas mixtures. This technology, which is based on the difference in the boiling points of the components, can simultaneously measure the mass transport properties of each component. The permeation of a binary mixture of H2O(v)/CO2 was measured on a composite polymer membrane to demonstrate the feasibility of the technology. The method is low-cost and convenient for the future study of the permeation/separation of such gas mixtures as natural gas, flue gas, etc

Towards Accurate One-Stage Object Detection with AP-Loss

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We verify good convergence property of the proposed algorithm theoretically and empirically. Experimental results demonstrate notable performance improvement in state-of-the-art one-stage detectors based on AP-loss over different kinds of classification-losses on various benchmarks, without changing the network architectures. Code is available at https://github.com/cccorn/AP-loss.Comment: 13 pages, 7 figures, 4 tables, main paper + supplementary material, accepted to CVPR 201

Unitarity estimation for quantum channels

The unitarity is a measure giving information on how much a quantum channel is unitary. Learning the unitarity of an unknown quantum channel $\mathcal{E}$ is a basic and important task in quantum device certification and benchmarking. Generally, this task can be performed with either coherent or incoherent access. For coherent access, there are no restrictions on learning algorithms; while for incoherent access, at each time, after preparing the input state and applying $\mathcal{E}$, the output is measured such that no coherent quantum information can survive or be acted upon by $\mathcal{E}$ again. Quantum algorithms with only incoherent access allow practical implementations without the use of persistent quantum memory, and thus is more suitable for near-term devices. In this paper, we study unitarity estimation in both settings. For coherent access, we provide an ancilla-efficient algorithm that uses $O(\epsilon^{-2})$ calls to $\mathcal{E}$ where $\epsilon$ is the required precision; we show that this algorithm is query-optimal, giving a matching lower bound $\Omega(\epsilon^{-2})$. For incoherent access, we provide a non-adaptive, non-ancilla-assisted algorithm that uses $O(\sqrt{d}\cdot \epsilon^{-2})$ calls to $\mathcal{E}$, where $d$ is the dimension of the system that $\mathcal{E}$ acts on; we show that this algorithm cannot be substantially improved, giving an $\Omega(\sqrt{d}+\epsilon^{-2})$ lower bound, even if adaptive strategies and ancilla systems are allowed. As part of our results, we settle the query complexity of the distinguishing problem for depolarizing and unitary channels with incoherent access by giving a matching lower bound $\Omega(\sqrt{d})$, improving the prior best lower bound $\Omega(\sqrt[3]{d})$ by Aharonov, Cotler, and Qi (Nat. Commun. 2022) and Chen, Cotler, Huang, and Li (FOCS 2021).Comment: 35 page

The role of cities in reducing smoking in China

China is the epicenter of the global tobacco epidemic. China grows more tobacco, produces more cigarettes, makes more profits from tobacco and has more smokers than any other nation in the world. Approximately one million smokers in China die annually from diseases caused by smoking, and this estimate is expected to reach over two million by 2020. China cities have a unique opportunity and role to play in leading the tobacco control charge from the “bottom up”. The Emory Global Health Institute—China Tobacco Control Partnership supported 17 cities to establish tobacco control programs aimed at changing social norms for tobacco use. Program assessments showed the Tobacco Free Cities grantees’ progress in establishing tobacco control policies and raising public awareness through policies, programs and education activities have varied from modest to substantial. Lessons learned included the need for training and tailored technical support to build staff capacity and the importance of government and organizational support for tobacco control. Tobacco control, particularly in China, is complex, but the potential for significant public health impact is unparalleled. Cities have a critical role to play in changing social norms of tobacco use, and may be the driving force for social norm change related to tobacco use in China

Retire: Robust Expectile Regression in High Dimensions

High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer useful tools to detect heteroscedasticity in high-dimensional data. The former is computationally challenging due to the non-smooth nature of the check loss, and the latter is sensitive to heavy-tailed error distributions. In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted $\ell_1$-penalization which reduces the estimation bias from $\ell_1$-penalization and leads to oracle properties. Theoretically, we establish the statistical properties of the retire estimator under two regimes: (i) low-dimensional regime in which $d \ll n$; (ii) high-dimensional regime in which $s\ll n\ll d$ with $s$ denoting the number of significant predictors. In the high-dimensional setting, we carefully characterize the solution path of the iteratively reweighted $\ell_1$-penalized retire estimation, adapted from the local linear approximation algorithm for folded-concave regularization. Under a mild minimum signal strength condition, we show that after as many as $\log(\log d)$ iterations the final iterate enjoys the oracle convergence rate. At each iteration, the weighted $\ell_1$-penalized convex program can be efficiently solved by a semismooth Newton coordinate descent algorithm. Numerical studies demonstrate the competitive performance of the proposed procedure compared with either non-robust or quantile regression based alternatives

LEARNING AQUAPONICS POST COVID-19 THROUGH START-UP INDUSTRY PARTNERSHIPS

PROBLEM Pre-COVID-19 lockdowns (2019), we usually organise for our students to visit a commercial aquaponics facility, Green Camel1, which is a start-up company located within The University of Sydney’s Cobbitty campus. Green Camel produces both barramundi (fish) and pesticide-free organic vegetables, such as tomatoes and basil. Effluent from the barramundi is passed through a bioreactor which converts ammonium to nitrate, which is then utilised by the vegetables in a closed-loop system. During the COVID-19 pandemic (2020-21), we had to live-stream and video field visits to remotely located students and international students who are unable to travel to Australia to experience the field visits. We were not very happy with video recording field practicals, since the students did not get a hands-on experience with aquaponics. PLAN After the lifting of the COVID-19 restrictions, the first author partnered with start-up company, Farmwall2, in its STEM Pilot Program. In this Program, Farmwall provided students with an aquaponic ecosystem classroom kit including a fish tank, plant trays, plants, seeds, gravel and micro-organisms for converting ammonium to nitrate, as well as a separate hydroponics kit. An online education platform was also provided with detailed instructions for setting up the aquaponics kit, as well as step by step video instructions on how to maintain the fish tank and grow the vegetables including microgreens. Teachers and students were able to engage with the step-by-step process of setting up the aquaponics system as well as monitoring the health of the system (e.g., pH, ammonium, nitrate and nitrite levels). ACTION Farmwall provided the students with an aquaponic classroom kit so that they can engage in setting up and maintaining a model aquaponics unit. One of the students also contributed biological filtration, white cloud mountain minnows (Tanichthys micagemmae; fish) and aquatic plants. During the lab practicals, students harvested and tasted the snow pea microgreens grown using the aquaponic classroom unit. Some students were also inspired to convert their home fish tanks into home mini-aquaponic systems. REFLECTION In addition to visiting or watching videos of field visits, students learnt to set up and maintain an aquaponics unit to produce vegetables such as microgreens, which is a life skill that they can use in the post-COVID-19 world. Live-streamed and in person practicals provided useful information on how students could set up and produce vegetables including microgreens, becoming potentially self-sufficient. In addition to learning the theory of aquaponic production, students gained the life skills of a close-loop system to produce their own organic vegetables at home. 1https://greencamel.com.au/ 2https://farmwall.com

Membrane Separation for Gases: Materials, Preparation Methods and Transport Mechanism

Gas adsorption and permeation of CO2 gas weremeasured on two carbon molecular sieve membranes over awide pressure range. The two membrane samples werefabricated under similar conditions but with different degreesof carbonization. The pressure dependence of the permeationtime lag was investigated and it is found that the diffusioncoefficient takes a stronger functional dependence on loadingthan the Darken relation and this dependence increases withthe degree of carbonization

Function-informed transcriptome analysis of Drosophila renal tubule

BACKGROUND: Comprehensive, tissue-specific, microarray analysis is a potent tool for the identification of tightly defined expression patterns that might be missed in whole-organism scans. We applied such an analysis to Drosophila melanogaster Malpighian (renal) tubule, a defined differentiated tissue. RESULTS: The transcriptome of the D. melanogaster Malpighian tubule is highly reproducible and significantly different from that obtained from whole-organism arrays. More than 200 genes are more than 10-fold enriched and over 1,000 are significantly enriched. Of the top 200 genes, only 18 have previously been named, and only 45% have even estimates of function. In addition, 30 transcription factors, not previously implicated in tubule development, are shown to be enriched in adult tubule, and their expression patterns respect precisely the domains and cell types previously identified by enhancer trapping. Of Drosophila genes with close human disease homologs, 50 are enriched threefold or more, and eight enriched 10-fold or more, in tubule. Intriguingly, several of these diseases have human renal phenotypes, implying close conservation of renal function across 400 million years of divergent evolution. CONCLUSIONS: From those genes that are identifiable, a radically new view of the function of the tubule, emphasizing solute transport rather than fluid secretion, can be obtained. The results illustrate the phenotype gap: historically, the effort expended on a model organism has tended to concentrate on a relatively small set of processes, rather than on the spread of genes in the genome