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Recommender systems (RSs) have become an indispensable part of online platforms. With the growing concerns of algorithmic fairness, RSs are not only expected to deliver high-quality personalized content, but are also demanded not to discriminate against users based on their demographic information. However, existing RSs could capture undesirable correlations between sensitive features and observed user behaviors, leading to biased recommendations. Most fair RSs tackle this problem by completely blocking the influences of sensitive features on recommendations. But since sensitive features may also affect user interests in a fair manner (e.g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities. To address this challenge, we propose a path-specific fair RS (PSF-RS) for recommendations. Specifically, we summarize all fair and unfair correlations between sensitive features and observed ratings into two latent proxy mediators, where the concept of path-specific bias (PS-Bias) is defined based on path-specific counterfactual inference. Inspired by Pearl's minimal change principle, we address the PS-Bias by minimally transforming the biased factual world into a hypothetically fair world, where a fair RS model can be learned accordingly by solving a constrained optimization problem. For the technical part, we propose a feasible implementation of PSF-RS, i.e., PSF-VAE, with weakly-supervised variational inference, which robustly infers the latent mediators such that unfairness can be mitigated while necessary recommendation diversities can be maximally preserved simultaneously. Experiments conducted on semi-simulated and real-world datasets demonstrate the effectiveness of PSF-RS. 

A plant’s oxygen supply can vary from normal (normoxia) to total depletion (anoxia). Tolerance to anoxia is relevant to wetland species, rice (Oryza sativa) cultivation, and submergence tolerance of crops. Decoding and transmitting calcium (Ca) signals may be an important component to anoxia tolerance; however, the contribution of intracellular Ca transporters to this process is poorly understood. Four functional cation/proton exchangers (CAX1–4) in Arabidopsis (Arabidopsis thaliana) help regulate Ca homeostasis around the vacuole. Our results demonstrate that cax1 mutants are more tolerant to both anoxic conditions and submergence. Using phenotypic measurements, RNA-sequencing, and proteomic approaches, we identified cax1-mediated anoxia changes that phenocopy changes present in anoxia-tolerant crops: altered metabolic processes, diminished reactive oxygen species production post anoxia, and altered hormone signaling. Comparing wild-type and cax1 expressing genetically encoded Ca indicators demonstrated altered cytosolic Ca signals in cax1 during reoxygenation. Anoxia-induced Ca signals around the plant vacuole are involved in the control of numerous signaling events related to adaptation to low oxygen stress. This work suggests that cax1 anoxia response pathway could be engineered to circumvent the adverse effects of flooding that impair production agriculture.

 

Several COVID-19 vaccines have been on the market since early 2021 and may vary in their effectiveness and safety. This study characterizes hesitancy about accepting COVID-19 vaccines among parents in Shanghai, China, and identifies how sensitive they are to changes in vaccine safety and effectiveness profiles. Schools in each township of Minhang District, Shanghai, were sampled, and parents in the WeChat group of each school were asked to participate in this cross-sectional Internet-based survey. Parents responded to questions about hesitancy and were given information about five different COVID-19 vaccine candidates, the effectiveness of which varied between 50 and 95% and which had a risk of fever as a side effect between 5 and 20%. Overall, 3673 parents responded to the survey. Almost 90% would accept a vaccine for themselves (89.7%), for their child (87.5%) or for an elderly parent (88.5%) with the most ideal attributes (95% effectiveness with 5% risk of fever). But with the least ideal attributes (50% effectiveness and a 20% risk of fever) these numbers dropped to 33.5%, 31.3%, and 31.8%, respectively. Vaccine hesitancy, age at first child’s birth, and relative income were all significantly related to sensitivity to vaccine safety and effectiveness. Parents showed a substantial shift in attitudes towards a vaccine based on its safety and effectiveness profile. These findings indicate that COVID-19 vaccine acceptance may be heavily influenced by how effective the vaccine actually is and could be impeded or enhanced based on vaccines already on the market. 

Jumping spiders (Salticidae) rely on accurate depth perception for predation and navigation. They accomplish depth perception, despite their tiny brains, by using specialized optics. Each principal eye includes a multitiered retina that simultaneously receives multiple images with different amounts of defocus, and from these images, distance is decoded with relatively little computation. We introduce a compact depth sensor that is inspired by the jumping spider. It combines metalens optics, which modifies the phase of incident light at a subwavelength scale, with efficient computations to measure depth from image defocus. Instead of using a multitiered retina to transduce multiple simultaneous images, the sensor uses a metalens to split the light that passes through an aperture and concurrently form 2 differently defocused images at distinct regions of a single planar photosensor. We demonstrate a system that deploys a 3-mm-diameter metalens to measure depth over a 10-cm distance range, using fewer than 700 floating point operations per output pixel. Compared with previous passive depth sensors, our metalens depth sensor is compact, single-shot, and requires a small amount of computation. This integration of nanophotonics and efficient computation brings artificial depth sensing closer to being feasible on millimeter-scale, microwatts platforms such as microrobots and microsensor networks. 

The focal track sensor is a monocular and computationally efficient depth sensor that is based on defocus controlled by a liquid membrane lens. It synchronizes small lens oscillations with a photosensor to produce real-time depth maps by means of differential defocus, and it couples these oscillations with bigger lens deformations that adapt the defocus working range to track objects over large axial distances. To create the focal track sensor, we derive a texture-invariant family of equations that relate image derivatives to scene depth when a lens changes its focal length differentially. Based on these equations, we design a feed-forward sequence of computations that: robustly incorporates image derivatives at multiple scales; produces confidence maps along with depth; and can be trained endto- end to mitigate against noise, aberrations, and other non-idealities. Our prototype with 1-inch optics produces depth and confidence maps at 100 frames per second over an axial range of more than 75cm.