The Impact of Sharing Economy on Local Employment: Evidence from Airbnb

A growing literature has documented the impact of home sharing. We study a new externality in which local people can enjoy more job opportunities because of the entry of home sharing. Using data from Airbnb and employing a quasi-experimental design, we find the following evidences. We find that home sharing provides more employment options to locals especially for lesseducation and low-income population. As home sharing becomes an incentive for local employment, our study provides important understanding of home sharing and its implications to local welfare, Airbnb hosts, and policy makers

An energetically beneficial leader-linker interaction abolishes ligand-binding cooperativity in glycine riboswitches

Comprised of two aptamers connected by a short nucleotide linker, the glycine riboswitch was the first example of naturally occurring RNA elements reported to bind small organic molecules cooperatively. Earlier works have shown binding of glycine to the second aptamer allows tertiary interactions to be made between the two aptamers, which facilitates binding of a separate glycine molecule to the first aptamer, leading to glycine-binding cooperativity. Prompted by a distinctive protection pattern in the linker region of a minimal glycine riboswitch construct, we have identified a highly conserved ( \u3e 90%) leader-linker duplex involving leader nucleotides upstream of the previously reported consensus glycine riboswitch sequences. In \u3e 50% of the glycine riboswitches, the leader-linker interaction forms a kink-turn motif. Characterization of three glycine ribsowitches showed that the leader-linker interaction improved the glycine-binding affinities by 4.5- to 86-fold. In-line probing and native gel assays with two aptamers in trans suggested synergistic action between glycine-binding and interaptamer interaction during global folding of the glycine riboswitch. Mutational analysis showed that there appeared to be no ligand-binding cooperativity in the glycine riboswitch when the leader-linker interaction is present, and the previously measured cooperativity is simply an artifact of a truncated construct missing the leader sequence

Comparison of preprocessing methods and storage times for touch DNA samples

Aim To select appropriate preprocessing methods for different substrates by comparing the effects of four different preprocessing methods on touch DNA samples and to determine the effect of various storage times on the results of touch DNA sample analysis. Method Hand touch DNA samples were used to investigate the detection and inspection results of DNA on different substrates. Four preprocessing methods, including the direct cutting method, stubbing procedure, double swab technique, and vacuum cleaner method, were used in this study. DNA was extracted from mock samples with four different preprocessing methods. The best preprocess protocol determined from the study was further used to compare performance after various storage times. DNA extracted from all samples was quantified and amplified using standard procedures. Results The amounts of DNA and the number of alleles detected on the porous substrates were greater than those on the non-porous substrates. The performances of the four preprocessing methods varied with different substrates. The direct cutting method displayed advantages for porous substrates, and the vacuum cleaner method was advantageous for non-porous substrates. No significant degradation trend was observed as the storage times increased. Conclusion Different substrates require the use of different preprocessing method in order to obtain the highest DNA amount and allele number from touch DNA samples. This study provides a theoretical basis for explorations of touch DNA samples and may be used as a reference when dealing with touch DNA samples in case work

Meteor: Improved Secure 3-Party Neural Network Inference with Reducing Online Communication Costs

Secure neural network inference has been a promising solution to private Deep-Learning-as-a-Service, which enables the service provider and user to execute neural network inference without revealing their private inputs. However, the expensive overhead of current schemes is still an obstacle when applied in real applications. In this work, we present \textsc{Meteor}, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. The main contributions of \textsc{Meteor} are two-fold: \romannumeral1) We propose a new and improved 3-party secret sharing scheme stemming from the \textit{linearity} of replicated secret sharing, and design efficient protocols for the basic cryptographic primitives, including linear operations, multiplication, most significant bit extraction, and multiplexer. \romannumeral2) Furthermore, we build efficient and secure blocks for the widely used neural network operators such as Matrix Multiplication, ReLU, and Maxpool, along with exploiting several specific optimizations for better efficiency. Our total communication with the setup phase is a little larger than SecureNN (PoPETs\u2719) and \textsc{Falcon} (PoPETs\u2721), two state-of-the-art solutions, but the gap is not significant when the online phase must be optimized as a priority. Using \textsc{Meteor}, we perform extensive evaluations on various neural networks. Compared to SecureNN and \textsc{Falcon}, we reduce the online communication costs by up to $25.6\times$ and $1.5\times$, and improve the running-time by at most $9.8\times$ (resp. $8.1\times$) and $1.5\times$ (resp. $2.1\times$) in LAN (resp. WAN) for the online inference