Asset Prices, Monetary Policy, and Aggregate Fluctuations: An Empirical Investigation

This paper studies empirically the dynamic interactions between asset prices, monetary policy, and aggregate fluctuations during the Volcker-Greenspan period. Using a simple structural vector autoregression framework, we investigate the effects of monetary policy on output, inflation and asset prices, the interactions of asset prices with the aggregate economy, as well as the relationship between stock price and house price. Several robust findings emerge. The systematic response of monetary policy to output and inflation is also found to play an important role in stabilizing the aggregate economy. In addition, the results call for special attention to be paid to house price when studying the dynamic relationships between asset prices and macroeconomic fluctuations.House prices; stock prices; systematic monetary policy; structural vector autoregressions.

GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data

Geometric information in the normalized digital surface models (nDSM) is highly correlated with the semantic class of the land cover. Exploiting two modalities (RGB and nDSM (height)) jointly has great potential to improve the segmentation performance. However, it is still an under-explored field in remote sensing due to the following challenges. First, the scales of existing datasets are relatively small and the diversity of existing datasets is limited, which restricts the ability of validation. Second, there is a lack of unified benchmarks for performance assessment, which leads to difficulties in comparing the effectiveness of different models. Last, sophisticated multi-modal semantic segmentation methods have not been deeply explored for remote sensing data. To cope with these challenges, in this paper, we introduce a new remote-sensing benchmark dataset for multi-modal semantic segmentation based on RGB-Height (RGB-H) data. Towards a fair and comprehensive analysis of existing methods, the proposed benchmark consists of 1) a large-scale dataset including co-registered RGB and nDSM pairs and pixel-wise semantic labels; 2) a comprehensive evaluation and analysis of existing multi-modal fusion strategies for both convolutional and Transformer-based networks on remote sensing data. Furthermore, we propose a novel and effective Transformer-based intermediary multi-modal fusion (TIMF) module to improve the semantic segmentation performance through adaptive token-level multi-modal fusion.The designed benchmark can foster future research on developing new methods for multi-modal learning on remote sensing data. Extensive analyses of those methods are conducted and valuable insights are provided through the experimental results. Code for the benchmark and baselines can be accessed at \url{https://github.com/EarthNets/RSI-MMSegmentation}.Comment: 13 page

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation

Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date. Despite the recent success, current S2ST models still suffer from distinct degradation in noisy environments and fail to translate visual speech (i.e., the movement of lips and teeth). In this work, we present AV-TranSpeech, the first audio-visual speech-to-speech (AV-S2ST) translation model without relying on intermediate text. AV-TranSpeech complements the audio stream with visual information to promote system robustness and opens up a host of practical applications: dictation or dubbing archival films. To mitigate the data scarcity with limited parallel AV-S2ST data, we 1) explore self-supervised pre-training with unlabeled audio-visual data to learn contextual representation, and 2) introduce cross-modal distillation with S2ST models trained on the audio-only corpus to further reduce the requirements of visual data. Experimental results on two language pairs demonstrate that AV-TranSpeech outperforms audio-only models under all settings regardless of the type of noise. With low-resource audio-visual data (10h, 30h), cross-modal distillation yields an improvement of 7.6 BLEU on average compared with baselines. Audio samples are available at https://AV-TranSpeech.github.ioComment: Accepted to ACL 202

From natural images to spaceborne imagery: an empirical study of self-supervised learning for Earth observation

In this work, we provide an empirical study on the performance of self-supervised learning for spaceborne imagery. Specifically, we conduct extensive experiments on three well-known remote sensing datasets BigEarthNet, SEN12MS and LCZ42 using four representative state-of-the-art SSL algorithms MoCo, SwAV, SimSiam and Barlow Twins. We analyze the performance of SSL algorithms under different data regimes and compare them to vanilla supervised learning. In addition, we explore the impact of data augmentation, which is known to be a key component in the design and tuning of modern SSL methods

Self-supervised Learning in Remote Sensing: A Review

In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper, we provide an introduction to, and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for earth observation (SSL4EO) to pave the way for fruitful interaction of both domains.Comment: Accepted by IEEE Geoscience and Remote Sensing Magazine. 32 pages, 22 content page

Uncovering differences in the composition and function of phage communities and phage-bacterium interactions in raw soy sauce

IntroductionAlthough the composition and succession of microbial communities in soy sauce fermentation have been well-characterized, the understanding of phage communities in soy sauce remains limited.MethodsThis study determined the diversity, taxonomic composition, and predicted function of phage communities and the phage-host interactions in two types of raw soy sauce (Cantonese-type fermentation, NJ; Japanese-type fermentation, PJ) using shotgun metagenomics.Results and discussionThese two raw soy sauces showed differences in phage composition (121 viral operational taxonomic units (vOTUs) in NJ and 387 vOTUs in PJ), with a higher abundance of the family Siphoviridae (58.50%) in the NJ phage community and a higher abundance of Myoviridae (33.01%) in PJ. Auxiliary metabolic functional annotation analyses showed that phages in the raw soy sauces mostly encoded genes with unknown functions (accounting for 66.33% of COG profiles), but the NJ sample contained genes mostly annotated to conventional functions related to carbohydrate metabolism (0.74%) and lipid metabolism (0.84%), while the PJ sample presented a higher level of amino acid metabolism functions (0.12%). Thirty auxiliary metabolism genes (AMGs) were identified in phage genomes, which were associated with carbohydrate utilization, cysteine and methionine metabolism, and aspartic acid biosynthesis for the host. To identify phage-host interactions, 30 host genomes (affiliated with 22 genera) were also recruited from the metagenomic dataset. The phage-host interaction analysis revealed a wide range of phage hosts, for which a total of 57 phage contigs were associated with 17 host genomes, with Shewanella fodinae and Weissella cibaria infected by the most phages. This study provides a comprehensive understanding of the phage community composition, auxiliary metabolic functions, and interactions with hosts in two different types of raw soy sauce

Immunogenicity and protective potential of chimeric virus-like particles containing SARS-CoV-2 spike and H5N1 matrix 1 proteins

Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has posed a constant threat to human beings and the world economy for more than two years. Vaccination is the first choice to control and prevent the pandemic. However, an effective SARS-CoV-2 vaccine against the virus infection is still needed. This study designed and prepared four kinds of virus-like particles (VLPs) using an insect expression system. Two constructs encoded wild-type SARS-CoV-2 spike (S) fused with or without H5N1 matrix 1 (M1) (S and SM). The other two constructs contained a codon-optimized spike gene and/or M1 gene (mS and mSM) based on protein expression, stability, and ADE avoidance. The results showed that the VLP-based vaccine could induce high SARS-CoV-2 specific antibodies in mice, including specific IgG, IgG1, and IgG2a. Moreover, the mSM group has the most robust ability to stimulate humoral immunity and cellular immunity than the other VLPs, suggesting the mSM is the best immunogen. Further studies showed that the mSM combined with Al/CpG adjuvant could stimulate animals to produce sustained high-level antibodies and establish an effective protective barrier to protect mice from challenges with mouse-adapted strain. The vaccine based on mSM and Al/CpG adjuvant is a promising candidate vaccine to prevent the COVID-19 pandemic

Strategies for improving the production of bio-based vanillin

Abstract Vanillin (4-hydroxy-3-methoxybenzaldehyde) is one of the most popular flavors with wide applications in food, fragrance, and pharmaceutical industries. However, the high cost and limited yield of plant extraction failed to meet the vast market demand of natural vanillin. Vanillin biotechnology has emerged as a sustainable and cost-effective alternative to supply vanillin. In this review, we explored recent advances in vanillin biosynthesis and highlighted the potential of vanillin biotechnology. In particular, we addressed key challenges in using microorganisms and provided promising approaches for improving vanillin production with a special focus on chassis development, pathway construction and process optimization. Future directions of vanillin biosynthesis using inexpensive precursors are also thoroughly discussed