ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Chunk-Based Bi-Scale Decoder for Neural Machine Translation

In typical neural machine translation~(NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.Comment: Accepted as a short paper by ACL 201

Recombinant protein production provoked accumulation of ATP, fructose-1,6-bisphosphate and pyruvate in E. coli K12 strain TG1

Background: Recently it was shown that production of recombinant proteins in E. coli BL21(DE3) using pET based expression vectors leads to metabolic stress comparable to a carbon overfeeding response. Opposite to original expectations generation of energy as well as catabolic provision of precursor metabolites were excluded as limiting factors for growth and protein production. On the contrary, accumulation of ATP and precursor metabolites revealed their ample formation but insufficient withdrawal as a result of protein production mediated constraints in anabolic pathways. Thus, not limitation but excess of energy and precursor metabolites were identified as being connected to the protein production associated metabolic burden. Results: Here we show that the protein production associated accumulation of energy and catabolic precursor metabolites is not unique to E. coli BL21(DE3) but also occurs in E. coli K12. Most notably, it was demonstrated that the IPTG-induced production of hFGF-2 using a tac-promoter based expression vector in the E. coli K12 strain TG1 was leading to persistent accumulation of key regulatory molecules such as ATP, fructose-1,6-bisphosphate and pyruvate. Conclusions: Excessive energy generation, respectively, accumulation of ATP during recombinant protein production is not unique to the BL21(DE3)/T7 promoter based expression system but also observed in the E. coli K12 strain TG1 using another promoter/vector combination. These findings confirm that energy is not a limiting factor for recombinant protein production. Moreover, the data also show that an accelerated glycolytic pathway flux aggravates the protein production associated “metabolic burden”. Under conditions of compromised anabolic capacities cells are not able to reorganize their metabolic enzyme repertoire as required for reduced carbon processing. © 2021, The Author(s)

Metabolic balance during protein production with recombinant Escherichia coli

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Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification

This paper aims to learn a domain-generalizable (DG) person re-identification (ReID) representation from large-scale videos \textbf{without any annotation}. Prior DG ReID methods employ limited labeled data for training due to the high cost of annotation, which restricts further advances. To overcome the barriers of data and annotation, we propose to utilize large-scale unsupervised data for training. The key issue lies in how to mine identity information. To this end, we propose an Identity-seeking Self-supervised Representation learning (ISR) method. ISR constructs positive pairs from inter-frame images by modeling the instance association as a maximum-weight bipartite matching problem. A reliability-guided contrastive loss is further presented to suppress the adverse impact of noisy positive pairs, ensuring that reliable positive pairs dominate the learning process. The training cost of ISR scales approximately linearly with the data size, making it feasible to utilize large-scale data for training. The learned representation exhibits superior generalization ability. \textbf{Without human annotation and fine-tuning, ISR achieves 87.0\% Rank-1 on Market-1501 and 56.4\% Rank-1 on MSMT17}, outperforming the best supervised domain-generalizable method by 5.0\% and 19.5\%, respectively. In the pre-training$\rightarrow$fine-tuning scenario, ISR achieves state-of-the-art performance, with 88.4\% Rank-1 on MSMT17. The code is at \url{https://github.com/dcp15/ISR_ICCV2023_Oral}.Comment: ICCV 2023 Ora