Parameter Estimation for Class A Modeled Ocean Ambient Noise

A Gaussian distribution is used by all traditional underwater acoustic signal processors, thus neglecting the impulsive property of ocean ambient noise in shallow waters. Undoubtedly, signal processors designed with a Gaussian model are sub-optimal in the presence of non-Gaussian noise. To solve this problem, firstly a quantile-quantile (Q-Q) plot of real data was analyzed, which further showed the necessity of investigating a non-Gaussian noise model. A Middleton Class A noise model considering impulsive noise was used to model non-Gaussian noise in shallow waters. After that, parameter estimation for the Class A model was carried out with the characteristic function. Lastly, the effectiveness of the method proposed in this paper was verified by using simulated data and real data

Engineering DNA polymerases for application in DNB-based sequencing technology

DNA polymerases serve as the core engine to afford sequence information in sequencing technologies that have revolutionized modern biological research. For application in the DNB-based sequencing platform, an assemblage of DNA polymerases was engineered to catalyze the requisite biochemical reaction. In the process, naturally occurring polymerases were tapped into through deep-learning algorithms for constraints between individual protein residues to narrow down the protein sequence space and to annotate protein sequences in light of their catalytic properties. And the constraints were subsequently applied in designing potential polymerase candidates with the guidance of the sequence annotations. Additionally, ancestral protein sequences were estimated to expand the candidate repertoire. Furthermore, the candidates were subjected to in silico screening before examined by an HTS methodology based on fluorescence signal. Finally, the resulting proteins were expressed and purified for testing in the DNB-based sequencing platform. Our sequencing data suggested that these proteins behave better than their existing counterparts

Parameter Estimation for Class a Modeled Ocean Ambient Noise

A Gaussian distribution is used by all traditional underwater acoustic signal processors, thus neglecting the impulsive property of ocean ambient noise in shallow waters. Undoubtedly, signal processors designed with a Gaussian model are sub-optimal in the presence of non-Gaussian noise. To solve this problem, firstly a quantile-quantile (Q-Q) plot of real data was analyzed, which further showed the necessity of investigating a non-Gaussian noise model. A Middleton Class A noise model considering impulsive noise was used to model non-Gaussian noise in shallow waters. After that, parameter estimation for the Class A model was carried out with the characteristic function. Lastly, the effectiveness of the method proposed in this paper was verified by using simulated data and real data

SPIDER-WEB enables stable, repairable, and encryptible algorithms under arbitrary local biochemical constraints in DNA-based storage

DNA has been considered as a promising medium for storing digital information. Despite the biochemical progress in DNA synthesis and sequencing, novel coding algorithms need to be constructed under the specific constraints in DNA-based storage. Many functional operations and storage carriers were introduced in recent years, bringing in various biochemical constraints including but not confined to long single-nucleotide repeats and abnormal GC content. Existing coding algorithms are not applicable or unstable due to more local biochemical constraints and their combinations. In this paper, we design a graph-based architecture, named SPIDER-WEB, to generate corresponding graph-based algorithms under arbitrary local biochemical constraints. These generated coding algorithms could be used to encode arbitrary digital data as DNA sequences directly or served as a benchmark for the follow-up construction of coding algorithms. To further consider recovery and security issues existing in the storage field, it also provides pluggable algorithmic patches based on the generated coding algorithms: path-based correcting and mapping shuffling. They provide approaches for probabilistic error correction and symmetric encryption respectively.Comment: 30 pages; 12 figures; 2 table

Digital Infrastructure: Overcoming the digital divide in emerging economies. CEPS Special Report, 5 April 2017

Since the 1990s when the internet began to be commercialised globally, the debate on how to close the digital divide has attracted widespread attention. In this Policy Brief, we review the literature on the digital divide in emerging economies with a view to explaining: 1) how internet connectivity promotes social and economic inclusiveness, efficiency and innovation; 2) why the physical access to the internet alone is insufficient to capture the full benefits of digital technology and what other social conditions should be considered; and 3) how to further connect the unconnected population. The digital divide prevents societies from harnessing the full benefits that information and communication technologies can deliver. In this context, actions to foster physical access to the internet remain essential, but they are not sufficient to ensure a truly inclusive information society. Therefore, strong leadership is needed at the global and local levels, to ensure more coordinated efforts among governments, local authorities and actors on the ground. Conversely, maintaining the status quo, while technology progressively pervades every sector of the economy, may critically widen disparities across countries and within national territories. This report offers two sets of policy recommendations: 1) a set of general principles that the G20 should endorse to overcome disparities between emerging and advanced economies; and 2) a set of policy guidelines each nation should follow to bridge the digital divide and foster inclusiveness

Digital Infrastructure: Overcoming the digital divide in China and the European Union. CEPS Research Report, November 2017

This study is the result of collaboration among a group of researchers from CEPS and Zhejiang University (ZJU), who decided to team up and analyse the experience of China and the EU in bridging the digital divide. While acknowledging that both China and Europe have undertaken major efforts to reduce socio-economic and geographical disparities by providing network access to ever more citizens, the authors found that investing in physical access alone is not sufficient to enhance inclusion in the information society. They argue that public authorities should also adopt corollary policies to spur social and economic cohesion through innovations that enable disadvantaged regions to catch up with more developed urban areas. In this context, the report calls upon governments to promote digital innovation and entrepreneurship, foster coordinated efforts and adapt their educational systems to the changing labour market

Mixed Pseudo Labels for Semi-Supervised Object Detection

While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach. Specifically, the pseudo-label method tends to amplify the inherent strengths of the detector while accentuating its weaknesses, which is manifested in the missed detection of pseudo-labels, particularly for small and tail category objects. To overcome these challenges, this paper proposes Mixed Pseudo Labels (MixPL), consisting of Mixup and Mosaic for pseudo-labeled data, to mitigate the negative impact of missed detections and balance the model's learning across different object scales. Additionally, the model's detection performance on tail categories is improved by resampling labeled data with relevant instances. Notably, MixPL consistently improves the performance of various detectors and obtains new state-of-the-art results with Faster R-CNN, FCOS, and DINO on COCO-Standard and COCO-Full benchmarks. Furthermore, MixPL also exhibits good scalability on large models, improving DINO Swin-L by 2.5% mAP and achieving nontrivial new records (60.2% mAP) on the COCO val2017 benchmark without extra annotations