59 research outputs found
Deep learning based enhancement of ordered statistics decoding of LDPC codes
Aiming at designing plausible decoders with channel information free, low
complexity, high throughput, and approaching maximum likelihood performance, we
put forward a streamlined architecture which concatenates sequentially three
components. Specifically, to tackle the decoding failures of normalized
min-sum, the whole decoding trajectory, not limited to the last iteration
information conventionally, is fed into a trained convolutional neural network
to yield new reliability metric for each sequence bit, termed decoding
information aggregation. Then an adapted order statistics decoding, following
the suggested decoding path, is adopted to process the sequence ordered with
new metric more efficiently in that many invalid searches contained in
conventional methods otherwise are evaded. The role of decoding information
aggregation is elaborated via statistics data to reveal that it can arrange
more error-prone bits into the fore part of most reliable basis of order
statistics decoding, which is vital for the effective decoding enhancement. We
argue the superposition of improved bitwise reliability of the most reliable
basis and the imposed rigorous code structure by OSD enables the proposed
architecture being a competitive rival of the state of the art decoders, which
was verified in extensive simulation in terms of performance, complexity and
latency for short and moderate LDPC codes.Comment: 9 pages, 6 figures, 2 table
Establishment and characterization of clear cell renal cell carcinoma cell lines with different metastatic potential from Chinese patients
1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses
Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage
We report new Gaussian boson sampling experiments with
pseudo-photon-number-resolving detection, which register up to 255 photon-click
events. We consider partial photon distinguishability and develop a more
complete model for characterization of the noisy Gaussian boson sampling. In
the quantum computational advantage regime, we use Bayesian tests and
correlation function analysis to validate the samples against all current
classical mockups. Estimating with the best classical algorithms to date,
generating a single ideal sample from the same distribution on the
supercomputer Frontier would take ~ 600 years using exact methods, whereas our
quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample.
Generating the hardest sample from the experiment using an exact algorithm
would take Frontier ~ 3.1*10^10 years.Comment: submitted on 10 Apri
Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response
The SARS-CoV-2 virus, the causative agent of COVID-19, is undergoing constant mutation. Here, we utilized an integrative approach combining epidemiology, virus genome sequencing, clinical phenotyping, and experimental validation to locate mutations of clinical importance. We identified 35 recurrent variants, some of which are associated with clinical phenotypes related to severity. One variant, containing a deletion in the Nsp1-coding region (D500-532), was found in more than 20% of our sequenced samples and associates with higher RT-PCR cycle thresholds and lower serum IFN-beta levels of infected patients. Deletion variants in this locus were found in 37 countries worldwide, and viruses isolated from clinical samples or engineered by reverse genetics with related deletions in Nsp1 also induce lower IFN-beta responses in infected Calu-3 cells. Taken together, our virologic surveillance characterizes recurrent genetic diversity and identified mutations in Nsp1 of biological and clinical importance, which collectively may aid molecular diagnostics and drug design.Peer reviewe
Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects
Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by
Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects
Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by
Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects
Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by
Burglars blocked by barriers? The impact of physical and social barriers on residential burglars' target location choices in China
Based on an offender spatial decision-making perspective, this burglary target location choice study aims to understand how physical and social barriers affect why residential burglars commit their crimes at particular locations in a major Chinese city. Using data on 3860 residential burglaries committed by 3772 burglars between January 2012 and June 2016 in ZG city, China, conditional logit (discrete choice) models were estimated to assess residential burglars' target location choice preferences. Three types of physical barriers were distinguished: major roads with access control, major roads without access control, and major rivers. Social barriers were constructed based on the Hukou system to reflect how local and nonlocal residents live segregated lives. Results show that residential burglars are less likely to target areas for which they have to cross a physical barrier and even less likely to do so if they have to cross multiple rivers. Local burglars are more likely to target communities with a majority of local residents than communities with a majority nonlocal population or a mixed community. Such a social barrier was less pronounced for nonlocal burglars. These findings add new insight that physical and social barriers affect, to various degrees, where residential burglars in China commit their crimes
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