Two Binary trees of Rational numbers -- the S-tree and the SC-tree

In this study, we explore a novel approach to demonstrate the countability of rational numbers and illustrate the relationship between the Calkin-Wilf tree and the Stern-Brocot tree in a more intuitive manner. By employing a growth pattern akin to that of the Calkin-Wilf tree, we construct the S-tree and establish a one-to-one correspondence between the vertices of the S-tree and the rational numbers in the interval $(0,1]$ using 0-1 sequences. To broaden the scope of this concept, we further develop the SC-tree, which is proven to encompass all positive rational numbers, with each rational number appearing only once. We also delve into the interplay among these four trees and offer some applications for the newly introduced tree structures.Comment: 24 pages, 15 figures, v

A novel radar signal recognition method based on a deep restricted Boltzmann machine

Radar signal recognition is of great importance in the field of electronic intelligence reconnaissance. To deal with the problem of parameter complexity and agility of multi-function radars in radar signal recognition, a new model called radar signal recognition based on the deep restricted Boltzmann machine (RSRDRBM) is proposed to extract the feature parameters and recognize the radar emitter. This model is composed of multiple restricted Boltzmann machines. A bottom-up hierarchical unsupervised learning is used to obtain the initial parameters, and then the traditional back propagation (BP) algorithm is conducted to fine-tune the network parameters. Softmax algorithm is used to classify the results at last. Simulation and comparison experiments show that the proposed method has the ability of extracting the parameter features and recognizing the radar emitters, and it is characterized with strong robustness as well as highly correct recognition rate

"It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools

While revolutionary AI-powered code generation tools have been rising rapidly, we know little about how and how to help software developers form appropriate trust in those AI tools. Through a two-phase formative study, we investigate how online communities shape developers' trust in AI tools and how we can leverage community features to facilitate appropriate user trust. Through interviewing 17 developers, we find that developers collectively make sense of AI tools using the experiences shared by community members and leverage community signals to evaluate AI suggestions. We then surface design opportunities and conduct 11 design probe sessions to explore the design space of using community features to support user trust in AI code generation systems. We synthesize our findings and extend an existing model of user trust in AI technologies with sociotechnical factors. We map out the design considerations for integrating user community into the AI code generation experience

FMRP Links Optimal Codons to mRNA stability in Neurons [preprint]

Fragile X syndrome (FXS) is caused by inactivation of the FMR1 gene and loss of encoded FMRP, an RNA binding protein that represses translation of some of its target transcripts. Here we use ribosome profiling and RNA-seq to investigate the dysregulation of translation in the mouse brain cortex. We find that most changes in ribosome occupancy on hundreds of mRNAs are largely driven by dysregulation in transcript abundance. Many downregulated mRNAs, which are mostly responsible for neuronal and synaptic functions, are highly enriched for FMRP binding targets. RNA metabolic labeling demonstrates that in FMRP-deficient cortical neurons, mRNA downregulation is caused by elevated degradation, and is correlated with codon optimality. Moreover, FMRP preferentially binds mRNAs with optimal codons, suggesting that it stabilizes such transcripts through direct interactions via the translational machinery. Finally, we show that the paradigm of genetic rescue of FXS-like phenotypes in FMRP-deficient mice by deletion of the Cpeb1 gene is mediated by restoration of steady state RNA levels and consequent rebalancing of translational homeostasis. Our data establish an essential role of FMRP in codon optimality-dependent mRNA stability as an important factor in FXS

Supervised Contrastive Learning for Fine-grained Chromosome Recognition

Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research. However, existing classification methods face significant challenges due to the inter-class similarity and intra-class variation of chromosomes. To address this issue, we propose a supervised contrastive learning strategy that is tailored to train model-agnostic deep networks for reliable chromosome classification. This method enables extracting fine-grained chromosomal embeddings in latent space. These embeddings effectively expand inter-class boundaries and reduce intra-class variations, enhancing their distinctiveness in predicting chromosome types. On top of two large-scale chromosome datasets, we comprehensively validate the power of our contrastive learning strategy in boosting cutting-edge deep networks such as Transformers and ResNets. Extensive results demonstrate that it can significantly improve models' generalization performance, with an accuracy improvement up to +4.5%. Codes and pretrained models will be released upon acceptance of this work

Effect of genotype on the physicochemical, nutritional, and antioxidant properties of hempseed

Hempseed products has been used as nutraceutical supplements and pharmaceutical products. However, hempseed has been underutilized as a food crop for human consumption. To fill the gap of limited knowledge of the variation of hempseed for food consumption, thirteen hemp varieties were selected to evaluate the effect of genotype on the physicochemical, nutritional, and antioxidant properties of hempseed. The tested hempseed contains 26.48–32.03% crude protein with average of 28.48%, 28.03–33.23% crude oil with average of 29.54%, 28.78–36.55% crude fiber with average of 33.49%, and 5.43%–6.32% ash with average of 5.89. Average test weight of 36.85 lbs/bu was relatively low compared to the standard test weight of 44 lbs/bu. Hempseed oil contained high portions of about 80% unsaturated fatty acids such as linoleic and α-linolenic acid. The DPPH scavenging activities varied greatly (0.37–28.78%) for the hydrolysates from different hempseed varieties. This study provides comprehensive understanding of the nutritional value of hempseed for human food and potential of a new crop in agricultural food system

FMRP Control of Ribosome Translocation Promotes Chromatin Modifications and Alternative Splicing of Neuronal Genes Linked to Autism [preprint]

Silencing of FMR1 and loss of its gene product FMRP results in Fragile X Syndrome. FMRP binds brain mRNAs and inhibits polypeptide elongation. Using ribosome profiling of the hippocampus, we find that ribosome footprint levels in Fmr1-deficient tissue mostly reflect changes in RNA abundance. Profiling over a time course of ribosome runoff in wildtype tissue reveals a wide range of ribosome translocation rates; on many mRNAs, the ribosomes are stalled. Sucrose gradient ultracentrifugation of hippocampal slices after ribosome runoff reveals that FMRP co-sediments with stalled ribosomes; and its loss results in decline of ribosome stalling on specific mRNAs. One such mRNA encodes SETD2, a lysine methyltransferase that catalyzes H3K36me3. ChIP-Seq demonstrates that loss of FMRP alters the deployment of this epigenetic mark on chromatin. H3K36me3 is associated with alternative pre-RNA processing, which we find occurs in an FMRP-dependent manner on transcripts linked to neural function and autism spectrum disorders