Single-cell RNA sequencing (scRNA-seq) has made significant strides in unraveling the intricate cellular diversity within complex tissues. This is particularly critical in the brain, presenting a greater diversity of cell types than other tissue types, to gain a deeper understanding of brain function within various cellular contexts. However, analyzing scRNA-seq data remains a challenge due to inherent measurement noise stemming from dropout events and the limited utilization of extensive gene expression information. In this work, we introduce scHyena, a foundation model designed to address these challenges and enhance the accuracy of scRNAseq analysis in the brain. Specifically, inspired by the recent Hyena operator, we design a novel Transformer architecture called singe-cell Hyena (scHyena) that is equipped with a linear adaptor layer, the positional encoding via gene-embedding, and a bidirectional Hyena operator. This enables us to process full-length scRNAseq data without losing any information from the raw data. In particular, our model learns generalizable features of cells and genes through pre-training scHyena using the full length of scRNA-seq data. We demonstrate the superior performance of scHyena compared to other benchmark methods in downstream tasks, including cell type classification and scRNA-seq imputation. Single-cell RNA sequencing (scRNA-seq) is a powerful technique for profiling gene expression levels at single-cell resolution, enabling molecular characteristics of complex biological systems in both normal and disease states (Saliba et al., 2014; Rood et al., 2022). Through scRNA-seq, several key objectives can be achieved, including cell type annotation (Li et al., 2020; Hao et al., 2021), the discovery of novel cell types (Villani et al., 2017), the identification of marker genes (Jaitin et al., 2014), and the analysis of cellular heterogeneity (Papalexi & Satija, 2018; Kinker et al., 2020). It is worth noting that the brain exhibits a particularly diverse range of cell types compared to other tissues (Saunders et al., 2018; Hodge et al., 2019). Therefore, conducting scRNA-seq analysis in the brain is especially important to gain a deeper understanding of brain function within various cellular contexts.

Analyzing data from the Large Hadron Collider (LHC) present a hyper challenge. A given collision event may result in hundreds of outgoing particles, each with many features (momentum, electric charge, etc.). This hyper variate phase space is then observed by complex multi-channel detectors that are essentially hyperspectral cameras. The LHC detectors have millions of readout channels and dimensionality reduction is essential for data analysis. One natural and nearly lossless reduction is the reconstruction of charged particle trajectories ('tracks'). The innermost layers of the detectors at the LHC are constructed to register the passage of charged particles without significantly altering the particle energy or direction. In the ATLAS and CMS detectors, this is achieved using silicon sensors that are finely segmented in one or two directions and are called strips and pixels, respectively. We will focus on pixels, although our methodology applies more generally. Typically, the first step in a tracking algorithm is the construction of seeds, which are sets of three or more hit pixel clusters that can be used to fit charged-particle trajectories (see e.g.

In attaining these objectives certain other desirable features of programming languages had to be relegated to secondary importance: (vi) Fast arithmetical facilities on integer and real numbers.

The relation between self awareness and intelligence is an open problem these days. Despite the fact that self awarness is usually related to Emotional Intelligence, this is not the case here. The problem described in this paper is how to model an agent which knows (Cognitive) Binary Logic and which is also able to pass (without any mistake) a certain family of Turing Tests designed to verify its knowledge and its discourse about the modal states of truth corresponding to well-formed formulae within the language of Propositional Binary Logic.

Humans make optimal perceptual decisions in noisy and ambiguous conditions. Computations underlying such optimal behavior have been shown to rely on probabilistic inference according to generative models whose structure is usually taken to be known a priori. We argue that Bayesian model selection is ideal for inferring similar and even more complex model structures from experience. We find in experiments that humans learn subtle statistical properties of visual scenes in a completely unsupervised manner. We show that these findings are well captured by Bayesian model learning within a class of models that seek to explain observed variables by independent hidden causes.

Humans make optimal perceptual decisions in noisy and ambiguous conditions. Computations underlying such optimal behavior have been shown to rely on probabilistic inference according to generative models whose structure is usually taken to be known a priori. We argue that Bayesian model selection is ideal for inferring similar and even more complex model structures from experience. We find in experiments that humans learn subtle statistical properties of visual scenes in a completely unsupervised manner. We show that these findings are well captured by Bayesian model learning within a class of models that seek to explain observed variables by independent hidden causes.

There is a growing number of studies supporting the Classical View of perception as probabilistic inference [1, 2].