Segmentation of nuclei regions from histological images is an important task for automated computer-aided analysis of histological images, particularly in the presence of impermissible color variation in the color appearance of stained tissue images. While color normalization enables better nuclei segmentation, accurate segmentation of nuclei structures makes color normalization rather trivial. In this respect, the paper proposes a novel deep generative model for simultaneously segmenting nuclei structures and normalizing color appearance of stained histological images.This model judiciously integrates the merits of truncated normal distribution and spatial attention. The model assumes that the latent color appearance information, corresponding to a particular histological image, is independent of respective nuclei segmentation map as well as embedding map information. The disentangled representation makes the model generalizable and adaptable as the modification or loss in color appearance information cannot be able to affect the nuclei segmentation map as well as embedding information. Also, for dealing with the stain overlap of associated histochemical reagents, the prior for latent color appearance code is assumed to be a mixture of truncated normal distributions. The proposed model incorporates the concept of spatial attention for segmentation of nuclei regions from histological images. The performance of the proposed approach, along with a comparative analysis with related state-of-the-art algorithms, has been demonstrated on publicly available standard histological image data sets.

Feline-focused cyberpunk adventure Stray is officially coming to Mac. The critically-acclaimed title will be available for all Apple silicon models, from the most powerful Mac Studio desktops to standard Macbook Air laptops. This is only for silicon models, however, so older Intel-based Macs need not apply. There's no release date yet but developer BlueTwelve Studio and publisher Annapurna Interactive urge fans to keep an eye on its Twitter accounts for up-to-date information. Stray originally launched last year for PS4, PS5 and PC via Steam.

We are developing special-purpose, low-power analog-to-digital converters for speech and music applications, that feature analog circuit models of biological audition to process the audio signal before conversion. This paper describes our most recent converter design, and a working system that uses several copies ofthe chip to compute multiple representations of sound from an analog input. This multi-representation system demonstrates the plausibility of inexpensively implementing an auditory scene analysis approach to sound processing.

Detectim of the periodicity of amplitude modulatim is a major step in the determinatim of the pitch of a SOODd. In this article we will present a silicm model that uses synchrroicity of spiking neurms to extract the fundamental frequency of a SOODd. It is based m the observatim that the so called'Choppers' in the mammalian Cochlear Nucleus synchrmize well for certain rates of amplitude modulatim, depending m the cell's intrinsic chopping frequency. Our silicm model uses three different circuits, i.e., an artificial cochlea, an Inner Hair Cell circuit, and a spiking neuron circuit

Detectim of the periodicity of amplitude modulatim is a major step in the determinatim of the pitch of a SOODd. In this article we will present a silicm model that uses synchrroicity of spiking neurms to extract the fundamental frequency of a SOODd. It is based m the observatim that the so called'Choppers' in the mammalian Cochlear Nucleus synchrmize well for certain rates of amplitude modulatim, depending m the cell's intrinsic chopping frequency. Our silicm model uses three different circuits, i.e., an artificial cochlea, an Inner Hair Cell circuit, and a spiking neuron circuit

An electrmic model can be especially helpful, when the parameters of the model can be easily controlled, and when the model will operate in real time.

We are developing special-purpose, low-power analog-to-digital converters for speech and music applications, that feature analog circuit models of biological audition to process the audio signal before conversion. This paper describes our most recent converter design, and a working system that uses several copies ofthe chip to compute multiple representations of sound from an analog input. This multi-representation system demonstrates the plausibility of inexpensively implementing an auditory scene analysis approach to sound processing. 1. INTRODUCTION The visual system computes multiple representations of the retinal image, such as motion, orientation, and stereopsis, as an early step in scene analysis. Likewise, the auditory brainstem computes secondary representations of sound, emphasizing properties such as binaural disparity, periodicity, and temporal onsets. Recent research in auditory scene analysis involves using computational models of these auditory brainstem representations in engineering applications. Computation is a major limitation in auditory scene analysis research: the complete auditory processing system described in (Brown and Cooke, 1994) operates at approximately 4000 times real time, running under UNIX on a Sun SPARCstation 1. Standard approaches to hardware acceleration for signal processing algorithms could be used to ease this computational burden in a research environment; a variety of parallel, fixed-point hardware products would work well on these algorithms.

We are developing special-purpose, low-power analog-to-digital converters for speech and music applications, that feature analog circuit models of biological audition to process the audio signal before conversion. This paper describes our most recent converter design, and a working system that uses several copies ofthe chip to compute multiple representations of sound from an analog input. This multi-representation system demonstrates the plausibility of inexpensively implementing an auditory scene analysis approach to sound processing. 1. INTRODUCTION The visual system computes multiple representations of the retinal image, such as motion, orientation, and stereopsis, as an early step in scene analysis. Likewise, the auditory brainstem computes secondary representations of sound, emphasizing properties such as binaural disparity, periodicity, and temporal onsets. Recent research in auditory scene analysis involves using computational models of these auditory brainstem representations in engineering applications. Computation is a major limitation in auditory scene analysis research: the complete auditoryprocessing system described in (Brown and Cooke, 1994) operates at approximately 4000 times real time, running under UNIX on a Sun SPARCstation 1. Standard approaches to hardware acceleration for signal processing algorithms could be used to ease this computational burden in a research environment; a variety of parallel, fixed-point hardware products would work well on these algorithms.

The biological network is small and relatively well understood, and the silicon model can therefore span three levels of organization in the leech nervous system (neuron, ganglion, system); it represents one of the first comprehensive models of leech swimming operating in real-time. The circuit employs biophysically motivated analog neurons networked to form multiple biologically inspired silicon ganglia. These ganglia are coupled using known interganglionic connections. Thus the model retains the flavor of its biological counterpart, and though simplified, the output of the silicon circuit is similar to the output of the leech swim central pattern generator. The model operates on the same time-and spatial-scale as the leech nervous system and will provide an excellent platform with which to explore real-time adaptive locomotion in the leech and other "simple" invertebrate nervous systems.

The biological network is small and relatively well understood, and the silicon model can therefore span three levels of organization in the leech nervous system (neuron, ganglion, system); it represents one of the first comprehensive models of leech swimming operating in real-time. The circuit employs biophysically motivated analog neurons networked to form multiple biologically inspired silicon ganglia. These ganglia are coupled using known interganglionic connections. Thus the model retains the flavor of its biological counterpart, and though simplified, the output of the silicon circuit is similar to the output of the leech swim central pattern generator. The model operates on the same time-and spatial-scale as the leech nervous system and will provide an excellent platform with which to explore real-time adaptive locomotion in the leech and other "simple" invertebrate nervous systems.