Abstract: Examples described herein relate to an optical device. The optical device includes a first microring resonator (MRR) laser having a first resonant frequency and a first free spectral range (FSR). The first FSR is greater than a channel spacing of the optical device. Further, the optical device includes a first frequency-dependent filter formed along a portion of the first MRR laser via a common bus waveguide to attenuate one or more frequencies different from the first resonant frequency. A length of the common bus waveguide is chosen to achieve a second FSR of the common bus waveguide to be substantially equal to the channel spacing to enable a single-mode operation for the optical device. Moreover, the optical device includes a first reflector formed at a first end of the common bus waveguide to enhance a unidirectionality of optical signal within the first MRR laser.

Abstract: Techniques for determining value of a home by applying one or more neural network models to images of spaces in the home. The techniques include: obtaining at least one image of a first space inside or outside of a home; determining a type of the first space by processing the at least one image of the first space with a first neural network model; identifying at least one feature in the first space by processing the at least one image with a second neural network model different from the first neural network model and trained using images of spaces of a same type as the first space; and determining a value of the home at least in part by using the at least one feature as input to a machine learning model different from the first neural network model and the second neural network model.

Abstract: Techniques for determining value of a home by applying one or more neural network models to images of spaces in the home. The techniques include: obtaining at least one image of a first space inside or outside of a home; determining a type of the first space by processing the at least one image of the first space with a first neural network model; identifying at least one feature in the first space by processing the at least one image with a second neural network model different from the first neural network model and trained using images of spaces of a same type as the first space; and determining a value of the home at least in part by using the at least one feature as input to a machine learning model different from the first neural network model and the second neural network model.

Abstract: The present disclosure relates to a method of culturing quiescent hematopoietic stem cells. This method involves providing a culture medium and introducing, into the culture medium, quiescent hematopoietic stem cells to culture the stem cells and maintain quiescence of the stem cells. The culture medium comprises a vacuolar-H+ adenosine triphosphate ATPase (“v-ATPase”) inhibitor. Also disclosed are methods of treating a subject for a hematological disorder, methods of culturing leukemic stem cells, and methods of enhancing the hematopoietic reconstitution ability of a population of human hematopoietic stem cells.

Abstract: Techniques for determining value of a home by applying one or more neural network models to images of spaces in the home. The techniques include: obtaining at least one image of a first space inside or outside of a home; determining a type of the first space by processing the at least one image of the first space with a first neural network model; identifying at least one feature in the first space by processing the at least one image with a second neural network model different from the first neural network model and trained using images of spaces of a same type as the first space; and determining a value of the home at least in part by using the at least one feature as input to a machine learning model different from the first neural network model and the second neural network model.

Abstract: Techniques for determining value of a home by applying one or more neural network models to images of spaces in the home. The techniques include: obtaining at least one image of a first space inside or outside of a home; determining a type of the first space by processing the at least one image of the first space with a first neural network model; identifying at least one feature in the first space by processing the at least one image with a second neural network model different from the first neural network model and trained using images of spaces of a same type as the first space; and determining a value of the home at least in part by using the at least one feature as input to a machine learning model different from the first neural network model and the second neural network model.

Abstract: An image scanner includes a light source which illuminates a document being scanned, a reflex mirror set receiving a first light signal which results from light rays that are reflected by the document and a lens barrel which has an imaging lens arranged therein and which receives the first light signal from the reflex mirror set. A flexible optical transmission element has an input end provided in close proximity with the light source so as to receive a second light signal therefrom. A line image sensor is disposed adjacent to the lens barrel and receives the first light signal from the reflex mirror set and the second light signal from the optical transmission element. The line image sensor generates an image scan signal which corresponds to the first light signal and a brightness reference signal which corresponds to the second light signal. A signal compensation circuit receives the image scan signal and the brightness reference signal from the line image sensor.