Abstract: The present invention relates to a pharmaceutical combination comprising a KRAS G12C inhibitor and one or more therapeutic agents which is selected from an agent targeting the MARK pathway or an agent targeting parallel pathways; and pharmaceutical compositions comprising the same. The invention also relates to KRAS G12C inhibitors alone or said combinations for use in methods of treating a cancer or a tumor, in particular wherein the cancer or tumor is KRAS G12C mutant.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: It relates to a pharmaceutical combination comprising a KRAS G12C inhibitor, in particular 1-{6-[(4M)-4-(5-Chloro-6-methyl-1H-indazol-4-yl)-5-methyl-3-(1-methyl-1H-indazol-5-yl)-1H-pyrazol-1-yl]-2-azaspiro [3.3] heptan-2-yl} prop-2-en-1-one (Compound A) and one or more therapeutically active agents selected from a SHP2 inhibitor (e.g. TNO155) and a PD-1 inhibitor, pharmaceutical compositions comprising the same; and methods of using such combinations and compositions in the treatment or prevention of a cancer or a tumor, in particular wherein the cancer or tumor is KRAS G12C mutated.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Disclosed is device for calibrating oil-water two-phase flow sensor, the device includes a wellbore model, an oil-water separation mechanism, an oil-water mixing mechanism, an oil inlet mechanism and a water inlet mechanism, the oil-water separation mechanism has a mixture inlet, an oil outlet, and a water outlet. The beneficial effect of the technical scheme proposed in the disclosure is: the oil-water mixture flowing out of the wellbore model is separated by an oil-water separation mechanism, and the separated oil and water are introduced into the oil-water mixing mechanism through an oil inlet mechanism and a water inlet mechanism, respectively, for remixing, the oil-water mixture formed by re-mixing enters the wellbore model, and enters the next cycle, which can realize the reuse of oil and water, the miniaturization of the device, and reduction of the production cost of the device.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Disclosed is device for calibrating oil-water two-phase flow sensor, the device includes a wellbore model, an oil-water separation mechanism, an oil-water mixing mechanism, an oil inlet mechanism and a water inlet mechanism, the oil-water separation mechanism has a mixture inlet, an oil outlet, and a water outlet. The beneficial effect of the technical scheme proposed in the disclosure is: the oil-water mixture flowing out of the wellbore model is separated by an oil-water separation mechanism, and the separated oil and water are introduced into the oil-water mixing mechanism through an oil inlet mechanism and a water inlet mechanism, respectively, for remixing, the oil-water mixture formed by re-mixing enters the wellbore model, and enters the next cycle, which can realize the reuse of oil and water, the miniaturization of the device, and reduction of the production cost of the device.

Abstract: A casting device of a large non-ferrous metal thin-walled structural component. A liquid outlet of the casting device is communicated with a casting sand box. The casting device comprises an L-shaped liquid storage cylinder, a pressure supplying cylinder, and a crystallization treater. Protective gas with the first gas pressure can be inflated into the top of the L-shaped liquid storage cylinder. The pressure supplying cylinder and the L-shaped liquid storage cylinder are integrally connected to form a U-shaped tube connector. Protective gas with the second gas pressure can be inflated into the top of the pressure supplying cylinder. A liquid inlet of the crystallization treater is communicated with the pressure supplying cylinder while a liquid outlet is communicated with the pouring system and the mold cavity. The crystallization treater is provided with a grain refining mechanism.

Abstract: A casting device of a large non-ferrous metal thin-walled structural component. A liquid outlet of the casting device is communicated with a casting sand box. The casting device comprises an L-shaped liquid storage cylinder, a pressure supplying cylinder, and a crystallization treater. Protective gas with the first gas pressure can be inflated into the top of the L-shaped liquid storage cylinder. The pressure supplying cylinder and the L-shaped liquid storage cylinder are integrally connected to form a U-shaped tube connector. Protective gas with the second gas pressure can be inflated into the top of the pressure supplying cylinder. A liquid inlet of the crystallization treater is communicated with the pressure supplying cylinder while a liquid outlet is communicated with the pouring system and the mold cavity. The crystallization treater is provided with a grain refining mechanism.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: A fiber optic array is miniaturized in a configuration that includes an input collimator, a first birefringence crystal, a waveplate, a Faraday rotator, a second birefringence crystal and a second collimator. The elements are aligned along a common optical axis. A light beam along the optical axis that is passed through the first collimator emerges off of the optical axis and through the remaining elements. It re-emerges from the fiber optic array on axis. The configuration provides substantial attenuation in a backward direction.