Sonnenberg Harrison Partners

A computer-implemented method for training a machine learning model (27) using a training dataset (28) having a set of distributional parameters θ is disclosed. The method comprises selecting (S205) stochastically a subset (28a) from the training dataset (28), calculating a gradient of the subset (28a) by encoding (S210) a cost function representative of the gradient into a quantum circuit, amplifying (S220) the amplitude of the quantum circuit, constructing a likelihood function (S230), and minimising the cost function in a variational quantum circuit to optimise the distributional parameters, extracting the optimised distributional parameters, and entering the optimised distributional parameters into the machine learning model (27).

A shut-off valve comprising a housing (10) having main fluid passage extending along a main flow axis (X-X) between a first port connectable to a first pipe and a second port connectable to second pipe; a shut-off element (30) movable along a shut-off axis (Y-Y) between an open position in which the shut-off element allows a fluid flow between the first port and the second port, and a shut-off position blocking the fluid flow through the shut-off element (30), wherein the shut-off element (30) comprises a shut-off body (34) extending along said shut-off axis, with at least one shutter (33) on at least one of two diametrically opposed ends of the shut-off element (30) to block the flow in the shut-off position, and a shaft or spindle (60), to move the shut-off element wherein the shut-off valve comprises at least one load limiting element.

A shut-off valve is provided. The shut-off valve comprises a housing (10) having main fluid passage extending along a main flow axis (X-X) between a first port connectable to a first pipe and a second port connectable to second pipe, the housing having a first housing section (12) and a second housing section (14) connected by a shut-off section (16). A shut-off element (30) is disposed in the shut-off section, wherein the shut-off element (30) is movable along a shut-off axis (Y-Y) between an open position in which the shut-off element allows a fluid flow between the first port and the second port, and a shut-off position blocking the fluid flow through the shut-off element (30). The shut-off element (30) comprises a shut-off body (34) extending along said shut-off axis, with at least one shutter (33) on at least one of two diametrically opposed ends of the shut-off element (30) to block the flow in the shut-off position, said shutter (33) having a contour (43). A sealing lip (50) is provided on the contour (43) of the shut-off element or on the housing (10). The housing (10) is made of a first material and the shut-off element (30) is made of a second material, wherein the second material is non-metallic and harder than the first material.

A system may include a vehicle for delivering items and a moveable track that cooperates with the vehicle. The moveable track may cooperate with a storage system having storage locations for storing items. The vehicle may drive into the moveable track and lift the track using a vertical drive mechanism. The vehicle may include a horizontal drive system operable to drive the vehicle horizontally along the ground to carry the moveable track to a position adjacent the storage location. The vehicle may operate the vertical drive system to drop the moveable track adjacent the storage system. Additionally, the vehicle may operate the vertical drive to drive up the moveable track to an elevated position adjacent one of the storage locations in the storage system. The vehicle may include a transfer mechanism for transferring items between the vehicle and the storage location while the vehicle is in the elevated position.

The present invention proposes a system and method of reducing a footprint of a predictive computational model, and a method of predicting a protein structure using such a method of reducing footprint of the predictive model. The method of reducing a footprint of a predictive computational model comprises compressing the predictive computational model to come up with a compressed model, using a model compressor based on advanced network structures, and retraining the compressed model to adjust the compressed model and thereby produce an optimised predictive computational model with a reduced footprint.

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Zusammenfassung wird geladen …

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