Abstract: The invention is directed to biocompatible composite materials for medical applications such as tissue regeneration. In particular, the present invention is directed to biocompatible composite materials that may be used for the treatment of lost bone or bone defects. According to the invention there is provided an anhydrous biocompatible composite material comprising a biodegradable polymeric material and a granular synthetic material, wherein the polymeric material essentially consists of at least one block copolymer that comprises at least one hydrophilic block and at least one hydrophobic block.

Abstract: The invention is directed to biocompatible composite materials for medical applications such as tissue regeneration. In particular, the present invention is directed to biocompatible composite materials that may be used for the treatment of lost bone or bone defects. According to the invention there is provided an anhydrous biocompatible composite material comprising a biodegradable polymeric material and a granular synthetic material, wherein the polymeric material essentially consists of at least one block copolymer that comprises at least one hydrophilic block and at least one hydrophobic block.

Abstract: A method for ordering and/or selection of latent elements for modeling low dimensional data within a latent space representation, the low dimensional data being a reduced dimensionality representation of input data as determined by a first model component of a model, comprising the steps of training said model and selecting one of said latent element selections based on said training, said training comprising: reducing a dimensionality of the input data to generate said low dimensional data in said latent space representation; training a second model component of said model for each of one or more latent element selections; and optimizing an approximation of the input data as output by said second model component for each said latent element selection, thereby ranking at least one of said plurality of latent elements in the latent space representation based on a contribution of each latent element to the input data.

Abstract: Autoencoder models may be used in the field of lithography to estimate, infer or predict a parameter of interest (e.g., metrology metrics). An autoencoder model is trained to predict a parameter by training it with measurement data (e.g., pupil images) of a substrate obtained from a measurement tool (e.g., optical metrology tool). Disclosed are methods and systems for synchronizing two or more autoencoder models for in-device metrology. Synchronizing two autoencoder models may configure the encoders of both autoencoder models to map from different signal spaces (e.g., measurement data obtained from different machines) to the same latent space, and the decoders to map from the same latent space to each autoencoder's respective signal space. Synchronizing may be performed for various purposes, including matching a measurement performance of one tool with another tool, and configuring a model to adapt to measurement process changes (e.g., changes in characteristics of the tool) over time.

Abstract: A modular autoencoder model is described. The modular autoencoder model comprises input models configured to process one or more inputs to a first level of dimensionality suitable for combination with other inputs: a common model configured to: reduce a dimensionality of combined processed inputs to generate low dimensional data in a latent space; and expand the low dimensional data in the latent space into one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; output models configured to use the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs; and a prediction model configured to estimate one or more parameters based on the low dimensional data in the latent space.

Abstract: A method for controlling an agent. The method includes collecting training data for multiple representations of states of the agent; for every representation and using the training data, training a state encoder, a state decoder, an action encoder and an action decoder, and a transition model, shared for the representations, for latent states, and a Q function model, shared by the representations, for latent states; receiving a state of the agent in one of the representations for which a control action is to be ascertained; mapping the state to one or more latent state(s) using the state encoder for the one of the representations; determining Q values for the state(s) for a set of actions using the Q function model; selecting the control action having the best Q value from the set of actions as the control action; and controlling the agent according to the selected control action.

Abstract: The invention is directed to biocompatible composite materials for medical applications such as tissue regeneration. In particular, the present invention is directed to biocompatible composite materials that may be used for the treatment of lost bone or bone defects. According to the invention there is provided an anhydrous biocompatible composite material comprising a biodegradable polymeric material and a granular synthetic material, wherein the polymeric material essentially consists of at least one block copolymer that comprises at least one hydrophilic block and at least one hydrophobic block.

Abstract: The invention is directed to biocompatible composite materials for medical applications such as tissue regeneration. In particular, the present invention is directed to biocompatible composite materials that may be used for the treatment of lost bone or bone defects. According to the invention there is provided an anhydrous biocompatible composite material comprising a biodegradable polymeric material and a granular synthetic material, wherein the polymeric material essentially consists of at least one block copolymer that comprises at least one hydrophilic block and at least one hydrophobic block.

Abstract: The present invention relates porous osteoinductive nanocomposites and for the methods to prepare osteoinductive nanocomposites. The osteoinductive composites consist of biocompatible polymers and non-heated (or non-sintered), needle-shaped or plate-like calcium phosphate nanocrystals and the methods comprise the steps of: a. adding phosphate to a calcium solution thereby evoking precipitation of calcium phosphate nanocrystals; b. washing the nanocrystals with water and suspending them in an organic solvent or getting dry calcium phosphate nanocrystals after evaporating organic solvent; c.

Abstract: Disclosed is an injectable and moldable ceramic material comprising porous calcium phosphate having surface-microporosity, and a water-free carrier, wherein the carrier is selected so as to disintegrate under physiological circumstances. The latter refers to the property of a carrier to dissolve, disassociate, or otherwise disintegrate after placement (e.g. through injection or implantation) into the human body. By selection of a water-free polymer or polymer blend which is combined with surface-microporous calcium phosphates, the favourable osteoinductive properties by virtue of the surface-microporosity can be retained for prolonged shelf life.

Abstract: The present invention relates porous osteoinductive nanocomposites and for the methods to prepare osteoinductive nanocomposites. The osteoinductive composites consist of biocompatible polymers and non-heated (or non-sintered), needle-shaped or plate-like calcium phosphate nanocrystals and the methods comprise the steps of: a. adding phosphate to a calcium solution thereby evoking precipitation of calcium phosphate nanocrystals; b. washing the nanocrystals with water and suspending them in an organic solvent or getting dry calcium phosphate nanocrystals after evaporating organic solvent; c.