Siemens Aktiengesellschaft

Panoratio Database Images Gmbh

Volker Tresp

Denis Krompaß

Rudolf Sollacher

Sebnem Rusitschka

Reimar Hofmann

Xueyan Jiang

Ralph Neuneier

Yunpu Ma

Maximilian Nickel

Marcus Pfister

Philipp Bernhardt

Volkmar Sterzing

Clemens Otte

Ulrich Münz

Hans-Georg Zimmermann

Florian Steinke

Andreas Litzinger

Martin Schlang

Michael Haft

Bernd Schurmann

Yi Huang

Joshua Lamar Moore

Stephan Baier

Thomas Briegel

J&ouml;rg Maschlanka

Zhiliang Wu

Denis Krompass

The invention relates to a computer-implemented method for providing a trained function () to determine a treatment. To allow a provision of improved treatment recommendations the method comprises the following steps: receiving (S) a plurality of training data sets () each relating to a former patient and each comprising treatment data (), which represents a former treatment chosen by a physician for the respective former patient, and patient data (), which relates to a respective health condition of the respective former patient; determining (S) a propensity score (), which represents a relative frequency for a former treatment of the plurality to be chosen for each health conditions; determining (S) an outcome score (), which is a measure of how the health data () and/or the health condition of the respective patient changed; and determining (S) a treatment recommendation score () based on the propensity score () and the outcome score (); and providing (S) the trained function ().

Training method for giving treatment recommendations to a physician based on a propensity score and an outcome score

A method of performing an inference task on a knowledge graph comprising semantic triples of entities, wherein entity types are subject, object and predicate, and wherein each semantic triple comprises one of each entity type, using a quantum computing device, wherein a first entity of a first type and a second entity of a second type are given and the inference task is to infer a third entity of the third type. By performing specific steps and choosing values according to specific prescriptions, an efficient and resource-saving method is developed that utilizes the power of quantum computing systems for inference tasks on large knowledge graphs. An advantageous value for a cutoff threshold for a cutoff based on singular values of a singular value tensor decomposition is prescribed, and a sequence of steps is developed in which only the squares of the singular values are of consequence and their signs are not.

Quantum machine learning algorithm for knowledge graphs

A method for computing joint torques applied by actuators to perform a control of a movement of a robot arm having several degrees of freedom is provided. The method includes the act of providing, by a trajectory generator, trajectory vectors specifying a desired trajectory of the robot arm for each degree of freedom. The trajectory vectors are mapped to corresponding latent representation vectors that capture inherent properties of the robot arm using basis functions with trained parameters. The latent representation vectors are multiplied with trained core tensors to compute the joint torques for each degree of freedom.

Method and apparatus for performing control of a movement of a robot arm

Various examples generally relate to knowledge graphs including entities and links associated with semantic triples including subject-predicate-object. Various examples specifically relate to quantum-machine learning of knowledge graphs. Further examples relate to a quantum-machine-assisted inference on knowledge graphs.

Quantum-machine training of knowledge graphs

A method for operating a network, such as an automation network, for example, has multiple node devices provided that are networked to one another. There is a global time available, and the node devices record their operating parameters. The operating parameters are allocated to a respective address element as content elements in order to be stored in a tensorial database structure. Control or adaptation of the operation of the network with its node devices and couplings is facilitated thereby. The method is suitable particularly for use in supply networks, automated production installations, communication networks, transport networks and logistical networks. The proposed storing allows easy visualisation, depiction and evaluation of operating states of the network and of its node devices.

Method for operating a network having multiple node devices, and network

A method and apparatus for automatic recognition of similarities between perturbations in a network, the apparatus includes a memory unit for storing a first data array of multiple perturbation data snapshots each recorded in response to a perturbation observed in the network; a generation unit adapted to generate by machine learning a data model of perturbations trained on the first data array, wherein the trained data model provides a latent vector representation for each of the perturbations; a recording unit adapted to record a perturbation data snapshot if a perturbation is observed during operation of said network and adapted to provide a corresponding second data array for the recorded perturbation data snapshot; and a processing unit adapted to derive a latent vector representation for the observed perturbation from the second data array using the trained data model of perturbations.

Method and apparatus for automatic recognition of similarities between perturbations in a network

A method is provided for calculating a relation indicator for a relation between entities based on an optimization procedure. The method combines the strong relational learning ability and the good scalability of the RESCAL model with the linear regression model, which may deal with observed patterns to model known relations. The method may be used to determine relations between objects, for instance entries in a database, such as a shopping platform, medical treatments, production processes, or in the context of the Internet of Things, in a fast and precise manner.

Calculating a relation indicator for a relation between entities

Systems and methods are provided for deriving a prediction from existing data by utilizing information extraction and machine learning, wherein both approaches can be optimized independently from each other. Optionally, deductive reasoning may also be combined with information extraction and machine learning and may as well be optimized independently from the other two functionalities. The two or three functionalities may utilize at least one set of data and may (at least partially) process various sets of data. The combined approach may produce significantly improved results, and may be implemented in various technical fields, applications and use cases involving, e.g., data mining or processing of huge amounts of data. The disclosed systems and methods may be applicable for all kinds of technical systems, e.g., medical, genetic research, or industry and automation systems.

Deriving predictions from data using information extraction and machine learning

A method, system, and computer software for information retrieval in semantic networks, has the steps of: acquiring a graph of interest, assuming a novel metric regarding the acquired graph, specifying a query node of interest on the obtained graph, calculating a shortest-path distance from the query node of interest to a plurality of other nodes on the acquired graph, obtaining a ranked list of nodes based on the calculated shortest-path distance, and displaying for a user the retrieved information.

Method, system, and computer program for information retrieval in semantic networks

After initial clusters having only one component are formed, a conditional probability P(Ci|C'k) is determined for the cluster Ci being included in an order on condition that cluster C&#x2032;k is included in the order. If P(Ci|C&#x2032;k) is greater than a first threshold value Sa new cluster Cn having all the components of clusters Ci, C&#x2032;k is formed and the operations are repeated until no new clusters are formed.

Method and device for detecting clusters amongst data arranged in orders for cluster creation and removal based on conditional probabilities between clusters

A method and a suitable x-ray device () for carrying out the method are specified for effective and operationally simplified user-specific optimization of a parameter configuration (K) of an x-ray device (), said configuration comprising at least one recording parameter (U,I,t,F). It is accordingly provided that a user () is shown a plurality of reference images (V,V) for different reference parameter sets (PV) from a reference memory () in which are stored a large number of reference images (V,V) each with an associated reference parameter set (PV), that for each reference image (V,V) shown the user () submits an assessment (BM) of the image quality (V,V), and that on the basis of the submitted assessments (BM) an optimized parameter configuration (K) is created from the reference parameter sets (PV) of the reference images (V,V) shown.

Method and device for user-specific parameterization of an x-ray device

The invention relates to a method and a system for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least on e of the state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by using the sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Method and system for modeling a system

The training phase of a neural network NN is stopped before an error function, which is to be minimized in the training phase, reaches a minimum (,). A first variable (EG) is defined using, for example, the optimal brain damage method or the optimal brain surgeon method, on the assumption that the error function is at the minimum. Furthermore, a second variable (ZG) is determined which provides an indication of the manner in which the value of the error function varies when a weight (w,) is removed from the neural network (NN). The first variable (EG) and the second variable (ZG) are used to classify the weight (w,) as being suitable or unsuitable for removal from the neural network (NN).

Method for determination of weights, suitable for elimination, of a neural network using a computer

In a method and arrangement for the neural modelling of a dynamic system with non-linear stochastic behavior wherein only a few measured values of the influencing variable are available and the remaining values of the time series are modelled, a combination of a non-linear computerized recurrent neural predictive network and a linear error model are employed to produce a prediction with the application of maximum likelihood adaption rules. The computerized recurrent neural network can be trained with the assistance of the real-time recurrent learning rule, and the linear error model is trained with the assistance of the error model adaption rule that is implemented on the basis of forward-backward Kalman equations. This model is utilized in order to predict values of the glucose-insulin metabolism of a diabetes patient.

Method and arrangement for the neural modelling of a dynamic system with non-linear stochastic behavior

A state vector (SV,) is determined with elements that characterize a financial market (,). Taking into account predetermined evaluation variables, an evaluation (V,) is determined (,) for the state vector (SV,). In addition, a chronologically following state vector (SV,) is determined (,) and evaluated (V,). On the basis of the two evaluations (V,, V,), weights (w,) of the neural network (NN) are adapted (,) using a reinforcement learning method (&Dgr;w,).

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Munich, Germany

Volker Tresp