Abstract: A method for localizing an object in the surroundings of an apparatus generating a stray magnetic field, wherein the object has a sensor arrangement including at least one magnetic field sensor, the method comprising: ascertaining at least one item of object information based on (i) stray-field information describing the spatial profile of the stray magnetic field at least within a region and (ii) at least one measured value measured with the sensor arrangement describing a location-dependent property of the stray magnetic field, the at least one item of object information describing at least one of the position or orientation of the object in the region.

Abstract: The object of the present invention is a method for the production of block polymers comprising a firstand a second block comprising the method steps: A) providing a first block having a nucleophilic peptide sequence for afirst transpeptidase enzyme, B) providing a second block having a peptide recognition sequence for the first transpeptidase enzyme, C) linking the first block to the second block by means of the first transpeptidase enzyme, wherein the first and second blocks are independently selected from nanoparticles, non-peptide polymers, and recombinant proteins. Such a production method makes it possible to build block polymers from identical or different blocks in a particularly simple and controlled manner.

Abstract: Embodiments use a hierarchy of machine learning models to predict datacenter behavior at multiple hardware levels of a datacenter without accessing operating system generated hardware utilization information. The accuracy of higher-level models in the hierarchy of models is increased by including, as input to the higher-level models, hardware utilization predictions from lower-level models. The hierarchy of models includes: server utilization models and workload/OS prediction models that produce predictions at a server device-level of a datacenter; and also top-of-rack switch models and backbone switch models that produce predictions at higher levels of the datacenter. These models receive, as input, hardware utilization information from non-OS sources. Based on datacenter-level network utilization predictions from the hierarchy of models, the datacenter automatically configures its hardware to avoid any predicted over-utilization of hardware in the datacenter.

Abstract: Described herein is a communication system including a cloud server, a first server, and at least one second server. The first server includes a first communication interface configured to provide reference spectral information referring to at least one reference sample and reference analytical data to the cloud server. Each second server includes a second communication interface configured to provide spectral information related to at least one substance to the cloud server. The cloud server is configured to: generate a calibration model, where the calibration model comprises at least one parameter; apply the calibration model to the spectral information, whereby at least one value for the at least one parameter is extracted; and provide the at least one value for the at least one parameter to the first server. The first server is further configured to determine treatment data using the at least one value for the at least one parameter.

Abstract: Disclosed herein are a communication system, a monitoring system for in-situ monitoring of a substance used in a gas scrubbing process, and related methods. The monitoring system can be used to monitor the at least one substance and provide treatment data for treating the at least one substance. The communication system includes a cloud server, a first server, a second server, and a third server. The first and second servers respectively include first and second communication interfaces configured to provide spectral information to the cloud server. The cloud server is configured to generate a calibration model including at least one parameter; apply the calibration model to the spectral information provided by the second server, whereby at least one value for the at least one parameter is extracted; and provide the at least one value for the at least one parameter to the first server via the first communication interface.

Abstract: Herein are machine learning (ML) feature processing and analytic techniques to detect anomalies in parse trees of logic statements, database queries, logic scripts, compilation units of general-purpose programing language, extensible markup language (XML), JavaScript object notation (JSON), and document object models (DOM). In an embodiment, a computer identifies an operational trace that contains multiple parse trees. Values of explicit features are generated from a single respective parse tree of the multiple parse trees of the operational trace. Values of implicit features are generated from more than one respective parse tree of the multiple parse trees of the operational trace. The explicit and implicit features are stored into a same feature vector. With the feature vector as input, an ML model detects whether or not the operational trace is anomalous, based on the explicit features of each parse tree of the operational trace and the implicit features of multiple parse trees of the operational trace.

Abstract: The present invention relates to compounds represented by general formula (Ia), (Ib), (IIa) or (IIb). These compounds are suitable for imaging alpha-synuclein and for diagnosing diseases which are associated with the aggregation of alpha-synuclein. The compounds are also useful for treating and preventing diseases which are associated with the aggregation of alpha-synuclein.

Abstract: Techniques are described herein for predicting disk drive failure using a machine learning model. The framework involves receiving disk drive sensor attributes as training data, preprocessing the training data to select a set of enhanced feature sequences, and using the enhanced feature sequences to train a machine learning model to predict disk drive failures from disk drive sensor monitoring data. Prior to the training phase, the RNN LSTM model is tuned using a set of predefined hyper-parameters. The preprocessing, which is performed during the training and evaluation phase as well as later during the prediction phase, involves using predefined values for a set of parameters to generate the set of enhanced sequences from raw sensor reading. The enhanced feature sequences are generated to maintain a desired healthy/failed disk ratio, and only use samples leading up to a last-valid-time sample in order to honor a pre-specified heads-up-period alert requirement.

Abstract: Herein are machine learning techniques that adjust reconstruction loss of a reconstructive model, such as a principal component analysis (PCA), based on importances of features. In an embodiment having a reconstructive model that more or less accurately reconstructs its input, a computer measures, for each feature, a respective importance that is based on the reconstructive model. For example, importance may be based on grading samples that the reconstructive model correctly or incorrectly inferenced. For each feature during production inferencing, a respective original loss from the reconstructive model measures a difference between a value of the feature in an input and a reconstructed value of the feature generated by the reconstructive model. For each feature, the respective importance of the feature is applied to the respective original loss to generate a respective weighted loss, which compensates for concept drift.

Abstract: Herein are machine learning techniques that adjust reconstruction loss of a reconstructive model such as an autoencoder based on importances of values of features. In an embodiment and before, during, or after training, the reconstructive model that more or less accurately reconstructs its input, a computer measures, for each distinct value of each feature, a respective importance that is not based on the reconstructive model. For example, importance may be based solely on a training corpus. For each feature during or after training, a respective original loss from the reconstructive model measures a difference between a value of the feature in an input and a reconstructed value of the feature generated by the reconstructive model. For each feature, the respective importance of the input value of the feature is applied to the respective original loss to generate a respective weighted loss. The weighted losses of the features of the input are collectively detected as anomalous or non-anomalous.

Abstract: Embodiments use a hierarchy of machine learning models to predict datacenter behavior at multiple hardware levels of a datacenter without accessing operating system generated hardware utilization information. The accuracy of higher-level models in the hierarchy of models is increased by including, as input to the higher-level models, hardware utilization predictions from lower-level models. The hierarchy of models includes: server utilization models and workload/OS prediction models that produce predictions at a server device-level of a datacenter; and also top-of-rack switch models and backbone switch models that produce predictions at higher levels of the datacenter. These models receive, as input, hardware utilization information from non-OS sources. Based on datacenter-level network utilization predictions from the hierarchy of models, the datacenter automatically configures its hardware to avoid any predicted over-utilization of hardware in the datacenter.

Abstract: Techniques are provided for sparse ensembling of unsupervised machine learning models. In an embodiment, the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models.

Abstract: Approaches herein relate to machine learning for detection of anomalous logic syntax. Herein is acceleration for comparison of parse trees such as suspicious database queries. In an embodiment, a computer identifies subtrees in each of many trees. A respective subset of participating subtrees is selected in each tree. A respective root node of each participating subtree should directly have a child node that is a leaf and/or should have a degree that exceeds a branching threshold such as one. For each pairing of a respective first tree with a respective second tree, based on a count of subtree matches between the participating subset of subtrees in the first tree and the participating subset of subtrees in the second tree, a respective tree similarity score is calculated. A machine learning model inferences based on the tree similarity scores of the many trees. In an embodiment, each tree similarity score is a convolution kernel.

Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.

Abstract: Herein are feature extraction mechanisms that receive parsed log messages as inputs and transform them into numerical feature vectors for machine learning models (MLMs). In an embodiment, a computer extracts fields from a log message. Each field specifies a name, a text value, and a type. For each field, a field transformer for the field is dynamically selected based the field's name and/or the field's type. The field transformer converts the field's text value into a value of the field's type. A feature encoder for the value of the field's type is dynamically selected based on the field's type and/or a range of the field's values that occur in a training corpus of an MLM. From the feature encoder, an encoding of the value of the field's typed is stored into a feature vector. Based on the MLM and the feature vector, the log message is detected as anomalous or not.

Abstract: Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.

Abstract: Embodiments use a hierarchy of machine learning models to predict datacenter behavior at multiple hardware levels of a datacenter without accessing operating system generated hardware utilization information. The accuracy of higher-level models in the hierarchy of models is increased by including, as input to the higher-level models, hardware utilization predictions from lower-level models. The hierarchy of models includes: server utilization models and workload/OS prediction models that produce predictions at a server device-level of a datacenter; and also top-of-rack switch models and backbone switch models that produce predictions at higher levels of the datacenter. These models receive, as input, hardware utilization information from non-OS sources. Based on datacenter-level network utilization predictions from the hierarchy of models, the datacenter automatically configures its hardware to avoid any predicted over-utilization of hardware in the datacenter.

Abstract: Unsorted sparse dictionary encodings are transformed into unsorted-dense or sorted-dense dictionary encodings. Sparse domain codes have large gaps between codes that are adjacent in order. Unlike spare codes, dense codes have smaller gaps between adjacent codes; consecutive codes are dense codes that have no gaps between adjacent codes. The techniques described herein are relational approaches that may be used to generate sparse composite codes and sorted codes.

Abstract: Techniques are described herein for estimating CPU, memory, and I/O utilization for a workload via out-of-band sensor readings using a machine learning model. The framework involves receiving sensor data associated with executing benchmark applications, obtaining ground truth utilization values for the benchmarks, preprocessing the training data to select a set of enhanced sequences, and using the enhanced sequences to train a random forest model to estimate CPU, memory, and I/O utilization given sensor monitoring data. Prior to the training phase, a machine learning model is trained using a set of predefined hyper-parameters. The trained models are used to generate estimations for CPU, memory, and I/O utilizations values. The utilization values are used with workload context information to assess the deployment and generate one or more recommendations for machine types that will best serve the workload in terms of system utilization.

Abstract: Herein are machine learning (ML) feature processing and analytic techniques to detect anomalies in parse trees of logic statements, database queries, logic scripts, compilation units of general-purpose programing language, extensible markup language (XML), JavaScript object notation (JSON), and document object models (DOM). In an embodiment, a computer identifies an operational trace that contains multiple parse trees. Values of explicit features are generated from a single respective parse tree of the multiple parse trees of the operational trace. Values of implicit features are generated from more than one respective parse tree of the multiple parse trees of the operational trace. The explicit and implicit features are stored into a same feature vector. With the feature vector as input, an ML model detects whether or not the operational trace is anomalous, based on the explicit features of each parse tree of the operational trace and the implicit features of multiple parse trees of the operational trace.