Abstract: Herein is a universal anomaly threshold based on several labeled datasets and transformation of anomaly scores from one or more anomaly detectors. In an embodiment, a computer meta-learns from each anomaly detection algorithm and each labeled dataset as follows. A respective anomaly detector based on the anomaly detection algorithm is trained based on the dataset. The anomaly detector infers respective anomaly scores for tuples in the dataset. The following are ensured in the anomaly scores from the anomaly detector: i) regularity that an anomaly score of zero cannot indicate an anomaly and ii) normality that an inclusive range of zero to one contains the anomaly scores from the anomaly detector. A respective anomaly threshold is calculated for the anomaly scores from the anomaly detector. After all meta-learning, a universal anomaly threshold is calculated as an average of the anomaly thresholds. An anomaly is detected based on the universal anomaly threshold.

Abstract: Techniques for computing global feature explanations using adaptive sampling are provided. In one technique, first and second samples from an dataset are identified. A first set of feature importance values (FIVs) is generated based on the first sample and a machine-learned model. A second set of FIVs is generated based on the second sample and the model. If a result of a comparison between the first and second FIV sets does not satisfy criteria, then: (i) an aggregated set is generated based on the last two FIV sets; (ii) a new sample that is double the size of a previous sample is identified from the dataset; (iii) a current FIV set is generated based on the new sample and the model; (iv) determine whether a result of a comparison between the current and aggregated FIV sets satisfies criteria; repeating (i)-(iv) until the result of the last comparison satisfies the criteria.

Abstract: A medical apparatus for a patient includes an external system configured to transmit one or more transmission signals, each transmission signal having at least power or data. An implantable system is configured to receive the one or more transmission signals from the external system, and the external system includes a first external device with at least one external antenna configured to transmit a first transmission signal to the implantable system. The first transmission signal includes at least power or data, and an external transmitter is configured to drive the at least one external antenna. An external power supply is configured to provide power to at least the external transmitter, and an external controller is configured to control the external transmitter. A first implantable device includes at least one implantable antenna configured to receive the first transmission signal from the first external device.

Abstract: An implantable device is provided that can include any number of features. In some embodiments, the device includes a coil antenna configured to receive wireless power from a power source external to the patient. The device can include at least one sensor configured to sense a bodily parameter of the patient. The device can also include electronics configured to communicate the sensed bodily parameter of to a device located external to the patient. Methods of use are also described.

Abstract: Herein are timeseries preprocessing, model selection, and hyperparameter tuning techniques for forecasting development based on temporal statistics of a timeseries and a single feed-forward pass through a machine learning (ML) pipeline. In an embodiment, a computer hosts and operates the ML pipeline that automatically measures temporal statistic(s) of a timeseries. ML algorithm selection, cross validation, and hyperparameters tuning is based on the temporal statistics of the timeseries. The result from the ML pipeline is a rigorously trained and production ready ML model that is validated to have increased accuracy for multiple prediction horizons. Based on the temporal statistics, efficiency is achieved by asymmetry of investment of computer resources in the tuning and training of the most promising ML algorithm(s). Compared to other approaches, this ML pipeline produces a more accurate ML model for a given amount of computer resources and consumes fewer computer resources to achieve a given accuracy.

Abstract: A medical apparatus configured to neuromodulate tissue and/or record patient information is provided. The apparatus includes an external system to transmit transmission signal(s), each signal having at least power or data, and an implantable system to receive the transmission signal(s). The data transfer between the external and implantable systems is asynchronous. The external system includes external antenna(s) to transmit a transmission signal. The transmission signal is an amplitude modulated signal modulated by varying a load on the external antenna(s) that causes an impedance mismatch prior to amplifying the signal for transmission. An implantable device includes implantable antenna(s) to receive the transmission signal. The implantable system comprises a receiver to receive the transmission signal from the implantable antenna(s), implantable transmission module(s) to transmit data to the external system, and a variable load connected to the implantable antenna(s).

Abstract: Techniques are provided for selection of machine learning algorithms based on performance predictions by using hyperparameter predictors. In an embodiment, for each mini-machine learning model (MML model), a respective hyperparameter predictor set that predicts a respective set of hyperparameter settings for a data set is trained. Each MML model represents a respective reference machine learning model (RML model). Data set samples are generated from the data set. Meta-feature sets are generated, each meta-feature set describing a respective data set sample. A respective target set of hyperparameter settings are generated for said each MML model using a hypertuning algorithm. The meta-feature sets and the respective target set of hyperparameter settings are used to train the respective hyperparameter predictor set. Each hyperparameter predictor set is used during training and inference to improve the accuracy of automatically selecting a RML model per data set.

Abstract: Provided herein are methods of treating a patient comprising providing a medical apparatus comprising an external system and an implantable system, implanting the implantable system, and delivering at least one of power or data to the implantable system with the external system. The external system comprises: at least one external antenna configured to transmit a first transmission signal to the implantable system; an external transmitter configured to drive the at least one external antenna; an external power supply; and an external controller. The implantable system comprises: at least one implantable antenna configured to receive the first transmission signal from the first external device; an implantable receiver; at least one implantable functional element configured to interface with the patient; an implantable controller; an implantable energy storage assembly; and an implantable housing surrounding at least the implantable controller and the implantable receiver. Medical apparatus are also provided.

Abstract: According to an embodiment, a method includes generating a first dataset sample from a dataset, calculating a first validation score for the first dataset sample and a machine learning model, and determining whether a difference in validation score between the first validation score and a second validation score satisfies a first criteria. If the difference in validation score does not satisfy the first criteria, the method includes generating a second dataset sample from the dataset. If the difference in validation score does satisfy the first criteria, the method includes updating a convergence value and determining whether the updated convergence value satisfies a second criteria. If the updated convergence value satisfies the second criteria, the method includes returning the first dataset sample. If the updated convergence value does not satisfy the second criteria, the method includes generating the second dataset sample from the dataset.

Abstract: An apparatus includes a first device having a clock signal and configured to communicate, via a data bus, with a second device configured to assert a data strobe signal and a plurality of data bit signals on the data bus. The first device may include a control circuit configured, during a training phase, to determine relative timing between the clock signal, the plurality of data bit signals, and the data strobe signal. The first device may determine, using a first set of sampling operations, a first timing relationship of the plurality of data bit signals relative to the data strobe signal, and determine, using a second set of sampling operations, a second timing relationship of the plurality of data bit signals and the data strobe signal relative to the clock signal. During an operational phase, the control circuit may be configured to use delays based on the first and second timing relationships to sample data from the second device on the data bus.

Abstract: Herein are techniques for exploring hyperparameters of a machine learning model (MLM) and to train a regressor to predict a time needed to train the MLM based on a hyperparameter configuration and a dataset. In an embodiment that is deployed in production inferencing mode, for each landmark configuration, each containing values for hyperparameters of a MLM, a computer configures the MLM based on the landmark configuration and measures time spent training the MLM on a dataset. An already trained regressor predicts time needed to train the MLM based on a proposed configuration of the MLM, dataset meta-feature values, and training durations and hyperparameter values of landmark configurations of the MLM. When instead in training mode, a regressor in training ingests a training corpus of MLM performance history to learn, by reinforcement, to predict a training time for the MLM for new datasets and/or new hyperparameter configurations.

Abstract: Provided herein are methods of treating a patient comprising providing a medical apparatus comprising an external system and an implantable system, implanting the implantable system, and delivering at least one of power or data to the implantable system with the external system. The external system comprises: at least one external antenna configured to transmit a first transmission signal to the implantable system; an external transmitter configured to drive the at least one external antenna; an external power supply; and an external controller. The implantable system comprises: at least one implantable antenna configured to receive the first transmission signal from the first external device; an implantable receiver; at least one implantable functional element configured to interface with the patient; an implantable controller; an implantable energy storage assembly; and an implantable housing surrounding at least the implantable controller and the implantable receiver. Medical apparatus are also provided.

Abstract: In an embodiment based on computer(s), an ML model is trained to detect outliers. The ML model calculates anomaly scores that include a respective anomaly score for each item in a validation dataset. The anomaly scores are automatically organized by sorting and/or clustering. Based on the organized anomaly scores, a separation is measured that indicates fitness of the ML model. In an embodiment, a computer performs two-clustering of anomaly scores into a first organization that consists of a first normal cluster of anomaly scores and a first anomaly cluster of anomaly scores. The computer performs three-clustering of the same anomaly scores into a second organization that consists of a second normal cluster of anomaly scores, a second anomaly cluster of anomaly scores, and a middle cluster of anomaly scores. A distribution difference between the first organization and the second organization is measured. An ML model is processed based on the distribution difference.

Abstract: A medical apparatus for a patient comprises an external system configured to transmit one or more transmission signals, each transmission signal comprising at least power or data; and an implantable system configured to receive the one or more transmission signals from the external system. The apparatus can be configured to treat a patient and/or record patient data. Methods of treating a patient and recording patient data are also provided.

Abstract: Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.

Abstract: Some embodiments of the present invention provide an apparatus for providing therapies and/or diagnostics with an implanted system. Some embodiments of the present invention include methods and apparatus for modulating tissues with conventional methods and/or new methods using mechanical forces. Some embodiments of the present invention include methods and apparatus for minimally invasive delivery of implanted systems. Some embodiments of the present invention include methods and apparatus for extensions of the implanted system that can expand, unroll, unfold, and/or unfurl.

Abstract: A proxy-based automatic non-iterative machine learning (PANI-ML) pipeline is described, which predicts machine learning model configuration performance and outputs an automatically-configured machine learning model for a target training dataset. Techniques described herein use one or more proxy models—which implement a variety of machine learning algorithms and are pre-configured with tuned hyperparameters—to estimate relative performance of machine learning model configuration parameters at various stages of the PANI-ML pipeline. The PANI-ML pipeline implements a radically new approach of rapidly narrowing the search space for machine learning model configuration parameters by performing algorithm selection followed by algorithm-specific adaptive data reduction (i.e., row- and/or feature-wise dataset sampling), and then hyperparameter tuning.

Abstract: Distributions of on-chip inductors for monolithic voltage regulation are described. On-chip voltage regulation may be provided by integrated voltage regulators (IVRs), such as a buck converter with integrated inductors. On-chip inductors may be placed to ensure optimal voltage regulation for high power density applications. With this technology, integrated circuits may have many independent voltage domains for fine-grained dynamic voltage and frequency scaling that allows for higher overall power efficiency for the system.

Abstract: An apparatus includes a first device having a clock signal and configured to communicate, via a data bus, with a second device configured to assert a data strobe signal and a plurality of data bit signals on the data bus. The first device may include a control circuit configured, during a training phase, to determine relative timing between the clock signal, the plurality of data bit signals, and the data strobe signal. The first device may determine, using a first set of sampling operations, a first timing relationship of the plurality of data bit signals relative to the data strobe signal, and determine, using a second set of sampling operations, a second timing relationship of the plurality of data bit signals and the data strobe signal relative to the clock signal. During an operational phase, the control circuit may be configured to use delays based on the first and second timing relationships to sample data from the second device on the data bus.

Abstract: Systems, devices, and methods for neurostimulation using a combination of implantable and external devices to treat pain are disclosed.