Embodiments of the disclosed technology concern transforming a high-level quantum-computer program to one or more symbolic expressions. Because the transformations lead to symbolic expressions in the compiled code, one can extract these to arrive at symbolic resource estimates for the quantum program. In cases where these transformations do not yield closed-form solutions, they can still be evaluated many orders of magnitude faster than it was possible using other resource estimation tools. Having access to such symbolic or near-symbolic expressions not only greatly improves the performance of accuracy management and resource estimation, but also better informs quantum software developers of the bottlenecks that may be present in the quantum program. In turn, the underlying quantum-computer program can be improved as appropriate.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

Techniques for database and file management herein include a processor and a memory device storing instructions that cause the processor to perform operations comprising creating a request based on an extensible markup language (XML) or an interpreted scripting language object, wherein the request comprises unauthenticated data for validation. The operations can also include transmitting the request to a remote device), updating metadata corresponding to the request to indicate the successful validation by the remote device, validating a response file, and detecting a discrepancy between the unauthenticated data and the authenticated data accessible by the remote device. Additionally, the operations include obtaining correction data to resolve the discrepancy, and executing a transaction based on the request and the correction data.

An example computing device includes a power supply. The computing device also includes a storage device to receive power from the power supply during an error collection operation. The storage device is also to receive power via radio waves from a client device during a diagnostic operation. The computing device further includes a controller to receive error information associated with a component of the computing device. The controller is also to store the error information in the storage device during the error collection operation.

An anomaly detection apparatus includes an anomaly detection unit configured to perform anomaly detection on time series data. The anomaly detection unit includes an encoding unit configured to encode the time series data by using a plurality of LSTM cells, an attention layer configured to calculate a weight of attention on an output from the encoding unit, a context generation unit configured to generate a context vector by applying the weight to the output from the encoding unit, and a decoding unit configured to reconfigure the time series data by using the plurality of LSTM cells in accordance with the context vector, and thereby, enables improvement in accuracy for the anomaly detection and efficient learning.

A method of verifying processing logic of a graphics processing unit receives a test task including a predefined set of instructions for execution on the graphics processing unit, the predefined set of instructions being configured to perform a predetermined set of operations on the graphics processing unit when executed for predefined input data. In a test phase, the test task is processed by executing the predefined set of instructions for the predefined input data first and second times at the graphics processing unit so as to, respectively, generate first and second outputs. A fault signal is raised if the first and second outputs do not match.

A method and/or system for automated detection and automated remediation of anomalies in Robotic Process Automation (RPA) environment is disclosed. The method comprises auto discovering resources (RPA components and its dependencies) in an RPA platform. The discovered resources are monitored though observation metrics whose values are obtained by executing pre-defined scripts. The obtained values are validated against threshold values to determine if there are any anomalies, wherein the threshold values may either be static values or dynamic values. If there is a breach of threshold, a remediation plan is automatically executed causing the remediation of anomalies. The system is trained to determine the dynamic threshold values through machine learning models which are developed and trained through metrics data and by determining error patterns from the historic unstructured log data.

Apparatus and method for high-performance page fault handling. For example, one embodiment of an apparatus comprises: one or more accelerator engines to process work descriptors submitted by clients to a plurality of work queues; fault processing hardware logic associated with the one or more accelerator engines, the fault processing hardware logic to implement a specified page fault handling mode for each work queue of the plurality of work queues, the page fault handling modes including a first page fault handling mode and a second page fault handling mode.

An evaluation unit (100) classifies a current treatment situation for a signal connected medical system. A receiving module (110) receives a plurality of treatment signals (112) that indicates a value (116) for a patient parameter (115) and a signal quality. A processing module stores values of patient parameters received at past times with a respective time stamp (125) indicating the time and a quality index (127) assigned to the respective value or group of values and detects a presence of a trigger signal (124) and calculates an evaluation score (134) using a stored calculation rule (132). The calculation uses the stored values of patient parameters as a function of the respective time stamp. The processing module calculates a quality indicator (138) using a stored quality metric (136) depending on the associated quality indices of the values of patient parameters necessary for the calculation of the evaluation score.

A memory device is provided to include: a plurality of memory cells; a peripheral circuit configured to perform an operation on the plurality of memory cells; a temperature circuit configured to measure a temperature of the memory device; a monitoring component configured to generate, based on whether a measured temperature is within a reference range, monitoring information representing an operation mode that is either a normal mode in which the operation is performed or a protection mode in which the operation is suspended; and an operation controller configured to output a signal for controlling the operation according to the monitoring information. The monitoring component is further configured to store the monitoring information and output the monitoring information to the operation controller in response to receiving the measured temperature from the temperature circuit.