Abstract: A weakly paired image style transfer method based on a pose self-supervised generative adversarial network, relating to the field of image processing. The method is suitable for style transfer of weakly paired images, different styles of pictures having certain overlap are used to perform model training of an adversarial neural network, so that the model is insensitive to poses and focuses on style learning, and therefore, in an actual application process, a source style can be converted into a target style, but a pose is kept unchanged. In addition, in the model training process of the adversarial neural network, a differentiable pose solver capable of estimating a relative pose of any two images is introduced, a phase correlation algorithm is optimized to be differentiable, and the phase correlation algorithm is embedded into an end-to-end learning network framework to achieve pose estimation.

Abstract: The disclosure relates to an enzymatic preparation method of inclusion complexes of tributyrin, and belongs to the technical field of oil microencapsulation. The disclosure combines enzymatic synthesis of cyclodextrin and inclusion of tributyrin with cyclodextrin, including enzymatic preparation of cyclodextrin with a CGT enzyme. Tributyrin is added in the preparation process; after reaction, Tween is added, and homogenization and spray drying are carried out. The effect of the finally obtained tributyrin powder is much better than that of single inclusion of tributyrin with cyclodextrin. The disclosure is simple in process, low in cost and convenient in operation; reaction processes are free of toxicity and pollution; there are no toxic reagent residues; the inclusion effect is obvious, and better utilization of a nutritional additive tributyrin in actual production is facilitated.

Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.

Abstract: A system is provided for training an inferential model based on selected training vectors. During operation, the system receives training data comprising observations for a set of time-series signals gathered from sensors in a monitored system during normal fault-free operation. Next, the system divides the observations into N subgroups comprising non-overlapping time windows of observations. The system then selects observations with a local minimum value and a local maximum value for all signals from each subgroup to be training vectors for the inferential model. Finally, the system trains the inferential model using the selected training vectors. Note that by selecting observations with local minimum and maximum values to be training vectors, the system maximizes an operational range for the training vectors, which reduces clipping in estimates subsequently produced by the inferential model and thereby reduces false alarms.

Abstract: Example comment adding methods and communication terminals are disclosed. One example comment adding method includes displaying, by an electronic device, in a first region, first content at a content layer. The electronic device receives a first operation of a finger and a second operation of a stylus within a first preset duration range. The electronic device loads a comment layer on the content layer in response to the first operation and the second operation. The electronic device receives, at the comment layer in response to a third operation of the stylus, a first comment input by the stylus for the first content. The electronic device displays, in the first region in response to the first comment, the first content including the first comment. The first region is any region that can be displayed on the display, and a display position of the first content in the first region remains unchanged.

Abstract: The disclosed embodiments relate to a system that trains an inferential model based on selected training vectors. During operation, the system receives training data comprising observations for a set of time-series signals gathered from sensors in a monitored system during normal fault-free operation. Next, the system divides the observations into N subgroups comprising non-overlapping time windows of observations. The system then selects observations with a local minimum value and a local maximum value for all signals from each subgroup to be training vectors for the inferential model. Finally, the system trains the inferential model using the selected training vectors. Note that by selecting observations with local minimum and maximum values to be training vectors, the system maximizes an operational range for the training vectors, which reduces clipping in estimates subsequently produced by the inferential model and thereby reduces false alarms.

Abstract: A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.

Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.

Abstract: A computer-implemented system for determining trials using a metastatic condition of a patient may include at least one processor programmed to receive a selection of a patient; access, in response to the selection of the patient, a patient dataset associated with the patient; receive a predicted metastatic condition associated with the patient; cause display of at least a first portion of the patient dataset and the predicted metastatic condition; determine, based on at least a second portion of the patient dataset or the predicted metastatic condition, a subset of trials for the patient, wherein the subset of trials for the patient is determined from a plurality of trials; and cause display of at least the subset of the trials for the patient.

Abstract: A graphical user interface for displaying an electronic medical record associated with a patient is provided. The graphical user interface may include an area configured to display patient information, which may include at least a name of the patient. The graphical user interface may also include an indicator displayed in association with the name of the patient. The indicator may include information specifying that the patient is potentially eligible for one or more trials, the patient is participating in one or more trials, or the patient has completed one or more trials.

Abstract: An augmented reality and virtual reality head mounted display is described. The head mounted display comprises a processor to initiate display of an image stream of its physical surroundings, enabling equipped with the head mounted display to view the physical environment.