Abstract: A method for generating a layout of a Josephson junction array includes obtaining an original script, in which geometric and action parameters are defined in the original script, the geometric parameters comprise a first structural parameter and a second structural parameter, the action parameters comprise an initial position parameter and a connection parameter; obtaining a first parameter value, a second parameter value, an initial position value, and a connection parameter value; in the original script, respectively assigning the first parameter value, the second parameter value, the initial position value, and the connection parameter value to the first structural parameter, the second structural parameter, the initial position parameter, and the connection parameter, so as to obtain a target script; and performing the target script to obtain a layout of a Josephson junction array having multiple connected Josephson junctions.

Abstract: According to one embodiment, a method, computer system, and computer program product for probabilistic inference from imprecise knowledge is provided. The embodiment may include identifying a knowledge base of one or more statements and first probability distributions corresponding to each of the one or more statements. The embodiment may also include identifying one or more queries. The embodiment may further include determining logical inferences about and second probability distributions for queries from the one or more queries or statements from the one or more statements based on information in the knowledge base.

Abstract: The present disclosure relates to polymers which contain a hydrophobic and hydrophilic segment which is sensitive to pH. In some aspects, the polymers form a micelle which is sensitive to pH and results in a change in fluorescence based upon the particular pH. In some aspects, the disclosure also provides methods of using the polymers for the imaging of cellular or extracellular environment or delivering a drug.

Abstract: A surge protection device with a high breaking capacity includes a housing with at least two lead-out electrodes, and a voltage limiting device and a thermal tripping mechanism that are installed in the housing. The voltage limiting device includes a voltage limiter, a first electrode and a second electrode that are positioned and installed in an insulating cover. The thermal tripping mechanism includes a fixed assembly, a movable assembly and a thermal trigger device. The fixed assembly and the movable assembly form a plurality of displacement switches arranged in series. The thermal trigger device is disposed in linkage with the movable assembly and includes a metal trigger sheet, a fusible alloy and an energy storage member. One end of the metal trigger sheet is fixed on the movable assembly, and the other end of the metal trigger sheet is fixed on the second electrode through welding by the fusible alloy.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate modeling bi-directional effects between events and system variables are provided. According to an embodiment, a system can comprise a processor that executes components stored in memory. The computer executable components comprise a machine learning component that learns mutual dependencies jointly over event occurrence data and transition data, wherein the transition data comprises state variable transitions observed over a multivariate state variable set.

Abstract: A computer implemented method of modeling agent interactions, includes receiving event occurrence data. One or more parent-event types and one or more corresponding child-event types are learned from the event occurrence data. A timeline of the one or more parent-event types and one or more corresponding child-event types is modeled from the event occurrence data. Agent interactions are predicted based on an order of the parent-event types in a predetermined history window.

Abstract: A set of sentences within a natural language text document are parsed, generating a word-level graph corresponding to a sentence in the set of sentences. Within the word-level graph using a trained entity identification model, a set of entity candidates are identified. From a set of graphs modelling relationships between portions of the set of sentences, a set of embeddings is generated. From a set of pairs of embeddings in the set of embeddings using a set of deconvolution layers, a set of links between entity candidates within the set of entity candidates is extracted. From the set of links and the set of entity candidates, an output graph modelling linkages between portions of the set of sentences within the natural language text document is generated.

Abstract: A computer system, computer program product, and computer-implemented method are provided that includes learning a tree ordered graphical event model from an event dataset. Temporal relationships between one or more events in received temporal event data is modeled, and an ordered graphical event model (OGEM) graph is learned. The learned OGEM graph is configured to capture ordinal historical dependence. Leveraging the learned OGEM graph, a parameter sharing architecture is learned, including order dependent statistical and causal co-occurrence relationships among event types. A control signal to an operatively coupled event device that is associated with at least one event type reflected in the learned parameter sharing environment is dynamically issued. The control signal is configured to selectively control an event injection.

Abstract: The present disclosure provides a scanning probe, a method and an apparatus for manufacturing the scanning probe. The scanning probe includes a base and a micro-tip disposed on an end of the base, wherein at least a section of the micro-tip comprises a lateral surface with a concavely curved generatrix. In the method, an end of a probe precursor is immersed in a corrosive solution by having a length direction of the probe precursor inclined with a liquid surface of the corrosive solution. The probe precursor is corroded by the corrosive solution while a corrosion current of the corroding is monitored. The probe precursor is moved away from the corrosive solution after a magnitude of the corrosion current has a plunge. The apparatus includes a container containing the corrosive solution, and a driving device configured to move the probe precursor in the container through a fastener.

Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, identify a plurality of data variables within a multivariate event dataset. Embodiments of the present invention can then formalize a causal inference between at least two identified data variables within the multivariate event dataset and generate a structural framework of an average effect value for the multivariate event dataset based on the formalization of the causal inference of the identified data variables. Embodiments of the present invention can then calculate an inverse propensity score for the generated structural framework of the average effect based on a type of identified variable, a predetermined time associated with the identified variable, and a causal connection strength between the identified variables.

Abstract: A classifying neural network (CNN) obtains a mixed data set of a priori information and outcomes information for treated units and untreated units. Classify units as treated or untreated, by running the CNN on the a priori information. Deliver a latent representation of the classified units from an intermediate layer of the CNN to a self-organizing map (SOM) engine. Generate an SOM based on the latent representation. Train the CNN to optimize a combined total loss of the classification and of the SOM. Estimate average treatment effect on the treated units by comparing the outcome information of the treated units to outcome information for untreated units that are nearest-neighbors of the treated units on the SOM.

Abstract: A surge protection device with a high breaking capacity includes a housing with at least two lead-out electrodes, and a voltage limiting device and a thermal tripping mechanism that are installed in the housing. The voltage limiting device includes a voltage limiter, a first electrode and a second electrode that are positioned and installed in an insulating cover. The thermal tripping mechanism includes a fixed assembly, a movable assembly and a thermal trigger device. The fixed assembly and the movable assembly form a plurality of displacement switches arranged in series. The thermal trigger device is disposed in linkage with the movable assembly and includes a metal trigger sheet, a fusible alloy and an energy storage member. One end of the metal trigger sheet is fixed on the movable assembly, and the other end of the metal trigger sheet is fixed on the second electrode through welding by the fusible alloy.

Abstract: Aspects of the present disclosure relate to knowledge graph compression. An input knowledge graph (KG) can be received. The input KG can be encoded to receive a first set of node embeddings. The input KG can be compressed into an output KG. The output KG can be encoded to receive a second set of node embeddings. A model for KG compression can be trained using optimal transport based on a distance matrix between the first set of node embeddings and the second set of node embeddings.

Abstract: An embodiment includes identifying, from a training dataset for training a model, a first unlabeled datapoint to present for labelling according to a first query strategy. The embodiment also includes issuing a query requesting a label for the first unlabeled datapoint. The embodiment also includes receiving a labeled datapoint in response to the query, the labeled datapoint comprising the first unlabeled datapoint as labeled by an oracle. The embodiment also includes generating a causal network based on labeled datapoints from the training dataset. The embodiment also includes receiving an instruction to modify the causal network. The embodiment also includes replacing the first query strategy with a second query strategy based on the instruction to modify the causal network. The embodiment also includes identifying, from the training dataset, a second unlabeled datapoint to present for labelling according to the second query strategy.

Abstract: A set of sentences within a natural language text document are parsed, generating a word-level graph corresponding to a sentence in the set of sentences. Within the word-level graph using a trained entity identification model, a set of entity candidates are identified. From a set of graphs modelling relationships between portions of the set of sentences, a set of embeddings is generated. From a set of pairs of embeddings in the set of embeddings using a set of deconvolution layers, a set of links between entity candidates within the set of entity candidates is extracted. From the set of links and the set of entity candidates, an output graph modelling linkages between portions of the set of sentences within the natural language text document is generated.

Abstract: The disclosure provides a method and an apparatus for determining coping capability boundary information of an unmanned vehicle and an electronic device. A set of indicator combination for evaluating a coping capability of an unmanned vehicle to be tested in a preset driving scenario is obtained, where the indicator combination includes at least one indicator item and an expected value range of the indicator item; historical driving behavior information of the unmanned vehicle to be tested is obtained according to a scenario feature corresponding to the preset driving scenario and the indicator item; and coping capability boundary information corresponding to the indicator combination of the unmanned vehicle to be tested in the preset driving scenario is obtained according to the historical driving behavior information, which realizes the coping capability boundary of the unmanned vehicle corresponding to the indicator combination that need to be tested.

Abstract: The present disclosure provides a tip-enhanced Raman spectroscope system. The system includes a laser emitting unit, a laser excitation unit, a first dichroic beam splitter, a first Raman spectrometer, and a confocal detecting unit. The laser excitation unit includes a sample stage and a first scanning probe. The sample stage is configured to have a sample disposed thereon such that a first incident laser beam emitted from the laser emitting unit is transmitted to the sample to excite first scattered light. The first dichroic beam splitter is configured to split a first Raman scattered light from the first Rayleigh scattered light. The first Raman spectrometer is disposed on a first Raman optical path of the first Raman scattered light. The confocal detecting unit is disposed on a first Rayleigh optical path of the first Rayleigh scattered light to image the sample.

Abstract: The present disclosure provides a tip-enhanced Raman spectroscope system. The system includes a laser emitting unit, a laser excitation unit, a first dichroic beam splitter, a first Raman spectrometer, and a confocal detecting unit. The laser excitation unit includes a sample stage and a first scanning probe. The sample stage is configured to have a sample disposed thereon such that a first incident laser beam emitted from the laser emitting unit is transmitted to the sample to excite first scattered light. The first dichroic beam splitter is configured to split a first Raman scattered light from the first Rayleigh scattered light. The first Raman spectrometer is disposed on a first Raman optical path of the first Raman scattered light. The confocal detecting unit is disposed on a first Rayleigh optical path of the first Rayleigh scattered light to image the sample.

Abstract: A graphical event model method, system, and computer program product, include learning statistical and causal co-occurrence relationships among multiple event-types of data, requiring no complex input, and generating a representation that explains a mutual dynamic of the multiple event-types in a form of a graphical event model.

Abstract: Analyzing complex systems by receiving labeled event data describing events occurring in association with a complex system, generating a first machine learning model according to the distribution of labeled event data, receiving state variable transition data describing state variable transitions occurring in association with a complex system, training a second machine learning model according to a combination of a distribution of state variable transitions and the first machine learning model, and using the second machine learning model to predict the effects of events upon state variables within the complex system according to new state variable transition and new labeled event data.