Abstract: An ablation probe having a separable outer sleeve and a freezing function, comprising: an inner probe (101, 201, 301, 401), an outer sleeve (102, 202, 302, 402), and a handle (105, 205, 305, 405). The inner probe (101, 201, 301, 401) is slidably connected to the outer sleeve (102, 202, 302, 402); one end of the inner probe (101, 201, 301, 401) is fixed to one end of the handle (105, 205, 305, 405); said ablation probe further comprises a separating assembly; the separating assembly comprises a first separable member (103) and a second separable member (104); the first separable member (103) and the second separable member (104) are detachably connected; the first separable member (103) is fixed to the outer sleeve (102, 202, 302, 402); the second separable member (104) is fixed to the handle (105, 205, 305, 405); the outer sleeve (102, 202, 302, 402) is a heat-insulating tube.

Abstract: In some embodiments, an apparatus includes a memory and a processor operatively coupled to the memory. The processor can be configured to receive a knowledge graph data structure, the knowledge graph data structure including at least an association of a first entity record from an entity dataset with a second entity record from the entity dataset. The processor can be configured to train a Bayesian Program Learning (BPL) model that generates multiple hypotheses based on the entity dataset and the knowledge graph data structure. The processor can be configured to generate, using the BPL model, at least one hypothesis in response to input data associated with an entity. The processor can be configured to receive feedback on the at least one hypothesis. The processor can be configured to further train the BPL model based on the feedback.

Abstract: In some embodiments, an apparatus includes a memory and a processor operatively coupled to the memory. The processor can be configured to receive multiple heterogeneous data records from at least one data source. The processor is configured to extract a set of features from the multiple data records, and to normalize the extracted set of features. The processor is configured to selectively combine the extracted set of features to define entity records. The processor is configured to associate two or more entity records to form relationships that have an indication of relation type and an indication of relation likelihood. In some embodiments, the processor can be configured to generate a knowledge graph data structure on the entity records and relationships.