Abstract: Systems and methods for pre-training and fine-tuning of neural-network-based language models to reason directly over tables without generating logical forms. In some examples, a language model can be pre-trained using masked-language modeling tasks synthetically generated from tables pulled from a knowledge corpus. In some examples, the language model may be further pre-trained using pairs of counterfactual statements generated from those tables, and/or one or more statements that compare selected data from those tables. The language model may then be fine-tuned using examples that include only a question, an answer, and a table, allowing fine-tuning examples to be harvested directly from existing benchmark datasets or synthetically generated.

Abstract: Systems and methods for pre-training and fine-tuning of neural-network-based language models to reason directly over tables without generating logical forms. In some examples, a language model can be pre-trained using masked-language modeling tasks synthetically generated from tables pulled from a knowledge corpus. In some examples, the language model may be further pre-trained using pairs of counterfactual statements generated from those tables, and/or one or more statements that compare selected data from those tables. The language model may then be fine-tuned using examples that include only a question, an answer, and a table, allowing fine-tuning examples to be harvested directly from existing benchmark datasets or synthetically generated.

Abstract: Systems and methods for pre-training and fine-tuning of neural-network-based language models to reason directly over tables without generating logical forms. In some examples, a language model can be pre-trained using masked-language modeling tasks synthetically generated from tables pulled from a knowledge corpus. In some examples, the language model may be further pre-trained using pairs of counterfactual statements generated from those tables, and/or one or more statements that compare selected data from those tables. The language model may then be fine-tuned using examples that include only a question, an answer, and a table, allowing fine-tuning examples to be harvested directly from existing benchmark datasets or synthetically generated.

Abstract: The invention concerns a process for removal of organics in liquids, especially dilute, toxic organics in water, wherein the liquid is contacted with microcapsules containing oxidizing agents, in combination with a photo-catalytic membrane. The invention is also related to a system for removal of organics in liquids.

Abstract: The present invention provides a 2-amino-5-piperidinylimidazolone compound of formula I The present invention also provides methods and compositions for the inhibition of ?-secretase (BACE) and the treatment of ?-amyloid deposits and neurofibrillary tangles.

Abstract: The present invention provides a 2-amino-5-cycloalkyl-hydantoin compound of formula I The present invention also provides methods and compositions for the inhibition of ?-secretase (BACE) and the treatment of ?-amyloid deposits and neurofibrillary tangles.

Abstract: The present invention provides an amino-imidazolone compound of formula I Also provided are compositions and methods for the use thereof to inhibit ?-secretase (BACE) and treat ?-amyloid deposits and neurofibrillary tangles.