Abstract: Aspects of the present application relate to an optical phase shifter including a first waveguide defined in a first semiconductor layer, the first waveguide comprising a single-mode portion, a multi-mode portion, and a tapered portion coupling the single-mode portion to the multi-mode portion. A second waveguide is defined in a second semiconductor layer, the second waveguide having a tapered portion and a tip, wherein the tapered portion of the second waveguide overlaps with the tapered portion of the first waveguide. For tuning the phase change, a first electrically resistive path, defined at least partially in the first semiconductor layer, is included. The first electrically resistive path intersects the multi-mode portion of the first waveguide.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: Provided herein are modified DNAzymes, including DNAzymes with overhang sequences not complementary to the target RNA, in particular, overhangs with high G content. Also provided are compositions comprising the DNAzyme; vectors including the DNAzymes; pharmaceutical compositions including the vectors; and methods including any of the above for cleaving a target RNA; inducing cell death; and treating various diseases and disorders.

Abstract: Provided herein are compositions and methods comprising a DNAzyme targeting a transcript encoding a cell wall synthesis enzyme, for use in reducing an amount of a biofilm in a subject with a bacterial infection; increasing or enhancing antibiotic susceptibility in a subject with a bacterial infection; and inhibiting bacterial growth in a subject with a bacterial infection.

Abstract: An oligonucleotide is provided. The oligonucleotide comprising a nucleic acid sequence of at least one DNAzyme, the DNAzyme being capable of silencing at least one target gene of a bacteria to thereby render the bacteria susceptible to antibiotic treatment.

Abstract: Provided herein are DNAzymes conjugated to an organic moiety and methods of facilitating entry of DNAzymes into bacteria, utilizing same. Also provided are methods of targeting bacterial target genes, methods of treating or inhibiting the progression of bacterial infections, and methods of increasing susceptibility of bacteria to an antibiotic, using the described DNAzymes, which are optionally capable of silencing at least one target gene of bacteria and/or rendering bacteria susceptible to antibiotic treatment.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: Methods and apparatuses for operating with writeable media. In one aspect of the invention, an exemplary method includes inserting a writeable media into a drive system which is coupled to a data processing system, instructing the data processing system to write or erase first data on the writeable media, and instructing the data processing system to eject the writeable media from the drive system, wherein upon the instructing of the data processing system to eject, the data processing system writes or erases the first data on the writeable media. The present invention includes other methods and apparatuses which perform at least one of these methods, including data processing systems which perform at least one of these methods and computer readable media which when executed on data processing systems cause the systems to perform at least one of these methods.

Abstract: Disclosed are methods of detecting cardiovascular disorders using reference profiles. Also disclosed are methods of identifying agents for treating cardiovascular disorders.

Abstract: Disclosed are methods of detecting urinary tract and prostatic disorders using reference profiles. Also disclosed are methods of identifying agents for treating urinary tract and prostatic disorders.

Abstract: Operations that involve denormalized numbers are handled by restructuring the input values for an operation as normalized numbers, and performing calculations on the normalized numbers. As a first step in the process of performing an operation, a determination is made whether input values for the operation contain one or more denormalized numbers. For certain types of operations, a determination is made whether the input values are such that the output value from the operation will be a denormalized number. For each operation in which either the input values or output values comprise a denormalized number, the input values are scaled to produce values that are not denormalized. Once the appropriate factoring has been carried out, the requested operation is performed, using normalized numbers, to produce an intermediate result which is then adjusted to account for the initial scaling.

Abstract: An apparatus capable of determining the clotting time of blood, without requiring continuous inspection of the sample, is disclosed. The apparatus includes a support frame, which is capable of supporting therein a syringe containing a blood sample, and a turntable adapted to rotate at a known rate of speed. Blood from the syringe drops onto the turntable, the clotting time being automatically and graphically depicted by a chart rotatively carried upon the turntable. The apparatus can also be employed for determining variations in the viscosity of blood plasma and other fluids.