Abstract: Structures for a waveguide escalator, as well as methods of forming such structures. The structure comprises a first waveguide core on a substrate, a second waveguide core, and a back-end-of-line stack including a third waveguide core disposed between the first waveguide core and the second waveguide core. The third waveguide core comprises a layer stack that includes a first layer, a second layer, and a third layer between the first layer and the second layer. The first layer and the second layer comprise a first dielectric material with a first refractive index, and the third layer comprises a second dielectric material with a second refractive index that is less than the first refractive index.

Abstract: Disclosed are embodiments of a structure for polarization-independent coarse wavelength division demultiplexing. The structure includes a polarization splitter-rotator (PSR), which receives a dual polarization mode multi-wavelength optical signal and outputs a single polarization mode multi-wavelength optical signal. The structure includes a wavelength division demultiplexer (WDD) and a thermo-optic phase-shifter (TOPS), which is coupled to the PSR and either coupled to the input section of the WDD or integrated into the input section of the WDD. The structure includes at least one photodetector and a thermal control system (TCS). The photodetector is coupled to an unused end of a waveguide in either the TOPS or WDD (depending on the embodiment).

Abstract: Structures for a wavelength-division-multiplexing filter and methods of forming a structure for a wavelength-division-multiplexing filter. The structure comprises a first waveguide core including a first phase delay arm and a second waveguide core including a second phase delay arm. The first phase delay arm has a first section and a second section connected to the first section. The first section of the first phase delay arm and the second section of the first phase delay arm are asymmetrical.

Abstract: Structures for a phase shifter and methods of forming such structures. The structure comprises a phase shifter including a first section, a second section, and a strip laterally between the first section and the second section. The structure further comprises a waveguide core including a portion laterally between the first section and the second section of the phase shifter, and a dielectric layer between the waveguide core and the phase shifter. The strip is laterally offset relative to the portion of the waveguide core toward the first section of the phase shifter.

Abstract: Structures for a waveguide escalator, as well as methods of forming such structures. The structure comprises a first waveguide core on a substrate, a second waveguide core, and a back-end-of-line stack including a third waveguide core disposed between the first waveguide core and the second waveguide core. The third waveguide core comprises a layer stack that includes a first layer, a second layer, and a third layer between the first layer and the second layer. The first layer and the second layer comprise a first dielectric material with a first refractive index, and the third layer comprises a second dielectric material with a second refractive index that is less than the first refractive index.

Abstract: Structures for an optical switch and methods of forming such structures. The structure comprises a first waveguide core including a first portion and a second portion, a second waveguide core including a first portion and a second portion, a ring resonator having a first portion adjacent to the first portion of the first waveguide core and a second portion adjacent to the first portion of the second waveguide core, and an optical coupler coupled to the second portion of first waveguide core and the second portion of the second waveguide core. The first portion of the ring resonator is spaced from the first portion of the first waveguide core by a first gap over a first light coupling region, and the second portion of the ring resonator is spaced from the first portion of the second waveguide core by a second gap over a second light coupling region.

Abstract: Disclosed is a thermal stabilization circuit including a heater, which is adjacent and thermally coupled to a closed-curve waveguide of an optical ring resonator, and an analog feedback circuit, which includes a fully autonomous analog feedback loop from a drop port of a bus waveguide of the optical ring resonator to the heater. This analog feedback circuit is configured to dynamically control the electrical power provided to the heater and, thereby to dynamically control the thermal output of the heater in order to tune the ring resonance wavelength to the operating laser wavelength. The analog feedback circuit is further configured to be independent of input power, to be power efficient, to have a relatively small footprint, to have a tunable time constant and to facilitate adjustable wavelength locking. Also disclosed is a device (e.g., a ring-based transceiver or the like), which includes multiple optical ring resonators and corresponding thermal stabilization circuits.

Abstract: Disclosed are a multi-photodetector circuit and an optical receiver incorporating the circuit. The circuit includes parallel-connected photodiodes. In some embodiments, photodiodes are connected, in a same direction, between a positive power supply line and an output line. They receive equal power optical signals or a series of 1:2 optical dividers is employed so each photodiode receives a progressively lower power signal until the last two photodiodes, which receive equal power optical signals. In other embodiments, first photodiodes are connected, in one direction, between a positive power supply line and an output line and second photodiodes are connected, in an opposite direction, between a negative power supply line and the output line. First photodiodes receive equal power optical signals from an optical divider in response to an optical input signal and second photodiodes receive equal power optical signals from another optical divider in response to an inverted optical input signal.

Abstract: Disclosed is a thermal stabilization circuit including a heater, which is adjacent and thermally coupled to a closed-curve waveguide of an optical ring resonator, and an analog feedback circuit, which includes a fully autonomous analog feedback loop from a drop port of a bus waveguide of the optical ring resonator to the heater. This analog feedback circuit is configured to dynamically control the electrical power provided to the heater and, thereby to dynamically control the thermal output of the heater in order to tune the ring resonance wavelength to the operating laser wavelength. The analog feedback circuit is further configured to be independent of input power, to be power efficient, to have a relatively small footprint, to have a tunable time constant and to facilitate adjustable wavelength locking. Also disclosed is a device (e.g., a ring-based transceiver or the like), which includes multiple optical ring resonators and corresponding thermal stabilization circuits.

Abstract: A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.

Abstract: A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include generating a user profile specifying a plurality of attribute values for a plurality of principle attributes, by processing a corpus of electronic documents using a first trained machine learning model. In an embodiment, the method further comprises generating a provider profile specifying a plurality of attribute values for the plurality of principle attributes, for each of a plurality of providers, by processing a respective corpus of electronic documents associated with each respective provider using a second trained machine learning model. A plurality of match coefficients based on comparing the user profile and the plurality of provider profiles are determined. Finally, one or more online interactions between the user and the target provider are influenced based on the determined match coefficients.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include receiving, from a first user of a plurality of users, a first set of electronic documents, where each electronic document in the first set of electronic documents corresponds to a respective user in the plurality of users. The methods also include identifying a plurality of user profiles, where each of the plurality of user profiles was generated by processing a corpus of electronic documents associated with each respective user using a first trained machine learning model. The methods include determining a plurality of match coefficients, based on comparing a plurality of user profiles associated with each respective user in the plurality of users, filtering the first set of electronic documents based on the plurality of match coefficients, and providing the filtered first set of electronic documents to the first user.

Abstract: A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.

Abstract: A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.

Abstract: A computer-implemented mechanism is disclosed. The mechanism includes receiving a data signal, and comparing the data signal to one or more predefined patterns to determine one or more long/short term predictor scores. A discount factor is generated in response to the long/short term predictor scores. A set of expected rewards is generated. The set of expected rewards correspond to an action set specific to the data signal. The set of expected rewards are generated according to reinforced learning. The set of expected rewards are adjusted based on the discount factor. A selected action is selected from the action set based on the set of expected rewards. The selected action is initiated.

Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.

Abstract: A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.