Microsoft Technology Licensing, LLC

Oracle International Corporation

Read Rite Corporation

Yan Yan

Abhishek Gupta

Jianghui Su

Vijay K Dialani

Shakti Dhirendraji Sinha

Viet Thuc Ha

Xianren Wu

Ye Xu

Satya Kanduri

Dawei Huang

Xun Zhang

Deqiang Song

Zuxu Qin

Sahin Cem Geyik

Baoqing Huang

Sifang You

Pu Chuang

Jieda Li

Ali Gokhan Ileri

In an example embodiment, one or more specified ideal candidates are used to perform a search in a database. One or more attributes are extracted from one or more ideal candidate member profiles. A search query is then generated based on the extracted one or more attributes. Then, a search is performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles.

Query building for search by ideal candidates

Techniques for using recruiter review data to create training, validation and test sets for automated sourcing are described. An example system obtains sample suggested candidate member profiles and sample search result member profiles in an online system. The system identifies unique pairs of member profiles, each pair consisting of one of the suggested candidate profiles and one of the search result profiles. Additionally, the system generates a label for each of the unique pairs of profiles. The label is generated using a score generated from log information of the online system, the log information including historical records of searcher feedback regarding members of the online system, the score being higher if the searcher accepted the sample search result member profile in a search session. Furthermore, the system inputs the labels into a machine learning algorithm to train a combined ranking model that outputs ranking scores for search result member profiles.

Using log data to train for automated sourcing

In an example embodiment, one or more sample ideal candidate member profiles in a social networking service are obtained, as well as one or more sample search result member profiles in the social networking service. Then, for each unique pair of sample ideal candidate member profile and sample search result member profile, a label is generated using a score generated from log information of the social networking service, the log information including records of communications between a searcher and members of the social networking service, the score being higher if the searcher communicated with both the member corresponding sample ideal candidate member profile and the member corresponding to the sample search result member profile in a same search session. The generated labels are fed into a machine learning algorithm to train a combined ranking model used to output ranking scores for search result member profiles.

Generation of training data for ideal candidate search ranking model

In an example embodiment, a query for search results is received, the query including at least one value for one facet, a facet defining a categorical dimension for the search results. It is then determined that the facet in the query is exclusive. In response to the determination that the facet is exclusive: for each potential facet different from the facet in the query: for each potential value in the potential facet: conditional entropy gain of the value in the query and the potential value is determined. The potential value in the potential facet that has the highest conditional entropy gain is determined, as is the potential facet with the minimum maximum conditional entropy gain. Then the potential facet with the minimum maximum is input into a machine learning model, causing the machine learning model to output one or more suggested facets to add to the query.

Smart suggestions for query refinements

An embodiment includes a receiver circuit, a feedback circuit and a control circuit. The receiver circuit is configured to receive each data bit of a plurality of data bits. The feedback circuit is configured to measure a first interference level generated by a first data bit of a first subset of the plurality of data bits on a second data bit of the plurality of data bits to generate one of a first plurality of feedback values. The feedback circuit is also configured to measure a second interference level generated by a third data bit of a second subset of the plurality of data bits on a fourth data bit of the plurality of data bits to generate one of a second plurality of feedback values. The control circuit is configured to determine a duty cycle dependent upon a comparison of the first plurality to the second plurality.

Method for duty cycle distortion detection through decision feedback equalizer taps

Embodiments include systems and methods for increasing frequency offset tracking in clock data recovery (CDR) systems. For example, in asynchronous clocking environments, the receiver-side clock frequency can be offset from the transmitter-side clock. While traditional CDR systems can handle some amount of offset, they are typically ineffective at accurately adapting the receiver-side clocking to an optimal data sampling rate when the offset is excessive. Embodiments include a CDR frequency offset adaptation loop that generates an adaptation signal, which can be monitored to detect an adaptation error arising from excessive frequency offset. In response to the detecting, an offset seed can be selected and injected into the frequency offset adaptation loop, thereby reinitializing the frequency offset adaptation loop with a less stressful seed.

Clock data recovery with increased frequency offset tracking

A system may include one or more high-speed serial interfaces for moving data. A system may include a transmission unit configured to serially transmit data bits, and a receiving unit coupled to the transmission unit. The receiving unit may receive a stream of data bits from the transmission unit and establish an initial sample point. The receiving unit may then sample the bits at multiple offsets from the initial sample point, reestablishing the initial sample point between each offset. The receiving unit may also calculate bit error rates (BERs) for the samples taken at each sample point. Based on the BERs, the receiving unit may set a data sampling point for receiving a second stream of data bits from the transmitter unit. The receiving unit may limit the amount of time the data sampling point is used and recalculate the data sampling point when the amount of time has expired.

Precursor adaptation algorithm for asynchronously clocked SERDES

A method and apparatus for duty cycle distortion compensation is disclosed. In one embodiment, an integrated circuit includes a differential signal transmitter having a main data path and a compensation data path. The main data path includes a first and second differential driver circuits each having output terminals coupled to a differential output. A transmission controller is configured to transmit data into the main and compensation data paths, the data corresponding to pairs of sequentially transmitted bits including an odd data bit followed by an even data bit, and further configured to determine respective duty cycle widths for each of the odd and even data bits as received by the transmission controller. The transmission controller is configured to cause the first and second driver circuits to equalize the respective duty cycle widths of the odd and even data bits, as transmitted, based their respective duty cycle widths as received.

Method and apparatus for duty cycle distortion compensation

In a disk drive, an interconnect adapter for coupling a head of a suspension assembly to front-end read/write circuitry includes a pad set with a plurality of specially designed pads. The pads are formed with an integrated frame and multiple tabs. The tabs enable removal and replacement of the pad set from the suspension assembly to enable suspension assembly rework. The interconnect adapter is designed to adapt to polarity specifications of various head and read/write circuitry designs. The pad set design is configured for connection with a variety of traces, with a piezoelectric microactuator, pre-amplification circuitry, and any of a variety of heads.

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San Jose, CA, United States of America

Yan Yan