Abstract: The present disclosure relates to TNF-alpha variants and TNF-alpha variant fusion molecules and therapeutic uses of such thereof.

Abstract: To automatically identify a sequence of recommended account/product pairs with highest likelihood of becoming a realized opportunity, an account/product sequence recommender uses an account propensity (AP) model and a reinforcement learning (RL) model and target engagement sequence generators trained on historical time series data, firmographic data, and product data. The trained AP model assigns propensity values to each product corresponding to received account characteristics. The trained RL model generates an optimal sequence of products that maximizes the reward over future realized opportunities. The target engagement sequence generators create target engagement sequences corresponding to the optimal sequence of products. The recommender prunes the optimal sequence of products based on the propensity values from the trained AP model, the completeness of these target engagement sequences, and a desired product sequence length.

Abstract: A submersible vehicle (210) includes a plurality of outlet nozzles (301, 302, 303) arranged to receive pressurised fluid from a remote supply and expel the pressurised fluid to create propulsion to manoeuvre the vehicle (210); and a plurality of valves (401) in fluid communication with the outlet nozzles (301, 302, 303) and operable to provide variable pressure and/or flow to each outlet nozzle (301, 302, 303). The outlet nozzles (301, 302, 303) are arranged about the vehicle to provide six-degrees of freedom movement and control of the submersible vehicle (210).

Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.

Abstract: A catheter handle includes a housing extending from a proximal end to a distal end along a longitudinal axis. The housing defines an internal cavity. The catheter handle also includes at least one wire extending through the internal cavity between the proximal end and the distal end. The catheter handle further includes a wire management cap positioned within the internal cavity between a movable component of the catheter handle and one of the proximal and distal ends. The wire management cap defines a wire receiver cavity sized to receive a portion of the wire therein. The wire management cap is oriented such that the portion of the wire n the wire receiver cavity is separated from the movable component by the wire management cap to prevent interference between the movable component and the wire.

Abstract: Dynamic content tags are generated as content is received by a dynamic content tagging system. A natural language processor (NLP) tokenizes the content and extracts contextual N-grams based on local or global context for the tokens in each document in the content. The contextual N-grams are used as input to a generative model that computes a weighted vector of likelihood values that each contextual N-gram corresponds to one of a set of unlabeled topics. A tag is generated for each unlabeled topic comprising the contextual N-gram having a highest likelihood to correspond to that unlabeled topic. Topic-based deep learning models having tag predictions below a threshold confidence level are retrained using the generated tags, and the retrained topic-based deep learning models dynamically tag the content.

Abstract: Dynamic content tags are generated as content is received by a dynamic content tagging system. A natural language processor (NLP) tokenizes the content and extracts contextual N-grams based on local or global context for the tokens in each document in the content. The contextual N-grams are used as input to a generative model that computes a weighted vector of likelihood values that each contextual N-gram corresponds to one of a set of unlabeled topics. A tag is generated for each unlabeled topic comprising the contextual N-gram having a highest likelihood to correspond to that unlabeled topic. Topic-based deep learning models having tag predictions below a threshold confidence level are retrained using the generated tags, and the retrained topic-based deep learning models dynamically tag the content.

Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.

Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.

Abstract: An advertising system has limited computing resources to spend evaluating advertisements of advertisers to determine a “best” advertisement to serve to users of a social networking system. The computing resources are allocated (e.g., by varying the number of advertisements that are considered for presentation to a user) based on the neediness of the user and/or the advertiser on a per impression basis. The neediness of a user may be determined by grouping users into groups and determining a yield curve of expected revenue per computing resource used. Then, the revenue may be maximized across impression opportunities for multiple users. The neediness of an advertiser may be determined by biasing the selection of one advertiser's advertisements over another advertiser's advertisements based on an expected revenue, an expected number of interactions of the advertisement, or otherwise maximizing a satisfaction coefficient for the advertiser.

Abstract: Dynamic content tags are generated as content is received by a dynamic content tagging system. A natural language processor (NLP) tokenizes the content and extracts contextual N-grams based on local or global context for the tokens in each document in the content. The contextual N-grams are used as input to a generative model that computes a weighted vector of likelihood values that each contextual N-gram corresponds to one of a set of unlabeled topics. A tag is generated for each unlabeled topic comprising the contextual N-gram having a highest likelihood to correspond to that unlabeled topic. Topic-based deep learning models having tag predictions below a threshold confidence level are retrained using the generated tags, and the retrained topic-based deep learning models dynamically tag the content.

Abstract: The system and method for using morpho photonic structures to form small, lightweight imagers for use with SWIR, MWIR and LWIR. In some cases, the morpho photonic structure imagers are used in googles. The morpho photonic structure imagers have a frame rate ranging from 100 Hz to 200 Hz. In some cases, using a cluster of short wave infrared, mid wave infrared, and long wave infrared sensors to form a multi-spectral image is used to scan for chemical fingerprints.

Abstract: Actuators for steerable medical devices are disclosed that not only deflect or steer a portion of a medical device (e.g., a distal portion of a catheter shaft), but also include mechanisms for actively returning the deflected portion of the medical device to an initial configuration (e.g., straight or substantially straight). These active return-to-straight mechanisms may return a catheter shaft from a deflected configuration to a substantially straight configuration throughout a medical procedure, may employ one or more tension members extending along the catheter shaft, and may comprise a gross return actuator and a fine return actuator. For example, the gross return actuator may be configured to partially reverse the deflection of the distal portion of the catheter; and the fine return actuator may be configured to continue reversing the deflection. The gross return actuator may automatically trigger or actuate (mechanically or electromechanically) the fine return actuator.

Abstract: To automatically identify a sequence of recommended account/product pairs with highest likelihood of becoming a realized opportunity, an account/product sequence recommender uses an account propensity (AP) model and a reinforcement learning (RL) model and target engagement sequence generators trained on historical time series data, firmographic data, and product data. The trained AP model assigns propensity values to each product corresponding to received account characteristics. The trained RL model generates an optimal sequence of products that maximizes the reward over future realized opportunities. The target engagement sequence generators create target engagement sequences corresponding to the optimal sequence of products. The recommender prunes the optimal sequence of products based on the propensity values from the trained AP model, the completeness of these target engagement sequences, and a desired product sequence length.

Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.

Abstract: An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.

Abstract: Based on prior interactions associated with a user, an online system predicts an amount of interaction by the user with an object associated with an advertisement. Using the predicted amount of user interaction, the online system determines an expected value of presenting the advertisement to the user. The advertisement is ranked among other advertisements based on the expected values associated with the advertisements, and one or more advertisements are selected for presentation to the user based on the ranking. An advertisement may also specify a threshold amount of interaction with an associated object as targeting criteria, so the predicted amount of interaction with the object associated with the advertisement may determine if a user is eligible to be presented with the advertisement.

Abstract: An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.

Abstract: The system and method for using morpho photonic structures to form small, lightweight imagers for use with SWIR, MWIR and LWIR. In some cases, the morpho photonic structure imagers are used in googles. The morpho photonic structure imagers have a frame rate ranging from 100 Hz to 200 Hz. In some cases, using a cluster of short wave infrared, mid wave infrared, and long wave infrared sensors to form a multi-spectral image is used to scan for chemical fingerprints.

Abstract: Described herein are game devices for use in individual and/or group play. These game devices can compose a base and an intermediate structure comprising an opening which a user can attempt to throw a ball or other object into. The base can be configured such that it can connect to a substantially planar surface such as a wall or ceiling. The intermediate structure can be flexible like a basketball net or can be rigid. In some embodiments, the opening in the intermediate structure can be connected to a rim that hold the opening in an “open” position. In some embodiments, the rim can further comprise bristle-like protrusion structures.