Abstract: The present disclosure relates to dynamically scheduling resource requests in a distributed system based on usage quotas. One example method includes identifying usage information for a distributed system including atoms, each atom representing a distinct item used by users of the distributed system; determining that a usage quota associated with the distributed system has been exceeded based on the usage information, the usage quota representing an upper limit for a particular type of usage of the distributed system; receiving a first request for a particular atom requiring invocation of the particular type of usage represented by the usage quota; determining that a second request for a different type of usage of the particular atom is waiting to be processed; and processing the second request for the particular atom before processing the first request.

Abstract: The present invention relates to novel anti-PD1 and anti-LAG3 antibody molecules. The invention also relates to nucleic acids encoding such antibody molecules; to methods for preparing such antibody molecules; to host cells expressing or capable of expressing such antibody molecules; to compositions comprising such antibody molecules; and to uses of such antibody molecules or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: The present invention relates to novel B7H6/CD3 binding proteins. The invention also relates to nucleic acids encoding such proteins; to methods for preparing such proteins; to host cells expressing or capable of expressing such proteins; to compositions comprising such proteins; and to uses of such proteins or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: The present invention relates to novel anti-PD1 and anti-LAG3 antibody molecules. The invention also relates to nucleic acids encoding such antibody molecules; to methods for preparing such antibody molecules; to host cells expressing or capable of expressing such antibody molecules; to compositions comprising such antibody molecules; and to uses of such antibody molecules or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: The present invention relates to novel DLL3/CD3 binding proteins. The invention also relates to nucleic acids encoding such proteins; to methods for preparing such proteins; to host cells expressing or capable of expressing such proteins; to compositions comprising such proteins; and to uses of such proteins or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: The present invention relates to novel B7H6/CD3 binding proteins. The invention also relates to nucleic acids encoding such proteins; to methods for preparing such proteins; to host cells expressing or capable of expressing such proteins; to compositions comprising such proteins; and to uses of such proteins or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: The present invention relates to anti-SIRP? (Signal regulatory protein alpha) antibodies and antigen-binding fragments thereof for therapeutic and diagnostic methods and compositions using them.

Abstract: Recommender Systems (RS) tend to recommend more popular items instead of the relevant long-tail items. Mitigating such popularity bias is crucial to ensure that less popular but relevant items are recommended. System described herein analyses popularity bias in session-based RS obtained via deep learning (DL) models. DL models trained on historical user-item interactions in session logs (having long-tailed item-click distributions) tend to amplify popularity bias. To understand source of this bias amplification, potential sources of bias at data-generation stage (user-item interactions captured as session logs) and model training stage are considered by the system for recommendation wherein popularity of item has causal effect on user-item interactions via conformity bias, and item ranking from models via biased training process due to class imbalance.

Abstract: A method of enhancing one or more fields of a master database record based on a consumption of one or more data points provided via a third-party data subscription service is disclosed. An aggregated view corresponding to a master database record is accessed. The aggregated view identifies correspondences between the one or more fields of the master database record and the one or more data points provided via the third-party data subscription service. Based on a determination that one or more consumption criteria are met, the one or more data points are consumed and stored in a central database. The one or more fields of the master database record are updated based on the one or more consumed data points. The enhanced master database record is provided via a network to one or more computerized processes associated with one or more consumers of the enhanced master database record.

Abstract: The present invention relates to anti-SIRP? (Signal regulatory protein alpha) antibodies and antigen-binding fragments thereof for therapeutic and diagnostic methods and compositions using them.

Abstract: The present invention relates to anti-SIRP? (Signal regulatory protein alpha) antibodies and antigen-binding fragments thereof for therapeutic and diagnostic methods and compositions using them.

Abstract: The present invention relates to novel tri-specific binding molecules. The invention also relates to nucleic acids encoding such binding molecules; to methods for preparing such binding molecules; to host cells expressing or capable of expressing such binding molecules; to compositions comprising such binding molecules; and to uses of such binding molecules or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: The present disclosure relates to dynamically scheduling resource requests in a distributed system based on usage quotas. One example method includes identifying usage information for a distributed system including atoms, each atom representing a distinct item used by users of the distributed system; determining that a usage quota associated with the distributed system has been exceeded based on the usage information, the usage quota representing an upper limit for a particular type of usage of the distributed system; receiving a first request for a particular atom requiring invocation of the particular type of usage represented by the usage quota; determining that a second request for a different type of usage of the particular atom is waiting to be processed; and processing the second request for the particular atom before processing the first request.

Abstract: The present invention relates to novel DLL3/CD3 binding proteins. The invention also relates to nucleic acids encoding such proteins; to methods for preparing such proteins; to host cells expressing or capable of expressing such proteins; to compositions comprising such proteins; and to uses of such proteins or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: Traditional systems and methods have implemented hand-crafted feature extraction from varying length time series that results in complexity and requires domain knowledge. Building classification models requires large labeled data and is computationally expensive. Embodiments of the present disclosure implement learning models for classification tasks in multi-dimensional time series by performing feature extraction from entity's parameters via unsupervised encoder and build a non-temporal linear classifier model. A fixed-dimensional feature vector is outputted using a pre-trained unsupervised encoder, which acts as off-the shelf feature extractor. Extracted features are concatenated to learn a non-temporal linear classification model and weight is assigned to each extracted feature during learning which helps to determine relevant parameters for each class. Mapping from parameters to target class is considered while constraining the linear model to use only subset of large number of features.

Abstract: This disclosure relates generally to method and system for handling popularity bias in item recommendations. In an embodiment the method includes initializing an item embedding look-up matrix corresponding to items in a sequence of item-clicks with respect to a training data. L2 norm is applied to the item embedding look-up matrix to learn a normalized item embeddings. Using a neural network, a session embeddings corresponding to the sequences of item-clicks is modeled and L2 norm is applied to the session embeddings to obtain a normalized session embeddings. Relevance scores corresponding to each of the plurality of items arc obtained based on similarity between the normalized item embeddings and the normalized session embeddings. A multi-dimensional probability vector corresponding to the relevance scores for the items to be clicked in the sequence is obtained. A list of the items ordered based on the multi-dimensional probability vector is provided as recommendation.

Abstract: Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. However, most existing approaches for SR either rely on costly online interactions with real users (model-free approaches) or rely on potentially biased rule-based or data-driven user-behavior models (model-based approaches) for learning. This disclosure relates to a system and method for selecting session-based recommendation policies using historical recommendations and user feedback. Herein, the learning of recommendation policies given offline or batch data from old recommendation policies based on a Distributional Reinforcement Learning (DRL) based recommender system in the offline or batch-constrained setting without requiring access to a user-behavior model or real-interactions with the users.

Abstract: The present invention relates to novel DLL3/CD3 binding proteins. The invention also relates to nucleic acids encoding such proteins; to methods for preparing such proteins; to host cells expressing or capable of expressing such proteins; to compositions comprising such proteins; and to uses of such proteins or such compositions, in particular for therapeutic purposes in the field of cancer diseases.

Abstract: The present invention relates to antibodies and fragments thereof that target semaphorin 3A (Sema3A). More specifically, anti-Sema3A antibodies and methods of use for the treatment of various diseases or disorders are disclosed.

Abstract: The present invention relates to novel antagonizing antibodies for CD73. The invention also relates to nucleic acids encoding such antibody molecules; to methods for preparing such antibody molecules; to host cells expressing or capable of expressing such antibody molecules; to compositions comprising such antibody molecules; and to uses of such antibody molecules or such compositions, in particular for therapeutic purposes in the field of cancer diseases.