International Business Machines Corporation

Google Inc.

Bogdan Prisacari

Cyriel Johan Minkenberg

German Rodriguez Herrera

Balint Miklos

Wolfgang Emil Denzel

Felix Weissenberger

Matthew Nirvan Sharifi

Victor Carbune

Luv Kothari

Jacopo Sannazzaro Natta

Domenico Carbotta

Jae Seo

Ray Chen

Kevin Fu

Fo Lee

Mucun Lu

Neha Garg

Barbara Poblocka

Matthew Miao

Thomas Qian

Philip Heidelberger

Lonny Lambrecht

Christopher Jon Johnson

Baba L Arimilli

Maria Gabrani

Timothy Youngjin Sohn

Andreea Anghel

Julia Proskurnia

Chiara Marchiori

Paul Roland Lambert

Matthew Vincent Dierker

Jacqueline Amy Tsay

Thijs Van As

Victor Anchidin

Alexander Froemmgen

Bryan Kenneth Rea

German R Herrera

Erwan Zerhouni

Implementations described herein relate to causing certain reasoning with respect to why an automated assistant performed (or did not perform) certain fulfillment and/or alternate fulfillment of an assistant command. For example, implementations can receive user input that includes the assistant command, process the user input to determine data to be utilized in performance of the certain fulfillment or the alternate fulfillment of the assistant command, and cause the automated assistant to utilize the data to perform the certain fulfillment or the alternate fulfillment of the assistant command. In some implementations, output that includes the certain reasoning can be provided for presentation to a user in response to additional user input that requests the certain reasoning. In some implementations, a selectable element can be visually rendered and, when selected by the user, the output that includes the certain reasoning can be provided for presentation to the user.

Providing certain reasoning with respect to fulfillment of an assistant command

An example method includes receiving, by a computing system, an identification of a recipient of an electronic message being composed from a message account associated with a user; predicting, by the computing system and based on text contained in previous electronic messages sent from the message account, text for a body of the electronic message; and outputting, for display, the predicted text for optional selection and insertion into the body of the electronic message.

Recipient based text prediction for electronic messaging

Implementations set forth herein relate to an automated assistant that can control a camera according to one or more conditions specified by a user. A condition can be satisfied when, for example, the automated assistant detects a particular environment feature is apparent. In this way, the user can rely on the automated assistant to identify and capture certain moments without necessarily requiring the user to constantly monitor a viewing window of the camera. In some implementations, a condition for the automated assistant to capture media data can be based on application data and/or other contextual data that is associated with the automated assistant. For instance, a relationship between content in a camera viewing window and other content of an application interface can be a condition upon which the automated assistant captures certain media data using a camera.

Conditional camera control via automated assistant commands

Apparatus, methods, and computer-readable media are provided for simultaneous feature extraction and dictionary learning from heterogeneous tissue images, without the need of prior local labeling. A convolutional autoencoder is adapted and enhanced to jointly learn a feature extraction algorithm and a dictionary of representative atoms. While training the autoencoder an image patch is tiled in sub-patches and only the highest activation value per sub-patch is kept. Thus, only a subset of spatially constrained values per patch is used for reconstruction. The deconvolutional filters are the dictionary elements, and only a deconvolution layer is used for these elements. Embodiments described herein may be provided for use in models for representing local tissue heterogeneity for better disease progression understanding and thus treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.

Simultaneous feature extraction and dictionary learning using deep learning architectures for characterization of images of heterogeneous tissue samples

Disclosed herein is a system comprising a plurality of agents that desire access to a resource; a finite amount of an internal state; a processor that is configured to enforce an arbitration mechanism to translate a quota specification for the plurality of agents according to a set of rules comprising i) all classes of service that are serviceable are served according to the quota specification; ii) if an underserviced class is not serviceable, a serviceable class continues to be granted access to the one or more resources even if the granted access leads to an increase in the underservice of a non-serviceable class; iii) as soon as the underserviced class becomes serviceable, it will in priority recuperate its accumulated disadvantage until its quota specification is reached; and iv) the internal state is updated upon reaching boundary conditions.

Enablement of quota-based quality of service

A method for all-to-all message exchange between program tasks including N&#x3e;1 hierarchy levels l, n=1 to N, in which a first level lincludes a plurality of group tasks and each higher level lincludes at least one group of level lgroups to which that task belongs in respective hierarchy levels of the network topology; sending a message via the interconnection network to a respective destination task whose hierarchical identifier is determined; and using the hierarchical identifier to send the program task and the network topology, such that the resulting exchange pattern for the all-to-all message exchange exploits a hierarchical distance in the network topology in a desired manner.

All-to-all message exchange in parallel computing systems

A computer-implemented method including: (i) receiving a computational task to be processed by the datacenter environment; (ii) receiving a set of computational task parameters, The computational task parameters specifying characteristics of the computational task to be processed; (iii) receiving a set of computing entity parameters, The computing entity parameters specifying characteristics of the computing entities; (iv) transmitting the computational task parameters and the computing entity parameters to a scheduling entity; and (v) selecting one or more computing entities of The plurality of computing entities for processing the computational task by the scheduling entity, based on various factors.

Datacenter scheduling of applications using machine learning techniques

A multiprocessor computer system includes a plurality of processor nodes and at least a three-tier hierarchical network interconnecting the processor nodes. The hierarchical network includes a plurality of routers interconnected such that each router is connected to a subset of the plurality of processor nodes; the plurality of routers are arranged in a hierarchy of n&#x2267;3 tiers (T, . . . , T); the plurality of routers are partitioned into disjoint groups at the first tier T, the groups at tier Tbeing partitioned into disjoint groups (of complete Tgroups) at the next tier Tand a top tier Tincluding a single group containing all of the plurality of routers; and for all tiers 1&#x2266;i&#x2266;n, each tier-Tsubgroup within a tier Tgroup is connected by at least one link to all other tier-Tsubgroups within the same tier Tgroup.

Interconnection network topology for large scale high performance computing (HPC) systems

Disclosed herein is a method for deadlock avoidance in a network comprising partitioning in a module, a set of virtual lanes into deadlock avoidance virtual channels; where each deadlock avoidance virtual channel comprises a plurality of virtual lanes; assigning each incoming packet at the module to one of the deadlock avoidance virtual channels, and within the deadlock avoidance virtual channel to a virtual lane whose relative index corresponds to an absolute index of the virtual lane the packet would have been assigned to if the network was not provisioned with deadlock avoidance virtual channels; and transitioning each packet as it traverses the module from one deadlock avoidance virtual channel to another deadlock avoidance virtual channel when a deadlock avoidance policy so demands it.

Mechanisms for deadlock avoidance support in network fabrics

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Adliswil, Switzerland

Bogdan Prisacari