Xerox Corporation

Palo Alto Research Center Inc.

Jean-Marc Andreoli

Guillaume M Bouchard

Stefania Castellani

Jean-Luc Meunier

Victor Ciriza

Yves Hoppenot

Onno R Zoeter

Uwe M Borghoff

Natalie S Glance

Diane Larlus-Larrondo

Cedric Archambeau

Lionel Cazenave

Shengbo Guo

Michael Niemaz

Remo Pareschi

Marie-Luise Schneider

Fran&ccedil;ois Pacull

Gunnar Teege

Daniele Pagani

A power meter generates a time series of observations of power drawn by a device configured to operate in an active mode and to transition through a sequence of inactive modes comprising one or more intermediate inactive modes each having a bounded time duration terminating in a final inactive mode of unbounded time duration, and further configured to transition from any of the inactive modes to the active mode in response to a device activation signal. An electronic data processing device is programmed to determine statistical characteristics of the power draw of the device in each of the inactive modes by fitting a state model to the time series of observations. The state model represents each intermediate inactive mode including its bounded time duration and further includes a probabilistic representation of the transition from any of the inactive modes to the active mode in response to a device activation signal.

Printer device power consumption profiling and power management systems using same

A system and method predict the translation quality of a translated input document. The method includes receiving an input document pair composed of a plurality of sentence pairs, each sentence pair including a source sentence in a source language and a machine translation of the source language sentence to a target language sentence. For each of the sentence pairs, a representation of the sentence pair is generated, based on a set of features extracted for the sentence pair. Using a generative model, a representation of the input document pair is generated, based on the sentence pair representations. A translation quality of the translated input document is computed, based on the representation of the input document pair.

Predicting the quality of automatic translation of an entire document

Methods and systems input an energy consumption profile for each of a plurality of different sleep modes available for a device, and input a probability distribution of interjob times for the device. The methods and systems then compute the optimal time-out period for each sleep mode based on the energy consumption profile of each sleep mode and the probability distribution of interjob times. Further, such methods and systems monitor the usage of the device to determine the current interjob time, and switch between sleep modes to relatively lower power sleep modes as the current interjob time becomes larger.

Multi-mode device power-saving optimization

A control system reduces energy consumption in a multi-device system comprising a plurality of devices. The control system includes at least one processor. The processor is programmed to receive a job to be executed, as well as a selection of one of the plurality of devices for executing the job and a transfer cost for transferring the job from the selected device to each of the plurality of devices. A device to execute the job is determined through optimization of a first cost function. The first cost function is based on the device selection and the transfer costs. The job is assigned to the determined device and a time-out for each device in the multi-device system is determined through optimization of a second cost function. The second cost function is based on an expected energy consumption by the multi-device system. The devices are provided with the determined time-outs.

Multi-device powersaving

Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D&#x2267;1 and P&#x2267;1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.

Multi-task learning using bayesian model with enforced sparsity and leveraging of task correlations

A system and method of localizing elements (shared devices and/or their users) in a device infrastructure, such as a printing network, are provided. The method includes mapping a structure in which the elements of a device infrastructure are located, the elements comprising shared devices and users of the shared devices. Probable locations of fewer than all of the elements in the structure are mapped, with at least some of the elements being initially assigned to an unknown location. Usage logs for a plurality of the shared devices are acquired. The acquired usage log for each device includes a user identifier for each of a set of uses of the device, each of the uses being initiated from a respective location within the mapped structure by one of the users. Based on the acquired usage logs and the input probable locations of some of the elements, locations of at least some of the elements initially assigned to an unknown location are predicted. The prediction is based a model which infers that for each of a plurality of the users, a usage of at least some of the shared devices by the user is a function of respective distances between the user and each of those devices.

User and device localization using probabilistic device log trilateration

An apparatus operating on a time sequence of events includes an event handling module configured to generate a predicted label for a current observed event of the time sequence of events and a true label handling module configured to process a true label revealed for an observed event of the time sequence of events. The event handling module and the true label handling module cooperatively model stochastic dependence of a true label for the current observed event based on the time sequence of events and revealed true labels for the past observed events of the time sequence of events. The event handling module and the true label handing module operate asynchronously. The event handling module and the true response handling module suitably operate as one or more digital processors.

On-line autoregressive prediction in time series with delayed disclosure and monitor systems using same

In a monitoring method, a time sequence of information pertaining to a monitored device, network, or system is recorded, comprising observations of the monitored device, network, or system and known prior correct action recommendations for the monitored device, network, or system. A hidden Markov model (HMM) operating on the time sequence of information is maintained. The HMM comprises a hidden state of the monitored device, network, or system. A current state of the monitored device, network, or system is classified using a classification value comprising an emission of the HMM that depends on an estimate of the distribution of the hidden state and on a selected portion of the time sequence of information. An action recommendation is generated for the current state of the monitored device, network, or system based on the classification value.

Monitoring a device, network, or system with adaptive dynamic classification employing a hidden Markov model operating on a time sequence of information

An events analysis method comprises: optimizing respective to a set of training data a set of branching transition likelihood parameters associating parent events of type k with child events of type k' in branching processes; inferring a most probable branching process for a set of input data comprising events based on the optimized set of branching transition likelihood parameters; and identifying rare or unusual events of the set of input data based on the inferred most probable branching process. An events analysis apparatus includes a probabilistic branching process learning engine configured to optimize the set of branching transition likelihood parameters, and a probabilistic branching process inference engine configured to infer the most probable branching process.

Temporal events analysis employing tree induction

A method for analyzing hidden dynamics, includes acquiring discrete observations, each discrete observation having an observed value selected from two or more allowed discrete values. A factorial hidden Markov model (FHMM) relating the discrete observations with a plurality of hidden dynamics is constructed. A contribution of the state of each hidden dynamic to the discrete observation may be represented in the FHMM as a parameter of a nominal distribution which is scaled by a function of the state of the hidden dynamic. States of the hidden dynamics are inferred from the discrete observations based on the FHMM. Information corresponding to at least one inferred state of at least one of the hidden dynamics is output. The parameters of the contribution of each dynamic to the hidden states may be learnt from a large number of observations. An example of a networked printing system is used to demonstrate the applicability of the method.

Factorial hidden Markov model with discrete observations

In a method for monitoring hardware devices of the hardware devices network to detect failures, past logged jobs are analyzed to determine a pattern of past usage of hardware devices by users of the hardware devices network. A failure of a hardware device is inferred based on a deviation in usage of the hardware devices from the pattern of past usage. In some embodiments, a state model is constructed that assigns one of two or more available operational states to each hardware device of the hardware devices network based on a recent load of jobs logged to the hardware device and a stored history of logged jobs indicative of typical loading of the hardware devices. One or more failing hardware devices are identified based on the assigned operational states.

Soft failure detection in a network of devices

A constraint solver is encapsulated in a service adapted to participate in a negotiation. In operation, the system constructs a graph in its memory for negotiating a set of actions to be performed by selected ones of a plurality of participants. Each node of the graph defines a negotiation context that has associated therewith a constraint store with at least a set of domain constraints stored therein that limit during the negotiation a set of semantic constraints of the service on variables of the constraint solver.

Graph-based negotiation system with encapsulated constraint solver

Methods are disclosed for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs. In operation, usage job data of observed users and devices carrying out the jobs is recorded. A probability model is defined with an observed user variable, an observed device variable, a latent job cluster variable, and a latent job service class variable. A range of job service classes associated with the shared devices is determined, and an initial number of job clusters is selected. Parameters of the probability model are learned using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters. The learned parameters of the probability model are applied to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.

Method, apparatus, and article of manufacture for estimating parameters of a probability model on shared device usage probabilistic semantic analysis

A plurality of components are specified to perform a negotiation, where each component is a participant, a coordinator, or both. Each component is provided with a conversion table that maps a set of parameters between invocation patterns instantiated by the participants. Each coordinator is provided with a negotiation graph that it modifies to coordinate its neighborhood of negotiation graphs, and each participant is provided with a negotiation graph that it modifies for each invocation pattern it instantiates. Two message types are sent between components that communicate directly with each other in each neighborhood of negotiation graphs. The two message types are sent between the components to collaboratively mirror their negotiation graphs using their conversion tables in each neighborhood of negotiation graphs so that each participant only views information concerning those aspects in its negotiation graph that relate to the parameters of the invocation patterns it instantiated.

System for negotiation with mirroring

A negotiation is carried out between a plurality of participants. Each participant defines an invocation pattern. A set of coordinator parameters are attached to each invocation pattern, where the set of coordinator parameters define interdependencies between parameters of the plurality of participants that are shared across their invocation patterns. A graph is constructed to carry out the negotiation of a set of actions to be performed by selected ones of the plurality of participants, with nodes of a first node type that represents a negotiating context or a second node type that represents a decision point. The participants construct the graph with primitives instantiated by the invocation patterns. At least one node in the graph is a decision point that merges two or more negotiation contexts into a single decision point. The single decision point combines values of the parameters from the two or more negotiation contexts.

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Meylan, France

Jean-Marc Andreoli