Switched networks with maximum weight policies: Fluid approximation and multiplicative state space collapse

We consider a queueing network in which there are constraints on which queues may be served simultaneously; such networks may be used to model input-queued switches and wireless networks. The scheduling policy for such a network specifies which queues to serve at any point in time. We consider a family of scheduling policies, related to the maximum-weight policy of Tassiulas and Ephremides [IEEE Trans. Automat. Control 37 (1992) 1936--1948], for single-hop and multihop networks. We specify a fluid model and show that fluid-scaled performance processes can be approximated by fluid model solutions. We study the behavior of fluid model solutions under critical load, and characterize invariant states as those states which solve a certain network-wide optimization problem. We use fluid model results to prove multiplicative state space collapse. A notable feature of our results is that they do not assume complete resource pooling.Comment: Published in at http://dx.doi.org/10.1214/11-AAP759 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

The price of choice: models, paradoxes, and inference for 'mobility as a service'

A city’s transportation network is made up of subsystems, often under separate management, linked together through the choices made by users. This paper introduces a transport model which combines a discrete choice model of users, with a resource allocation model of a subsystems. This combined model gives a direct economic interpretation of tradeoffs in the system. For example, it tells us how much of a rideshare price is attributable to the cost of running the platform and how much is profit-making. The model can also be used to predict knock-on effects in the style of Braess’s paradox, where an improvement in one part of the network might induce problems in other parts because of selfish choices made by users and by subsystems.Toyota Mobility Foundatio

Rationale for tau aggregation inhibitor therapy in Alzheimer's disease and other tauopathies

Preprin

Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective

In this study, we introduce a novel, probabilistic viewpoint on adversarial examples, achieved through box-constrained Langevin Monte Carlo (LMC). Proceeding from this perspective, we develop an innovative approach for generating semantics-aware adversarial examples in a principled manner. This methodology transcends the restriction imposed by geometric distance, instead opting for semantic constraints. Our approach empowers individuals to incorporate their personal comprehension of semantics into the model. Through human evaluation, we validate that our semantics-aware adversarial examples maintain their inherent meaning. Experimental findings on the MNIST and SVHN datasets demonstrate that our semantics-aware adversarial examples can effectively circumvent robust adversarial training methods tailored for traditional adversarial attacks.Comment: 17 pages, 14 figure

Interference is not noise

This paper looks at the problem of designing wireless medium access algorithms. Inter-user interference at the receivers is an important characteristic of wireless networks. We show that decoding (or canceling) this interference results in significant improvement in the system performance over protocols that either treat interference as noise, or explicitly avoid interference at the receivers by allowing at most one of the transmitters in its range to transmit. This improvement in performance is realized by means of a medium access algorithm with: (a) polynomial computational complexity per timeslot, (b) polynomially bounded expected queue-length at the transmitters, and (c) a throughput region that is at least a polylogarithmic fraction of the largest possible throughput-region under any algorithm operating using that treats interference as noise. Thus, the hardness of low-delay network scheduling (a result by Shah, Tse and Tsitsiklis [1]) is an artifact of explicitly avoiding interference, or treating it as noise and can be overcome by a rather simple medium access algorithm that does not require information theoretic "block codes".United States. Defense Advanced Research Projects Agency. Information Theory for Mobile Ad-Hoc Networks Progra

On the path to AI

This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data

On the path to AI

This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data

Taylorformer: Probabilistic Predictions for Time Series and other Processes

We propose the Taylorformer for time series and other random processes. Its two key components are: 1) the LocalTaylor wrapper to learn how and when to use Taylor series-based approximations for predictions, and 2) the MHA-X attention block which makes predictions in a way inspired by how Gaussian Processes' mean predictions are linear smoothings of contextual data. Taylorformer outperforms the state-of-the-art on several forecasting datasets, including electricity, oil temperatures and exchange rates with at least 14% improvement in MSE on all tasks, and better likelihood on 5/6 classic Neural Process tasks such as meta-learning 1D functions. Taylorformer combines desirable features from the Neural Process (uncertainty-aware predictions and consistency) and forecasting (predictive accuracy) literature, two previously distinct bodies.Comment: 18 pages, 6 figure

Tau Aggregation Inhibitor Therapy : An Exploratory Phase 2 Study in Mild or Moderate Alzheimer's Disease

ACKNOWLEDGMENTS We thank patients and their caregivers for their participation in the study and are indebted to all the investigators involved in the study, particularly Drs. Douglas Fowlie and Donald Mowat for their helpful contributions to the clinical execution of the study in Scotland. We thank Sharon Eastwood, Parexel, for assistance in preparing initial drafts of the manuscript. We acknowledge constructive comments provided by Professors G. Wilcock and S. Gauthier on drafts of the article. CMW, CRH, and JMDS are officers of, and hold beneficial interests in, TauRx Therapeutics. RTS, PB, KK, and DJW are paid consultants to TauRx Therapeutics. The study was financed entirely by TauRx TherapeuticsPeer reviewedPublisher PD

Transportation system reconstruction

2035-03-17 [Patent Duration]Current Assignees: Urban Engines Inc Google LLCA system for reconstructing vehicle itinerary include a processor and a memory storing instructions, implemented by the processor, to cluster historical trip records into a plurality of clusters, each of the plurality of clusters including a set of historical trip records that describe events occurring within a predetermined time range at one location; identify a sequence of clusters that includes a cluster at each location; and estimate an itinerary for a vehicle based on the sequence of clusters and constraint data describing physical constraints, the itinerary for the vehicle describing a sequence of arrival and departure times at a sequence of locations for the vehicle