24,235 research outputs found
Automated Synthesis of Distributed Self-Stabilizing Protocols
In this paper, we introduce an SMT-based method that automatically
synthesizes a distributed self-stabilizing protocol from a given high-level
specification and network topology. Unlike existing approaches, where synthesis
algorithms require the explicit description of the set of legitimate states,
our technique only needs the temporal behavior of the protocol. We extend our
approach to synthesize ideal-stabilizing protocols, where every state is
legitimate. We also extend our technique to synthesize monotonic-stabilizing
protocols, where during recovery, each process can execute an most once one
action. Our proposed methods are fully implemented and we report successful
synthesis of well-known protocols such as Dijkstra's token ring, a
self-stabilizing version of Raymond's mutual exclusion algorithm,
ideal-stabilizing leader election and local mutual exclusion, as well as
monotonic-stabilizing maximal independent set and distributed Grundy coloring
Boom and Bust Carbon-Nitrogen Dynamics during Reforestation
Legacies of historical land use strongly shape contemporary ecosystem dynamics. In old-field secondary forests, tree growth embodies a legacy of soil changes affected by previous cultivation. Three patterns of biomass accumulation during reforestation have been hypothesized previously, including monotonic to steady state, non-monotonic with a single peak then decay to steady state, and multiple oscillations around the steady state. In this paper, the conditions leading to the emergence of these patterns is analyzed. Using observations and models, we demonstrate that divergent reforestation patterns can be explained by contrasting time-scales in ecosystem carbon-nitrogen cycles that are influenced by land use legacies. Model analyses characterize non-monotonic plant-soil trajectories as either single peaks or multiple oscillations during an initial transient phase controlled by soil carbon-nitrogen conditions at the time of planting. Oscillations in plant and soil pools appear in modeled systems with rapid tree growth and low initial soil nitrogen, which stimulate nitrogen competition between trees and decomposers and lead the forest into a state of acute nitrogen deficiency. High initial soil nitrogen dampens oscillations, but enhances the magnitude of the tree biomass peak. These model results are supported by data derived from the long-running Calhoun Long-Term Soil-Ecosystem Experiment from 1957 to 2007. Observed carbon and nitrogen pools reveal distinct tree growth and decay phases, coincident with soil nitrogen depletion and partial re-accumulation. Further, contemporary tree biomass loss decreases with the legacy soil C:N ratio. These results support the idea that non-monotonic reforestation trajectories may result from initial transients in the plant-soil system affected by initial conditions derived from soil changes associated with land-use history
Error correcting code using tree-like multilayer perceptron
An error correcting code using a tree-like multilayer perceptron is proposed.
An original message \mbi{s}^0 is encoded into a codeword \boldmath{y}_0
using a tree-like committee machine (committee tree) or a tree-like parity
machine (parity tree). Based on these architectures, several schemes featuring
monotonic or non-monotonic units are introduced. The codeword \mbi{y}_0 is
then transmitted via a Binary Asymmetric Channel (BAC) where it is corrupted by
noise. The analytical performance of these schemes is investigated using the
replica method of statistical mechanics. Under some specific conditions, some
of the proposed schemes are shown to saturate the Shannon bound at the infinite
codeword length limit. The influence of the monotonicity of the units on the
performance is also discussed.Comment: 23 pages, 3 figures, Content has been extended and revise
The Flexible Group Spatial Keyword Query
We present a new class of service for location based social networks, called
the Flexible Group Spatial Keyword Query, which enables a group of users to
collectively find a point of interest (POI) that optimizes an aggregate cost
function combining both spatial distances and keyword similarities. In
addition, our query service allows users to consider the tradeoffs between
obtaining a sub-optimal solution for the entire group and obtaining an
optimimized solution but only for a subgroup.
We propose algorithms to process three variants of the query: (i) the group
nearest neighbor with keywords query, which finds a POI that optimizes the
aggregate cost function for the whole group of size n, (ii) the subgroup
nearest neighbor with keywords query, which finds the optimal subgroup and a
POI that optimizes the aggregate cost function for a given subgroup size m (m
<= n), and (iii) the multiple subgroup nearest neighbor with keywords query,
which finds optimal subgroups and corresponding POIs for each of the subgroup
sizes in the range [m, n]. We design query processing algorithms based on
branch-and-bound and best-first paradigms. Finally, we provide theoretical
bounds and conduct extensive experiments with two real datasets which verify
the effectiveness and efficiency of the proposed algorithms.Comment: 12 page
Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments
Qualitative models are often more suitable than classical quantitative models in tasks such as Model-based Diagnosis (MBD), explaining system behavior, and designing novel devices from first principles. Monotonicity is an important feature to leverage when constructing qualitative models. Detecting monotonic pieces robustly and efficiently from sensor or simulation data remains an open problem. This paper presents scale-based monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. sensor data or simulation results, can be partitioned into quasi-monotonic segments, i.e. segments monotonic with respect to a scale, in linear time. A novel segmentation algorithm is introduced along with a scale-based definition of "flatness"
The Case for Learned Index Structures
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the
position of a record within a sorted array, a Hash-Index as a model to map a
key to a position of a record within an unsorted array, and a BitMap-Index as a
model to indicate if a data record exists or not. In this exploratory research
paper, we start from this premise and posit that all existing index structures
can be replaced with other types of models, including deep-learning models,
which we term learned indexes. The key idea is that a model can learn the sort
order or structure of lookup keys and use this signal to effectively predict
the position or existence of records. We theoretically analyze under which
conditions learned indexes outperform traditional index structures and describe
the main challenges in designing learned index structures. Our initial results
show, that by using neural nets we are able to outperform cache-optimized
B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over
several real-world data sets. More importantly though, we believe that the idea
of replacing core components of a data management system through learned models
has far reaching implications for future systems designs and that this work
just provides a glimpse of what might be possible
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