196,143 research outputs found
Base station cooperation in MIMO-aided multi-user multi-cell systems employing distributed probabilistic data association based soft reception
Inter-cell co-channel interference (CCI) mitigation is investigated in the context of cellular systems relying on dense frequency reuse. A distributed Base Station (BS) cooperation aided soft reception scheme using the Probabilistic Data Association (PDA) algorithm and Soft Combining (SC) is proposed for the uplink of multi-user multi-cell MIMO systems. The realistic hexagonal cellular model relying on unity Frequency Reuse (FR) is considered, where both the BSs and the Mobile Stations (MSs) are equipped with multiple antennas. Local cooperation based message passing is used instead of a global message passing chain for the sake of reducing the backhaul traffic. The PDA algorithm is employed as a low complexity solution for producing soft information, which facilitates the employment of SC at the individual BSs in order to generate the final soft decision metric. Our simulations and analysis demonstrate that despite its low additional complexity and backhaul traffic, the proposed distributed PDA-aided reception scheme significantly outperforms the conventional non-cooperative bench markers
Dynamic Verification of SystemC with Statistical Model Checking
Many embedded and real-time systems have a inherent probabilistic behaviour
(sensors data, unreliable hardware,...). In that context, it is crucial to
evaluate system properties such as "the probability that a particular hardware
fails". Such properties can be evaluated by using probabilistic model checking.
However, this technique fails on models representing realistic embedded and
real-time systems because of the state space explosion. To overcome this
problem, we propose a verification framework based on Statistical Model
Checking. Our framework is able to evaluate probabilistic and temporal
properties on large systems modelled in SystemC, a standard system-level
modelling language. It is fully implemented as an extension of the Plasma-lab
statistical model checker. We illustrate our approach on a multi-lift system
case study
Probabilistic model checking multi-agent behaviors in dispersion games using counter abstraction
Accurate analysis of the stochastic dynamics of multi-agent system is important but challenging. Probabilistic model checking, a formal technique for analysing a system which exhibits stochastic behaviors, can be a natural solution to analyse multi-agent systems. In this paper, we investigate this problem in the context of dispersion games focusing on two strategies: basic simple strategy (BSS) and extended simple strategies (ESS). We model the system using discrete-time Markov chain (DTMC) and reduce the state space of the models by applying counter abstraction technique. Two important properties of the system are considered: convergence and convergence rate. We show that these kinds of properties can be automatically analysed and verified using probabilistic model checking techniques. Better understanding of the dynamics of the strategies can be obtained compared with empirical evaluations in previous work. Through the analysis, we are able to demonstrate that probabilistic model checking technique is applicable, and indeed useful for automatic analysis and verification of multi-agent dynamics.No Full Tex
Contextualized Programs for Ontology-Mediated Probabilistic System Analysis
Modeling context-dependent systems for their analysis is challenging as verification tools usually rely on an input language close to imperative programming languages which need not support description of contexts well. We introduce the concept of contextualized programs where operational behaviors and context knowledge are modeled separately using domain-specific formalisms. For behaviors specified in stochastic guarded-command language and contextual knowledge given by OWL description logic ontologies, we develop a technique to efficiently incorporate contextual information into behavioral descriptions by reasoning about the ontology. We show how our presented concepts support and facilitate the quantitative analysis of context-dependent systems using probabilistic model checking. For this, we evaluate our implementation on a case study issuing a multi-server system
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
For decades, context-dependent phonemes have been the dominant sub-word unit
for conventional acoustic modeling systems. This status quo has begun to be
challenged recently by end-to-end models which seek to combine acoustic,
pronunciation, and language model components into a single neural network. Such
systems, which typically predict graphemes or words, simplify the recognition
process since they remove the need for a separate expert-curated pronunciation
lexicon to map from phoneme-based units to words. However, there has been
little previous work comparing phoneme-based versus grapheme-based sub-word
units in the end-to-end modeling framework, to determine whether the gains from
such approaches are primarily due to the new probabilistic model, or from the
joint learning of the various components with grapheme-based units.
In this work, we conduct detailed experiments which are aimed at quantifying
the value of phoneme-based pronunciation lexica in the context of end-to-end
models. We examine phoneme-based end-to-end models, which are contrasted
against grapheme-based ones on a large vocabulary English Voice-search task,
where we find that graphemes do indeed outperform phonemes. We also compare
grapheme and phoneme-based approaches on a multi-dialect English task, which
once again confirm the superiority of graphemes, greatly simplifying the system
for recognizing multiple dialects
Structure Inference for Bayesian Multisensory Perception and Tracking
We investigate a solution to the problem of multisensor
perception and tracking by formulating it in
the framework of Bayesian model selection. Humans
robustly associate multi-sensory data as appropriate,
but previous theoretical work has focused
largely on purely integrative cases, leaving
segregation unaccounted for and unexploited by
machine perception systems. We illustrate a unifying,
Bayesian solution to multi-sensor perception
and tracking which accounts for both integration
and segregation by explicit probabilistic reasoning
about data association in a temporal context. Unsupervised
learning of such a model with EM is illustrated
for a real world audio-visual application
Bayesian Quadrature for Multiple Related Integrals
Bayesian probabilistic numerical methods are a set of tools providing
posterior distributions on the output of numerical methods. The use of these
methods is usually motivated by the fact that they can represent our
uncertainty due to incomplete/finite information about the continuous
mathematical problem being approximated. In this paper, we demonstrate that
this paradigm can provide additional advantages, such as the possibility of
transferring information between several numerical methods. This allows users
to represent uncertainty in a more faithful manner and, as a by-product,
provide increased numerical efficiency. We propose the first such numerical
method by extending the well-known Bayesian quadrature algorithm to the case
where we are interested in computing the integral of several related functions.
We then prove convergence rates for the method in the well-specified and
misspecified cases, and demonstrate its efficiency in the context of
multi-fidelity models for complex engineering systems and a problem of global
illumination in computer graphics.Comment: Proceedings of the 35th International Conference on Machine Learning
(ICML), PMLR 80:5369-5378, 201
PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition
In the context of biometrics, matching confidence refers to the confidence
that a given matching decision is correct. Since many biometric systems operate
in critical decision-making processes, such as in forensics investigations,
accurately and reliably stating the matching confidence becomes of high
importance. Previous works on biometric confidence estimation can well
differentiate between high and low confidence, but lack interpretability.
Therefore, they do not provide accurate probabilistic estimates of the
correctness of a decision. In this work, we propose a probabilistic
interpretable comparison (PIC) score that accurately reflects the probability
that the score originates from samples of the same identity. We prove that the
proposed approach provides optimal matching confidence. Contrary to other
approaches, it can also optimally combine multiple samples in a joint PIC score
which further increases the recognition and confidence estimation performance.
In the experiments, the proposed PIC approach is compared against all biometric
confidence estimation methods available on four publicly available databases
and five state-of-the-art face recognition systems. The results demonstrate
that PIC has a significantly more accurate probabilistic interpretation than
similar approaches and is highly effective for multi-biometric recognition. The
code is publicly-available
CSM-H-R: An Automatic Context Reasoning Framework for Interoperable Intelligent Systems and Privacy Protection
Automation of High-Level Context (HLC) reasoning for intelligent systems at
scale is imperative due to the unceasing accumulation of contextual data in the
IoT era, the trend of the fusion of data from multi-sources, and the intrinsic
complexity and dynamism of the context-based decision-making process. To
mitigate this issue, we propose an automatic context reasoning framework
CSM-H-R, which programmatically combines ontologies and states at runtime and
the model-storage phase for attaining the ability to recognize meaningful HLC,
and the resulting data representation can be applied to different reasoning
techniques. Case studies are developed based on an intelligent elevator system
in a smart campus setting. An implementation of the framework - a CSM Engine,
and the experiments of translating the HLC reasoning into vector and matrix
computing especially take care of the dynamic aspects of context and present
the potentiality of using advanced mathematical and probabilistic models to
achieve the next level of automation in integrating intelligent systems;
meanwhile, privacy protection support is achieved by anonymization through
label embedding and reducing information correlation. The code of this study is
available at: https://github.com/songhui01/CSM-H-R.Comment: 11 pages, 8 figures, Keywords: Context Reasoning, Automation,
Intelligent Systems, Context Modeling, Context Dynamism, Privacy Protection,
Context Sharing, Interoperability, System Integratio
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