64 research outputs found
Deterministic Multi-sensor Measurement-adaptive Birth using Labeled Random Finite Sets
Measurement-adaptive track initiation remains a critical design requirement
of many practical multi-target tracking systems. For labeled random finite sets
multi-object filters, prior work has been established to construct a labeled
multi-object birth density using measurements from multiple sensors. A
truncation procedure has also been provided that leverages a stochastic Gibbs
sampler to truncate the birth density for scalability. In this work, we
introduce a deterministic herded Gibbs sampling truncation solution for
efficient multi-sensor adaptive track initialization. Removing the stochastic
behavior of the track initialization procedure without impacting average
tracking performance enables a more robust tracking solution more suitable for
safety-critical applications. Simulation results for linear sensing scenarios
are provided to verify performance.Comment: Accepted to the 2023 Proc. IEEE 26th Int. Conf. Inf. Fusio
On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking
Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms
because of its simplicity, scalability, and wide applicability within many
fields of statistics, science, and engineering. In the labeled random finite
sets literature, Gibbs sampling procedures have recently been applied to
efficiently truncate the single-sensor and multi-sensor -generalized
labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive
labeled multi-Bernoulli birth distribution. However, only a limited discussion
has been provided regarding key Gibbs sampler architecture details including
the Markov chain Monte Carlo sample generation technique and early termination
criteria. This paper begins with a brief background on Markov chain Monte Carlo
methods and a review of the Gibbs sampler implementations proposed for labeled
random finite sets filters. Next, we propose a short chain, multi-simulation
sample generation technique that is well suited for these applications and
enables a parallel processing implementation. Additionally, we present two
heuristic early termination criteria that achieve similar sampling performance
with substantially fewer Markov chain observations. Finally, the benefits of
the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.Comment: Accepted to the 2023 Proc. IEEE 26th Int. Conf. Inf. Fusio
Transferability of Convolutional Neural Networks in Stationary Learning Tasks
Recent advances in hardware and big data acquisition have accelerated the
development of deep learning techniques. For an extended period of time,
increasing the model complexity has led to performance improvements for various
tasks. However, this trend is becoming unsustainable and there is a need for
alternative, computationally lighter methods. In this paper, we introduce a
novel framework for efficient training of convolutional neural networks (CNNs)
for large-scale spatial problems. To accomplish this we investigate the
properties of CNNs for tasks where the underlying signals are stationary. We
show that a CNN trained on small windows of such signals achieves a nearly
performance on much larger windows without retraining. This claim is supported
by our theoretical analysis, which provides a bound on the performance
degradation. Additionally, we conduct thorough experimental analysis on two
tasks: multi-target tracking and mobile infrastructure on demand. Our results
show that the CNN is able to tackle problems with many hundreds of agents after
being trained with fewer than ten. Thus, CNN architectures provide solutions to
these problems at previously computationally intractable scales.Comment: 14 pages, 7 figures, for associated code see
https://github.com/damowerko/mt
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack
The multi-armed bandit formalism has been extensively studied under various
attack models, in which an adversary can modify the reward revealed to the
player. Previous studies focused on scenarios where the attack value either is
bounded at each round or has a vanishing probability of occurrence. These
models do not capture powerful adversaries that can catastrophically perturb
the revealed reward. This paper investigates the attack model where an
adversary attacks with a certain probability at each round, and its attack
value can be arbitrary and unbounded if it attacks. Furthermore, the attack
value does not necessarily follow a statistical distribution. We propose a
novel sample median-based and exploration-aided UCB algorithm (called
med-E-UCB) and a median-based -greedy algorithm (called
med--greedy). Both of these algorithms are provably robust to the
aforementioned attack model. More specifically we show that both algorithms
achieve pseudo-regret (i.e., the optimal regret without
attacks). We also provide a high probability guarantee of
regret with respect to random rewards and random occurrence of attacks. These
bounds are achieved under arbitrary and unbounded reward perturbation as long
as the attack probability does not exceed a certain constant threshold. We
provide multiple synthetic simulations of the proposed algorithms to verify
these claims and showcase the inability of existing techniques to achieve
sublinear regret. We also provide experimental results of the algorithm
operating in a cognitive radio setting using multiple software-defined radios.Comment: Published at AAAI'2
Observation of associated near-side and away-side long-range correlations in âsNN=5.02ââTeV proton-lead collisions with the ATLAS detector
Two-particle correlations in relative azimuthal angle (ÎÏ) and pseudorapidity (Îη) are measured in âsNN=5.02ââTeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1ââÎŒb-1 of data as a function of transverse momentum (pT) and the transverse energy (ÎŁETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Îη|<5) ânear-sideâ (ÎÏâŒ0) correlation that grows rapidly with increasing ÎŁETPb. A long-range âaway-sideâ (ÎÏâŒÏ) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ÎŁETPb, is found to match the near-side correlation in magnitude, shape (in Îη and ÎÏ) and ÎŁETPb dependence. The resultant ÎÏ correlation is approximately symmetric about Ï/2, and is consistent with a dominant cosâĄ2ÎÏ modulation for all ÎŁETPb ranges and particle pT
Search for R-parity-violating supersymmetry in events with four or more leptons in sqrt(s) =7 TeV pp collisions with the ATLAS detector
A search for new phenomena in final states with four or more leptons (electrons or muons) is presented. The analysis is based on 4.7 fbâ1 of proton-proton collisions delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in two signal regions: one that requires moderate values of missing transverse momentum and another that requires large effective mass. The results are interpreted in a simplified model of R-parity-violating supersymmetry in which a 95% CL exclusion region is set for charged wino masses up to 540 GeV. In an R-parity-violating MSUGRA/CMSSM model, values of m 1/2 up to 820 GeV are excluded for 10 < tan ÎČ < 40
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