75 research outputs found
Advanced photon counting techniques for long-range depth imaging
The Time-Correlated Single-Photon Counting (TCSPC) technique has emerged as a
candidate approach for Light Detection and Ranging (LiDAR) and active depth imaging
applications. The work of this Thesis concentrates on the development and
investigation of functional TCSPC-based long-range scanning time-of-flight (TOF)
depth imaging systems. Although these systems have several different configurations
and functions, all can facilitate depth profiling of remote targets at low light levels and
with good surface-to-surface depth resolution. Firstly, a Superconducting Nanowire
Single-Photon Detector (SNSPD) and an InGaAs/InP Single-Photon Avalanche Diode
(SPAD) module were employed for developing kilometre-range TOF depth imaging
systems at wavelengths of ~1550 nm. Secondly, a TOF depth imaging system at a
wavelength of 817 nm that incorporated a Complementary Metal-Oxide-Semiconductor
(CMOS) 32×32 Si-SPAD detector array was developed. This system was used with
structured illumination to examine the potential for covert, eye-safe and high-speed
depth imaging. In order to improve the light coupling efficiency onto the detectors, the
arrayed CMOS Si-SPAD detector chips were integrated with microlens arrays using
flip-chip bonding technology. This approach led to the improvement in the fill factor by
up to a factor of 15. Thirdly, a multispectral TCSPC-based full-waveform LiDAR
system was developed using a tunable broadband pulsed supercontinuum laser source
which can provide simultaneous multispectral illumination, at wavelengths of 531, 570,
670 and ~780 nm. The investigated multispectral reflectance data on a tree was used to
provide the determination of physiological parameters as a function of the tree depth
profile relating to biomass and foliage photosynthetic efficiency. Fourthly, depth
images were estimated using spatial correlation techniques in order to reduce the
aggregate number of photon required for depth reconstruction with low error. A depth
imaging system was characterised and re-configured to reduce the effects of scintillation
due to atmospheric turbulence. In addition, depth images were analysed in terms of
spatial and depth resolution
Lidar waveform based analysis of depth images constructed using sparse single-photon data
This paper presents a new Bayesian model and algorithm used for depth and
intensity profiling using full waveforms from the time-correlated single photon
counting (TCSPC) measurement in the limit of very low photon counts. The model
proposed represents each Lidar waveform as a combination of a known impulse
response, weighted by the target intensity, and an unknown constant background,
corrupted by Poisson noise. Prior knowledge about the problem is embedded in a
hierarchical model that describes the dependence structure between the model
parameters and their constraints. In particular, a gamma Markov random field
(MRF) is used to model the joint distribution of the target intensity, and a
second MRF is used to model the distribution of the target depth, which are
both expected to exhibit significant spatial correlations. An adaptive Markov
chain Monte Carlo algorithm is then proposed to compute the Bayesian estimates
of interest and perform Bayesian inference. This algorithm is equipped with a
stochastic optimization adaptation mechanism that automatically adjusts the
parameters of the MRFs by maximum marginal likelihood estimation. Finally, the
benefits of the proposed methodology are demonstrated through a serie of
experiments using real data
Robust Bayesian target detection algorithm for depth imaging from sparse single-photon data
This paper presents a new Bayesian model and associated algorithm for depth
and intensity profiling using full waveforms from time-correlated single-photon
counting (TCSPC) measurements in the limit of very low photon counts (i.e.,
typically less than 20 photons per pixel). The model represents each Lidar
waveform as an unknown constant background level, which is combined in the
presence of a target, to a known impulse response weighted by the target
intensity and finally corrupted by Poisson noise. The joint target detection
and depth imaging problem is expressed as a pixel-wise model selection and
estimation problem which is solved using Bayesian inference. Prior knowledge
about the problem is embedded in a hierarchical model that describes the
dependence structure between the model parameters while accounting for their
constraints. In particular, Markov random fields (MRFs) are used to model the
joint distribution of the background levels and of the target presence labels,
which are both expected to exhibit significant spatial correlations. An
adaptive Markov chain Monte Carlo algorithm including reversible-jump updates
is then proposed to compute the Bayesian estimates of interest. This algorithm
is equipped with a stochastic optimization adaptation mechanism that
automatically adjusts the parameters of the MRFs by maximum marginal likelihood
estimation. Finally, the benefits of the proposed methodology are demonstrated
through a series of experiments using real data.Comment: arXiv admin note: text overlap with arXiv:1507.0251
In Search of the Long-Tail: Systematic Generation of Long-Tail Knowledge via Logical Rule Guided Search
Since large language models have approached human-level performance on many
tasks, it has become increasingly harder for researchers to find tasks that are
still challenging to the models. Failure cases usually come from the long-tail
distribution - data that an oracle language model could assign a probability on
the lower end of its distribution. Current methodology such as prompt
engineering or crowdsourcing are insufficient for creating long-tail examples
because humans are constrained by cognitive bias. We propose a
Logic-Induced-Knowledge-Search (LINK) framework for systematically generating
long-tail knowledge statements. Grounded by a symbolic rule, we search for
long-tail values for each variable of the rule by first prompting a LLM, then
verifying the correctness of the values with a critic, and lastly pushing for
the long-tail distribution with a reranker. With this framework we construct a
dataset, Logic-Induced-Long-Tail (LINT), consisting of 200 symbolic rules and
50K knowledge statements spanning across four domains. Human annotations find
that 84% of the statements in LINT are factually correct. In contrast, ChatGPT
and GPT4 struggle with directly generating long-tail statements under the
guidance of logic rules, each only getting 56% and 78% of their statements
correct. Moreover, their "long-tail" generations in fact fall into the higher
likelihood range, and thus are not really long-tail. Our findings suggest that
LINK is effective for generating data in the long-tail distribution while
enforcing quality. LINT can be useful for systematically evaluating LLMs'
capabilities in the long-tail distribution. We challenge the models with a
simple entailment classification task using samples from LINT. We find that
ChatGPT and GPT4's capability in identifying incorrect knowledge drop by ~3% in
the long-tail distribution compared to head distribution
Unusual conservation of mitochondrial gene order in Crassostrea oysters: evidence for recent speciation in Asia
<p>Abstract</p> <p>Background</p> <p>Oysters are morphologically plastic and hence difficult subjects for taxonomic and evolutionary studies. It is long been suspected, based on the extraordinary species diversity observed, that Asia Pacific is the epicenter of oyster speciation. To understand the species diversity and its evolutionary history, we collected five <it>Crassostrea </it>species from Asia and sequenced their complete mitochondrial (mt) genomes in addition to two newly released Asian oysters (<it>C. iredalei </it>and <it>Saccostrea mordax</it>) for a comprehensive analysis.</p> <p>Results</p> <p>The six Asian <it>Crassostrea </it>mt genomes ranged from 18,226 to 22,446 bp in size, and all coded for 39 genes (12 proteins, 2 rRNAs and 25 tRNAs) on the same strand. Their genomes contained a split of the <it>rrnL </it>gene and duplication of <it>trnM</it>, <it>trnK </it>and <it>trnQ </it>genes. They shared the same gene order that differed from an Atlantic sister species by as many as nine tRNA changes (6 transpositions and 3 duplications) and even differed significantly from <it>S. mordax </it>in protein-coding genes. Phylogenetic analysis indicates that the six Asian <it>Crassostrea </it>species emerged between 3 and 43 Myr ago, while the Atlantic species evolved 83 Myr ago.</p> <p>Conclusions</p> <p>The complete conservation of gene order in the six Asian <it>Crassostrea </it>species over 43 Myr is highly unusual given the remarkable rate of rearrangements in their sister species and other bivalves. It provides strong evidence for the recent speciation of the six <it>Crassostrea </it>species in Asia. It further indicates that changes in mt gene order may not be strictly a function of time but subject to other constraints that are presently not well understood.</p
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