6,999 research outputs found
Adaptive frequency-domain equalization for the transmission of the fundamental mode in a few-mode fiber
We propose and experimentally demonstrate single-carrier adaptive frequency-domain equalization (SC-FDE) to mitigate multipath interference (MPI) for the transmission of the fundamental mode in a few-mode fiber. The FDE approach reduces computational complexity significantly compared to the time-domain equalization (TDE) approach while maintaining the same performance. Both FDE and TDE methods are evaluated by simulating long-haul fundamental-mode transmission using a few-mode fiber. For the fundamental mode operation, the required tap length of the equalizer depends on the differential mode group delay (DMGD) of a single span rather than DMGD of the entire link
4-(4-ChloroÂbenzoÂyl)-3-methyl-1-phenyl-1H-pyrazol-5-yl 4-chloroÂbenzoate
In the title compound, C24H16Cl2N2O3, the three benzene rings are twisted with respect to the central pyrazole ring, making dihedral angles of 71.56 (9) (4-chloroÂbenzoÂyloxy), 57.55 (8) (4-chloroÂbenzoÂyl) and 39.33 (1)° (phenÂyl)
Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training
Universal lesion detection has great value for clinical practice as it aims
to detect various types of lesions in multiple organs on medical images. Deep
learning methods have shown promising results, but demanding large volumes of
annotated data for training. However, annotating medical images is costly and
requires specialized knowledge. The diverse forms and contrasts of objects in
medical images make fully annotation even more challenging, resulting in
incomplete annotations. Directly training ULD detectors on such datasets can
yield suboptimal results. Pseudo-label-based methods examine the training data
and mine unlabelled objects for retraining, which have shown to be effective to
tackle this issue. Presently, top-performing methods rely on a dynamic
label-mining mechanism, operating at the mini-batch level. However, the model's
performance varies at different iterations, leading to inconsistencies in the
quality of the mined labels and limits their performance enhancement. Inspired
by the observation that deep models learn concepts with increasing complexity,
we introduce an innovative exploratory training to assess the reliability of
mined lesions over time. Specifically, we introduce a teacher-student detection
model as basis, where the teacher's predictions are combined with incomplete
annotations to train the student. Additionally, we design a prediction bank to
record high-confidence predictions. Each sample is trained several times,
allowing us to get a sequence of records for each sample. If a prediction
consistently appears in the record sequence, it is likely to be a true object,
otherwise it may just a noise. This serves as a crucial criterion for selecting
reliable mined lesions for retraining. Our experimental results substantiate
that the proposed framework surpasses state-of-the-art methods on two medical
image datasets, demonstrating its superior performance
Photoluminescence pressure coefficients of InAs/GaAs quantum dots
We have investigated the band-gap pressure coefficients of self-assembled
InAs/GaAs quantum dots by calculating 17 systems with different quantum dot
shape, size, and alloying profile using atomistic empirical pseudopotential
method within the ``strained linear combination of bulk bands'' approach. Our
results confirm the experimentally observed significant reductions of the band
gap pressure coefficients from the bulk values. We show that the nonlinear
pressure coefficients of the bulk InAs and GaAs are responsible for these
reductions. We also find a rough universal pressure coefficient versus band gap
relationship which agrees quantitatively with the experimental results. We find
linear relationships between the percentage of electron wavefunction on the
GaAs and the quantum dot band gaps and pressure coefficients. These linear
relationships can be used to get the information of the electron wavefunctions.Comment: 8 pages, 2 tables, 4 figure
Chaos: a bridge from microscopic uncertainty to macroscopic randomness
It is traditionally believed that the macroscopic randomness has nothing to
do with the micro-level uncertainty. Besides, the sensitive dependence on
initial condition (SDIC) of Lorenz chaos has never been considered together
with the so-called continuum-assumption of fluid (on which Lorenz equations are
based), from physical and statistic viewpoints. A very fine numerical technique
(Liao, 2009) with negligible truncation and round-off errors, called here the
"clean numerical simulation" (CNS), is applied to investigate the propagation
of the micro-level unavoidable uncertain fluctuation (caused by the
continuum-assumption of fluid) of initial conditions for Lorenz equation with
chaotic solutions. Our statistic analysis based on CNS computation of 10,000
samples shows that, due to the SDIC, the uncertainty of the micro-level
statistic fluctuation of initial conditions transfers into the macroscopic
randomness of chaos. This suggests that chaos might be a bridge from
micro-level uncertainty to macroscopic randomness, and thus would be an origin
of macroscopic randomness. We reveal in this article that, due to the SDIC of
chaos and the inherent uncertainty of initial data, accurate long-term
prediction of chaotic solution is not only impossible in mathematics but also
has no physical meanings. This might provide us a new, different viewpoint to
deepen and enrich our understandings about the SDIC of chaos.Comment: 9 pages, 2 figure
Supermodes for optical transmission
In this paper, the concept of supermode is introduced for long-distance optical transmission systems. The supermodes exploit coupling between the cores of a multi-core fiber, in which the core-to-core distance is much shorter than that in conventional multi-core fiber. The use of supermodes leads to a larger mode effective area and higher mode density than the conventional multi-core fiber. Through simulations, we show that the proposed coupled multi-core fiber allows lower modal dependent loss, mode coupling and differential modal group delay than few-mode fibers. These properties suggest that the coupled multi-core fiber could be a good candidate for both spatial division multiplexing and single-mode operation
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