2,925 research outputs found
DC-Informative Joint Color-Frequency Modulation for Visible Light Communications
In this paper, we consider the problem of constellation design for a visible
light communication (VLC) system using red/green/blue light-emitting diodes
(RGB LED), and propose a method termed DC-informative joint color-frequency
modulation (DCI-JCFM). This method jointly utilizes available diversity
resources including different optical wavelengths, multiple baseband
subcarriers, and adaptive DC-bias. Constellation is designed in a high
dimensional space, where the compact sphere packing advantage over lower
dimensional counterparts is utilized. Taking into account multiple practical
illumination constraints, a non-convex optimization problem is formulated,
seeking the least error rate with a fixed spectral efficiency. The proposed
scheme is compared with a decoupled scheme, where constellation is designed
separately for each LED. Notable gains for DCI-JCFM are observed through
simulations where balanced, unbalanced and very unbalanced color illuminations
are considered.Comment: submitted to Journal of Lightwave Technology, Aug. 5th 201
Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data
With the successful application of automatic fare collection (AFC) system in urban rail transit (URT), the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt), exit walking time (exwt), and transfer walking time (twt). Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT
Discovering New Intents with Deep Aligned Clustering
Discovering new intents is a crucial task in dialogue systems. Most existing
methods are limited in transferring the prior knowledge from known intents to
new intents. They also have difficulties in providing high-quality supervised
signals to learn clustering-friendly features for grouping unlabeled intents.
In this work, we propose an effective method, Deep Aligned Clustering, to
discover new intents with the aid of the limited known intent data. Firstly, we
leverage a few labeled known intent samples as prior knowledge to pre-train the
model. Then, we perform k-means to produce cluster assignments as
pseudo-labels. Moreover, we propose an alignment strategy to tackle the label
inconsistency problem during clustering assignments. Finally, we learn the
intent representations under the supervision of the aligned pseudo-labels. With
an unknown number of new intents, we predict the number of intent categories by
eliminating low-confidence intent-wise clusters. Extensive experiments on two
benchmark datasets show that our method is more robust and achieves substantial
improvements over the state-of-the-art methods. The codes are released at
https://github.com/thuiar/DeepAligned-Clustering.Comment: Accepted by AAAI 2021 (Main Track, Long Paper
Learning From Biased Soft Labels
Knowledge distillation has been widely adopted in a variety of tasks and has
achieved remarkable successes. Since its inception, many researchers have been
intrigued by the dark knowledge hidden in the outputs of the teacher model.
Recently, a study has demonstrated that knowledge distillation and label
smoothing can be unified as learning from soft labels. Consequently, how to
measure the effectiveness of the soft labels becomes an important question.
Most existing theories have stringent constraints on the teacher model or data
distribution, and many assumptions imply that the soft labels are close to the
ground-truth labels. This paper studies whether biased soft labels are still
effective. We present two more comprehensive indicators to measure the
effectiveness of such soft labels. Based on the two indicators, we give
sufficient conditions to ensure biased soft label based learners are
classifier-consistent and ERM learnable. The theory is applied to three
weakly-supervised frameworks. Experimental results validate that biased soft
labels can also teach good students, which corroborates the soundness of the
theory
Symmetry dictated universal helicity redistribution of Dirac fermions in transport
Helicity is a fundamental property of Dirac fermions. Yet, how it changes in
transport processes remains largely mysterious. We uncover, theoretically, the
rule of spinor state transformation and consequently universal helicity
redistribution in two cases of transport through potentials of electrostatic
and mass types, respectively. The former is dictated by Lorentz boost and its
complex counterpart in Klein tunneling regime. The latter is governed by an
abstract rotation group we identified, which reduces to SO(2) when acting on
the plane of effective mass and momentum. This endows an extra structure
foliating the Hilbert space of Dirac spinors, establishes miraculously a
unified yet latent connection between helicity, Klein tunneling, and Lorentz
boost. Our results thus deepen the understanding of relativistic quantum
transport, and may open a new window for exotic helicity-based physics and
applications in mesoscopic systems.Comment: 6 pages, 4 figure
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