7,520 research outputs found
Observation of recoil-induced resonances and electromagnetically induced absorption of cold atoms in diffuse light
In this paper we report an experiment on the observation of the
recoil-induced resonances (RIR) and electromagnetically induced absorption
(EIA) of cold Rb87 atoms in diffuse light. The pump light of the RIR and the
EIA comes from the diffuse light in an integrating sphere, which also serves
the cooling light. The probe light beam is a weak laser split from the cooling
laser in order to keep the cooling and probe lasers correlated. We measured the
RIR and the EIA signal varying with the detuning of the diffuse laser light,
and also measured the temperature of the cold atoms at the different detunings.
The mechanism of RIR and EIA in the configuration with diffuse-light pumping
and laser probing is discussed, and the difference of nonlinear spectra of cold
atoms between in diffuse-light cooling system and in optical molasses as well
as in a magneto-optical trap (MOT) are studied.Comment: 9 pages, 6 figure
A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring
Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal − more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods
Mixture Selection, Mechanism Design, and Signaling
We pose and study a fundamental algorithmic problem which we term mixture
selection, arising as a building block in a number of game-theoretic
applications: Given a function from the -dimensional hypercube to the
bounded interval , and an matrix with bounded entries,
maximize over in the -dimensional simplex. This problem arises
naturally when one seeks to design a lottery over items for sale in an auction,
or craft the posterior beliefs for agents in a Bayesian game through the
provision of information (a.k.a. signaling).
We present an approximation algorithm for this problem when
simultaneously satisfies two smoothness properties: Lipschitz continuity with
respect to the norm, and noise stability. The latter notion, which
we define and cater to our setting, controls the degree to which
low-probability errors in the inputs of can impact its output. When is
both -Lipschitz continuous and -stable, we obtain an (additive)
PTAS for mixture selection. We also show that neither assumption suffices by
itself for an additive PTAS, and both assumptions together do not suffice for
an additive FPTAS.
We apply our algorithm to different game-theoretic applications from
mechanism design and optimal signaling. We make progress on a number of open
problems suggested in prior work by easily reducing them to mixture selection:
we resolve an important special case of the small-menu lottery design problem
posed by Dughmi, Han, and Nisan; we resolve the problem of revenue-maximizing
signaling in Bayesian second-price auctions posed by Emek et al. and Miltersen
and Sheffet; we design a quasipolynomial-time approximation scheme for the
optimal signaling problem in normal form games suggested by Dughmi; and we
design an approximation algorithm for the optimal signaling problem in the
voting model of Alonso and C\^{a}mara
Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models
Motivated by a sampling problem basic to computational statistical inference,
we develop a nearly optimal algorithm for a fundamental problem in spectral
graph theory and numerical analysis. Given an SDDM matrix , and a constant , our algorithm gives efficient
access to a sparse linear operator such that
The
solution is based on factoring into a product of simple and
sparse matrices using squaring and spectral sparsification. For
with non-zero entries, our algorithm takes work nearly-linear in , and
polylogarithmic depth on a parallel machine with processors. This gives the
first sampling algorithm that only requires nearly linear work and i.i.d.
random univariate Gaussian samples to generate i.i.d. random samples for
-dimensional Gaussian random fields with SDDM precision matrices. For
sampling this natural subclass of Gaussian random fields, it is optimal in the
randomness and nearly optimal in the work and parallel complexity. In addition,
our sampling algorithm can be directly extended to Gaussian random fields with
SDD precision matrices
Res2Net: A New Multi-scale Backbone Architecture
Representing features at multiple scales is of great importance for numerous
vision tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate stronger multi-scale representation ability, leading to
consistent performance gains on a wide range of applications. However, most
existing methods represent the multi-scale features in a layer-wise manner. In
this paper, we propose a novel building block for CNNs, namely Res2Net, by
constructing hierarchical residual-like connections within one single residual
block. The Res2Net represents multi-scale features at a granular level and
increases the range of receptive fields for each network layer. The proposed
Res2Net block can be plugged into the state-of-the-art backbone CNN models,
e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these
models and demonstrate consistent performance gains over baseline models on
widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies
and experimental results on representative computer vision tasks, i.e., object
detection, class activation mapping, and salient object detection, further
verify the superiority of the Res2Net over the state-of-the-art baseline
methods. The source code and trained models are available on
https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure
Single deep ultraviolet light emission from boron nitride nanotube film
Light in deep ultraviolet DUV region has a wide range of applications and the demand for finding
DUV light emitting materials at nanoscale is increasingly urgent as they are vital for building
miniaturized optic and optoelectronic devices. We discover that boron nitride nanotubes BNNTs
with a well-crystallized cylindrical multiwall structure and diameters smaller than 10 nm can have
single DUV emission at 225 nm 5.51 eV. The measured BNNTs are grown on substrate in the form
of a thin film. This study suggests that BNNTs may work as nanosized DUV light sources for
various applications. © 20
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