7,348 research outputs found
Generalized Second Price Auction with Probabilistic Broad Match
Generalized Second Price (GSP) auctions are widely used by search engines
today to sell their ad slots. Most search engines have supported broad match
between queries and bid keywords when executing GSP auctions, however, it has
been revealed that GSP auction with the standard broad-match mechanism they are
currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g.,
its theoretical properties are known only for the single-slot case and
full-information setting, and even in this simple setting, the corresponding
worst-case social welfare can be rather bad). To address this issue, we propose
a novel broad-match mechanism, which we call the Probabilistic Broad-Match
(PBM) mechanism. Different from SBM that puts together the ads bidding on all
the keywords matched to a given query for the GSP auction, the GSP with PBM
(denoted as PBM-GSP) randomly samples a keyword according to a predefined
probability distribution and only runs the GSP auction for the ads bidding on
this sampled keyword. We perform a comprehensive study on the theoretical
properties of the PBM-GSP. Specifically, we study its social welfare in the
worst equilibrium, in both full-information and Bayesian settings. The results
show that PBM-GSP can generate larger welfare than SBM-GSP under mild
conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in
Bayesian setting. To the best of our knowledge, this is the first work on
broad-match mechanisms for GSP that goes beyond the single-slot case and the
full-information setting
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
Non-autoregressive translation (NAT) models, which remove the dependence on
previous target tokens from the inputs of the decoder, achieve significantly
inference speedup but at the cost of inferior accuracy compared to
autoregressive translation (AT) models. Previous work shows that the quality of
the inputs of the decoder is important and largely impacts the model accuracy.
In this paper, we propose two methods to enhance the decoder inputs so as to
improve NAT models. The first one directly leverages a phrase table generated
by conventional SMT approaches to translate source tokens to target tokens,
which are then fed into the decoder as inputs. The second one transforms
source-side word embeddings to target-side word embeddings through
sentence-level alignment and word-level adversary learning, and then feeds the
transformed word embeddings into the decoder as inputs. Experimental results
show our method largely outperforms the NAT baseline~\citep{gu2017non} by
BLEU scores on WMT14 English-German task and BLEU scores on WMT16
English-Romanian task.Comment: AAAI 201
Non-Autoregressive Machine Translation with Auxiliary Regularization
As a new neural machine translation approach, Non-Autoregressive machine
Translation (NAT) has attracted attention recently due to its high efficiency
in inference. However, the high efficiency has come at the cost of not
capturing the sequential dependency on the target side of translation, which
causes NAT to suffer from two kinds of translation errors: 1) repeated
translations (due to indistinguishable adjacent decoder hidden states), and 2)
incomplete translations (due to incomplete transfer of source side information
via the decoder hidden states).
In this paper, we propose to address these two problems by improving the
quality of decoder hidden representations via two auxiliary regularization
terms in the training process of an NAT model. First, to make the hidden states
more distinguishable, we regularize the similarity between consecutive hidden
states based on the corresponding target tokens. Second, to force the hidden
states to contain all the information in the source sentence, we leverage the
dual nature of translation tasks (e.g., English to German and German to
English) and minimize a backward reconstruction error to ensure that the hidden
states of the NAT decoder are able to recover the source side sentence.
Extensive experiments conducted on several benchmark datasets show that both
regularization strategies are effective and can alleviate the issues of
repeated translations and incomplete translations in NAT models. The accuracy
of NAT models is therefore improved significantly over the state-of-the-art NAT
models with even better efficiency for inference.Comment: AAAI 201
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Rotation detection is a challenging task due to the difficulties of locating
the multi-angle objects and separating them effectively from the background.
Though considerable progress has been made, for practical settings, there still
exist challenges for rotating objects with large aspect ratio, dense
distribution and category extremely imbalance. In this paper, we propose an
end-to-end refined single-stage rotation detector for fast and accurate object
detection by using a progressive regression approach from coarse to fine
granularity. Considering the shortcoming of feature misalignment in existing
refined single-stage detector, we design a feature refinement module to improve
detection performance by getting more accurate features. The key idea of
feature refinement module is to re-encode the position information of the
current refined bounding box to the corresponding feature points through
pixel-wise feature interpolation to realize feature reconstruction and
alignment. For more accurate rotation estimation, an approximate SkewIoU loss
is proposed to solve the problem that the calculation of SkewIoU is not
derivable. Experiments on three popular remote sensing public datasets DOTA,
HRSC2016, UCAS-AOD as well as one scene text dataset ICDAR2015 show the
effectiveness of our approach. Tensorflow and Pytorch version codes are
available at https://github.com/Thinklab-SJTU/R3Det_Tensorflow and
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection, and R3Det is also
integrated in our open source rotation detection benchmark:
https://github.com/yangxue0827/RotationDetection.Comment: 13 pages, 12 figures, 9 table
Systematic investigation of the rotational bands in nuclei with using a particle-number conserving method based on a cranked shell model
The rotational bands in nuclei with are investigated
systematically by using a cranked shell model (CSM) with the pairing
correlations treated by a particle-number conserving (PNC) method, in which the
blocking effects are taken into account exactly. By fitting the experimental
single-particle spectra in these nuclei, a new set of Nilsson parameters
( and ) and deformation parameters ( and
) are proposed. The experimental kinematic moments of inertia
for the rotational bands in even-even, odd- and odd-odd nuclei, and the
bandhead energies of the 1-quasiparticle bands in odd- nuclei, are
reproduced quite well by the PNC-CSM calculations. By analyzing the
-dependence of the occupation probability of each cranked Nilsson
orbital near the Fermi surface and the contributions of valence orbitals in
each major shell to the angular momentum alignment, the upbending mechanism in
this region is understood clearly.Comment: 21 pages, 24 figures, extended version of arXiv: 1101.3607 (Phys.
Rev. C83, 011304R); added refs.; added Fig. 4 and discussions; Phys. Rev. C,
in pres
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