30,168 research outputs found
Performance of a Multiple-Access DCSK-CC System over Nakagami- Fading Channels
In this paper, we propose a novel cooperative scheme to enhance the
performance of multiple-access (MA) differential-chaos-shift-keying (DCSK)
systems. We provide the bit-error-rate (BER) performance and throughput
analyses for the new system with a decode-and-forward (DF) protocol over
Nakagami- fading channels. Our simulated results not only show that this
system significantly improves the BER performance as compared to the existing
DCSK non-cooperative (DCSK-NC) system and the multiple-input multiple-output
DCSK (MIMO-DCSK) system, but also verify the theoretical analyses. Furthermore,
we show that the throughput of this system approximately equals that of the
DCSK-NC system, both of which have prominent improvements over the MIMO-DCSK
system. We thus believe that the proposed system can be a good framework for
chaos-modulation-based wireless communications.Comment: 4 pages, 5 figures, accepted, IEEE ISCAS, 201
Does Downloading PowerPoint Slides Before the Lecture Lead to Better Student Achievement?: Reply
This reply responds to a comment by Cannon (2011) that opens the debate on consistency of the effect of downloading PowerPoint slides before lectures on students’ exam performance. Cannon (2011) points out potential endogeneity problems in Chen and Lin (2008) and attempts to explore the unconditional mean effect of downloading PowerPoint slides for the full sample. In this reply, we firstly argue that the estimates in our original article are consistent since the effect of interest is the “conditional†treatment effect but not the unconditional mean effect. We provide explanations for our rationale of estimating the “conditional†treatment effect. Secondly, we propose a modified downloading variable to replicate Cannon’s analysis. Our results suggest that downloading PowerPoint slides before the exam does not produce a significant effect on absent students’ exam performance which is different from the results in Cannon (2011). Our analysis does support Cannon’s argument that students fixed effects are different across different attendance status.
Collaborative Feature Learning from Social Media
Image feature representation plays an essential role in image recognition and
related tasks. The current state-of-the-art feature learning paradigm is
supervised learning from labeled data. However, this paradigm requires
large-scale category labels, which limits its applicability to domains where
labels are hard to obtain. In this paper, we propose a new data-driven feature
learning paradigm which does not rely on category labels. Instead, we learn
from user behavior data collected on social media. Concretely, we use the image
relationship discovered in the latent space from the user behavior data to
guide the image feature learning. We collect a large-scale image and user
behavior dataset from Behance.net. The dataset consists of 1.9 million images
and over 300 million view records from 1.9 million users. We validate our
feature learning paradigm on this dataset and find that the learned feature
significantly outperforms the state-of-the-art image features in learning
better image similarities. We also show that the learned feature performs
competitively on various recognition benchmarks
Does generalization performance of regularization learning depend on ? A negative example
-regularization has been demonstrated to be an attractive technique in
machine learning and statistical modeling. It attempts to improve the
generalization (prediction) capability of a machine (model) through
appropriately shrinking its coefficients. The shape of a estimator
differs in varying choices of the regularization order . In particular,
leads to the LASSO estimate, while corresponds to the smooth
ridge regression. This makes the order a potential tuning parameter in
applications. To facilitate the use of -regularization, we intend to
seek for a modeling strategy where an elaborative selection on is
avoidable. In this spirit, we place our investigation within a general
framework of -regularized kernel learning under a sample dependent
hypothesis space (SDHS). For a designated class of kernel functions, we show
that all estimators for attain similar generalization
error bounds. These estimated bounds are almost optimal in the sense that up to
a logarithmic factor, the upper and lower bounds are asymptotically identical.
This finding tentatively reveals that, in some modeling contexts, the choice of
might not have a strong impact in terms of the generalization capability.
From this perspective, can be arbitrarily specified, or specified merely by
other no generalization criteria like smoothness, computational complexity,
sparsity, etc..Comment: 35 pages, 3 figure
Assisted optimal state discrimination without entanglement
A fundamental problem in quantum information is to explore the roles of
different quantum correlations in a quantum information procedure. Recent work
[Phys. Rev. Lett., 107 (2011) 080401] shows that the protocol for assisted
optimal state discrimination (AOSD) may be implemented successfully without
entanglement, but with another correlation, quantum dissonance. However, both
the original work and the extension to discrimination of states [Phys. Rev.
A, 85 (2012) 022328] have only proved that entanglement can be absent in the
case with equal a \emph{priori} probabilities. By improving the protocol in
[Sci. Rep., 3 (2013) 2134], we investigate this topic in a simple case to
discriminate three nonorthogonal states of a qutrit, with positive real
overlaps. In our procedure, the entanglement between the qutrit and an
auxiliary qubit is found to be completely unnecessary. This result shows that
the quantum dissonance may play as a key role in optimal state discrimination
assisted by a qubit for more general cases.Comment: 6 pages, 3 figures. Accepted by EPL. We extended the protocol for
assisted optimal state discrimination to the case with positive real
overlaps, and presented a proof for the absence of entanglemen
Single-ancilla ground state preparation via Lindbladians
We design an early fault-tolerant quantum algorithm for ground state
preparation. As a Monte Carlo-style quantum algorithm, our method features a
Lindbladian where the target state is stationary, and its evolution can be
efficiently implemented using just one ancilla qubit. Our algorithm can prepare
the ground state even when the initial state has zero overlap with the ground
state, bypassing the most significant limitation of methods like quantum phase
estimation. As a variant, we also propose a discrete-time algorithm, which
demonstrates even better efficiency, providing a near-optimal simulation cost
for the simulation time and precision. Numerical simulation using Ising models
and Hubbard models demonstrates the efficacy and applicability of our method
- …