10,745 research outputs found
Successive Refinement of Shannon Cipher System Under Maximal Leakage
We study the successive refinement setting of Shannon cipher system (SCS)
under the maximal leakage constraint for discrete memoryless sources under
bounded distortion measures. Specifically, we generalize the threat model for
the point-to-point rate-distortion setting of Issa, Wagner and Kamath (T-IT
2020) to the multiterminal successive refinement setting. Under mild conditions
that correspond to partial secrecy, we characterize the asymptotically optimal
normalized maximal leakage region for both the joint excess-distortion
probability (JEP) and the expected distortion reliability constraints. Under
JEP, in the achievability part, we propose a type-based coding scheme, analyze
the reliability guarantee for JEP and bound the leakage of the information
source through compressed versions. In the converse part, by analyzing a
guessing scheme of the eavesdropper, we prove the optimality of our
achievability result. Under expected distortion, the achievability part is
established similarly to the JEP counterpart. The converse proof proceeds by
generalizing the corresponding results for the rate-distortion setting of SCS
by Schieler and Cuff (T-IT 2014) to the successive refinement setting. Somewhat
surprisingly, the normalized maximal leakage regions under both JEP and
expected distortion constraints are identical under certain conditions,
although JEP appears to be a stronger reliability constraint
Resource allocation for maximizing outage throughput in OFDMA systems with finite-rate feedback
Previous works on orthogonal frequency division multiple access (OFDMA) systems with quantized channel state information (CSI) were mainly based on suboptimal quantization methods. In this paper, we consider the performance limit of OFDMA systems with quantized CSI over independent Rayleigh fading channels using the rate-distortion theory. First, we establish a lower bound on the capacity of the feedback channel and build the test channel that achieves this lower bound. Then, with the derived test channel, we characterize the system performance with the outage throughput and formulate the outage throughput maximization problem with quantized channel state information (CSI). To solve this problem in low complexity, we develop a suboptimal algorithm that performs resource allocation in two steps: subcarrier allocation and power allocation. Using this approach, we can numerically evaluate the outage throughput in terms of feedback rate. Numerical results show that this suboptimal algorithm can provide a near optimal performance (with a performance loss of less than 5%) and the outage throughput with a limited feedback rate can be close to that with perfect CSI.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000294918800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Engineering, Electrical & ElectronicTelecommunicationsSCI(E)1ARTICLEnul
Transport properties of a holographic model with novel gauge-axion coupling
We investigate the transport properties within a holographic model
characterized by a novel gauge-axion coupling. A key innovation is the
introduction of the direct coupling between axion fields, the antisymmetric
tensor, and the gauge field in our bulk theory. This novel coupling term leads
to the emergence of non-diagonal components in the conductivity tensor. An
important characteristic is that the off-diagonal elements manifest
antisymmetry. Remarkably, the conductivity behavior in this model akin to that
of Hall conductivity. Additionally, this model can also achieve metal-insulator
transition.Comment: 28 pages, 11 figures, References adde
Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression
In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
Metro origin-destination prediction is a crucial yet challenging time-series
analysis task in intelligent transportation systems, which aims to accurately
forecast two specific types of cross-station ridership, i.e.,
Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete
OD matrices of previous time intervals can not be obtained immediately in
online metro systems, and conventional methods only used limited information to
forecast the future OD and DO ridership separately. In this work, we proposed a
novel neural network module termed Heterogeneous Information Aggregation
Machine (HIAM), which fully exploits heterogeneous information of historical
data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices)
to jointly learn the evolutionary patterns of OD and DO ridership.
Specifically, an OD modeling branch estimates the potential destinations of
unfinished orders explicitly to complement the information of incomplete OD
matrices, while a DO modeling branch takes DO matrices as input to capture the
spatial-temporal distribution of DO ridership. Moreover, a Dual Information
Transformer is introduced to propagate the mutual information among OD features
and DO features for modeling the OD-DO causality and correlation. Based on the
proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD
and DO ridership simultaneously. Extensive experiments conducted on two
large-scale benchmarks demonstrate the effectiveness of our method for online
metro origin-destination prediction
Electrochemically primed functional redox mediator generator from the decomposition of solid state electrolyte.
Recent works into sulfide-type solid electrolyte materials have attracted much attention among the battery community. Specifically, the oxidative decomposition of phosphorus and sulfur based solid state electrolyte has been considered one of the main hurdles towards practical application. Here we demonstrate that this phenomenon can be leveraged when lithium thiophosphate is applied as an electrochemically "switched-on" functional redox mediator-generator for the activation of commercial bulk lithium sulfide at up to 70 wt.% lithium sulfide electrode content. X-ray adsorption near-edge spectroscopy coupled with electrochemical impedance spectroscopy and Raman indicate a catalytic effect of generated redox mediators on the first charge of lithium sulfide. In contrast to pre-solvated redox mediator species, this design decouples the lithium sulfide activation process from the constraints of low electrolyte content cell operation stemming from pre-solvated redox mediators. Reasonable performance is demonstrated at strict testing conditions
InfoEntropy Loss to Mitigate Bias of Learning Difficulties for Generative Language Models
Generative language models are usually pretrained on large text corpus via
predicting the next token (i.e., sub-word/word/phrase) given the previous ones.
Recent works have demonstrated the impressive performance of large generative
language models on downstream tasks. However, existing generative language
models generally neglect an inherent challenge in text corpus during training,
i.e., the imbalance between frequent tokens and infrequent ones. It can lead a
language model to be dominated by common and easy-to-learn tokens, thereby
overlooking the infrequent and difficult-to-learn ones. To alleviate that, we
propose an Information Entropy Loss (InfoEntropy Loss) function. During
training, it can dynamically assess the learning difficulty of a to-be-learned
token, according to the information entropy of the corresponding predicted
probability distribution over the vocabulary. Then it scales the training loss
adaptively, trying to lead the model to focus more on the difficult-to-learn
tokens. On the Pile dataset, we train generative language models at different
scales of 468M, 1.2B, and 6.7B parameters. Experiments reveal that models
incorporating the proposed InfoEntropy Loss can gain consistent performance
improvement on downstream benchmarks
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