108 research outputs found
Aggregating Dependent Signals with Heavy-Tailed Combination Tests
Combining dependent p-values to evaluate the global null hypothesis presents
a longstanding challenge in statistical inference, particularly when
aggregating results from diverse methods to boost signal detection. P-value
combination tests using heavy-tailed distribution based transformations, such
as the Cauchy combination test and the harmonic mean p-value, have recently
garnered significant interest for their potential to efficiently handle
arbitrary p-value dependencies. Despite their growing popularity in practical
applications, there is a gap in comprehensive theoretical and empirical
evaluations of these methods. This paper conducts an extensive investigation,
revealing that, theoretically, while these combination tests are asymptotically
valid for pairwise quasi-asymptotically independent test statistics, such as
bivariate normal variables, they are also asymptotically equivalent to the
Bonferroni test under the same conditions. However, extensive simulations
unveil their practical utility, especially in scenarios where stringent type-I
error control is not necessary and signals are dense. Both the heaviness of the
distribution and its support substantially impact the tests' non-asymptotic
validity and power, and we recommend using a truncated Cauchy distribution in
practice. Moreover, we show that under the violation of quasi-asymptotic
independence among test statistics, these tests remain valid and, in fact, can
be considerably less conservative than the Bonferroni test. We also present two
case studies in genetics and genomics, showcasing the potential of the
combination tests to significantly enhance statistical power while effectively
controlling type-I errors
Characterizations of Network Auctions and Generalizations of VCG
With the growth of networks, promoting products through social networks has
become an important problem. For auctions in social networks, items are needed
to be sold to agents in a network, where each agent can bid and also diffuse
the sale information to her neighbors. Thus, the agents' social relations are
intervened with their bids in the auctions. In network auctions, the classical
VCG mechanism fails to retain key properties. In order to better understand
network auctions, in this paper, we characterize network auctions for the
single-unit setting with respect to weak budget balance, individual
rationality, incentive compatibility, efficiency, and other properties. For
example, we present sufficient conditions for mechanisms to be efficient and
(weakly) incentive compatible. With the help of these properties and new
concepts such as rewards, participation rewards, and so on, we show how to
design efficient mechanisms to satisfy incentive compatibility as much as
possible, and incentive compatibility mechanisms to maximize the revenue. Our
results provide insights into understanding auctions in social networks.Comment: To appear in ECAI 202
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
We study property prediction for crystal materials. A crystal structure
consists of a minimal unit cell that is repeated infinitely in 3D space. How to
accurately represent such repetitive structures in machine learning models
remains unresolved. Current methods construct graphs by establishing edges only
between nearby nodes, thereby failing to faithfully capture infinite repeating
patterns and distant interatomic interactions. In this work, we propose several
innovations to overcome these limitations. First, we propose to model
physics-principled interatomic potentials directly instead of only using
distances as in many existing methods. These potentials include the Coulomb
potential, London dispersion potential, and Pauli repulsion potential. Second,
we model the complete set of potentials among all atoms, instead of only
between nearby atoms as in existing methods. This is enabled by our
approximations of infinite potential summations with provable error bounds. We
further develop efficient algorithms to compute the approximations. Finally, we
propose to incorporate our computations of complete interatomic potentials into
message passing neural networks for representation learning. We perform
experiments on the JARVIS and Materials Project benchmarks for evaluation.
Results show that the use of interatomic potentials and complete interatomic
potentials leads to consistent performance improvements with reasonable
computational costs. Our code is publicly available as part of the AIRS library
(https://github.com/divelab/AIRS)
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training
Specific emitter identification (SEI) plays an increasingly crucial and
potential role in both military and civilian scenarios. It refers to a process
to discriminate individual emitters from each other by analyzing extracted
characteristics from given radio signals. Deep learning (DL) and deep neural
networks (DNNs) can learn the hidden features of data and build the classifier
automatically for decision making, which have been widely used in the SEI
research. Considering the insufficiently labeled training samples and large
unlabeled training samples, semi-supervised learning-based SEI (SS-SEI) methods
have been proposed. However, there are few SS-SEI methods focusing on
extracting the discriminative and generalized semantic features of radio
signals. In this paper, we propose an SS-SEI method using metric-adversarial
training (MAT). Specifically, pseudo labels are innovatively introduced into
metric learning to enable semi-supervised metric learning (SSML), and an
objective function alternatively regularized by SSML and virtual adversarial
training (VAT) is designed to extract discriminative and generalized semantic
features of radio signals. The proposed MAT-based SS-SEI method is evaluated on
an open-source large-scale real-world automatic-dependent
surveillance-broadcast (ADS-B) dataset and WiFi dataset and is compared with
state-of-the-art methods. The simulation results show that the proposed method
achieves better identification performance than existing state-of-the-art
methods. Specifically, when the ratio of the number of labeled training samples
to the number of all training samples is 10\%, the identification accuracy is
84.80\% under the ADS-B dataset and 80.70\% under the WiFi dataset. Our code
can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI.Comment: 12 pages, 5 figures, Journa
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
A time-frequency diagram is a commonly used visualization for observing the
time-frequency distribution of radio signals and analyzing their time-varying
patterns of communication states in radio monitoring and management. While it
excels when performing short-term signal analyses, it becomes inadaptable for
long-term signal analyses because it cannot adequately depict signal
time-varying patterns in a large time span on a space-limited screen. This
research thus presents an abstract signal time-frequency (ASTF) diagram to
address this problem. In the diagram design, a visual abstraction method is
proposed to visually encode signal communication state changes in time slices.
A time segmentation algorithm is proposed to divide a large time span into time
slices.Three new quantified metrics and a loss function are defined to ensure
the preservation of important time-varying information in the time
segmentation. An algorithm performance experiment and a user study are
conducted to evaluate the effectiveness of the diagram for long-term signal
analyses.Comment: 11 pages, 9 figure
1xN Pattern for Pruning Convolutional Neural Networks
Though network pruning receives popularity in reducing the complexity of
convolutional neural networks (CNNs), it remains an open issue to concurrently
maintain model accuracy as well as achieve significant speedups on general
CPUs. In this paper, we propose a novel 1xN pruning pattern to break this
limitation. In particular, consecutive N output kernels with the same input
channel index are grouped into one block, which serves as a basic pruning
granularity of our pruning pattern. Our 1xN pattern prunes these blocks
considered unimportant. We also provide a workflow of filter rearrangement that
first rearranges the weight matrix in the output channel dimension to derive
more influential blocks for accuracy improvements and then applies similar
rearrangement to the next-layer weights in the input channel dimension to
ensure correct convolutional operations. Moreover, the output computation after
our 1xN pruning can be realized via a parallelized block-wise vectorized
operation, leading to significant speedups on general CPUs. The efficacy of our
pruning pattern is proved with experiments on ILSVRC-2012. For example, Given
the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements
over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it
obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Our
project is made available at https://github.com/lmbxmu/1xN
Cell separation using tilted-angle standing surface acoustic waves
Separation of cells is a critical process for studying cell properties, disease diagnostics, and therapeutics. Cell sorting by acoustic waves offers a means to separate cells on the basis of their size and physical properties in a label-free, contactless, and biocompatible manner. The separation sensitivity and efficiency of currently available acoustic-based approaches, however, are limited, thereby restricting their widespread application in research and health diagnostics. In this work, we introduce a unique configuration of tilted-angle standing surface acoustic waves (taSSAW), which are oriented at an optimally designed inclination to the flow direction in the microfluidic channel. We demonstrate that this design significantly improves the efficiency and sensitivity of acoustic separation techniques. To optimize our device design, we carried out systematic simulations of cell trajectories, matching closely with experimental results. Using numerically optimized design of taSSAW, we successfully separated 2- and 10-µm-diameter polystyrene beads with a separation efficiency of ~99%, and separated 7.3- and 9.9-µm-polystyrene beads with an efficiency of ~97%. We illustrate that taSSAW is capable of effectively separating particles–cells of approximately the same size and density but different compressibility. Finally, we demonstrate the effectiveness of the present technique for biological–biomedical applications by sorting MCF-7 human breast cancer cells from nonmalignant leukocytes, while preserving the integrity of the separated cells. The method introduced here thus offers a unique route for separating circulating tumor cells, and for label-free cell separation with potential applications in biological research, disease diagnostics, and clinical practice.National Institutes of Health (U.S.) (Grant U01HL114476)National Institutes of Health (U.S.) (New Innovator Award 1DP2OD007209-01)National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Grant DMR-0820404
Time-series MODIS Image-based Retrieval and Distribution Analysis of Total Suspended Matter Concentrations in Lake Taihu (China)
Although there has been considerable effort to use remotely sensed images to provide synoptic maps of total suspended matter (TSM), there are limited studies on universal TSM retrieval models. In this paper, we have developed a TSM retrieval model for Lake Taihu using TSM concentrations measured in situ and a time series of quasi-synchronous MODIS 250 m images from 2005. After simple geometric and atmospheric correction, we found a significant relationship (R = 0.8736, N = 166) between in situ measured TSM concentrations and MODIS band normalization difference of band 3 and band 1. From this, we retrieved TSM concentrations in eight regions of Lake Taihu in 2007 and analyzed the characteristic distribution and variation of TSM. Synoptic maps of model-estimated TSM of 2007 showed clear geographical and seasonal variations. TSM in Central Lake and Southern Lakeshore were consistently higher than in other regions, while TSM in East Taihu was generally the lowest among the regions throughout the year. Furthermore, a wide range of TSM concentrations appeared from winter to summer. TSM in winter could be several times that in summer
- …