38 research outputs found

    The Capacity of Wireless Channels: A Physical Approach

    Full text link
    In this paper, the capacity of wireless channels is characterized based on electromagnetic and antenna theories with only minimal assumptions. We assume the transmitter can generate an arbitrary current distribution inside a spherical region and the receive antennas are uniformly distributed on a bigger sphere surrounding the transmitter. The capacity is shown to be (αP/N0)loge(\alpha P/N_0) \log e [bits/sec] in the limit of large number of receive antennas, where PP is the transmit power constraint, α\alpha is the normalized density of the receive antennas and N0N_0 is the noise power spectral density. Although this result may look trivial, it is surprising in two ways. First, this result holds regardless of the bandwidth (bandwidth can even be negligibly small). Second, this result shows that the capacity is irrespective of the size of the region containing the transmitter. This is against some previous results that claimed the maximum degrees of freedom is proportional to the surface area containing the transmitter normalized by the square of the wavelength. Our result has important practical implications since it shows that even a compact antenna array with negligible bandwidth and antenna spacing well below the wavelength can provide a huge throughput as if the array was big enough so that the antenna spacing is on the order of the wavelength.Comment: 5 pages, to appear in proceedings of 2013 IEEE ISI

    Membership Dynamics and Network Stability in the Open-Source Community: The Ising Perspective

    Get PDF
    In this paper, we address the following two questions: (1)How does a participant’s membership decision affect the others (neighbors) with whom he has collaborated over an extended period of time in an open source software (OSS) network? (2) To what extent do network characteristics (i.e, size and connectivity) mediate the impact of external factors on the OSS participants’ dynamic membership decisions and hence the stability of the network? From the Ising perspective, we present fresh theoretical insight into the dynamic and reciprocal membership relations between OSS participants. We also performed simulations based on empirical data that were collected from two actual OSS communities. Some of the key findings include that (1) membership herding is highly present when the external force is weak, but decreases significantly when the force increases, (2) the propensity for membership herding is most likely to be seen in a large network with a random connectivity, and (3) for large networks, at low external force a random connectivity will perform better than a scale-free counterpart in terms of the network strength. However, as the temperature (external force) increases, the reverse phenomenon is observed. In addition, the scale-free connectivity appears to be less volatile than with the random connectivity in response to the increase in the temperature. We conclude with several implications that may be of significance to OSS stakeholders

    Real-time delay-multiply-and-sum beamforming with coherence factor for in vivo clinical photoacoustic imaging of humans

    Get PDF
    In the clinical photoacoustic (PA) imaging, ultrasound (US) array transducers are typically used to provide B-mode images in real-time. To form a B-mode image, delay-and-sum (DAS) beamforming algorithm is the most commonly used algorithm because of its ease of implementation. However, this algorithm suffers from low image resolution and low contrast drawbacks. To address this issue, delay-multiply-and-sum (DMAS) beamforming algorithm has been developed to provide enhanced image quality with higher contrast, and narrower main lobe compared but has limitations on the imaging speed for clinical applications. In this paper, we present an enhanced real-time DMAS algorithm with modified coherence factor (CF) for clinical PA imaging of humans in vivo. Our algorithm improves the lateral resolution and signal-to-noise ratio (SNR) of original DMAS beam-former by suppressing the background noise and side lobes using the coherence of received signals. We optimized the computations of the proposed DMAS with CF (DMAS-CF) to achieve real-time frame rate imaging on a graphics processing unit (GPU). To evaluate the proposed algorithm, we implemented DAS and DMAS with/without CF on a clinical US/PA imaging system and quantitatively assessed their processing speed and image quality. The processing time to reconstruct one B-mode image using DAS, DAS with CF (DAS-CF), DMAS, and DMAS-CF algorithms was 7.5, 7.6, 11.1, and 11.3 ms, respectively, all achieving the real-time imaging frame rate. In terms of the image quality, the proposed DMAS-CF algorithm improved the lateral resolution and SNR by 55.4% and 93.6 dB, respectively, compared to the DAS algorithm in the phantom imaging experiments. We believe the proposed DMAS-CF algorithm and its real-time implementation contributes significantly to the improvement of imaging quality of clinical US/PA imaging system.11Ysciescopu

    Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

    Full text link
    Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator. This alternated optimization is known to be delicate in practice since it compounds unstable adversarial training with brittle and sample-inefficient reinforcement learning. We propose to remove the burden of the policy optimization steps by leveraging a novel discriminator formulation. Specifically, our discriminator is explicitly conditioned on two policies: the one from the previous generator's iteration and a learnable policy. When optimized, this discriminator directly learns the optimal generator's policy. Consequently, our discriminator's update solves the generator's optimization problem for free: learning a policy that imitates the expert does not require an additional optimization loop. This formulation effectively cuts by half the implementation and computational burden of Adversarial Imitation Learning algorithms by removing the Reinforcement Learning phase altogether. We show on a variety of tasks that our simpler approach is competitive to prevalent Imitation Learning methods

    Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions

    Full text link
    We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces. Our work is motivated by practical scenarios where the target policy needs to be deterministic due to domain requirements, such as prescription of treatment dosage and duration in medicine. Although importance sampling (IS) provides a basic principle for OPE, it is ill-posed for the deterministic target policy with continuous actions. Our main idea is to relax the target policy and pose the problem as kernel-based estimation, where we learn the kernel metric in order to minimize the overall mean squared error (MSE). We present an analytic solution for the optimal metric, based on the analysis of bias and variance. Whereas prior work has been limited to scalar action spaces or kernel bandwidth selection, our work takes a step further being capable of vector action spaces and metric optimization. We show that our estimator is consistent, and significantly reduces the MSE compared to baseline OPE methods through experiments on various domains

    Neural Topological Ordering for Computation Graphs

    Full text link
    Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance. In this paper, we consider the problem of finding an optimal topological order on a directed acyclic graph with focus on the memory minimization problem which arises in compilers. We propose an end-to-end machine learning based approach for topological ordering using an encoder-decoder framework. Our encoder is a novel attention based graph neural network architecture called \emph{Topoformer} which uses different topological transforms of a DAG for message passing. The node embeddings produced by the encoder are converted into node priorities which are used by the decoder to generate a probability distribution over topological orders. We train our model on a dataset of synthetically generated graphs called layered graphs. We show that our model outperforms, or is on-par, with several topological ordering baselines while being significantly faster on synthetic graphs with up to 2k nodes. We also train and test our model on a set of real-world computation graphs, showing performance improvements.Comment: To appear in NeurIPS 202

    The Seoul National University AGN Monitoring Project. II. BLR Size and Black Hole Mass of Two AGNs

    Get PDF
    Active galactic nuclei (AGNs) show a correlation between the size of the broad line region and the monochromatic continuum luminosity at 5100 Å, allowing black hole mass estimation based on single-epoch spectra. However, the validity of the correlation is yet to be clearly tested for high-luminosity AGNs. We present the first reverberation mapping results of the Seoul National University AGN Monitoring Project (SAMP), which is designed to focus on luminous AGNs for probing the high end of the size–luminosity relation. We report time lag measurements of two AGNs, namely, 2MASS J10261389+5237510 and SDSS J161911.24+501109.2, using the light curves obtained over an ∼1000 days period with an average cadence of 10 and 20 days, respectively, for photometry and spectroscopy monitoring. Based on a cross-correlation analysis and Hβ line width measurements, we determine the Hβ lag as and days in the observed frame, and black hole mass as and , respectively, for 2MASS J1026 and SDSS J1619

    The Seoul National University AGN Monitoring Project. IV. Hα Reverberation Mapping of Six AGNs and the Hα Size–Luminosity Relation

    Get PDF
    The broad-line region (BLR) size–luminosity relation has paramount importance for estimating the mass of black holes in active galactic nuclei (AGNs). Traditionally, the size of the Hβ BLR is often estimated from the optical continuum luminosity at 5100 Å, while the size of the Hα BLR and its correlation with the luminosity is much less constrained. As a part of the Seoul National University AGN Monitoring Project, which provides 6 yr photometric and spectroscopic monitoring data, we present our measurements of the Hα lags of high-luminosity AGNs. Combined with the measurements for 42 AGNs from the literature, we derive the size–luminosity relations of the Hα BLR against the broad Hα and 5100 Å continuum luminosities. We find the slope of the relations to be 0.61 ± 0.04 and 0.59 ± 0.04, respectively, which are consistent with the Hβ size–luminosity relation. Moreover, we find a linear relation between the 5100 Å continuum luminosity and the broad Hα luminosity across 7 orders of magnitude. Using these results, we propose a new virial mass estimator based on the Hα broad emission line, finding that the previous mass estimates based on scaling relations in the literature are overestimated by up to 0.7 dex at masses lower than 107M⊙
    corecore