The Carriers of the Interstellar Unidentified Infrared Emission Features: Constraints from the Interstellar C-H Stretching Features at 3.2-3.5 Micrometers

The unidentified infrared emission (UIE) features at 3.3, 6.2, 7.7, 8.6, and 11.3 micrometer, commonly attributed to polycyclic aromatic hydrocarbon (PAH) molecules, have been recently ascribed to mixed aromatic/aliphatic organic nanoparticles. More recently, an upper limit of <9% on the aliphatic fraction (i.e., the fraction of carbon atoms in aliphatic form) of the UIE carriers based on the observed intensities of the 3.4 and 3.3 micrometer emission features by attributing them to aliphatic and aromatic C-H stretching modes, respectively, and assuming A_34./A_3.3~0.68 derived from a small set of aliphatic and aromatic compounds, where A_3.4 and A_3.3 are respectively the band strengths of the 3.4 micrometer aliphatic and 3.3 micrometer aromatic C-H bonds. To improve the estimate of the aliphatic fraction of the UIE carriers, here we analyze 35 UIE sources which exhibit both the 3.3 and 3.4 micrometer C-H features and determine I_3.4/I_3.3, the ratio of the power emitted from the 3.4 micrometer feature to that from the 3.3 micrometer feature. We derive the median ratio to be ~ 0.12. We employ density functional theory and second-order perturbation theory to compute A_3.4/A_3.3 for a range of methyl-substituted PAHs. The resulting A_3.4/A_3.3 ratio well exceeds 1.4, with an average ratio of ~1.76. By attributing the 3.4 micrometer feature exclusively to aliphatic C-H stretch (i.e., neglecting anharmonicity and superhydrogenation), we derive the fraction of C atoms in aliphatic form to be ~2%. We therefore conclude that the UIE emitters are predominantly aromatic.Comment: 14 pages, 5 figures, 1 table; accepted for publication in The Astrophysical Journa

Micromachined membrane particle filters

We report here several particle membrane filters (8 x 8 mm^2) with circular, hexagonal and rectangular through holes. By varying hole dimensions from 6 to 12 pm, opening factors from 4 to 45 % are achieved. In order to improve the filter robustness, a composite silicon nitride/Parylene membrane technology is developed. More importantly, fluid dynamic performance of the filters is also studied by both experiments and numerical simulations. It is found that the gaseous flow through the filters depends strongly on opening factors, and the measured pressure drops are much lower than that from numerical simulation using the Navier-Stokes equation. Interestingly, surface velocity slip can only account for a minor part of the discrepancy. This suggests that a very interesting topic for micro fluid mechanics research is identified

Constrained structure of ancient Chinese poetry facilitates speech content grouping

Ancient Chinese poetry is constituted by structured language that deviates from ordinary language usage [1, 2]; its poetic genres impose unique combinatory constraints on linguistic elements [3]. How does the constrained poetic structure facilitate speech segmentation when common linguistic [4, 5, 6, 7, 8] and statistical cues [5, 9] are unreliable to listeners in poems? We generated artificial Jueju, which arguably has the most constrained structure in ancient Chinese poetry, and presented each poem twice as an isochronous sequence of syllables to native Mandarin speakers while conducting magnetoencephalography (MEG) recording. We found that listeners deployed their prior knowledge of Jueju to build the line structure and to establish the conceptual flow of Jueju. Unprecedentedly, we found a phase precession phenomenon indicating predictive processes of speech segmentation—the neural phase advanced faster after listeners acquired knowledge of incoming speech. The statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segmentation, which provides an alternative perspective on how statistical cues facilitate speech segmentation. Our findings suggest that constrained poetic structures serve as a temporal map for listeners to group speech contents and to predict incoming speech signals. Listeners can parse speech streams by using not only grammatical and statistical cues but also their prior knowledge of the form of language

An Online Approach to Dynamic Channel Access and Transmission Scheduling

Making judicious channel access and transmission scheduling decisions is essential for improving performance as well as energy and spectral efficiency in multichannel wireless systems. This problem has been a subject of extensive study in the past decade, and the resulting dynamic and opportunistic channel access schemes can bring potentially significant improvement over traditional schemes. However, a common and severe limitation of these dynamic schemes is that they almost always require some form of a priori knowledge of the channel statistics. A natural remedy is a learning framework, which has also been extensively studied in the same context, but a typical learning algorithm in this literature seeks only the best static policy, with performance measured by weak regret, rather than learning a good dynamic channel access policy. There is thus a clear disconnect between what an optimal channel access policy can achieve with known channel statistics that actively exploits temporal, spatial and spectral diversity, and what a typical existing learning algorithm aims for, which is the static use of a single channel devoid of diversity gain. In this paper we bridge this gap by designing learning algorithms that track known optimal or sub-optimal dynamic channel access and transmission scheduling policies, thereby yielding performance measured by a form of strong regret, the accumulated difference between the reward returned by an optimal solution when a priori information is available and that by our online algorithm. We do so in the context of two specific algorithms that appeared in [1] and [2], respectively, the former for a multiuser single-channel setting and the latter for a single-user multichannel setting. In both cases we show that our algorithms achieve sub-linear regret uniform in time and outperforms the standard weak-regret learning algorithms.Comment: 10 pages, to appear in MobiHoc 201

ASAP : towards accurate, stable and accelerative penetrating-rank estimation on large graphs

Pervasive web applications increasingly require a measure of similarity among objects. Penetrating-Rank (P-Rank) has been one of the promising link-based similarity metrics as it provides a comprehensive way of jointly encoding both incoming and outgoing links into computation for emerging applications. In this paper, we investigate P-Rank efficiency problem that encompasses its accuracy, stability and computational time. (1) We provide an accuracy estimate for iteratively computing P-Rank. A symmetric problem is to find the iteration number K needed for achieving a given accuracy ε. (2) We also analyze the stability of P-Rank, by showing that small choices of the damping factors would make P-Rank more stable and well-conditioned. (3) For undirected graphs, we also explicitly characterize the P-Rank solution in terms of matrices. This results in a novel non-iterative algorithm, termed ASAP , for efficiently computing P-Rank, which improves the CPU time from O(n 4) to O( n 3 ). Using real and synthetic data, we empirically verify the effectiveness and efficiency of our approaches