Damped Lyman alpha Absorbing Galaxies At Low Redshifts z<1 From Hierarchical Galaxy Formation Models

We investigate Damped Ly-alpha absorbing galaxies (DLA galaxies) at low redshifts z<1 in the hierarchical structure formation scenario to clarify the nature of DLA galaxies because observational data of such galaxies mainly at low redshifts are currently available. We find that our model well reproduces distributions of fundamental properties of DLA galaxies such as luminosities, column densities, impact parameters obtained by optical and near-infrared imagings. Our results suggest that DLA systems primarily consist of low luminosity galaxies with small impact parameters (typical radius about 3 kpc, surface brightness from 22 to 27 mag arcsec^{-2}) similar to low surface brightness (LSB) galaxies. In addition, we investigate selection biases arising from the faintness and from the masking effect which prevents us from identifying a DLA galaxy hidden or contaminated by a point spread function of a background quasar. We find that the latter affects the distributions of DLA properties more seriously rather than the former, and that the observational data are well reproduced only when taking into account the masking effect. The missing rate of DLA galaxies by the masking effect attains 60-90 % in the sample at redshift 0<z<1 when an angular size limit is as small as 1 arcsec. Furthermore we find a tight correlation between HI mass and cross section of DLA galaxies, and also find that HI-rich galaxies with M(HI) \sim 10^{9} M_sun dominate DLA systems. These features are entirely consistent with those from the Arecibo Dual-Beam Survey which is a blind 21 cm survey. Finally we discuss star formation rates, and find that they are typically about 10^{-2} M_sun yr^{-1} as low as those in LSB galaxies.Comment: 21 pages, 13 figures, Accepted for publication in Astrophsical Journa

A mathematical framework to quantify the masking effect associated with the confidence intervals of measures of disproportionality

Background: The lower bound of the 95% confidence interval of measures of disproportionality (Lower95CI) is widely used in signal detection. Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other medicines. The primary objective of our study is to develop and validate a mathematical framework for assessing the masking effect of Lower95CI. Methods: We have developed our new algorithm based on the masking ratio (MR) developed for the measures of disproportionality. A MR for the Lower95CI (MRCI) is proposed. A simulation study to validate this algorithm was also conducted. Results: We have established the existence of a very close mathematical relation between MR and MRCI. For a given drug–event pair, the same product will be responsible for the highest masking effect with the measure of disproportionality and its Lower95CI. The extent of masking is likely to be very similar across the two methods. An important proportion of identical drug–event associations affected by the presence of an important masking effect is revealed by the unmasking exercise, whether the proportional reporting ratio (PRR) or its confidence interval are used. Conclusion: The detection of the masking effect of Lower95CI can be automated. The real benefits of this unmasking in terms of new true-positive signals (rate of true-positive/false-positive) or time gained by the revealing of signals using this method have not been fully assessed. These benefits should be demonstrated in the context of prospective studies. </jats:sec

Remote-Frequency Masking and Speech Perception in Adults

The primary purpose of this study is threefold: to use SRT measurements to examine the effect of various remote-frequency, narrowband maskers on adult’s perception of narrowband speech, to compare the performance between low and high band speech stimuli, and to evaluate the combination of these approaches by examining the correlation between the masking effect observed with speech and pure tone stimuli. Twelve subjects aged 22-34, with hearing thresholds no worse than 15 dB HL for frequencies 500-8000 Hz, participated in two listening tasks. In the speech perception task, coordinate response measure (CRM) sentences and their maskers were separately filtered into two ½-octave wide frequency bands with respective center frequencies of 500 Hz (low-band) and 2500 Hz (high-band). Three types of maskers were utilized: Gaussian noise, CRM sentences spoken by a talker different from the talker of the target sentences (speech-masked conditions), and time-reversed CRM sentences. Speech reception thresholds (SRTs) of either low- or high-band sentences were assessed in quiet and in the presence of a high- or low-band masker. Speech recognition scores (SRSs), or the percentages of keywords correctly identified, were measured in the same conditions. In the informational masking task, detection thresholds of a 1 kHz tone were measured in quiet and in the presence of a muli-tonal masker. SRTs in quiet were found to be significantly higher than in GNB and reverse speech maskers. SRTs were also found to be lower for high band target speech. In the SRS task, only the forward speech masker produced significantly worse recognition scores. Using pure tone stimuli, an average masking effect of approximately 18 dB was observed across participants. The pure tone masking effect was not found to correlate with the SRT masking effect, however, a trend of correlation appears to exist that may potentially reach significance with a larger sample size

Perception of low-frequency acoustic signals by a harbour porpoise (Phocoena phocoena) in the presence of simulated offshore wind turbine noise

Using auditory evoked potential (AEP) methods, a study was conducted on a harbour porpoise (Phocoena phocoena) at the Dolfinarium Harderwijk in The Netherlands. The study measured the audible range of wind turbine sounds and their potential masking effects on the acoustic perception of the animal. AEPs were evoked with two types of acoustic stimuli: (1) click-type signals and (2) amplitude-modulated signals. The masking noise resembling the underwater sound emissions of an operational wind turbine was simulated. At first, the animal’s hearing threshold was measured at frequencies between 0.7 and 16 kHz. Subsequently, these measurements were repeated at frequencies between 0.7 and 2.8 kHz in the presence of two different levels of masking noise. The resulting data show a masking effect of the simulated wind turbine sound at 128 dB re 1 μPa at 0.7, 1.0, and 2.0 kHz. This masking effect varied between 4.8 and 7.3 dB at those frequencies. No significant masking was measured at a masking level of 115 dB re 1 μPa. The available data indicate that the potential masking effect would be limited to short ranges in the open sea, but limitations exist to this conclusion and all estimates are based on existing turbine types, not taking into account future developments of larger and potentially noisier turbine types

Subband coding of digital audio signals without loss of quality

A subband coding system for high quality digital audio signals is described. To achieve low bit rates at a high quality level, it exploits the simultaneous masking effect of the human ear. It is shown how this effect can be used in an adaptive bit-allocation scheme. The proposed approach has been applied in two coding systems, a complex system in which signal is split into 26 subbands, each approximately one third of an octave wide, and a simpler 20-band system. Both systems have been designed for coding stereophonic 16-bit compact disk signals with a sampling frequency of 44.1 kHz. With the 26-band system high-quality results can be obtained at bit rates of 220 kb/s. With the 20-band system, similar results can be obtained at bit rates of 360 kb/

Not all adversarial examples require a complex defense : identifying over-optimized adversarial examples with IQR-based logit thresholding

Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase the prediction likelihood of a target class for a particular data point. During this process, the adversarial example can be further optimized, even when it has already been wrongly classified with 100% confidence, thus making the adversarial example even more difficult to detect. For this kind of adversarial examples, which we refer to as over-optimized adversarial examples, we discovered that the logits of the model provide solid clues on whether the data point at hand is adversarial or genuine. In this context, we first discuss the masking effect of the softmax function for the prediction made and explain why the logits of the model are more useful in detecting over-optimized adversarial examples. To identify this type of adversarial examples in practice, we propose a non-parametric and computationally efficient method which relies on interquartile range, with this method becoming more effective as the image resolution increases. We support our observations throughout the paper with detailed experiments for different datasets (MNIST, CIFAR-10, and ImageNet) and several architectures