385 research outputs found
Direct tests of General Relativity under screening effect with galaxy-scale strong lensing systems
Observations of galaxy-scale strong gravitational lensing (SGL) systems have
enabled unique tests of nonlinear departures from general relativity (GR) on
the galactic and supergalactic scales. One of the most important cases of such
tests is constraints on the gravitational slip between two scalar gravitational
potentials. In this paper, we use a newly compiled sample of strong
gravitational lenses to test the validity of GR, focusing on the screening
effects on the apparent positions of lensed sources relative to the GR
predictions. This is the first simultaneous measurement of the Post-Newtonian
(PN) parameter () and the screening radius () without any
assumptions about the contents of the Universe. Our results suggest that the
measured PPN is marginally consistent with GR () with increasing
screening radius (kpc), although the choice of lens models
may have a significant influence on the final measurements. Based on a
well-defined sample of 5000 simulated strong lenses from the forthcoming LSST,
our methodology will provide a strong extragalactic test of GR with an accuracy
of 0.5\%, assessed up to scales of kpc. For the current and
future observations of available SGL systems, there is no noticeable evidence
indicating some specific cutoff scales on kpc-Mpc scales, beyond which new
gravitational degrees of freedom are expressed.Comment: 14 pages, 9 figures, accepted for publication in Ap
Learning Local to Global Feature Aggregation for Speech Emotion Recognition
Transformer has emerged in speech emotion recognition (SER) at present.
However, its equal patch division not only damages frequency information but
also ignores local emotion correlations across frames, which are key cues to
represent emotion. To handle the issue, we propose a Local to Global Feature
Aggregation learning (LGFA) for SER, which can aggregate longterm emotion
correlations at different scales both inside frames and segments with entire
frequency information to enhance the emotion discrimination of utterance-level
speech features. For this purpose, we nest a Frame Transformer inside a Segment
Transformer. Firstly, Frame Transformer is designed to excavate local emotion
correlations between frames for frame embeddings. Then, the frame embeddings
and their corresponding segment features are aggregated as different-level
complements to be fed into Segment Transformer for learning utterance-level
global emotion features. Experimental results show that the performance of LGFA
is superior to the state-of-the-art methods.Comment: This paper has been accepted on INTERSPEECH 202
Pseudo-solidification of dredged marine soils with cement - fly ash for reuse in coastal development
The dislodged and removed sediments from the seabed, termed dredged marine soils, are generally classified as a waste material requiring special disposal procedures. This is due to the potential contamination risks of transporting and disposing the dredged soils, and the fact that the material is of poor engineering quality, unsuitable for usage as a conventional good soil in construction. Also, taking into account the incurred costs and risk exposure in transferring the material to the dump site, whether on land or offshore, it is intuitive to examine the possibilities of reusing the dredged soils, especially in coastal development where the transportation route would be of shorter distance between the dredged site and the construction location. Pseudo-solidification of soils is not a novel idea though, where hydraulic binders are injected and mixed with soils to improve the inherent engineering properties for better load bearing capacity. It is commonly used on land in areas with vast and deep deposits of soft, weak soils. However, to implement the technique on the displaced then replaced dredged soil would require careful study, as the material is far more poorly than their land counterparts, and that the deployment of equipment and workforce in a coastal environment is understandably more challenging. The paper illustrates the laboratory investigation of the improved engineering performance of dredged marine soil sample with cement and fly ash blend. Some key findings include optimum dosage of cement and fly ash mix to produce up to 30 times of small strain stiffness improvement, pre-yield settlement reduction of the treated soil unaffected by prolonged curing period, and damage of the cementitious bonds formed by the rather small dosage of admixtures in the soil post-yield. In short, the test results show a promising reuse potential of the otherwise discarded dredged marine soils
Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
In this paper, we propose a new unsupervised domain adaptation (DA) method
called layer-adapted implicit distribution alignment networks (LIDAN) to
address the challenge of cross-corpus speech emotion recognition (SER). LIDAN
extends our previous ICASSP work, deep implicit distribution alignment networks
(DIDAN), whose key contribution lies in the introduction of a novel
regularization term called implicit distribution alignment (IDA). This term
allows DIDAN trained on source (training) speech samples to remain applicable
to predicting emotion labels for target (testing) speech samples, regardless of
corpus variance in cross-corpus SER. To further enhance this method, we extend
IDA to layer-adapted IDA (LIDA), resulting in LIDAN. This layer-adpated
extention consists of three modified IDA terms that consider emotion labels at
different levels of granularity. These terms are strategically arranged within
different fully connected layers in LIDAN, aligning with the increasing
emotion-discriminative abilities with respect to the layer depth. This
arrangement enables LIDAN to more effectively learn emotion-discriminative and
corpus-invariant features for SER across various corpora compared to DIDAN. It
is also worthy to mention that unlike most existing methods that rely on
estimating statistical moments to describe pre-assumed explicit distributions,
both IDA and LIDA take a different approach. They utilize an idea of target
sample reconstruction to directly bridge the feature distribution gap without
making assumptions about their distribution type. As a result, DIDAN and LIDAN
can be viewed as implicit cross-corpus SER methods. To evaluate LIDAN, we
conducted extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA
corpora. The experimental results demonstrate that LIDAN surpasses recent
state-of-the-art explicit unsupervised DA methods in tackling cross-corpus SER
tasks
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