Broad Band Polarimetry of Supernovae: SN1994D, SN1994Y, SN1994ae, SN1995D and SN 1995H

We have made polarimetric observations of three Type Ia supernovae (SN Ia) and two type II supernovae (SN II). No significant polarization was detected for any of the SN Ia down to the level of 0.2\%, while polarization of order $1.0\%$ was detected for the two SN II 1994Y and 1995H. A catalog of all the SNe with polarization data is compiled that shows a distinct trend that all the 5 SN II with sufficient polarimetric data show polarizations at about 1\%, while none of the 9 SN Ia in the sample show intrinsic polarization. This systematic difference in polarization of supernovae, if confirmed, raises many interesting questions concerning the mechanisms leading to supernova explosions. Our observations enhance the use of SN Ia as tools for determining the distance scale through various techniques, but suggest that one must be very cautious in utilizing Type II for distance determinations. However, we caution that the link between the asphericity of a supernova and the measured ``intrinsic'' polarization is complicated by reflected light from the circumstellar material and the intervening interstellar material, the so-called light echo. This effect may contribute more substantially to SN II than to SN Ia. The tight limits on polarization of SN Ia may constrain progenitor models with extensive scattering nebulae such as symbiotic stars and other systems of extensive mass loss.Comment: 27 pages, 3 Postscript figure

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion

Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning

This paper describes a novel algorithm for underdetermined speech separation problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two steps. The unknown mixing matrix is firstly estimated from the speech mixtures in the transform domain by using K-means clustering algorithm. In the second step, the speech sources are recovered based on an autocalibration sparse Bayesian learning algorithm for speech signal. Numerical experiments including the comparison with other sparse representation approaches are provided to show the achieved performance improvement

A Novel Color Parameter As A Luminosity Calibrator for Type Ia Supernovae

Type Ia supernovae (SNe Ia) provide us with a unique tool for measuring extragalactic distances and determining cosmological parameters. As a result, the precise and effective calibration for peak luminosities of SNe Ia becomes extremely crucial and thus is critically scrutinized for cosmological explorations. In this Letter, we reveal clear evidence for a tight linear correlation between peak luminosities of SNe Ia and their $B-V$ colors $\sim 12$ days after the $B$ maximum denoted by $\Delta C_{12}$. By introducing such a novel color parameter, $\Delta C_{12}$, this empirical correlation allows us to uniformly standardize SNe Ia with decline rates $\Delta m_{15}$ in the range of $0.8<\Delta m_{15}<2.0$ and to reduce scatters in estimating their peak luminosities from $\sim 0.5$ mag to the levels of 0.18 and 0.12 mag in the $V$ and $I$ bands, respectively. For a sample of SNe Ia with insignificant reddenings of host galaxies [e.g., E(B-V)_{host}\lsim 0.06 mag], the scatter drops further to only 0.07 mag (or 3-4% in distance), which is comparable to observational accuracies and is better than other calibrations for SNe Ia. This would impact observational and theoretical studies of SNe Ia and cosmological scales and parameters.Comment: 13 pages, including 3 figures. To appear in ApJL (2005 Feb issue

Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm

IntroductionActive tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication.MethodologyUtilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently, a pyroptosis-related module was garnered, facilitating the identification of a pyroptosis-related signature (PRS) diagnostic model through the application of a neural network algorithm. With the aid of Single Sample Gene Set Enrichment Analysis (ssGSEA), we further examined the immune cells engaged in the pyroptosis process in the context of active ATB. Lastly, dataset GSE37250 played a crucial role as a validating cohort, aimed at evaluating the diagnostic prowess of our model.ResultsIn executing the Weighted Gene Co-expression Network Analysis (WGCNA), a total of nine discrete co-expression modules were lucidly elucidated. Module 1 demonstrated a potent correlation with pyroptosis. A predictive diagnostic paradigm comprising three pyroptosis-related signatures, specifically AIM2, CASP8, and NAIP, was devised accordingly. The established PRS model exhibited outstanding accuracy across both cohorts, with the area under the curve (AUC) being respectively articulated as 0.946 and 0.787.ConclusionThe present research succeeded in identifying the pyroptosis-related signature within the pathogenetic framework of ATB. Furthermore, we developed a diagnostic model which exuded a remarkable potential for efficient and accurate diagnosis

Hot isostatic pressing of in-situ TiB/Ti-6Al-4V composites with novel reinforcement architecture, enhanced hardness and elevated tribological properties

In this study, titanium borides reinforced Ti-6Al-4V composites have been successfully prepared by hot isostatic pressing (HIPing). The microstructure of the as-fabricated samples was investigated using X-ray diffraction technique, secondary electron microscopy and electron backscatter diffraction and the mechanical properties evaluated through micro-hardness and wear resistance measurements together with nano-indentation. It was found that during HIPing the additive particles TiB2 have transformed into TiB needles which tend to decorate at prior particle boundaries of the consolidated powder particles to form a network structure. Under the same HIPing condition, the needles became increasingly coarser and agglomerated with increased addition of TiB2. The micro-hardness of the synthesized materials increased with increased volume fraction of TiB. Nano-indentation measurement demonstrates that the TiB network structure shows much higher nanohardness than the surrounding matrix regions. The friction coefficient of the synthesized composites decreased continuously with increased volume fraction of TiB, indicating improved wear resistance. High resolution transmission electron microscopy analysis on wear debris revealed the formation of a series of oxides suggesting that chemical reaction between alloy elements and oxygen in air may have happened. It is thus believed that the wearing of the current samples is a result of both friction and chemical reaction