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

Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies

Large Language Models (LLMs) have made significant strides in both scientific research and practical applications. Existing studies have demonstrated the state-of-the-art (SOTA) performance of LLMs in various natural language processing tasks. However, the question of how to further enhance LLMs' performance in specific task using prompting strategies remains a pivotal concern. This paper explores the enhancement of LLMs' performance in sentiment analysis through the application of prompting strategies. We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis: RolePlaying (RP) prompting and Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT prompting strategy which is a combination of RP prompting and CoT prompting. We conduct comparative experiments on three distinct domain datasets to evaluate the effectiveness of the proposed sentiment analysis strategies. The results demonstrate that the adoption of the proposed prompting strategies leads to a increasing enhancement in sentiment analysis accuracy. Further, the CoT prompting strategy exhibits a notable impact on implicit sentiment analysis, with the RP-CoT prompting strategy delivering the most superior performance among all strategies

The Progenitor of Supernova 2004dj in a Star Cluster

The progenitor of type II-plateau supernova (SN) 2004dj is identified with a supergiant in a compact star cluster known as "Sandage Star 96" (S96) in the nearby spiral galaxy NGC 2403, which was fortuitously imaged as part of the Beijing-Arizona-Taiwan-Connecticut (BATC) Multicolor Sky Survey from Feb 1995 to Dec 2003 prior to SN 2004dj. The superior photometry of BATC images for S96, taken with 14 intermediate-band filters covering 3000-10000\AA, unambiguously establishes the star cluster nature of S96 with an age of $\sim 20$Myr, a reddening of $\hbox{E}(B-V)\sim 0.35$ mag and a total mass of $\sim 96,000$M$_{\odot}$. The compact star cluster nature of S96 is also consistent with the lack of light variations in the past decade. The SN progenitor is estimated to have a main-sequence mass of $\sim$12M$_{\odot}$. The comparison of our intermediate-band data of S96 with the post-outburst photometry obtained as the SN has significantly dimmed, may hopefully conclusively establish the nature of the progenitor.Comment: 4 pages; 3 figures. To accept for Publications in ApJ Letters, but slightly longer in this perprin

Broad targeting of angiogenesis for cancer prevention and therapy

Deregulation of angiogenesis – the growth of new blood vessels from an existing vasculature – is a main driving force in many severe human diseases including cancer. As such, tumor angiogenesis is important for delivering oxygen and nutrients to growing tumors, and therefore considered an essential pathologic feature of cancer, while also playing a key role in enabling other aspects of tumor pathology such as metabolic deregulation and tumor dissemination/metastasis. Recently, inhibition of tumor angiogenesis has become a clinical anti-cancer strategy in line with chemotherapy, radiotherapy and surgery, which underscore the critical importance of the angiogenic switch during early tumor development. Unfortunately the clinically approved anti-angiogenic drugs in use today are only effective in a subset of the patients, and many who initially respond develop resistance over time. Also, some of the anti-angiogenic drugs are toxic and it would be of great importance to identify alternative compounds, which could overcome these drawbacks and limitations of the currently available therapy. Finding “the most important target” may, however, prove a very challenging approach as the tumor environment is highly diverse, consisting of many different cell types, all of which may contribute to tumor angiogenesis. Furthermore, the tumor cells themselves are genetically unstable, leading to a progressive increase in the number of different angiogenic factors produced as the cancer progresses to advanced stages. As an alternative approach to targeted therapy, options to broadly interfere with angiogenic signals by a mixture of non-toxic natural compound with pleiotropic actions were viewed by this team as an opportunity to develop a complementary anti-angiogenesis treatment option. As a part of the “Halifax Project” within the “Getting to know cancer” framework, we have here, based on a thorough review of the literature, identified 10 important aspects of tumor angiogenesis and the pathological tumor vasculature which would be well suited as targets for anti-angiogenic therapy: (1) endothelial cell migration/tip cell formation, (2) structural abnormalities of tumor vessels, (3) hypoxia, (4) lymphangiogenesis, (5) elevated interstitial fluid pressure, (6) poor perfusion, (7) disrupted circadian rhythms, (8) tumor promoting inflammation, (9) tumor promoting fibroblasts and (10) tumor cell metabolism/acidosis. Following this analysis, we scrutinized the available literature on broadly acting anti-angiogenic natural products, with a focus on finding qualitative information on phytochemicals which could inhibit these targets and came up with 10 prototypical phytochemical compounds: (1) oleanolic acid, (2) tripterine, (3) silibinin, (4) curcumin, (5) epigallocatechin-gallate, (6) kaempferol, (7) melatonin, (8) enterolactone, (9) withaferin A and (10) resveratrol. We suggest that these plant-derived compounds could be combined to constitute a broader acting and more effective inhibitory cocktail at doses that would not be likely to cause excessive toxicity. All the targets and phytochemical approaches were further cross-validated against their effects on other essential tumorigenic pathways (based on the “hallmarks” of cancer) in order to discover possible synergies or potentially harmful interactions, and were found to generally also have positive involvement in/effects on these other aspects of tumor biology. The aim is that this discussion could lead to the selection of combinations of such anti-angiogenic compounds which could be used in potent anti-tumor cocktails, for enhanced therapeutic efficacy, reduced toxicity and circumvention of single-agent anti-angiogenic resistance, as well as for possible use in primary or secondary cancer prevention strategies

Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.Comment: 11pages, 5 figures, 6 table

Poisoning Attacks Against Contrastive Recommender Systems

Contrastive learning (CL) has recently gained significant popularity in the field of recommendation. Its ability to learn without heavy reliance on labeled data is a natural antidote to the data sparsity issue. Previous research has found that CL can not only enhance recommendation accuracy but also inadvertently exhibit remarkable robustness against noise. However, this paper identifies a vulnerability of CL-based recommender systems: Compared with their non-CL counterparts, they are even more susceptible to poisoning attacks that aim to promote target items. Our analysis points to the uniform dispersion of representations led by the CL loss as the very factor that accounts for this vulnerability. We further theoretically and empirically demonstrate that the optimization of CL loss can lead to smooth spectral values of representations. Based on these insights, we attempt to reveal the potential poisoning attacks against CL-based recommender systems. The proposed attack encompasses a dual-objective framework: One that induces a smoother spectral value distribution to amplify the CL loss's inherent dispersion effect, named dispersion promotion; and the other that directly elevates the visibility of target items, named rank promotion. We validate the destructiveness of our attack model through extensive experimentation on four datasets. By shedding light on these vulnerabilities, we aim to facilitate the development of more robust CL-based recommender systems.Comment: 14pages,6 figures,5 table

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

PAH exposure is associated with enhanced risk for pediatric dyslipidemia through serum SOD reduction

Background: Exposure to polycyclic aromatic hydrocarbons (PAHs) is linked to abnormal lipid metabolism, but evidence regarding PAHs as risk factors for dyslipidemia is lacking. Objective: To investigate the respective role and interaction of PAH exposure and antioxidant consumption in the risk for pediatric dyslipidemia. Methods: We measured the concentrations of serum lipids, superoxide dismutase (SOD) and urinary hydroxylated PAHs (OH-PAHs) in 403 children, of which 203 were from an e-waste-exposed area (Guiyu) and 200 were from a reference area (Haojiang). Biological interactions were calculated by additive models. Results: Guiyu children had higher serum triglyceride concentration and dyslipidemia incidence, and lower serum concentration of high-density lipoprotein (HDL) than Haojiang children. Elevated OH-PAH concentration, and concomitant SOD reduction, were both associated with lower HDL concentration and higher hypo-HDL risk (S3OH-Phes: B for lgHDL = 0.048, P <0.01; OR for hypo-HDL = 3.708, 95% CI: 1.200, 11.453; SOD: BT3 for lgHDL = 0.061, P <0.01; ORT3 for hypo-HDL = 0.168, 95% CI: 0.030, 0.941; all were adjusted for confounders). Biological interaction between phenanthrol exposure and SOD reduction was linked to dyslipidemia risk (RERI = 2.783, AP = 0.498, S = 2.537). Children with both risk factors (higher S3OH-Phes and lower SOD) had 5.594times (95% CI: 1.119, 27.958) the dyslipidemia risk than children with neither risk factors (lower S3OH-Phes and higher SOD). Conclusion: High PAH exposure combined with SOD reduction is recommended for predicting elevated risk for pediatric dyslipidemia. Risk assessment of PAH-related dyslipidemia should take antioxidant concentration into consideration