348 research outputs found
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
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 Myr, a
reddening of mag and a total mass of M. 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 12M. 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 â the growth of new blood vessels from an existing vasculature â 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 âthe most important targetâ 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 âHalifax Projectâ within the âGetting to know cancerâ 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 âhallmarksâ 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
Poisoning Attacks against Recommender Systems: A Survey
Modern recommender systems (RS) have seen substantial success, yet they
remain vulnerable to malicious activities, notably poisoning attacks. These
attacks involve injecting malicious data into the training datasets of RS,
thereby compromising their integrity and manipulating recommendation outcomes
for gaining illicit profits. This survey paper provides a systematic and
up-to-date review of the research landscape on Poisoning Attacks against
Recommendation (PAR). A novel and comprehensive taxonomy is proposed,
categorizing existing PAR methodologies into three distinct categories:
Component-Specific, Goal-Driven, and Capability Probing. For each category, we
discuss its mechanism in detail, along with associated methods. Furthermore,
this paper highlights potential future research avenues in this domain.
Additionally, to facilitate and benchmark the empirical comparison of PAR, we
introduce an open-source library, ARLib, which encompasses a comprehensive
collection of PAR models and common datasets. The library is released at
https://github.com/CoderWZW/ARLib.Comment: 9 pages,3 figure
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 colors days after the maximum denoted by . By introducing such
a novel color parameter, , this empirical correlation allows us
to uniformly standardize SNe Ia with decline rates in the range
of and to reduce scatters in estimating their peak
luminosities from mag to the levels of 0.18 and 0.12 mag in the
and 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
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