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

Design, purification and assessment of GRP78 binding peptide-linked Subunit A of Subtilase cytotoxic for targeting cancer cells

The sequence of primers for GBP-SubA and optimization of E. coli strain and vector of GBP-SubA expression. (DOC 710 kb

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

The Data Security Risks and Responses in China-ASEAN Digital Cooperation

As China-ASEAN digital cooperation deepens, both parties face multidimensional data security risks in digital infrastructure connectivity, trade rule reconstruction, collaborative technological research, and industry ecosystem building. This study systematically analyzes the complex security challenges and their causes in China-ASEAN digital cooperation from a South-South cooperation perspective, and proposes targeted response strategies. The study finds that data security risks manifest across physical, informational, and sovereignty layers: geopolitical competition in technological standards, conflicts between data sovereignty and cross-border flow regulations, and intervention by major power rules collectively form the deep-rooted contradictions in regional digital cooperation. Specifically, digital infrastructure is hindered by technical compatibility and cybersecurity disputes, digital trade rules are trapped in “institutional competition and cooperation” due to fragmented regulation, core technology supply chains are impacted by geopolitical tensions, and digital industry collaboration faces dual challenges of cultural identity and regulatory misalignment.The study further reveals that the root causes of these risks lie in the delayed regional risk awareness, imbalanced governance resources within ASEAN, and the geopolitical rule competition by major powers. To address this, the paper proposes four cooperative pathways: first, building a tiered governance mechanism under the RCEP framework to balance sovereignty and circulation through data categorization, classification, and mutual recognition; second, deepening strategic collaboration by establishing a cross-border data flow joint regulatory committee and a joint cybersecurity defense and control system; third, accelerating the construction of the “Digital Silk Road” by using quantum communication and edge computing to bridge the digital divide; and fourth, strengthening legal mutual recognition, resisting external rule fragmentation, and maintaining regional governance autonomy. These pathways provide a new paradigm of “co-building rules, co-researching technology, and sharing risks” for digital security governance in developing countries.The study also highlights current limitations, such as the uneven data openness among ASEAN member states and insufficient empirical data in sensitive areas. Future research may focus on the compliance risks and financial stability impacts of the cross-border application of digital RMB, as well as technological and institutional innovations to bridge the digital divide. By deepening differentiated analysis and practical validation, future studies can offer more actionable theoretical support for the high-quality development of China-ASEAN digital cooperation

Chandra Observation of the Cluster of Galaxies MS 0839.9+2938 at z=0.194: the Central Excess Iron and SN Ia Enrichment

We present the Chandra study of the intermediately distant cluster of galaxies MS 0839.9+2938. By performing both the projected and deprojected spectral analyses, we find that the gas temperature is approximately constant at about 4 keV in 130-444h_70^-1 kpc. In the inner regions, the gas temperature descends towards the center, reaching <~ 3 keV in the central 37h_70^-1 kpc. This infers that the lower and upper limits of the mass deposit rate are 9-34 M_sun yr^-1 and 96-126 M_sun yr^-1, respectively within 74h_70^-1 kpc where the gas is significantly colder. Along with the temperature drop, we detect a significant inward iron abundance increase from about 0.4 solar in the outer regions to about 1 solar within the central 37h_70^-1 kpc. Thus MS 0839.9+2938 is the cluster showing the most significant central iron excess at z>~ 0.2. We argue that most of the excess iron should have been contributed by SNe Ia. By utilizing the observed SN Ia rate and stellar mass loss rate, we estimate that the time needed to enrich the central region with excess iron is 6.4-7.9 Gyr, which is similar to those found for the nearby clusters. Coinciding with the optical extension of the cD galaxy (up to about 30h_70^-1 kpc), the observed X-ray surface brightness profile exhibits an excess beyond the distribution expected by either the beta model or the NFW model, and can be well fitted with an empirical two-beta model that leads to a relatively flatter mass profile in the innermost region.Comment: Accepted for publication in Ap

DeFiTail: DeFi Protocol Inspection through Cross-Contract Execution Analysis

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding \$77 billion. However, detection methods for malicious DeFi events are still lacking. In this paper, we propose DeFiTail, the first framework that utilizes deep learning to detect access control and flash loan exploits that may occur on DeFi. Since the DeFi protocol events involve invocations with multi-account transactions, which requires execution path unification with different contracts. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, we validate the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Experimental results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset

StateGuard: Detecting State Derailment Defects in Decentralized Exchange Smart Contract

Decentralized Exchanges (DEXs), leveraging blockchain technology and smart contracts, have emerged in decentralized finance. However, the DEX project with multi-contract interaction is accompanied by complex state logic, which makes it challenging to solve state defects. In this paper, we conduct the first systematic study on state derailment defects of DEXs. These defects could lead to incorrect, incomplete, or unauthorized changes to the system state during contract execution, potentially causing security threats. We propose StateGuard, a deep learning-based framework to detect state derailment defects in DEX smart contracts. StateGuard constructs an Abstract Syntax Tree (AST) of the smart contract, extracting key features to generate a graph representation. Then, it leverages a Graph Convolutional Network (GCN) to discover defects. Evaluating StateGuard on 46 DEX projects with 5,671 smart contracts reveals its effectiveness, with a precision of 92.24%. To further verify its practicality, we used StateGuard to audit real-world smart contracts and successfully authenticated multiple novel CVEs.Comment: 5 pages,2 figures, prepared for Conference WWW 202

RecDiff: Diffusion Model for Social Recommendation

Social recommendation has emerged as a powerful approach to enhance personalized recommendations by leveraging the social connections among users, such as following and friend relations observed in online social platforms. The fundamental assumption of social recommendation is that socially-connected users exhibit homophily in their preference patterns. This means that users connected by social ties tend to have similar tastes in user-item activities, such as rating and purchasing. However, this assumption is not always valid due to the presence of irrelevant and false social ties, which can contaminate user embeddings and adversely affect recommendation accuracy. To address this challenge, we propose a novel diffusion-based social denoising framework for recommendation (RecDiff). Our approach utilizes a simple yet effective hidden-space diffusion paradigm to alleivate the noisy effect in the compressed and dense representation space. By performing multi-step noise diffusion and removal, RecDiff possesses a robust ability to identify and eliminate noise from the encoded user representations, even when the noise levels vary. The diffusion module is optimized in a downstream task-aware manner, thereby maximizing its ability to enhance the recommendation process. We conducted extensive experiments to evaluate the efficacy of our framework, and the results demonstrate its superiority in terms of recommendation accuracy, training efficiency, and denoising effectiveness. The source code for the model implementation is publicly available at: https://github.com/HKUDS/RecDiff

What Makes a Helpful Online Review When Information Overload Exists?

With the increasing of online reviews, information overload has become a major problem in online community. What makes a helpful online review when information overload exists? In this study, the research model is developed to examine the helpfulness of online consumer reviews when information overload exists. Information quality is measured by review length and pictures in the model. The result is showed the relationship between review length and review helpfulness is usually described as an inverted U curve. The impact of review length and picture review on helpfulness is stronger when information overload exists. The impact of is also stronger with negative reviews than without negative reviews. As a result, our findings help extend the literature on information diagnosticity within the context of information overload