ESSAYS ON RISK AND SOCIAL PREFERENCES: EVIDENCE FROM GENES, CULTURE, AND STRATEGIC INTERACTIONS

Ph.DDOCTOR OF PHILOSOPH

Diffractive lensing of nano-Hertz gravitational waves emitted from supermassive binary black holes by intervening galaxies

Pulsar timing array (PTA) experiments are expected to detect nano-Hertz gravitational waves (GWs) emitted from individual inspiralling supermassive binary black holes (SMBBHs). The GW signals from a small fraction of these SMBBHs may be diffractively lensed by intervening galaxies. In this paper, we investigate the diffractive lensing effects on the continuous GW signals from the lensed SMBBHs and estimate the detectable number of such signals by PTAs, such as the Chinese PTA (CPTA) and the Square Kilometer Array (SKA) PTA. We find that the amplitude of the lensed GW signals may be only amplified by a factor of $\sim 1.01-1.14$ ($16\%-84\%$ range) and the phase of the signals may shift somewhat due to the lensing, significantly different from those strongly lensed high frequency GW signals from compact binary mergers in the geometric optics. We estimate that $\sim 0.01\%$ of all detected nano-Hertz GW signals from individual SMBBHs by future PTA experiments are lensed by foreground galaxies (i.e., up to $\sim 106$ for CPTA and up to $\sim 289$ for SKA-PTA). However, the lensed nano-Hertz GW signals are difficult to be distinguished from those without lensing by the PTA observations only. We further discuss the possibility about the identification of the lensed nano-Hertz GW signals from SMBBHs via the electromagnetic detection of their host galaxies or active galactic nuclei.Comment: 12 pages, 8 figures, Accepted for publication in MNRA

Contrastive Prompt Learning-based Code Search based on Interaction Matrix

Code search aims to retrieve the code snippet that highly matches the given query described in natural language. Recently, many code pre-training approaches have demonstrated impressive performance on code search. However, existing code search methods still suffer from two performance constraints: inadequate semantic representation and the semantic gap between natural language (NL) and programming language (PL). In this paper, we propose CPLCS, a contrastive prompt learning-based code search method based on the cross-modal interaction mechanism. CPLCS comprises:(1) PL-NL contrastive learning, which learns the semantic matching relationship between PL and NL representations; (2) a prompt learning design for a dual-encoder structure that can alleviate the problem of inadequate semantic representation; (3) a cross-modal interaction mechanism to enhance the fine-grained mapping between NL and PL. We conduct extensive experiments to evaluate the effectiveness of our approach on a real-world dataset across six programming languages. The experiment results demonstrate the efficacy of our approach in improving semantic representation quality and mapping ability between PL and NL

Accountability of Perfect Concurrent Signature

Concurrent signature provided a novel idea for fair exchange protocol without trusted third party. Perfect Concurrent Signature is proposed to strengthen theambiguity of the concurrent signature. Wang et al, pointed out there exist an attack against the fairness of Perfect Concurrent Signature and proposed the improved perfect concurrent signature. This paper find that in proposed (perfect) concurrent signature protocol, no matter two party or multi-party, the signer could bind multiple messages with one keystone set but let the other signers know only one of the messages. This is a new unfair case in the application of concurrent signature. Based on this observation, we propose that accountability should be one of the security properties of (perfect) concurrent signature and we give the definition of accountability of concurrent signature. To illustrate this idea, we give an attack scene against the accountability of improved perfect concurrent signature proposed by Wang et al, and propose an update version of perfect concurrent signature to avoid such attack

High-quality mesoporous graphene particles as high-energy and fast-charging anodes for lithium-ion batteries.

The application of graphene for electrochemical energy storage has received tremendous attention; however, challenges remain in synthesis and other aspects. Here we report the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles through chemical vapor deposition with magnesium-oxide particles as the catalyst and template. Such particles possess excellent structural and electrochemical stability, electronic and ionic conductivity, enabling their use as high-performance anodes with high reversible capacity, outstanding rate performance (e.g., 1,138 mA h g-1 at 0.2 C or 440 mA h g-1 at 60 C with a mass loading of 1 mg cm-2), and excellent cycling stability (e.g., >99% capacity retention for 500 cycles at 2 C with a mass loading of 1 mg cm-2). Interestingly, thick electrodes could be fabricated with high areal capacity and current density (e.g., 6.1 mA h cm-2 at 0.9 mA cm-2), providing an intriguing class of materials for lithium-ion batteries with high energy and power performance