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Wasm runtime is a fundamental component in the Wasm ecosystem, as it directly impacts whether Wasm applications can be executed as expected. Bugs in Wasm runtime bugs are frequently reported, thus our research community has made a few attempts to design automated testing frameworks for detecting bugs in Wasm runtimes. However, existing testing frameworks are limited by the quality of test cases, i.e., they face challenges of generating both semantic-rich and syntactic-correct Wasm binaries, thus complicated bugs cannot be triggered. In this work, we present WRTester, a novel differential testing framework that can generated complicated Wasm test cases by disassembling and assembling of real-world Wasm binaries, which can trigger hidden inconsistencies among Wasm runtimes. For further pinpointing the root causes of unexpected behaviors, we design a runtime-agnostic root cause location method to accurately locate bugs. Extensive evaluation suggests that WRTester outperforms SOTA techniques in terms of both efficiency and effectiveness. We have uncovered 33 unique bugs in popular Wasm runtimes, among which 25 have been confirmed.

The black hole superradiance phenomena state that when a scalar wave perturbation is properly scattering off a charged or rotating black hole, the energy of the reflected scalar wave may be amplified. If this amplification process can occur back and forth through certain confining mechanisms, it will lead to strong instability of the black hole, which is so-called "black hole bomb". In this paper, the superradiant stability is investigated for dyonic black holes in string theory. Although the electric charged black hole in string theory has been proved superrdiantly stable, it is found that the dyonic black hole is more unstable than an RN black hole due to the magnetic charge. Furthermore, by our analysis of the effective potential outside the event horizon, we give the region of the parameter associated with the superradiance stability on dyonic stringly black hole., Comment: 9 pages, 3 figures. arXiv admin note: text overlap with arXiv:1809.07345

The superradiant stability of five and six-dimensional extremal Reissner-Nordstrom black holes under charged massive scalar perturbation is studied. In each case, it is analytically proved that the effective potential experienced by the scalar perturbation has only one maximum outside the black hole horizon and no potential well exists for the superradiance modes. So the five and six-dimensional extremal Reissner-Nordstrom black holes are superradiantly stable. In the proof, we develop a new method which is based on the Descartes' rule of signs for the polynomial equations. Our results generalize the previous study that four-dimensional extremal Reissner-Nordstrom black hole is superradiantly stable under charged massive scalar perturbation., Comment: 15 pages, no figures

Aggregating risks from multiple sources can be complex and demanding, and decision makers usually adopt heuristics to simplify the evaluation process. This paper axiomatizes two closed related and yet different heuristics, narrow bracketing and correlation neglect, by relaxing the independence axiom in the expected utility theory. The flexibility of our framework allows for applications in various economic problems. First, our model can explain the experimental evidence of narrow bracketing over monetary gambles. Second, when one source represents background risk, we can accommodate Rabin (2000)'s critique and explain risk aversion over small gambles. Finally, when different sources represent consumptions in different periods, we unify three seemingly distinct models of time preferences and propose a novel model that simultaneously satisfies indifference to temporal resolution of uncertainty, separation of time and risk preferences, and recursivity in the domain of lotteries. As a direct application to macroeconomics and finance, we provide an alternative to Epstein and Zin (1989) which avoids the unreasonably high timing premium discussed in Epstein, Farhi, and Strzalecki (2014).

During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides differential privacy often remains a manual task to be completed by an expert in the field. Language-based techniques have been proposed for fully automating proofs of differential privacy via type system design, however these results have lagged behind advances in differentially-private algorithms, leaving a noticeable gap in programs which can be automatically verified while also providing state-of-the-art bounds on privacy. We propose Duet, an expressive higher-order language, linear type system and tool for automatically verifying differential privacy of general-purpose higher-order programs. In addition to general purpose programming, Duet supports encoding machine learning algorithms such as stochastic gradient descent, as well as common auxiliary data analysis tasks such as clipping, normalization and hyperparameter tuning - each of which are particularly challenging to encode in a statically verified differential privacy framework. We present a core design of the Duet language and linear type system, and complete key proofs about privacy for well-typed programs. We then show how to extend Duet to support realistic machine learning applications and recent variants of differential privacy which result in improved accuracy for many practical differentially private algorithms. Finally, we implement several differentially private machine learning algorithms in Duet which have never before been automatically verified by a language-based tool, and we present experimental results which demonstrate the benefits of Duet's language design in terms of accuracy of trained machine learning models., Comment: Extended version of OOPSLA 2019 paper