AntFuzzer: A Grey-Box Fuzzing Framework for EOSIO Smart Contracts

In the past few years, several attacks against the vulnerabilities of EOSIO smart contracts have caused severe financial losses to this prevalent blockchain platform. As a lightweight test-generation approach, grey-box fuzzing can open up the possibility of improving the security of EOSIO smart contracts. However, developing a practical grey-box fuzzer for EOSIO smart contracts from scratch is time-consuming and requires a deep understanding of EOSIO internals. In this work, we proposed AntFuzzer, the first highly extensible grey-box fuzzing framework for EOSIO smart contracts. AntFuzzer implements a novel approach that interfaces AFL to conduct AFL-style grey-box fuzzing on EOSIO smart contracts. Compared to black-box fuzzing tools, AntFuzzer can effectively trigger those hard-to-cover branches. It achieved an improvement in code coverage on 37.5% of smart contracts in our benchmark dataset. AntFuzzer provides unified interfaces for users to easily develop new detection plugins for continually emerging vulnerabilities. We have implemented 6 detection plugins on AntFuzzer to detect major vulnerabilities of EOSIO smart contracts. In our large-scale fuzzing experiments on 4,616 real-world smart contracts, AntFuzzer successfully detected 741 vulnerabilities. The results demonstrate the effectiveness and efficiency of AntFuzzer and our detection p

Feature Weaken: Vicinal Data Augmentation for Classification

Deep learning usually relies on training large-scale data samples to achieve better performance. However, over-fitting based on training data always remains a problem. Scholars have proposed various strategies, such as feature dropping and feature mixing, to improve the generalization continuously. For the same purpose, we subversively propose a novel training method, Feature Weaken, which can be regarded as a data augmentation method. Feature Weaken constructs the vicinal data distribution with the same cosine similarity for model training by weakening features of the original samples. In especially, Feature Weaken changes the spatial distribution of samples, adjusts sample boundaries, and reduces the gradient optimization value of back-propagation. This work can not only improve the classification performance and generalization of the model, but also stabilize the model training and accelerate the model convergence. We conduct extensive experiments on classical deep convolution neural models with five common image classification datasets and the Bert model with four common text classification datasets. Compared with the classical models or the generalization improvement methods, such as Dropout, Mixup, Cutout, and CutMix, Feature Weaken shows good compatibility and performance. We also use adversarial samples to perform the robustness experiments, and the results show that Feature Weaken is effective in improving the robustness of the model.Comment: 9 pages,6 figure

Surface structure and multigap superconductivity of V3Si (111) revealed by scanning tunneling microscopy

V3Si, a classical silicide superconductor with relatively high TC (~16 K), is promising for constructing silicon-based superconducting devices and hetero-structures. However, real space characterization on its surfaces and superconducting properties are still limited. Here we report the first low-temperature scanning tunnelling microscopy (STM) study on cleaned V3Si (111) single crystal surface. We observed a r3 by r3 superstructure which displays mirror symmetry between adjacent terraces, indicating the surface is V-terminated and reconstructed. The tunneling spectrum shows full superconducting gap with double pairs of coherence peaks, but has a relatively small gap size with comparing to bulk TC. Impurity induced in-gap state is absent on surface defects but present on introduced magnetic adatoms. Upon applying magnetic field, a hexagonal vortex lattice is visualized. Interestingly, the vortex size is found to be field dependent, and the coherence length measured from single vortex at low field is significantly larger than estimated value from bulk H_c2. These results reflect V3Si is a multi-band, s- wave superconductor

Lyα\alpha profile, dust, and prediction of Lyα\alpha escape fraction in Green Pea Galaxies

We studied Lyman-$\alpha$ (Ly$\alpha$) escape in a statistical sample of 43 Green Peas with HST/COS Ly$\alpha$ spectra. Green Peas are nearby star-forming galaxies with strong [OIII]$\lambda$5007 emission lines. Our sample is four times larger than the previous sample and covers a much more complete range of Green Pea properties. We found that about 2/3 of Green Peas are strong Ly$\alpha$ line emitters with rest-frame Ly$\alpha$ equivalent width $>20$ \AA. The Ly$\alpha$ profiles of Green Peas are diverse. The Ly$\alpha$ escape fraction, defined as the ratio of observed Ly$\alpha$ flux to intrinsic Ly$\alpha$ flux, shows anti-correlations with a few Ly$\alpha$ kinematic features -- both the blue peak and red peak velocities, the peak separations, and FWHM of the red portion of the Ly$\alpha$ profile. Using properties measured from SDSS optical spectra, we found many correlations -- Ly$\alpha$ escape fraction generally increases at lower dust reddening, lower metallicity, lower stellar mass, and higher [OIII]/[OII] ratio. We fit their Ly$\alpha$ profiles with the HI shell radiative transfer model and found Ly$\alpha$ escape fraction anti-correlates with the best-fit $N_{HI}$. Finally, we fit an empirical linear relation to predict Ly$\alpha$ escape fraction from the dust extinction and Ly$\alpha$ red peak velocity. The standard deviation of this relation is about 0.3 dex. This relation can be used to isolate the effect of IGM scatterings from Ly$\alpha$ escape and to probe the IGM optical depth along the line of sight of each $z>7$ Ly$\alpha$ emission line galaxy in the JWST era.Comment: 15 pages, 11 figures, 3 tables, machine-readable tables included. ApJ in-pres

Impurity-induced bound states in superconductors with topological order

The study of classical spins in topological insulators [Phys. Rev. B {\bf 80}, 115216 (2009)] is generalized to topological superconductors. Based on the characteristic features of the so-called $F$-function, Bogoliubov-de Gennes Hamiltonian for superconductors is classified to positive, negative, and zero "gap" categories for topologically trivial and nontrivial phases of a topological superconductor as well as a BCS superconductor respectively. It is found that the $F$-function determines directly the presence or absence of localized excited states, induced by bulk classical spins and nonmagnetic impurities, in superconducting gap and their persistence with respect to impurity strength. Our results provide an alternative way to identify topologically insulating and superconducting phases in experiments while without resorting to the surface properties.Comment: 6 pages, 4 figures. More discussions and references are added. Accepted Accepted for publication in Journal of Physics: Condensed Matte

Emission Line Metallicities From The Faint Infrared Grism Survey and VLT/MUSE

We derive direct measurement gas-phase metallicities of $7.4 < 12 + \log(O/H) < 8.4$ for 14 low-mass Emission Line Galaxies (ELGs) at $0.3 < z < 0.8$ identified in the Faint Infrared Grism Survey (FIGS). We use deep slitless G102 grism spectroscopy of the Hubble Ultra Deep Field (HUDF), dispersing light from all objects in the field at wavelengths between 0.85 and 1.15 microns. We run an automatic search routine on these spectra to robustly identify 71 emission line sources, using archival data from VLT/MUSE to measure additional lines and confirm redshifts. We identify 14 objects with $0.3 < z < 0.8$ with measurable O[III]$\lambda$4363 \AA\ emission lines in matching VLT/MUSE spectra. For these galaxies, we derive direct electron-temperature gas-phase metallicities with a range of $7.4 < 12 + \log(O/H) < 8.4$. With matching stellar masses in the range of $10^{7.9} M_{\odot} < M_{\star} < 10^{10.4} M_{\odot}$, we construct a mass-metallicity (MZ) relation and find that the relation is offset to lower metallicities compared to metallicities derived from alternative methods (e.g.,$R_{23}$, O3N2, N2O2) and continuum selected samples. Using star formation rates (SFR) derived from the $H\alpha$ emission line, we calculate our galaxies' position on the Fundamental Metallicity Relation (FMR), where we also find an offset toward lower metallicities. This demonstrates that this emission-line-selected sample probes objects of low stellar masses but even lower metallicities than many comparable surveys. We detect a trend suggesting galaxies with higher Specific Star Formation (SSFR) are more likely to have lower metallicity. This could be due to cold accretion of metal-poor gas that drives star formation, or could be because outflows of metal-rich stellar winds and SNe ejecta are more common in galaxies with higher SSFR.Comment: 14 pages, 11 figures, accepted in Ap

The Grizzly, April 14, 2016

White House Honors Alum Who Started Nonprofit • Greek Week Begins • We Stand Together Kicks Off • International Perspective: Cultural Differences Between Students • Students Explore Racial Issues Through Theater and Discussion • Passion, Pride and Protection • Making the Classroom a Place for Performance • Opinions: Minority Religions Deserve Accommodation; Choose Two: Sleep, Study or Socialize • Racket Up • Women\u27s Golf Makes History as Men Look to Regain Strokehttps://digitalcommons.ursinus.edu/grizzlynews/1689/thumbnail.jp

VBench: Comprehensive Benchmark Suite for Video Generative Models

Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end, we present VBench, a comprehensive benchmark suite that dissects "video generation quality" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods. VBench has three appealing properties: 1) Comprehensive Dimensions: VBench comprises 16 dimensions in video generation (e.g., subject identity inconsistency, motion smoothness, temporal flickering, and spatial relationship, etc). The evaluation metrics with fine-grained levels reveal individual models' strengths and weaknesses. 2) Human Alignment: We also provide a dataset of human preference annotations to validate our benchmarks' alignment with human perception, for each evaluation dimension respectively. 3) Valuable Insights: We look into current models' ability across various evaluation dimensions, and various content types. We also investigate the gaps between video and image generation models. We will open-source VBench, including all prompts, evaluation methods, generated videos, and human preference annotations, and also include more video generation models in VBench to drive forward the field of video generation.Comment: Equal contributions from first four authors. Project page: https://vchitect.github.io/VBench-project/ Code: https://github.com/Vchitect/VBenc