Decompilation of binary code has arisen as a highly-important application in the space of Ethereum VM (EVM) smart contracts. Major new decompilers appear nearly every year and attain popularity, for a multitude of reverse-engineering or tool-building purposes. Technically, the problem is fundamental: it consists of recovering high-level control flow from a highly-optimized continuation-passing-style (CPS) representation. Architecturally, decompilers can be built using either static analysis or symbolic execution techniques. We present Shrknr, a static-analysis-based decompiler succeeding the state-of-the-art Elipmoc decompiler. Shrknr manages to achieve drastic improvements relative to the state of the art, in all significant dimensions: scalability, completeness, precision. Chief among the techniques employed is a new variant of static analysis context: shrinking context sensitivity. Shrinking context sensitivity performs deep cuts in the static analysis context, eagerly "forgetting" control-flow history, in order to leave room for further precise reasoning. We compare Shrnkr to state-of-the-art decompilers, both static-analysis- and symbolic-execution-based. In a standard benchma
The perovskite oxide EuTiO3 (ETO) has attracted increased scientific interest due to its potential multiferroic properties and magnetic activity above and below its structural phase transition at TS=282K. Various experiments have indirectly evidenced that this transition is neither a cubic tetragonal nor the only one occurring in ETO. Here, we show new results demonstrating two further instabilities below TS based on lattice dynamics and spin-phonon interactions combined with a Landau free energy model with coupled order parameters. The new transition temperatures perfectly agree with available experimental data where further instabilities have been anticipated.
Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.
We propose three kinds of explicit formulas for the elliptic lambda function by the elliptic modular function. Further, we derive incredible cubic identities as a corollary of our explicit formulas and evaluate some singular values of the elliptic lambda function explicitly.
Scientists have uncovered a surprising navigation system in pigeons: iron-filled immune cells in the liver that may act like tiny magnetic sensors。 Birds deprived of these cells struggled to find their way home under overcast skies, indicating they rely on Earth’s magnetic field for guidance。 The discovery could solve a decades-old mystery about an
The NASA Kepler mission -in flight since March 2009- is producing an enormous number of high-quality continuous light curves. Now, and for the first time ever, we are able to do ensemble asteroseismology, i.e., to do an asteroseismic analysis with a statistically significant sub-sample of solar-like stars covering a wide range of stellar characteristics. In the present work, I highlight some of the most recent studies carried out using these data.
We comment on progress in measurements of the Casimir force and discuss what is the actual reliability of different experiments. In this connection a more rigorous approach to the usage of such concepts as accuracy, precision, and measure of agreement between experiment and theory, is presented. We demonstrate that all measurements of the Casimir force employing spherical lenses with centimeter-size curvature radii are fundamentally flawed due to the presence of bubbles and pits on their surfaces. The commonly used formulation of the proximity force approximation is shown to be inapplicable for centimeter-size lenses. New expressions for the Casimir force are derived taking into account surface imperfections. Uncontrollable deviations of the Casimir force from the values predicted using the assumption of perfect sphericity vary by a few tens of percent within the separation region from 1 to $3\,μ$m. This makes impractical further use of centimeter-size lenses in experiments on measuring the Casimir force.
How much can pruning algorithms teach us about the fundamentals of learning representations in neural networks? And how much can these fundamentals help while devising new pruning techniques? A lot, it turns out. Neural network pruning has become a topic of great interest in recent years, and many different techniques have been proposed to address this problem. The decision of what to prune and when to prune necessarily forces us to confront our assumptions about how neural networks actually learn to represent patterns in data. In this work, we set out to test several long-held hypotheses about neural network learning representations, approaches to pruning and the relevance of one in the context of the other. To accomplish this, we argue in favor of pruning whole neurons as opposed to the traditional method of pruning weights from optimally trained networks. We first review the historical literature, point out some common assumptions it makes, and propose methods to demonstrate the inherent flaws in these assumptions. We then propose our novel approach to pruning and set about analyzing the quality of the decisions it makes. Our analysis led us to question the validity of many wide
Life and the mathematical legacy of the great mathematician A.V. Pogorelov.
Using M(atrix) Theory, the dualities of toroidally compactified M-theory can be formulated as properties of super Yang Mills theories in various dimensions. We consider the cases of compactification on one, two, three, four and five dimensional tori. The dualities required by string theory lead to conjectures of remarkable symmetries and relations between field theories as well as extremely unusual dynamical properties. By studying the theories in the limit of vanishingly small tori, a wealth of information is obtained about strongly coupled fixed points of super Yang-Mills theories in various dimensions. Perhaps the most striking behavior, as noted by Rozali in this context, is the emergence of an additional dimension of space in the case of a four torus.
Scalar Field Dark Matter (SFDM) comprised of ultralight ($\gtrsim 10^{-22}$ eV) bosons is an alternative to standard, collisionless Cold Dark Matter (CDM) that is CDM-like on large scales but inhibits small-scale structure formation. As a Bose-Einstein condensate, its free-field ("fuzzy") limit (FDM) suppresses structure below the de Broglie wavelength, $λ_\text{deB}$, creating virialized haloes with central cores of radius $\simλ_\text{deB}$, surrounded by CDM-like envelopes, and a halo mass function (HMF) with a sharp cut-off on small scales. With a strong enough repulsive self-interaction (SI), structure is inhibited, instead, below the Thomas-Fermi (TF) radius, $R_\text{TF}$ (the size of an SI-pressure-supported ($n=1$)-polytrope), when $R_\text{TF} > λ_\text{deB}$. Previously, we developed tools to describe SFDM dynamics on scales above $λ_\text{deB}$ and showed that SFDM-TF haloes formed by Jeans-unstable collapse from non-cosmological initial conditions have $R_\text{TF}$-sized cores, surrounded by CDM-like envelopes. Revisiting SFDM-TF in the cosmological context, we simulate halo formation by cosmological infall and collapse, and derive its transfer function from linear
Recent experimental results from the LHC have placed strong constraints on the masses of QCD-charged superpartners. The MSSM parameter space is also constrained by the measurement of the Higgs boson mass, and the requirement that the relic density of lightest neutralinos be consistent with observations. Although large regions of the MSSM parameter space can be excluded by these combined bounds, leptophilic versions of the MSSM can survive these constraints. In this paper we consider a scenario in which the requirements of minimal flavor violation, vanishing $CP$-violation, and mass universality are relaxed, specifically focusing on scenarios with light sleptons. We find a large region of parameter space, analogous to the original bulk region, for which the lightest neutralino is a thermal relic with an abundance consistent with that of dark matter. We find that these leptophilic models are constrained by measurements of the magnetic and electric dipole moments of the electron and muon, and that these models have interesting signatures at a variety of indirect detection experiments.
Vision-Language(-Action) Models (VLMs) are increasingly applied to interactive environments, yet existing benchmarks often overlook the complex physical reasoning required for point-and-click puzzle games. This paper introduces Vision-Language Against The Incredible Machine (VLATIM), a benchmark designed to evaluate human-like logical problem-solving capabilities within the classic physics puzzle game The Incredible Machine 2 (TIM). Unlike existing benchmarks, VLATIM specifically targets the critical gap between high-level logical reasoning and continuous action spaces requiring precise mouse interactions. This benchmark is structured into five progressive parts, assessing capabilities that range from basic visual grounding and domain understanding to multi-step manipulation and full puzzle solving. Our results reveal a significant disparity between reasoning and execution. While large proprietary models demonstrate superior planning abilities, they struggle with precise visual grounding. Consequently, they do not yet show human-like problem-solving capabilities.
The intersection of artificial intelligence (AI) and digital forensics (DF) is becoming increasingly complex, ubiquitous, and pervasive, with overlapping techniques and technologies being adopted in all types of scientific and technical inquiry. Despite incredible advances, forensic sciences are not exempt from errors and remain vulnerable to fallibility. To mitigate the limitations of errors in DF, the systemic complexity is identified and addressed with the adoption of human-readable artifacts and open standards. A DF AI model schema based on the state of the art is outlined.
Apéry's remarkable discovery of rapidly converging continued fractions with small coefficients for $ζ(2)$ and $ζ(3)$ has led to a flurry of important activity in an incredible variety of different directions. Our purpose is to show that modifications of Apéry's continued fractions can give interesting results including new rapidly convergent continued fractions for certain interesting constants.
With the aid of large language models, current conversational recommender system (CRS) has gaining strong abilities to persuade users to accept recommended items. While these CRSs are highly persuasive, they can mislead users by incorporating incredible information in their explanations, ultimately damaging the long-term trust between users and the CRS. To address this, we propose a simple yet effective method, called PC-CRS, to enhance the credibility of CRS's explanations during persuasion. It guides the explanation generation through our proposed credibility-aware persuasive strategies and then gradually refines explanations via post-hoc self-reflection. Experimental results demonstrate the efficacy of PC-CRS in promoting persuasive and credible explanations. Further analysis reveals the reason behind current methods producing incredible explanations and the potential of credible explanations to improve recommendation accuracy.
We propose an optomechanical scheme for reaching quantum entanglement in vibration polaritons. The system involves $N$ molecules, whose vibrations can be fairly entangled with plasmonic cavities. We find that the vibration-photon entanglement can exist at room temperature and is robust against thermal noise. We further demonstrate the quantum entanglement between the vibrational modes through the plasmonic cavities, which shows a delocalized nature and an incredible enhancement with the number of molecules. The underlying mechanism for the entanglement is attributed to the strong vibration-cavity coupling which possesses collectivity. Our results provide a molecular optomechanical scheme which offers a promising platform for the study of noise-free quantum resources and macroscopic quantum phenomena.
From healing wounds to maintaining homeostasis in cyclically loaded tissue, living systems have a phenomenal ability to sense, store, and respond to mechanical stimuli. Broadly speaking, there is significant interest in designing engineered systems to recapitulate this incredible functionality. In engineered systems, we have seen significant recent computationally driven advances in sensing and control. And, there has been a growing interest - inspired in part by the incredible distributed and emergent functionality observed in the natural world - in exploring the ability of engineered systems to perform computation through mechanisms that are fundamentally driven by physical laws. In this work, we focus on a small segment of this broad and evolving field: locality sensitive hashing via mechanical behavior. Specifically, we will address the question: can mechanical information (i.e., loads) be transformed by mechanical systems (i.e., converted into sensor readouts) such that the mechanical system meets the requirements for a locality sensitive hash function? Overall, we not only find that mechanical systems are able to perform this function, but also that different mechanical syste
Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019). To address this issue, we have created an energy estimation pipeline1, which allows practitioners to estimate the energy needs of their models in advance, without actually running or training them. We accomplished this, by collecting high-quality energy data and building a first baseline model, capable of predicting the energy consumption of DL models by accumulating their estimated layer-wise energies.
The enigmatic nonlocal quantum correlation that was famously derided by Einstein as "spooky action at a distance" has now been experimentally demonstrated to be authentic. The quantum entanglement and nonlocal correlations emerged as inevitable consequences of John Bell's epochal paper on Bell's inequality. However, in spite of some extraordinary applications as well as attempts to explain the reason for quantum nonlocality, a satisfactory account of how Nature accomplishes this astounding phenomenon is yet to emerge. A cogent mechanism for the occurrence of this incredible event is presented in terms of a plausible quantum mechanical Einstein-Rosen bridge.