搜索结果：weakest

A system of polynomial ordinary differential equations (ODEs) is specified via a vector of multivariate polynomials, or vector field, $F$. A safety assertion $ψ\rightarrow[F]φ$ means that the trajectory of the system will lie in a subset $φ$ (the postcondition) of the state-space, whenever the initial state belongs to a subset $ψ$ (the precondition). We consider the case when $φ$ and $ψ$ are algebraic varieties, that is, zero sets of polynomials. In particular, polynomials specifying the postcondition can be seen as a system's conservation laws implied by $ψ$. Checking the validity of algebraic safety assertions is a fundamental problem in, for instance, hybrid systems. We consider a generalized version of this problem, and offer an algorithm that, given a user specified polynomial set $P$ and an algebraic precondition $ψ$, finds the largest subset of polynomials in $P$ implied by $ψ$ (relativized strongest postcondition). Under certain assumptions on $φ$, this algorithm can also be used to find the largest algebraic invariant included in $φ$ and the weakest algebraic precondition for $φ$. Applications to continuous semialgebraic systems are also considered. The effectiveness of th

Forgetting is an important concept in knowledge representation and automated reasoning with widespread applications across a number of disciplines. A standard forgetting operator, characterized in [Lin and Reiter'94] in terms of model-theoretic semantics and primarily focusing on the propositional case, opened up a new research subarea. In this paper, a new operator called weak forgetting, dual to standard forgetting, is introduced and both together are shown to offer a new more uniform perspective on forgetting operators in general. Both the weak and standard forgetting operators are characterized in terms of entailment and inference, rather than a model theoretic semantics. This naturally leads to a useful algorithmic perspective based on quantifier elimination and the use of Ackermman's Lemma and its fixpoint generalization. The strong formal relationship between standard forgetting and strongest necessary conditions and weak forgetting and weakest sufficient conditions is also characterized quite naturally through the entailment-based, inferential perspective used. The framework used to characterize the dual forgetting operators is also generalized to the first-order case and i

Ethereum holds multiple billions of U.S. dollars in the form of Ether cryptocurrency and ERC-20 tokens, with millions of deployed smart contracts algorithmically operating these funds. Unsurprisingly, the security of Ethereum smart contracts has been under rigorous scrutiny. In recent years, numerous defense tools have been developed to detect different types of smart contract code vulnerabilities. When opportunities for exploiting code vulnerabilities diminish, the attackers start resorting to social engineering attacks, which aim to influence humans -- often the weakest link in the system. The only known class of social engineering attacks in Ethereum are honeypots, which plant hidden traps for attackers attempting to exploit existing vulnerabilities, thereby targeting only a small population of potential victims. In this work, we explore the possibility and existence of new social engineering attacks beyond smart contract honeypots. We present two novel classes of Ethereum social engineering attacks - Address Manipulation and Homograph - and develop six zero-day social engineering attacks. To show how the attacks can be used in popular programming patterns, we conduct a case stu

Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which causes them to overlook information at certain positions. Whether these failures stem from an inability to locate evidence (recognition failure) or integrate it (synthesis failure) is unclear. We introduce Multi-Focus Attention Instruction (MFAI), a semantic probe to disentangle these mechanisms by explicitly steering attention towards selected positions. Across 5 LLMs on two multi-hop QA tasks (MuSiQue and NeoQA), we identify the "Weakest Link Effect": in our 18-document, 3-bucket setting, multi-hop reasoning performance collapses to the level of the least visible evidence, governed by absolute position rather than the linear distance between facts. While matched MFAI resolves recognition bottlenecks, improving accuracy by up to 11.49% in low-visibility positions, misleading MFAI yields divergent effects modulated by task topology: entity-centric tasks with vertical reasoning chains are vulnerable, whereas event-centric tasks with horizontal evidence structures are more resilient. Finally, we demonstrate that thinking models utilizing Syst

The nature of dispersal of many invasive pests and pathogens in agricultural and forestry makes it necessary to consider how the actions of one manager affect neighbouring properties. In addition to the direct effects of a potential spread of a pest and the resulting economic loss, there are also indirect consequences that affect whole regions and that require coordinated actions to manage and/or to eradicate it (like movement restrictions). In this paper we address the emergence and stability of cooperation among agents who respond to a threat of an invasive pest or disease. The model, based on the weakest-link paradigm, uses repeated multi-participant coordination games where players' pay-offs depend on management decisions to prevent the invasion on their own land as well as of their neighbours on a network. We show that for the basic cooperation game agents select the risk-dominant strategy of a Stag hunt game over the pay-off dominant strategy of implementing control measures. However, cooperation can be achieved by the social planner offering a biosecurity payment. The critical level of this payment depends on the details of the decision-making process, with higher trust (bas

This paper tackles the problem of synthesizing specifications for nondeterministic programs. For such programs, useful specifications can capture demonic properties, which hold for every nondeterministic execution, but also angelic properties, which hold for some nondeterministic execution. We build on top of a recently proposed a framework by Park et al. in which given (i) a quantifier-free query posed about a set of function definitions (i.e., the behavior for which we want to generate a specification), and (ii) a language L in which each extracted property is to be expressed (we call properties in the language L-properties), the goal is to synthesize a conjunction of L-properties such that each of the conjunct is a strongest L-consequence for the query: each property is an over-approximation of the query and there is no other L-property that over-approximates the query and is strictly more precise than each property. This framework does not apply to nondeterministic programs for two reasons: it does not support existential quantifiers in queries (which are necessary to expressing nondeterminism) and it can only compute L-consequences, i.e., it is unsuitable for capturing both an

Percentage Brain Volume Change (PBVC) derived from Magnetic Resonance Imaging (MRI) is a widely used biomarker of brain atrophy, with SIENA among the most established methods for its estimation. However, SIENA relies on classical image processing steps, particularly skull stripping and tissue segmentation, whose failures can propagate through the pipeline and bias atrophy estimates. In this work, we examine whether targeted deep learning substitutions can improve SIENA while preserving its established and interpretable framework. To this end, we integrate SynthStrip and SynthSeg into SIENA and evaluate three pipeline variants on the ADNI and PPMI longitudinal cohorts. Performance is assessed using three complementary criteria: correlation with longitudinal clinical and structural decline, scan-order consistency, and end-to-end runtime. Replacing the skull-stripping module yields the most consistent gains: in ADNI, it substantially strengthens associations between PBVC and multiple measures of disease progression relative to the standard SIENA pipeline, while across both datasets it markedly improves robustness under scan reversal. The fully integrated pipeline achieves the stronges

We analyze how frequently instant runoff voting (IRV) selects the weakest (or least popular) candidate in three-candidate elections. We consider four definitions of ``weakest candidate'': the Borda loser, the Bucklin loser, the candidate with the most last-place votes, and the candidate with minimum social utility. We determine the probability that IRV selects the weakest candidate under the impartial anonymous culture and impartial culture models of voter behavior, and use Monte Carlo simulations to estimate these probabilities under several spatial models. We also examine this question empirically using a large dataset of real elections. Our results show that IRV can select the weakest candidates under each of these definitions, but such outcomes are generally rare. Across most models, the probability that IRV elects a given type of weakest candidate is at most 5\%. Larger probabilities arise only when the electorate is extremely polarized.

This document presents the tool named ''Application of Hoare Logic and Dijkstra's Weakest Proposition Calculus to Biological Regulatory Networks Using Path Programs with Branching First-Order Logic Operators'' or Hoare-fol for short. This tool consists in an implementation of the theoretical work developed in [Bernot et al., 2019] and contains the following features: (1) computation of the weakest precondition of a Hoare triple, (2) simplification of this weakest precondition using De Morgan laws and partial knowledge on the initial state, and (3) translation into Answer Set Programming to allow a solving of all compatible solutions.

Such large-structure tools of cohomology as toposes and derived categories stay close to arithmetic in practice, yet existing foundations for them go beyond the strong set theory ZFC. We formalize the practical insight by founding the theorems of EGA and SGA, plus derived categories, at the level of finite order arithmetic. This is the weakest possible foundation for these tools since one elementary topos of sets with infinity is already this strong.