278 research outputs found
Safety Model Checking with Complementary Approximations
Formal verification techniques such as model checking, are becoming popular
in hardware design. SAT-based model checking techniques such as IC3/PDR, have
gained a significant success in hardware industry. In this paper, we present a
new framework for SAT-based safety model checking, named Complementary
Approximate Reachability (CAR). CAR is based on standard reachability analysis,
but instead of maintaining a single sequence of reachable- state sets, CAR
maintains two sequences of over- and under- approximate reachable-state sets,
checking safety and unsafety at the same time. To construct the two sequences,
CAR uses standard Boolean-reasoning algorithms, based on satisfiability
solving, one to find a satisfying cube of a satisfiable Boolean formula, and
one to provide a minimal unsatisfiable core of an unsatisfiable Boolean
formula. We applied CAR to 548 hardware model-checking instances, and compared
its performance with IC3/PDR. Our results show that CAR is able to solve 42
instances that cannot be solved by IC3/PDR. When evaluated against a portfolio
that includes IC3/PDR and other approaches, CAR is able to solve 21 instances
that the other approaches cannot solve. We conclude that CAR should be
considered as a valuable member of any algorithmic portfolio for safety model
checking
A case crossover study on the impact of heat waves on non-accidental deaths in Jinan, China
Background: Heat waves can not only cause direct death from heat stroke but also lead to excess deaths due to other illnesses. Identifying contributing factors of population vulnerability to heat waves is particularly crucial because heat waves will affect the most disadvantaged populations aggravating health disparities. There has been little evidence on the risk of deaths from heat waves and associated contributing factors to the population vulnerability in Jinan. Purpose: To assess the impact of heat waves on non-accidental deaths and identify individual vulnerability factors to heat wave-related deaths in Jinan, China
Experimenting a New Programming Practice with LLMs
The recent development on large language models makes automatically
constructing small programs possible. It thus has the potential to free
software engineers from low-level coding and allow us to focus on the perhaps
more interesting parts of software development, such as requirement engineering
and system testing. In this project, we develop a prototype named AISD
(AI-aided Software Development), which is capable of taking high-level
(potentially vague) user requirements as inputs, generates detailed use cases,
prototype system designs, and subsequently system implementation. Different
from existing attempts, AISD is designed to keep the user in the loop, i.e., by
repeatedly taking user feedback on use cases, high-level system designs, and
prototype implementations through system testing. AISD has been evaluated with
a novel benchmark of non-trivial software projects. The experimental results
suggest that it might be possible to imagine a future where software
engineering is reduced to requirement engineering and system testing only
Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework
Machine learning is widely used to make decisions with societal impact such
as bank loan approving, criminal sentencing, and resume filtering. How to
ensure its fairness while maintaining utility is a challenging but crucial
issue. Fairness is a complex and context-dependent concept with over 70
different measurement metrics. Since existing regulations are often vague in
terms of which metric to use and different organizations may prefer different
fairness metrics, it is important to have means of improving fairness
comprehensively. Existing mitigation techniques often target at one specific
fairness metric and have limitations in improving multiple notions of fairness
simultaneously. In this work, we propose CFU (Comprehensive Fairness-Utility),
a reinforcement learning-based framework, to efficiently improve the
fairness-utility trade-off in machine learning classifiers. A comprehensive
measurement that can simultaneously consider multiple fairness notions as well
as utility is established, and new metrics are proposed based on an in-depth
analysis of the relationship between different fairness metrics. The reward
function of CFU is constructed with comprehensive measurement and new metrics.
We conduct extensive experiments to evaluate CFU on 6 tasks, 3 machine learning
models, and 15 fairness-utility measurements. The results demonstrate that CFU
can improve the classifier on multiple fairness metrics without sacrificing its
utility. It outperforms all state-of-the-art techniques and has witnessed a
37.5% improvement on average
SAT-based Explicit LTLf Satisfiability Checking
We present here a SAT-based framework for LTLf (Linear Temporal Logic on
Finite Traces) satisfiability checking. We use propositional SAT-solving
techniques to construct a transition system for the input LTLf formula;
satisfiability checking is then reduced to a path-search problem over this
transition system. Furthermore, we introduce CDLSC (Conflict-Driven LTLf
Satisfiability Checking), a novel algorithm that leverages information produced
by propositional SAT solvers from both satisfiability and unsatisfiability
results. Experimental evaluations show that CDLSC outperforms all other
existing approaches for LTLf satisfiability checking, by demonstrating an
approximate four-fold speedup compared to the second-best solver
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