RustHorn: CHC-based Verification for Rust Programs

Reduction to satisfiability of constrained Horn clauses (CHCs) is a widely studied approach to automated program verification. Current CHC-based methods, however, do not work very well for pointer-manipulating programs, especially those with dynamic memory allocation. This article presents a novel reduction of pointer-manipulating Rust programs into CHCs, which clears away pointers and memory states by leveraging Rust’s guarantees on permission. We formalize our reduction for a simplified core of Rust and prove its soundness and completeness. We have implemented a prototype verifier for a subset of Rust and confirmed the effectiveness of our method.

Landslide Displacement Prediction Method Based on GA-Elman Model

The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.

A fully-coupled 3D model of a large Greenlandic outlet glacier with evolving subglacial hydrology, frontal plume melting and calving

We present the first fully coupled 3D full-Stokes model of a tidewater glacier, incorporating ice flow, subglacial hydrology, plume-induced frontal melting and calving. We apply the model to Store Glacier (Sermeq Kujalleq) in west Greenland to simulate a year of high melt (2012) and one of low melt (2017). In terms of modelled hydrology, we find perennial channels extending 5 km inland from the terminus and up to 41 and 29 km inland in summer 2012 and 2017, respectively. We also report a hydrodynamic feedback that suppresses channel growth under thicker ice inland and allows water to be stored in the distributed system. At the terminus, we find hydrodynamic feedbacks exert a major control on calving through their impact on velocity. We show that 2012 marked a year in which Store Glacier developed a fully channelised drainage system, unlike 2017, where it remained only partially developed. This contrast in modelled behaviour indicates that tidewater glaciers can experience a strong hydrological, as well as oceanic, control, which is consistent with observations showing glaciers switching between types of behaviour. The fully coupled nature of the model allows us to demonstrate the likely lack of any hydrological or ice-dynamic memory at Store Glacier.

Permchecker: a toolchain for debugging memory managers with typestate

Dynamic memory managers are a crucial component of almost every modern software system. In addition to implementing efficient allocation and reclamation, memory managers provide the essential abstraction of memory as distinct objects, which underpins the properties of memory safety and type safety. Bugs in memory managers, while not common, are extremely hard to diagnose and fix. One reason is that their implementations often involve tricky pointer calculations, raw memory manipulation, and complex memory state invariants. While these properties are often documented, they are not specified in any precise, machine-checkable form. A second reason is that memory manager bugs can break the client application in bizarre ways that do not immediately implicate the memory manager at all. A third reason is that existing tools for debugging memory errors, such as Memcheck, cannot help because they rely on correct allocation and deallocation information to work. In this paper we present Permchecker, a tool designed specifically to detect and diagnose bugs in memory managers. The key idea in Permchecker is to make the expected structure of the heap explicit by associating typestates with each piece of memory. Typestate captures elements of both type (e.g., page, block, or cell) and state (e.g., allocated, free, or forwarded). Memory manager developers annotate their implementation with information about the expected typestates of memory and how heap operations change those typestates. At runtime, our system tracks the typestates and ensures that each memory access is consistent with the expected typestates. This technique detects errors quickly, before they corrupt the application or the memory manager itself, and it often provides accurate information about the reason for the error. The implementation of Permchecker uses a combination of compile-time annotation and instrumentation, and dynamic binary instrumentation (DBI). Because the overhead of DBI is fairly high, Permchecker is suitable for a testing and debugging setting and not for deployment. It works on a wide variety of existing systems, including explicit malloc/free memory managers and garbage collectors, such as those found in JikesRVM and OpenJDK. Since bugs in these systems are not numerous, we developed a testing methodology in which we automatically inject bugs into the code using bug patterns derived from real bugs. This technique allows us to test Permchecker on hundreds or thousands of buggy variants of the code. We find that Permchecker effectively detects and localizes errors in the vast majority of cases; without it, these bugs result in strange, incorrect behaviors usually long after the actual error occurs.

Disconnection between Rat’s Left and Right Hemisphere Impairs Short-Term Memory but Not Long-Term Memory

Split-brain experiments, which have been actively conducted since the twentieth century, have provided a great deal of insight into functional asymmetry and inter-hemispheric interactions. However, how communication between the left and right hemispheres directly contributes to memory formation is still poorly understood. To address this issue, we cut the rat commissural fibers prior to performing behavioral tests, which consisted of two short-term and two long-term memory tasks. The result showed that cutting the commissural fibers impairs short-term memory but not long-term memory. This suggests that the left-right hemispheric interaction through the commissural fibers contributes to the appropriate formation of short-term memory, but not that of long-term memory. Our findings would help to elucidate dynamic memory formation between the two hemispheres and contribute to the development of therapeutics for some neurological diseases which cause a reduction in the inter-hemispheric interaction.

Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains. The technique balances memory by detecting pseudo-domains, representing different style clusters within the data stream. Evaluation of two different tasks, cardiac segmentation in magnetic resonance imaging and lung nodule detection in computed tomography, demonstrate a consistent advantage of the method.