Iterative Circuit Repair Against Formal Specifications

We present a deep learning approach for repairing sequential circuits against formal specifications given in linear-time temporal logic (LTL). Given a defective circuit and its formal specification, we train Transformer models to output circuits that satisfy the corresponding specification. We propose a separated hierarchical Transformer for multimodal representation learning of the formal specification and the circuit. We introduce a data generation algorithm that enables generalization to more complex specifications and out-of-distribution datasets. In addition, our proposed repair mechanism significantly improves the automated synthesis of circuits from LTL specifications with Transformers. It improves the state-of-the-art by 6.8 percentage points on held-out instances and 11.8 percentage points on an out-of-distribution dataset from the annual reactive synthesis competition

Neural Circuit Synthesis from Specification Patterns

We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study

nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models

A rigorous formalization of desired system requirements is indispensable when performing any verification task. This often limits the application of verification techniques, as writing formal specifications is an error-prone and time-consuming manual task. To facilitate this, we present nl2spec, a framework for applying Large Language Models (LLMs) to derive formal specifications (in temporal logics) from unstructured natural language. In particular, we introduce a new methodology to detect and resolve the inherent ambiguity of system requirements in natural language: we utilize LLMs to map subformulas of the formalization back to the corresponding natural language fragments of the input. Users iteratively add, delete, and edit these sub-translations to amend erroneous formalizations, which is easier than manually redrafting the entire formalization. The framework is agnostic to specific application domains and can be extended to similar specification languages and new neural models. We perform a user study to obtain a challenging dataset, which we use to run experiments on the quality of translations. We provide an open-source implementation, including a web-based frontend

Teaching Temporal Logics to Neural Networks

We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the problem to directly predict a solution, i.e. a trace, to a given LTL formula. The training data is generated with classical solvers, which, however, only provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions to formulas, and that Transformers can predict solutions even to formulas from benchmarks from the literature on which the classical solver timed out. Transformers also generalize to the semantics of the logics: while they often deviate from the solutions found by the classical solvers, they still predict correct solutions to most formulas

Deep Learning for Temporal Logics

Temporal logics are a well established formal specification paradigm to specify the behavior of systems, and serve as inputs to industrial-strength verification tools. We report on current advances in applying deep learning to temporal logical reasoning tasks, showing that models can even solve instances where competitive classical algorithms timed out

Deconstructing 3D Structured Materials by Modern Ultramicrotomy for Multimodal Imaging and Volume Analysis across Length Scales

Based on the rapid advances in additive manufacturing, micro-patterned heterostructures of soft materials have become available that need to be characterized down to the nanoscale. Advanced function-structure relationships are designed by direct 3D structuring of the object and – in the future – fine control over material functionality in 3D will produce complex functional objects. To control their design, fabrication and final structure, morphological and spectroscopical imaging in 3D at nanometer resolution are critically required. With examples of carbon-based objects, it is demonstrated how serial ultramicrotomy, that is, cutting a large number of successive ultrathin sections, can be utilized to gain access to the interior of 3D objects. Array tomography, hierarchical imaging and correlative light and electron microscopy can bridge length scales over several orders of magnitude and provide multimodal information of the sample\u27s inner structure. Morphology data derived from scanning electron microscopy are correlated with spectroscopy in analytical transmission electron microscopy and probe microscopy at nanometer resolution, using TEM-electron energy loss spectroscopy and infrared-scanning-near-field microscopy. The correlation of different imaging modalities and spectroscopy of carbon-based materials in 3D provides a powerful toolbox of complementary techniques for understanding emerging functions from nanoscopic structuring

Gas-phase microsolvation of ubiquitin: investigation of crown ether complexation sites using ion mobility-mass spectrometry.

In this study the gas-phase structure of ubiquitin and its lysine-to-arginine mutants was investigated using ion mobility-mass spectrometry (IM-MS) and electron transfer dissociation-mass spectrometry (ETD-MS). Crown ether molecules were attached to positive charge sites of the proteins and the resulting non-covalent complexes were analysed. Collision induced dissociation (CID) experiments revealed relative energy differences between the wild type and the mutant crown-ether complexes. ETD-MS experiments were performed to identify the crown ether binding sites. Although not all of the binding sites could be revealed, the data confirm that the first crown ether is able to bind to the N-terminus. IM-MS experiments show a more compact structure for specific charge states of wild type ubiquitin when crown ethers are attached. However, data on ubiquitin mutants reveal that only specific lysine residues contribute to the effect of charge microsolvation. A compaction is only observed for one of the investigated mutants, in which the lysine has no proximate interaction partner. On the other hand when the lysine residues are involved in salt bridges, attachment of crown ethers has little effect on the structure

Resilience trinity: safeguarding ecosystem functioning and services across three different time horizons and decision contexts

Ensuring ecosystem resilience is an intuitive approach to safeguard the functioning of ecosystems and hence the future provisioning of ecosystem services (ES). However, resilience is a multi-faceted concept that is difficult to operationalize. Focusing on resilience mechanisms, such as diversity, network architectures or adaptive capacity, has recently been suggested as means to operationalize resilience. Still, the focus on mechanisms is not specific enough. We suggest a conceptual framework, resilience trinity, to facilitate management based on resilience mechanisms in three distinctive decision contexts and time-horizons: i) reactive, when there is an imminent threat to ES resilience and a high pressure to act, ii) adjustive, when the threat is known in general but there is still time to adapt management, and iii) provident, when time horizons are very long and the nature of the threats is uncertain, leading to a low willingness to act. Resilience has different interpretations and implications at these different time horizons, which also prevail in different disciplines. Social ecology, ecology, and engineering are often implicitly focussing on provident, adjustive, or reactive resilience, respectively, but these different notions and of resilience and their corresponding social, ecological, and economic trade-offs need to be reconciled. Otherwise, we keep risking unintended consequences of reactive actions, or shying away from provident action because of uncertainties that cannot be reduced. The suggested trinity of time horizons and their decision contexts could help ensuring that longer-term management actions are not missed while urgent threats to ES are given priority

The ABC130 barrel module prototyping programme for the ATLAS strip tracker

For the Phase-II Upgrade of the ATLAS Detector, its Inner Detector, consisting of silicon pixel, silicon strip and transition radiation sub-detectors, will be replaced with an all new 100 % silicon tracker, composed of a pixel tracker at inner radii and a strip tracker at outer radii. The future ATLAS strip tracker will include 11,000 silicon sensor modules in the central region (barrel) and 7,000 modules in the forward region (end-caps), which are foreseen to be constructed over a period of 3.5 years. The construction of each module consists of a series of assembly and quality control steps, which were engineered to be identical for all production sites. In order to develop the tooling and procedures for assembly and testing of these modules, two series of major prototyping programs were conducted: an early program using readout chips designed using a 250 nm fabrication process (ABCN-25) and a subsequent program using a follow-up chip set made using 130 nm processing (ABC130 and HCC130 chips). This second generation of readout chips was used for an extensive prototyping program that produced around 100 barrel-type modules and contributed significantly to the development of the final module layout. This paper gives an overview of the components used in ABC130 barrel modules, their assembly procedure and findings resulting from their tests.Comment: 82 pages, 66 figure