The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or language translation. While tools like GitHub Copilot are already in widespread use in interactive settings, automated approaches require a higher level of reliability before being usable in industrial practice. In this paper, we focus on three aspects that directly influence the quality of the results: a) the effect of automated feedback loops, b) the choice of Large Language Model (LLM), and c) the influence of behavior-preserving code changes. We study the effect of these three variables on an automated C-to-Rust translation system. Code translation from C to Rust is an attractive use case in industry due to Rust's safety guarantees. The translation system is based on a generate-and-check pattern, in which Rust code generated by the LLM is automatically checked for compilability and behavioral equivalence with the original C code. For negative checking results, the LLM is re-prompted in a feedback loop to repair its output. These checks also allow us to evaluate and compare the respective success rates of the translation system when varying the three variables. Our results show that without feedback loops LLM selection has a large effect on translation success. However, when the translation system uses feedback loops the differences across models diminish. We observe this for the average performance of the system as well as its robustness under code perturbations. Finally, we also identify that diversity provided by code perturbations can even result in improved system performance.

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has been growing interest in modular neural architectures that represent the data using sparsely interacting modules. These models can be more robust out-of-distribution, computationally efficient, and capable of sample-efficient adaptation to new data. However, they tend to make domain-specific assumptions about the data, and present challenges in how module behavior (i.e., parameterization) and connectivity (i.e., their layout) can be jointly learned. In this work, we introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) that jointly learns the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs are best understood as the combination of two systems that are jointly trained end-to-end: one that determines the module configuration and the other that executes it on an input. We demonstrate qualitatively that NACs learn diverse and meaningful module configurations on the Natural Language and Visual Reasoning for Real (NLVR2) dataset without additional supervision. Quantitatively, we show that by incorporating modularity in this way, NACs improve upon a strong non-modular baseline in terms of low-shot adaptation on CIFAR and Caltech-UCSD Birds dataset (CUB) by about 10 percent, and OOD robustness on Tiny ImageNet-R by about 2.5 percent. Further, we find that NACs can achieve an 8x speedup at inference time while losing less than 3 percent performance. Finally, we find NACs to yield competitive results on diverse data modalities spanning point-cloud classification, symbolic processing and text-classification from ASCII bytes, thereby confirming its general purpose nature.

For various applications such as formation of electronically coupled truck convoys, status monitoring of freight trains, and operation of production lines, a line topology is a natural and suitable network structure. It is natural, because the physical layout in these applications is linear; it is suitable, because it supports the functionality of these applications, e.g. fast detection of convoy splits, network clustering, and routing between network nodes. In this paper, we propose an approach for the dynamic establishment and repair of an overlay line topology in wireless networks. To form this topology, only stable bidirectional links, i.e. links that are sufficiently reliable in both directions, are selected. For this purpose, we present an approach for the dynamic detection and statistical assessment of links. We have devised a protocol for both approaches, and have implemented, deployed, and tested this protocol on an industrial hardware platform using Bluetooth Low Energy.

Among the many new phenomena that have arisen since the divestiture of AT&T in the United States, and the liberalization of information technology markets worldwide, the emergence of consortia explicitly related to the standards setting process. While R&D consortia and user groups have existed for many years, consortia in the standards-setting process possess unique properties. This article develops a classification of the standard-setting consortia and develops a theoretical basis for their emergence. This theory is based on the confluence of two classical economic theories: political theory and the theory of markets where network externalities exist. We also consider the implications of these consortia on the standards-setting process from theoretical and practical perspectives. © 1992 John Wiley & Sons, Inc.