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Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
Pure functional epidemics
Agent-Based Simulation (ABS) is a methodology in which a system is simulated in a bottom-up approach by modelling the micro interactions of its constituting parts, called agents, out of which the global system behaviour emerges. So far mainly object-oriented techniques and languages have been used in ABS. Using the SIR model of epidemiology, which simulates the spreading of an infectious disease through a population, we demonstrate how to use pure Functional Reactive Programming to implement ABS. With our approach we can guarantee the reproducibility of the simulation at compile time and rule out specific classes of run-time bugs, something that is not possible with traditional object-oriented languages. Also, we found that the representation in a purely functional format is conceptually quite elegant and opens the way to formally reason about ABS
Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
We present Perceiver-VL, a vision-and-language framework that efficiently
handles high-dimensional multimodal inputs such as long videos and text.
Powered by the iterative latent cross-attention of Perceiver, our framework
scales with linear complexity, in contrast to the quadratic complexity of
self-attention used in many state-of-the-art transformer-based models. To
further improve the efficiency of our framework, we also study applying
LayerDrop on cross-attention layers and introduce a mixed-stream architecture
for cross-modal retrieval. We evaluate Perceiver-VL on diverse video-text and
image-text benchmarks, where Perceiver-VL achieves the lowest GFLOPs and
latency while maintaining competitive performance. In addition, we also provide
comprehensive analyses of various aspects of our framework, including
pretraining data, scalability of latent size and input size, dropping
cross-attention layers at inference to reduce latency, modality aggregation
strategy, positional encoding, and weight initialization strategy. Our code and
checkpoints are available at: https://github.com/zinengtang/Perceiver_VLComment: WACV 2023 (first two authors contributed equally
Towards a Uniform Theory of Effectful State Machines
Using recent developments in coalgebraic and monad-based semantics, we
present a uniform study of various notions of machines, e.g. finite state
machines, multi-stack machines, Turing machines, valence automata, and weighted
automata. They are instances of Jacobs' notion of a T-automaton, where T is a
monad. We show that the generic language semantics for T-automata correctly
instantiates the usual language semantics for a number of known classes of
machines/languages, including regular, context-free, recursively-enumerable and
various subclasses of context free languages (e.g. deterministic and real-time
ones). Moreover, our approach provides new generic techniques for studying the
expressivity power of various machine-based models.Comment: final version accepted by TOC
Energy Efficient Window Development
The paper investigates the development of energy efficient windows in the past 30 years. The focus is on the development and interlinkages among technology, actors´ interaction and market diffusion in a broader policy context. The paper shows that in singular development cycles, different factors and the interfaces among these factors influenced the improvement and penetration of energy efficient window technologies. Such factors includes a) surrounding factors, such as climate characteristics, oil crisis and international concerns and strategies, b) policy instruments, like building codes and technology procurement programs, as well as c) industry initiatives, including niche market strategies
Nonterrestrial utilization of materials: Automated space manufacturing facility
Four areas related to the nonterrestrial use of materials are included: (1) material resources needed for feedstock in an orbital manufacturing facility, (2) required initial components of a nonterrestrial manufacturing facility, (3) growth and productive capability of such a facility, and (4) automation and robotics requirements of the facility
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Whether current or near-term AI systems could be conscious is a topic of
scientific interest and increasing public concern. This report argues for, and
exemplifies, a rigorous and empirically grounded approach to AI consciousness:
assessing existing AI systems in detail, in light of our best-supported
neuroscientific theories of consciousness. We survey several prominent
scientific theories of consciousness, including recurrent processing theory,
global workspace theory, higher-order theories, predictive processing, and
attention schema theory. From these theories we derive "indicator properties"
of consciousness, elucidated in computational terms that allow us to assess AI
systems for these properties. We use these indicator properties to assess
several recent AI systems, and we discuss how future systems might implement
them. Our analysis suggests that no current AI systems are conscious, but also
suggests that there are no obvious technical barriers to building AI systems
which satisfy these indicators
VeriGen: A Large Language Model for Verilog Code Generation
In this study, we explore the capability of Large Language Models (LLMs) to
automate hardware design by generating high-quality Verilog code, a common
language for designing and modeling digital systems. We fine-tune pre-existing
LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We
evaluate the functional correctness of the generated Verilog code using a
specially designed test suite, featuring a custom problem set and testing
benches. Here, our fine-tuned open-source CodeGen-16B model outperforms the
commercial state-of-the-art GPT-3.5-turbo model with a 1.1% overall increase.
Upon testing with a more diverse and complex problem set, we find that the
fine-tuned model shows competitive performance against state-of-the-art
gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41%
improvement in generating syntactically correct Verilog code across various
problem categories compared to its pre-trained counterpart, highlighting the
potential of smaller, in-house LLMs in hardware design automation.Comment: arXiv admin note: text overlap with arXiv:2212.1114
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