Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking

This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications

Attentional capture by meaning: A multi-level modelling study

We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models

Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning

An important open question pertaining to the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge, and external unstructured knowledge. Most existing prompting methods either rely solely on one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. In this work, we overcome these limitations by introducing a novel semi-structured prompting approach that seamlessly integrates the model's parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. Experimental results on open-domain multi-hop question answering datasets demonstrate that our prompting method significantly surpasses existing techniques, even exceeding those which require fine-tuning

Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering

Answering time-sensitive questions from long documents requires temporal reasoning over the times in questions and documents. An important open question is whether large language models can perform such reasoning solely using a provided text document, or whether they can benefit from additional temporal information extracted using other systems. We address this research question by applying existing temporal information extraction systems to construct temporal graphs of events, times, and temporal relations in questions and documents. We then investigate different approaches for fusing these graphs into Transformer models. Experimental results show that our proposed approach for fusing temporal graphs into input text substantially enhances the temporal reasoning capabilities of Transformer models with or without fine-tuning. Additionally, our proposed method outperforms various graph convolution-based approaches and establishes a new state-of-the-art performance on SituatedQA and three splits of TimeQA.Comment: EMNLP 2023 Finding

Understanding mechanisms of genetic risk for adolescent internalizing and externalizing problems: The mediating role of parenting and personality

Genetic predispositions play an important role in the development of internalizing and externalizing behaviors. Understanding the mechanisms through which genetic risk unfolds to influence these developmental outcomes is critical for developing prevention and intervention efforts, capturing key elements of Irv's research agenda and scientific legacy. In this study, we examined the role of parenting and personality in mediating the effect of genetic risk on adolescents' major depressive disorder and conduct disorder symptoms. Longitudinal data were drawn from a sample of 709 European American adolescents and their mothers from the Collaborative Studies on Genetics of Alcoholism. Results from multivariate path analysis indicated that adolescents' depressive symptoms genome-wide polygenic scores (DS_GPS) predicted lower parental knowledge, which in turn was associated with more subsequent major depressive disorder and conduct disorder symptoms. Adolescents' DS_GPS also had indirect effects on these outcomes via personality, with a mediating effect via agreeableness but not via other dimensions of personality. Findings revealed that the pattern of associations was similar across adolescent gender. Our findings emphasize the important role of evocative gene-environment correlation processes and intermediate phenotypes in the pathways of risk from genetic predispositions to complex adolescent outcomes

Understanding the Clean Interface between Covalent Si and Ionic Al2O3

The atomic and electronic structures of the (001)-Si/(001)-gamma-Al2O3 heterointerface are investigated by first principles total energy calculations combined with a newly developed "modified basin-hopping" method. It is found that all interface Si atoms are fourfold coordinated due to the formation of Si-O and unexpected covalent Si-Al bonds in the new abrupt interface model. And the interface has perfect electronic properties in that the unpassivated interface has a large LDA band gap and no gap levels. These results show that it is possible to have clean semiconductor-oxide interfaces

Superconductivity in Ca-doped graphene

Graphene, a zero-gap semimetal, can be transformed into a metallic, semiconducting or insulating state by either physical or chemical modification. Superconductivity is conspicuously missing among these states despite considerable experimental efforts as well as many theoretical proposals. Here, we report superconductivity in calcium-decorated graphene achieved by intercalation of graphene laminates that consist of well separated and electronically decoupled graphene crystals. In contrast to intercalated graphite, we find that Ca is the only dopant that induces superconductivity in graphene laminates above 1.8 K among intercalants used in our experiments such as potassium, caesium and lithium. Ca-decorated graphene becomes superconducting at ~ 6 K and the transition temperature is found to be strongly dependent on the confinement of the Ca layer and the induced charge carrier concentration. In addition to the first evidence for superconducting graphene, our work shows a possibility of inducing and studying superconductivity in other 2D materials using their laminates

Experimental investigation of cutting vibration during micro-end-milling of the straight groove

Micro-end-milling is a cutting technology that removes redundant material from machined workpieces by small-diameter end mills, and is widely used to manufacture miniature complex parts. During micro-end-milling, the cutting vibration caused by weak tool rigidity and high spindle speed is known as a key factor for decreasing machined quality and accelerating tool failure. This study reports on experiments of micro-end-milling of the straight groove for AISI 1045 steel. The waveform characteristics of acceleration vibration were revealed, the relationship between acceleration and milling parameters were analyzed and two types of relationship models were developed. The results show that, during micro-end-milling of the straight groove, the components of acceleration vibration from largest to smallest are in turn the transverse acceleration αY, the feed acceleration αX and the axial acceleration αZ. Compared with feed velocity vf and axial depth of cut ap, the spindle speed n has the highest influence on cutting vibration. The response surface model of acceleration vibration was shown to have a higher prediction accuracy compared to the power function model and is more suitable for the prediction and control of cutting vibration during micro-end-milling