Recursive Processing of Nested Structures in Monkeys? Two alternative accounts

Ferrigno et al. [2020] introduced an ingenious task to investigate recursion in human and non-human primates. American adults, Tsimane adults, and 3-5 year-old children successfully performed the task. Macaque monkeys required additional training, but two out of three eventually showed good generalization and scored above many Tsimane and child participants. Moreover, when tested on sequences composed of new bracket signs, the monkeys still showed good performance. The authors thus concluded that recursive nesting is not unique to humans. Here, we dispute the claim by showing that at least two alternative interpretations remain tenable. We first examine this conclusion in light of recent findings from modern artificial recurrent neural networks (RNNs), regarding how these networks encode sequences. We show that although RNNs, like monkeys, succeed on demanding generalization tasks as in Ferrigno et al., the underlying neural mechanisms are not recursive. Moreover, we show that when the networks are tested on sequences with deeper center-embedded structures compared to training, the networks fail to generalize. We then discuss an additional interpretation of the results in light of a simple model of sequence memory.

Spatial Component Analysis to Improve Mineral Estimation Using Sentinel-2 Band Ratio: Application to a Greek Bauxite Residue

Remote sensing can be fruitfully used in the characterization of metals within stockpiles and tailings, produced from mining activities. Satellite information, in the form of band ratio, can act as an auxiliary variable, with a certain correlation with the ground primary data. In the presence of this auxiliary variable, modeled with nested structures, the spatial components without correlation can be filtered out, so that the useful correlation with ground data grows. This paper investigates the possibility to substitute in a co-kriging system, the whole band ratio information, with only the correlated components. The method has been applied over a bauxite residues case study and presents three estimation alternatives: ordinary kriging, co-kriging, component co-kriging. Results have shown how using the most correlated component reduces the estimation variance and improves the estimation results. In general terms, when a good correlation with ground samples exists, co-kriging of the satellite band-ratio Component improves the reconstruction of mineral grade distribution, thus affecting the selectivity. On the other hand, the use of the components approach exalts the distance variability.

Fabrication of Three-Dimensional Microvalves of Internal Nested Structures Inside Fused Silica

Nested structures inside the hard material play a pivotal role in the microfluidics systems, such as the microvalve and the micropump. In this article, we demonstrate a novel and facile method of fabricating nested structures inside the fused silica with a two-step process femtosecond laser wet etching (FLWE) process. Inside fused silica, a spherical structure was made with a diameter of nearly 80 µm in a square chamber. In addition, we designed a simple microvalve with this sphere controlling the current’s flow. The novel microvalve structure can be easily integrated into the functional microfluidics systems and will be widely applied in the Lab-on-chip (LOC) system.

Recursively Binary Modification Model for Nested Named Entity Recognition

Recently, there has been an increasing interest in identifying named entities with nested structures. Existing models only make independent typing decisions on the entire entity span while ignoring strong modification relations between sub-entity types. In this paper, we present a novel Recursively Binary Modification model for nested named entity recognition. Our model utilizes the modification relations among sub-entities types to infer the head component on top of a Bayesian framework and uses entity head as a strong evidence to determine the type of the entity span. The process is recursive, allowing lower-level entities to help better model those on the outer-level. To the best of our knowledge, our work is the first effort that uses modification relation in nested NER task. Extensive experiments on four benchmark datasets demonstrate that our model outperforms state-of-the-art models in nested NER tasks, and delivers competitive results with state-of-the-art models in flat NER task, without relying on any extra annotations or NLP tools.

Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition

Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is usually difficult to detect entities with nested structures. The span-based method that can easily detect nested entities in different subsequences is naturally suitable for the nested NER problem. However, previous span-based methods have two main issues. First, classifying all subsequences is computationally expensive and very inefficient at inference. Second, the span-based methods mainly focus on learning span representations but lack of explicit boundary supervision. To tackle the above two issues, we propose a boundary enhanced neural span classification model. In addition to classifying the span, we propose incorporating an additional boundary detection task to predict those words that are boundaries of entities. The two tasks are jointly trained under a multitask learning framework, which enhances the span representation with additional boundary supervision. In addition, the boundary detection model has the ability to generate high-quality candidate spans, which greatly reduces the time complexity during inference. Experiments show that our approach outperforms all existing methods and achieves 85.3, 83.9, and 78.3 scores in terms of F1 on the ACE2004, ACE2005, and GENIA datasets, respectively.

STRUCTURE OF TEMPLE COMPLEXES UNDER CONDITIONS OF COMPLEX RELIEF AND DESIGN PRINCIPLES

The main direction of the research includes the analysis of the structure of temple complexes in difficult terrain. The influence of landscape morphology on the specifics of the structure and architecture of religious buildings is considered. One of the important components is the historical outline of the construction of temple complexes as unique events, as well as time as one of the main “building materials”. Based on the analysis, the design principles were developed: uniqueness, regional affiliation, continuity, contextuality, spatial narrative, structurality, geomorphological certainty, genios loci (Greek. genius of place), integrity, sacredness, nested structures, koinos bios (Greek. common life).