Disentangling hand and tool processing: distal effects of neuromodulation

The neural processing within a brain region that responds to more than one object category can be separated by looking at the horizontal modulations established by that region, which suggests that local representations can be affected by connections to distal areas, in a category-specific way. Here we first wanted to test whether by applying transcranial direct current stimulation (tDCS) to a region that responds both to hands and tools (posterior middle temporal gyrus; pMTG), while participants performed either a hand- or tool-related training task, we would be able to specifically target the trained category, and thereby dissociate the overlapping neural processing. Second, we wanted to see if these effects were limited to the target area or extended to distal but functionally connected brain areas. After each combined tDCS and training session, participants therefore viewed images of tools, hands, and animals, in an fMRI scanner. Using multivoxel pattern analysis, we found that tDCS stimulation to pMTG indeed improved the classification accuracy between tools vs. animals, but only when combined with a tool training task (not a hand training task). However, surprisingly, tDCS stimulation to pMTG also improved the classification accuracy between hands vs. animals when combined with a tool training task (not a hand training task). Our findings suggest that overlapping but functionally-specific networks can be separated by using a category-specific training task together with tDCS - a strategy that can be applied more broadly to other cognitive domains using tDCS - and demonstrates the importance of horizontal modulations in object-category representations.

Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model

State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel Charge Equilibration (kQEq). This model is based on classical charge equilibration models like QEq, expanded with an environment dependent electronegativity. In contrast to previously reported neural network models with a similar concept, kQEq takes advantage of the linearity of both QEq and Kernel Ridge Regression to obtain a closed-form linear algebra expression for training the models. Furthermore, we avoid the ambiguity of charge partitioning schemes by using dipole moments as reference data. As a first application, we show that kQEq can be used to generate accurate and highly data-efficient models for molecular dipole moments.

HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation

Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance. Recently, a sequence of Transformer-based methodologies emerges in the field of image processing, which brings great generalization and performance in various tasks. On the other hand, traditional CNNs have their own advantages, such as rapid convergence and local representations. Therefore, we analyze a hybrid multimodal segmentation method based on Transformers and CNNs and propose a novel architecture, HybridCTrm network. We conduct experiments using HybridCTrm on two benchmark datasets and compare with HyperDenseNet, a network based on fully CNNs. Results show that our HybridCTrm outperforms HyperDenseNet on most of the evaluation metrics. Furthermore, we analyze the influence of the depth of Transformer on the performance. Besides, we visualize the results and carefully explore how our hybrid methods improve on segmentations.

Multi-granularity Deep Local Representations for Irregular Scene Text Recognition

Recognizing irregular text from natural scene images is challenging due to the unconstrained appearance of text, such as curvature, orientation, and distortion. Recent recognition networks regard this task as a text sequence labeling problem and most networks capture the sequence only from a single-granularity visual representation, which to some extent limits the performance of recognition. In this article, we propose a hierarchical attention network to capture multi-granularity deep local representations for recognizing irregular scene text. It consists of several hierarchical attention blocks, and each block contains a Local Visual Representation Module (LVRM) and a Decoder Module (DM). Based on the hierarchical attention network, we propose a scene text recognition network. The extensive experiments show that our proposed network achieves the state-of-the-art performance on several benchmark datasets including IIIT-5K, SVT, CUTE, SVT-Perspective, and ICDAR datasets under shorter training time.

Multivariate spatial feature selection in fMRI

Multivariate neuroimaging analyses constitute a powerful class of techniques to identify psychological representations. However, not all psychological processes are represented at the same spatial scale throughout the brain. This heterogeneity is apparent when comparing hierarchically organized local representations of perceptual processes to flexible transmodal representations of more abstract cognitive processes such as social and affective operations. An open question is how the spatial scale of analytic approaches interacts with the spatial scale of the representations under investigation. In this article, we describe how multivariate analyses can be viewed as existing on a spatial spectrum, anchored by searchlights used to identify locally distributed patterns of information on one end, whole brain approach used to identify diffuse neural representations at the other and region-based approaches in between. We describe how these distinctions are an important and often overlooked analytic consideration and provide heuristics to compare these different techniques to choose based on the analyst’s inferential goals.

The body and the verb

This paper explores the figurative expression of emotion in Gija, a non-Pama-Nyungan language from the East Kimberley in Western Australia. As in many Australian languages, Gija displays a large number of metaphors of emotion where miscellaneous body parts – frequently, the belly – contribute to the figurative representation of emotions. In addition, in Gija certain verbal constructions describe the experience of emotion via metaphors of physical impact or damage. This second profile of metaphors is far less widespread, in Australia and elsewhere in the world, and has also attracted far fewer descriptions. This article explores both types of metaphors in turn. Body-based metaphors will be discussed first, and we will highlight the specificity of Gija in this respect, so as to offer data that can be compared to other languages, in Australia and elsewhere. The second part of the article will present verbal metaphors. Given that this phenomenon is not yet very well undersood, this account aims to take a first step into documenting a previously unexplored domain in the language thereby contributing to the broader typology that this issue forms a part of. Throughout the text, we also endeavour to connect the discussion of metaphors with local representations and understanding of emotions.

A Tri-Attention Neural Network Model-BasedRecommendation

Heterogeneous information network (HIN), which contains various types of nodes and links, has been applied in recommender systems. Although HIN-based recommendation approaches perform better than the traditional recommendation approaches, they still have the following problems: for example, meta-paths are manually selected, not automatically; meta-path representations are rarely explicitly learned; and the global and local information of each node in HIN has not been simultaneously explored. To solve the above deficiencies, we propose a tri-attention neural network (TANN) model for recommendation task. The proposed TANN model applies the stud genetic algorithm to automatically select meta-paths at first. Then, it learns global and local representations of each node, as well as the representations of meta-paths existing in HIN. After that, a tri-attention mechanism is proposed to enhance the mutual influence among users, items, and their related meta-paths. Finally, the encoded interaction information among the user, the item, and their related meta-paths, which contain more semantic information can be used for recommendation task. Extensive experiments on the Douban Movie, MovieLens, and Yelp datasets have demonstrated the outstanding performance of the proposed approach.

Introduction

This introductory chapter provides an overview of the relationship between information technology and military power. Digital systems now mediate almost every effort to gather, store, display, analyze, and communicate information. As a result, military personnel now have to struggle with their own information systems as much as with the enemy. Local representations of the world must be coordinated with whatever distant reality they represent. When personnel can perceive things that are relevant to their mission, distinguish friend from foe, predict the effects of their operations, and get reliable feedback on the results, then they can fight more effectively. When they cannot do these things, however, then tragedies like friendly fire, civilian deaths, missed opportunities, and other counterproductive actions become more likely. If military organizations are unable to coordinate their representations with reality, then all of their advantages in weaponry or manpower will count for little. The chapter describes the organizational effort to coordinate knowledge and control as information practice. It argues that the quality of practice, and thus military performance, depends on the interaction between strategic problems and organizational solutions.