Visual Concept Reasoning Networks

A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties. Dependencies or interactions between these representations are typically defined by dense and local operations, however, without any adaptiveness or high-level reasoning. In this work, we propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. We associate each branch with a visual concept and derive a compact concept state by selecting a few local descriptors through an attention module. These concept states are then updated by graph-based interaction and used to adaptively modulate the local descriptors. We describe our proposed model by split-transform-attend-interact-modulate-merge stages, which are implemented by opting for a highly modularized architecture. Extensive experiments on visual recognition tasks such as image classification, semantic segmentation, object detection, scene recognition, and action recognition show that our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%.Comment: Preprin

Selective Token Generation for Few-shot Natural Language Generation

Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. Among them, additive learning that incorporates a task-specific adapter on top of the fixed large-scale PLM has been popularly used in the few-shot setting. However, this added adapter is still easy to disregard the knowledge of the PLM especially for few-shot natural language generation (NLG) since an entire sequence is usually generated by only the newly trained adapter. Therefore, in this work, we develop a novel additive learning algorithm based on reinforcement learning (RL) that selectively outputs language tokens between the task-general PLM and the task-specific adapter during both training and inference. This output token selection over the two generators allows the adapter to take into account solely the task-relevant parts in sequence generation, and therefore makes it more robust to overfitting as well as more stable in RL training. In addition, to obtain the complementary adapter from the PLM for each few-shot task, we exploit a separate selecting module that is also simultaneously trained using RL. Experimental results on various few-shot NLG tasks including question answering, data-to-text generation and text summarization demonstrate that the proposed selective token generation significantly outperforms the previous additive learning algorithms based on the PLMs.Comment: COLING 202

Whole Genome Analysis of the Red-Crowned Crane Provides Insight into Avian Longevity

The red-crowned crane (Grus japonensis) is an endangered, large-bodied crane native to East Asia. It is a traditional symbol of longevity and its long lifespan has been confirmed both in captivity and in the wild. Lifespan in birds is known to be positively correlated with body size and negatively correlated with metabolic rate, though the genetic mechanisms for the red-crowned crane's long lifespan have not previously been investigated. Using whole genome sequencing and comparative evolutionary analyses against the grey-crowned crane and other avian genomes, including the long-lived common ostrich, we identified red-crowned crane candidate genes with known associations with longevity. Among these are positively selected genes in metabolism and immunity pathways (NDUFA5, NDUFA8, NUDT12, SOD3, CTH, RPA1, PHAX, HNMT, HS2ST1, PPCDC, PSTK CD8B, GP9, IL-9R, and PTPRC). Our analyses provide genetic evidence for low metabolic rate and longevity, accompanied by possible convergent adaptation signatures among distantly related large and long-lived birds. Finally, we identified low genetic diversity in the red-crowned crane, consistent with its listing as an endangered species, and this genome should provide a useful genetic resource for future conservation studies of this rare and iconic species

Hexa: Self-Improving for Knowledge-Grounded Dialogue System

A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data for such steps are often inaccessible compared to those of dialogue responses as they are unobservable in an ordinary dialogue. To fill in the absence of these data, we develop a self-improving method to improve the generative performances of intermediate steps without the ground truth data. In particular, we propose a novel bootstrapping scheme with a guided prompt and a modified loss function to enhance the diversity of appropriate self-generated responses. Through experiments on various benchmark datasets, we empirically demonstrate that our method successfully leverages a self-improving mechanism in generating intermediate and final responses and improves the performances on the task of knowledge-grounded dialogue generation

Steering Algorithm for a Flexible Microrobot to Enhance Guidewire Control in a Coronary Angioplasty Application

Magnetically driven microrobots have been widely studied for various biomedical applications in the past decade. An important application of these biomedical microrobots is heart disease treatment. In intravascular treatments, a particular challenge is the submillimeter-sized guidewire steering; this requires a new microrobotic approach. In this study, a flexible microrobot was fabricated by the replica molding method, which consists of three parts: (1) a flexible polydimethylsiloxane (PDMS) body, (2) two permanent magnets, and (3) a micro-spring connector. A mathematical model was developed to describe the relationship between the magnetic field and the deformation. A system identification approach and an algorithm were proposed for steering. The microrobot was fabricated, and the models for steering were experimentally validated under a magnetic field intensity of 15 mT. Limitations to control were identified, and the microrobot was steered in an arbitrary path using the proposed model. Furthermore, the flexible microrobot was steered using the guidewire within a three-dimensional (3D) transparent phantom of the right coronary artery filled with water, to show the potential application in a realistic environment. The flexible microrobot presented here showed promising results for enhancing guidewire steering in percutaneous coronary intervention (PCI)