The Hippocampus is Preferentially Associated with Memory for Spatial Context

The existence of a functional-anatomic dissociation for retrieving item versus contextual information within subregions of the medial temporal lobe (MTL) is currently under debate. We used a spatial source memory paradigm during event-related functional magnetic resonance imaging to investigate this issue. At study, abstract shapes were presented to the left or right of fixation. During test, old and new shapes were presented at fixation. Participants responded whether each shape had been previously presented on the “left,” the “right,” or was “new.” Activity associated with contextual memory (i.e., source memory) was isolated by contrasting accurate versus inaccurate memory for spatial location. Item-memory-related activity was isolated by contrasting accurate item recognition without contextual memory with forgotten items. Source memory was associated with activity in the hippocampus and parahippocampal cortex. Although item memory was not associated with unique MTL activity at our original threshold, a region-of-interest (ROI) analysis revealed item-memory-related activity in the perirhinal cortex. Furthermore, a functional-anatomic dissociation within the parietal cortex for retrieving item and contextual information was not found in any of three ROIs. These results support the hypothesis that specific subregions in the MTL are associated with item memory and memory for context

Accuracy and Timeliness in ML Based Activity Recognition

While recent Machine Learning (ML) based techniques for activity recognition show great promise, there remain a number of questions with respect to the relative merits of these techniques. To provide a better understanding of the relative strengths of contemporary Activity Recognition methods, in this paper we present a comparative analysis of Hidden Markov Model, Bayesian, and Support Vector Machine based human activity recognition models. The study builds on both pre-existing and newly annotated data which includes interleaved activities. Results demonstrate that while Support Vector Machine based techniques perform well for all data sets considered, simple representations of sensor histories regularly outperform more complex count based models

A Comparative Study of the Effect of Sensor Noise on Activity Recognition Models

To provide a better understanding of the relative strengths of Machine Learning based Activity Recognition methods, in this paper we present a comparative analysis of the robustness of three popular methods with respect to sensor noise. Specifically we evaluate the robustness of Naive Bayes classifier, Support Vector Machine, and Random Forest based activity recognition models in three cases which span sensor errors from dead to poorly calibrated sensors. Test data is partially synthesized from a recently annotated activity recognition corpus which includes both interleaved activities and a range of both temporally long and short activities. Results demonstrate that the relative performance of Support Vector Machine classifiers over Naive Bayes classifiers reduces in noisy sensor conditions, but that overall the Random Forest classifier provides best activity recognition accuracy across all noise conditions synthesized in the corpus. Moreover, we find that activity recognition is equally robust across classification techniques with the relative performance of all models holding up under almost all sensor noise conditions considered

Some Sources Of Variation In Structural Characteristics Of Douglas-Fir Bark

This study examines variations in structure and formation of Douglas-fir bark. Development of a classification system based on the external appearance of the bark surface that would correlate with anatomical characteristics of the bark was not possible. Modification of bark by fungi was observed to occur in several specific ways, such as attacking cell walls of sclereids, removing contents from lumina of various cell types, and affecting formation of cork layers in regions associated with radial checks and fissures in the bark

Language-Driven Region Pointer Advancement for Controllable Image Captioning

Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the advancement step as a natural part of the language structure via a NEXT-token, motivated by a strong correlation to the sentence structure in the training data. We find that our timing agrees with the ground-truth timing in the Flickr30k Entities test data with a precision of 86.55% and a recall of 97.92%. Our model implementing this technique improves the state-of-the-art on standard captioning metrics while additionally demonstrating a considerably larger effective vocabulary size

Evaluation of a Substitution Method for Idiom Transformation in Statistical Machine Translation

We evaluate a substitution based technique for improving Statistical Machine Translation performance on idiomatic multiword expressions. The method operates by performing substitution on the original idiom with its literal meaning before translation, with a second substitution step replacing literal meanings with idioms following translation. We detail our approach, outline our implementation and provide an evaluation of the method for the language pair English/Brazilian-Portuguese. Our results show improvements in translation accuracy on sentences containing either morphosyntactically constrained or unconstrained idioms. We discuss the consequences of our results and outline potential extensions to this process

Entity-Grounded Image Captioning

An urgent limitation in current Image Captioning models is their tendency to produce generic captions that avoid the interesting detail which makes each image unique. To address this limitation, we propose an approach that enforces a stronger alignment between image regions and specific segments of text. The model architecture is composed of a visual region proposer, a region-order planner and a region-guided caption generator. The region-guided caption generator incorporates a novel information gate which allows visual and textual input of different frequencies and dimensionalities in a Recurrent Neural Network