Tokenization inherently affects the way information is processed within Large Language Models (LLMs), with dramatic implications for performance. Common strategies such as Byte Pair Encoding (BPE) used in the likes of GPT-2 tend to output linguistically inconsequential segments simply because they operate statistically, posing a risk of impeding predictability of models (quantified by perplexity). This work explores tokenization techniques to get lower perplexity. We present AMEST, a new hybrid tokenization that leverages both linguistic insight (data-driven morphological segmentation) and statistical soundness (probabilistic sampling of Unigram), with primary emphasis on favouring morphemes for familiar terms and sampling to handle unknown ones. We measured by training models of GPT-2 Small from scratch across eight varied sets of data to demonstrate that AMEST reliably registers significant perplexity decreases, improvements in convergence and competitive throughput vs. GPT-2 BPE and Baseline Unigram tokenizer. Our analysis, including ablation studies and cross-domain evaluations, confirms that the synergy between morphological awareness and probabilistic fallback is key to these improvements.
Thanks to recent advances in deep learning based algorithms, humanoid social robots are increasingly exhibiting human-like behaviors. In this context, the analysis of soft biometrics, particularly emotion recognition, is crucial for enhancing communication between social robots and humans, facilitating emotion-aware dialogues. In light of these considerations, we propose a multimodal emotion recognition system tailored for social robotics applications. The system processes both video and audio data to classify the emotion in one among six different classes, employing 3D convolutional operations that eliminate the need for transformer-based architecture, effectively reducing the model's size and making the network able to run over low power embedded devices mounted directly on board of the robot. The proposed approach was trained on the CREMA-D dataset and demonstrates impressive performance when compared to video-only and audio-only counterparts, outperforming state-of-the-art methods both unimodal and multimodal.
Data valuation and monetization are becoming increasingly important across domains such as eXtended Reality (XR) and digital media. In the context of 3D scene reconstruction from a set of images -whether casually or professionally captured - not all inputs contribute equally to the final output. Neural Radiance Fields (NeRFs) enable photorealistic 3D reconstruction of scenes by optimizing a volumetric radiance field given a set of images. However, in-the-wild scenes often include image captures of varying quality, occlusions, and transient objects, resulting in uneven utility across inputs. In this paper we propose a method to quantify the individual contribution of each image to NeRF-based reconstructions of in-the-wild image sets. Contribution is assessed through reconstruction quality metrics based on PSNR and MSE. We validate our approach by removing low-contributing images during training and measuring the resulting impact on reconstruction fidelity.
Fungi are living organisms that inhabit the soil and can cause severe root diseases in various crops. These diseases result from the interaction between the pathogen, the host, and the biotic and abiotic components of the soil. These fungi are generally resilient and remain inactive in the absence of the host plant. However, in the presence of a vulnerable host in the rhizosphere or the absence of adequate nutrients, these resilient structures infect the plant. The fungi in the soil can spread to other plants, in some cases without inducing symptoms that may be visible, and it can survive in crop residues. Therefore, this set of characteristics of biology, ecology, and resilience in the soil results in a complex management situation in cases of root diseases caused by fungi, which mainly cause root rot. Analysis methods are usually based on manually scoring the severity of the disease. The method proposed in this work was to create a system capable of detecting the presence of fungi that cause root rot leveraging a state-of-the-art convolutional neural network model, a YOLO-based object detection framework, and to evaluate its reliability and accuracy by comparing it with other recent work on predicting the state of plant health. The proposed network was trained with 125 samples from 15 bean genotype lines. We obtained a confidence level of 85
