arXiv:2402.04536v3 Announce Type: replace-cross Abstract: We introduce GEOTACT, the first robotic system capable of grasping and retrieving objects of potentially unknown shapes buried in a granular environment. While important in many applications, ranging from mining and exploration to search and rescue, this type of interaction with granular media is difficult due to the uncertainty stemming […]
GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
arXiv:2501.07890v3 Announce Type: replace-cross Abstract: Traditional Mixture-of-Experts (MoE) networks benefit from utilizing multiple smaller expert models as opposed to a single large network. However, these experts typically operate independently, leaving a question open about whether interconnecting these models could enhance the performance of MoE networks. In response, we introduce GRAPHMOE, a novel method aimed at […]
FGDCC: Fine-Grained Deep Cluster Categorization — A Framework for Intra-Class Variability Problems in Plant Classification
arXiv:2512.19960v1 Announce Type: new Abstract: Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially when such classes are also underrepresented, which is a very common scenario in Fine-Grained […]
Improving Local Training in Federated Learning via Temperature Scaling
arXiv:2401.09986v3 Announce Type: replace-cross Abstract: Federated learning is inherently hampered by data heterogeneity: non-i.i.d. training data over local clients. We propose a novel model training approach for federated learning, FLex&Chill, which exploits the Logit Chilling method. Through extensive evaluations, we demonstrate that, in the presence of non-i.i.d. data characteristics inherent in federated learning systems, this […]
Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injection
arXiv:2512.20140v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated effectiveness as zero-shot time series (TS) forecasters. The key challenge lies in tokenizing TS data into textual representations that align with LLMs’ pre-trained knowledge. While existing work often relies on fine-tuning specialized modules to bridge this gap, a distinct, yet challenging, paradigm aims to […]
Methods for Analyzing RNA Pseudoknots via Chord Diagrams and Intersection Graphs
arXiv:2512.19939v1 Announce Type: new Abstract: RNA molecules are known to form complex secondary structures including pseudoknots. A systematic framework for the enumeration, classification and prediction of secondary structures is critical to determine the biological significance of the molecular configurations of RNA. Chord diagrams are mathematical objects widely used to represent RNA secondary structures and to […]
A Bidirectional Gated Recurrent Unit Model for PUE Prediction in Data Centers
arXiv:2512.20161v1 Announce Type: new Abstract: Data centers account for significant global energy consumption and a carbon footprint. The recent increasing demand for edge computing and AI advancements drives the growth of data center storage capacity. Energy efficiency is a cost-effective way to combat climate change, cut energy costs, improve business competitiveness, and promote IT and […]
Zero-Shot Segmentation through Prototype-Guidance for Multi-Label Plant Species Identification
arXiv:2512.19957v1 Announce Type: new Abstract: This paper presents an approach developed to address the PlantClef 2025 challenge, which consists of a fine-grained multi-label species identification, over high-resolution images. Our solution focused on employing class prototypes obtained from the training dataset as a proxy guidance for training a segmentation Vision Transformer (ViT) on the test set […]
Concept Generalization in Humans and Large Language Models: Insights from the Number Game
arXiv:2512.20162v1 Announce Type: new Abstract: We compare human and large language model (LLM) generalization in the number game, a concept inference task. Using a Bayesian model as an analytical framework, we examined the inductive biases and inference strategies of humans and LLMs. The Bayesian model captured human behavior better than LLMs in that humans flexibly […]
Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
arXiv:2512.19937v1 Announce Type: new Abstract: Recent research has explored using very large language models (LLMs) as proxies for humans in tasks such as simulation, surveys, and studies. While LLMs do not possess a human psychology, they often can emulate human behaviors with sufficiently high fidelity to drive simulations to test human behavioral hypotheses, exhibiting more […]