In this work, we introduce a method for predicting environment steppability -- the ability of a legged robot platform to place a foothold at a particular location in the local environment -- in the image space. This novel environment representation captures this critical geometric property of the local terrain while allowing us to exploit the computational benefits of sensing and planning in the image space. We adapt a primitive shapes-based synthetic data generation scheme to create geometrically rich and diverse simulation scenes and extract ground truth semantic information in order to train a steppability model. We then integrate this steppability model into an existing interleaved graph search and trajectory optimization-based footstep planner to demonstrate how this steppability paradigm can inform footstep planning in complex, unknown environments. We analyze the steppability model performance to demonstrate its validity, and we deploy the perception-informed footstep planner both in offline and online settings to experimentally verify planning performance.

We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base. An ontology is authored by generating Competency Questions (CQ) on knowledge base to discover knowledge scope, extracting relations from CQs, and attempt to replace equivalent relations by their counterpart in Wikidata. To ensure consistency and interpretability in the resulting KG, we ground generation of KG with the authored ontology based on extracted relations. Evaluation on benchmark datasets demonstrates competitive performance in knowledge graph construction task. Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs, which are interoperable with Wikidata semantics for potential knowledge base expansion.

To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring new tasks. Recently, prompt-based methods have emerged as potent alternatives to rehearsal-based strategies, demonstrating strong empirical performance. However, upon analyzing existing prompt-based approaches for CRE, we identified several critical limitations, such as inaccurate prompt selection, inadequate mechanisms for mitigating forgetting in shared parameters, and suboptimal handling of cross-task and within-task variances. To overcome these challenges, we draw inspiration from the relationship between prefix-tuning and mixture of experts, proposing a novel approach that employs a prompt pool for each task, capturing variations within each task while enhancing cross-task variances. Furthermore, we incorporate a generative model to consolidate prior knowledge within shared parameters, eliminating the need for explicit data storage. Extensive experiments validate the efficacy of our approach, demonstrating superior performance over state-of-the-art prompt-based and rehearsal-free methods in continual relation extraction.

Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such as Chain-of-Thought (CoT), assuming the resulting generation will have a more granular exploration and reasoning over the question space and scope. However, such methods struggle with generating outputs that are faithful to the intermediate chain of reasoning produced by the model. On the other end of the spectrum, neuro-symbolic methods such as Faithful CoT (F-CoT) propose to combine LLMs with external symbolic solvers. While such approaches boast a high degree of faithfulness, they usually require a model trained for code generation and struggle with tasks that are ambiguous or hard to formalise strictly. We introduce $\textbf{F}$aithful $\textbf{L}$ogic-$\textbf{A}$ided $\textbf{R}$easoning and $\textbf{E}$xploration ($\textbf{FLARE}$), a novel interpretable approach for traversing the problem space using task decompositions. We use the LLM to plan a solution, soft-formalise the query into facts and predicates using a logic programming code and simulate that code execution using an exhaustive multi-hop search over the defined space. Our method allows us to compute the faithfulness of the reasoning process w.r.t. the generated code and analyse the steps of the multi-hop search without relying on external solvers. Our methods achieve SOTA results on $\mathbf{7}$ out of $\mathbf{9}$ diverse reasoning benchmarks. We also show that model faithfulness positively correlates with overall performance and further demonstrate that $\textbf{FLARE}$ allows pinpointing the decisive factors sufficient for and leading to the correct answer with optimal reasoning during the multi-hop search.

Long-term time series prediction involves forecasting future trends over extended periods based on historical changes. This approach is crucial in various fields such as weather [1], traffic [2], and power [3]. The exceptionally long forecast horizon and the complex correlations between channels pose significant challenges to modeling. Traditional methods often fall short in capturing the sequence and inter-channel relationships. In contrast, deep learning architectures, with their superior fitting capabilities, have emerged as effective tools for addressing long-term time series prediction. Consequently, the primary methodologies in this field have shifted towards deep learning models. The core issue in long time series analysis is extracting dependencies within sequences and correlations across channels, which significantly benefits model performance in multi-channel prediction and robustness. Various methods have been developed to capture this information from time series data. Commonly used techniques include Transformers [4, 5, 6, 7, 8, 9, 10], which apply attention mechanisms to effectively capture correlations both within sequences and across channels; Convolutional Neural Networks (CNN) [11, 12] that use 1D or multidimensional convolutions to capture these dependencies; and structures based on Multilayer Perceptrons[13, 14, 15, 16, 17], such as DLinear, which decompose sequences and apply multiple linear layers to capture sequence correlations.

Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative importance scaling, while preserving interpretability. DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. The method is available in the Python library DADApy.

Connected Vehicles (CVs) can leverage the unique features of 5G and future 6G/NextG networks to enhance Intelligent Transportation System (ITS) services. However, even with advancements in cellular network generations, CV applications may experience communication interruptions in high-mobility scenarios due to frequent changes of serving base station, also known as handovers (HOs). This paper proposes the adoption of Open Radio Access Network (Open RAN/O-RAN) and deep learning models for decision-making to prevent Quality of Service (QoS) degradation due to HOs and to ensure the timely connectivity needed for CV services. The solution utilizes the O-RAN Software Community (OSC), an open-source O-RAN platform developed by the collaboration between the O-RAN Alliance and Linux Foundation, to develop xApps that are executed in the near-Real-Time RIC of OSC. To demonstrate the proposal's effectiveness, an integrated framework combining the OMNeT++ simulator and OSC was created. Evaluations used real-world datasets in urban application scenarios, such as video streaming transmission and over-the-air (OTA) updates. Results indicate that the proposal achieved superior performance and reduced latency compared to the standard 3GPP HO procedure.

Recent works show that these biases may stem from uneven cultural representation in pretraining corpora. This work investigates how pretraining leads to biased culture-conditioned generations by analyzing how models associate entities with cultures based on pretraining data patterns. Additionally, the model favors generating entities with extraordinarily high frequency regardless of the conditioned culture, reflecting biases toward frequent pretraining terms irrespective of relevance. Our findings reflect trends observed specifically within OLMo-7B's pretraining data and are limited to this dataset. We make no claims about whether these results reflect real-world conditions.] In open-ended generative tasks like narrative writing or dialogue, language models often show bias against marginalized social groups based on gender, race, or culture (Gallegos et al., 2024; Manvi et al., 2024; Li et al., 2024b). Cultural bias is particularly notable due to the vast number of cultures to account for. Cultures are often unevenly represented in the pretraining corpora, with some mentioned more frequently than others, irrespective of their real-world prevalence (Li et al., 2024a). Recent studies reveal that models favor entities (Naous et al., 2023) and opinions (Ryan et al., 2024) from frequently represented cultures in pretraining while showing inadequate knowledge and templated answers for less frequent ones (Li et al., 2024b). Such biases in culture-conditioned generations can be linked to studies showing that LLMs' memorization and generalization are constrained by pretraining data imbalances. Zhang et al. (2024) find that these imbalances cause models to overgeneralize to high-frequency knowledge, overshadowing lower-frequency knowledge.

I arrive in Hermanus, a picturesque South African coastal village an hour-and-a-half from Cape Town, at about 11am on a sunny October morning. Ignoring the restaurants and art galleries on the main drag and the throngs of tourists watching southern right whales from the cliff path, I drive straight to the harbour to meet Els Vermeulen, the Belgium-born scientist who heads up the whale unit for the University of Pretoria's Mammal Research Institute. She is waiting for her colleagues to return from the last whale-tagging sortie of the 2024 season. "I would normally be out on the boat with the team," says Vermeulen, who is dressed in a bold geometric print dress and a denim jacket. "But I had to drop my kids at school and couldn't get down here early enough." The water next to the concrete pier is so clear that I can see a giant orange starfish inching its way along the rocky seabed.

In this paper, we explore the paradox of trust and vulnerability in human-machine interactions, inspired by Alexander Reben's BlabDroid project. This project used small, unassuming robots that actively engaged with people, successfully eliciting personal thoughts or secrets from individuals, often more effectively than human counterparts. This phenomenon raises intriguing questions about how trust and self-disclosure operate in interactions with machines, even in their simplest forms. We study the change of trust in technology through analyzing the psychological processes behind such encounters. The analysis applies theories like Social Penetration Theory and Communication Privacy Management Theory to understand the balance between perceived security and the risk of exposure when personal information and secrets are shared with machines or AI. Additionally, we draw on philosophical perspectives, such as posthumanism and phenomenology, to engage with broader questions about trust, privacy, and vulnerability in the digital age. Rapid incorporation of AI into our most private areas challenges us to rethink and redefine our ethical responsibilities.