Abstract

We present a diffusion-based generative model for coordinated trajectory planning in multi-UAV swarms. The proposed method represents each UAV’s trajectory in a Bernstein polynomial coefficient space and employs a de- noising diffusion process with self-attention layers to generate diverse, feasible motion plans. A safety filter is integrated into the generation pipeline to refine candidate trajectories, enforcing inter-drone collision avoidance and other feasibility constraints. The model is trained offline on a large set of expert demonstration trajectories, eliminating the need for reinforcement learning and manual reward function design. In experiments with a 16-UAV swarm using a dataset of collision-free trajectories, the approach achieved a high success rate in producing safe and smooth flight paths. These results demonstrate that the learned planner can rapidly generate a diverse set of smooth, collision-free trajectories for the swarm. Keywords- Swarm, Diffusion, UAV path planning, Joint Opti- mization

Abstract

Machine Translation (MT) revolutionizes cross-lingual communication but is prone to errors, necessitating thorough evaluation for enhancement. Translation quality can be assessed by humans and automatic evaluation metrics. Human evaluation, though valuable, is costly and subject to limitations in scalability and consistency. While automated metrics supplement manual evaluations, this field still has considerable potential for development. However, there exists prior survey work on automatic evaluation metrics, it is worth noting that most of these are focused on resource-rich languages, leaving a significant gap in evaluating MT outputs across other language families. To bridge this gap, we present an exhaustive survey, encompassing discussions on MT meta-evaluation datasets, human assessments, and diverse metrics. We categorize both human and automatic evaluation approaches, and offer decision trees to aid in selecting the appropriate approach. Additionally, we evaluate sentences across languages, domains and linguistic features, and further meta-evaluate the metrics by correlating them with human scores. We critically examine the limitations and challenges inherent in current datasets and evaluation approaches. We propose suggestions for future research aimed at enhancing MT evaluation, including the importance of diverse and well-distributed datasets, the refinement of human evaluation methodologies, and the development of robust metrics that closely align with human judgments.

Abstract

Current computational approaches for analysing or generating code-mixed sentences do not explicitly model ``naturalness'' or ``acceptability'' of code-mixed sentences, but rely on training corpora to reflect distribution of acceptable code-mixed sentences. Modelling human judgement for the acceptability of code-mixed text can help in distinguishing natural code-mixed text and enable quality-controlled generation of code-mixed text. To this end, we construct Cline - a dataset containing human acceptability judgements for English-Hindi~(en-hi) code-mixed text. Cline is the largest of its kind with 16,642 sentences, consisting of samples sourced from two sources: synthetically generated code-mixed text and samples collected from online social media. Our analysis establishes that popular code-mixing metrics such as CMI, Number of Switch Points, Burstines, which are used to filter/curate/compare code-mixed corpora have low correlation with human acceptability judgements, underlining the necessity of our dataset. Experiments using Cline demonstrate that simple Multilayer Perceptron (MLP) models when trained solely using code-mixing metrics as features are outperformed by fine-tuned pre-trained Multilingual Large Language Models (MLLMs). Specifically, among Encoder models XLM-Roberta and Bernice outperform IndicBERT across different configurations. Among Encoder-Decoder models, mBART performs better than mT5, however Encoder-Decoder models are not able to outperform Encoder-only models. Decoder-only models perform the best when compared to all other MLLMS, with Llama 3.2 - 3B models outperforming similarly sized Qwen, Phi models. Comparison with zero and fewshot capabilitites of ChatGPT show that MLLMs fine-tuned on larger data outperform ChatGPT, providing scope for improvement in code-mixed tasks. Zero-shot transfer from En-Hi to En-Te acceptability judgments are better than random baselines.

Abstract

Large Language Models (LLMs) have made significant contributions to cognitive science research. One area of application is narrative understanding. Sap et al. (2022) introduced sequentiality, an LLM-derived measure that assesses the coherence of a story based on word probability distributions. They reported that recalled stories flowed less sequentially than imagined stories. However, the robustness and generalizability of this narrative flow measure remain unverified. To assess generalizability, we apply sequentiality derived from three different LLMs to a new dataset of matched autobiographical and biographical paragraphs. Contrary to previous results, we fail to find a significant difference in narrative flow between autobiographies and biographies. Further investigation reveals biases in the original data collection process, where topic selection systematically influences sequentiality scores. Adjusting for these biases substantially reduces the originally reported effect size. A validation exercise using LLM-generated stories with "good'' and "poor'' flow further highlights the flaws in the original formulation of sequentiality. Our findings suggest that LLM-based narrative flow quantification is susceptible to methodological artifacts. Finally, we provide some suggestions for modifying the sequentiality formula to accurately capture narrative flow.

Abstract

Creating photorealistic, animatable, and relightable 3D head avatars traditionally requires expensive Lightstage with multiple calibrated cameras, making it inaccessible for widespread adoption. To bridge this gap, we present a novel, cost-effective approach for creating high-quality relightable head avatars using only a smartphone equipped with polaroid filters. Our approach involves simultaneously capturing cross-polarized and parallel-polarized video streams in a dark room with a single point-light source, separating the skin's diffuse and specular components during dynamic facial performances. We introduce a hybrid representation that embeds 2D Gaussians in the UV space of a parametric head model, facilitating efficient real-time rendering while preserving high-fidelity geometric details. Our learning-based neural analysis-by-synthesis pipeline decouples pose and expression-dependent geometrical offsets from appearance, decomposing the surface into albedo, normal, and specular UV texture maps, along with the environment maps. We collect a unique dataset of various subjects performing diverse facial expressions and head movements.

Abstract

To understand a prompt, Vision-Language models (VLMs) must perceive the image, comprehend the text, and build associations within and across both modalities. For instance, given an `image of a red toy car', the model should associate this image to phrases like `car', `red toy', `red object', etc. Prior work proposed the Binding ID mechanism in LLMs, suggesting that the entity and its corresponding attribute tokens share a Binding ID in the model activations. We investigate this for image-text binding in VLMs using a synthetic dataset and task that requires models to associate 3D objects in an image with their descriptions in the text. Our experiments demonstrate that VLMs assign a distinct Binding ID to an object's image tokens and its textual references, enabling in-context association.

Abstract

We motivate weakly supervised learning as an effective learning paradigm for problems where curating perfectly annotated datasets is expensive and may require domain expertise such as fine-grained classification. We focus on Partial Label Learning (PLL), a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels (partial label), one of which is the true label. Noisy PLL (NPLL) relaxes this constraint by allowing some partial labels to not contain the true label, enhancing the practicality of the problem. Our work centres on NPLL and presents a framework that initially assigns pseudo-labels to images by exploiting the noisy partial labels through a weighted nearest neighbour algorithm. These pseudo-label and image pairs are then used to train a deep neural network classifier with label smoothing. The classifier's features and predictions are subsequently employed to refine and enhance the accuracy of pseudo-labels. We perform thorough experiments on seven datasets and compare against nine NPLL and PLL methods. We achieve state-of-the-art results in all studied settings from the prior literature, obtaining substantial gains in the simulated fine-grained benchmarks. Further, we show the promising generalisation capability of our framework in realistic, fine-grained, crowd-sourced datasets.

Abstract

Line-search methods are commonly used to solve optimization problems. The simplest line search method is steepest descent where one always moves in the direction of the negative gradient. Newton's method on the other hand is a second-order method that uses the curvature information in the Hessian to pick the descent direction. In this work, we propose a new line-search method called Constrained Gradient Descent (CGD) that implicitly changes the landscape of the objective function for efficient optimization. CGD is formulated as a solution to the constrained version of the original problem where the constraint is on a function of the gradient. We optimize the corresponding Lagrangian function thereby favourably changing the landscape of the objective function. This results in a line search procedure where the Lagrangian penalty acts as a control over the descent direction and can therefore be used to iterate over points that have smaller gradient values, compared to iterates of vanilla steepest descent. We establish global linear convergence rates for CGD and provide numerical experiments on synthetic test functions to illustrate the performance of CGD. We also provide two practical variants of CGD, CGD-FD which is a Hessian free variant and CGD-QN, a quasi-Newton variant and demonstrate their effectiveness.

Abstract

The detrimental impact of the long-overdue pandemic has been widely acknowledged. However, the varied responses to its unprecedented challenges, particularly among young adults in college, are not well known. This study explores the role of psychological immunity in COVID-related trauma among Indian students. A significant proportion of students reported psychological distress, depression, and stress during the second wave between May and June 2021, with COVID-19 infection and related worries correlating with poorer mental health. Mediation analysis indicated that psychological flexibility negatively predicted distress, depression, and stress, while avoidance coping strategies showed a positive association with these outcomes. These findings suggest that psychological flexibility serves as a protective buffer against the impact of the pandemic, fostering resilience, while avoidant coping exacerbates its adverse effects. Interventions like Acceptance and Commitment Therapy (ACT) may enhance psychological flexibility and mitigate maladaptive coping, improving student mental well-being.

Abstract

Word-level prominence plays a crucial role in spoken language understanding, as it helps to interpret the speaker’s intent. However, automatic word-level prominence detection remains underexplored, particularly in non-native speech, where prominence patterns are more variable due to native language influence. While prominence is determined by acoustic features such as energy, duration, and pitch, capturing statistics on these acoustics at the word level provides only a global representation, missing finer suprasegmental variations. In this study, we consider syllables as the suprasegmental unit and propose a methodology that jointly models syllable prominence variations and their contribution to word prominence using sequential neural networks. This enables learning word-level prominence representations with minimal labeled data. Our method outperforms an unsupervised n-gram-based baseline by 24.01% and a supervised SVM by 5.73%, demonstrating its effectiveness over both approaches.