pwc-archive/papers-with-abstracts huggingface dataset:
Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization
Authors: Hamed Nozari, Hoessein Abdi, Agnieszka Szmelter-Jarosz
Date: 2025-03-04 00:00:00
Tasks: global-optimization, Metaheuristic Optimization

This paper presents the Goat Optimization Algorithm (GOA), a novel bio-inspired metaheuristic optimization technique inspired by goats' adaptive foraging, strategic movement, and parasite avoidance behaviors.GOA is designed to balance exploration and exploitation effectively by incorporating three key mechanisms, adaptive foraging for global search, movement toward the best solution for local refinement, and a jump strategy to escape local optima.A solution filtering mechanism is introduced to enhance robustness and maintain population diversity. The algorithm's performance is evaluated on standard unimodal and multimodal benchmark functions, demonstrating significant improvements over existing metaheuristics, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Artificial Bee Colony (ABC). Comparative analysis highlights GOA's superior convergence rate, enhanced global search capability, and higher solution accuracy.A Wilcoxon rank-sum test confirms the statistical significance of GOA's exceptional performance. Despite its efficiency, computational complexity and parameter sensitivity remain areas for further optimization. Future research will focus on adaptive parameter tuning, hybridization with other metaheuristics, and real-world applications in supply chain management, bioinformatics, and energy optimization. The findings suggest that GOA is a promising advancement in bio-inspired optimization techniques.
Nonlinear energy-preserving model reduction with lifting transformations that quadratize the energy
Authors: Harsh Sharma, Juan Diego Draxl Giannoni, Boris Kramer
Date: 2025-03-04 00:00:00
Tasks: Computational Efficiency

Existing model reduction techniques for high-dimensional models of conservative partial differential equations (PDEs) encounter computational bottlenecks when dealing with systems featuring non-polynomial nonlinearities. This work presents a nonlinear model reduction method that employs lifting variable transformations to derive structure-preserving quadratic reduced-order models for conservative PDEs with general nonlinearities. We present an energy-quadratization strategy that defines the auxiliary variable in terms of the nonlinear term in the energy expression to derive an equivalent quadratic lifted system with quadratic system energy. The proposed strategy combined with proper orthogonal decomposition model reduction yields quadratic reduced-order models that conserve the quadratized lifted energy exactly in high dimensions. We demonstrate the proposed model reduction approach on four nonlinear conservative PDEs: the one-dimensional wave equation with exponential nonlinearity, the two-dimensional sine-Gordon equation, the two-dimensional Klein-Gordon equation with parametric dependence, and the two-dimensional Klein-Gordon-Zakharov equations. The numerical results show that the proposed lifting approach is competitive with the state-of-the-art structure-preserving hyper-reduction method in terms of both accuracy and computational efficiency in the online stage while providing significant computational gains in the offline stage.
Online Pseudo-average Shifting Attention(PASA) for Robust Low-precision LLM Inference: Algorithms and Numerical Analysis
Authors: Long Cheng, Qichen Liao, Fan Wu, Junlin Mu, Tengfei Han, Zhe Qiu, Lianqiang Li, Tianyi Liu, Fangzheng Miao, Keming Gao, Liang Wang, Zhen Zhang, Qiande Yin
Date: 2025-02-26 00:00:00
Tasks: Video Generation

Attention calculation is extremely time-consuming for long-sequence inference tasks, such as text or image/video generation, in large models. To accelerate this process, we developed a low-precision, mathematically-equivalent algorithm called PASA, based on Flash Attention. PASA introduces two novel techniques: online pseudo-average shifting and global recovering. These techniques enable the use of half-precision computation throughout the Flash Attention process without incurring overflow instability or unacceptable numerical accuracy loss. This algorithm enhances performance on memory-restricted AI hardware architectures, such as the Ascend Neural-network Processing Unit(NPU), by reducing data movement and increasing computational FLOPs. The algorithm is validated using both designed random benchmarks and real large models. We find that the large bias and amplitude of attention input data are critical factors contributing to numerical overflow ($>65504$ for half precision) in two different categories of large models (Qwen2-7B language models and Stable-Video-Diffusion multi-modal models). Specifically, overflow arises due to the large bias in the sequence dimension and the resonance mechanism between the query and key in the head dimension of the Stable-Video-Diffusion models. The resonance mechanism is defined as phase coincidence or 180-degree phase shift between query and key matrices. It will remarkably amplify the element values of attention score matrix. This issue also applies to the Qwen models. Additionally, numerical accuracy is assessed through root mean square error (RMSE) and by comparing the final generated texts and videos to those produced using high-precision attention.
Modelowanie nieliniowej charakterystyki szerokopasmowych wzmacniaczy radiowych o zmiennym napięciu zasilania; Modeling Nonlinear Characteristics of Wideband Radio Frequency Amplifiers with Variable Supply Voltage
Authors: Kornelia Kostrzewska, Paweł Kryszkiewicz
Date: 2025-03-05 00:00:00

The work aims to propose a new nonlinear characteristics model for a wideband radio amplifier of variable supply voltage. An extended Rapp model proposal is presented. The proposed model has been verified by measurements of three different amplifiers. This model can be used to design frontend-aware 6G systems. -- Praca ma na celu zaproponowanie nowego modelu dla nieliniowej charakterystyki wzmacniacza radiowego ze zmiennym napi\k{e}ciem zasilania pracuj\k{a}cym w szerokim zakresie cz\k{e}stotliwo\'sci. Przedstawiona zosta{\l}a propozycja rozszerzonego modelu Rappa. Zaproponowany model zweryfikowano na podstawie pomiar\'ow charakterystyk trzech r\'o\.znych wzmacniaczy. Model ten mo\.ze by\'c wykorzystany do projektowania system\'ow 6G "\'swiadomych" niedoskona{\l}o\'sci uk{\l}ad\'ow wej\'sciowo-wyj\'sciowych.
O-RAN xApps Conflict Management using Graph Convolutional Networks
Authors: Maryam Al Shami, Jun Yan, Emmanuel Thepie Fapi
Date: 2025-03-05 00:00:00
Tasks: Management

The lack of a unified mechanism to coordinate and prioritize the actions of different applications can create three types of conflicts (direct, indirect, and implicit). Conflict management in O-RAN refers to the process of identifying and resolving conflicts between network applications. In our paper, we introduce a novel data-driven GCN-based method called GRAPH-based Intelligent xApp Conflict Prediction and Analysis (GRAPHICA) based on Graph Convolutional Network (GCN). It predicts three types of conflicts (direct, indirect, and implicit) and pinpoints the root causes (xApps). GRAPHICA captures the complex and hidden dependencies among the xApps, controlled parameters, and KPIs in O-RAN to predict possible conflicts. Then, it identifies the root causes (xApps) contributing to the predicted conflicts. The proposed method was tested on highly imbalanced synthesized datasets where conflict instances range from 40% to 10%. The model is tested in a setting that simulates real-world scenarios where conflicts are rare to assess its performance. Experimental results demonstrate a high F1-score over 98% for the synthesized datasets with different levels of class imbalance.
Efficient and Optimal No-Regret Caching under Partial Observation
Authors: Younes Ben Mazziane, Francescomaria Faticanti, Sara Alouf, Giovanni Neglia
Date: 2025-03-04 00:00:00

Online learning algorithms have been successfully used to design caching policies with sublinear regret in the total number of requests, with no statistical assumption about the request sequence. Most existing algorithms involve computationally expensive operations and require knowledge of all past requests. However, this may not be feasible in practical scenarios like caching at a cellular base station. Therefore, we study the caching problem in a more restrictive setting where only a fraction of past requests are observed, and we propose a randomized caching policy with sublinear regret based on the classic online learning algorithm Follow-the-Perturbed-Leader (FPL). Our caching policy is the first to attain the asymptotically optimal regret bound while ensuring asymptotically constant amortized time complexity in the partial observability setting of requests. The experimental evaluation compares the proposed solution against classic caching policies and validates the proposed approach under synthetic and real-world request traces.
MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence
Authors: Stepan Mazokha, Fanchen Bao, George Sklivanitis, Jason O. Hallstrom
Date: 2025-03-04 00:00:00

WiFi-based mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movements. However, MAC address randomization introduces a significant obstacle in accurately estimating congestion levels and path trajectories. To this end, we consider radio frequency fingerprinting and re-identification for attributing WiFi traffic to emitting devices without the use of MAC addresses. We present MobRFFI, an AI-based device fingerprinting and re-identification framework for WiFi networks that leverages an encoder deep learning model to extract unique features based on WiFi chipset hardware impairments. It is entirely independent of frame type. When evaluated on the WiFi fingerprinting dataset WiSig, our approach achieves 94% and 100% device accuracy in multi-day and single-day re-identification scenarios, respectively. We also collect a novel dataset, MobRFFI, for granular multi-receiver WiFi device fingerprinting evaluation. Using the dataset, we demonstrate that the combination of fingerprints from multiple receivers boosts re-identification performance from 81% to 100% on a single-day scenario and from 41% to 100% on a multi-day scenario.
MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings
Authors: Andrea Gurioli, Federico Pennino, João Monteiro, Maurizio Gabbrielli
Date: 2025-03-04 00:00:00
Tasks: All, Code Search, Code Translation, Retrieval

Deploying language models often requires navigating accuracy vs. performance trade-offs to meet latency constraints while preserving utility. Traditional model distillation reduces size but incurs substantial costs through training separate models. We introduce ModularStarEncoder (MoSE), a 1-billion-parameter multi-exit encoder for code retrieval and classification that employs a novel Self-Distillation mechanism. This approach significantly enhances lower-layer representations, enabling flexible deployment of different model portions with favorable performance trade-offs. Our architecture improves text-to-code and code-to-code search by targeting specific encoder layers as exit heads, where higher layers guide earlier ones during training-improving intermediate representations at minimal additional cost. We further enhance MoSE with a repository-level contextual loss that maximizes training context window utilization. Additionally, we release a new dataset created through code translation that extends text-to-code benchmarks with cross-language code-to-code pairs. Evaluations demonstrate the effectiveness of Self-Distillation as a principled approach to trading inference cost for accuracy across various code understanding tasks.
OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction
Authors: Huang Huang, Fangchen Liu, Letian Fu, Tingfan Wu, Mustafa Mukadam, Jitendra Malik, Ken Goldberg, Pieter Abbeel
Date: 2025-03-05 00:00:00
Tasks: Vision-Language-Action, Zero-shot Generalization

Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visionlanguage models (VLMs) as visual and language features are independently fed into downstream policies, degrading the pre-trained semantic alignments. We propose OTTER, a novel VLA architecture that leverages these existing alignments through explicit, text-aware visual feature extraction. Instead of processing all visual features, OTTER selectively extracts and passes only task-relevant visual features that are semantically aligned with the language instruction to the policy transformer. This allows OTTER to keep the pre-trained vision-language encoders frozen. Thereby, OTTER preserves and utilizes the rich semantic understanding learned from large-scale pre-training, enabling strong zero-shot generalization capabilities. In simulation and real-world experiments, OTTER significantly outperforms existing VLA models, demonstrating strong zeroshot generalization to novel objects and environments. Video, code, checkpoints, and dataset: https://ottervla.github.io/.
Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames
Authors: Jun Yang, Wenjie Xue, Sahar Ghavidel, Steven L. Waslander
Date: 2025-03-05 00:00:00
Tasks: 6D Pose Estimation, Object, Pose Estimation, Template Matching

Estimating the 6D pose of textureless objects from RBG images is an important problem in robotics. Due to appearance ambiguities, rotational symmetries, and severe occlusions, single-view based 6D pose estimators are still unable to handle a wide range of objects, motivating research towards multi-view pose estimation and next-best-view prediction that addresses these limitations. In this work, we propose a comprehensive active perception framework for estimating the 6D poses of textureless objects using only RGB images. Our approach is built upon a key idea: decoupling the 6D pose estimation into a sequential two-step process can greatly improve both accuracy and efficiency. First, we estimate the 3D translation of each object, resolving scale and depth ambiguities inherent to RGB images. These estimates are then used to simplify the subsequent task of determining the 3D orientation, which we achieve through canonical scale template matching. Building on this formulation, we then introduce an active perception strategy that predicts the next best camera viewpoint to capture an RGB image, effectively reducing object pose uncertainty and enhancing pose accuracy. We evaluate our method on the public ROBI dataset as well as on a transparent object dataset that we created. When evaluated using the same camera viewpoints, our multi-view pose estimation significantly outperforms state-of-the-art approaches. Furthermore, by leveraging our next-best-view strategy, our method achieves high object pose accuracy with substantially fewer viewpoints than heuristic-based policies.
Curating Demonstrations using Online Experience
Authors: Annie S. Chen, Alec M. Lessing, Yuejiang Liu, Chelsea Finn
Date: 2025-03-05 00:00:00
Tasks: Imitation Learning

Many robot demonstration datasets contain heterogeneous demonstrations of varying quality. This heterogeneity may benefit policy pre-training, but can hinder robot performance when used with a final imitation learning objective. In particular, some strategies in the data may be less reliable than others or may be underrepresented in the data, leading to poor performance when such strategies are sampled at test time. Moreover, such unreliable or underrepresented strategies can be difficult even for people to discern, and sifting through demonstration datasets is time-consuming and costly. On the other hand, policy performance when trained on such demonstrations can reflect the reliability of different strategies. We thus propose for robots to self-curate based on online robot experience (Demo-SCORE). More specifically, we train and cross-validate a classifier to discern successful policy roll-outs from unsuccessful ones and use the classifier to filter heterogeneous demonstration datasets. Our experiments in simulation and the real world show that Demo-SCORE can effectively identify suboptimal demonstrations without manual curation. Notably, Demo-SCORE achieves over 15-35% higher absolute success rate in the resulting policy compared to the base policy trained with all original demonstrations.
REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation
Authors: Débora N. P. Oliveira, Joshua Knights, Sebastián Barbas Laina, Simon Boche, Wolfram Burgard, Stefan Leutenegger
Date: 2025-03-05 00:00:00
Tasks: Graph Neural Network

Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation. Current methods using dense point clouds for accurate place recognition do not scale well due to computationally expensive scan-to-scan comparisons. Alternative object-centric approaches are more efficient but often struggle with sensitivity to viewpoint variation. In this work, we introduce REGRACE, a novel approach that addresses these challenges of scalability and perspective difference in re-localization by using LiDAR-based submaps. We introduce rotation-invariant features for each labeled object and enhance them with neighborhood context through a graph neural network. To identify potential revisits, we employ a scalable bag-of-words approach, pooling one learned global feature per submap. Additionally, we define a revisit with geometrical consistency cues rather than embedding distance, allowing us to recognize far-away loop closures. Our evaluations demonstrate that REGRACE achieves similar results compared to state-of-the-art place recognition and registration baselines while being twice as fast.
A Generative System for Robot-to-Human Handovers: from Intent Inference to Spatial Configuration Imagery
Authors: Hanxin Zhang, Abdulqader Dhafer, Zhou Daniel Hao, Hongbiao Dong
Date: 2025-03-05 00:00:00
Tasks: Motion Planning, Motor Imagery

We propose a novel system for robot-to-human object handover that emulates human coworker interactions. Unlike most existing studies that focus primarily on grasping strategies and motion planning, our system focus on 1. inferring human handover intents, 2. imagining spatial handover configuration. The first one integrates multimodal perception-combining visual and verbal cues-to infer human intent. The second one using a diffusion-based model to generate the handover configuration, involving the spacial relationship among robot's gripper, the object, and the human hand, thereby mimicking the cognitive process of motor imagery. Experimental results demonstrate that our approach effectively interprets human cues and achieves fluent, human-like handovers, offering a promising solution for collaborative robotics. Code, videos, and data are available at: https://i3handover.github.io.
Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control
Authors: Jørgen Anker Olsen, Grzegorz Malczyk, Kostas Alexis
Date: 2025-03-05 00:00:00

Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot's design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated.
Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation
Authors: Xiaomeng Zhu, Yuyang Li, Leiyao Cui, Pengfei Li, Huan-ang Gao, Yixin Zhu, Hao Zhao
Date: 2025-03-05 00:00:00
Tasks: Object, Object Recognition

Object affordance reasoning, the ability to infer object functionalities based on physical properties, is fundamental for task-oriented planning and activities in both humans and Artificial Intelligence (AI). This capability, required for planning and executing daily activities in a task-oriented manner, relies on commonsense knowledge of object physics and functionalities, extending beyond simple object recognition. Current computational models for affordance reasoning from perception lack generalizability, limiting their applicability in novel scenarios. Meanwhile, comprehensive Large Language Models (LLMs) with emerging reasoning capabilities are challenging to deploy on local devices for task-oriented manipulations. Here, we introduce LVIS-Aff, a large-scale dataset comprising 1,496 tasks and 119k images, designed to enhance the generalizability of affordance reasoning from perception. Utilizing this dataset, we develop Afford-X, an end-to-end trainable affordance reasoning model that incorporates Verb Attention and Bi-Fusion modules to improve multi-modal understanding. This model achieves up to a 12.1% performance improvement over the best-reported results from non-LLM methods, while also demonstrating a 1.2% enhancement compared to our previous conference paper. Additionally, it maintains a compact 187M parameter size and infers nearly 50 times faster than the GPT-4V API. Our work demonstrates the potential for efficient, generalizable affordance reasoning models that can be deployed on local devices for task-oriented manipulations. We showcase Afford-X's effectiveness in enabling task-oriented manipulations for robots across various tasks and environments, underscoring its efficiency and broad implications for advancing robotics and AI systems in real-world applications.
Unified Human Localization and Trajectory Prediction with Monocular Vision
Authors: Po-Chien Luan, Yang Gao, Celine Demonsant, Alexandre Alahi
Date: 2025-03-05 00:00:00
Tasks: Prediction, Trajectory Prediction

Conventional human trajectory prediction models rely on clean curated data, requiring specialized equipment or manual labeling, which is often impractical for robotic applications. The existing predictors tend to overfit to clean observation affecting their robustness when used with noisy inputs. In this work, we propose MonoTransmotion (MT), a Transformer-based framework that uses only a monocular camera to jointly solve localization and prediction tasks. Our framework has two main modules: Bird's Eye View (BEV) localization and trajectory prediction. The BEV localization module estimates the position of a person using 2D human poses, enhanced by a novel directional loss for smoother sequential localizations. The trajectory prediction module predicts future motion from these estimates. We show that by jointly training both tasks with our unified framework, our method is more robust in real-world scenarios made of noisy inputs. We validate our MT network on both curated and non-curated datasets. On the curated dataset, MT achieves around 12% improvement over baseline models on BEV localization and trajectory prediction. On real-world non-curated dataset, experimental results indicate that MT maintains similar performance levels, highlighting its robustness and generalization capability. The code is available at https://github.com/vita-epfl/MonoTransmotion.