OpenRaiser/Intern-Atlas huggingface dataset:
Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion
Venue: arXiv cs.CE 2025
Year: 2025

Modeling plays a critical role in additive manufacturing (AM), enabling a deeper understanding of underlying processes. Parametric solutions for such models are of great importance, enabling the optimization of production processes and considerable cost reductions. However, the complexity of the problem and diversity of spatio-temporal scales involved in the process pose significant challenges for traditional numerical methods. Surrogate models offer a powerful alternative by accelerating simulations and facilitating real-time monitoring and control. The present study presents an operator learning approach that relies on the deep operator network (DeepONet) and physics-informed neural networks (PINN) to predict the three-dimensional temperature distribution during melting and consolidation in laser powder bed fusion (LPBF). Parametric solutions for both single-track and multi-track scenarios with respect to tool path are obtained. To address the challenges in obtaining parametric solutions for multi-track scenarios using DeepONet architecture, a sequential PINN approach is proposed to efficiently manage the increased training complexity inherent in those scenarios. The accuracy and consistency of the model are verified against finite-difference computations. The developed surrogate allows us to efficiently analyze the effect of scanning paths and laser parameters on the thermal history.
Meta-neural Topology Optimization: Knowledge Infusion with Meta-learning
Venue: arXiv cs.CE 2025
Year: 2025

Engineers learn from every design they create, building intuition that helps them quickly identify promising solutions for new problems. Topology optimization (TO) - a well-established computational method for designing structures with optimized performance - lacks this ability to learn from experience. Existing approaches treat design tasks in isolation, starting from a "blank canvas" design for each new problem, often requiring many computationally expensive steps to converge. We propose a meta-learning strategy, termed meta-neural TO, that finds effective initial designs through a systematic transfer of knowledge between related tasks, building on the mesh-agnostic representation provided by neural reparameterization. We compare our approach against established TO methods, demonstrating efficient optimization across diverse test cases without compromising design quality. Further, we demonstrate powerful cross-resolution transfer capabilities, where initializations learned on lower-resolution discretizations lead to superior convergence in 74.1% of tasks on a higher-resolution test set, reducing the average number of iterations by 33.6% compared to standard neural TO. Remarkably, we discover that meta-learning naturally gravitates toward the strain energy patterns found in uniform density designs as effective starting points, aligning with engineering intuition.
Preparing for Kyber in Securing Intelligent Transportation Systems Communications: A Case Study on Fault-Enabled Chosen-Ciphertext Attack
Venue: arXiv cs.CR 2025
Year: 2025

Intelligent transportation systems (ITS) are characterized by wired or wireless communication among different entities, such as vehicles, roadside infrastructure, and traffic management infrastructure. These communications demand different levels of security, depending on how sensitive the data is. The national ITS reference architecture (ARC-IT) defines three security levels, i.e., high, moderate, and low-security levels, based on the different security requirements of ITS applications. In this study, we present a generalized approach to secure ITS communications using a standardized key encapsulation mechanism, known as Kyber, designed for post-quantum cryptography (PQC). We modified the encryption and decryption systems for ITS communications while mapping the security levels of ITS applications to the three versions of Kyber, i.e., Kyber-512, Kyber-768, and Kyber-1024. Then, we conducted a case study using a benchmark fault-enabled chosen-ciphertext attack to evaluate the security provided by the different Kyber versions. The encryption and decryption times observed for different Kyber security levels and the total number of iterations required to recover the secret key using the chosen-ciphertext attack are presented. Our analyses show that higher security levels increase the time required for a successful attack, with Kyber-512 being breached in 183 seconds, Kyber-768 in 337 seconds, and Kyber-1024 in 615 seconds. In addition, attack time instabilities are observed for Kyber-512, 768, and 1024 under 5,000, 6,000, and 8,000 inequalities, respectively. The relationships among the different Kyber versions, and the respective attack requirements and performances underscore the ITS communication security Kyber could provide in the PQC era.
Learning Fine-to-Coarse Cuboid Shape Abstraction
Venue: arXiv cs.CV 2025
Year: 2025

The abstraction of 3D objects with simple geometric primitives like cuboids allows to infer structural information from complex geometry. It is important for 3D shape understanding, structural analysis and geometric modeling. We introduce a novel fine-to-coarse unsupervised learning approach to abstract collections of 3D shapes. Our architectural design allows us to reduce the number of primitives from hundreds (fine reconstruction) to only a few (coarse abstraction) during training. This allows our network to optimize the reconstruction error and adhere to a user-specified number of primitives per shape while simultaneously learning a consistent structure across the whole collection of data. We achieve this through our abstraction loss formulation which increasingly penalizes redundant primitives. Furthermore, we introduce a reconstruction loss formulation to account not only for surface approximation but also volume preservation. Combining both contributions allows us to represent 3D shapes more precisely with fewer cuboid primitives than previous work. We evaluate our method on collections of man-made and humanoid shapes comparing with previous state-of-the-art learning methods on commonly used benchmarks. Our results confirm an improvement over previous cuboid-based shape abstraction techniques. Furthermore, we demonstrate our cuboid abstraction in downstream tasks like clustering, retrieval, and partial symmetry detection.
Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Accurate and robust 3D object detection is essential for autonomous driving, where fusing data from sensors like LiDAR and camera enhances detection accuracy. However, sensor malfunctions such as corruption or disconnection can degrade performance, and existing fusion models often struggle to maintain reliability when one modality fails. To address this, we propose ReliFusion, a novel LiDAR-camera fusion framework operating in the bird's-eye view (BEV) space. ReliFusion integrates three key components: the Spatio-Temporal Feature Aggregation (STFA) module, which captures dependencies across frames to stabilize predictions over time; the Reliability module, which assigns confidence scores to quantify the dependability of each modality under challenging conditions; and the Confidence-Weighted Mutual Cross-Attention (CW-MCA) module, which dynamically balances information from LiDAR and camera modalities based on these confidence scores. Experiments on the nuScenes dataset show that ReliFusion significantly outperforms state-of-the-art methods, achieving superior robustness and accuracy in scenarios with limited LiDAR fields of view and severe sensor malfunctions.
Explaining Automatic Image Assessment
Venue: arXiv cs.CV 2025
Year: 2025

Previous work in aesthetic categorization and explainability utilizes manual labeling and classification to explain aesthetic scores. These methods require a complex labeling process and are limited in size. Our proposed approach attempts to explain aesthetic assessment models through visualizing dataset trends and automatic categorization of visual aesthetic features through training neural networks on different versions of the same dataset. By evaluating the models adapted to each specific modality using existing and novel metrics, we can capture and visualize aesthetic features and trends.
A Multi-Objective Framework for Optimizing GPU-Enabled VM Placement in Cloud Data Centers with Multi-Instance GPU Technology
Venue: arXiv cs.DC 2025
Year: 2025

The extensive use of GPUs in cloud computing and the growing need for multitenancy have driven the development of innovative solutions for efficient GPU resource management. Multi-Instance GPU (MIG) technology from NVIDIA enables shared GPU usage in cloud data centers by providing isolated instances. However, MIG placement rules often lead to fragmentation and suboptimal resource utilization. In this work, we formally model the MIG-enabled VM placement as a multi-objective Integer Linear Programming (ILP) problem aimed at maximizing request acceptance, minimizing active hardware usage, and reducing migration overhead. Building upon this formulation, we propose GRMU, a multi-stage placement framework designed to address MIG placement challenges. GRMU performs intra-GPU migrations for defragmentation of a single GPU and inter-GPU migrations for consolidation and resource efficiency. It also employs a quota-based partitioning approach to allocate GPUs into two distinct baskets: one for large-profile workloads and another for smaller-profile workloads. Each basket has predefined capacity limits, ensuring fair resource distribution and preventing large-profile workloads from monopolizing resources. Evaluations on a real-world Alibaba GPU cluster trace reveal that GRMU improves acceptance rates by 22%, reduces active hardware by 17%, and incurs migration for only 1% of MIG-enabled VMs, demonstrating its effectiveness in minimizing fragmentation and improving resource utilization.
On the Core of the $b$-Matching Game
Venue: arXiv cs.GT 2025
Year: 2025

The core is a quintessential solution concept for profit sharing in cooperative game theory. An imputation allocates the worth of the given game among its agents. The imputation lies in the core of the game if, for each sub-coalition, the amount allocated to its agents is at least the worth of this sub-coalition. Hence, under a core imputation, each of exponentially many sub-coalitions gets satisfied. The following computational question has received much attention: Given an imputation, does it lie in the core? Clearly, this question lies in co-NP, since a co-NP certificate for this problem would be a sub-coalition which is not satisfied under the imputation. This question is in P for the assignment game [SS71] and has been shown to be co-NP-hard for several natural games, including max-flow [FZCD02] and MST [FKFH97]. The one natural game for which this question has remained open is the b-matching game when the number of times an edge can be matched is unconstrained; in case each edge can be matched at most once, it is co-NP-hard [BKPW18]. At the outset, it was not clear which way this open question would resolve: on the one hand, for all but one game, this problem was shown co-NP-hard and on the other hand, proximity to the assignment problem and the deep structural properties of matching could lead to a positive result. In this paper, we show that the problem is indeed co-NP-hard.
Anomaly Detection via Autoencoder Composite Features and NCE
Venue: arXiv cs.LG 2025
Year: 2025

Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently identify anomalous, out-of-distribution inputs by high reconstruction error or low likelihood, respectively. However, AEs may generalize and achieve small reconstruction errors on abnormal inputs. We propose a decoupled training approach for anomaly detection that both an AE and a likelihood model trained with noise contrastive estimation (NCE). After training the AE, NCE estimates a probability density function, to serve as the anomaly score, on the joint space of the AE's latent representation combined with features of the reconstruction quality. To further reduce the false negative rate in NCE we systematically varying the reconstruction features to augment the training and optimize the contrastive Gaussian noise distribution. Experimental assessments on multiple benchmark datasets demonstrate that the proposed approach matches the performance of prevalent state-of-the-art anomaly detection algorithms.
Improving Software Engineering Team Communication Through Stronger Social Networks
Venue: arXiv cs.SE 2025
Year: 2025

Students working in teams in software engineering group project often communicate ineffectively, which reduces the quality of deliverables, and is therefore detrimental for project success. An important step towards addressing areas of improvement is identifying which changes to communication will improve team performance the most. We applied two different communication analysis techniques, triad census and socio-technical congruence, to data gathered from a two-semester software engineering group project. Triad census uses the presence of edges between groups of three nodes as a measure of network structure, while socio-technical congruence compares the fit of a team's communication to their technical dependencies. Our findings suggest that each team's triad census for a given sprint is promising as a predictor of the percentage of story points they pass, which is closely linked to project success. Meanwhile, socio-technical congruence is inadequate as the sole metric for predicting project success in this context. We discuss these findings, and their potential applications improve communication in a software engineering group project.
Can LLMs Maintain Fundamental Abilities under KV Cache Compression?
Venue: arXiv cs.CL 2025
Year: 2025

This paper investigates an underexplored challenge in large language models (LLMs): the impact of KV cache compression methods on LLMs' fundamental capabilities. Although existing methods achieve impressive compression ratios on long-context benchmarks, their effects on core model capabilities remain understudied. We present a comprehensive benchmark KVFundaBench to systematically evaluate the effects of KV cache compression across diverse fundamental LLM capabilities, spanning world knowledge, commonsense reasoning, arithmetic reasoning, code generation, safety, and long-context understanding and generation.Our analysis reveals serval key findings: (1) \textit{Task-Dependent Degradation}; (2) \textit{Model-Type Robustness} (3) \textit{Prompt Length Vulnerability}; (4) \textit{Chunk-Level Superiority}; (5) \textit{Prompt-Gain Sensitivity}; (6) \textit{Long-Context Generation Sensitivity}. Based on our analysis of attention patterns and cross-task compression performance, we propose ShotKV, a novel compression approach that distinctly handles prefill and decoding phases while maintaining shot-level semantic coherence. Empirical results show that ShotKV achieves $9\%$-$18\%$ performance improvements on long-context generation tasks under aggressive compression ratios.
Optimizing Spot Instance Reliability and Security Using Cloud-Native Data and Tools
Venue: arXiv cs.CR 2025
Year: 2025

This paper represents "Cloudlab", a comprehensive, cloud - native laboratory designed to support network security research and training. Built on Google Cloud and adhering to GitOps methodologies, Cloudlab facilitates the the creation, testing, and deployment of secure, containerized workloads using Kubernetes and serverless architectures. The lab integrates tools like Palo Alto Networks firewalls, Bridgecrew for "Security as Code," and automated GitHub workflows to establish a robust Continuous Integration/Continuous Machine Learning pipeline. By providing an adaptive and scalable environment, Cloudlab supports advanced security concepts such as role-based access control, Policy as Code, and container security. This initiative enables data scientists and engineers to explore cutting-edge practices in a dynamic cloud-native ecosystem, fostering innovation and improving operational resilience in modern IT infrastructures.
Online Adaptive Traversability Estimation through Interaction for Unstructured, Densely Vegetated Environments
Venue: arXiv cs.RO 2025
Year: 2025

Navigating densely vegetated environments poses significant challenges for autonomous ground vehicles. Learning-based systems typically use prior and in-situ data to predict terrain traversability but often degrade in performance when encountering out-of-distribution elements caused by rapid environmental changes or novel conditions. This paper presents a novel, lidar-only, online adaptive traversability estimation (TE) method that trains a model directly on the robot using self-supervised data collected through robot-environment interaction. The proposed approach utilises a probabilistic 3D voxel representation to integrate lidar measurements and robot experience, creating a salient environmental model. To ensure computational efficiency, a sparse graph-based representation is employed to update temporarily evolving voxel distributions. Extensive experiments with an unmanned ground vehicle in natural terrain demonstrate that the system adapts to complex environments with as little as 8 minutes of operational data, achieving a Matthews Correlation Coefficient (MCC) score of 0.63 and enabling safe navigation in densely vegetated environments. This work examines different training strategies for voxel-based TE methods and offers recommendations for training strategies to improve adaptability. The proposed method is validated on a robotic platform with limited computational resources (25W GPU), achieving accuracy comparable to offline-trained models while maintaining reliable performance across varied environments.
Rethinking Timesteps Samplers and Prediction Types
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models suffer from the huge consumption of time and resources to train. For example, diffusion models need hundreds of GPUs to train for several weeks for a high-resolution generative task to meet the requirements of an extremely large number of iterations and a large batch size. Training diffusion models become a millionaire's game. With limited resources that only fit a small batch size, training a diffusion model always fails. In this paper, we investigate the key reasons behind the difficulties of training diffusion models with limited resources. Through numerous experiments and demonstrations, we identified a major factor: the significant variation in the training losses across different timesteps, which can easily disrupt the progress made in previous iterations. Moreover, different prediction types of $x_0$ exhibit varying effectiveness depending on the task and timestep. We hypothesize that using a mixed-prediction approach to identify the most accurate $x_0$ prediction type could potentially serve as a breakthrough in addressing this issue. In this paper, we outline several challenges and insights, with the hope of inspiring further research aimed at tackling the limitations of training diffusion models with constrained resources, particularly for high-resolution tasks.
The $\text{FP}^\text{NP}$ versus #P dichotomy for #EO
Venue: arXiv cs.CC 2025
Year: 2025

The complexity classification of the Holant problem has remained unresolved for the past fifteen years. Counting complex-weighted Eulerian orientation problems, denoted as #EO, is regarded as one of the most significant challenges to the comprehensive complexity classification of the Holant problem. This article presents an $\text{FP}^\text{NP}$ vs. #P dichotomy for #EO, demonstrating that #EO defined by a signature set is either #P-hard or polynomial-time computable with a specific NP oracle. This result provides a comprehensive complexity classification for #EO, and potentially leads to a dichotomy for the Holant problem. Furthermore, we derive three additional dichotomies related to the Holant problem from the dichotomy for #EO.
Improving Wireless Federated Learning via Joint Downlink-Uplink Beamforming over Analog Transmission
Venue: arXiv cs.IT 2025
Year: 2025

Federated learning (FL) over wireless networks using analog transmission can efficiently utilize the communication resource but is susceptible to errors caused by noisy wireless links. In this paper, assuming a multi-antenna base station, we jointly design downlink-uplink beamforming to maximize FL training convergence over time-varying wireless channels. We derive the round-trip model updating equation and use it to analyze the FL training convergence to capture the effects of downlink and uplink beamforming and the local model training on the global model update. Aiming to maximize the FL training convergence rate, we propose a low-complexity joint downlink-uplink beamforming (JDUBF) algorithm, which adopts a greedy approach to decompose the multi-round joint optimization and convert it into per-round online joint optimization problems. The per-round problem is further decomposed into three subproblems over a block coordinate descent framework, where we show that each subproblem can be efficiently solved by projected gradient descent with fast closed-form updates. An efficient initialization method that leads to a closed-form initial point is also proposed to accelerate the convergence of JDUBF. Simulation demonstrates that JDUBF substantially outperforms the conventional separate-link beamforming design.
Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in machine learning (ML), natural language processing (NLP), and foundational models have shown promise for real-life applications in critical, albeit compute-constrainted fields like healthcare. In such areas, combining foundational models with supervised ML offers potential for automating tasks like diagnosis and treatment planning, but the limited availability of onsite computational resources pose significant challenges before applying these technologies effectively: Current approaches either yield subpar results when using pretrained models without task-specific adaptation, or require substantial computational resources for fine-tuning, which is often a barrier to entry in such environments. This renders them inaccessible in applications where performance and quality standards are high, but computational resources are scarce. To bridge the gap between best-in-class performance and accessibility, we propose a novel method for adapting foundational, multimodal embeddings to downstream tasks, without the need of expensive fine-tuning processes. Our method leverages frozen embeddings from Large Language Models (LLMs) and Vision Models, and uses contrastive learning to train a small, task-specific nonlinear projection that can be used in the downstream task, without having to fine-tune the original foundational models. We show that this efficient procedure leads to significant performance improvements across various downstream tasks, and perhaps more importantly with minimal computational overhead, offering a practical solution for the use of advanced, foundational ML models in resource-constrained settings.