OpenRaiser/Intern-Atlas huggingface dataset:
TAPOR: 3D Hand Pose Reconstruction with Fully Passive Thermal Sensing for Around-Device Interactions
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents the design and implementation of TAPOR, a privacy-preserving, non-contact, and fully passive sensing system for accurate and robust 3D hand pose reconstruction for around-device interaction using a single low-cost thermal array sensor. Thermal sensing using inexpensive and miniature thermal arrays emerges with an excellent utility-privacy balance, offering an imaging resolution significantly lower than cameras but far superior to RF signals like radar or WiFi. The design of TAPOR, however, is challenging, mainly because the captured temperature maps are low-resolution and textureless. To overcome the challenges, we investigate thermo-depth and thermo-pose properties, proposing a novel physics-inspired neural network that learns effective 3D spatial representations of potential hand poses. We then formulate the 3D pose reconstruction problem as a distinct retrieval task, enabling accurate hand pose determination from the input temperature map. To deploy TAPOR on IoT devices, we introduce an effective heterogeneous knowledge distillation method, reducing computation by 377x. TAPOR is fully implemented and tested in real-world scenarios, showing remarkable performance, supported by four gesture control and finger tracking case studies. We envision TAPOR to be a ubiquitous interface for around-device control and have open-sourced it at https://github.com/aiot-lab/TAPOR.
Drivetrain simulation using variational autoencoders
Venue: arXiv cs.LG 2025
Year: 2025

This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk signals from torque demand in the context of limited real-world drivetrain datasets. We implement both unconditional and conditional VAEs, trained on experimental data from two variants of a fully electric SUV with differing torque and drivetrain configurations. The VAEs synthesize jerk signals that capture characteristics from multiple drivetrain scenarios by leveraging the learned latent space. A performance comparison with baseline physics-based and hybrid models confirms the effectiveness of the VAEs, without requiring detailed system parametrization. Unconditional VAEs generate realistic jerk signals without prior system knowledge, while conditional VAEs enable the generation of signals tailored to specific torque inputs. This approach reduces the dependence on costly and time-intensive real-world experiments and extensive manual modeling. The results support the integration of generative models such as VAEs into drivetrain simulation pipelines, both for data augmentation and for efficient exploration of complex operational scenarios, with the potential to streamline validation and accelerate vehicle development.
FeatureGS: Eigenvalue-Feature Optimization in 3D Gaussian Splatting for Geometrically Accurate and Artifact-Reduced Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian Splatting (3DGS) has emerged as a powerful approach for 3D scene reconstruction using 3D Gaussians. However, neither the centers nor surfaces of the Gaussians are accurately aligned to the object surface, complicating their direct use in point cloud and mesh reconstruction. Additionally, 3DGS typically produces floater artifacts, increasing the number of Gaussians and storage requirements. To address these issues, we present FeatureGS, which incorporates an additional geometric loss term based on an eigenvalue-derived 3D shape feature into the optimization process of 3DGS. The goal is to improve geometric accuracy and enhance properties of planar surfaces with reduced structural entropy in local 3D neighborhoods.We present four alternative formulations for the geometric loss term based on 'planarity' of Gaussians, as well as 'planarity', 'omnivariance', and 'eigenentropy' of Gaussian neighborhoods. We provide quantitative and qualitative evaluations on 15 scenes of the DTU benchmark dataset focusing on following key aspects: Geometric accuracy and artifact-reduction, measured by the Chamfer distance, and memory efficiency, evaluated by the total number of Gaussians. Additionally, rendering quality is monitored by Peak Signal-to-Noise Ratio. FeatureGS achieves a 30 % improvement in geometric accuracy, reduces the number of Gaussians by 90 %, and suppresses floater artifacts, while maintaining comparable photometric rendering quality. The geometric loss with 'planarity' from Gaussians provides the highest geometric accuracy, while 'omnivariance' in Gaussian neighborhoods reduces floater artifacts and number of Gaussians the most. This makes FeatureGS a strong method for geometrically accurate, artifact-reduced and memory-efficient 3D scene reconstruction, enabling the direct use of Gaussian centers for geometric representation.
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
Venue: arXiv cs.LG 2025
Year: 2025

Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments. However, DRL agents are vulnerable to noisy observations and adversarial attacks, and concerns about the adversarial robustness of DRL systems have emerged. Recent efforts have focused on addressing these robustness issues by establishing rigorous theoretical guarantees for the returns achieved by DRL agents in adversarial settings. Among these approaches, policy smoothing has proven to be an effective and scalable method for certifying the robustness of DRL agents. Nevertheless, existing certifiably robust DRL relies on policies trained with simple Gaussian augmentations, resulting in a suboptimal trade-off between certified robustness and certified return. To address this issue, we introduce a novel paradigm dubbed \texttt{C}ertified-r\texttt{A}dius-\texttt{M}aximizing \texttt{P}olicy (\texttt{CAMP}) training. \texttt{CAMP} is designed to enhance DRL policies, achieving better utility without compromising provable robustness. By leveraging the insight that the global certified radius can be derived from local certified radii based on training-time statistics, \texttt{CAMP} formulates a surrogate loss related to the local certified radius and optimizes the policy guided by this surrogate loss. We also introduce \textit{policy imitation} as a novel technique to stabilize \texttt{CAMP} training. Experimental results demonstrate that \texttt{CAMP} significantly improves the robustness-return trade-off across various tasks. Based on the results, \texttt{CAMP} can achieve up to twice the certified expected return compared to that of baselines. Our code is available at https://github.com/NeuralSec/camp-robust-rl.
Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements
Venue: arXiv cs.LG 2025
Year: 2025

The interconnected nature of the economic variables influencing a firm's performance makes the prediction of a company's earning trend a challenging task. Existing methodologies often rely on simplistic models and financial ratios failing to capture the complexity of interacting influences. In this paper, we apply Machine Learning techniques to raw financial statements data taken from AIDA, a Database comprising Italian listed companies' data from 2013 to 2022. We present a comparative study of different models and following the European AI regulations, we complement our analysis by applying explainability techniques to the proposed models. In particular, we propose adopting an eXplainable Artificial Intelligence method based on Game Theory to identify the most sensitive features and make the result more interpretable.
vNV-Heap: An Ownership-Based Virtually Non-Volatile Heap for Embedded Systems
Venue: arXiv cs.OS 2025
Year: 2025

The Internet of Batteryless Things might revolutionize our understanding of connected devices by harvesting required operational energy from the environment. These systems come with the system-software challenge that the intermittently powered IoT devices have to checkpoint their state in non-volatile memory to later resume with this state when sufficient energy is available. The scarce energy resources demand that only modified data is persisted before a power failure, which requires precise modification tracking. We present vNV-Heap, the first ownership-based virtually Non-Volatile Heap for intermittently powered systems with guaranteed power-failure resilience. The heap exploits ownership systems, a zero-cost (i.e., compile-time) abstraction for example implemented by Rust, to track modifications and virtualize object persistence. To achieve power-failure resilience, our heap is designed and implemented to guarantee bounded operations by static program code analysis: For example, the heap allows for determining a worst-case energy consumption for the operation of persisting modified and currently volatile objects. The evaluation of our open-source implementation on an embedded hardware platform (i.e., ESP32-C3) shows that using our heap abstraction is more energy efficient than existing approaches while also providing runtime guarantees by static worst-case bounds.
STGCN-LSTM for Olympic Medal Prediction: Dynamic Power Modeling and Causal Policy Optimization
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes a novel hybrid model, STGCN-LSTM, to forecast Olympic medal distributions by integrating the spatio-temporal relationships among countries and the long-term dependencies of national performance. The Spatial-Temporal Graph Convolution Network (STGCN) captures geographic and interactive factors-such as coaching exchange and socio-economic links-while the Long Short-Term Memory (LSTM) module models historical trends in medal counts, economic data, and demographics. To address zero-inflated outputs (i.e., the disparity between countries that consistently yield wins and those never having won medals), a Zero-Inflated Compound Poisson (ZICP) framework is incorporated to separate random zeros from structural zeros, providing a clearer view of potential breakthrough performances. Validation includes historical backtracking, policy shock simulations, and causal inference checks, confirming the robustness of the proposed method. Results shed light on the influence of coaching mobility, event specialization, and strategic investment on medal forecasts, offering a data-driven foundation for optimizing sports policies and resource allocation in diverse Olympic contexts.
BitMLx: Secure Cross-chain Smart Contracts For Bitcoin-style Cryptocurrencies
Venue: arXiv cs.CR 2025
Year: 2025

A smart contract is an interactive program that governs funds in the realm of a single cryptocurrency. Yet, the many existing cryptocurrencies have spurred the design of cross-chain applications that require interactions with multiple cryptocurrencies simultaneously. Currently, cross-chain applications are implemented as use-case-specific cryptographic protocols that serve as overlay to synchronize smart contract executions in the different cryptocurrencies. Hence, their design requires substantial expertise, as well as a security analysis in complex cryptographic frameworks. In this work, we present BitMLx, the first domain-specific language for cross-chain smart contracts, enabling interactions with several users that hold funds across multiple Bitcoin-like cryptocurrencies. We contribute a compiler to automatically translate a BitMLx contract into one contract per involved cryptocurrency and a user strategy that synchronizes the execution of these contracts. We prove that an honest user, who follows the prescribed strategy when interacting with the several contracts, ends up with at least as many funds as in the corresponding execution of the BitMLx contract. Last, but not least, we implement the BitMLx compiler and demonstrate its utility in the design of illustrative examples of cross-chain applications such as multi-chain donations or loans across different cryptocurrencies.
Yin-Yang: Developing Motifs With Long-Term Structure And Controllability
Venue: arXiv cs.SD 2025
Year: 2025

Transformer models have made great strides in generating symbolically represented music with local coherence. However, controlling the development of motifs in a structured way with global form remains an open research area. One of the reasons for this challenge is due to the note-by-note autoregressive generation of such models, which lack the ability to correct themselves after deviations from the motif. In addition, their structural performance on datasets with shorter durations has not been studied in the literature. In this study, we propose Yin-Yang, a framework consisting of a phrase generator, phrase refiner, and phrase selector models for the development of motifs into melodies with long-term structure and controllability. The phrase refiner is trained on a novel corruption-refinement strategy which allows it to produce melodic and rhythmic variations of an original motif at generation time, thereby rectifying deviations of the phrase generator. We also introduce a new objective evaluation metric for quantifying how smoothly the motif manifests itself within the piece. Evaluation results show that our model achieves better performance compared to state-of-the-art transformer models while having the advantage of being controllable and making the generated musical structure semi-interpretable, paving the way for musical analysis. Our code and demo page can be found at https://github.com/keshavbhandari/yinyang.
2SSP: A Two-Stage Framework for Structured Pruning of LLMs
Venue: arXiv cs.CL 2025
Year: 2025

We propose a novel Two-Stage framework for Structured Pruning (\textsc{2SSP}) for pruning Large Language Models (LLMs), which combines two different strategies of pruning, namely Width and Depth Pruning. The first stage (Width Pruning) removes entire neurons, hence their corresponding rows and columns, aiming to preserve the connectivity among the pruned structures in the intermediate state of the Feed-Forward Networks in each Transformer block. This is done based on an importance score measuring the impact of each neuron on the output magnitude. The second stage (Depth Pruning), instead, removes entire Attention submodules. This is done by applying an iterative process that removes the Attention with the minimum impact on a given metric of interest (in our case, perplexity). We also propose a novel mechanism to balance the sparsity rate of the two stages w.r.t. to the desired global sparsity. We test \textsc{2SSP} on four LLM families and three sparsity rates (25\%, 37.5\%, and 50\%), measuring the resulting perplexity over three language modeling datasets as well as the performance over six downstream tasks. Our method consistently outperforms five state-of-the-art competitors over three language modeling and six downstream tasks, with an up to two-order-of-magnitude gain in terms of pruning time. The code is available at https://github.com/FabrizioSandri/2SSP.
Iso-Recursive Multiparty Sessions and their Automated Verification -- Technical Report
Venue: arXiv cs.PL 2025
Year: 2025

Most works on session types take an equi-recursive approach and do not distinguish among a recursive type and its unfolding. This becomes more important in recent type systems which do not require global types, also known as generalised multiparty session types (GMST). In GMST, in order to establish properties as deadlock-freedom, the environments which type processes are assumed to satisfy extensional properties holding in all infinite sequences. This is a problem because: (1) the mechanisation of GMST and equi-recursion in proof assistants is utterly complex and eventually requires co-induction; and (2) the implementation of GMST in type checkers relies on model checkers for environment verification, and thus the program analysis is not self-contained. In this paper, we overcome these limitations by providing an iso-recursive typing system that computes the behavioural properties of environments. The type system relies on a terminating function named compliance that computes all final redexes of an environment, and determines when these redexes do not contain mismatches or deadlocks: compliant environments cannot go wrong. The function is defined theoretically by introducing the novel notions of deterministic LTS of environments and of environment closure, and can be implemented in mainstream programming languages and compilers. We showcase an implementation in OCaml by using exception handling to tackle the inherent non-determinism of synchronisation of branching and selection types. We assess that the implementation provides the desired properties, namely absence of mismatches and of deadlocks in environments, by resorting to automated deductive verification performed in tools of the OCaml ecosystem relying on Why3.
AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing
Venue: arXiv cs.LG 2025
Year: 2025

In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize a process parameter defect dataset to fine-tune a collection of models, titled AdditiveLLM, for the purpose of predicting potential defect regimes including Keyholing, Lack of Fusion, and Balling. We compare different methods of input formatting in order to gauge the model's performance to correctly predict defect regimes on our sparse Baseline dataset and our natural language Prompt dataset. The model displays robust predictive capability, achieving an accuracy of 93\% when asked to provide the defect regimes associated with a set of process parameters. The incorporation of natural language input further simplifies the task of process parameters selection, enabling users to identify optimal settings specific to their build.
BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights
Venue: arXiv cs.CL 2025
Year: 2025

We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyphone disambiguation in the language. Building upon CosyVoice, we incorporate a $S^{3}$ tokenizer, a large language model (LLM), an optimal-transport conditional flow matching model (OT-CFM), and a grapheme to phoneme prediction model, to generate realistic speech that closely mimics human utterances. Our evaluation demonstrates BreezyVoice's superior performance in both general and code-switching contexts, highlighting its robustness and effectiveness in generating high-fidelity speech. Additionally, we address the challenges of generalizability in modeling long-tail speakers and polyphone disambiguation. Our approach significantly enhances performance and offers valuable insights into the workings of neural codec TTS systems.
Leveraging Multimodal LLM for Inspirational User Interface Search
Venue: arXiv cs.HC 2025
Year: 2025

Inspirational search, the process of exploring designs to inform and inspire new creative work, is pivotal in mobile user interface (UI) design. However, exploring the vast space of UI references remains a challenge. Existing AI-based UI search methods often miss crucial semantics like target users or the mood of apps. Additionally, these models typically require metadata like view hierarchies, limiting their practical use. We used a multimodal large language model (MLLM) to extract and interpret semantics from mobile UI images. We identified key UI semantics through a formative study and developed a semantic-based UI search system. Through computational and human evaluations, we demonstrate that our approach significantly outperforms existing UI retrieval methods, offering UI designers a more enriched and contextually relevant search experience. We enhance the understanding of mobile UI design semantics and highlight MLLMs' potential in inspirational search, providing a rich dataset of UI semantics for future studies.
P-TAME: Explain Any Image Classifier with Trained Perturbations
Venue: arXiv cs.CV 2025
Year: 2025

The adoption of Deep Neural Networks (DNNs) in critical fields where predictions need to be accompanied by justifications is hindered by their inherent black-box nature. In this paper, we introduce P-TAME (Perturbation-based Trainable Attention Mechanism for Explanations), a model-agnostic method for explaining DNN-based image classifiers. P-TAME employs an auxiliary image classifier to extract features from the input image, bypassing the need to tailor the explanation method to the internal architecture of the backbone classifier being explained. Unlike traditional perturbation-based methods, which have high computational requirements, P-TAME offers an efficient alternative by generating high-resolution explanations in a single forward pass during inference. We apply P-TAME to explain the decisions of VGG-16, ResNet-50, and ViT-B-16, three distinct and widely used image classifiers. Quantitative and qualitative results show that our method matches or outperforms previous explainability methods, including model-specific approaches. Code and trained models will be released upon acceptance.
SMT-Boosted Security Types for Low-Level MPC
Venue: arXiv cs.CR 2025
Year: 2025

Secure Multi-Party Computation (MPC) is an important enabling technology for data privacy in modern distributed applications. We develop a new type theory to automatically enforce correctness,confidentiality, and integrity properties of protocols written in the \emph{Prelude/Overture} language framework. Judgements in the type theory are predicated on SMT verifications in a theory of finite fields, which supports precise and efficient analysis. Our approach is automated, compositional, scalable, and generalizes to arbitrary prime fields for data and key sizes.
The Right to AI
Venue: arXiv cs.CY 2025
Year: 2025

This paper proposes a Right to AI, which asserts that individuals and communities should meaningfully participate in the development and governance of the AI systems that shape their lives. Motivated by the increasing deployment of AI in critical domains and inspired by Henri Lefebvre's concept of the Right to the City, we reconceptualize AI as a societal infrastructure, rather than merely a product of expert design. In this paper, we critically evaluate how generative agents, large-scale data extraction, and diverse cultural values bring new complexities to AI oversight. The paper proposes that grassroots participatory methodologies can mitigate biased outcomes and enhance social responsiveness. It asserts that data is socially produced and should be managed and owned collectively. Drawing on Sherry Arnstein's Ladder of Citizen Participation and analyzing nine case studies, the paper develops a four-tier model for the Right to AI that situates the current paradigm and envisions an aspirational future. It proposes recommendations for inclusive data ownership, transparent design processes, and stakeholder-driven oversight. We also discuss market-led and state-centric alternatives and argue that participatory approaches offer a better balance between technical efficiency and democratic legitimacy.