OpenRaiser/Intern-Atlas huggingface dataset:
Geometric Impedance Control on SE(3) for Robotic Manipulators
Venue: arXiv cs.RO 2025
Year: 2025

After its introduction, impedance control has been utilized as a primary control scheme for robotic manipulation tasks that involve interaction with unknown environments. While impedance control has been extensively studied, the geometric structure of SE(3) for the robotic manipulator itself and its use in formulating a robotic task has not been adequately addressed. In this paper, we propose a differential geometric approach to impedance control. Given a left-invariant error metric in SE(3), the corresponding error vectors in position and velocity are first derived. We then propose the impedance control schemes that adequately account for the geometric structure of the manipulator in SE(3) based on a left-invariant potential function. The closed-loop stabilities for the proposed control schemes are verified using Lyapunov function-based analysis. The proposed control design clearly outperformed a conventional impedance control approach when tracking challenging trajectory profiles.
Psychophysiology-aided Perceptually Fluent Speech Analysis of Children Who Stutter
Venue: arXiv cs.SD 2025
Year: 2025

This paper presents a novel approach named PASAD that detects changes in perceptually fluent speech acoustics of young children. Particularly, analysis of perceptually fluent speech enables identifying the speech-motor-control factors that are considered as the underlying cause of stuttering disfluencies. Recent studies indicate that the speech production of young children, especially those who stutter, may get adversely affected by situational physiological arousal. A major contribution of this paper is leveraging the speaker's situational physiological responses in real-time to analyze the speech signal effectively. The presented PASAD approach adapts a Hyper-Network structure to extract temporal speech importance information leveraging physiological parameters. Moreover, we collected speech and physiological sensing data from 73 preschool-age children who stutter (CWS) and who do not stutter (CWNS) in different conditions. PASAD's unique architecture enables identifying speech attributes distinct to a CWS's fluent speech and mapping them to the speaker's respective speech-motor-control factors. Extracted knowledge can enhance understanding of children's speech-motor-control and stuttering development. Our comprehensive evaluation shows that PASAD outperforms state-of-the-art multi-modal baseline approaches in different conditions, is expressive and adaptive to the speaker's speech and physiology, generalizable, robust, and is real-time executable.
Introduction to Online Control
Venue: arXiv cs.LG 2025
Year: 2025

This text presents an introduction to an emerging paradigm in control of dynamical systems and differentiable reinforcement learning called online nonstochastic control. The new approach applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. The primary distinction between online nonstochastic control and other frameworks is the objective. In optimal control, robust control, and other control methodologies that assume stochastic noise, the goal is to perform comparably to an offline optimal strategy. In online nonstochastic control, both the cost functions as well as the perturbations from the assumed dynamical model are chosen by an adversary. Thus the optimal policy is not defined a priori. Rather, the target is to attain low regret against the best policy in hindsight from a benchmark class of policies. This objective suggests the use of the decision making framework of online convex optimization as an algorithmic methodology. The resulting methods are based on iterative mathematical optimization algorithms, and are accompanied by finite-time regret and computational complexity guarantees.
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
Venue: arXiv cs.LG 2025
Year: 2025

High fidelity design evaluation processes such as Computational Fluid Dynamics and Finite Element Analysis are often replaced with data driven surrogates to reduce computational cost in engineering design optimization. However, building accurate surrogate models still requires a large number of expensive simulations. To address this challenge, we introduce epsilon HQS, a scalable active learning strategy that leverages a student teacher framework to train deep neural networks efficiently. Unlike Bayesian AL methods, which are computationally demanding with DNNs, epsilon HQS selectively queries informative samples to reduce labeling cost. Applied to CFD, FEA, and propeller design tasks, our method achieves higher accuracy under fixed labeling cost budgets.
Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics
Venue: arXiv cs.CV 2025
Year: 2025

High-quality surface normal can help improve geometry estimation in problems faced by autonomous vehicles, such as collision avoidance and occlusion inference. While a considerable volume of literature focuses on densely scanned indoor scenarios, normal estimation during autonomous driving remains an intricate problem due to the sparse, non-uniform, and noisy nature of real-world LiDAR scans. In this paper, we introduce a multi-modal technique that leverages 3D point clouds and 2D colour images obtained from LiDAR and camera sensors for surface normal estimation. We present the Hybrid Geometric Transformer (HGT), a novel transformer-based neural network architecture that proficiently fuses visual semantic and 3D geometric information. Furthermore, we developed an effective learning strategy for the multi-modal data. Experimental results demonstrate the superior effectiveness of our information fusion approach compared to existing methods. It has also been verified that the proposed model can learn from a simulated 3D environment that mimics a traffic scene. The learned geometric knowledge is transferable and can be applied to real-world 3D scenes in the KITTI dataset. Further tasks built upon the estimated normal vectors in the KITTI dataset show that the proposed estimator has an advantage over existing methods.
Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets
Venue: arXiv cs.LG 2025
Year: 2025

This work proposes a method to evaluate synthetic tabular data generated to augment small sample datasets. While data augmentation techniques can increase sample counts for machine learning applications, traditional validation approaches fail when applied to extremely limited sample sizes. Our experiments across four datasets reveal significant inconsistencies between global metrics and topological measures, with statistical tests producing unreliable significance values due to insufficient sample sizes. We demonstrate that common metrics like propensity scoring and MMD often suggest similarity where fundamental topological differences exist. Our proposed normalized Bottleneck distance based metric provides complementary insights but suffers from high variability across experimental runs and occasional values exceeding theoretical bounds, showing inherent instability in topological approaches for very small datasets. These findings highlight the critical need for multi-faceted evaluation methodologies when validating synthetic data generated from limited samples, as no single metric reliably captures both distributional and structural similarity.
Multi-head Ensemble of Smoothed Classifiers for Certified Robustness
Venue: arXiv cs.LG 2025
Year: 2025

Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple Deep Neural Networks (DNNs) has shown state-of-the-art performances due to its variance reduction effect over Gaussian noises. However, such an ensemble brings heavy computation burdens in both training and certification, and yet under-exploits individual DNNs and their mutual effects, as the communication between these classifiers is commonly ignored in optimization. In this work, we consider a novel ensemble-based training way for a single DNN with multiple augmented heads, named as SmOothed Multi-head Ensemble (SOME). In SOME, similar to the pursuit of variance reduction via ensemble, an ensemble of multiple heads imposed with a cosine constraint inside a single DNN is employed with much cheaper training and certification computation overloads in RS. In such network structure, an associated training strategy is designed by introducing a circular communication flow among those augmented heads. That is, each head teaches its neighbor with the self-paced learning strategy using smoothed losses, which are specifically designed in relation to certified robustness. The deployed multi-head structure and the circular-teaching scheme in SOME jointly contribute to the diversities among multiple heads and benefit their ensemble, leading to a competitively stronger certifiably-robust RS-based defense than ensembling multiple DNNs (effectiveness) at the cost of much less computational expenses (efficiency), verified by extensive experiments and discussions.
The distribution of syntactic dependency distances
Venue: arXiv cs.CL 2025
Year: 2025

The syntactic structure of a sentence can be represented as a graph, where vertices are words and edges indicate syntactic dependencies between them. In this setting, the distance between two linked words is defined as the difference between their positions. Here we wish to contribute to the characterization of the actual distribution of syntactic dependency distances, which has previously been argued to follow a power-law distribution. Here we propose a new model with two exponential regimes in which the probability decay is allowed to change after a break-point. This transition could mirror the transition from the processing of word chunks to higher-level structures. We find that a two-regime model - where the first regime follows either an exponential or a power-law decay - is the most likely one in all 20 languages we considered, independently of sentence length and annotation style. Moreover, the break-point exhibits low variation across languages and averages values of 4-5 words, suggesting that the amount of words that can be simultaneously processed abstracts from the specific language to a high degree. The probability decay slows down after the breakpoint, consistently with a universal chunk-and-pass mechanism. Finally, we give an account of the relation between the best estimated model and the closeness of syntactic dependencies as function of sentence length, according to a recently introduced optimality score.
Posterior Sampling for Continuing Environments
Venue: arXiv cs.LG 2025
Year: 2025

We develop an extension of posterior sampling for reinforcement learning (PSRL) that is suited for a continuing agent-environment interface and integrates naturally into agent designs that scale to complex environments. The approach, continuing PSRL, maintains a statistically plausible model of the environment and follows a policy that maximizes expected $γ$-discounted return in that model. At each time, with probability $1-γ$, the model is replaced by a sample from the posterior distribution over environments. For a choice of discount factor that suitably depends on the horizon $T$, we establish an $\tilde{O}(τS \sqrt{A T})$ bound on the Bayesian regret, where $S$ is the number of environment states, $A$ is the number of actions, and $τ$ denotes the reward averaging time, which is a bound on the duration required to accurately estimate the average reward of any policy. Our work is the first to formalize and rigorously analyze the resampling approach with randomized exploration.
FedCC: Robust Federated Learning against Model Poisoning Attacks
Venue: arXiv cs.CR 2025
Year: 2025

Federated learning is a distributed framework designed to address privacy concerns. However, it introduces new attack surfaces, which are especially prone when data is non-Independently and Identically Distributed. Existing approaches fail to effectively mitigate the malicious influence in this setting; previous approaches often tackle non-IID data and poisoning attacks separately. To address both challenges simultaneously, we present FedCC, a simple yet effective novel defense algorithm against model poisoning attacks. It leverages the Centered Kernel Alignment similarity of Penultimate Layer Representations for clustering, allowing the identification and filtration of malicious clients, even in non-IID data settings. The penultimate layer representations are meaningful since the later layers are more sensitive to local data distributions, which allows better detection of malicious clients. The sophisticated utilization of layer-wise Centered Kernel Alignment similarity allows attack mitigation while leveraging useful knowledge obtained. Our extensive experiments demonstrate the effectiveness of FedCC in mitigating both untargeted model poisoning and targeted backdoor attacks. Compared to existing outlier detection-based and first-order statistics-based methods, FedCC consistently reduces attack confidence to zero. Specifically, it significantly minimizes the average degradation of global performance by 65.5\%. We believe that this new perspective on aggregation makes it a valuable contribution to the field of FL model security and privacy. The code will be made available upon acceptance.
Integration in Cones
Venue: arXiv cs.LO 2025
Year: 2025

Measurable cones, with linear and measurable functions as morphisms, are a model of intuitionistic linear logic and of call-by-name probabilistic PCF which accommodates "continuous data types" such as the real line. So far however, they lacked a major feature to make them a model of more general probabilistic programming languages (notably call-by-value and call-by-push-value languages): a theory of integration for functions whose codomain is a cone, which is the key ingredient for interpreting the sampling programming primitives. The goal of this paper is to develop such a theory: our definition of integrals is an adaptation to cones of Pettis integrals in topological vector spaces. We prove that such integrable cones, with integral-preserving linear maps as morphisms, form a model of Linear Logic for which we develop two exponential comonads: the first based on a notion of stable and measurable functions introduced in earlier work and the second based on a new notion of integrable analytic function on cones.
Petri Nets-based Methods on Automatically Detecting for Concurrency Bugs in Rust Programs
Venue: arXiv cs.PL 2025
Year: 2025

Rust's memory safety guarantees, notably ownership and lifetime systems, have driven its widespread adoption. Concurrency bugs still occur in Rust programs, and existing detection approaches exhibit significant limitations: static analyzers suffer from context insensitivity and high false positives, while dynamic methods incur prohibitive runtime costs due to exponential path exploration. This paper presents a Petri net-based method for efficient, precise detection of Rust concurrency bugs. The method rests on three pillars: (1) A syntax-preserving program-to-Petri-net transformation tailored for target bug classes; (2) Semantics-preserving state compression via context-aware slicing; (3) Bug detection through efficient Petri net reachability analysis. The core innovation is its rigorous, control-flow-driven modeling of Rust's ownership semantics and synchronization primitives within the Petri net structure, with data operations represented as token movements. Integrated pointer analysis automates alias identification during transformation. Experiments on standard Rust concurrency benchmarks demonstrate that our method outperforms the state-of-the-art methods LockBud and Miri that are both tools of detecting concurrency bugs of Rust programs. Compared to LockBud, our approach reduces false positives by 35.7\% and false negatives by 28.3\% , which is obtained through our precise flow-sensitive pointer analysis. Compared with Miri that is a dynamic analysis tool, although Miri can obtain the same detection results, our method achieves 100% faster verification speed since our method takes a state reduce algorithm.
The Formal Theory of Monads, Univalently
Venue: arXiv cs.LO 2025
Year: 2025

We develop the formal theory of monads, as established by Street, in univalent foundations. This allows us to formally reason about various kinds of monads on the right level of abstraction. In particular, we define the bicategory of monads internal to a bicategory, and prove that it is univalent. We also define Eilenberg-Moore objects, and we show that both Eilenberg-Moore categories and Kleisli categories give rise to Eilenberg-Moore objects. Finally, we relate monads and adjunctions in arbitrary bicategories. Our work is formalized in Coq using the UniMath library.
Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms
Venue: arXiv cs.DC 2025
Year: 2025

Serverless is an attractive computing model that offers seamless scalability and elasticity; it takes the infrastructure management burden away from users and enables a pay-as-you-use billing model. As a result, serverless is becoming increasingly popular to support highly elastic and bursty workloads. However, existing platforms are supported by bloated virtualization stacks, which, combined with bursty and irregular invocations, lead to high memory and latency overheads. To reduce the virtualization stack bloat, we propose Hydra, a virtualized multi-language runtime and platform capable of hosting multiple sandboxes running concurrently. To fully leverage Hydra's virtualized runtime, we revisit the existing serverless platform design to make it colocation-aware across owners and functions, and to feature a caching layer of pre-allocated Hydra instances that can be used by different functions written in different languages to reduce cold starts. We also propose a snapshotting mechanism to checkpoint and restore individual sandboxes. By consolidating multiple serverless function invocations through Hydra, we improve the overall function density (ops/GB-sec) by 2.41x on average compared to OpenWhisk runtimes, the state-of-the-art single-language runtimes used in most serverless platforms, and by 1.43x on average compared to Knative runtimes supporting invocation colocation within the same function. When reproducing the Azure Functions trace, our serverless platform operating Hydra instances reduces the overall memory footprint by 21.3-43.9% compared to operating OpenWhisk instances and by 14.5-30% compared to operating Knative instances. Hydra eliminates cold starts thanks to the pool of pre-warmed runtime instances, reducing p99 latency by 45.3-375.5x compared to OpenWhisk and by 1.9-51.4x compared to Knative.
Learned-Database Systems Security
Venue: arXiv cs.CR 2025
Year: 2025

A learned database system uses machine learning (ML) internally to improve performance. We can expect such systems to be vulnerable to some adversarial-ML attacks. Often, the learned component is shared between mutually-distrusting users or processes, much like microarchitectural resources such as caches, potentially giving rise to highly-realistic attacker models. However, compared to attacks on other ML-based systems, attackers face a level of indirection as they cannot interact directly with the learned model. Additionally, the difference between the attack surface of learned and non-learned versions of the same system is often subtle. These factors obfuscate the de-facto risks that the incorporation of ML carries. We analyze the root causes of potentially-increased attack surface in learned database systems and develop a framework for identifying vulnerabilities that stem from the use of ML. We apply our framework to a broad set of learned components currently being explored in the database community. To empirically validate the vulnerabilities surfaced by our framework, we choose 3 of them and implement and evaluate exploits against these. We show that the use of ML cause leakage of past queries in a database, enable a poisoning attack that causes exponential memory blowup in an index structure and crashes it in seconds, and enable index users to snoop on each others' key distributions by timing queries over their own keys. We find that adversarial ML is an universal threat against learned components in database systems, point to open research gaps in our understanding of learned-systems security, and conclude by discussing mitigations, while noting that data leakage is inherent in systems whose learned component is shared between multiple parties.
BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection
Venue: arXiv cs.NI 2025
Year: 2025

Recent advances in distributed intelligence have driven impressive progress across a diverse range of applications, from industrial automation to autonomous transportation. Nevertheless, deploying distributed learning services over wireless networks poses numerous challenges. These arise from inherent uncertainties in wireless environments (e.g., random channel fluctuations), limited resources (e.g., bandwidth and transmit power), and the presence of coexisting services on the network. In this paper, we investigate a mixed service scenario wherein high-priority ultra-reliable low latency communication (URLLC) and low-priority distributed learning services run concurrently over a network. Utilizing device selection, we aim to minimize the convergence time of distributed learning while simultaneously fulfilling the requirements of the URLLC service. We formulate this problem as a Markov decision process and address it via BSAC-CoEx, a framework based on the branching soft actor-critic (BSAC) algorithm that determines each device's participation decision through distinct branches in the actor's neural network. We evaluate our solution with a realistic simulator that is compliant with 3GPP standards for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delays of the distributed learning service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all wireless resources.
Human Activity Recognition in an Open World
Venue: arXiv cs.CV 2025
Year: 2025

Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current state-of-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The protocol as an algorithm for reproducing experiments using the KOWL-718 benchmark will be publicly released with code and containers at https://github.com/prijatelj/human-activity-recognition-in-an-open-world. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released.