OpenRaiser/Intern-Atlas huggingface dataset:
Neuradicon: operational representation learning of neuroimaging reports
Venue: arXiv cs.CL 2025
Year: 2025

Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of radiological services to throughput undifferentiated by content, impeding specific, targeted operational optimization. Here we present Neuradicon, a natural language processing (NLP) framework for quantitative analysis of neuroradiological reports. Our framework is a hybrid of rule-based and artificial intelligence models to represent neurological reports in succinct, quantitative form optimally suited to operational guidance. We demonstrate the application of Neuradicon to operational phenotyping of a corpus of 336,569 reports, and report excellent generalizability across time and two independent healthcare institutions.
Using Visual Anomaly Detection for Task Execution Monitoring
Venue: arXiv cs.RO 2025
Year: 2025

Execution monitoring is essential for robots to detect and respond to failures. Since it is impossible to enumerate all failures for a given task, we learn from successful executions of the task to detect visual anomalies during runtime. Our method learns to predict the motions that occur during the nominal execution of a task, including camera and robot body motion. A probabilistic U-Net architecture is used to learn to predict optical flow, and the robot's kinematics and 3D model are used to model camera and body motion. The errors between the observed and predicted motion are used to calculate an anomaly score. We evaluate our method on a dataset of a robot placing a book on a shelf, which includes anomalies such as falling books, camera occlusions, and robot disturbances. We find that modeling camera and body motion, in addition to the learning-based optical flow prediction, results in an improvement of the area under the receiver operating characteristic curve from 0.752 to 0.804, and the area under the precision-recall curve from 0.467 to 0.549.
An Operator Splitting View of Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Over the past few years, the federated learning ($\texttt{FL}$) community has witnessed a proliferation of new $\texttt{FL}$ algorithms. However, our understating of the theory of $\texttt{FL}$ is still fragmented, and a thorough, formal comparison of these algorithms remains elusive. Motivated by this gap, we show that many of the existing $\texttt{FL}$ algorithms can be understood from an operator splitting point of view. This unification allows us to compare different algorithms with ease, to refine previous convergence results and to uncover new algorithmic variants. In particular, our analysis reveals the vital role played by the step size in $\texttt{FL}$ algorithms. The unification also leads to a streamlined and economic way to accelerate $\texttt{FL}$ algorithms, without incurring any communication overhead. We perform numerical experiments on both convex and nonconvex models to validate our findings.
A Unified Framework for Hopsets and Spanners
Venue: arXiv cs.DS 2025
Year: 2025

Given an undirected graph $G=(V,E)$, an {\em $(α,β)$-spanner} $H=(V,E')$ is a subgraph that approximately preserves distances; for every $u,v\in V$, $d_H(u,v)\le α\cdot d_G(u,v)+β$. An $(α,β)$-hopset is a graph $H=(V,E")$, so that adding its edges to $G$ guarantees every pair has an $α$-approximate shortest path that has at most $β$ edges (hops), that is, $d_G(u,v)\le d_{G\cup H}^{(β)}(u,v)\le α\cdot d_G(u,v)$. Given the usefulness of spanners and hopsets for fundamental algorithmic tasks, several different algorithms and techniques were developed for their construction, for various regimes of the stretch parameter $α$. In this work we develop a single algorithm that can attain all state-of-the-art spanners and hopsets for general graphs, by choosing the appropriate input parameters. In fact, in some cases it also improves upon the previous best results. We also show a lower bound on our algorithm. In \cite{BP20}, given a parameter $k$, a $(O(k^ε),O(k^{1-ε}))$-hopset of size $\tilde{O}(n^{1+1/k})$ was shown for any $n$-vertex graph and parameter $0<ε<1$, and they asked whether this result is best possible. We resolve this open problem, showing that any $(α,β)$-hopset of size $O(n^{1+1/k})$ must have $α\cdot β\geΩ(k)$.
Construction of 2-D Z-Complementary Array Code Sets with Flexible Lengths for Different System Requirements
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we propose a new and optimal construction of two-dimensional (2-D) Z-complementary array code set (ZCACS) using multivariable extended Boolean functions (EBFs). The proposed 2-D arrays have many applications in modern wireless communications, such as multi-carrier code division multiple access (MC-CDMA), massive multiple input multiple output (mMIMO), etc. The main theoretical problem for sequences and 2-D arrays for application in MC-CDMA lies in the efficient construction of such sequences and arrays, which have low peak-to-mean envelope power ratio (PMEPR) and flexible parameter values. The PMEPR measures the power efficiency of the concerned system and hence has been an important research topic for past several years. The proposed construction produces a better PMEPR upper bound than the existing constructions. We also propose a tighter upper bound for the set size which translates more number of supported users in the communication system. We show that for some special cases, the proposed code set is optimal with respect to that bound. Finally, We derive 2-D Golay complementary array set (GCAS) and Golay complementary set (GCS) from the proposed construction, which has significant application in uniform rectangular array (URA)-based massive multiple-input multiple-output (mMIMO) system to achieve omnidirectional transmission. The simulation result shows the performance benefits of the derived arrays. In essence, we show that the flexibility of the parameters of the proposed 2-D ZCACS makes it a good candidate for practical use cases, both in theory and simulation.
Benchmarking the Robustness of Instance Segmentation Models
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a comprehensive evaluation of instance segmentation models with respect to real-world image corruptions as well as out-of-domain image collections, e.g. images captured by a different set-up than the training dataset. The out-of-domain image evaluation shows the generalization capability of models, an essential aspect of real-world applications and an extensively studied topic of domain adaptation. These presented robustness and generalization evaluations are important when designing instance segmentation models for real-world applications and picking an off-the-shelf pretrained model to directly use for the task at hand. Specifically, this benchmark study includes state-of-the-art network architectures, network backbones, normalization layers, models trained starting from scratch versus pretrained networks, and the effect of multi-task training on robustness and generalization. Through this study, we gain several insights. For example, we find that group normalization enhances the robustness of networks across corruptions where the image contents stay the same but corruptions are added on top. On the other hand, batch normalization improves the generalization of the models across different datasets where statistics of image features change. We also find that single-stage detectors do not generalize well to larger image resolutions than their training size. On the other hand, multi-stage detectors can easily be used on images of different sizes. We hope that our comprehensive study will motivate the development of more robust and reliable instance segmentation models.
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
Venue: arXiv cs.LG 2025
Year: 2025

The recent breakthroughs of deep neural networks (DNNs) and the advent of billions of Internet of Things (IoT) devices have excited an explosive demand for intelligent IoT devices equipped with domain-specific DNN accelerators. However, the deployment of DNN accelerator enabled intelligent functionality into real-world IoT devices still remains particularly challenging. First, powerful DNNs often come at prohibitive complexities, whereas IoT devices often suffer from stringent resource constraints. Second, while DNNs are vulnerable to adversarial attacks especially on IoT devices exposed to complex real-world environments, many IoT applications require strict security. Existing DNN accelerators mostly tackle only one of the two aforementioned challenges (i.e., efficiency or adversarial robustness) while neglecting or even sacrificing the other. To this end, we propose a 2-in-1 Accelerator, an integrated algorithm-accelerator co-design framework aiming at winning both the adversarial robustness and efficiency of DNN accelerators. Specifically, we first propose a Random Precision Switch (RPS) algorithm that can effectively defend DNNs against adversarial attacks by enabling random DNN quantization as an in-situ model switch. Furthermore, we propose a new precision-scalable accelerator featuring (1) a new precision-scalable MAC unit architecture which spatially tiles the temporal MAC units to boost both the achievable efficiency and flexibility and (2) a systematically optimized dataflow that is searched by our generic accelerator optimizer. Extensive experiments and ablation studies validate that our 2-in-1 Accelerator can not only aggressively boost both the adversarial robustness and efficiency of DNN accelerators under various attacks, but also naturally support instantaneous robustness-efficiency trade-offs adapting to varied resources without the necessity of DNN retraining.
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Venue: arXiv cs.LG 2025
Year: 2025

Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting, ensembles of local decision rules can gain good predictive performance on data involving global structure. Meanwhile, machine learning models are being increasingly used to solve problems in high-stake domains including healthcare and finance. Here, there is an emerging consensus regarding the need for practitioners to understand whether and how those models could perform robustly in the deployment environments, in the presence of distributional shifts. Past research on local decision rules has focused mainly on maximizing discriminant patterns, without due consideration of robustness against distributional shifts. In order to fill this gap, we propose a new method to learn and ensemble local decision rules, that are robust both in the training and deployment environments. Specifically, we propose to leverage causal knowledge by regarding the distributional shifts in subpopulations and deployment environments as the results of interventions on the underlying system. We propose two regularization terms based on causal knowledge to search for optimal and stable rules. Experiments on both synthetic and benchmark datasets show that our method is effective and robust against distributional shifts in multiple environments.
Zeropod: Simplifying Datacenter Networking with Future-Proof Zero-Buffer Packet Switches
Venue: arXiv cs.NI 2025
Year: 2025

With the rapid growth of traffic volume in datacenter networks (DCNs), packet switches suffer from insufficient switching chip capacity and difficulties in transmission control, making it challenging to provide high goodput and low latency for emerging cloud applications. We present Zeropod, a future-proof DCN architecture featuring simplified zero-buffer packet switches inside the point-of-delivery (pod). Within each pod, traffic transmission is scheduled by a per-pod centralized scheduler for collision avoidance, enabling a highly simplified data plane, facilitating benefits like higher switching capacity and precise transmission control. Among the pods, buffered Core switches work as barriers and relay inter-pod data, limiting the scope of centralized scheduling and thus simplifying the control plane. Zeropod combines host-level and flow-level scheduling for high performance with low overhead. Evaluation results show that Zeropod consistently performs better or equivalent to traditional buffered DCN, particularly regarding flow completion time (FCT). When accounting for the increased switching capacity due to the removal of buffers, its performance is further improved. Zeropod explores an extreme end of the design spectrum, and we hope it can encourage further exploration in the DCN community.
Combinatorial Contracts
Venue: arXiv cs.GT 2025
Year: 2025

We introduce a new model of combinatorial contracts in which a principal delegates the execution of a costly task to an agent. To complete the task, the agent can take any subset of a given set of unobservable actions, each of which has an associated cost. The cost of a set of actions is the sum of the costs of the individual actions, and the principal's reward as a function of the chosen actions satisfies some form of diminishing returns. The principal incentivizes the agents through a contract, based on the observed outcome. Our main results are for the case where the task delegated to the agent is a project, which can be successful or not. We show that if the success probability as a function of the set of actions is gross substitutes, then an optimal contract can be computed with polynomially many value queries, whereas if it is submodular, the optimal contract is NP-hard. All our results extend to linear contracts for higher-dimensional outcome spaces, which we show to be robustly optimal given first moment constraints. Our analysis uncovers a new property of gross substitutes functions, and reveals many interesting connections between combinatorial contracts and combinatorial auctions, where gross substitutes is known to be the frontier for efficient computation.
Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview
Venue: arXiv cs.LG 2025
Year: 2025

Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other. By using a squared differential length as a Lyapunov-like function, its nonlinear stability analysis boils down to finding a suitable contraction metric that satisfies a stability condition expressed as a linear matrix inequality, indicating that many parallels can be drawn between well-known linear systems theory and contraction theory for nonlinear systems. Furthermore, contraction theory takes advantage of a superior robustness property of exponential stability used in conjunction with the comparison lemma. This yields much-needed safety and stability guarantees for neural network-based control and estimation schemes, without resorting to a more involved method of using uniform asymptotic stability for input-to-state stability. Such distinctive features permit the systematic construction of a contraction metric via convex optimization, thereby obtaining an explicit exponential bound on the distance between a time-varying target trajectory and solution trajectories perturbed externally due to disturbances and learning errors. The objective of this paper is, therefore, to present a tutorial overview of contraction theory and its advantages in nonlinear stability analysis of deterministic and stochastic systems, with an emphasis on deriving formal robustness and stability guarantees for various learning-based and data-driven automatic control methods. In particular, we provide a detailed review of techniques for finding contraction metrics and associated control and estimation laws using deep neural networks.
Design and Operation of Shared Machine Learning Clusters on Campus
Venue: arXiv cs.DC 2025
Year: 2025

Amid the rapid advancements in large machine learning (ML) models, universities worldwide are investing substantial funds and efforts into GPU clusters. However, managing a shared GPU cluster poses a pyramid of challenges, from hardware configuration to resource allocation among users. This paper introduces SING, a full-stack solution designed to streamline the management of shared GPU clusters in academic institutions. Motivated by the pressing need for efficient resource sharing and the challenges posed by limited staffing, we present a comprehensive view of SING's architecture and design choices, which achieves operational efficiency (i.e., low maintenance cost and high resource utilization). We also share experience and insights from the real-world operations of SING, including analysis of its usage patterns and management of incidents and failures. This paper is part of our ongoing effort to improve the management of shared ML clusters. We open-source relevant resources to facilitate the development and operation of similar clusters for ML.
Verify Linearizability of Concurrent Stacks
Venue: arXiv cs.SE 2025
Year: 2025

Proving linearizability of concurrent data structures is crucial for ensuring their correctness, but is challenging especially for implementations that employ sophisticated synchronization techniques. In this paper, we propose a new proof technique for verifying linearizability of concurrent stacks. We first prove the soundness of the elimination mechanism, a common optimization used in concurrent stacks, which enables simplifying the linearizability proofs. We then present a stack theorem that reduces the problem of proving linearizability to establishing a set of conditions based on the happened-before order of operations. The key idea is to use an extended partial order to capture when a pop operation can observe the effect of a push operation. We apply our proof technique to verify two concurrent stack algorithms: the Treiber stack and the Time-Stamped stack, demonstrating its practicality. Our approach provides a systematic and compositional way to prove linearizability of concurrent stacks.
Diagonalization of Polynomial-Time Deterministic Turing Machines via Nondeterministic Turing Machines
Venue: arXiv cs.CC 2025
Year: 2025

The {\em diagonalization technique} was invented by Georg Cantor to show that there are more real numbers than algebraic numbers and is very crucial in {\em theoretical computer science}. In this work, we enumerate all of the polynomial-time deterministic Turing machines and diagonalize against all of them by a universal nondeterministic Turing machine. As a result, we obtain that there is a language $L_d$ not accepted by any polynomial-time deterministic Turing machines but accepted by a nondeterministic Turing machine running within time $O(n^k)$ for any $k\in\mathbb{N}_1$. Based on these, we further show that $L_d\in\mathcal{NP}$. That is, in this work, we present a proof that $\mathcal{P}$ and $\mathcal{NP}$ differ. Meanwhile, we show that there exists a language $L_s$ in $\mathcal{P}$, but the machine accepting it also runs within time $O(n^k)$ for all $k\in\mathbb{N}_1$. Lastly, we show that if $\mathcal{P}^O=\mathcal{NP}^O$ and on some rational base assumptions, then the set $P^O$ of all polynomial-time deterministic oracle Turing machines with oracle $O$ is not enumerable, thus demonstrating that the diagonalization technique ({\em via a universal nondeterministic oracle Turing machine}) will generally {\em not} apply to the relativized versions of the $\mathcal{P}$ versus $\mathcal{NP}$ problem.
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data
Venue: arXiv cs.LG 2025
Year: 2025

Large network logs, recording multivariate time series generated from heterogeneous devices and sensors in a network, can often reveal important information about abnormal activities, such as network intrusions and device malfunctions. Existing machine learning methods for anomaly detection on multivariate time series typically assume that 1) normal sequences would have consistent behavior for training unsupervised models, or 2) require a large set of labeled normal and abnormal sequences for supervised models. However, in practice, normal network activities can demonstrate significantly varying sequence patterns (e.g., before and after rerouting partial network traffic). Also, the recorded abnormal events can be sparse. This paper presents a novel semi-supervised method that efficiently captures dependencies between network time series and across time points to generate meaningful representations of network activities for predicting abnormal events. The method can use the limited labeled data to explicitly learn separable embedding space for normal and abnormal samples and effectively leverage unlabeled data to handle training data scarcity. The experiments demonstrate that our approach significantly outperformed state-of-the-art approaches for event detection on a large real-world network log.
Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information
Venue: arXiv cs.CL 2025
Year: 2025

Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to humans; the bigger the performance gap, the harder the dataset is said to be. However, this comparison provides little understanding of how difficult each instance in a given distribution is, or what attributes make the dataset difficult for a given model. To address these questions, we frame dataset difficulty -- w.r.t. a model $\mathcal{V}$ -- as the lack of $\mathcal{V}$-$\textit{usable information}$ (Xu et al., 2019), where a lower value indicates a more difficult dataset for $\mathcal{V}$. We further introduce $\textit{pointwise $\mathcal{V}$-information}$ (PVI) for measuring the difficulty of individual instances w.r.t. a given distribution. While standard evaluation metrics typically only compare different models for the same dataset, $\mathcal{V}$-$\textit{usable information}$ and PVI also permit the converse: for a given model $\mathcal{V}$, we can compare different datasets, as well as different instances/slices of the same dataset. Furthermore, our framework allows for the interpretability of different input attributes via transformations of the input, which we use to discover annotation artefacts in widely-used NLP benchmarks.
Vision Transformer for Classification of Breast Ultrasound Images
Venue: arXiv cs.CV 2025
Year: 2025

Medical ultrasound (US) imaging has become a prominent modality for breast cancer imaging due to its ease-of-use, low-cost and safety. In the past decade, convolutional neural networks (CNNs) have emerged as the method of choice in vision applications and have shown excellent potential in automatic classification of US images. Despite their success, their restricted local receptive field limits their ability to learn global context information. Recently, Vision Transformer (ViT) designs that are based on self-attention between image patches have shown great potential to be an alternative to CNNs. In this study, for the first time, we utilize ViT to classify breast US images using different augmentation strategies. The results are provided as classification accuracy and Area Under the Curve (AUC) metrics, and the performance is compared with the state-of-the-art CNNs. The results indicate that the ViT models have comparable efficiency with or even better than the CNNs in classification of US breast images.