OpenRaiser/Intern-Atlas huggingface dataset:
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
Venue: arXiv cs.CV 2024
Year: 2024

Organ at risk (OAR) segmentation is a critical process in radiotherapy treatment planning such as head and neck tumors. Nevertheless, in clinical practice, radiation oncologists predominantly perform OAR segmentations manually on CT scans. This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy. Additionally, CT scans offer lower soft-tissue contrast compared to MRI. Despite MRI providing superior soft-tissue visualization, its time-consuming nature makes it infeasible for real-time treatment planning. To address these challenges, we propose a method called SegReg, which utilizes Elastic Symmetric Normalization for registering MRI to perform OAR segmentation. SegReg outperforms the CT-only baseline by 16.78% in mDSC and 18.77% in mIoU, showing that it effectively combines the geometric accuracy of CT with the superior soft-tissue contrast of MRI, making accurate automated OAR segmentation for clinical practice become possible. See project website https://steve-zeyu-zhang.github.io/SegReg
Towards Climate Variable Prediction with Conditioned Spatio-Temporal Normalizing Flows
Venue: arXiv cs.LG 2024
Year: 2024

This study investigates how conditional normalizing flows can be applied to remote sensing data products in climate science for spatio-temporal prediction. The method is chosen due to its desired properties such as exact likelihood computation, predictive uncertainty estimation and efficient inference and sampling which facilitates faster exploration of climate scenarios. Experimental findings reveal that the conditioned spatio-temporal flow surpasses both deterministic and stochastic baselines in prolonged rollout scenarios. It exhibits stable extrapolation beyond the training time horizon for extended rollout durations. These findings contribute valuable insights to the field of spatio-temporal modeling, with potential applications spanning diverse scientific disciplines.
Robust Regression over Averaged Uncertainty
Venue: arXiv cs.LG 2024
Year: 2024

We propose a new formulation of robust regression by integrating all realizations of the uncertainty set and taking an averaged approach to obtain the optimal solution for the ordinary least squares regression problem. We show that this formulation recovers ridge regression exactly and establishes the missing link between robust optimization and the mean squared error approaches for existing regression problems. We further demonstrate that the condition of this equivalence relies on the geometric properties of the defined uncertainty set. We provide exact, closed-form, in some cases, analytical solutions to the equivalent regularization strength under uncertainty sets induced by $\ell_p$ norm, Schatten $p$-norm, and general polytopes. We then show in synthetic datasets with different levels of uncertainties, a consistent improvement of the averaged formulation over the existing worst-case formulation in out-of-sample performance. In real-world regression problems obtained from UCI datasets, similar improvements are seen in the out-of-sample datasets.
Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
Venue: arXiv cs.SE 2024
Year: 2024

The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous operations. This convergence has accelerated the development and deployment of a range of real-world applications, such as autonomous vehicles, delivery drones, service robots, and telemedicine procedures. However, the software development life cycle (SDLC) for AI-infused CPS diverges significantly from traditional approaches, featuring data and learning as two critical components. Existing verification and validation techniques are often inadequate for these new paradigms. In this study, we pinpoint the main challenges in ensuring formal safety for learningenabled CPS.We begin by examining testing as the most pragmatic method for verification and validation, summarizing the current state-of-the-art methodologies. Recognizing the limitations in current testing approaches to provide formal safety guarantees, we propose a roadmap to transition from foundational probabilistic testing to a more rigorous approach capable of delivering formal assurance.
P4-PSFP: P4-Based Per-Stream Filtering and Policing for Time-Sensitive Networking
Venue: arXiv cs.NI 2024
Year: 2024

Time-Sensitive Networking (TSN) extends Ethernet to enable real-time communication. In TSN, bounded latency and zero congestion-based packet loss are achieved through mechanisms such as the Credit-Based Shaper (CBS) for bandwidth shaping and the Time-Aware Shaper (TAS) for traffic scheduling. Generally, TSN requires streams to be explicitly admitted before being transmitted. To ensure that admitted traffic conforms with the traffic descriptors indicated for admission control, Per-Stream Filtering and Policing (PSFP) has been defined. For credit-based metering, well-known token bucket policers are applied. However, time-based metering requires time-dependent switch behavior and time synchronization with sub-microsecond precision. While TSN-capable switches support various TSN traffic shaping mechanisms, a full implementation of PSFP is still not available. To bridge this gap, we present a P4-based implementation of PSFP on a 100 Gb/s per port hardware switch. We explain the most interesting aspects of the PSFP implementation whose code is available on GitHub. We demonstrate credit-based and time-based policing and synchronization capabilities to validate the functionality and effectiveness of P4-PSFP. The implementation scales up to 35840 streams depending on the stream identification method. P4-PSFP can be used in practice as long as appropriate TSN switches lack this function. Moreover, its implementation may be helpful for other P4-based hardware implementations that require time synchronization.
Sidekick compilation with xDSL
Venue: arXiv cs.PL 2024
Year: 2024

Traditionally, compiler researchers either conduct experiments within an existing production compiler or develop their own prototype compiler; both options come with trade-offs. On one hand, prototyping in a production compiler can be cumbersome, as they are often optimized for program compilation speed at the expense of software simplicity and development speed. On the other hand, the transition from a prototype compiler to production requires significant engineering work. To bridge this gap, we introduce the concept of sidekick compiler frameworks, an approach that uses multiple frameworks that interoperate with each other by leveraging textual interchange formats and declarative descriptions of abstractions. Each such compiler framework is specialized for specific use cases, such as performance or prototyping. Abstractions are by design shared across frameworks, simplifying the transition from prototyping to production. We demonstrate this idea with xDSL, a sidekick for MLIR focused on prototyping and teaching. xDSL interoperates with MLIR through a shared textual IR and the exchange of IRs through an IR Definition Language. The benefits of sidekick compiler frameworks are evaluated by showing on three use cases how xDSL impacts their development: teaching, DSL compilation, and rewrite system prototyping. We also investigate the trade-offs that xDSL offers, and demonstrate how we simplify the transition between frameworks using the IRDL dialect. With sidekick compilation, we envision a future in which engineers minimize the cost of development by choosing a framework built for their immediate needs, and later transitioning to production with minimal overhead.
ChartCheck: Explainable Fact-Checking over Real-World Chart Images
Venue: arXiv cs.CL 2024
Year: 2024

Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked. Charts are commonly used in the real-world to summarize and communicate key information, but they can also be easily misused to spread misinformation and promote certain agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset for explainable fact-checking against real-world charts, consisting of 1.7k charts and 10.5k human-written claims and explanations. We systematically evaluate ChartCheck using vision-language and chart-to-table models, and propose a baseline to the community. Finally, we study chart reasoning types and visual attributes that pose a challenge to these models
Ara2: Exploring Single- and Multi-Core Vector Processing with an Efficient RVV 1.0 Compliant Open-Source Processor
Venue: arXiv cs.AR 2024
Year: 2024

Vector processing is highly effective in boosting processor performance and efficiency for data-parallel workloads. In this paper, we present Ara2, the first fully open-source vector processor to support the RISC-V V 1.0 frozen ISA. We evaluate Ara2's performance on a diverse set of data-parallel kernels for various problem sizes and vector-unit configurations, achieving an average functional-unit utilization of 95% on the most computationally intensive kernels. We pinpoint performance boosters and bottlenecks, including the scalar core, memories, and vector architecture, providing insights into the main vector architecture's performance drivers. Leveraging the openness of the design, we implement Ara2 in a 22nm technology, characterize its PPA metrics on various configurations (2-16 lanes), and analyze its microarchitecture and implementation bottlenecks. Ara2 achieves a state-of-the-art energy efficiency of 37.8 DP-GFLOPS/W (0.8V) and 1.35GHz of clock frequency (critical path: ~40 FO4 gates). Finally, we explore the performance and energy-efficiency trade-offs of multi-core vector processors: we find that multiple vector cores help overcome the scalar core issue-rate bound that limits short-vector performance. For example, a cluster of eight 2-lane Ara2 (16 FPUs) achieves more than 3x better performance than a 16-lane single-core Ara2 (16 FPUs) when executing a 32x32x32 matrix multiplication, with 1.5x improved energy efficiency.
Modeling Choice via Self-Attention
Venue: arXiv cs.AI 2024
Year: 2024

Models of choice are a fundamental input to many now-canonical optimization problems in the field of Operations Management, including assortment, inventory, and price optimization. Naturally, accurate estimation of these models from data is a critical step in the application of these optimization problems in practice. Concurrently, recent advancements in deep learning have sparked interest in integrating these techniques into choice modeling. However, there is a noticeable research gap at the intersection of deep learning and choice modeling, particularly with both theoretical and empirical foundations. Thus motivated, we first propose a choice model that is the first to successfully (both theoretically and practically) leverage a modern neural network architectural concept (self-attention). Theoretically, we show that our attention-based choice model is a low-rank generalization of the Halo Multinomial Logit (Halo-MNL) model. We prove that whereas the Halo-MNL requires $Ω(m^2)$ data samples to estimate, where $m$ is the number of products, our model supports a natural nonconvex estimator (in particular, that which a standard neural network implementation would apply) which admits a near-optimal stationary point with $O(m)$ samples. Additionally, we establish the first realistic-scale benchmark for choice model estimation on real data, conducting the most extensive evaluation of existing models to date, thereby highlighting our model's superior performance.
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Venue: arXiv cs.LG 2024
Year: 2024

Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved through weight pruning, has been identified as a powerful technique to compress the number of model parameters and reduce the computational operations of neural networks. Yet, sparse activations, while omnipresent in both biological neural networks and deep learning systems, have not been fully utilized as a compression technique in deep learning. Moreover, the interaction between sparse activations and weight pruning is not fully understood. In this work, we demonstrate that activity sparsity can compose multiplicatively with parameter sparsity in a recurrent neural network model based on the GRU that is designed to be activity sparse. We achieve up to $20\times$ reduction of computation while maintaining perplexities below $60$ on the Penn Treebank language modeling task. This magnitude of reduction has not been achieved previously with solely sparsely connected LSTMs, and the language modeling performance of our model has not been achieved previously with any sparsely activated recurrent neural networks or spiking neural networks. Neuromorphic computing devices are especially good at taking advantage of the dynamic activity sparsity, and our results provide strong evidence that making deep learning models activity sparse and porting them to neuromorphic devices can be a viable strategy that does not compromise on task performance. Our results also drive further convergence of methods from deep learning and neuromorphic computing for efficient machine learning.
Matching aggregate posteriors in the variational autoencoder
Venue: arXiv cs.LG 2024
Year: 2024

The variational autoencoder (VAE) is a well-studied, deep, latent-variable model (DLVM) that efficiently optimizes the variational lower bound of the log marginal data likelihood and has a strong theoretical foundation. However, the VAE's known failure to match the aggregate posterior often results in \emph{pockets/holes} in the latent distribution (i.e., a failure to match the prior) and/or \emph{posterior collapse}, which is associated with a loss of information in the latent space. This paper addresses these shortcomings in VAEs by reformulating the objective function associated with VAEs in order to match the aggregate/marginal posterior distribution to the prior. We use kernel density estimate (KDE) to model the aggregate posterior in high dimensions. The proposed method is named the \emph{aggregate variational autoencoder} (AVAE) and is built on the theoretical framework of the VAE. Empirical evaluation of the proposed method on multiple benchmark data sets demonstrates the effectiveness of the AVAE relative to state-of-the-art (SOTA) methods.
SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification
Venue: arXiv cs.CV 2024
Year: 2024

Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early detection and effective treatment. To address this challenge, we employed deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. We conducted experiments on the "ChestX-ray14" dataset using various pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical models. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%. By combining the predictions of all trained models using a weighted average ensemble where the weight of each model was determined using differential evolution, we further improved the AUROC to 85.4%, outperforming other state-of-the-art methods in this field. Our findings demonstrate the potential of deep learning techniques, particularly ensemble deep learning, for improving the accuracy of automatic diagnosis of thoracic diseases from chest X-rays. Code available at:https://github.com/syednabilashraf/SynthEnsemble
Predicting the First Response Latency of Maintainers and Contributors in Pull Requests
Venue: arXiv cs.SE 2024
Year: 2024

The success of a Pull Request (PR) depends on the responsiveness of the maintainers and the contributor during the review process. Being aware of the expected waiting times can lead to better interactions and managed expectations for both the maintainers and the contributor. In this paper, we propose a machine-learning approach to predict the first response latency of the maintainers following the submission of a PR, and the first response latency of the contributor after receiving the first response from the maintainers. We curate a dataset of 20 large and popular open-source projects on GitHub and extract 21 features to characterize projects, contributors, PRs, and review processes. Using these features, we then evaluate seven types of classifiers to identify the best-performing models. We also conduct permutation feature importance and SHAP analyses to understand the importance and the impact of different features on the predicted response latencies. We find that our CatBoost models are the most effective for predicting the first response latencies of both maintainers and contributors. We also observe that PRs submitted earlier in the week, containing an average number of commits, and with concise descriptions are more likely to receive faster first responses from the maintainers. Similarly, PRs with a lower first response latency from maintainers, that received the first response of maintainers earlier in the week, and containing an average number of commits tend to receive faster first responses from the contributors. Additionally, contributors with a higher acceptance rate and a history of timely responses in the project are likely to both obtain and provide faster first responses. Moreover, we show the effectiveness of our approach in a cross-project setting.
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
Venue: arXiv cs.LG 2024
Year: 2024

We address two major challenges in scientific machine learning (SciML): interpretability and computational efficiency. We increase the interpretability of certain learning processes by establishing a new theoretical connection between optimization problems arising from SciML and a generalized Hopf formula, which represents the viscosity solution to a Hamilton-Jacobi partial differential equation (HJ PDE) with time-dependent Hamiltonian. Namely, we show that when we solve certain regularized learning problems with integral-type losses, we actually solve an optimal control problem and its associated HJ PDE with time-dependent Hamiltonian. This connection allows us to reinterpret incremental updates to learned models as the evolution of an associated HJ PDE and optimal control problem in time, where all of the previous information is intrinsically encoded in the solution to the HJ PDE. As a result, existing HJ PDE solvers and optimal control algorithms can be reused to design new efficient training approaches for SciML that naturally coincide with the continual learning framework, while avoiding catastrophic forgetting. As a first exploration of this connection, we consider the special case of linear regression and leverage our connection to develop a new Riccati-based methodology for solving these learning problems that is amenable to continual learning applications. We also provide some corresponding numerical examples that demonstrate the potential computational and memory advantages our Riccati-based approach can provide.
A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control
Venue: arXiv cs.RO 2024
Year: 2024

The development of intelligent robots requires control policies that can handle dynamic environments and evolving tasks. Pre-training reinforcement learning has emerged as an effective approach to address these demands by enabling robots to acquire reusable motor skills. However, they often rely on large datasets or expert-designed goal spaces, limiting adaptability. Additionally, these methods need help to generate dynamic and diverse skills in high-dimensional state spaces, reducing their effectiveness for downstream tasks. In this paper, we propose CMS-PRL, a pre-training reinforcement learning method inspired by the Central Motor System (CMS). First, we introduce a fusion reward mechanism that combines the basic motor reward with mutual information reward, promoting the discovery of dynamic skills during pre-training without reliance on external data. Second, we design a skill encoding method inspired by the motor program of the basal ganglia, providing rich and continuous skill instructions during pre-training. Finally, we propose a skill activity function to regulate motor skill activity, enabling the generation of skills with different activity levels, thereby enhancing the robot's flexibility in downstream tasks. We evaluate the model on four types of robots in a challenging set of sparse-reward tasks. Experimental results demonstrate that CMS-PRL generates diverse, reusable motor skills to solve various downstream tasks and outperforms baseline methods, particularly in high-degree-of-freedom robots and complex tasks.
Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)
Venue: arXiv cs.CV 2024
Year: 2024

The advent of foundation models signals a new era in artificial intelligence. The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. The increasing requirement for annotated eye-image datasets presents a significant opportunity for SAM to redefine the landscape of data annotation in gaze estimation. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks. Our results are consistent with studies in other domains, demonstrating that SAM's segmentation effectiveness can be on-par with specialized models depending on the feature, with prompts improving its performance, evidenced by an IoU of 93.34% for pupil segmentation in one dataset. Foundation models like SAM could revolutionize gaze estimation by enabling quick and easy image segmentation, reducing reliance on specialized models and extensive manual annotation.
SAIE Framework: Support Alone Isn't Enough -- Advancing LLM Training with Adversarial Remarks
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) can justify or critique their predictions through discussions with other models or humans, thereby enriching their intrinsic understanding of instances. While proactive discussions in the inference phase have been shown to boost performance, such interactions have not been extensively explored during the training phase. We hypothesize that incorporating interactive discussions into the training process can enhance the models' understanding and improve their reasoning and verbal expression abilities during inference. This work introduces the SAIE framework, which facilitates supportive and adversarial discussions between learner and partner models. The learner model receives responses from the partner, and its parameters are then updated based on this discussion. This dynamic adjustment process continues throughout the training phase, responding to the evolving outputs of the learner model. Our empirical evaluation across various tasks, including math problems, commonsense reasoning, and multi-domain knowledge, demonstrates that models fine-tuned with the SAIE framework outperform those trained with conventional fine-tuning approaches. Furthermore, our method enhances the models' reasoning capabilities, improving both individual and multi-agent inference performance.