OpenRaiser/Intern-Atlas huggingface dataset:
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
Venue: arXiv cs.LG 2024
Year: 2024

Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments. With the advent of deep learning, numerous innovative models have been introduced for diagnosing arrhythmias using Electrocardiogram (ECG) signals. However, recent studies solely focus on the performance of models, neglecting the interpretation of their results. This leads to a considerable lack of transparency, posing a significant risk in the actual diagnostic process. To solve this problem, this paper introduces MambaCapsule, a deep neural networks for ECG arrhythmias classification, which increases the explainability of the model while enhancing the accuracy.Our model utilizes Mamba for feature extraction and Capsule networks for prediction, providing not only a confidence score but also signal features. Akin to the processing mechanism of human brain, the model learns signal features and their relationship between them by reconstructing ECG signals in the predicted selection. The model evaluation was conducted on MIT-BIH and PTB dataset, following the AAMI standard. MambaCapsule has achieved a total accuracy of 99.54% and 99.59% on the test sets respectively. These results demonstrate the promising performance of under the standard test protocol.
The Realizability of Revision and Contraction Operators in Epistemic Spaces
Venue: arXiv cs.AI 2024
Year: 2024

This paper studies the realizability of belief revision and belief contraction operators in epistemic spaces. We observe that AGM revision and AGM contraction operators for epistemic spaces are only realizable in precisely determined epistemic spaces. We define the class of linear change operators, a special kind of maxichoice operator. When AGM revision, respectively, AGM contraction, is realizable, linear change operators are a canonical realization.
Automatically Removing Unnecessary Stubbings from Test Suites
Venue: arXiv cs.SE 2024
Year: 2024

Most modern software systems are characterized by a high number of components whose interactions can affect and complicate testing activities. During testing, developers can account for the interactions by isolating the code under test using test doubles and stubbings. During the evolution of a test suite, stubbings might become unnecessary, and developers should remove unnecessary stubbings, as their definitions can introduce unreliable test results in future versions of the test suite. Unfortunately, removing unnecessary stubbings is still a manual task that can be complex and time-consuming. To help developers in this task, we propose ARUS, a technique to automatically remove unnecessary stubbings from test suites. Given a software project and its test suite, the technique executes the tests to identify unnecessary stubbings and then removes them using different approaches based on the characteristics of the stubbings. We performed an empirical evaluation based on 128 Java projects that use Mockito for stubbing and contain 280 stubbing definitions that lead to 1,529 unnecessary stubbings. Overall, our technique provides a solution for 276 of the definitions (98.6% resolution rate), ARUS' time cost is negligible, and, on average, the technique's changes introduce a limited increase in code complexity. We submitted ARUS' changes to the projects through pull requests and 83 resolutions are already merged.
Synthesis of Resource-Efficient Superconducting Circuits with Clock-Free Alternating Logic
Venue: arXiv cs.AR 2024
Year: 2024

Gate-level clocking, typical in traditional approaches to Single Flux Quantum (SFQ) technology, makes the effective synthesis of superconducting circuits a significant engineering hurdle. This paper addresses this challenge by employing the recently introduced alternating SFQ (xSFQ) logic family. xSFQ leverages dual-rail alternating encoding to eliminate the clock dependency from the superconducting gate semantics. This obviates the need for ad hoc modifications to existing synthesis tools and avoids unnecessary circuit resource overheads, marking a significant advancement in superconducting circuit design automation. Our implementation results demonstrate an average reduction of over 80\% in the Josephson junction count for circuits from the ISCAS85, EPFL, and ISCAS89 benchmark suites.
SoK: Payment Channel Networks
Venue: arXiv cs.CR 2024
Year: 2024

Payment Channel Networks (PCNs) have been proposed as an alternative solution to the scalability, throughput, and cost overhead problems associated with blockchain transactions. By facilitating offchain execution of transactions, PCNs significantly reduce the burden on the blockchain, leading to faster transaction processing, reduced transaction fees, and enhanced privacy. Despite these advantages, the current state-of-the-art in PCNs presents a variety of challenges that require further exploration. In this paper, we survey several fundamental aspects of PCNs, such as pathfinding and routing, virtual channels, state channels, payment channel hubs, and rebalancing protocols. We aim to provide the reader with a detailed understanding of the various aspects of PCN research, highlighting important advancements. Additionally, we highlight the various unresolved challenges in this area. Specifically, this paper seeks to answer the following crucial question: What are the various interesting and non-trivial challenges in fundamental infrastructure design leading to efficient transaction processing in PCN research that require immediate attention from the academic and research community? By addressing this question, we aim to identify the most pressing problems and future research directions, and we hope to inspire researchers and practitioners to tackle these challenges to make PCNs more secure and versatile
Distributed Symmetric Key Establishment: a Scalable Quantum-Safe Key Distribution Protocol
Venue: arXiv cs.CR 2024
Year: 2024

Pre-shared keys (PSK) have been widely used in network security. Nonetheless, existing PSK solutions are not scalable. Moreover, whenever a new user joins a network, PSK requires an existing user to get a new key before they are able to communicate with the new user. The key issue is how to distribute the PSK between different users. Here, we solve this problem by proposing a new protocol called Distributed Symmetric Key Establishment (DSKE). DSKE has the advantage of being scalable. Unlike standard public key infrastructure (PKI) which relies on computational assumptions, DSKE provides information-theoretic security in a universally composable security framework. Specifically, we prove the security (correctness and confidentiality) and robustness of this protocol against a computationally unbounded adversary, who additionally may have fully compromised a bounded number of the intermediaries and can eavesdrop on all communication. DSKE also achieves distributed trust through secret sharing. We present several implementations of DSKE in real environments, such as providing client services to link encryptors, network encryptors, and mobile phones, as well as the implementation of intermediaries, called Security Hubs, and associated test data as evidence for its versatility. As DSKE is highly scalable in a network setting with no distance limit, it is expected to be a cost-effective quantum-safe cryptographic solution to the network security threat presented by quantum computers.
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning
Venue: arXiv cs.CV 2024
Year: 2024

Recent advancements in Contrastive Language-Image Pre-training (CLIP) have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples. We introduce a novel language-image Contrastive Learning method with an Efficient large language model and prompt Fine-Tuning (CLEFT) that harnesses the strengths of the extensive pre-trained language and visual models. Furthermore, we present an efficient strategy for learning context-based prompts that mitigates the gap between informative clinical diagnostic data and simple class labels. Our method demonstrates state-of-the-art performance on multiple chest X-ray and mammography datasets compared with various baselines. The proposed parameter efficient framework can reduce the total trainable model size by 39% and reduce the trainable language model to only 4% compared with the current BERT encoder.
They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns. We address this challenge by introducing ProtoDep, a novel, explainable framework for Twitter-based depression detection. ProtoDep leverages prototype learning and the generative power of Large Language Models to provide transparent explanations at three levels: (i) symptom-level explanations for each tweet and user, (ii) case-based explanations comparing the user to similar individuals, and (iii) transparent decision-making through classification weights. Evaluated on five benchmark datasets, ProtoDep achieves near state-of-the-art performance while learning meaningful prototypes. This multi-faceted approach offers significant potential to enhance the reliability and transparency of depression detection on social media, ultimately aiding mental health professionals in delivering more informed care.
Knowledge Models for Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering
Venue: arXiv cs.CL 2024
Year: 2024

An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and transforms it into a programmatically interactable, easy-to-update structured model with minimal human intervention. The existing automated algorithms have minimal scope and cannot handle the varying complexity of the knowledge content in the CPGs for different cancer types. This work proposes an improved automated knowledge modeling algorithm to create knowledge models from the National Comprehensive Cancer Network (NCCN) CPGs in Oncology for different cancer types. The proposed algorithm has been evaluated with NCCN CPGs for four different cancer types. We also proposed an algorithm to compare the knowledge models for different versions of a guideline to discover the specific changes introduced in the treatment protocol of a new version. We created a question-answering (Q&A) framework with the guideline knowledge models as the augmented knowledge base to study our ability to query the knowledge models. We compiled a set of 32 question-answer pairs derived from two reliable data sources for the treatment of Non-Small Cell Lung Cancer (NSCLC) to evaluate the Q&A framework. The framework was evaluated against the question-answer pairs from one data source, and it can generate the answers with 54.5% accuracy from the treatment algorithm and 81.8% accuracy from the discussion part of the NCCN NSCLC guideline knowledge model.
High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models
Venue: arXiv cs.SE 2024
Year: 2024

Ensuring fault tolerance of Highly Automated Vehicles (HAVs) is crucial for their safety due to the presence of potentially severe faults. Hence, Fault Injection (FI) testing is conducted by practitioners to evaluate the safety level of HAVs. To fully cover test cases, various driving scenarios and fault settings should be considered. However, due to numerous combinations of test scenarios and fault settings, the testing space can be complex and high-dimensional. In addition, evaluating performance in all newly added scenarios is resource-consuming. The rarity of critical faults that can cause security problems further strengthens the challenge. To address these challenges, we propose to accelerate FI testing under the low-rank Smoothness Regularized Matrix Factorization (SRMF) framework. We first organize the sparse evaluated data into a structured matrix based on its safety values. Then the untested values are estimated by the correlation captured by the matrix structure. To address high dimensionality, a low-rank constraint is imposed on the testing space. To exploit the relationships between existing scenarios and new scenarios and capture the local regularity of critical faults, three types of smoothness regularization are further designed as a complement. We conduct experiments on car following and cut in scenarios. The results indicate that SRMF has the lowest prediction error in various scenarios and is capable of predicting rare critical faults compared to other machine learning models. In addition, SRMF can achieve 1171 acceleration rate, 99.3% precision and 91.1% F1 score in identifying critical faults. To the best of our knowledge, this is the first work to introduce low-rank models to FI testing of HAVs.
Diffusion Mechanism Design in Tree-Structured Social Network
Venue: arXiv cs.GT 2024
Year: 2024

We design a fixed-price auction mechanism for a seller to sell multiple items in a tree-structured market. The buyers have independently drawn valuation from a uniform distribution, and the seller would like to incentivize buyers to invite more people to the auction. We prove that our mechanism is individual rational, and incentivize compatible with regard to the buyers' action. Furthermore, we show the approximation ratio of our mechanism to the optimal fixed-price auction in two ways, theoretically and via Monte-Carlo simulation, and show a high practical ratio. Finally, we discuss several factors affecting the behavior of our mechanism and its feasibility in reality.
Learning Stable Robot Grasping with Transformer-based Tactile Control Policies
Venue: arXiv cs.RO 2024
Year: 2024

Measuring grasp stability is an important skill for dexterous robot manipulation tasks, which can be inferred from haptic information with a tactile sensor. Control policies have to detect rotational displacement and slippage from tactile feedback, and determine a re-grasp strategy in term of location and force. Classic stable grasp task only trains control policies to solve for re-grasp location with objects of fixed center of gravity. In this work, we propose a revamped version of stable grasp task that optimises both re-grasp location and gripping force for objects with unknown and moving center of gravity. We tackle this task with a model-free, end-to-end Transformer-based reinforcement learning framework. We show that our approach is able to solve both objectives after training in both simulation and in a real-world setup with zero-shot transfer. We also provide performance analysis of different models to understand the dynamics of optimizing two opposing objectives.
GenRec: Generative Sequential Recommendation with Large Language Models
Venue: arXiv cs.IR 2024
Year: 2024

Sequential recommendation is a task to capture hidden user preferences from historical user item interaction data and recommend next items for the user. Significant progress has been made in this domain by leveraging classification based learning methods. Inspired by the recent paradigm of 'pretrain, prompt and predict' in NLP, we consider sequential recommendation as a sequence to sequence generation task and propose a novel model named Generative Recommendation (GenRec). Unlike classification based models that learn explicit user and item representations, GenRec utilizes the sequence modeling capability of Transformer and adopts the masked item prediction objective to effectively learn the hidden bidirectional sequential patterns. Different from existing generative sequential recommendation models, GenRec does not rely on manually designed hard prompts. The input to GenRec is textual user item sequence and the output is top ranked next items. Moreover, GenRec is lightweight and requires only a few hours to train effectively in low-resource settings, making it highly applicable to real-world scenarios and helping to democratize large language models in the sequential recommendation domain. Our extensive experiments have demonstrated that GenRec generalizes on various public real-world datasets and achieves state-of-the-art results. Our experiments also validate the effectiveness of the the proposed masked item prediction objective that improves the model performance by a large margin.
Towards an Integrated Performance Framework for Fire Science and Management Workflows
Venue: arXiv cs.LG 2024
Year: 2024

Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National Data Platform, which leverages multiple use-cases including the WIFIRE Data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intelligence and machine learning (AI/ML) approach to performance assessment and optimization of scientific workflows. An associated early AI/ML framework spanning performance data collection, prediction and optimization is applied to wildfire science applications within the WIFIRE BurnPro3D (BP3D) platform for proactive fire management and mitigation.
Robust Box Prompt based SAM for Medical Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

The Segment Anything Model (SAM) can achieve satisfactory segmentation performance under high-quality box prompts. However, SAM's robustness is compromised by the decline in box quality, limiting its practicality in clinical reality. In this study, we propose a novel Robust Box prompt based SAM (\textbf{RoBox-SAM}) to ensure SAM's segmentation performance under prompts with different qualities. Our contribution is three-fold. First, we propose a prompt refinement module to implicitly perceive the potential targets, and output the offsets to directly transform the low-quality box prompt into a high-quality one. We then provide an online iterative strategy for further prompt refinement. Second, we introduce a prompt enhancement module to automatically generate point prompts to assist the box-promptable segmentation effectively. Last, we build a self-information extractor to encode the prior information from the input image. These features can optimize the image embeddings and attention calculation, thus, the robustness of SAM can be further enhanced. Extensive experiments on the large medical segmentation dataset including 99,299 images, 5 modalities, and 25 organs/targets validated the efficacy of our proposed RoBox-SAM.
Seamless Parametrization in Penner Coordinates
Venue: arXiv cs.GR 2024
Year: 2024

We introduce a conceptually simple and efficient algorithm for seamless parametrization, a key element in constructing quad layouts and texture charts on surfaces. More specifically, we consider the construction of parametrizations with prescribed holonomy signatures i.e., a set of angles at singularities, and rotations along homology loops, preserving which is essential for constructing parametrizations following an input field, as well as for user control of the parametrization structure. Our algorithm performs exceptionally well on a large dataset based on Thingi10k [Zhou and Jacobson 2016], (16156 meshes) as well as on a challenging smaller dataset of [Myles et al. 2014], converging, on average, in 9 iterations. Although the algorithm lacks a formal mathematical guarantee, presented empirical evidence and the connections between convex optimization and closely related algorithms, suggest that a similar formulation can be found for this algorithm in the future.
ProSpec RL: Plan Ahead, then Execute
Venue: arXiv cs.LG 2024
Year: 2024

Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition. However, mainstream model-free Reinforcement Learning (RL) methods lack the ability to proactively envision future scenarios, plan, and guide strategies. These methods typically rely on trial and error to adjust policy functions, aiming to maximize cumulative rewards or long-term value, even if such high-reward decisions place the environment in extremely dangerous states. To address this, we propose the Prospective (ProSpec) RL method, which makes higher-value, lower-risk optimal decisions by imagining future n-stream trajectories. Specifically, ProSpec employs a dynamic model to predict future states (termed "imagined states") based on the current state and a series of sampled actions. Furthermore, we integrate the concept of Model Predictive Control and introduce a cycle consistency constraint that allows the agent to evaluate and select the optimal actions from these trajectories. Moreover, ProSpec employs cycle consistency to mitigate two fundamental issues in RL: augmenting state reversibility to avoid irreversible events (low risk) and augmenting actions to generate numerous virtual trajectories, thereby improving data efficiency. We validated the effectiveness of our method on the DMControl benchmarks, where our approach achieved significant performance improvements. Code will be open-sourced upon acceptance.
Exploring the Role of Social Support when Integrating Generative AI into Small Business Workflows
Venue: arXiv cs.HC 2024
Year: 2024

Small business owners stand to benefit from generative AI technologies due to limited resources, yet they must navigate increasing legal and ethical risks. In this paper, we interview 11 entrepreneurs and support personnel to investigate existing practices of how entrepreneurs integrate generative AI technologies into their business workflows. Specifically, we build on scholarship in HCI which emphasizes the role of small, offline networks in supporting entrepreneurs' technology maintenance. We detail how entrepreneurs resourcefully leveraged their local networks to discover new use cases of generative AI (e.g., by sharing accounts), assuage heightened techno-anxieties (e.g., by recruiting trusted confidants), overcome barriers to sustained use (e.g., by receiving wrap-around support), and establish boundaries of use. Further, we suggest how generative AI platforms may be redesigned to better support entrepreneurs, such as by taking into account the benefits and tensions of use in a social context.