OpenRaiser/Intern-Atlas huggingface dataset:
FILS: Self-Supervised Video Feature Prediction In Semantic Language Space
Venue: arXiv cs.CV 2024
Year: 2024

This paper demonstrates a self-supervised approach for learning semantic video representations. Recent vision studies show that a masking strategy for vision and natural language supervision has contributed to developing transferable visual pretraining. Our goal is to achieve a more semantic video representation by leveraging the text related to the video content during the pretraining in a fully self-supervised manner. To this end, we present FILS, a novel self-supervised video Feature prediction In semantic Language Space (FILS). The vision model can capture valuable structured information by correctly predicting masked feature semantics in language space. It is learned using a patch-wise video-text contrastive strategy, in which the text representations act as prototypes for transforming vision features into a language space, which are then used as targets for semantically meaningful feature prediction using our masked encoder-decoder structure. FILS demonstrates remarkable transferability on downstream action recognition tasks, achieving state-of-the-art on challenging egocentric datasets, like Epic-Kitchens, Something-SomethingV2, Charades-Ego, and EGTEA, using ViT-Base. Our efficient method requires less computation and smaller batches compared to previous works.
AD-H: Autonomous Driving with Hierarchical Agents
Venue: arXiv cs.CV 2024
Year: 2024

Due to the impressive capabilities of multimodal large language models (MLLMs), recent works have focused on employing MLLM-based agents for autonomous driving in large-scale and dynamic environments. However, prevalent approaches often directly translate high-level instructions into low-level vehicle control signals, which deviates from the inherent language generation paradigm of MLLMs and fails to fully harness their emergent powers. As a result, the generalizability of these methods is highly restricted by autonomous driving datasets used during fine-tuning. To tackle this challenge, we propose to connect high-level instructions and low-level control signals with mid-level language-driven commands, which are more fine-grained than high-level instructions but more universal and explainable than control signals, and thus can effectively bridge the gap in between. We implement this idea through a hierarchical multi-agent driving system named AD-H, including a MLLM planner for high-level reasoning and a lightweight controller for low-level execution. The hierarchical design liberates the MLLM from low-level control signal decoding and therefore fully releases their emergent capability in high-level perception, reasoning, and planning. We build a new dataset with action hierarchy annotations. Comprehensive closed-loop evaluations demonstrate several key advantages of our proposed AD-H system. First, AD-H can notably outperform state-of-the-art methods in achieving exceptional driving performance, even exhibiting self-correction capabilities during vehicle operation, a scenario not encountered in the training dataset. Second, AD-H demonstrates superior generalization under long-horizon instructions and novel environmental conditions, significantly surpassing current state-of-the-art methods. We will make our data and code publicly accessible at https://github.com/zhangzaibin/AD-H
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning
Venue: arXiv cs.CR 2024
Year: 2024

Asynchronous federated learning (AFL) is an effective method to address the challenge of device heterogeneity in cross-device federated learning. However, AFL is usually incompatible with existing secure aggregation protocols used to protect user privacy in federated learning because most existing secure aggregation protocols are based on synchronous aggregation. To address this problem, we propose a novel secure aggregation protocol named buffered asynchronous secure aggregation (BASA) in this paper. Compared with existing protocols, BASA is fully compatible with AFL and provides secure aggregation under the condition that each user only needs one round of communication with the server without relying on any synchronous interaction among users. Based on BASA, we propose the first AFL method which achieves secure aggregation without extra requirements on hardware. We empirically demonstrate that BASA outperforms existing secure aggregation protocols for cross-device federated learning in terms of training efficiency and scalability.
A Simple Learning-Augmented Algorithm for Online Packing with Concave Objectives
Venue: arXiv cs.DS 2024
Year: 2024

Learning-augmented algorithms has been extensively studied recently in the computer-science community, due to the potential of using machine learning predictions in order to improve the performance of algorithms. Predictions are especially useful for online algorithms making irrevocable decisions without knowledge of the future. Such learning-augmented algorithms aim to overcome the limitations of classical online algorithms when the predictions are accurate, and still perform comparably when the predictions are inaccurate. A common approach is to adapt existing online algorithms to the particular advice notion employed, which often involves understanding previous sophisticated algorithms and their analyses. However, ideally, one would simply use previous online solutions in a black-box fashion, without much loss in the approximation guarantees. Such clean solutions that avoid opening up black-boxes are often rare, and may be even missed the first time around. For example, Grigorescu et al. (NeurIPS 22) proposed a learning-augmented algorithms for online covering linear programs, but it later turned out that their results can be subsumed by a natural approach that switches between the advice and an online algorithm given as a black-box, as noted in their paper. In this work, we introduce and analyze a simple learning-augmented algorithm for online packing problems with linear constraints and concave objectives. We exhibit several direct applications of our framework including online packing linear programming, knapsack, resource management benefit, throughput maximization, and network utility maximization. We further raise the problem of understanding necessary and sufficient conditions for when such simple black-box solutions may be optimal. We believe this is an important direction of research that would unify many ad-hoc approaches from the literature.
A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming
Venue: arXiv cs.LG 2024
Year: 2024

Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networks, which are opaque, symbolic regression aims to model and map data in a way that can be understood by scientists. Recent advancements, have attempted to bridge the gap between these two fields; new methodologies attempt to fuse the mapping power of neural networks and deep learning techniques with the explanatory power of symbolic regression. In this paper, we examine these new emerging systems and test the performance of an end-to-end transformer model for symbolic regression versus the reigning traditional methods based on genetic programming that have spearheaded symbolic regression throughout the years. We compare these systems on novel datasets to avoid bias to older methods who were improved on well-known benchmark datasets. Our results show that traditional GP methods as implemented e.g., by Operon still remain superior to two recently published symbolic regression methods.
Why is "Problems" Predictive of Positive Sentiment? A Case Study of Explaining Unintuitive Features in Sentiment Classification
Venue: arXiv cs.HC 2024
Year: 2024

Explainable AI (XAI) algorithms aim to help users understand how a machine learning model makes predictions. To this end, many approaches explain which input features are most predictive of a target label. However, such explanations can still be puzzling to users (e.g., in product reviews, the word "problems" is predictive of positive sentiment). If left unexplained, puzzling explanations can have negative impacts. Explaining unintuitive associations between an input feature and a target label is an underexplored area in XAI research. We take an initial effort in this direction using unintuitive associations learned by sentiment classifiers as a case study. We propose approaches for (1) automatically detecting associations that can appear unintuitive to users and (2) generating explanations to help users understand why an unintuitive feature is predictive. Results from a crowdsourced study (N=300) found that our proposed approaches can effectively detect and explain predictive but unintuitive features in sentiment classification.
Towards the Development of a Tendon-Actuated Galvanometer for Endoscopic Surgical Laser Scanning
Venue: arXiv cs.RO 2024
Year: 2024

There is a need for precision pathological sensing, imaging, and tissue manipulation in neurosurgical procedures, such as brain tumor resection. Precise tumor margin identification and resection can prevent further growth and protect critical structures. Surgical lasers with small laser diameters and steering capabilities can allow for new minimally invasive procedures by traversing through complex anatomy, then providing energy to sense, visualize, and affect tissue. In this paper, we present the design of a small-scale tendon-actuated galvanometer (TAG) that can serve as an end-effector tool for a steerable surgical laser. The galvanometer sensor design, fabrication, and kinematic modeling are presented and derived. It can accurately rotate up to 30.14 degrees (or a laser reflection angle of 60.28 degrees). A kinematic mapping of input tendon stroke to output galvanometer angle change and a forward-kinematics model relating the end of the continuum joint to the laser end-point are derived and validated.
Task and Motion Planning for Execution in the Real
Venue: arXiv cs.RO 2024
Year: 2024

Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reasoning necessitates task domain models and enough information to ground actions to motion planning queries. Gaps in this knowledge often arise from sources like occlusion or imprecise modeling. This work generates task and motion plans that include actions cannot be fully grounded at planning time. During execution, such an action is handled by a provided human-designed or learned closed-loop behavior. Execution combines offline planned motions and online behaviors till reaching the task goal. Failures of behaviors are fed back as constraints to find new plans. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework and compare against state-of-the-art. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise. The experiment data is shared for researchers to simulate these settings. The work shows promise in expanding the applicable class of realistic partially grounded problems that robots can address.
3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Video Object Segmentation (VOS) is a vital task in computer vision, focusing on distinguishing foreground objects from the background across video frames. Our work draws inspiration from the Cutie model, and we investigate the effects of object memory, the total number of memory frames, and input resolution on segmentation performance. This report validates the effectiveness of our inference method on the coMplex video Object SEgmentation (MOSE) dataset, which features complex occlusions. Our experimental results demonstrate that our approach achieves a J\&F score of 0.8139 on the test set, securing the third position in the final ranking. These findings highlight the robustness and accuracy of our method in handling challenging VOS scenarios.
New MDS codes of non-GRS type and NMDS codes
Venue: arXiv cs.IT 2024
Year: 2024

Maximum distance separable (MDS) and near maximum distance separable (NMDS) codes have been widely used in various fields such as communication systems, data storage, and quantum codes due to their algebraic properties and excellent error-correcting capabilities. This paper focuses on a specific class of linear codes and establishes necessary and sufficient conditions for them to be MDS or NMDS. Additionally, we employ the well-known Schur method to demonstrate that they are non-equivalent to generalized Reed-Solomon codes.
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction
Venue: arXiv cs.MM 2024
Year: 2024

In the field of information extraction (IE), tasks across a wide range of modalities and their combinations have been traditionally studied in isolation, leaving a gap in deeply recognizing and analyzing cross-modal information. To address this, this work for the first time introduces the concept of grounded Multimodal Universal Information Extraction (MUIE), providing a unified task framework to analyze any IE tasks over various modalities, along with their fine-grained groundings. To tackle MUIE, we tailor a multimodal large language model (MLLM), Reamo, capable of extracting and grounding information from all modalities, i.e., recognizing everything from all modalities at once. Reamo is updated via varied tuning strategies, equipping it with powerful capabilities for information recognition and fine-grained multimodal grounding. To address the absence of a suitable benchmark for grounded MUIE, we curate a high-quality, diverse, and challenging test set, which encompasses IE tasks across 9 common modality combinations with the corresponding multimodal groundings. The extensive comparison of Reamo with existing MLLMs integrated into pipeline approaches demonstrates its advantages across all evaluation dimensions, establishing a strong benchmark for the follow-up research. Our resources are publicly released at https://haofei.vip/MUIE.
Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning
Venue: arXiv cs.CR 2024
Year: 2024

Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities, resulting in poor performance in real-world scenarios beyond the training instances. To tackle this challenge, we introduce VulLLM, a novel framework that integrates multi-task learning with Large Language Models (LLMs) to effectively mine deep-seated vulnerability features. Specifically, we construct two auxiliary tasks beyond the vulnerability detection task. First, we utilize the vulnerability patches to construct a vulnerability localization task. Second, based on the vulnerability features extracted from patches, we leverage GPT-4 to construct a vulnerability interpretation task. VulLLM innovatively augments vulnerability classification by leveraging generative LLMs to understand complex vulnerability patterns, thus compelling the model to capture the root causes of vulnerabilities rather than overfitting to spurious features of a single task. The experiments conducted on six large datasets demonstrate that VulLLM surpasses seven state-of-the-art models in terms of effectiveness, generalization, and robustness.
High-Order Continuous Geometrical Validity
Venue: arXiv cs.CG 2024
Year: 2024

We propose a conservative algorithm to test the geometrical validity of simplicial (triangles, tetrahedra), tensor product (quadrilaterals, hexahedra), and mixed (prisms) elements of arbitrary polynomial order as they deform over a piecewise-linear trajectory. Our algorithm uses a combination of adaptive Bézier refinement and bisection search to determine if, when, and where the Jacobian determinant of an element's polynomial geometric map becomes negative in the transition from one configuration to another. Unlike previous approaches, our method preserves its properties also when implemented using floating point arithmetic: This feature comes at a small additional runtime cost compared to existing inexact methods, making it a drop-in replacement for current validity tests, while providing superior robustness and generality. To prove the practical effectiveness of our algorithm, we demonstrate its use in a high-order Incremental Potential Contact (IPC) elastodynamic simulator, and we experimentally show that it prevents invalid, simulation-breaking configurations that would otherwise occur using inexact methods, without the need for manual parameter tuning.
CORTEX: Large-Scale Brain Simulator Utilizing Indegree Sub-Graph Decomposition on Fugaku Supercomputer
Venue: arXiv cs.DC 2024
Year: 2024

We introduce CORTEX, an algorithmic framework designed for large-scale brain simulation. Leveraging the computational capacity of the Fugaku Supercomputer, CORTEX maximizes available problem size and processing performance. Our primary innovation, Indegree Sub-Graph Decomposition, along with a suite of parallel algorithms, facilitates efficient domain decomposition by segmenting the global graph structure into smaller, identically structured sub-graphs. This segmentation allows for parallel processing of synaptic interactions without inter-process dependencies, effectively eliminating data racing at the thread level without necessitating mutexes or atomic operations. Additionally, this strategy enhances the overlap of communication and computation. Benchmark tests conducted on spiking neural networks, characterized by biological parameters, have demonstrated significant enhancements in both problem size and simulation performance, surpassing the capabilities of the current leading open-source solution, the NEST Simulator. Our work offers a powerful new tool for the field of neuromorphic computing and understanding brain function.
Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation
Venue: arXiv cs.IT 2024
Year: 2024

Semantic communication, notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, reduces transmission length, and mitigates channel noise. However, most studies overlook multi-user scenarios and resource availability, limiting real-world application. This paper addresses this gap by focusing on downlink communication from a base station to multiple users with varying computing capacities. Users employ variants of Swin transformer models for source decoding and a simple architecture for channel decoding. We propose a novel training regimen, incorporating transfer learning and knowledge distillation to improve low-computing users' performance. Extensive simulations validate the proposed methods.
Adaptive Lightweight Security for Performance Efficiency in Critical Healthcare Monitoring
Venue: arXiv cs.CR 2024
Year: 2024

The healthcare infrastructure requires robust security procedures, technologies, and policies due to its critical nature. Since the Internet of Things (IoT) with its diverse technologies has become an integral component of future healthcare systems, its security requires a thorough analysis due to its inherent security limitations that arise from resource constraints. Existing communication technologies used for IoT connectivity, such as 5G, provide communications security with the underlying communication infrastructure to a certain level. However, the evolving healthcare paradigm requires adaptive security procedures and technologies that can adapt to the varying resource constraints of IoT devices. This need for adaptive security is particularly pronounced when considering components outside the security sandbox of 5G, such as IoT nodes and M2M connections, which introduce additional security challenges. This article brings forth the unique healthcare monitoring requirements and studies the existing encryption-based security approaches to provide the necessary security. Furthermore, this research introduces a novel approach to optimizing security and performance in IoT in healthcare, particularly in critical use cases such as remote patient monitoring. Finally, the results from the practical implementation demonstrate a marked improvement in the system performance.
Performance of large language models in numerical vs. semantic medical knowledge: Benchmarking on evidence-based Q&As
Venue: arXiv cs.CL 2024
Year: 2024

Clinical problem-solving requires processing of semantic medical knowledge such as illness scripts and numerical medical knowledge of diagnostic tests for evidence-based decision-making. As large language models (LLMs) show promising results in many aspects of language-based clinical practice, their ability to generate non-language evidence-based answers to clinical questions is inherently limited by tokenization. Therefore, we evaluated LLMs' performance on two question types: numeric (correlating findings) and semantic (differentiating entities) while examining differences within and between LLMs in medical aspects and comparing their performance to humans. To generate straightforward multi-choice questions and answers (QAs) based on evidence-based medicine (EBM), we used a comprehensive medical knowledge graph (encompassed data from more than 50,00 peer-reviewed articles) and created the "EBMQA". EBMQA contains 105,000 QAs labeled with medical and non-medical topics and classified into numerical or semantic questions. We benchmarked this dataset using more than 24,500 QAs on two state-of-the-art LLMs: Chat-GPT4 and Claude3-Opus. We evaluated the LLMs accuracy on semantic and numerical question types and according to sub-labeled topics. For validation, six medical experts were tested on 100 numerical EBMQA questions. We found that both LLMs excelled more in semantic than numerical QAs, with Claude3 surpassing GPT4 in numerical QAs. However, both LLMs showed inter and intra gaps in different medical aspects and remained inferior to humans. Thus, their medical advice should be addressed carefully.
GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation
Venue: arXiv cs.SE 2024
Year: 2024

Scenario-based testing is considered state-of-the-art for verifying and validating Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). However, the practical application of scenario-based testing requires an efficient method to generate or collect the scenarios that are needed for the safety assessment. In this paper, we propose Goal-conditioned Scenario Generation (GOOSE), a goal-conditioned reinforcement learning (RL) approach that automatically generates safety-critical scenarios to challenge ADASs or ADSs. In order to simultaneously set up and optimize scenarios, we propose to control vehicle trajectories at the scenario level. Each step in the RL framework corresponds to a scenario simulation. We use Non-Uniform Rational B-Splines (NURBS) for trajectory modeling. To guide the goal-conditioned agent, we formulate test-specific, constraint-based goals inspired by the OpenScenario Domain Specific Language(DSL). Through experiments conducted on multiple pre-crash scenarios derived from UN Regulation No. 157 for Active Lane Keeping Systems (ALKS), we demonstrate the effectiveness of GOOSE in generating scenarios that lead to safety-critical events.