OpenRaiser/Intern-Atlas huggingface dataset:
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
Venue: arXiv cs.IR 2024
Year: 2024

Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation learning task, concentrating on enhancing graph encoders. However, the decoding process in EA - essential for effective operation and alignment accuracy - has received limited attention and remains tailored to specific datasets and model architectures, necessitating both entity and additional explicit relation embeddings. This specificity limits its applicability, particularly in GNN-based models. To address this gap, we introduce a novel, generalized, and efficient decoding approach for EA, relying solely on entity embeddings. Our method optimizes the decoding process by minimizing Dirichlet energy, leading to the gradient flow within the graph, to maximize graph homophily. The discretization of the gradient flow produces a fast and scalable approach, termed Triple Feature Propagation (TFP). TFP innovatively generalizes adjacency matrices to multi-views matrices:entity-to-entity, entity-to-relation, relation-to-entity, and relation-to-triple. The gradient flow through generalized matrices enables TFP to harness the multi-view structural information of KGs. Rigorous experimentation on diverse public datasets demonstrates that our approach significantly enhances various EA methods. Notably, the approach achieves these advancements with less than 6 seconds of additional computational time, establishing a new benchmark in efficiency and adaptability for future EA methods.
Enhancements for 5G NR PRACH Reception: An AI/ML Approach
Venue: arXiv cs.IT 2024
Year: 2024

Random Access is an important step in enabling the initial attachment of a User Equipment (UE) to a Base Station (gNB). The UE identifies itself by embedding a Preamble Index (RAPID) in the phase rotation of a known base sequence, which it transmits on the Physical Random Access Channel (PRACH). The signal on the PRACH also enables the estimation of propagation delay, often known as Timing Advance (TA), which is induced by virtue of the UE's position. Traditional receivers estimate the RAPID and TA using correlation-based techniques. This paper presents an alternative receiver approach that uses AI/ML models, wherein two neural networks are proposed, one for the RAPID and one for the TA. Different from other works, these two models can run in parallel as opposed to sequentially. Experiments with both simulated data and over-the-air hardware captures highlight the improved performance of the proposed AI/ML-based techniques compared to conventional correlation methods.
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces
Venue: arXiv cs.LG 2024
Year: 2024

We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. The suggested Iterated Relevance Matrix Analysis (IRMA) identifies a linear subspace representing the classification specific information of the considered data sets using Generalized Matrix Learning Vector Quantization (GMLVQ). By iteratively determining a new discriminative subspace while projecting out all previously identified ones, a combined subspace carrying all class-specific information can be found. This facilitates a detailed analysis of feature relevances, and enables improved low-dimensional representations and visualizations of labeled data sets. Additionally, the IRMA-based class-discriminative subspace can be used for dimensionality reduction and the training of robust classifiers with potentially improved performance.
From Understanding to Utilization: A Survey on Explainability for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Explainability for Large Language Models (LLMs) is a critical yet challenging aspect of natural language processing. As LLMs are increasingly integral to diverse applications, their "black-box" nature sparks significant concerns regarding transparency and ethical use. This survey underscores the imperative for increased explainability in LLMs, delving into both the research on explainability and the various methodologies and tasks that utilize an understanding of these models. Our focus is primarily on pre-trained Transformer-based LLMs, such as LLaMA family, which pose distinctive interpretability challenges due to their scale and complexity. In terms of existing methods, we classify them into local and global analyses, based on their explanatory objectives. When considering the utilization of explainability, we explore several compelling methods that concentrate on model editing, control generation, and model enhancement. Additionally, we examine representative evaluation metrics and datasets, elucidating their advantages and limitations. Our goal is to reconcile theoretical and empirical understanding with practical implementation, proposing exciting avenues for explanatory techniques and their applications in the LLMs era.
Model-Free $δ$-Policy Iteration Based on Damped Newton Method for Nonlinear Continuous-Time H$\infty$ Tracking Control
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents a δ-PI algorithm which is based on damped Newton method for the H{\infty} tracking control problem of unknown continuous-time nonlinear system. A discounted performance function and an augmented system are used to get the tracking Hamilton-Jacobi-Isaac (HJI) equation. Tracking HJI equation is a nonlinear partial differential equation, traditional reinforcement learning methods for solving the tracking HJI equation are mostly based on the Newton method, which usually only satisfies local convergence and needs a good initial guess. Based upon the damped Newton iteration operator equation, a generalized tracking Bellman equation is derived firstly. The δ-PI algorithm can seek the optimal solution of the tracking HJI equation by iteratively solving the generalized tracking Bellman equation. On-policy learning and off-policy learning δ-PI reinforcement learning methods are provided, respectively. Off-policy version δ-PI algorithm is a model-free algorithm which can be performed without making use of a priori knowledge of the system dynamics. NN-based implementation scheme for the off-policy δ-PI algorithms is shown. The suitability of the model-free δ-PI algorithm is illustrated with a nonlinear system simulation.
Secure Spatial Signal Design for ISAC in a Cell-Free MIMO Network
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we study a cell-free multiple-input multiple-output network equipped with integrated sensing and communication (ISAC) access points (APs). The distributed APs are used to jointly serve the communication needs of user equipments (UEs) while sensing a target, assumed to be an eavesdropper (Eve). To increase the system's robustness towards said Eve, we develop an ISAC waveform model that includes artificial noise (AN) aimed at degrading the Eve channel quality. The central processing unit receives the observations from each AP and calculates the optimal precoding and AN covariance matrices by solving a semi-definite relaxation of a constrained Cramer-Rao bound (CRB) minimization problem. Simulation results highlight an underlying trade-off between sensing and communication performances: in particular, the UEs signal-to-noise and interference ratio and the maximum Eve's signal to noise ratio are directly proportional to the CRB. Furthermore, the optimal AN covariance matrix is rank-1 and has a peak in the eve's direction, leading to a surprising inverse-proportionality between the UEs-Eve distance and optimal-CRB magnitude.
Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization
Venue: arXiv cs.CV 2024
Year: 2024

We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of the input or suffer from substantial computational costs, thereby limiting their practicality. To address this challenge, Coverage Axis++ proposes a heuristic algorithm to select skeletal points, offering a high-accuracy approximation of the Medial Axis Transform (MAT) while significantly mitigating computational intensity for various shape representations. We introduce a simple yet effective strategy that considers shape coverage, uniformity, and centrality to derive skeletal points. The selection procedure enforces consistency with the shape structure while favoring the dominant medial balls, which thus introduces a compact underlying shape representation in terms of MAT. As a result, Coverage Axis++ allows for skeletonization for various shape representations (e.g., water-tight meshes, triangle soups, point clouds), specification of the number of skeletal points, few hyperparameters, and highly efficient computation with improved reconstruction accuracy. Extensive experiments across a wide range of 3D shapes validate the efficiency and effectiveness of Coverage Axis++. Our codes are available at https://github.com/Frank-ZY-Dou/Coverage_Axis.
Understanding Emojis :) in Useful Code Review Comments
Venue: arXiv cs.SE 2024
Year: 2024

Emojis and emoticons serve as non-verbal cues and are increasingly prevalent across various platforms, including Modern Code Review. These cues often carry emotive or instructive weight for developers. Our study dives into the utility of Code Review comments (CR comments) by scrutinizing the sentiments and semantics conveyed by emojis within these comments. To assess the usefulness of CR comments, we augment traditional 'textual' features and pre-trained embeddings with 'emoji-specific' features and pre-trained embeddings. To fortify our inquiry, we expand an existing dataset with emoji annotations, guided by existing research on GitHub emoji usage, and re-evaluate the CR comments accordingly. Our models, which incorporate textual and emoji-based sentiment features and semantic understandings of emojis, substantially outperform baseline metrics. The often-overlooked emoji elements in CR comments emerge as key indicators of usefulness, suggesting that these symbols carry significant weight.
Minimizing the Age of Two Heterogeneous Sources With Packet Drops Via Cyclic Schedulers
Venue: arXiv cs.IT 2024
Year: 2024

In a communication setting where multiple sources share a single channel to provide status updates to a remote monitor, source transmissions need to be scheduled appropriately to maintain timely communication between each of the sources and the monitor. We consider age-agnostic scheduling policies which are advantageous due to their simplicity of implementation. Further, we focus on a special class of age-agnostic policies, called cyclic schedulers, where each source is scheduled based on a fixed cyclic pattern. We use weighted average age of information (AoI) to quantify the timeliness of communication. We develop a Markov chain formulation to compute the exact mean AoI for the case of two-source cyclic schedulers. Based on the obtained age expression, we develop an algorithm that generates near-optimal cyclic schedulers to minimize the weighted average AoI for two heterogeneous sources, in the presence of channel errors.
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents
Venue: arXiv cs.RO 2024
Year: 2024

Foundation models that incorporate language, vision, and more recently actions have revolutionized the ability to harness internet scale data to reason about useful tasks. However, one of the key challenges of training embodied foundation models is the lack of data grounded in the physical world. In this paper, we propose AutoRT, a system that leverages existing foundation models to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision. AutoRT leverages vision-language models (VLMs) for scene understanding and grounding, and further uses large language models (LLMs) for proposing diverse and novel instructions to be performed by a fleet of robots. Guiding data collection by tapping into the knowledge of foundation models enables AutoRT to effectively reason about autonomy tradeoffs and safety while significantly scaling up data collection for robot learning. We demonstrate AutoRT proposing instructions to over 20 robots across multiple buildings and collecting 77k real robot episodes via both teleoperation and autonomous robot policies. We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.
On the Efficacy of Text-Based Input Modalities for Action Anticipation
Venue: arXiv cs.CV 2024
Year: 2024

Anticipating future actions is a highly challenging task due to the diversity and scale of potential future actions; yet, information from different modalities help narrow down plausible action choices. Each modality can provide diverse and often complementary context for the model to learn from. While previous multi-modal methods leverage information from modalities such as video and audio, we primarily explore how text descriptions of actions and objects can also lead to more accurate action anticipation by providing additional contextual cues, e.g., about the environment and its contents. We propose a Multi-modal Contrastive Anticipative Transformer (M-CAT), a video transformer architecture that jointly learns from multi-modal features and text descriptions of actions and objects. We train our model in two stages, where the model first learns to align video clips with descriptions of future actions, and is subsequently fine-tuned to predict future actions. Compared to existing methods, M-CAT has the advantage of learning additional context from two types of text inputs: rich descriptions of future actions during pre-training, and, text descriptions for detected objects and actions during modality feature fusion. Through extensive experimental evaluation, we demonstrate that our model outperforms previous methods on the EpicKitchens datasets, and show that using simple text descriptions of actions and objects aid in more effective action anticipation. In addition, we examine the impact of object and action information obtained via text, and perform extensive ablations.
Identifying Risk Patterns in Brazilian Police Reports Preceding Femicides: A Long Short Term Memory (LSTM) Based Analysis
Venue: arXiv cs.CL 2024
Year: 2024

Femicide refers to the killing of a female victim, often perpetrated by an intimate partner or family member, and is also associated with gender-based violence. Studies have shown that there is a pattern of escalating violence leading up to these killings, highlighting the potential for prevention if the level of danger to the victim can be assessed. Machine learning offers a promising approach to address this challenge by predicting risk levels based on textual descriptions of the violence. In this study, we employed the Long Short Term Memory (LSTM) technique to identify patterns of behavior in Brazilian police reports preceding femicides. Our first objective was to classify the content of these reports as indicating either a lower or higher risk of the victim being murdered, achieving an accuracy of 66%. In the second approach, we developed a model to predict the next action a victim might experience within a sequence of patterned events. Both approaches contribute to the understanding and assessment of the risks associated with domestic violence, providing authorities with valuable insights to protect women and prevent situations from escalating.
A General-purpose AI Avatar in Healthcare
Venue: arXiv cs.CL 2024
Year: 2024

Recent advancements in machine learning and natural language processing have led to the rapid development of artificial intelligence (AI) as a valuable tool in the healthcare industry. Using large language models (LLMs) as conversational agents or chatbots has the potential to assist doctors in diagnosing patients, detecting early symptoms of diseases, and providing health advice to patients. This paper focuses on the role of chatbots in healthcare and explores the use of avatars to make AI interactions more appealing to patients. A framework of a general-purpose AI avatar application is demonstrated by using a three-category prompt dictionary and prompt improvement mechanism. A two-phase approach is suggested to fine-tune a general-purpose AI language model and create different AI avatars to discuss medical issues with users. Prompt engineering enhances the chatbot's conversational abilities and personality traits, fostering a more human-like interaction with patients. Ultimately, the injection of personality into the chatbot could potentially increase patient engagement. Future directions for research include investigating ways to improve chatbots' understanding of context and ensuring the accuracy of their outputs through fine-tuning with specialized medical data sets.
A Comprehensive Survey of Text Classification Techniques and Their Research Applications: Observational and Experimental Insights
Venue: arXiv cs.CL 2024
Year: 2024

The exponential growth of textual data presents substantial challenges in management and analysis, notably due to high storage and processing costs. Text classification, a vital aspect of text mining, provides robust solutions by enabling efficient categorization and organization of text data. These techniques allow individuals, researchers, and businesses to derive meaningful patterns and insights from large volumes of text. This survey paper introduces a comprehensive taxonomy specifically designed for text classification based on research fields. The taxonomy is structured into hierarchical levels: research field-based category, research field-based sub-category, methodology-based technique, methodology sub-technique, and research field applications. We employ a dual evaluation approach: empirical and experimental. Empirically, we assess text classification techniques across four critical criteria. Experimentally, we compare and rank the methodology sub-techniques within the same methodology technique and within the same overall research field sub-category. This structured taxonomy, coupled with thorough evaluations, provides a detailed and nuanced understanding of text classification algorithms and their applications, empowering researchers to make informed decisions based on precise, field-specific insights.
Crowdsourced Adaptive Surveys
Venue: arXiv cs.CL 2024
Year: 2024

Public opinion surveys are vital for informing democratic decision-making, but responding to rapidly evolving information environments and measuring beliefs within niche communities can be challenging for traditional survey methods. This paper introduces a crowdsourced adaptive survey methodology (CSAS) that unites advances in natural language processing and adaptive algorithms to generate question banks that evolve with user input. The CSAS method converts open-ended text provided by participants into survey items and applies a multi-armed bandit algorithm to determine which questions should be prioritized in the survey. The method's adaptive nature allows for the exploration of new survey questions, while imposing minimal costs in survey length. Applications in the domains of Latino information environments, national issue importance, and local politics showcase CSAS's ability to identify topics that might otherwise escape the notice of survey researchers. I conclude by highlighting CSAS's potential to bridge conceptual gaps between researchers and participants in survey research.
A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes
Venue: arXiv cs.CL 2024
Year: 2024

Background: Electronic health records (EHRs) are a data source for opioid research. Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes. Objectives: Our goals were 1) to identify problematic opioid use from a full range of clinical notes; and 2) to compare the characteristics of patients identified as having problematic opioid use, exclusively documented in clinical notes, to those having documented ICD opioid use disorder diagnostic codes. Materials and Methods: We developed and applied a natural language processing (NLP) tool to the clinical notes of a patient cohort (n=222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. We compared the demographic and clinical characteristics of patients identified only through NLP, to those of patients identified through ICD codes. Results: NLP exclusively identified 57,331 patients; 6,997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, more care encounters, and less likely to be married. Patients in the NLP and ICD groups had substantially elevated comorbidity levels compared to patients not documented as experiencing problematic opioid use. Conclusions: NLP is a feasible approach for identifying problematic opioid use not otherwise recorded by ICD codes. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.
Contractive Diffusion Probabilistic Models
Venue: arXiv cs.LG 2024
Year: 2024

Diffusion probabilistic models (DPMs) have emerged as a promising technique in generative modeling. The success of DPMs relies on two ingredients: time reversal of diffusion processes and score matching. In view of possibly unguaranteed score matching, we propose a new criterion -- the contraction property of backward sampling in the design of DPMs, leading to a novel class of contractive DPMs (CDPMs). Our key insight is that, the contraction property can provably narrow score matching errors and discretization errors, thus our proposed CDPMs are robust to both sources of error. For practical use, we show that CDPM can leverage weights of pretrained DPMs by a simple transformation, and does not need retraining. We corroborated our approach by experiments on synthetic 1-dim examples, Swiss Roll, MNIST, CIFAR-10 32$\times$32 and AFHQ 64$\times$64 dataset. Notably, CDPM steadily improves the performance of baseline score-based diffusion models.
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations
Venue: arXiv cs.LG 2024
Year: 2024

Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.