OpenRaiser/Intern-Atlas huggingface dataset:
VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling
Venue: arXiv cs.CV 2024
Year: 2024

VAR is a new generation paradigm that employs 'next-scale prediction' as opposed to 'next-token prediction'. This innovative transformation enables auto-regressive (AR) transformers to rapidly learn visual distributions and achieve robust generalization. However, the original VAR model is constrained to class-conditioned synthesis, relying solely on textual captions for guidance. In this paper, we introduce VAR-CLIP, a novel text-to-image model that integrates Visual Auto-Regressive techniques with the capabilities of CLIP. The VAR-CLIP framework encodes captions into text embeddings, which are then utilized as textual conditions for image generation. To facilitate training on extensive datasets, such as ImageNet, we have constructed a substantial image-text dataset leveraging BLIP2. Furthermore, we delve into the significance of word positioning within CLIP for the purpose of caption guidance. Extensive experiments confirm VAR-CLIP's proficiency in generating fantasy images with high fidelity, textual congruence, and aesthetic excellence. Our project page are https://github.com/daixiangzi/VAR-CLIP
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems
Venue: arXiv cs.AI 2024
Year: 2024

Reinforcement Learning (RL) is used extensively in Autonomous Systems (AS) as it enables learning at runtime without the need for a model of the environment or predefined actions. However, most applications of RL in AS, such as those based on Q-learning, can only optimize one objective, making it necessary in multi-objective systems to combine multiple objectives in a single objective function with predefined weights. A number of Multi-Objective Reinforcement Learning (MORL) techniques exist but they have mostly been applied in RL benchmarks rather than real-world AS systems. In this work, we use a MORL technique called Deep W-Learning (DWN) and apply it to the Emergent Web Servers exemplar, a self-adaptive server, to find the optimal configuration for runtime performance optimization. We compare DWN to two single-objective optimization implementations: ε-greedy algorithm and Deep Q-Networks. Our initial evaluation shows that DWN optimizes multiple objectives simultaneously with similar results than DQN and ε-greedy approaches, having a better performance for some metrics, and avoids issues associated with combining multiple objectives into a single utility function.
Game Theory Based Community-Aware Opinion Dynamics
Venue: arXiv cs.GT 2024
Year: 2024

Examining the mechanisms underlying the formation and evolution of opinions within real-world social systems, which consist of numerous individuals, can provide valuable insights for effective social functioning and informed business decision making. The focus of our study is on the dynamics of opinions inside a networked multi-agent system. We provide a novel approach called the Game Theory Based Community-Aware Opinion Formation Process (GCAOFP) to accurately represent the co-evolutionary dynamics of communities and opinions in real-world social systems. The GCAOFP algorithm comprises two distinct steps in each iteration. 1) The Community Dynamics Process conceptualizes the process of community formation as a non-cooperative game involving a finite number of agents. Each individual agent aims to maximize their own utility by adopting a response that leads to the most favorable update of the community label. 2) The Opinion Formation Process involves the updating of an individual agent's opinion within a community-aware framework that incorporates bounded confidence. This process takes into account the updated matrix of community members and ensures that an agent's opinion aligns with the opinions of others within their community, within certain defined limits. The present study provides a theoretical proof that under any initial conditions, the aforementioned co-evolutionary dynamics process will ultimately reach an equilibrium state. In this state, both the opinion vector and community member matrix will stabilize after a finite number of iterations. In contrast to conventional opinion dynamics models, the guaranteed convergence of agent opinion within the same community ensures that the convergence of opinions takes place exclusively inside a given community.
Zoomable Level-of-Detail ChartTables for Interpreting Probabilistic Model Outputs for Reactionary Train Delays
Venue: arXiv cs.HC 2024
Year: 2024

"Reactionary delay" is a result of the accumulated cascading effects of knock-on train delays which is increasing on UK railways due to increasing utilisation of the railway infrastructure. The chaotic nature of its effects on train lateness is notoriously hard to predict. We use a stochastic Monte-Carto-style simulation of reactionary delay that produces whole distributions of likely reactionary delay and delays this causes. We demonstrate how Zoomable Level-of-Detail ChartTables - case-by-variable tables where cases are rows, variables are columns, variables are complex composite metrics that incorporate distributions, and cells contain mini-charts that depict these as different levels of detail through zoom interaction - help interpret whole distributions of model outputs to help understand the causes and effects of reactionary delay, how they inform timetable robustness testing, and how they could be used in other contexts.
Markov Decision Processes with Sure Parity and Multiple Reachability Objectives
Venue: arXiv cs.GT 2024
Year: 2024

This paper considers the problem of finding strategies that satisfy a mixture of sure and threshold objectives in Markov decision processes. We focus on a single $ω$-regular objective expressed as parity that must be surely met while satisfying $n$ reachability objectives towards sink states with some probability thresholds too. We consider three variants of the problem: (a) strict and (b) non-strict thresholds on all reachability objectives, and (c) maximizing the thresholds with respect to a lexicographic order. We show that (a) and (c) can be reduced to solving parity games, and (b) can be solved in $\sf{EXPTIME}$. Strategy complexities as well as algorithms are provided for all cases.
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI classification, challenges such as computational efficiency and the need for extensive labeled data persist. This paper introduces WaveMamba, a novel approach that integrates wavelet transformation with the spatial-spectral Mamba architecture to enhance HSI classification. WaveMamba captures both local texture patterns and global contextual relationships in an end-to-end trainable model. The Wavelet-based enhanced features are then processed through the state-space architecture to model spatial-spectral relationships and temporal dependencies. The experimental results indicate that WaveMamba surpasses existing models, achieving an accuracy improvement of 4.5\% on the University of Houston dataset and a 2.0\% increase on the Pavia University dataset.
Entanglement Routing in Quantum Networks: A Comprehensive Survey
Venue: arXiv cs.ET 2024
Year: 2024

Entanglement routing in near-term quantum networks consists of choosing the optimal sequence of short-range entanglements to combine through swapping operations to establish end-to-end entanglement between two distant nodes. Similar to traditional routing technologies, a quantum routing protocol uses network information to choose the best paths to satisfy a set of end-to-end entanglement requests. However, in addition to network state information, a quantum routing protocol must also take into account the requested entanglement fidelity, the probabilistic nature of swapping operations, and the short lifetime of entangled states. In this work, we formulate a practical entanglement routing problem and analyze and categorize the main approaches to address it, drawing comparisons to, and inspiration from, classical network routing strategies where applicable. We classify and discuss the studied quantum routing schemes into reactive, proactive, opportunistic, and virtual routing
Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activities
Venue: arXiv cs.LG 2024
Year: 2024

Flow-guided localization using in-body nanodevices in the bloodstream is expected to be beneficial for early disease detection, continuous monitoring of biological conditions, and targeted treatment. The nanodevices face size and power constraints that produce erroneous raw data for localization purposes. On-body anchors receive this data, and use it to derive the locations of diagnostic events of interest. Different Machine Learning (ML) approaches have been recently proposed for this task, yet they are currently restricted to a reference bloodstream of a resting patient. As such, they are unable to deal with the physical diversity of patients' bloodstreams and cannot provide continuous monitoring due to changes in individual patient's activities. Toward addressing these issues for the current State-of-the-Art (SotA) flow-guided localization approach based on Graph Neural Networks (GNNs), we propose a pipeline for GNN adaptation based on individual physiological indicators including height, weight, and heart rate. Our results indicate that the proposed adaptions are beneficial in reconciling the individual differences between bloodstreams and activities.
SeCritMass: Threshold Secret Petitions
Venue: arXiv cs.CR 2024
Year: 2024

We introduce the notion of an $n$-threshold secret petition, in which users add encrypted signatures to a petition, and the signatures are decrypted if and only if at least $n$ signatures have been gathered. This solves the coordination problem in which users wish to sign a petition or commit to a cause, but do not want to be identified as having signed it before enough others have signed it too. We present an implementation of such a petition based on the ElGamal cryptosystem. Applications include reporting misconduct in situations were complainants hesitate to come forward alone, such as in allegations of sexual harassment or police brutality.
Does This Have a Particular Meaning? Interactive Pattern Explanation for Network Visualizations
Venue: arXiv cs.HC 2024
Year: 2024

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer's selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology.
A Systematic Mapping Study on SDN Controllers for Enhancing Security in IoT Networks
Venue: arXiv cs.CR 2024
Year: 2024

Context: The increase in Internet of Things (IoT) devices gives rise to an increase in deceptive manipulations by malicious actors. These actors should be prevented from targeting the IoT networks. Cybersecurity threats have evolved and become dynamically sophisticated, such that they could exploit any vulnerability found in IoT networks. However, with the introduction of the Software Defined Network (SDN) in the IoT networks as the central monitoring unit, IoT networks are less vulnerable and less prone to threats. %Although, the SDN itself is vulnerable to several threats. Objective: To present a comprehensive and unbiased overview of the state-of-the-art on IoT networks security enhancement using SDN controllers. Method: We review the current body of knowledge on enhancing the security of IoT networks using SDN with a Systematic Mapping Study (SMS) following the established guidelines. Results: The SMS result comprises 33 primary studies analyzed against four major research questions. The SMS highlights current research trends and identifies gaps in the SDN-IoT network security. Conclusion: We conclude that the SDN controller architecture commonly used for securing IoT networks is the centralized controller architecture. However, this architecture is not without its limitations. Additionally, the predominant technique utilized for risk mitigation is machine learning.
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only
Venue: arXiv cs.CL 2024
Year: 2024

Instruction fine-tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance. However, the annotation of instruction datasets has traditionally been expensive and laborious, often relying on manual annotations or costly API calls of proprietary LLMs. To address these challenges, we introduce FANNO, a fully autonomous, open-sourced framework that revolutionizes the annotation process without the need for pre-existing annotated data. Utilizing a Mistral-7b-instruct model, FANNO efficiently produces diverse and high-quality datasets through a structured process involving document pre-screening, instruction generation, and response generation. Experiments on Open LLM Leaderboard and AlpacaEval benchmark show that the FANNO can generate high-quality data with diversity and complexity for free, comparable to human-annotated or cleaned datasets like Alpaca-GPT4-Cleaned.
Leveraging Knowledge Graph Embedding for Effective Conversational Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to traditional recommender system, it learns the user preference better through interactions (i.e. conversations), and then further boosts the recommendation performance. However, existing studies on CRS ignore to address the relationship among attributes, users, and items effectively, which might lead to inappropriate questions and inaccurate recommendations. In this view, we propose a knowledge graph based conversational recommender system (referred as KG-CRS). Specifically, we first integrate the user-item graph and item-attribute graph into a dynamic graph, i.e., dynamically changing during the dialogue process by removing negative items or attributes. We then learn informative embedding of users, items, and attributes by also considering propagation through neighbors on the graph. Extensive experiments on three real datasets validate the superiority of our method over the state-of-the-art approaches in terms of both the recommendation and conversation tasks.
PC$^2$: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval
Venue: arXiv cs.MM 2024
Year: 2024

In the realm of cross-modal retrieval, seamlessly integrating diverse modalities within multimedia remains a formidable challenge, especially given the complexities introduced by noisy correspondence learning (NCL). Such noise often stems from mismatched data pairs, which is a significant obstacle distinct from traditional noisy labels. This paper introduces Pseudo-Classification based Pseudo-Captioning (PC$^2$) framework to address this challenge. PC$^2$ offers a threefold strategy: firstly, it establishes an auxiliary "pseudo-classification" task that interprets captions as categorical labels, steering the model to learn image-text semantic similarity through a non-contrastive mechanism. Secondly, unlike prevailing margin-based techniques, capitalizing on PC$^2$'s pseudo-classification capability, we generate pseudo-captions to provide more informative and tangible supervision for each mismatched pair. Thirdly, the oscillation of pseudo-classification is borrowed to assistant the correction of correspondence. In addition to technical contributions, we develop a realistic NCL dataset called Noise of Web (NoW), which could be a new powerful NCL benchmark where noise exists naturally. Empirical evaluations of PC$^2$ showcase marked improvements over existing state-of-the-art robust cross-modal retrieval techniques on both simulated and realistic datasets with various NCL settings. The contributed dataset and source code are released at https://github.com/alipay/PC2-NoiseofWeb.
Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents a novel coordinate descent algorithm leveraging a combination of one-directional line search and gradient information for parameter updates for a squared error loss function. Each parameter undergoes updates determined by either the line search or gradient method, contingent upon whether the modulus of the gradient of the loss with respect to that parameter surpasses a predefined threshold. Notably, a larger threshold value enhances algorithmic efficiency. Despite the potentially slower nature of the line search method relative to gradient descent, its parallelizability facilitates computational time reduction. Experimental validation conducted on a 2-layer Rectified Linear Unit network with synthetic data elucidates the impact of hyperparameters on convergence rates and computational efficiency.
FT K-means: A High-Performance K-means on GPU with Fault Tolerance
Venue: arXiv cs.DC 2024
Year: 2024

K-means is a widely used algorithm in clustering, however, its efficiency is primarily constrained by the computational cost of distance computing. Existing implementations suffer from suboptimal utilization of computational units and lack resilience against soft errors. To address these challenges, we introduce FT K-means, a high-performance GPU-accelerated implementation of K-means with online fault tolerance. We first present a stepwise optimization strategy that achieves competitive performance compared to NVIDIA's cuML library. We further improve FT K-means with a template-based code generation framework that supports different data types and adapts to different input shapes. A novel warp-level tensor-core error correction scheme is proposed to address the failure of existing fault tolerance methods due to memory asynchronization during copy operations. Our experimental evaluations on NVIDIA T4 GPU and A100 GPU demonstrate that FT K-means without fault tolerance outperforms cuML's K-means implementation, showing a performance increase of 10\%-300\% in scenarios involving irregular data shapes. Moreover, the fault tolerance feature of FT K-means introduces only an overhead of 11\%, maintaining robust performance even with tens of errors injected per second.
DebateQA: Evaluating Question Answering on Debatable Knowledge
Venue: arXiv cs.CL 2024
Year: 2024

The rise of large language models (LLMs) has enabled us to seek answers to inherently debatable questions on LLM chatbots, necessitating a reliable way to evaluate their ability. However, traditional QA benchmarks assume fixed answers are inadequate for this purpose. To address this, we introduce DebateQA, a dataset of 2,941 debatable questions, each accompanied by multiple human-annotated partial answers that capture a variety of perspectives. We develop two metrics: Perspective Diversity, which evaluates the comprehensiveness of perspectives, and Dispute Awareness, which assesses if the LLM acknowledges the question's debatable nature. Experiments demonstrate that both metrics align with human preferences and are stable across different underlying models. Using DebateQA with two metrics, we assess 12 popular LLMs and retrieval-augmented generation methods. Our findings reveal that while LLMs generally excel at recognizing debatable issues, their ability to provide comprehensive answers encompassing diverse perspectives varies considerably.
MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation
Venue: arXiv cs.CY 2024
Year: 2024

To enhance therapeutic outcomes from a pharmacological perspective, we propose MiranDa, designed for medication recommendation, which is the first actionable model capable of providing the estimated length of stay in hospitals (ELOS) as counterfactual outcomes that guide clinical practice and model training. In detail, MiranDa emulates the educational trajectory of doctors through two gradient-scaling phases shifted by ELOS: an Evidence-based Training Phase that utilizes supervised learning and a Therapeutic Optimization Phase grounds in reinforcement learning within the gradient space, explores optimal medications by perturbations from ELOS. Evaluation of the Medical Information Mart for Intensive Care III dataset and IV dataset, showcased the superior results of our model across five metrics, particularly in reducing the ELOS. Surprisingly, our model provides structural attributes of medication combinations proved in hyperbolic space and advocated "procedure-specific" medication combinations. These findings posit that MiranDa enhanced medication efficacy. Notably, our paradigm can be applied to nearly all medical tasks and those with information to evaluate predicted outcomes. The source code of the MiranDa model is available at https://github.com/azusakou/MiranDa.
LocalValueBench: A Collaboratively Built and Extensible Benchmark for Evaluating Localized Value Alignment and Ethical Safety in Large Language Models
Venue: arXiv cs.CY 2024
Year: 2024

The proliferation of large language models (LLMs) requires robust evaluation of their alignment with local values and ethical standards, especially as existing benchmarks often reflect the cultural, legal, and ideological values of their creators. \textsc{LocalValueBench}, introduced in this paper, is an extensible benchmark designed to assess LLMs' adherence to Australian values, and provides a framework for regulators worldwide to develop their own LLM benchmarks for local value alignment. Employing a novel typology for ethical reasoning and an interrogation approach, we curated comprehensive questions and utilized prompt engineering strategies to probe LLMs' value alignment. Our evaluation criteria quantified deviations from local values, ensuring a rigorous assessment process. Comparative analysis of three commercial LLMs by USA vendors revealed significant insights into their effectiveness and limitations, demonstrating the critical importance of value alignment. This study offers valuable tools and methodologies for regulators to create tailored benchmarks, highlighting avenues for future research to enhance ethical AI development.