OpenRaiser/Intern-Atlas huggingface dataset:
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Venue: arXiv cs.LG 2024
Year: 2024

Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing. This deficit stems, at least in part, from the lack of established/public RDB benchmarks as needed for training and evaluation purposes. As a result, related model development thus far often defaults to tabular approaches trained on ubiquitous single-table benchmarks, or on the relational side, graph-based alternatives such as GNNs applied to a completely different set of graph datasets devoid of tabular characteristics. To more precisely target RDBs lying at the nexus of these two complementary regimes, we explore a broad class of baseline models predicated on: (i) converting multi-table datasets into graphs using various strategies equipped with efficient subsampling, while preserving tabular characteristics; and (ii) trainable models with well-matched inductive biases that output predictions based on these input subgraphs. Then, to address the dearth of suitable public benchmarks and reduce siloed comparisons, we assemble a diverse collection of (i) large-scale RDB datasets and (ii) coincident predictive tasks. From a delivery standpoint, we operationalize the above four dimensions (4D) of exploration within a unified, scalable open-source toolbox called 4DBInfer. We conclude by presenting evaluations using 4DBInfer, the results of which highlight the importance of considering each such dimension in the design of RDB predictive models, as well as the limitations of more naive approaches such as simply joining adjacent tables. Our source code is released at https://github.com/awslabs/multi-table-benchmark .
Flood Data Analysis on SpaceNet 8 Using Apache Sedona
Venue: arXiv cs.CV 2024
Year: 2024

With the escalating frequency of floods posing persistent threats to human life and property, satellite remote sensing has emerged as an indispensable tool for monitoring flood hazards. SpaceNet8 offers a unique opportunity to leverage cutting-edge artificial intelligence technologies to assess these hazards. A significant contribution of this research is its application of Apache Sedona, an advanced platform specifically designed for the efficient and distributed processing of large-scale geospatial data. This platform aims to enhance the efficiency of error analysis, a critical aspect of improving flood damage detection accuracy. Based on Apache Sedona, we introduce a novel approach that addresses the challenges associated with inaccuracies in flood damage detection. This approach involves the retrieval of cases from historical flood events, the adaptation of these cases to current scenarios, and the revision of the model based on clustering algorithms to refine its performance. Through the replication of both the SpaceNet8 baseline and its top-performing models, we embark on a comprehensive error analysis. This analysis reveals several main sources of inaccuracies. To address these issues, we employ data visual interpretation and histogram equalization techniques, resulting in significant improvements in model metrics. After these enhancements, our indicators show a notable improvement, with precision up by 5%, F1 score by 2.6%, and IoU by 4.5%. This work highlights the importance of advanced geospatial data processing tools, such as Apache Sedona. By improving the accuracy and efficiency of flood detection, this research contributes to safeguarding public safety and strengthening infrastructure resilience in flood-prone areas, making it a valuable addition to the field of remote sensing and disaster management.
FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method
Venue: arXiv cs.CV 2024
Year: 2024

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216.
PatentGPT: A Large Language Model for Intellectual Property
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, large language models(LLMs) have attracted significant attention due to their exceptional performance across a multitude of natural language process tasks, and have been widely applied in various fields. However, the application of large language models in the Intellectual Property (IP) domain is challenging due to the strong need for specialized knowledge, privacy protection, processing of extremely long text in this field. In this technical report, we present for the first time a low-cost, standardized procedure for training IP-oriented LLMs, meeting the unique requirements of the IP domain. Using this standard process, we have trained the PatentGPT series models based on open-source pretrained models. By evaluating them on the open-source IP-oriented benchmark MOZIP, our domain-specific LLMs outperforms GPT-4, indicating the effectiveness of the proposed training procedure and the expertise of the PatentGPT models in the IP domain. Remarkably, our model surpassed GPT-4 on the 2019 China Patent Agent Qualification Examination, scoring 65 and matching human expert levels. Additionally, the PatentGPT model, which utilizes the SMoE architecture, achieves performance comparable to that of GPT-4 in the IP domain and demonstrates a better cost-performance ratio on long-text tasks, potentially serving as an alternative to GPT-4 within the IP domain.
Mapping 'when'-clauses in Latin American and Caribbean languages: an experiment in subtoken-based typology
Venue: arXiv cs.CL 2024
Year: 2024

Languages can encode temporal subordination lexically, via subordinating conjunctions, and morphologically, by marking the relation on the predicate. Systematic cross-linguistic variation among the former can be studied using well-established token-based typological approaches to token-aligned parallel corpora. Variation among different morphological means is instead much harder to tackle and therefore more poorly understood, despite being predominant in several language groups. This paper explores variation in the expression of generic temporal subordination ('when'-clauses) among the languages of Latin America and the Caribbean, where morphological marking is particularly common. It presents probabilistic semantic maps computed on the basis of the languages of the region, thus avoiding bias towards the many world's languages that exclusively use lexified connectors, incorporating associations between character $n$-grams and English $when$. The approach allows capturing morphological clause-linkage devices in addition to lexified connectors, paving the way for larger-scale, strategy-agnostic analyses of typological variation in temporal subordination.
Align, Minimize and Diversify: A Source-Free Unsupervised Domain Adaptation Method for Handwritten Text Recognition
Venue: arXiv cs.CV 2024
Year: 2024

This paper serves to introduce the Align, Minimize and Diversify (AMD) method, a Source-Free Unsupervised Domain Adaptation approach for Handwritten Text Recognition (HTR). This framework decouples the adaptation process from the source data, thus not only sidestepping the resource-intensive retraining process but also making it possible to leverage the wealth of pre-trained knowledge encoded in modern Deep Learning architectures. Our method explicitly eliminates the need to revisit the source data during adaptation by incorporating three distinct regularization terms: the Align term, which reduces the feature distribution discrepancy between source and target data, ensuring the transferability of the pre-trained representation; the Minimize term, which encourages the model to make assertive predictions, pushing the outputs towards one-hot-like distributions in order to minimize prediction uncertainty, and finally, the Diversify term, which safeguards against the degeneracy in predictions by promoting varied and distinctive sequences throughout the target data, preventing informational collapse. Experimental results from several benchmarks demonstrated the effectiveness and robustness of AMD, showing it to be competitive and often outperforming DA methods in HTR.
Pragmatic Formal Verification of Sequential Error Detection and Correction Codes (ECCs) used in Safety-Critical Design
Venue: arXiv cs.AI 2024
Year: 2024

Error Detection and Correction Codes (ECCs) are often used in digital designs to protect data integrity. Especially in safety-critical systems such as automotive electronics, ECCs are widely used and the verification of such complex logic becomes more critical considering the ISO 26262 safety standards. Exhaustive verification of ECC using formal methods has been a challenge given the high number of data bits to protect. As an example, for an ECC of 128 data bits with a possibility to detect up to four-bit errors, the combination of bit errors is given by 128C1 + 128C2 + 128C3 + 128C4 = 1.1 * 10^7. This vast analysis space often leads to bounded proof results. Moreover, the complexity and state-space increase further if the ECC has sequential encoding and decoding stages. To overcome such problems and sign-off the design with confidence within reasonable proof time, we present a pragmatic formal verification approach of complex ECC cores with several complexity reduction techniques and know-how that were learnt during the course of verification. We discuss using the linearity of the syndrome generator as a helper assertion, using the abstract model as glue logic to compare the RTL with the sequential version of the circuit, k-induction-based model checking and using mathematical relations captured as properties to simplify the verification in order to get an unbounded proof result within 24 hours of proof runtime.
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
Venue: arXiv cs.CL 2024
Year: 2024

Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) have demonstrated remarkable abilities in understanding text, which also introduced the potential for more expressive modeling in text-rich graphs. Despite these capabilities, efficiently applying LLMs to representation learning on graphs presents significant challenges. Recently, parameter-efficient fine-tuning methods for LLMs have enabled efficient new task generalization with minimal time and memory consumption. Inspired by this, we introduce Graph-aware Parameter-Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning with LLMs on text-rich graphs. Specifically, we utilize a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. This prompt is then inserted at the beginning of the text sequence. To improve the quality of graph prompts, we pre-trained the GNN to assist the frozen LLM in predicting the next token in the node text. Compared with existing joint GNN and LMs, our method directly generate the node embeddings from large language models with an affordable fine-tuning cost. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations. Our results demonstrate the efficacy and efficiency of our model, showing that it can be smoothly integrated with various large language models, including OPT, LLaMA and Falcon.
Bias Neutralization Framework: Measuring Fairness in Large Language Models with Bias Intelligence Quotient (BiQ)
Venue: arXiv cs.CL 2024
Year: 2024

The burgeoning influence of Large Language Models (LLMs) in shaping public discourse and decision-making underscores the imperative to address inherent biases within these AI systems. In the wake of AI's expansive integration across sectors, addressing racial bias in LLMs has never been more critical. This paper introduces a novel framework called Comprehensive Bias Neutralization Framework (CBNF) which embodies an innovative approach to quantifying and mitigating biases within LLMs. Our framework combines the Large Language Model Bias Index (LLMBI) [Oketunji, A., Anas, M., Saina, D., (2023)] and Bias removaL with No Demographics (BLIND) [Orgad, H., Belinkov, Y. (2023)] methodologies to create a new metric called Bias Intelligence Quotient (BiQ)which detects, measures, and mitigates racial bias in LLMs without reliance on demographic annotations. By introducing a new metric called BiQ that enhances LLMBI with additional fairness metrics, CBNF offers a multi-dimensional metric for bias assessment, underscoring the necessity of a nuanced approach to fairness in AI [Mehrabi et al., 2021]. This paper presents a detailed analysis of Latimer AI (a language model incrementally trained on black history and culture) in comparison to ChatGPT 3.5, illustrating Latimer AI's efficacy in detecting racial, cultural, and gender biases through targeted training and refined bias mitigation strategies [Latimer & Bender, 2023].
Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms
Venue: arXiv cs.AI 2024
Year: 2024

Recent work on decentralized computational trust models for open Multi Agent Systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee's perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents' continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q Learning (DQN), in a single-agent Reinforcement Learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.
Finding Understanding and Support: Navigating Online Communities to Share and Connect at the intersection of Abuse and Foster Care Experiences
Venue: arXiv cs.HC 2024
Year: 2024

Many children in foster care experience trauma that is rooted in unstable family relationships. Other members of the foster care system like foster parents and social workers face secondary trauma. Drawing on 10 years of Reddit data, we used a mixed methods approach to analyze how different members of the foster care system find support and similar experiences at the intersection of two Reddit communities - foster care, and abuse. Users who cross this boundary focus on trauma experiences specific to different roles in foster care. While representing a small number of users, boundary crossing users contribute heavily to both communities, and, compared to matching users, receive higher scores and more replies. We explore the roles boundary crossing users have both in the online community and in the context of foster care. Finally, we present design recommendations that would support trauma survivors find communities more suited to their personal experiences.
Pièces de viole des Cinq Livres and their statistical signatures: the musical work of Marin Marais and Jordi Savall
Venue: arXiv cs.SD 2024
Year: 2024

This study analyzes the spectrum of audio signals related to the work of "Pièces de viole des Cinq Livres" based on the collaborative work between Marin Marais and Jordi Savall for the underlying musical information. In particular, we explore the identification of possible statistical signatures related to this musical work. Based on the complex systems approach, we compute the spectrum of audio signals, analyze and identify their best-fit statistical distributions, and plot their relative frequencies using the scientific pitch notation. Findings suggest that the collection of frequency components related to the spectrum of each of the books that form this audio work show highly skewed and associated statistical distributions. Therefore, the most frequent statistical distribution that best describes the collection of these audio data and may be associated with a singular statistical signature is the exponential.
Improving Multi-Instance GPU Efficiency via Sub-Entry Sharing TLB Design
Venue: arXiv cs.DC 2024
Year: 2024

NVIDIA's Multi-Instance GPU (MIG) technology enables partitioning GPU computing power and memory into separate hardware instances, providing complete isolation including compute resources, caches, and memory. However, prior work identifies that MIG does not extend to partitioning the last-level TLB (i.e., L3 TLB), which remains shared among all instances. To enhance TLB reach, NVIDIA GPUs reorganized the TLB structure with 16 sub-entries in each L3 TLB entry that have a one-to-one mapping to the address translations for 16 pages of size 64KB located within the same 1MB aligned range. Our comprehensive investigation of address translation efficiency in MIG identifies two main issues caused by L3 TLB sharing interference: (i) it results in performance degradation for co-running applications, and (ii) TLB sub-entries are not fully utilized before eviction. Based on this observation, we propose STAR to improve the utilization of TLB sub-entries through dynamic sharing of TLB entries across multiple base addresses. STAR evaluates TLB entries based on their sub-entry utilization to optimize address translation storage, dynamically adjusting between a shared and non-shared status to cater to current demand. We show that STAR improves overall performance by an average of 30.2% across various multi-tenant workloads.
Reactive Composition of UAV Delivery Services in Urban Environments
Venue: arXiv cs.RO 2024
Year: 2024

We propose a novel failure-aware reactive UAV delivery service composition framework. A skyway network infrastructure is presented for the effective provisioning of services in urban areas. We present a formal drone delivery service model and a system architecture for reactive drone delivery services. We develop radius-based, cell density-based, and two-phased algorithms to reduce the search space and perform reactive service compositions when a service failure occurs. We conduct a set of experiments with a real drone dataset to demonstrate the effectiveness of our proposed approach.
Decomposition Model Assisted Energy-Saving Design in Radio Access Network
Venue: arXiv cs.NI 2024
Year: 2024

The continuous emergence of novel services and massive connections involve huge energy consumption towards ultra-dense radio access networks. Moreover, there exist much more number of controllable parameters that can be adjusted to reduce the energy consumption from a network-wide perspective. However, a network-level energy-saving intent usually contains multiple network objectives and constraints. Therefore, it is critical to decompose a network-level energy-saving intent into multiple levels of configurated operations from a top-down refinement perspective. In this work, we utilize a softgoal interdependency graph decomposition model to assist energy-saving scheme design. Meanwhile, we propose an energy-saving approach based on deep Q-network, which achieve a better trade-off among the energy consumption, the throughput, and the first packet delay. In addition, we illustrate how the decomposition model can assist in making energy-saving decisions. Evaluation results demonstrate the performance gain of the proposed scheme in accelerating the model training process.
3D Gaussian Splatting with Deferred Reflection
Venue: arXiv cs.CV 2024
Year: 2024

The advent of neural and Gaussian-based radiance field methods have achieved great success in the field of novel view synthesis. However, specular reflection remains non-trivial, as the high frequency radiance field is notoriously difficult to fit stably and accurately. We present a deferred shading method to effectively render specular reflection with Gaussian splatting. The key challenge comes from the environment map reflection model, which requires accurate surface normal while simultaneously bottlenecks normal estimation with discontinuous gradients. We leverage the per-pixel reflection gradients generated by deferred shading to bridge the optimization process of neighboring Gaussians, allowing nearly correct normal estimations to gradually propagate and eventually spread over all reflective objects. Our method significantly outperforms state-of-the-art techniques and concurrent work in synthesizing high-quality specular reflection effects, demonstrating a consistent improvement of peak signal-to-noise ratio (PSNR) for both synthetic and real-world scenes, while running at a frame rate almost identical to vanilla Gaussian splatting.
Clicks2Line: Using Lines for Interactive Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

For click-based interactive segmentation methods, reducing the number of clicks required to obtain a desired segmentation result is essential. Although recent click-based methods yield decent segmentation results, we observe that substantial amount of clicks are required to segment elongated regions. To reduce the amount of user-effort required, we propose using lines instead of clicks for such cases. In this paper, an interactive segmentation algorithm which adaptively adopts either clicks or lines as input is proposed. Experimental results demonstrate that using lines can generate better segmentation results than clicks for several cases.