OpenRaiser/Intern-Atlas huggingface dataset:
PANDORA: A Parallel Dendrogram Construction Algorithm for Single Linkage Clustering on GPU
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents \pandora, a novel parallel algorithm for efficiently constructing dendrograms for single-linkage hierarchical clustering, including \hdbscan. Traditional dendrogram construction methods from a minimum spanning tree (MST), such as agglomerative or divisive techniques, often fail to efficiently parallelize, especially with skewed dendrograms common in real-world data. \pandora addresses these challenges through a unique recursive tree contraction method, which simplifies the tree for initial dendrogram construction and then progressively reconstructs the complete dendrogram. This process makes \pandora asymptotically work-optimal, independent of dendrogram skewness. All steps in \pandora are fully parallel and suitable for massively threaded accelerators such as GPUs. Our implementation is written in Kokkos, providing support for both CPUs and multi-vendor GPUs (e.g., Nvidia, AMD). The multithreaded version of \pandora is 2.2$\times$ faster than the current best-multithreaded implementation, while the GPU \pandora implementation achieved 6-20$\times$ on \amdgpu and 10-37$\times$ on \nvidiagpu speed-up over multithreaded \pandora. These advancements lead to up to a 6-fold speedup for \hdbscan on GPUs over the current best, which only offload MST construction to GPUs and perform multithreaded dendrogram construction.
Harnessing Artificial Intelligence for Sustainable Agricultural Development in Africa: Opportunities, Challenges, and Impact
Venue: arXiv cs.CY 2024
Year: 2024

This paper explores the transformative potential of artificial intelligence (AI) in the context of sustainable agricultural development across diverse regions in Africa. Delving into opportunities, challenges, and impact, the study navigates through the dynamic landscape of AI applications in agriculture. Opportunities such as precision farming, crop monitoring, and climate-resilient practices are examined, alongside challenges related to technological infrastructure, data accessibility, and skill gaps. The article analyzes the impact of AI on smallholder farmers, supply chains, and inclusive growth. Ethical considerations and policy implications are also discussed, offering insights into responsible AI integration. By providing a nuanced understanding, this paper contributes to the ongoing discourse on leveraging AI for fostering sustainability in African agriculture.
VW-PINNs: A volume weighting method for PDE residuals in physics-informed neural networks
Venue: arXiv cs.CE 2024
Year: 2024

Physics-informed neural networks (PINNs) have shown remarkable prospects in the solving the forward and inverse problems involving partial differential equations (PDEs). The method embeds PDEs into the neural network by calculating PDE loss at a series of collocation points, providing advantages such as meshfree and more convenient adaptive sampling. However, when solving PDEs using nonuniform collocation points, PINNs still face challenge regarding inefficient convergence of PDE residuals or even failure. In this work, we first analyze the ill-conditioning of the PDE loss in PINNs under nonuniform collocation points. To address the issue, we define volume-weighted residual and propose volume-weighted physics-informed neural networks (VW-PINNs). Through weighting the PDE residuals by the volume that the collocation points occupy within the computational domain, we embed explicitly the spatial distribution characteristics of collocation points in the residual evaluation. The fast and sufficient convergence of the PDE residuals for the problems involving nonuniform collocation points is guaranteed. Considering the meshfree characteristics of VW-PINNs, we also develop a volume approximation algorithm based on kernel density estimation to calculate the volume of the collocation points. We verify the universality of VW-PINNs by solving the forward problems involving flow over a circular cylinder and flow over the NACA0012 airfoil under different inflow conditions, where conventional PINNs fail; By solving the Burgers' equation, we verify that VW-PINNs can enhance the efficiency of existing the adaptive sampling method in solving the forward problem by 3 times, and can reduce the relative error of conventional PINNs in solving the inverse problem by more than one order of magnitude.
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis
Venue: arXiv cs.LG 2024
Year: 2024

Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.
The Shapley Value in Database Management
Venue: arXiv cs.DB 2024
Year: 2024

Attribution scores can be applied in data management to quantify the contribution of individual items to conclusions from the data, as part of the explanation of what led to these conclusions. In Artificial Intelligence, Machine Learning, and Data Management, some of the common scores are deployments of the Shapley value, a formula for profit sharing in cooperative game theory. Since its invention in the 1950s, the Shapley value has been used for contribution measurement in many fields, from economics to law, with its latest researched applications in modern machine learning. Recent studies investigated the application of the Shapley value to database management. This article gives an overview of recent results on the computational complexity of the Shapley value for measuring the contribution of tuples to query answers and to the extent of inconsistency with respect to integrity constraints. More specifically, the article highlights lower and upper bounds on the complexity of calculating the Shapley value, either exactly or approximately, as well as solutions for realizing the calculation in practice.
Modularis: Modular Underwater Robot for Rapid Development and Validation of Autonomous Systems
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous underwater robots typically require higher cost and time for demonstrations compared to other domains due to the complexity of the environment. Due to the limited capacity and payload flexibility, it is challenging to find off-the-shelf underwater robots that are affordable, customizable, and subject to environmental variability. Custom-built underwater robots may be necessary for specialized applications or missions, but the process can be more costly and time-consuming than purchasing an off-the-shelf autonomous underwater vehicle (AUV). To address these challenges, we propose a modular underwater robot, Modularis, that can serve as an open-source testbed system. Our proposed system expedites the testing of perception, planning, and control algorithms.
Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning
Venue: arXiv cs.LG 2024
Year: 2024

In machine learning, the exponential growth of data and the associated ``curse of dimensionality'' pose significant challenges, particularly with expansive yet sparse datasets. Addressing these challenges, multi-view ensemble learning (MEL) has emerged as a transformative approach, with feature partitioning (FP) playing a pivotal role in constructing artificial views for MEL. Our study introduces the Semantic-Preserving Feature Partitioning (SPFP) algorithm, a novel method grounded in information theory. The SPFP algorithm effectively partitions datasets into multiple semantically consistent views, enhancing the MEL process. Through extensive experiments on eight real-world datasets, ranging from high-dimensional with limited instances to low-dimensional with high instances, our method demonstrates notable efficacy. It maintains model accuracy while significantly improving uncertainty measures in scenarios where high generalization performance is achievable. Conversely, it retains uncertainty metrics while enhancing accuracy where high generalization accuracy is less attainable. An effect size analysis further reveals that the SPFP algorithm outperforms benchmark models by large effect size and reduces computational demands through effective dimensionality reduction. The substantial effect sizes observed in most experiments underscore the algorithm's significant improvements in model performance.
AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints
Venue: arXiv cs.CV 2024
Year: 2024

Real-time and accurate information on fine-grained changes in crop cultivation is of great significance for crop growth monitoring, yield prediction and agricultural structure adjustment. Aiming at the problems of serious spectral confusion in visible high-resolution unmanned aerial vehicle (UAV) images of different phases, interference of large complex background and salt-and-pepper noise by existing semantic change detection (SCD) algorithms, in order to effectively extract deep image features of crops and meet the demand of agricultural practical engineering applications, this paper designs and proposes an agricultural geographic scene and parcel-scale constrained SCD framework for crops (AGSPNet). AGSPNet framework contains three parts: agricultural geographic scene (AGS) division module, parcel edge extraction module and crop SCD module. Meanwhile, we produce and introduce an UAV image SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple semantic variation types of crops in complex geographical scene. We conduct comparative experiments and accuracy evaluations in two test areas of this dataset, and the results show that the crop SCD results of AGSPNet consistently outperform other deep learning SCD models in terms of quantity and quality, with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over the sub-optimal method. The method proposed in this paper can clearly detect the fine-grained change information of crop types in complex scenes, which can provide scientific and technical support for smart agriculture monitoring and management, food policy formulation and food security assurance.
Frequency-Time Diffusion with Neural Cellular Automata
Venue: arXiv cs.CV 2024
Year: 2024

Despite considerable success, large Denoising Diffusion Models (DDMs) with UNet backbone pose practical challenges, particularly on limited hardware and in processing gigapixel images. To address these limitations, we introduce two Neural Cellular Automata (NCA)-based DDMs: Diff-NCA and FourierDiff-NCA. Capitalizing on the local communication capabilities of NCA, Diff-NCA significantly reduces the parameter counts of NCA-based DDMs. Integrating Fourier-based diffusion enables global communication early in the diffusion process. This feature is particularly valuable in synthesizing complex images with important global features, such as the CelebA dataset. We demonstrate that even a 331k parameter Diff-NCA can generate 512x512 pathology slices, while FourierDiff-NCA (1.1m parameters) reaches a three times lower FID score of 43.86, compared to the four times bigger UNet (3.94m parameters) with a score of 128.2. Additionally, FourierDiff-NCA can perform diverse tasks such as super-resolution, out-of-distribution image synthesis, and inpainting without explicit training.
Kimera2: Robust and Accurate Metric-Semantic SLAM in the Real World
Venue: arXiv cs.RO 2024
Year: 2024

We present improvements to Kimera, an open-source metric-semantic visual-inertial SLAM library. In particular, we enhance Kimera-VIO, the visual-inertial odometry pipeline powering Kimera, to support better feature tracking, more efficient keyframe selection, and various input modalities (eg monocular, stereo, and RGB-D images, as well as wheel odometry). Additionally, Kimera-RPGO and Kimera-PGMO, Kimera's pose-graph optimization backends, are updated to support modern outlier rejection methods - specifically, Graduated-Non-Convexity - for improved robustness to spurious loop closures. These new features are evaluated extensively on a variety of simulated and real robotic platforms, including drones, quadrupeds, wheeled robots, and simulated self-driving cars. We present comparisons against several state-of-the-art visual-inertial SLAM pipelines and discuss strengths and weaknesses of the new release of Kimera. The newly added features have been released open-source at https://github.com/MIT-SPARK/Kimera.
AffordanceLLM: Grounding Affordance from Vision Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Affordance grounding refers to the task of finding the area of an object with which one can interact. It is a fundamental but challenging task, as a successful solution requires the comprehensive understanding of a scene in multiple aspects including detection, localization, and recognition of objects with their parts, of geo-spatial configuration/layout of the scene, of 3D shapes and physics, as well as of the functionality and potential interaction of the objects and humans. Much of the knowledge is hidden and beyond the image content with the supervised labels from a limited training set. In this paper, we make an attempt to improve the generalization capability of the current affordance grounding by taking the advantage of the rich world, abstract, and human-object-interaction knowledge from pretrained large-scale vision language models. Under the AGD20K benchmark, our proposed model demonstrates a significant performance gain over the competing methods for in-the-wild object affordance grounding. We further demonstrate it can ground affordance for objects from random Internet images, even if both objects and actions are unseen during training. Project site: https://jasonqsy.github.io/AffordanceLLM/
Cognitive BPM as an Equalizer: Improving Access and Efficiency for Employees with (and without) Cognitive Disabilities
Venue: arXiv cs.AI 2024
Year: 2024

We examine ProcessGPT, an AI model designed to automate, augment, and improve business processes, to study the challenges of managing business processes within the cognitive limitations of the human workforce, particularly individuals with cognitive disabilities. ProcessGPT provides a blueprint for designing efficient business processes that take into account human cognitive limitations. By viewing this through the lens of cognitive disabilities, we show that ProcessGPT improves process usability for individuals with and without cognitive disabilities. We also demonstrate that organizations implementing ProcessGPT-like capabilities will realize increased productivity, morale, and inclusion.
New Lower Bounds in Merlin-Arthur Communication and Graph Streaming Verification
Venue: arXiv cs.DS 2024
Year: 2024

We show new lower bounds in the \emph{Merlin-Arthur} (MA) communication model and the related \emph{annotated streaming} or stream verification model. The MA communication model is an enhancement of the classical communication model, where in addition to the usual players Alice and Bob, there is an all-powerful but untrusted player Merlin who knows their inputs and tries to convince them about the output. Most functions have MA protocols with total communication significantly smaller than what would be needed without Merlin. We focus on the online MA (OMA) model, which is the MA analogue of one-way communication, and introduce the notion of \emph{non-trivial-OMA} complexity of a function. This is the minimum total communication needed by any non-trivial OMA protocol computing that function, where a trivial OMA protocol is one where Alice sends Bob roughly as many bits as she would have sent without Merlin. We prove a lower bound on the non-trivial-OMA complexity of a natural function \emph{Equals-Index} (basically the well-known Index problem on large domains) and identify it as a canonical problem for proving strong lower bounds on this complexity: reductions from it (i) reproduce and/or improve upon the lower bounds for all functions that were previously known to have large non-trivial-OMA complexity, (ii) exhibit the first explicit functions whose non-trivial-OMA complexity is superlinear, and even exponential, in their classical one-way complexity, and (iii) show functions on input size $n$ for which this complexity is as large as $n/\log n$. While exhibiting a function with $ω(\sqrt{n})$ (standard) OMA complexity is a longstanding open problem, we did not even know of any function with $ω(\sqrt{n})$ non-trivial-OMA complexity. We further extend the lower bounds to a related streaming model called annotated streaming.
An approach for mistranslation removal from popular dataset for Indic MT Task
Venue: arXiv cs.CL 2024
Year: 2024

The conversion of content from one language to another utilizing a computer system is known as Machine Translation (MT). Various techniques have come up to ensure effective translations that retain the contextual and lexical interpretation of the source language. End-to-end Neural Machine Translation (NMT) is a popular technique and it is now widely used in real-world MT systems. Massive amounts of parallel datasets (sentences in one language alongside translations in another) are required for MT systems. These datasets are crucial for an MT system to learn linguistic structures and patterns of both languages during the training phase. One such dataset is Samanantar, the largest publicly accessible parallel dataset for Indian languages (ILs). Since the corpus has been gathered from various sources, it contains many incorrect translations. Hence, the MT systems built using this dataset cannot perform to their usual potential. In this paper, we propose an algorithm to remove mistranslations from the training corpus and evaluate its performance and efficiency. Two Indic languages (ILs), namely, Hindi (HIN) and Odia (ODI) are chosen for the experiment. A baseline NMT system is built for these two ILs, and the effect of different dataset sizes is also investigated. The quality of the translations in the experiment is evaluated using standard metrics such as BLEU, METEOR, and RIBES. From the results, it is observed that removing the incorrect translation from the dataset makes the translation quality better. It is also noticed that, despite the fact that the ILs-English and English-ILs systems are trained using the same corpus, ILs-English works more effectively across all the evaluation metrics.
An Efficient and Scalable Clocking Assignment Algorithm for Multi-Threaded Multi-Phase Single Flux Quantum Circuits
Venue: arXiv cs.ET 2024
Year: 2024

A key distinguishing feature of single flux quantum (SFQ) circuits is that each logic gate is clocked. This feature forces the introduction of path-balancing flip-flops to ensure proper synchronization of inputs at each gate. This paper proposes a polynomial time complexity approximation algorithm for clocking assignments that minimizes the insertion of path balancing buffers for multi-threaded multi-phase clocking of SFQ circuits. Existing SFQ multi-phase clocking solutions have been shown to effectively reduce the number of required buffers inserted while maintaining high throughput, however, the associated clock assignment algorithms have exponential complexity and can have prohibitively long runtimes for large circuits, limiting the scalability of this approach. Our proposed algorithm is based on a linear program (LP) that leads to solutions that are experimentally on average within 5% of the optimum and helps accelerate convergence towards the optimal integer linear program (ILP) based solution. The improved LP and ILP runtimes permit multi-phase clocking schemes to scale to larger SFQ circuits than previous state of the art clocking assignment methods. We further extend the existing algorithm to support fanout sharing of the added buffers, saving, on average, an additional 10% of the inserted DFFs. Compared to traditional full path balancing (FPB) methods across 10 benchmarks, our enhanced LP saves 79.9%, 87.8%, and 91.2% of the inserted buffers for 2, 3, and 4 clock phases respectively. Finally, we extend this approach to the generation of circuits that completely mitigate potential hold-time violations at the cost of either adding on average less than 10% more buffers (for designs with 3 or more clock phases) or, more generally, adding a clock phase and thereby reducing throughput.
3D Reconstruction of Interacting Multi-Person in Clothing from a Single Image
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces a novel pipeline to reconstruct the geometry of interacting multi-person in clothing on a globally coherent scene space from a single image. The main challenge arises from the occlusion: a part of a human body is not visible from a single view due to the occlusion by others or the self, which introduces missing geometry and physical implausibility (e.g., penetration). We overcome this challenge by utilizing two human priors for complete 3D geometry and surface contacts. For the geometry prior, an encoder learns to regress the image of a person with missing body parts to the latent vectors; a decoder decodes these vectors to produce 3D features of the associated geometry; and an implicit network combines these features with a surface normal map to reconstruct a complete and detailed 3D humans. For the contact prior, we develop an image-space contact detector that outputs a probability distribution of surface contacts between people in 3D. We use these priors to globally refine the body poses, enabling the penetration-free and accurate reconstruction of interacting multi-person in clothing on the scene space. The results demonstrate that our method is complete, globally coherent, and physically plausible compared to existing methods.
Mutual Distillation Learning For Person Re-Identification
Venue: arXiv cs.CV 2024
Year: 2024

With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements. However, the majority of prior works have traditionally focused on solving the problem via extracting features solely from a single perspective, such as uniform partitioning, hard attention mechanisms, or semantic masks. While these approaches have demonstrated efficacy within specific contexts, they fall short in diverse situations. In this paper, we propose a novel approach, Mutual Distillation Learning For Person Re-identification (termed as MDPR), which addresses the challenging problem from multiple perspectives within a single unified model, leveraging the power of mutual distillation to enhance the feature representations collectively. Specifically, our approach encompasses two branches: a hard content branch to extract local features via a uniform horizontal partitioning strategy and a Soft Content Branch to dynamically distinguish between foreground and background and facilitate the extraction of multi-granularity features via a carefully designed attention mechanism. To facilitate knowledge exchange between these two branches, a mutual distillation and fusion process is employed, promoting the capability of the outputs of each branch. Extensive experiments are conducted on widely used person ReID datasets to validate the effectiveness and superiority of our approach. Notably, our method achieves an impressive $88.7\%/94.4\%$ in mAP/Rank-1 on the DukeMTMC-reID dataset, surpassing the current state-of-the-art results. Our source code is available at https://github.com/KuilongCui/MDPR.