OpenRaiser/Intern-Atlas huggingface dataset:
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2024
Year: 2024

Adapting machine learning models to new domains without labeled data, especially when source data is inaccessible, is a critical challenge in applications like medical imaging, autonomous driving, and remote sensing. This task, known as Source-Free Unsupervised Domain Adaptation (SFUDA), involves adapting a pre-trained model to a target domain using only unlabeled target data, which can lead to issues such as overfitting, underfitting, and poor generalization due to domain discrepancies and noise. Existing SFUDA methods often rely on single-model architectures, struggling with uncertainty and variability in the target domain. To address these challenges, we propose DRIVE (Dual-Robustness through Information Variability and Entropy), a novel SFUDA framework leveraging a dual-model architecture. The two models, initialized with identical weights, work in parallel to capture diverse target domain characteristics. One model is exposed to perturbations via projection gradient descent (PGD) guided by mutual information, focusing on high-uncertainty regions. We also introduce an entropy-aware pseudo-labeling strategy that adjusts label weights based on prediction uncertainty, ensuring the model focuses on reliable data while avoiding noisy regions. The adaptation process has two stages: the first aligns the models on stable features using a mutual information consistency loss, and the second dynamically adjusts the perturbation level based on the loss from the first stage, encouraging the model to explore a broader range of the target domain while preserving existing performance. This enhances generalization capabilities and robustness against interference. Evaluations on standard SFUDA benchmarks show that DRIVE consistently outperforms previous methods, delivering improved adaptation accuracy and stability across complex target domains.
Instantiation of Jerboa Rule Schemes, a Set-based Explanation
Venue: arXiv cs.CG 2024
Year: 2024

This report presents a set-theoretic framework for the instantiation of rule schemes in the Jerboa platform, a tool for developing domain-specific geometric modelers. Jerboa enables the design of geometric modeling operations as graph transformation rules generalized to rule schemes for genericity over the topological content of the operations. Current approaches to algebraic graph transformations are typically described within a finitary $\mathcal{M}$-adhesive category (where $\mathcal{M}$ is a suitable system of monomorphisms), employing compositional double-pushout (DPO) semantics for rewriting. In this report, we propose a lightweight, set-theoretic description that exploits the proximity between presheaf topoi and sets to provide an explanation that does not rely on extensive theoretical background. The proposed method simplifies the formal description of modeling operations to bridge the gap between abstract concepts and their practical application in geometric modeling. The framework offers a complementary perspective to categorical approaches at the foundation of Jerboa.
Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perception
Venue: arXiv cs.AR 2024
Year: 2024

We study the application of emerging chiplet-based Neural Processing Units to accelerate vehicular AI perception workloads in constrained automotive settings. The motivation stems from how chiplets technology is becoming integral to emerging vehicular architectures, providing a cost-effective trade-off between performance, modularity, and customization; and from perception models being the most computationally demanding workloads in a autonomous driving system. Using the Tesla Autopilot perception pipeline as a case study, we first breakdown its constituent models and profile their performance on different chiplet accelerators. From the insights, we propose a novel scheduling strategy to efficiently deploy perception workloads on multi-chip AI accelerators. Our experiments using a standard DNN performance simulator, MAESTRO, show our approach realizes 82% and 2.8x increase in throughput and processing engines utilization compared to monolithic accelerator designs.
Finding hypergraph immersion is fixed-parameter tractable
Venue: arXiv cs.DM 2024
Year: 2024

Immersion minor is an important variant of graph minor, defined through an injective mapping from vertices in a smaller graph $H$ to vertices in a larger graph $G$ where adjacent elements of the former are connected in the latter by edge-disjoint paths. Here, we consider the immersion problem in the emerging field of hypergraphs. We first define hypergraph immersion by extending the injective mapping to hypergraphs. We then prove that finding a hypergraph immersion is fixed-parameter tractable, namely, there exists an $O(N^6)$ polynomial-time algorithm to determine whether a fixed hypergraph $H$ can be immersed in a hypergraph $G$ with $N$ vertices. Additionally, we present the dual hypergraph immersion problem and provide further characteristics of the algorithmic complexity.
SAGEval: The frontiers of Satisfactory Agent based NLG Evaluation for reference-free open-ended text
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Model (LLM) integrations into applications like Microsoft365 suite and Google Workspace for creating/processing documents, emails, presentations, etc. has led to considerable enhancements in productivity and time savings. But as these integrations become more more complex, it is paramount to ensure that the quality of output from the LLM-integrated applications are relevant and appropriate for use. Identifying the need to develop robust evaluation approaches for natural language generation, wherein references/ground labels doesn't exist or isn't amply available, this paper introduces a novel framework called "SAGEval" which utilizes a critiquing Agent to provide feedback on scores generated by LLM evaluators. We show that the critiquing Agent is able to rectify scores from LLM evaluators, in absence of references/ground-truth labels, thereby reducing the need for labeled data even for complex NLG evaluation scenarios, like the generation of JSON-structured forms/surveys with responses in different styles like multiple choice, likert ratings, single choice questions, etc.
BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
Venue: arXiv cs.LG 2024
Year: 2024

Offline batch inference, which leverages the flexibility of request batching to achieve higher throughput and lower costs, is becoming more popular for latency-insensitive applications. Meanwhile, recent progress in model capability and modality makes requests more diverse in compute and memory demands, creating unique opportunities for throughput improvement by resource overlapping. However, a request schedule that maximizes resource overlapping can conflict with the schedule that maximizes prefix sharing, a widely-used performance optimization, causing sub-optimal inference throughput. We present BlendServe, a system that maximizes resource utilization of offline batch inference by combining the benefits of resource overlapping and prefix sharing using a resource-aware prefix tree. BlendServe exploits the relaxed latency requirements in offline batch inference to reorder and overlap requests with varied resource demands while ensuring high prefix sharing. We evaluate BlendServe on a variety of synthetic multi-modal workloads and show that it provides up to $1.44\times$ throughput boost compared to widely-used industry standards, vLLM and SGLang.
Revisiting Marr in Face: The Building of 2D--2.5D--3D Representations in Deep Neural Networks
Venue: arXiv cs.CV 2024
Year: 2024

David Marr's seminal theory of vision proposes that the human visual system operates through a sequence of three stages, known as the 2D sketch, the 2.5D sketch, and the 3D model. In recent years, Deep Neural Networks (DNN) have been widely thought to have reached a level comparable to human vision. However, the mechanisms by which DNNs accomplish this and whether they adhere to Marr's 2D--2.5D--3D construction theory remain unexplored. In this paper, we delve into the perception task to explore these questions and find evidence supporting Marr's theory. We introduce a graphics probe, a sub-network crafted to reconstruct the original image from the network's intermediate layers. The key to the graphics probe is its flexible architecture that supports image in both 2D and 3D formats, as well as in a transitional state between them. By injecting graphics probes into neural networks, and analyzing their behavior in reconstructing images, we find that DNNs initially encode images as 2D representations in low-level layers, and finally construct 3D representations in high-level layers. Intriguingly, in mid-level layers, DNNs exhibit a hybrid state, building a geometric representation that s sur normals within a narrow depth range, akin to the appearance of a low-relief sculpture. This stage resembles the 2.5D representations, providing a view of how DNNs evolve from 2D to 3D in the perception process. The graphics probe therefore serves as a tool for peering into the mechanisms of DNN, providing empirical support for Marr's theory.
Optimizing Winograd Convolution on ARMv8 processors
Venue: arXiv cs.PF 2024
Year: 2024

As Convolutional Neural Networks (CNNs) gain prominence in deep learning, algorithms like Winograd Convolution have been introduced to enhance computational efficiency. However, existing implementations often face challenges such as high transformation overhead, suboptimal computation efficiency, and reduced parallel performance in some layers. We propose a fused Winograd Convolution algorithm optimized for ARMv8 CPUs, integrating input transformation, filter transformation, computation, and output transformation into a single pipeline. By maintaining consecutive memory access and using a custom z-shaped data layout, our approach fully utilizes an optimized GEMM micro-kernel with a ping-pong technique. Additionally, we introduce a multi-dimensional parallel strategy that adapts to convolutional layer scales. To maximize performance, we manually optimize each kernel in AArch64 assembly and carefully tune blocking parameters. Experimental results show speedups of up to 4.74x, 4.10x, 4.72x, and 10.57x over NCNN, NNPACK, FastConv, and ACL on the Kunpeng 920 platform using multiple threads, with respective gains of 3.85x, 2.81x, 4.20x, and 7.80x on the AWS Graviton2, and 3.32x, 3.68x, 8.00x, and 9.28x on the Phytium 2000+.
MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
Venue: arXiv cs.LG 2024
Year: 2024

Transformer-based large language models (LLMs) have achieved remarkable success as model sizes continue to grow, yet their deployment remains challenging due to significant computational and memory demands. Quantization has emerged as a promising solution, and state-of-the-art quantization algorithms for LLMs introduce the need for mixed-precision matrix multiplication (mpGEMM), where lower-precision weights are multiplied with higher-precision activations. Despite its benefits, current hardware accelerators such as GPUs and TPUs lack native support for efficient mpGEMM, leading to inefficient dequantization operations in the main sequential loop. To address this limitation, we introduce MixPE, a specialized mixed-precision processing element designed for efficient low-bit quantization in LLM inference. MixPE leverages two key innovations to minimize dequantization overhead and unlock the full potential of low-bit quantization. First, recognizing that scale and zero point are shared within each quantization group, we propose performing dequantization after per-group mpGEMM, significantly reducing dequantization overhead. Second, instead of relying on conventional multipliers, MixPE utilizes efficient shift\&add operations for multiplication, optimizing both computation and energy efficiency. Our experimental results demonstrate that MixPE surpasses the state-of-the-art quantization accelerators by $2.6\times$ speedup and $1.4\times$ energy reduction.
Sparse patches adversarial attacks via extrapolating point-wise information
Venue: arXiv cs.CV 2024
Year: 2024

Sparse and patch adversarial attacks were previously shown to be applicable in realistic settings and are considered a security risk to autonomous systems. Sparse adversarial perturbations constitute a setting in which the adversarial perturbations are limited to affecting a relatively small number of points in the input. Patch adversarial attacks denote the setting where the sparse attacks are limited to a given structure, i.e., sparse patches with a given shape and number. However, previous patch adversarial attacks do not simultaneously optimize multiple patches' locations and perturbations. This work suggests a novel approach for sparse patches adversarial attacks via point-wise trimming dense adversarial perturbations. Our approach enables simultaneous optimization of multiple sparse patches' locations and perturbations for any given number and shape. Moreover, our approach is also applicable for standard sparse adversarial attacks, where we show that it significantly improves the state-of-the-art over multiple extensive settings. A reference implementation of the proposed method and the reported experiments is provided at \url{https://github.com/yanemcovsky/SparsePatches.git}
Text-to-Image Synthesis: A Decade Survey
Venue: arXiv cs.CV 2024
Year: 2024

When humans read a specific text, they often visualize the corresponding images, and we hope that computers can do the same. Text-to-image synthesis (T2I), which focuses on generating high-quality images from textual descriptions, has become a significant aspect of Artificial Intelligence Generated Content (AIGC) and a transformative direction in artificial intelligence research. Foundation models play a crucial role in T2I. In this survey, we review over 440 recent works on T2I. We start by briefly introducing how GANs, autoregressive models, and diffusion models have been used for image generation. Building on this foundation, we discuss the development of these models for T2I, focusing on their generative capabilities and diversity when conditioned on text. We also explore cutting-edge research on various aspects of T2I, including performance, controllability, personalized generation, safety concerns, and consistency in content and spatial relationships. Furthermore, we summarize the datasets and evaluation metrics commonly used in T2I research. Finally, we discuss the potential applications of T2I within AIGC, along with the challenges and future research opportunities in this field.
Any3DIS: Class-Agnostic 3D Instance Segmentation by 2D Mask Tracking
Venue: arXiv cs.CV 2024
Year: 2024

Existing 3D instance segmentation methods frequently encounter issues with over-segmentation, leading to redundant and inaccurate 3D proposals that complicate downstream tasks. This challenge arises from their unsupervised merging approach, where dense 2D instance masks are lifted across frames into point clouds to form 3D candidate proposals without direct supervision. These candidates are then hierarchically merged based on heuristic criteria, often resulting in numerous redundant segments that fail to combine into precise 3D proposals. To overcome these limitations, we propose a 3D-Aware 2D Mask Tracking module that uses robust 3D priors from a 2D mask segmentation and tracking foundation model (SAM-2) to ensure consistent object masks across video frames. Rather than merging all visible superpoints across views to create a 3D mask, our 3D Mask Optimization module leverages a dynamic programming algorithm to select an optimal set of views, refining the superpoints to produce a final 3D proposal for each object. Our approach achieves comprehensive object coverage within the scene while reducing unnecessary proposals, which could otherwise impair downstream applications. Evaluations on ScanNet200 and ScanNet++ confirm the effectiveness of our method, with improvements across Class-Agnostic, Open-Vocabulary, and Open-Ended 3D Instance Segmentation tasks.
SMGDiff: Soccer Motion Generation using diffusion probabilistic models
Venue: arXiv cs.CV 2024
Year: 2024

Soccer is a globally renowned sport with significant applications in video games and VR/AR. However, generating realistic soccer motions remains challenging due to the intricate interactions between the human player and the ball. In this paper, we introduce SMGDiff, a novel two-stage framework for generating real-time and user-controllable soccer motions. Our key idea is to integrate real-time character control with a powerful diffusion-based generative model, ensuring high-quality and diverse output motion. In the first stage, we instantly transform coarse user controls into diverse global trajectories of the character. In the second stage, we employ a transformer-based autoregressive diffusion model to generate soccer motions based on trajectory conditioning. We further incorporate a contact guidance module during inference to optimize the contact details for realistic ball-foot interactions. Moreover, we contribute a large-scale soccer motion dataset consisting of over 1.08 million frames of diverse soccer motions. Extensive experiments demonstrate that our SMGDiff significantly outperforms existing methods in terms of motion quality and condition alignment.
Poster: Could Large Language Models Perform Network Management?
Venue: arXiv cs.NI 2024
Year: 2024

Modern wireless communication systems have become increasingly complex due to the proliferation of wireless devices, increasing performance standards, and growing security threats. Managing these networks is becoming more challenging, requiring the use of advanced network management methods and tools. AI-driven network management systems such as Self-Optimizing Networks (SONs) are gaining attention. On the other hand, Large Language Models (LLMs) have been demonstrating exceptional zero-shot learning and generalization capabilities across several domains. In this paper, we leverage the potential of LLMs with SONs to enhance future network management systems. Specifically, we benchmark the use of various LLMs such as GPT-4, Llama, and Falcon, in a zero-shot setting based on their real-time network configuration recommendations. Our results indicate promising prospects for integrating LLMs into future network management systems.
Diagnosis of diabetic retinopathy using machine learning & deep learning technique
Venue: arXiv cs.CV 2024
Year: 2024

Fundus images are widely used for diagnosing various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. However, manual analysis of fundus images is time-consuming and prone to errors. In this report, we propose a novel method for fundus detection using object detection and machine learning classification techniques. We use a YOLO_V8 to perform object detection on fundus images and locate the regions of interest (ROIs) such as optic disc, optic cup and lesions. We then use machine learning SVM classification algorithms to classify the ROIs into different DR stages based on the presence or absence of pathological signs such as exudates, microaneurysms, and haemorrhages etc. Our method achieves 84% accuracy and efficiency for fundus detection and can be applied for retinal fundus disease triage, especially in remote areas around the world.
Transparent Neighborhood Approximation for Text Classifier Explanation
Venue: arXiv cs.CL 2024
Year: 2024

Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood construction posed by the unstructured nature of texts, thereby improving the quality of explanations. However, the deployed generators are usually implemented via neural networks and lack inherent explainability, sparking arguments over the transparency of the explanation process itself. To address this limitation while preserving neighborhood quality, this paper introduces a probability-based editing method as an alternative to black-box text generators. This approach generates neighboring texts by implementing manipulations based on in-text contexts. Substituting the generator-based construction process with recursive probability-based editing, the resultant explanation method, XPROB (explainer with probability-based editing), exhibits competitive performance according to the evaluation conducted on two real-world datasets. Additionally, XPROB's fully transparent and more controllable construction process leads to superior stability compared to the generator-based explainers.
Cluster-based human-in-the-loop strategy for improving machine learning-based circulating tumor cell detection in liquid biopsy
Venue: arXiv cs.CV 2024
Year: 2024

Detection and differentiation of circulating tumor cells (CTCs) and non-CTCs in blood draws of cancer patients pose multiple challenges. While the gold standard relies on tedious manual evaluation of an automatically generated selection of images, machine learning (ML) techniques offer the potential to automate these processes. However, human assessment remains indispensable when the ML system arrives at uncertain or wrong decisions due to an insufficient set of labeled training data. This study introduces a human-in-the-loop (HiL) strategy for improving ML-based CTC detection. We combine self-supervised deep learning and a conventional ML-based classifier and propose iterative targeted sampling and labeling of new unlabeled training samples by human experts. The sampling strategy is based on the classification performance of local latent space clusters. The advantages of the proposed approach compared to naive random sampling are demonstrated for liquid biopsy data from patients with metastatic breast cancer.
From CISC to RISC: language-model guided assembly transpilation
Venue: arXiv cs.PL 2024
Year: 2024

The transition from x86 to ARM architecture is becoming increasingly common across various domains, primarily driven by ARM's energy efficiency and improved performance across traditional sectors. However, this ISA shift poses significant challenges, mainly due to the extensive legacy ecosystem of x86 software and lack of portability across proprietary ecosystems and software stacks. This paper introduces CRT, a lightweight LLM-based transpiler that automatically converts x86 assembly to ARM assembly. Our approach bridges the fundamental architectural gap between x86's CISC-based and ARM's RISC-based computing paradigms while preserving program semantics and optimizing performance. We evaluate CRT on diverse real-world applications, achieving 79.25% translation accuracy from x86 to ARMv5 on our comprehensive test suite, and an 88.68% accuracy from x86 to RISC-V. In practical deployments on Apple M2 hardware (ARMv8), our transpiled code achieves 1.73$\times$ speedup compared to Apple's Rosetta 2 virtualization engine, while delivering 2.41$\times$ memory efficiency and 1.47$\times$ better energy consumption. Through testing and analysis, we show that CRT successfully navigates the CISC/RISC divide and generates correctly executable RISC code despite machine ``language'' barriers. We release our code, models, training datasets, and benchmarks at: \url{https://ahmedheakl.github.io/asm2asm/}.