OpenRaiser/Intern-Atlas huggingface dataset:
Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles
Venue: arXiv cs.RO 2024
Year: 2024

This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving. The decision making and trajectory planning tasks are jointly formulated as a nonlinear programming problem with an integrated objective function. However, integrating the discrete decision variables into the continuous trajectory optimization leads to a mixed-integer programming (MIP) problem with inherent nonlinearity and nonconvexity. To address the challenge in solving the problem, the original problem is decomposed into two sub-stages, and a two-stage optimization (TSO) based approach is presented to ensure the coherence in outcomes for the two stages. The optimization problem in the first stage determines the optimal decision sequence that acts as an informed initialization. With the outputs from the first stage, the second stage necessitates the use of a high-fidelity vehicle model and strict enforcement of the collision avoidance constraints as part of the trajectory planning problem. We evaluate the effectiveness of our proposed planner across diverse multi-lane scenarios. The results demonstrate that the proposed planner simultaneously generates a sequence of optimal decisions and the corresponding trajectory that significantly improves driving performance in terms of driving safety and traveling efficiency as compared to alternative methods. Additionally, we implement the closed-loop simulation in CARLA, and the results showcase the effectiveness of the proposed planner to adapt to changing driving situations with high computational efficiency.
Zero-shot Slot Filling in the Age of LLMs for Dialogue Systems
Venue: arXiv cs.CL 2024
Year: 2024

Zero-shot slot filling is a well-established subtask of Natural Language Understanding (NLU). However, most existing methods primarily focus on single-turn text data, overlooking the unique complexities of conversational dialogue. Conversational data is highly dynamic, often involving abrupt topic shifts, interruptions, and implicit references that make it difficult to directly apply zero-shot slot filling techniques, even with the remarkable capabilities of large language models (LLMs). This paper addresses these challenges by proposing strategies for automatic data annotation with slot induction and black-box knowledge distillation (KD) from a teacher LLM to a smaller model, outperforming vanilla LLMs on internal datasets by 26% absolute increase in F1 score. Additionally, we introduce an efficient system architecture for call center product settings that surpasses off-the-shelf extractive models by 34% relative F1 score, enabling near real-time inference on dialogue streams with higher accuracy, while preserving low latency.
USTCCTSU at SemEval-2024 Task 1: Reducing Anisotropy for Cross-lingual Semantic Textual Relatedness Task
Venue: arXiv cs.CL 2024
Year: 2024

Cross-lingual semantic textual relatedness task is an important research task that addresses challenges in cross-lingual communication and text understanding. It helps establish semantic connections between different languages, crucial for downstream tasks like machine translation, multilingual information retrieval, and cross-lingual text understanding.Based on extensive comparative experiments, we choose the XLM-R-base as our base model and use pre-trained sentence representations based on whitening to reduce anisotropy.Additionally, for the given training data, we design a delicate data filtering method to alleviate the curse of multilingualism. With our approach, we achieve a 2nd score in Spanish, a 3rd in Indonesian, and multiple entries in the top ten results in the competition's track C. We further do a comprehensive analysis to inspire future research aimed at improving performance on cross-lingual tasks.
Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks
Venue: arXiv cs.LG 2024
Year: 2024

Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization algorithms, which are computationally intensive due to online iterative steps. Existing learning-based methods, however, often fail to exploit the intrinsic structure of channel data and neglect pilot allocation information, leading to suboptimal performance, especially in large-scale networks with many users. This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network, a novel approach that integrates pilot allocation data into the network, effectively handling pilot contamination scenarios. PAPC employs the attention mechanism with a custom masking technique to utilize structural information and pilot data. The architecture includes tailored preprocessing and post-processing stages for efficient feature extraction and adherence to power constraints. Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm, showing comparable spectral efficiency fairness performance while significantly improving computational efficiency. Simulations demonstrate PAPC's superior performance over fully connected networks (FCNs) that lack pilot information, its scalability to large-scale CFmMIMO networks, and its computational efficiency improvement over APG. Additionally, by employing padding techniques, PAPC adapts to the dynamically varying number of users without retraining.
Mars-PO: Multi-Agent Reasoning System Preference Optimization
Venue: arXiv cs.AI 2024
Year: 2024

Mathematical reasoning is a fundamental capability for large language models (LLMs), yet achieving high performance in this domain remains a significant challenge. The auto-regressive generation process often makes LLMs susceptible to errors, hallucinations, and inconsistencies, particularly during multi-step reasoning. In this paper, we propose Mars-PO, a novel framework to improve the mathematical reasoning capabilities of LLMs through a multi-agent system. It combines high-quality outputs from multiple agents into a hybrid positive sample set and pairs them with agent-specific negative samples to construct robust preference pairs for training. By aligning agents with shared positive samples while addressing individual weaknesses, Mars-PO achieves substantial performance improvements on mathematical reasoning benchmarks. For example, it increases the accuracy on the MATH benchmark of the state-of-the-art instruction-tuned LLM, Llama3.1-8B-Instruct, from 50.38% to 57.82%. Experimental results further demonstrate that our method consistently outperforms other baselines, such as supervised fine-tuning, vanilla DPO, and its enhanced versions, highlighting the effectiveness of our approach.
Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection
Venue: arXiv cs.CV 2024
Year: 2024

Ensuring construction site safety requires accurate and real-time detection of workers' safety helmet use, despite challenges posed by cluttered environments, densely populated work areas, and hard-to-detect small or overlapping objects caused by building obstructions. This paper proposes a novel algorithm for safety helmet wearing detection, incorporating a dynamic attention within the detection head to enhance multi-scale perception. The mechanism combines feature-level attention for scale adaptation, spatial attention for spatial localization, and channel attention for task-specific insights, improving small object detection without additional computational overhead. Furthermore, a two-way fusion strategy enables bidirectional information flow, refining feature fusion through adaptive multi-scale weighting, and enhancing recognition of occluded targets. Experimental results demonstrate a 1.7% improvement in mAP@[.5:.95] compared to the best baseline while reducing GFLOPs by 11.9% on larger sizes. The proposed method surpasses existing models, providing an efficient and practical solution for real-world construction safety monitoring.
On Homogeneous Model of Fluted Languages
Venue: arXiv cs.LO 2024
Year: 2024

We study the fluted fragment of first-order logic which is often viewed as a multi-variable non-guarded extension to various systems of description logics lacking role-inverses. In this paper we show that satisfiable fluted sentences (even under reasonable extensions) admit special kinds of ``nice'' models which we call globally/locally homogeneous. Homogeneous models allow us to simplify methods for analysing fluted logics with counting quantifiers and establish a novel result for the decidability of the (finite) satisfiability problem for the fluted fragment with periodic counting. More specifically, we will show that the (finite) satisfiability problem for the language is ${\rm T{\small OWER}}$-complete. If only two variable are used, computational complexity drops to ${\rm NE{\small XP}T{\small IME}}$-completeness. We supplement our findings by showing that generalisations of fluted logics, such as the adjacent fragment, have finite and general satisfiability problems which are, respectively, $Π^0_1$- and $Σ^0_1$-complete. Additionally, satisfiability becomes $Σ^1_1$-complete if periodic counting quantifiers are permitted.
360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images
Venue: arXiv cs.CV 2024
Year: 2024

360-degree images offer a significantly wider field of view compared to traditional pinhole cameras, enabling sparse sampling and dense 3D reconstruction in low-texture environments. This makes them crucial for applications in VR, AR, and related fields. However, the inherent distortion caused by the wide field of view affects feature extraction and matching, leading to geometric consistency issues in subsequent multi-view reconstruction. In this work, we propose 360Recon, an innovative MVS algorithm for ERP images. The proposed spherical feature extraction module effectively mitigates distortion effects, and by combining the constructed 3D cost volume with multi-scale enhanced features from ERP images, our approach achieves high-precision scene reconstruction while preserving local geometric consistency. Experimental results demonstrate that 360Recon achieves state-of-the-art performance and high efficiency in depth estimation and 3D reconstruction on existing public panoramic reconstruction datasets.
PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI Inference Servers
Venue: arXiv cs.DC 2024
Year: 2024

NVIDIA's Multi-Instance GPU (MIG) is a feature that enables system designers to reconfigure one large GPU into multiple smaller GPU slices. This work characterizes this emerging GPU and evaluates its effectiveness in designing high-performance AI inference servers. Our study reveals that the data preprocessing stage of AI inference causes significant performance bottlenecks to MIG. To this end, we present PREBA, which is a hardware/software co-design targeting MIG inference servers. Our first proposition is an FPGA-based data preprocessing accelerator that unlocks the full potential of MIG with domain-specific acceleration of data preprocessing. The MIG inference server unleashed from preprocessing overheads is then augmented with our dynamic batching system that enables high-performance inference. PREBA is implemented end-to-end in real systems, providing a 3.7x improvement in throughput, 3.4x reduction in tail latency, 3.5x improvement in energy-efficiency, and 3.0x improvement in cost-efficiency.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Venue: arXiv cs.CV 2024
Year: 2024

The rapid advancement of generative models has introduced serious risks, including deepfake techniques for facial synthesis and editing. Traditional approaches rely on training classifiers and enhancing generalizability through various feature extraction techniques. Meanwhile, training-free detection methods address issues like limited data and overfitting by directly leveraging statistical properties from vision foundation models to distinguish between real and fake images. The current leading training-free approach, RIGID, utilizes DINOv2 sensitivity to perturbations in image space for detecting fake images, with fake image embeddings exhibiting greater sensitivity than those of real images. This observation prompts us to investigate how detection performance varies across model backbones, perturbation types, and datasets. Our experiments reveal that detection performance is closely linked to model robustness, with self-supervised (SSL) models providing more reliable representations. While Gaussian noise effectively detects general objects, it performs worse on facial images, whereas Gaussian blur is more effective due to potential frequency artifacts. To further improve detection, we introduce Contrastive Blur, which enhances performance on facial images, and MINDER (MINimum distance DetEctoR), which addresses noise type bias, balancing performance across domains. Beyond performance gains, our work offers valuable insights for both the generative and detection communities, contributing to a deeper understanding of model robustness property utilized for deepfake detection.
Advancing Generalization in PINNs through Latent-Space Representations
Venue: arXiv cs.LG 2024
Year: 2024

Physics-informed neural networks (PINNs) have made significant strides in modeling dynamical systems governed by partial differential equations (PDEs). However, their generalization capabilities across varying scenarios remain limited. To overcome this limitation, we propose PIDO, a novel physics-informed neural PDE solver designed to generalize effectively across diverse PDE configurations, including varying initial conditions, PDE coefficients, and training time horizons. PIDO exploits the shared underlying structure of dynamical systems with different properties by projecting PDE solutions into a latent space using auto-decoding. It then learns the dynamics of these latent representations, conditioned on the PDE coefficients. Despite its promise, integrating latent dynamics models within a physics-informed framework poses challenges due to the optimization difficulties associated with physics-informed losses. To address these challenges, we introduce a novel approach that diagnoses and mitigates these issues within the latent space. This strategy employs straightforward yet effective regularization techniques, enhancing both the temporal extrapolation performance and the training stability of PIDO. We validate PIDO on a range of benchmarks, including 1D combined equations and 2D Navier-Stokes equations. Additionally, we demonstrate the transferability of its learned representations to downstream applications such as long-term integration and inverse problems.
Examining Multimodal Gender and Content Bias in ChatGPT-4o
Venue: arXiv cs.CY 2024
Year: 2024

This study investigates ChatGPT-4o's multimodal content generation, highlighting significant disparities in its treatment of sexual content and nudity versus violent and drug-related themes. Detailed analysis reveals that ChatGPT-4o consistently censors sexual content and nudity, while showing leniency towards violence and drug use. Moreover, a pronounced gender bias emerges, with female-specific content facing stricter regulation compared to male-specific content. This disparity likely stems from media scrutiny and public backlash over past AI controversies, prompting tech companies to impose stringent guidelines on sensitive issues to protect their reputations. Our findings emphasize the urgent need for AI systems to uphold genuine ethical standards and accountability, transcending mere political correctness. This research contributes to the understanding of biases in AI-driven language and multimodal models, calling for more balanced and ethical content moderation practices.
Bounds for Quantum Circuits using Logic-Based Analysis
Venue: arXiv cs.LO 2024
Year: 2024

We explore ideas for scaling verification methods for quantum circuits using SMT (Satisfiability Modulo Theories) solvers. We propose two primary strategies: (1) decomposing proof obligations via compositional verification and (2) leveraging linear over-approximation techniques for gate effects. We present two examples and demonstrate the application of these ideas to proof Hamming weight preservation.
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving
Venue: arXiv cs.CV 2024
Year: 2024

Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, under adverse weather and changing lighting conditions, and in complex situations with many overlapping objects, these systems are not completely reliable. The spectral reflectance of the different objects in a driving scene beyond the visible spectrum can offer additional information to increase the reliability of these systems, especially under challenging driving conditions. Furthermore, this information may be significant enough to develop vision systems that allow for a better understanding and interpretation of the whole driving scene. In this work we explore the use of snapshot, video-rate hyperspectral imaging (HSI) cameras in ADAS on the assumption that the near infrared (NIR) spectral reflectance of different materials can help to better segment the objects in real driving scenarios. To do this, we have used the HSI-Drive 1.1 dataset to perform various experiments on spectral classification algorithms. However, the information retrieval of hyperspectral recordings in natural outdoor scenarios is challenging, mainly because of deficient colour constancy and other inherent shortcomings of current snapshot HSI technology, which poses some limitations to the development of pure spectral classifiers. In consequence, in this work we analyze to what extent the spatial features codified by standard, tiny fully convolutional network (FCN) models can improve the performance of HSI segmentation systems for ADAS applications. The abstract above is truncated due to submission limits. For the full abstract, please refer to the published article.
DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilities
Venue: arXiv cs.CL 2024
Year: 2024

The Needle-in-a-haystack (NIAH) test is a general task used to assess language models' (LMs') abilities to recall particular information from long input context. This framework however does not provide a means of analyzing what factors, beyond context length, contribute to LMs' abilities or inabilities to separate and recall needles from their haystacks. To provide a systematic means of assessing what features contribute to LMs' NIAH capabilities, we developed a synthetic benchmark called DENIAHL (Data-oriented Evaluation of NIAH for LLM's). Our work expands on previous NIAH studies by ablating NIAH features beyond typical context length including data type, size, and patterns. We find stark differences between GPT-3.5 and LLaMA 2-7B's performance on DENIAHL, and drops in recall performance when features like item size are increased, and to some degree when data type is changed from numbers to letters. This has implications for increasingly large context models, demonstrating factors beyond item-number impact NIAH capabilities.
A Simple Sparse Matrix Vector Multiplication Approach to Padded Convolution
Venue: arXiv cs.LG 2024
Year: 2024

We introduce an algorithm for efficiently representing convolution with zero-padding and stride as a sparse transformation matrix, applied to a vectorized input through sparse matrix-vector multiplication (SpMV). We provide a theoretical contribution with an explicit expression for the number of non-zero multiplications in convolutions with stride and padding, offering insight into the potential for leveraging sparsity in convolution operations. A proof-of-concept implementation is presented in Python, demonstrating the performance of our method on both CPU and GPU architectures. This work contributes to the broader exploration of sparse matrix techniques in convolutional algorithms, with a particular focus on leveraging matrix multiplications for parallelization. Our findings lay the groundwork for future advancements in exploiting sparsity to improve the efficiency of convolution operations in fields such as machine learning and signal processing.
Optimal Algorithm for Paired-Domination in Distance-Hereditary Graphs
Venue: arXiv cs.DS 2024
Year: 2024

The domination problem and its variants represent a classical domain within algorithmic graph theory. Among these variants, the paired-domination problem holds particular prominence due to its real-world implications in security and surveillance domains. Given an input graph $G$, the paired-domination problem involves identifying a minimum dominating set $D$ that induces a subgraph of $G$ with a perfect matching. Lin et al.~[\emph{Paired-domination problem on distance-hereditary graphs}, Algorithmica, 2020] previously presented a solution to this problem with a time complexity of $O(n^2)$. This paper significantly enhances their findings by introducing an $O(n+m)$-time algorithm. Furthermore, the time complexity of this algorithm can be reduced to $O(n)$ when provided with a decomposition tree for the graph $G$.
HE2C: A Holistic Approach for Allocating Latency-Sensitive AI Tasks across Edge-Cloud
Venue: arXiv cs.DC 2024
Year: 2024

The high computational, memory, and energy demands of Deep Learning (DL) applications often exceed the capabilities of battery-powered edge devices, creating difficulties in meeting task deadlines and accuracy requirements. Unlike previous solutions that optimize a single metric (e.g., accuracy or energy efficiency), HE2C framework is designed to holistically address the latency, memory, accuracy, throughput, and energy demands of DL applications across edge-cloud continuum, thereby, delivering a more comprehensive and effective user experience. HE2C comprises three key modules: (a) a "feasibility-check module that evaluates the likelihood of meeting deadlines across both edge and cloud resources; (b) a "resource allocation strategy" that maximizes energy efficiency without sacrificing the inference accuracy; and (c) a "rescue module" that enhances throughput by leveraging approximate computing to trade accuracy for latency when necessary. Our primary objective is to maximize system prolong battery lifespan, throughput, and accuracy while adhering to strict latency constraints. Experimental evaluations in the context of wearable technologies for blind and visually impaired users demonstrate that HE2C significantly improves task throughput via completing a larger number of tasks within their specified deadlines, while preserving edge device battery and maintaining prediction accuracy with minimal latency impact. These results underscore HE2C's potential as a robust solution for resource management in latency-sensitive, energy-constrained edge-to-cloud environments.