OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Embedding Dimension for Sparse Subspace Embeddings
Venue: arXiv cs.DS 2024
Year: 2024

A random $m\times n$ matrix $S$ is an oblivious subspace embedding (OSE) with parameters $ε>0$, $δ\in(0,1/3)$ and $d\leq m\leq n$, if for any $d$-dimensional subspace $W\subseteq R^n$, $P\big(\,\forall_{x\in W}\ (1+ε)^{-1}\|x\|\leq\|Sx\|\leq (1+ε)\|x\|\,\big)\geq 1-δ.$ It is known that the embedding dimension of an OSE must satisfy $m\geq d$, and for any $θ> 0$, a Gaussian embedding matrix with $m\geq (1+θ) d$ is an OSE with $ε= O_θ(1)$. However, such optimal embedding dimension is not known for other embeddings. Of particular interest are sparse OSEs, having $s\ll m$ non-zeros per column, with applications to problems such as least squares regression and low-rank approximation. We show that, given any $θ> 0$, an $m\times n$ random matrix $S$ with $m\geq (1+θ)d$ consisting of randomly sparsified $\pm1/\sqrt s$ entries and having $s= O(\log^4(d))$ non-zeros per column, is an oblivious subspace embedding with $ε= O_θ(1)$. Our result addresses the main open question posed by Nelson and Nguyen (FOCS 2013), who conjectured that sparse OSEs can achieve $m=O(d)$ embedding dimension, and it improves on $m=O(d\log(d))$ shown by Cohen (SODA 2016). We use this to construct the first oblivious subspace embedding with $O(d)$ embedding dimension that can be applied faster than current matrix multiplication time, and to obtain an optimal single-pass algorithm for least squares regression. We further extend our results to Leverage Score Sparsification (LESS), which is a recently introduced non-oblivious embedding technique. We use LESS to construct the first subspace embedding with low distortion $ε=o(1)$ and optimal embedding dimension $m=O(d/ε^2)$ that can be applied in current matrix multiplication time.
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Venue: arXiv cs.IR 2024
Year: 2024

Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency: they first select historical behaviors related to the candidate item and then deduce the user's interest from this narrowed-down behavior sub-sequence. This two-stage paradigm, though effective, leads to information loss. Solely using users' lifelong click behaviors doesn't provide a complete picture of their interests, leading to suboptimal performance. In our research, we introduce the Deep Group Interest Network (DGIN), an end-to-end method to model the user's entire behavior history. This includes all post-registration actions, such as clicks, cart additions, purchases, and more, providing a nuanced user understanding. We start by grouping the full range of behaviors using a relevant key (like item_id) to enhance efficiency. This process reduces the behavior length significantly, from O(10^4) to O(10^2). To mitigate the potential loss of information due to grouping, we incorporate two categories of group attributes. Within each group, we calculate statistical information on various heterogeneous behaviors (like behavior counts) and employ self-attention mechanisms to highlight unique behavior characteristics (like behavior type). Based on this reorganized behavior data, the user's interests are derived using the Transformer technique. Additionally, we identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence. The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy. Our comprehensive evaluation, both on industrial and public datasets, validates DGIN's efficacy and efficiency.
A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends
Venue: arXiv cs.CL 2024
Year: 2024

Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a systematic literature review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA. To our knowledge, it is also the first to systematically examine the interrelations among ABSA research and data distribution across domains, as well as trends in solution paradigms and approaches. Our sample includes 727 primary studies screened from 8550 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors
Venue: arXiv cs.CV 2024
Year: 2024

Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person's expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Some newer methods employ optical flow models to capture this temporal aspect, but they are computationally expensive. In contrast, we propose using the related but often ignored Motion Vectors (MVs) and Information Masks (IMs) from the H.264 video codec, to detect temporal inconsistencies in DeepFakes. Our experiments show that this approach is effective and has minimal computational costs, compared with per-frame RGB-only methods. This could lead to new, real-time temporally-aware DeepFake detection methods for video calls and streaming.
Evaluating the Relationship Between News Source Sharing and Political Beliefs
Venue: arXiv cs.SI 2024
Year: 2024

In an era marked by an abundance of news sources, access to information significantly influences public opinion. Notably, the bias of news sources often serves as an indicator of individuals' political leanings. This study explores this hypothesis by examining the news sharing behavior of politically active social media users, whose political ideologies were identified in a previous study. Using correspondence analysis, we estimate the Media Sharing Index (MSI), a measure that captures bias in media outlets and user preferences within a hidden space. During Argentina's 2019 election on Twitter, we observed a predictable pattern: center-right individuals predominantly shared media from center-right biased outlets. However, it is noteworthy that those with center-left inclinations displayed a more diverse media consumption, which is a significant finding. Despite a noticeable polarization based on political affiliation observed in a retweet network analysis, center-left users showed more diverse media sharing preferences, particularly concerning the MSI. Although these findings are specific to Argentina, the developed methodology can be applied in other countries to assess the correlation between users' political leanings and the media they share.
CAMRA: Copilot for AMR Annotation
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we introduce CAMRA (Copilot for AMR Annotatations), a cutting-edge web-based tool designed for constructing Abstract Meaning Representation (AMR) from natural language text. CAMRA offers a novel approach to deep lexical semantics annotation such as AMR, treating AMR annotation akin to coding in programming languages. Leveraging the familiarity of programming paradigms, CAMRA encompasses all essential features of existing AMR editors, including example lookup, while going a step further by integrating Propbank roleset lookup as an autocomplete feature within the tool. Notably, CAMRA incorporates AMR parser models as coding co-pilots, greatly enhancing the efficiency and accuracy of AMR annotators. To demonstrate the tool's capabilities, we provide a live demo accessible at: https://camra.colorado.edu
Channel Estimation for FAS-assisted Multiuser mmWave Systems
Venue: arXiv cs.IT 2024
Year: 2024

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.
Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Ultrasound imaging plays a critical role in the early detection of breast cancer. Accurate identification and segmentation of lesions are essential steps in clinical practice, requiring methods to assist physicians in lesion segmentation. However, ultrasound lesion segmentation models based on supervised learning require extensive manual labeling, which is both time-consuming and labor-intensive. In this study, we present a novel framework for weakly supervised lesion segmentation in early breast ultrasound images. Our method uses morphological enhancement and class activation map (CAM)-guided localization. Finally, we employ the Segment Anything Model (SAM), a computer vision foundation model, for detailed segmentation. This approach does not require pixel-level annotation, thereby reducing the cost of data annotation. The performance of our method is comparable to supervised learning methods that require manual annotations, achieving a Dice score of 74.39% and outperforming comparative supervised models in terms of Hausdorff distance in the BUSI dataset. These results demonstrate that our framework effectively integrates weakly supervised learning with SAM, providing a promising solution for breast cancer image analysis. The code for this study is available at: https://github.com/YueXin18/MorSeg-CAM-SAM.
HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Gait recognition has achieved promising advances in controlled settings, yet it significantly struggles in unconstrained environments due to challenges such as view changes, occlusions, and varying walking speeds. Additionally, efforts to fuse multiple modalities often face limited improvements because of cross-modality incompatibility, particularly in outdoor scenarios. To address these issues, we present a multi-modal Hierarchy in Hierarchy network (HiH) that integrates silhouette and pose sequences for robust gait recognition. HiH features a main branch that utilizes Hierarchical Gait Decomposer (HGD) modules for depth-wise and intra-module hierarchical examination of general gait patterns from silhouette data. This approach captures motion hierarchies from overall body dynamics to detailed limb movements, facilitating the representation of gait attributes across multiple spatial resolutions. Complementing this, an auxiliary branch, based on 2D joint sequences, enriches the spatial and temporal aspects of gait analysis. It employs a Deformable Spatial Enhancement (DSE) module for pose-guided spatial attention and a Deformable Temporal Alignment (DTA) module for aligning motion dynamics through learned temporal offsets. Extensive evaluations across diverse indoor and outdoor datasets demonstrate HiH's state-of-the-art performance, affirming a well-balanced trade-off between accuracy and efficiency.
M$^{2}$UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models
Venue: arXiv cs.SD 2024
Year: 2024

The current landscape of research leveraging large language models (LLMs) is experiencing a surge. Many works harness the powerful reasoning capabilities of these models to comprehend various modalities, such as text, speech, images, videos, etc. They also utilize LLMs to understand human intention and generate desired outputs like images, videos, and music. However, research that combines both understanding and generation using LLMs is still limited and in its nascent stage. To address this gap, we introduce a Multi-modal Music Understanding and Generation (M$^{2}$UGen) framework that integrates LLM's abilities to comprehend and generate music for different modalities. The M$^{2}$UGen framework is purpose-built to unlock creative potential from diverse sources of inspiration, encompassing music, image, and video through the use of pretrained MERT, ViT, and ViViT models, respectively. To enable music generation, we explore the use of AudioLDM 2 and MusicGen. Bridging multi-modal understanding and music generation is accomplished through the integration of the LLaMA 2 model. Furthermore, we make use of the MU-LLaMA model to generate extensive datasets that support text/image/video-to-music generation, facilitating the training of our M$^{2}$UGen framework. We conduct a thorough evaluation of our proposed framework. The experimental results demonstrate that our model achieves or surpasses the performance of the current state-of-the-art models.
Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we introduce a novel multimodal camo-perceptive framework (MMCPF) aimed at handling zero-shot Camouflaged Object Detection (COD) by leveraging the powerful capabilities of Multimodal Large Language Models (MLLMs). Recognizing the inherent limitations of current COD methodologies, which predominantly rely on supervised learning models demanding extensive and accurately annotated datasets, resulting in weak generalization, our research proposes a zero-shot MMCPF that circumvents these challenges. Although MLLMs hold significant potential for broad applications, their effectiveness in COD is hindered and they would make misinterpretations of camouflaged objects. To address this challenge, we further propose a strategic enhancement called the Chain of Visual Perception (CoVP), which significantly improves the perceptual capabilities of MLLMs in camouflaged scenes by leveraging both linguistic and visual cues more effectively. We validate the effectiveness of MMCPF on five widely used COD datasets, containing CAMO, COD10K, NC4K, MoCA-Mask and OVCamo. Experiments show that MMCPF can outperform all existing state-of-the-art zero-shot COD methods, and achieve competitive performance compared to weakly-supervised and fully-supervised methods, which demonstrates the potential of MMCPF. The Github link of this paper is \url{https://github.com/luckybird1994/MMCPF}.
Discrete approximations of Gaussian smoothing and Gaussian derivatives
Venue: arXiv cs.CV 2024
Year: 2024

This paper develops an in-depth treatment concerning the problem of approximating the Gaussian smoothing and Gaussian derivative computations in scale-space theory for application on discrete data. With close connections to previous axiomatic treatments of continuous and discrete scale-space theory, we consider three main ways discretizing these scale-space operations in terms of explicit discrete convolutions, based on either (i) sampling the Gaussian kernels and the Gaussian derivative kernels, (ii) locally integrating the Gaussian kernels and the Gaussian derivative kernels over each pixel support region and (iii) basing the scale-space analysis on the discrete analogue of the Gaussian kernel, and then computing derivative approximations by applying small-support central difference operators to the spatially smoothed image data. We study the properties of these three main discretization methods both theoretically and experimentally, and characterize their performance by quantitative measures, including the results they give rise to with respect to the task of scale selection, investigated for four different use cases, and with emphasis on the behaviour at fine scales. The results show that the sampled Gaussian kernels and derivatives as well as the integrated Gaussian kernels and derivatives perform very poorly at very fine scales. At very fine scales, the discrete analogue of the Gaussian kernel with its corresponding discrete derivative approximations performs substantially better. The sampled Gaussian kernel and the sampled Gaussian derivatives do, on the other hand, lead to numerically very good approximations of the corresponding continuous results, when the scale parameter is sufficiently large, in the experiments presented in the paper, when the scale parameter is greater than a value of about 1, in units of the grid spacing.
Unveiling Deception: Establishing a Taxonomic Framework for Disinformation within Scientific Discourse
Venue: arXiv cs.DL 2024
Year: 2024

Disinformation spreads among the public and in scientific discourse through the actions of individuals, organizations, and governments that distort scholarly communications, media narratives, and institutional trust. This taxonomy introduces a structured framework and specialized set of definitions to elucidate the key participants, platforms, and strategies employed in the propagation of disinformation. Enhanced comprehension of the mechanisms and pathways of scientific disinformation equips journalists and policymakers with the tools necessary to more effectively recognize and address these issues. The authors developed this taxonomy of disinformation through a multi-faceted approach, encompassing a literature review, expert review, and case study analysis. The literature review revealed a scarcity of taxonomical models amidst prevalent algorithmic detection studies. Subsequently, an expert review process refined our taxonomy through collaborative analysis of twenty-two cases of identified disinformation, categorized by their methods, motives, and impacts. Finally, we validated and fine-tuned our taxonomy through detailed case studies of twelve diverse disinformation instances, assessing the taxonomy's effectiveness in capturing the essential characteristics of each case and making necessary adjustments to ensure its relevance and accuracy in real-world applications.
Taking Complete Finite Prefixes To High Level, Symbolically
Venue: arXiv cs.LO 2024
Year: 2024

Unfoldings are a well known partial-order semantics of P/T Petri nets that can be applied to various model checking or verification problems. For high-level Petri nets, the so-called symbolic unfolding generalizes this notion. A complete finite prefix of a P/T Petri net's unfolding contains all information to verify, e.g., reachability of markings. We unite these two concepts and define complete finite prefixes of the symbolic unfolding of high-level Petri nets. For a class of safe high-level Petri nets, we generalize the well-known algorithm by Esparza et al. for constructing small such prefixes. We evaluate this extended algorithm through a prototype implementation on four novel benchmark families. Additionally, we identify a more general class of nets with infinitely many reachable markings, for which an approach with an adapted cut-off criterion extends the complete prefix methodology, in the sense that the original algorithm cannot be applied to the P/T net represented by a high-level net.
Predictions of Transient Vector Solution Fields with Sequential Deep Operator Network
Venue: arXiv cs.CE 2024
Year: 2024

The Deep Operator Network (DeepONet) structure has shown great potential in approximating complex solution operators with low generalization errors. Recently, a sequential DeepONet (S-DeepONet) was proposed to use sequential learning models in the branch of DeepONet to predict final solutions given time-dependent inputs. In the current work, the S-DeepONet architecture is extended by modifying the information combination mechanism between the branch and trunk networks to simultaneously predict vector solutions with multiple components at multiple time steps of the evolution history, which is the first in the literature using DeepONets. Two example problems, one on transient fluid flow and the other on path-dependent plastic loading, were shown to demonstrate the capabilities of the model to handle different physics problems. The use of a trained S-DeepONet model in inverse parameter identification via the genetic algorithm is shown to demonstrate the application of the model. In almost all cases, the trained model achieved an $R^2$ value of above 0.99 and a relative $L_2$ error of less than 10\% with only 3200 training data points, indicating superior accuracy. The vector S-DeepONet model, having only 0.4\% more parameters than a scalar model, can predict two output components simultaneously at an accuracy similar to the two independently trained scalar models with a 20.8\% faster training time. The S-DeepONet inference is at least three orders of magnitude faster than direct numerical simulations, and inverse parameter identifications using the trained model is highly efficient and accurate.
Correlation-based Dual-band THz Channel Measurements and Characterization in a Laboratory
Venue: arXiv cs.IT 2024
Year: 2024

The Terahertz band, spanning from 0.1~THz to 10~THz, is envisioned as a key technology to realize ultra-high data rates in the 6G and beyond mobile communication systems, due to its abundant bandwidth resource. However, to realize THz communications, one substantial step is to fully understand the THz channels, which relies on extensive channel measurements. In this paper, using a correlation-based time domain channel sounder, measurement campaigns are conducted in a laboratory at 140~GHz and 220~GHz. In the data post-processing procedures, the time drift of clock signals is corrected using a linear interpolation/extrapolation method. Based on the measured results, the main objects that provide significant once-scattering clusters are found, based on which the scattering losses are calculated and analyzed. Furthermore, the channel characteristics, including path loss, shadow fading, K-factor, etc. are calculated and compared to 3GPP standard values. The propagation analysis and channel characteristics are helpful to study channel modeling and guide system design for THz communications.