id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.04937 | Generalized Linear Models with 1-Bit Measurements: Asymptotics of the
Maximum Likelihood Estimator | [
"math.ST",
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] | This work establishes regularity conditions for consistency and asymptotic normality of the multiple parameter maximum likelihood estimator(MLE) from censored data, where the censoring mechanism is in the form of $1$-bit measurements. The underlying distribution of the uncensored data is assumed to belong to the expone... | {
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2501.04939 | Multi-Context Temporal Consistent Modeling for Referring Video Object
Segmentation | [
"cs.CV"
] | Referring video object segmentation aims to segment objects within a video corresponding to a given text description. Existing transformer-based temporal modeling approaches face challenges related to query inconsistency and the limited consideration of context. Query inconsistency produces unstable masks of different ... | {
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2501.04940 | A New Perspective on Privacy Protection in Federated Learning with
Granular-Ball Computing | [
"cs.LG",
"cs.CV"
] | Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. However, most existing articles attempt to address challenges within the model's internal parameters and corresponding outputs, while neglecting to solve them at the input level. To address this ... | {
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2501.04944 | MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification | [
"cs.CV"
] | Transformer has been extensively explored for hyperspectral image (HSI) classification. However, transformer poses challenges in terms of speed and memory usage because of its quadratic computational complexity. Recently, the Mamba model has emerged as a promising approach, which has strong long-distance modeling capab... | {
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2501.04945 | Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of
Large Language Models | [
"cs.CL",
"cs.AI"
] | It is crucial for large language models (LLMs) to follow instructions that involve multiple constraints. However, it is an unexplored area to enhance LLMs' ability to follow soft constraints. To bridge the gap, we initially design a pipeline to construct datasets with high-quality outputs automatically. Additionally, t... | {
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2501.04946 | Non-asymptotic analysis of the performance of the penalized least
trimmed squares in sparse models | [
"stat.ML",
"cs.LG"
] | The least trimmed squares (LTS) estimator is a renowned robust alternative to the classic least squares estimator and is popular in location, regression, machine learning, and AI literature. Many studies exist on LTS, including its robustness, computation algorithms, extension to non-linear cases, asymptotics, etc. The... | {
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2501.04947 | Seeing with Partial Certainty: Conformal Prediction for Robotic Scene
Recognition in Built Environments | [
"cs.CV"
] | In assistive robotics serving people with disabilities (PWD), accurate place recognition in built environments is crucial to ensure that robots navigate and interact safely within diverse indoor spaces. Language interfaces, particularly those powered by Large Language Models (LLM) and Vision Language Models (VLM), hold... | {
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2501.04950 | MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors
to Unseen Real-target Domain While Preserving Performance on Real-source
Domain | [
"cs.CV"
] | Deep neural network (DNN) based perception models are indispensable in the development of autonomous vehicles (AVs). However, their reliance on large-scale, high-quality data is broadly recognized as a burdensome necessity due to the substantial cost of data acquisition and labeling. Further, the issue is not a one-tim... | {
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2501.04952 | Open Problems in Machine Unlearning for AI Safety | [
"cs.LG",
"cs.AI",
"cs.CY"
] | As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research, and healthcare, ensuring their safety and alignment with human values is paramount. Machine unlearning -- the ability to selectively forget or suppress specific types of knowledg... | {
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2501.04958 | Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo
Development Assessment | [
"cs.CV",
"cs.AI"
] | Deep learning models in medical imaging face dual challenges: domain shift, where models perform poorly when deployed in settings different from their training environment, and class imbalance, where certain disease conditions are naturally underrepresented. We present Imbalance-Aware Domain Adaptation (IADA), a novel ... | {
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2501.04961 | Demystifying Domain-adaptive Post-training for Financial LLMs | [
"cs.CL",
"cs.AI",
"cs.CE",
"cs.LG"
] | Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challenges remain in identifying optimal adaptation criteria and training strategies across varying data and model configurations. To address these... | {
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2501.04962 | VoxEval: Benchmarking the Knowledge Understanding Capabilities of
End-to-End Spoken Language Models | [
"cs.CL",
"cs.SD",
"eess.AS"
] | With the rising need for speech-based interaction models, end-to-end Spoken Language Models (SLMs) have emerged as a promising solution. While these models require comprehensive world knowledge for meaningful and reliable human interactions, existing question-answering (QA) benchmarks fall short in evaluating SLMs' kno... | {
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2501.04964 | Promoting Shared Energy Storage Aggregation among High Price-Tolerance
Prosumer: An Incentive Deposit and Withdrawal Service | [
"eess.SY",
"cs.SY"
] | Many residential prosumers exhibit a high price-tolerance for household electricity bills and a low response to price incentives. This is because the household electricity bills are not inherently high, and the potential for saving on electricity bills through participation in conventional Shared Energy Storage (SES) i... | {
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2501.04966 | Emergence of Painting Ability via Recognition-Driven Evolution | [
"cs.CV"
] | From Paleolithic cave paintings to Impressionism, human painting has evolved to depict increasingly complex and detailed scenes, conveying more nuanced messages. This paper attempts to emerge this artistic capability by simulating the evolutionary pressures that enhance visual communication efficiency. Specifically, we... | {
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2501.04967 | Targeted Adversarial Denoising Autoencoders (TADA) for Neural Time
Series Filtration | [
"cs.LG"
] | Current machine learning (ML)-based algorithms for filtering electroencephalography (EEG) time series data face challenges related to cumbersome training times, regularization, and accurate reconstruction. To address these shortcomings, we present an ML filtration algorithm driven by a logistic covariance-targeted adve... | {
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2501.04969 | AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding
Predictive Architecture for Autonomous Driving with LiDAR Data | [
"cs.RO",
"cs.CV"
] | As opposed to human drivers, current autonomous driving systems still require vast amounts of labeled data to train. Recently, world models have been proposed to simultaneously enhance autonomous driving capabilities by improving the way these systems understand complex real-world environments and reduce their data dem... | {
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2501.04970 | Battling the Non-stationarity in Time Series Forecasting via Test-time
Adaptation | [
"cs.LG",
"cs.AI"
] | Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of time series undermines the reliability of pre-trained source time series forecasters in mission-critical deployment settings. In this study... | {
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2501.04971 | Self-Adaptive Ising Machines for Constrained Optimization | [
"cs.ET",
"cs.LG",
"cs.NE"
] | Ising machines (IM) are physics-inspired alternatives to von Neumann architectures for solving hard optimization tasks. By mapping binary variables to coupled Ising spins, IMs can naturally solve unconstrained combinatorial optimization problems such as finding maximum cuts in graphs. However, despite their importance ... | {
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2501.04974 | SensorQA: A Question Answering Benchmark for Daily-Life Monitoring | [
"cs.CL",
"cs.AI"
] | With the rapid growth in sensor data, effectively interpreting and interfacing with these data in a human-understandable way has become crucial. While existing research primarily focuses on learning classification models, fewer studies have explored how end users can actively extract useful insights from sensor data, o... | {
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2501.04975 | V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer | [
"cs.CV"
] | Concept Bottleneck Models (CBMs) offer inherent interpretability by initially translating images into human-comprehensible concepts, followed by a linear combination of these concepts for classification. However, the annotation of concepts for visual recognition tasks requires extensive expert knowledge and labor, cons... | {
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2501.04982 | CuRLA: Curriculum Learning Based Deep Reinforcement Learning for
Autonomous Driving | [
"cs.RO",
"cs.AI",
"cs.LG"
] | In autonomous driving, traditional Computer Vision (CV) agents often struggle in unfamiliar situations due to biases in the training data. Deep Reinforcement Learning (DRL) agents address this by learning from experience and maximizing rewards, which helps them adapt to dynamic environments. However, ensuring their gen... | {
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2501.04987 | TreeKV: Smooth Key-Value Cache Compression with Tree Structures | [
"cs.CL"
] | Efficient key-value (KV) cache compression is critical for scaling transformer-based Large Language Models (LLMs) in long sequences and resource-limited settings. Existing methods evict tokens based on their positions or importance scores, but position-based strategies can miss crucial information outside predefined re... | {
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2501.04988 | Intelligent Sailing Model for Open Sea Navigation | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Autonomous vessels potentially enhance safety and reliability of seaborne trade. To facilitate the development of autonomous vessels, high-fidelity simulations are required to model realistic interactions with other vessels. However, modeling realistic interactive maritime traffic is challenging due to the unstructured... | {
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2501.04989 | Error Floor of Spinal Codes under ML Decoding | [
"cs.IT",
"math.IT"
] | Spinal codes is a new family of capacity-achieving rateless codes that has been shown to achieve better rate performance compared to Raptor codes, Strider codes, and rateless Low-Density Parity-Check (LDPC) codes. This correspondence addresses the performance limitations of Spinal codes in the finite block length regim... | {
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2501.04995 | IPDN: Image-enhanced Prompt Decoding Network for 3D Referring Expression
Segmentation | [
"cs.CV",
"cs.AI"
] | 3D Referring Expression Segmentation (3D-RES) aims to segment point cloud scenes based on a given expression. However, existing 3D-RES approaches face two major challenges: feature ambiguity and intent ambiguity. Feature ambiguity arises from information loss or distortion during point cloud acquisition due to limitati... | {
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2501.04996 | A CT Image Classification Network Framework for Lung Tumors Based on
Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and
Market Analysis in the Medical field | [
"eess.IV",
"cs.CV"
] | In the medical field, accurate diagnosis of lung cancer is crucial for treatment. Traditional manual analysis methods have significant limitations in terms of accuracy and efficiency. To address this issue, this paper proposes a deep learning network framework based on the pre-trained MobileNetV2 model, initialized wit... | {
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2501.04997 | GiNet: Integrating Sequential and Context-Aware Learning for Battery
Capacity Prediction | [
"cs.LG",
"cs.AI"
] | The surging demand for batteries requires advanced battery management systems, where battery capacity modelling is a key functionality. In this paper, we aim to achieve accurate battery capacity prediction by learning from historical measurements of battery dynamics. We propose GiNet, a gated recurrent units enhanced I... | {
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2501.05000 | Load Forecasting for Households and Energy Communities: Are Deep
Learning Models Worth the Effort? | [
"cs.LG"
] | Accurate load forecasting is crucial for predictive control in many energy domain applications, with significant economic and ecological implications. To address these implications, this study provides an extensive benchmark of state-of-the-art deep learning models for short-term load forecasting in energy communities.... | {
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2501.05004 | A Fast Path-Planning Method for Continuous Harvesting of Table-Top Grown
Strawberries | [
"cs.RO"
] | Continuous harvesting and storage of multiple fruits in a single operation allow robots to significantly reduce the travel distance required for repetitive back-and-forth movements. Traditional collision-free path planning algorithms, such as Rapidly-Exploring Random Tree (RRT) and A-star (A), often fail to meet the de... | {
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2501.05005 | A High-accuracy Calibration Method of Transient TSEPs for Power
Semiconductor Devices | [
"cs.LG"
] | The thermal sensitive electrical parameter (TSEP) method is crucial for enhancing the reliability of power devices through junction temperature monitoring. The TSEP method comprises three key processes: calibration, regression, and application. While significant efforts have been devoted to improving regression algorit... | {
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2501.05006 | CHASE: A Native Relational Database for Hybrid Queries on Structured and
Unstructured Data | [
"cs.DB"
] | Querying both structured and unstructured data has become a new paradigm in data analytics and recommendation. With unstructured data, such as text and videos, are converted to high-dimensional vectors and queried with approximate nearest neighbor search (ANNS). State-of-the-art database systems implement vector search... | {
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2501.05007 | Quantum-enhanced causal discovery for a small number of samples | [
"quant-ph",
"cs.AI",
"cs.LG",
"stat.ME"
] | The discovery of causal relationships from observed data has attracted significant interest from disciplines such as economics, social sciences, epidemiology, and biology. In practical applications, considerable knowledge of the underlying systems is often unavailable, and real data are often associated with nonlinear ... | {
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2501.05009 | A Scalable System for Visual Analysis of Ocean Data | [
"cs.GR",
"cs.CV",
"cs.DC"
] | Oceanographers rely on visual analysis to interpret model simulations, identify events and phenomena, and track dynamic ocean processes. The ever increasing resolution and complexity of ocean data due to its dynamic nature and multivariate relationships demands a scalable and adaptable visualization tool for interactiv... | {
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2501.05014 | UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission
Generation | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple tex... | {
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2501.05015 | On Measuring Unnoticeability of Graph Adversarial Attacks: Observations,
New Measure, and Applications | [
"cs.LG",
"cs.AI"
] | Adversarial attacks are allegedly unnoticeable. Prior studies have designed attack noticeability measures on graphs, primarily using statistical tests to compare the topology of original and (possibly) attacked graphs. However, we observe two critical limitations in the existing measures. First, because the measures re... | {
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2501.05017 | Continuous Knowledge-Preserving Decomposition for Few-Shot Continual
Learning | [
"cs.CV"
] | Few-shot class-incremental learning (FSCIL) involves learning new classes from limited data while retaining prior knowledge, and often results in catastrophic forgetting. Existing methods either freeze backbone networks to preserve knowledge, which limits adaptability, or rely on additional modules or prompts, introduc... | {
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2501.05018 | Finding Needles in Emb(a)dding Haystacks: Legal Document Retrieval via
Bagging and SVR Ensembles | [
"cs.IR",
"cs.AI"
] | We introduce a retrieval approach leveraging Support Vector Regression (SVR) ensembles, bootstrap aggregation (bagging), and embedding spaces on the German Dataset for Legal Information Retrieval (GerDaLIR). By conceptualizing the retrieval task in terms of multiple binary needle-in-a-haystack subtasks, we show improve... | {
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2501.05020 | Perception-as-Control: Fine-grained Controllable Image Animation with
3D-aware Motion Representation | [
"cs.CV"
] | Motion-controllable image animation is a fundamental task with a wide range of potential applications. Recent works have made progress in controlling camera or object motion via various motion representations, while they still struggle to support collaborative camera and object motion control with adaptive control gran... | {
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2501.05030 | A General Retrieval-Augmented Generation Framework for Multimodal
Case-Based Reasoning Applications | [
"cs.AI",
"cs.CL"
] | Case-based reasoning (CBR) is an experience-based approach to problem solving, where a repository of solved cases is adapted to solve new cases. Recent research shows that Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) can support the Retrieve and Reuse stages of the CBR pipeline by retrieving s... | {
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2501.05031 | ECBench: Can Multi-modal Foundation Models Understand the Egocentric
World? A Holistic Embodied Cognition Benchmark | [
"cs.CV",
"cs.LG",
"cs.RO"
] | The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abilities of LVLMs based on egocentric videos are of great interest. However, current datasets for embodied video question answering lack comprehensive and systematic evaluation... | {
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2501.05032 | Enhancing Human-Like Responses in Large Language Models | [
"cs.CL",
"cs.AI"
] | This paper explores the advancements in making large language models (LLMs) more human-like. We focus on techniques that enhance natural language understanding, conversational coherence, and emotional intelligence in AI systems. The study evaluates various approaches, including fine-tuning with diverse datasets, incorp... | {
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2501.05033 | Towards High-Performance Network Coding: FPGA Acceleration With
Bounded-value Generators | [
"cs.AR",
"cs.IT",
"math.IT"
] | Network coding enhances performance in network communications and distributed storage by increasing throughput and robustness while reducing latency. Batched Sparse (BATS) codes are a class of capacity-achieving network codes, but their practical applications are hindered by their structure, computational intensity, an... | {
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2501.05034 | Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised
Deep Learning Approach | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Fingerprint mosaicking, which is the process of combining multiple fingerprint images into a single master fingerprint, is an essential process in modern biometric systems. However, it is prone to errors that can significantly degrade fingerprint image quality. This paper proposes a novel deep learning-based approach t... | {
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2501.05037 | LongViTU: Instruction Tuning for Long-Form Video Understanding | [
"cs.CV",
"cs.LG"
] | This paper introduce LongViTU, a large-scale (~121k QA pairs, ~900h videos), automatically generated dataset for long-form video understanding. We developed a systematic approach that organizes videos into a hierarchical tree structure and incorporates self-revision mechanisms to ensure high-quality QA pairs. Each QA p... | {
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2501.05040 | SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub
Issue Resolution | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks. One significant application of LLMs is in tackling software engineering challenges, particularly in resolving real-world tasks on GitHub by fixing code based on the issues reported by the users. However, many curren... | {
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2501.05053 | TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated
Learning | [
"cs.CR",
"cs.AI"
] | Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. However, current research indicates that traditional federated learning platforms are unable to ensure privacy due to privacy leaks caused by t... | {
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2501.05057 | LearningFlow: Automated Policy Learning Workflow for Urban Driving with
Large Language Models | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To t... | {
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2501.05058 | Simultaneous emulation and downscaling with physically-consistent deep
learning-based regional ocean emulators | [
"physics.ao-ph",
"cs.AI",
"cs.LG",
"nlin.CD",
"physics.geo-ph"
] | Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. Regional ocean emulation presents unique challenges owing to the complex bathymetry and lateral boundary conditions as we... | {
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2501.05066 | Improving Skeleton-based Action Recognition with Interactive Object
Information | [
"cs.CV",
"cs.AI"
] | Human skeleton information is important in skeleton-based action recognition, which provides a simple and efficient way to describe human pose. However, existing skeleton-based methods focus more on the skeleton, ignoring the objects interacting with humans, resulting in poor performance in recognizing actions that inv... | {
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2501.05067 | LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion
for Video Understanding | [
"cs.CV",
"cs.AI"
] | In this paper, we introduce LLaVA-Octopus, a novel video multimodal large language model. LLaVA-Octopus adaptively weights features from different visual projectors based on user instructions, enabling us to leverage the complementary strengths of each projector. We observe that different visual projectors exhibit dist... | {
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2501.05068 | D3RM: A Discrete Denoising Diffusion Refinement Model for Piano
Transcription | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | Diffusion models have been widely used in the generative domain due to their convincing performance in modeling complex data distributions. Moreover, they have shown competitive results on discriminative tasks, such as image segmentation. While diffusion models have also been explored for automatic music transcription,... | {
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2501.05069 | Commonsense Video Question Answering through Video-Grounded Entailment
Tree Reasoning | [
"cs.CV",
"cs.AI"
] | This paper proposes the first video-grounded entailment tree reasoning method for commonsense video question answering (VQA). Despite the remarkable progress of large visual-language models (VLMs), there are growing concerns that they learn spurious correlations between videos and likely answers, reinforced by their bl... | {
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2501.05072 | A Flexible and Scalable Framework for Video Moment Search | [
"cs.IR",
"cs.CV"
] | Video moment search, the process of finding relevant moments in a video corpus to match a user's query, is crucial for various applications. Existing solutions, however, often assume a single perfect matching moment, struggle with inefficient inference, and have limitations with hour-long videos. This paper introduces ... | {
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2501.05075 | A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for
General Industrial Process Tasks Based on Large Language Model | [
"cs.AI",
"cs.LG"
] | Data-driven soft sensors (DDSS) have become mainstream methods for predicting key performance indicators in process industries. However, DDSS development requires complex and costly customized designs tailored to various tasks during the modeling process. Moreover, DDSS are constrained to a single structured data modal... | {
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2501.05076 | TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging | [
"cs.CV",
"cs.LG"
] | Contactless fingerprint recognition systems offer a hygienic, user-friendly, and efficient alternative to traditional contact-based methods. However, their accuracy heavily relies on precise fingertip detection and segmentation, particularly under challenging background conditions. This paper introduces TipSegNet, a no... | {
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2501.05078 | Analyzing Memorization in Large Language Models through the Lens of
Model Attribution | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) are prevalent in modern applications but often memorize training data, leading to privacy breaches and copyright issues. Existing research has mainly focused on posthoc analyses, such as extracting memorized content or developing memorization metrics, without exploring the underlying archit... | {
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2501.05079 | Multimodal-to-Text Prompt Engineering in Large Language Models Using
Feature Embeddings for GNSS Interference Characterization | [
"cs.AI",
"eess.SP"
] | Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems. Despite their adaptability and efficiency, LLMs have not been extensively explored for signal processing tasks, particularly in the domain of global navigation satellite... | {
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2501.05081 | DriVLM: Domain Adaptation of Vision-Language Models in Autonomous
Driving | [
"cs.LG"
] | In recent years, large language models have had a very impressive performance, which largely contributed to the development and application of artificial intelligence, and the parameters and performance of the models are still growing rapidly. In particular, multimodal large language models (MLLM) can combine multiple ... | {
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2501.05082 | Comparison of Feature Learning Methods for Metadata Extraction from PDF
Scholarly Documents | [
"cs.IR",
"cs.CL",
"cs.DL",
"cs.LG"
] | The availability of metadata for scientific documents is pivotal in propelling scientific knowledge forward and for adhering to the FAIR principles (i.e. Findability, Accessibility, Interoperability, and Reusability) of research findings. However, the lack of sufficient metadata in published documents, particularly tho... | {
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2501.05085 | End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Objective: There exist several X-ray computed tomography (CT) scanning strategies to reduce a radiation dose, such as (1) sparse-view CT, (2) low-dose CT, and (3) region-of-interest (ROI) CT (called interior tomography). To further reduce the dose, the sparse-view and/or low-dose CT settings can be applied together wit... | {
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2501.05087 | Enhanced Quantile Regression with Spiking Neural Networks for Long-Term
System Health Prognostics | [
"cs.RO",
"cs.LG"
] | This paper presents a novel predictive maintenance framework centered on Enhanced Quantile Regression Neural Networks EQRNNs, for anticipating system failures in industrial robotics. We address the challenge of early failure detection through a hybrid approach that combines advanced neural architectures. The system lev... | {
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2501.05089 | Supervised Learning with Evolving Tasks and Performance Guarantees | [
"stat.ML",
"cs.LG"
] | Multiple supervised learning scenarios are composed by a sequence of classification tasks. For instance, multi-task learning and continual learning aim to learn a sequence of tasks that is either fixed or grows over time. Existing techniques for learning tasks that are in a sequence are tailored to specific scenarios, ... | {
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2501.05091 | ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual
Inference | [
"cs.CV",
"eess.IV"
] | The implementation of diffusion-based pansharpening task is predominantly constrained by its slow inference speed, which results from numerous sampling steps. Despite the existing techniques aiming to accelerate sampling, they often compromise performance when fusing multi-source images. To ease this limitation, we int... | {
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2501.05093 | Hierarchical Decomposed Dual-domain Deep Learning for Sparse-View CT
Reconstruction | [
"cs.LG",
"eess.SP"
] | Objective: X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the inadequate number of projection views, an analytic reconstruction method utilizing filtered backprojection results in severe streaking artifacts. Recently, deep ... | {
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2501.05094 | Convexity of Mutual Information along the Fokker-Planck Flow | [
"cs.IT",
"math.IT"
] | We study the convexity of mutual information as a function of time along the Fokker-Planck flow. The results are generalizations of that along heat flow and Ornstein-Ulenbeck flow, which were established by A. Wibisono and V. Jog. We prove the existence and uniqueness of the classical solutions to a class of Fokker-Pla... | {
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2501.05095 | Advancing ALS Applications with Large-Scale Pre-training: Dataset
Development and Downstream Assessment | [
"cs.CV",
"cs.AI"
] | The pre-training and fine-tuning paradigm has revolutionized satellite remote sensing applications. However, this approach remains largely underexplored for airborne laser scanning (ALS), an important technology for applications such as forest management and urban planning. In this study, we address this gap by constru... | {
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2501.05097 | A 1Mb mixed-precision quantized encoder for image classification and
patch-based compression | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Even if Application-Specific Integrated Circuits (ASIC) have proven to be a relevant choice for integrating inference at the edge, they are often limited in terms of applicability. In this paper, we demonstrate that an ASIC neural network accelerator dedicated to image processing can be applied to multiple tasks of dif... | {
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2501.05098 | Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset | [
"cs.CV"
] | In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing motion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions, and are typically limited to lab settings with manually labeled t... | {
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2501.05102 | Coordinated Control of Deformation and Flight for Morphing Aircraft via
Meta-Learning and Coupled State-Dependent Riccati Equations | [
"eess.SY",
"cs.SY"
] | In this paper, the coordinated control problem of deformation and flight for morphing aircraft (MA) is studied by using meta-learning (ML) and coupled state-dependent Riccati equations (CSDREs). Our method is built on two principal observations that dynamic models of MA under varying morphing conditions share a morphin... | {
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2501.05105 | Robust Score Matching | [
"stat.ML",
"cs.LG"
] | Proposed in Hyv\"arinen (2005), score matching is a parameter estimation procedure that does not require computation of distributional normalizing constants. In this work we utilize the geometric median of means to develop a robust score matching procedure that yields consistent parameter estimates in settings where th... | {
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2501.05107 | Harnessing the Power of Vibration Motors to Develop Miniature Untethered
Robotic Fishes | [
"cs.RO",
"physics.app-ph"
] | Miniature underwater robots play a crucial role in the exploration and development of marine resources, particularly in confined spaces and high-pressure deep-sea environments. This study presents the design, optimization, and performance of a miniature robotic fish, powered by the oscillation of bio-inspired fins. The... | {
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2501.05108 | Optimizing Multitask Industrial Processes with Predictive Action
Guidance | [
"cs.CV"
] | Monitoring complex assembly processes is critical for maintaining productivity and ensuring compliance with assembly standards. However, variability in human actions and subjective task preferences complicate accurate task anticipation and guidance. To address these challenges, we introduce the Multi-Modal Transformer ... | {
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2501.05109 | EquiBoost: An Equivariant Boosting Approach to Molecular Conformation
Generation | [
"cs.LG",
"physics.chem-ph",
"q-bio.BM"
] | Molecular conformation generation plays key roles in computational drug design. Recently developed deep learning methods, particularly diffusion models have reached competitive performance over traditional cheminformatical approaches. However, these methods are often time-consuming or require extra support from traditi... | {
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2501.05113 | Constrained Optimization of Charged Particle Tracking with Multi-Agent
Reinforcement Learning | [
"physics.comp-ph",
"cs.AI",
"cs.LG"
] | Reinforcement learning demonstrated immense success in modelling complex physics-driven systems, providing end-to-end trainable solutions by interacting with a simulated or real environment, maximizing a scalar reward signal. In this work, we propose, building upon previous work, a multi-agent reinforcement learning ap... | {
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2501.05120 | Improving the U-Net Configuration for Automated Delineation of Head and
Neck Cancer on MRI | [
"eess.IV",
"cs.CV"
] | Tumor volume segmentation on MRI is a challenging and time-consuming process that is performed manually in typical clinical settings. This work presents an approach to automated delineation of head and neck tumors on MRI scans, developed in the context of the MICCAI Head and Neck Tumor Segmentation for MR-Guided Applic... | {
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2501.05122 | Centurio: On Drivers of Multilingual Ability of Large Vision-Language
Model | [
"cs.CL",
"cs.CV"
] | Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output in the desired target language. Existing efforts mitigate these issues by adding multilingual training data, but do so in a largely ad-hoc ma... | {
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2501.05130 | Learning Compact and Robust Representations for Anomaly Detection | [
"cs.LG"
] | Distance-based anomaly detection methods rely on compact and separable in-distribution (ID) embeddings to effectively delineate anomaly boundaries. Single-positive contrastive formulations suffer from class collision, promoting unnecessary intra-class variance within ID samples. While multi-positive formulations can im... | {
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2501.05131 | 3DIS-FLUX: simple and efficient multi-instance generation with DiT
rendering | [
"cs.CV"
] | The growing demand for controllable outputs in text-to-image generation has driven significant advancements in multi-instance generation (MIG), enabling users to define both instance layouts and attributes. Currently, the state-of-the-art methods in MIG are primarily adapter-based. However, these methods necessitate re... | {
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2501.05132 | CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for
Real-time Object Detection | [
"cs.CV"
] | Real-time object detection takes an essential part in the decision-making process of numerous real-world applications, including collision avoidance and path planning in autonomous driving systems. This paper presents a novel real-time streaming perception method named CorrDiff, designed to tackle the challenge of dela... | {
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2501.05138 | Preference Queries over Taxonomic Domains | [
"cs.DB"
] | When composing multiple preferences characterizing the most suitable results for a user, several issues may arise. Indeed, preferences can be partially contradictory, suffer from a mismatch with the level of detail of the actual data, and even lack natural properties such as transitivity. In this paper we formally inve... | {
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2501.05141 | OfficeMate: Pilot Evaluation of an Office Assistant Robot | [
"cs.RO",
"cs.HC"
] | Office Assistant Robots (OARs) offer a promising solution to proactively provide in-situ support to enhance employee well-being and productivity in office spaces. We introduce OfficeMate, a social OAR designed to assist with practical tasks, foster social interaction, and promote health and well-being. Through a pilot ... | {
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2501.05147 | A Systematic Literature Review on Deep Learning-based Depth Estimation
in Computer Vision | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Depth estimation (DE) provides spatial information about a scene and enables tasks such as 3D reconstruction, object detection, and scene understanding. Recently, there has been an increasing interest in using deep learning (DL)-based methods for DE. Traditional techniques rely on handcrafted features that often strugg... | {
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2501.05153 | Assisting MoCap-Based Teleoperation of Robot Arm using Augmented Reality
Visualisations | [
"cs.RO",
"cs.HC"
] | Teleoperating a robot arm involves the human operator positioning the robot's end-effector or programming each joint. Whereas humans can control their own arms easily by integrating visual and proprioceptive feedback, it is challenging to control an external robot arm in the same way, due to its inconsistent orientatio... | {
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2501.05155 | Biomedical Relation Extraction via Adaptive Document-Relation
Cross-Mapping and Concept Unique Identifier | [
"cs.CL",
"cs.AI"
] | Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts, serving as a crucial subfield of biomedical text mining. Existing Bio-RE methods struggle with cross-sentence inference, which is essential for capturing relations spanning multiple sent... | {
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2501.05156 | State-Based Disassembly Planning | [
"cs.RO"
] | It has been shown recently that physics-based simulation significantly enhances the disassembly capabilities of real-world assemblies with diverse 3D shapes and stringent motion constraints. However, the efficiency suffers when tackling intricate disassembly tasks that require numerous simulations and increased simulat... | {
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2501.05162 | A Key Conditional Quotient Filter for Nonlinear, non-Gaussian and
non-Markovian System | [
"cs.CE"
] | This paper proposes a novel and efficient key conditional quotient filter (KCQF) for the estimation of state in the nonlinear system which can be either Gaussian or non-Gaussian, and either Markovian or non-Markovian. The core idea of the proposed KCQF is that only the key measurement conditions, rather than all measur... | {
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2501.05163 | Explainable AI based System for Supply Air Temperature Forecast | [
"eess.SY",
"cs.AI",
"cs.SY"
] | This paper explores the application of Explainable AI (XAI) techniques to improve the transparency and understanding of predictive models in control of automated supply air temperature (ASAT) of Air Handling Unit (AHU). The study focuses on forecasting of ASAT using a linear regression with Huber loss. However, having ... | {
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2501.05165 | Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in
Secure Software Engineering | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.CR",
"cs.ET"
] | Context. Developing secure and reliable software remains a key challenge in software engineering (SE). The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete. Secure software engineering (SSE) must continuously address vulnerabilities that... | {
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2501.05168 | KabaddiPy: A package to enable access to Professional Kabaddi Data | [
"cs.CE"
] | Kabaddi, a contact team sport of Indian origin, has seen a dramatic rise in global popularity, highlighted by the upcoming Kabaddi World Cup in 2025 with over sixteen international teams participating, alongside flourishing national leagues such as the Indian Pro Kabaddi League (230 million viewers) and the British Kab... | {
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2501.05170 | De-centering the (Traditional) User: Multistakeholder Evaluation of
Recommender Systems | [
"cs.IR",
"cs.LG"
] | Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakeholder, as is often the case of ... | {
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} |
2501.05171 | Emergence of human-like polarization among large language model agents | [
"cs.SI",
"cs.CY"
] | Rapid advances in large language models (LLMs) have empowered autonomous agents to establish social relationships, communicate, and form shared and diverging opinions on political issues. Our understanding of their collective behaviours and underlying mechanisms remains incomplete, however, posing unexpected risks to h... | {
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} |
2501.05177 | FaceMe: Robust Blind Face Restoration with Personal Identification | [
"cs.CV"
] | Blind face restoration is a highly ill-posed problem due to the lack of necessary context. Although existing methods produce high-quality outputs, they often fail to faithfully preserve the individual's identity. In this paper, we propose a personalized face restoration method, FaceMe, based on a diffusion model. Given... | {
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} |
2501.05179 | Compression with Global Guidance: Towards Training-free High-Resolution
MLLMs Acceleration | [
"cs.CV"
] | Multimodal large language models (MLLMs) have attracted considerable attention due to their exceptional performance in visual content understanding and reasoning. However, their inference efficiency has been a notable concern, as the increasing length of multimodal contexts leads to quadratic complexity. Token compress... | {
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} |
2501.05190 | RMTransformer: Accurate Radio Map Construction and Coverage Prediction | [
"eess.SP",
"cs.LG"
] | Radio map, or pathloss map prediction, is a crucial method for wireless network modeling and management. By leveraging deep learning to construct pathloss patterns from geographical maps, an accurate digital replica of the transmission environment could be established with less computational overhead and lower predicti... | {
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} |
2501.05195 | HipyrNet: Hypernet-Guided Feature Pyramid network for mixed-exposure
correction | [
"cs.CV"
] | Recent advancements in image translation for enhancing mixed-exposure images have demonstrated the transformative potential of deep learning algorithms. However, addressing extreme exposure variations in images remains a significant challenge due to the inherent complexity and contrast inconsistencies across regions. C... | {
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} |
2501.05197 | An Algorithmic Approach for Causal Health Equity: A Look at Race
Differentials in Intensive Care Unit (ICU) Outcomes | [
"cs.LG",
"cs.AI",
"stat.AP",
"stat.ME"
] | The new era of large-scale data collection and analysis presents an opportunity for diagnosing and understanding the causes of health inequities. In this study, we describe a framework for systematically analyzing health disparities using causal inference. The framework is illustrated by investigating racial and ethnic... | {
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} |
2501.05198 | Dexterous Manipulation of Deformable Objects via Pneumatic Gripping:
Lifting by One End | [
"cs.RO"
] | Manipulating deformable objects in robotic cells is often costly and not widely accessible. However, the use of localized pneumatic gripping systems can enhance accessibility. Current methods that use pneumatic grippers to handle deformable objects struggle with effective lifting. This paper introduces a method for the... | {
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} |
2501.05204 | Design and Control of a Bipedal Robotic Character | [
"cs.RO",
"cs.LG"
] | Legged robots have achieved impressive feats in dynamic locomotion in challenging unstructured terrain. However, in entertainment applications, the design and control of these robots face additional challenges in appealing to human audiences. This work aims to unify expressive, artist-directed motions and robust dynami... | {
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} |
2501.05205 | Discovering Hidden Visual Concepts Beyond Linguistic Input in Infant
Learning | [
"cs.CV",
"cs.AI"
] | Infants develop complex visual understanding rapidly, even preceding of the acquisition of linguistic inputs. As computer vision seeks to replicate the human vision system, understanding infant visual development may offer valuable insights. In this paper, we present an interdisciplinary study exploring this question: ... | {
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} |
2501.05207 | CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual
Alignment of Intent and Timeliness | [
"cs.MA",
"cs.LG"
] | Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challenging to meet in practice. However, real-world agents suffer from channel delays, receiving messages sent at different time points, termed {\it... | {
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} |
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