OpenRaiser/Intern-Atlas huggingface dataset:
A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks
Venue: arXiv cs.SI 2024
Year: 2024

Roadway reconfiguration is a crucial aspect of transportation planning, aiming to enhance traffic flow, reduce congestion, and improve overall road network performance with existing infrastructure and resources. This paper presents a novel roadway reconfiguration technique by integrating optimization based Brute Force search approach and decision support framework to rank various roadway configurations for better performance. The proposed framework incorporates a multi-criteria decision analysis (MCDA) approach, combining input from generated scenarios during the optimization process. By utilizing data from optimization, the model identifies total betweenness centrality (TBC), system travel time (STT), and total link traffic flow (TLTF) as the most influential decision variables. The developed framework leverages graph theory to model the transportation network topology and apply network science metrics as well as stochastic user equilibrium traffic assignment to assess the impact of each roadway configuration on the overall network performance. To rank the roadway configurations, the framework employs machine learning algorithms, such as ridge regression, to determine the optimal weights for each criterion (i.e., TBC, STT, TLTF). Moreover, the network-based analysis ensures that the selected configurations not only optimize individual roadway segments but also enhance system-level efficiency, which is particularly helpful as the increasing frequency and intensity of natural disasters and other disruptive events underscore the critical need for resilient transportation networks. By integrating multi-criteria decision analysis, machine learning, and network science metrics, the proposed framework would enable transportation planners to make informed and data-driven decisions, leading to more sustainable, efficient, and resilient roadway configurations.
A Rapid Review of Clustering Algorithms
Venue: arXiv cs.LG 2024
Year: 2024

Clustering algorithms aim to organize data into groups or clusters based on the inherent patterns and similarities within the data. They play an important role in today's life, such as in marketing and e-commerce, healthcare, data organization and analysis, and social media. Numerous clustering algorithms exist, with ongoing developments introducing new ones. Each algorithm possesses its own set of strengths and weaknesses, and as of now, there is no universally applicable algorithm for all tasks. In this work, we analyzed existing clustering algorithms and classify mainstream algorithms across five different dimensions: underlying principles and characteristics, data point assignment to clusters, dataset capacity, predefined cluster numbers and application area. This classification facilitates researchers in understanding clustering algorithms from various perspectives and helps them identify algorithms suitable for solving specific tasks. Finally, we discussed the current trends and potential future directions in clustering algorithms. We also identified and discussed open challenges and unresolved issues in the field.
Mask-adaptive Gated Convolution and Bi-directional Progressive Fusion Network for Depth Completion
Venue: arXiv cs.CV 2024
Year: 2024

Depth completion is a critical task for handling depth images with missing pixels, which can negatively impact further applications. Recent approaches have utilized Convolutional Neural Networks (CNNs) to reconstruct depth images with the assistance of color images. However, vanilla convolution has non-negligible drawbacks in handling missing pixels. To solve this problem, we propose a new model for depth completion based on an encoder-decoder structure. Our model introduces two key components: the Mask-adaptive Gated Convolution (MagaConv) architecture and the Bi-directional Progressive Fusion (BP-Fusion) module. The MagaConv architecture is designed to acquire precise depth features by modulating convolution operations with iteratively updated masks, while the BP-Fusion module progressively integrates depth and color features, utilizing consecutive bi-directional fusion structures in a global perspective. Extensive experiments on popular benchmarks, including NYU-Depth V2, DIML, and SUN RGB-D, demonstrate the superiority of our model over state-of-the-art methods. We achieved remarkable performance in completing depth maps and outperformed existing approaches in terms of accuracy and reliability.
The Fairness of Redistricting Ghost
Venue: arXiv cs.GT 2024
Year: 2024

We explore the fairness of a redistricting game introduced by Mixon and Villar, which provides a two-party protocol for dividing a state into electoral districts, without the participation of an independent authority. We analyze the game in an abstract setting that ignores the geographic distribution of voters and assumes that voter preferences are fixed and known. We show that the minority player can always win at least $p-1$ districts, where $p$ is proportional to the percentage of minority voters. We give an upper bound on the number of districts won by the minority based on a "cracking" strategy for the majority.
Zone-Specific CSI Feedback for Massive MIMO: A Situation-Aware Deep Learning Approach
Venue: arXiv cs.IT 2024
Year: 2024

Massive MIMO basestations, operating with frequency-division duplexing (FDD), require the users to feedback their channel state information (CSI) in order to design the precoding matrices. Given the powerful capabilities of deep neural networks in learning quantization codebooks, utilizing these networks in compressing the channels and reducing the massive MIMO CSI feedback overhead has recently gained increased interest. Learning one model, however, for the full cell or sector may not be optimal as the channel distribution could change significantly from one \textit{zone} (an area or region) to another. In this letter, we introduce the concept of \textit{zone-specific} CSI feedback. By partitioning the site space into multiple channel zones, the underlying channel distribution can be efficiently leveraged to reduce the CSI feedback. This concept leverages the implicit or explicit user position information to select the right zone-specific model and its parameters. To facilitate the evaluation of associated overhead, we introduce two novel metrics named \textit{model parameters transmission rate} (MPTR) and \textit{model parameters update rate} (MPUR). They jointly provide important insights and guidance for the system design and deployment. Simulation results show that significant gains could be achieved by the proposed framework. For example, using the large-scale Boston downtown scenario of DeepMIMO, the proposed zone-specific CSI feedback approach can on average achieve around 6dB NMSE gain compared to the other solutions, while keeping the same model complexity.
Utilizing deep learning models for the identification of enhancers and super-enhancers based on genomic and epigenomic features
Venue: arXiv cs.CL 2024
Year: 2024

This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our primary objective was to conduct a psycholinguistic and emotion analysis of social media content associated with these cryptocurrencies. To enable investigators to make more informed decisions. The study involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns that emerge within each coin's community. To achieve this, we utilized advanced text analysis techniques. Additionally, our work unveiled an intriguing Understanding of the interplay between these digital assets within the cryptocurrency community. By examining which coin pairs are mentioned together most frequently in the dataset, we established correlations between different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from Twitter. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable Perception into the linguistic nuances of various digital coins' online communities and provides a deeper understanding of their interactions in the cryptocurrency space.
Decentralized Interference-Aware Codebook Learning in Millimeter Wave MIMO Systems
Venue: arXiv cs.IT 2024
Year: 2024

Beam codebooks are integral components of the future millimeter wave (mmWave) multiple input multiple output (MIMO) system to relax the reliance on the instantaneous channel state information (CSI). The design of these codebooks, therefore, becomes one of the fundamental problems for these systems, and the well-designed codebooks play key roles in enabling efficient and reliable communications. Prior work has primarily focused on the codebook learning problem within a single cell/network and under stationary interference. In this work, we generalize the interference-aware codebook learning problem to networks with multiple cells/basestations. One of the key differences compared to the single-cell codebook learning problem is that the underlying environment becomes non-stationary, as the behavior of one base station will influence the learning of the others. Moreover, to encompass some of the challenging scenarios, information exchange between the different learning nodes is not allowed, which leads to a fully decentralized system with significantly increased learning difficulties. To tackle the non-stationarity, the averaging of the measurements is used to estimate the interference nulling performance of a particular beam, based on which a decision rule is provided. Furthermore, we theoretically justify the adoption of such estimator and prove that it is a sufficient statistic for the underlying quantity of interest in an asymptotic sense. Finally, a novel reward function based on averaging is proposed to fully decouple the learning of the multiple agents running at different nodes. Simulation results show that the developed solution is capable of learning well-shaped codebook patterns for different networks that significantly suppress the interference without information exchange, highlighting ...
Existence of MMS Allocations with Mixed Manna
Venue: arXiv cs.GT 2024
Year: 2024

Maximin share (MMS) allocations are a popular relaxation of envy-free allocations that have received wide attention in the fair division of indivisible items. Although MMS allocations of goods can fail to exist, previous work has found conditions under which they exist. Specifically, MMS allocations of goods exist whenever $m \leq n+5$, and this bound is tight in the sense that they can fail to exist when $m = n+6$. The techniques used to obtain these results do not apply to the mixed manna setting, leaving the question of whether similar results hold for the general setting. This paper addresses this by introducing new techniques to handle these settings. In particular, we are able to answer this question completely for the chores setting, and partially for the mixed manna setting. An agent $i$ is a {\em chores agent} if it considers every item to be a chore and a {\em non-negative agent} if its MMS guarantee is non-negative. In this paper, we prove that an MMS allocation exists as long as $m \leq n+5$ and either (i) every agent is a chores agent, or (ii) there exists a non-negative agent. In addition, for $n \leq 3$, we also prove that an MMS allocation exists as long as $m \leq n+5$, regardless of the types of agents. To the best of our knowledge, these are the first non-trivial results pertaining to the existence of exact MMS allocations in the mixed manna setting.
PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks
Venue: arXiv cs.CV 2024
Year: 2024

Despeckling is a crucial noise reduction task in improving the quality of synthetic aperture radar (SAR) images. Directly obtaining noise-free SAR images is a challenging task that has hindered the development of accurate despeckling algorithms. The advent of deep learning has facilitated the study of denoising models that learn from only noisy SAR images. However, existing methods deal solely with single-polarization images and cannot handle the multi-polarization images captured by modern satellites. In this work, we present an extension of the existing model for generating single-polarization SAR images to handle multi-polarization SAR images. Specifically, we propose a novel self-supervised despeckling approach called channel masking, which exploits the relationship between polarizations. Additionally, we utilize a spatial masking method that addresses pixel-to-pixel correlations to further enhance the performance of our approach. By effectively incorporating multiple polarization information, our method surpasses current state-of-the-art methods in quantitative evaluation in both synthetic and real-world scenarios.
Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering
Venue: arXiv cs.CL 2024
Year: 2024

ChatGPT explores a strategic blueprint of question answering (QA) in delivering medical diagnosis, treatment recommendations, and other healthcare support. This is achieved through the increasing incorporation of medical domain data via natural language processing (NLP) and multimodal paradigms. By transitioning the distribution of text, images, videos, and other modalities from the general domain to the medical domain, these techniques have expedited the progress of medical domain question answering (MDQA). They bridge the gap between human natural language and sophisticated medical domain knowledge or expert manual annotations, handling large-scale, diverse, unbalanced, or even unlabeled data analysis scenarios in medical contexts. Central to our focus is the utilizing of language models and multimodal paradigms for medical question answering, aiming to guide the research community in selecting appropriate mechanisms for their specific medical research requirements. Specialized tasks such as unimodal-related question answering, reading comprehension, reasoning, diagnosis, relation extraction, probability modeling, and others, as well as multimodal-related tasks like vision question answering, image caption, cross-modal retrieval, report summarization, and generation, are discussed in detail. Each section delves into the intricate specifics of the respective method under consideration. This paper highlights the structures and advancements of medical domain explorations against general domain methods, emphasizing their applications across different tasks and datasets. It also outlines current challenges and opportunities for future medical domain research, paving the way for continued innovation and application in this rapidly evolving field.
Temporal Link Prediction Using Graph Embedding Dynamics
Venue: arXiv cs.LG 2024
Year: 2024

Graphs are a powerful representation tool in machine learning applications, with link prediction being a key task in graph learning. Temporal link prediction in dynamic networks is of particular interest due to its potential for solving complex scientific and real-world problems. Traditional approaches to temporal link prediction have focused on finding the aggregation of dynamics of the network as a unified output. In this study, we propose a novel perspective on temporal link prediction by defining nodes as Newtonian objects and incorporating the concept of velocity to predict network dynamics. By computing more specific dynamics of each node, rather than overall dynamics, we improve both accuracy and explainability in predicting future connections. We demonstrate the effectiveness of our approach using two datasets, including 17 years of co-authorship data from PubMed. Experimental results show that our temporal graph embedding dynamics approach improves downstream classification models' ability to predict future collaboration efficacy in co-authorship networks by 17.34% (AUROC improvement relative to the baseline model). Furthermore, our approach offers an interpretable layer over traditional approaches to address the temporal link prediction problem.
Multi-Protocol Location Forwarding (MPLF) for Space Routing
Venue: arXiv cs.NI 2024
Year: 2024

The structure and routing architecture design is critical for achieving low latency and high capacity in future LEO space networks (SNs). Existing studies mainly focus on topologies of space networks, but there is a lack of analysis on constellation structures, which can greatly affect network performance. In addition, some routing architectures are designed for networks with a small number of network nodes such as Iridium while they introduce significant network overhead for high-density networks (i.e., mega-constellation networks containing thousands of satellites). In this paper, we conduct the quantitatively study on the design of network structure and routing architecture in space. The high density, high dynamics, and large scale nature of emerging Space Networks (SNs) pose significant challenges, such as unstable routing paths, low network reachability, high latency, and large jitter. To alleviate the above challenges, we design the structure of space network to maximum the connectivity through wisely adjusting the inter-plane inter satellite link. We further propose Multi-Protocol Location Forwarding (MPLF), a distributed routing architecture, targeting at minimizing the propagation latency with a distributed, convergence-free routing paradigm, while keeping routing stable and maximum the path diversity. Comprehensive experiments are conducted on a customized platform \textit{Space Networking Kits} (SNK) which demonstrate that our solution can outperform existing related schemes by about 14\% reduction of propagation latency and 66\% reduction of hops-count on average, while sustaining a high path diversity with only $O(1)$ time complexity.
Understanding the Indirect Effects of Interactive Systems Within Systems of Systems
Venue: arXiv cs.HC 2024
Year: 2024

Until recently, research into the sustainable design of interactive systems has primarily focused on the direct material impact of a system, through improving its energy efficiency and optimizing its lifecycle. Yet the way a system is designed and marketed often has wider repercussions, such as rebound effects, and systemic change in practices. These effects are harder to assess (and to anticipate) than the direct physical impact of the construction and use of the system itself. Current tools are unable to account for the complexity of these effects: the underlying causal mechanisms, their multi-level nature, their different temporalities, and the variety of their consequences (environmental and societal). This is why we are seeking to develop a specific methodology and tool, inspired by systemic design and system dynamics. These are intended for decision-makers and designers of interactive systems within systems of systems (for example, in the fields of agricultural robotics or public transportation). In this paper, we present this modeling approach and our prototype tool through the example of a second-hand clothing sales platform.
Call graph discovery in binary programs from unknown instruction set architectures
Venue: arXiv cs.CR 2024
Year: 2024

This study addresses the challenge of reverse engineering binaries from unknown instruction set architectures, a complex task with potential implications for software maintenance and cyber-security. We focus on the tasks of detecting candidate call and return opcodes for automatic extraction of call graphs in order to simplify the reverse engineering process. Empirical testing on a small dataset of binary files from different architectures demonstrates that the approach can accurately detect specific opcodes under conditions of noisy data. The method lays the groundwork for a valuable tool for reverse engineering where the reverse engineer has minimal a priori knowledge of the underlying instruction set architecture.
A greedy heuristic for graph burning
Venue: arXiv cs.DM 2024
Year: 2024

Given a graph $G$, the optimization version of the graph burning problem seeks for a sequence of vertices, $(u_1,u_2,...,u_p) \in V(G)^p$, with minimum $p$ and such that every $v \in V(G)$ has distance at most $p-i$ to some vertex $u_i$. The length $p$ of the optimal solution is known as the burning number and is denoted by $b(G)$, an invariant that helps quantify the graph's vulnerability to contagion. This paper explores the advantages and limitations of an $\mathcal{O}(mn + pn^2)$ deterministic greedy heuristic for this problem, where $n$ is the graph's order, $m$ is the graph's size, and $p$ is a guess on $b(G)$. This heuristic is based on the relationship between the graph burning problem and the clustered maximum coverage problem, and despite having limitations on paths and cycles, it found most of the optimal and best-known solutions of benchmark and synthetic graphs with up to 102400 vertices. Beyond practical advantages, our work unveils some of the fundamental aspects of graph burning: its relationship with a generalization of a classical coverage problem and compact integer programs. With this knowledge, better algorithms might be designed in the future.
Semantic Scene Segmentation for Robotics
Venue: arXiv cs.RO 2024
Year: 2024

Comprehensive scene understanding is a critical enabler of robot autonomy. Semantic segmentation is one of the key scene understanding tasks which is pivotal for several robotics applications including autonomous driving, domestic service robotics, last mile delivery, amongst many others. Semantic segmentation is a dense prediction task that aims to provide a scene representation in which each pixel of an image is assigned a semantic class label. Therefore, semantic segmentation considers the full scene context, incorporating the object category, location, and shape of all the scene elements, including the background. Numerous algorithms have been proposed for semantic segmentation over the years. However, the recent advances in deep learning combined with the boost in the computational capacity and the availability of large-scale labeled datasets have led to significant advances in semantic segmentation. In this chapter, we introduce the task of semantic segmentation and present the deep learning techniques that have been proposed to address this task over the years. We first define the task of semantic segmentation and contrast it with other closely related scene understanding problems. We detail different algorithms and architectures for semantic segmentation and the commonly employed loss functions. Furthermore, we present an overview of datasets, benchmarks, and metrics that are used in semantic segmentation. We conclude the chapter with a discussion of challenges and opportunities for further research in this area.