OpenRaiser/Intern-Atlas huggingface dataset:
Numbers Matter! Bringing Quantity-awareness to Retrieval Systems
Venue: arXiv cs.IR 2024
Year: 2024

Quantitative information plays a crucial role in understanding and interpreting the content of documents. Many user queries contain quantities and cannot be resolved without understanding their semantics, e.g., ``car that costs less than $10k''. Yet, modern search engines apply the same ranking mechanisms for both words and quantities, overlooking magnitude and unit information. In this paper, we introduce two quantity-aware ranking techniques designed to rank both the quantity and textual content either jointly or independently. These techniques incorporate quantity information in available retrieval systems and can address queries with numerical conditions equal, greater than, and less than. To evaluate the effectiveness of our proposed models, we introduce two novel quantity-aware benchmark datasets in the domains of finance and medicine and compare our method against various lexical and neural models. The code and data are available under https://github.com/satya77/QuantityAwareRankers.
Switch-Less Dragonfly on Wafers: A Scalable Interconnection Architecture based on Wafer-Scale Integration
Venue: arXiv cs.AR 2024
Year: 2024

Existing high-performance computing (HPC) interconnection architectures are based on high-radix switches, which limits the injection/local performance and introduces latency/energy/cost overhead. The new wafer-scale packaging and high-speed wireline technologies provide high-density, low-latency, and high-bandwidth connectivity, thus promising to support direct-connected high-radix interconnection architecture. In this paper, we propose a wafer-based interconnection architecture called Switch-Less-Dragonfly-on-Wafers. By utilizing distributed high-bandwidth networks-on-chip-on-wafer, costly high-radix switches of the Dragonfly topology are eliminated while increasing the injection/local throughput and maintaining the global throughput. Based on the proposed architecture, we also introduce baseline and improved deadlock-free minimal/non-minimal routing algorithms with only one additional virtual channel. Extensive evaluations show that the Switch-Less-Dragonfly-on-Wafers outperforms the traditional switch-based Dragonfly in both cost and performance. Similar approaches can be applied to other switch-based direct topologies, thus promising to power future large-scale supercomputers.
RecGS: Removing Water Caustic with Recurrent Gaussian Splatting
Venue: arXiv cs.CV 2024
Year: 2024

Water caustics are commonly observed in seafloor imaging data from shallow-water areas. Traditional methods that remove caustic patterns from images often rely on 2D filtering or pre-training on an annotated dataset, hindering the performance when generalizing to real-world seafloor data with 3D structures. In this paper, we present a novel method Recurrent Gaussian Splatting (RecGS), which takes advantage of today's photorealistic 3D reconstruction technology, 3DGS, to separate caustics from seafloor imagery. With a sequence of images taken by an underwater robot, we build 3DGS recurrently and decompose the caustic with low-pass filtering in each iteration. In the experiments, we analyze and compare with different methods, including joint optimization, 2D filtering, and deep learning approaches. The results show that our method can effectively separate the caustic from the seafloor, improving the visual appearance, and can be potentially applied on more problems with inconsistent illumination.
Public Discourse about COVID-19 Vaccinations: A Computational Analysis of the Relationship between Public Concerns and Policies
Venue: arXiv cs.CY 2024
Year: 2024

Societies worldwide have witnessed growing rifts separating advocates and opponents of vaccinations and other COVID-19 countermeasures. With the rollout of vaccination campaigns, German-speaking regions exhibited much lower vaccination uptake than other European regions. While Austria, Germany, and Switzerland (the DACH region) caught up over time, it remains unclear which factors contributed to these changes. Scrutinizing public discourses can help shed light on the intricacies of vaccine hesitancy and inform policy-makers tasked with making far-reaching decisions: policies need to effectively curb the spread of the virus while respecting fundamental civic liberties and minimizing undesired consequences. This study draws on Twitter data to analyze the topics prevalent in the public discourse. It further maps the topics to different phases of the pandemic and policy changes to identify potential drivers of change in public attention. We use a hybrid pipeline to detect and analyze vaccination-related tweets using topic modeling, sentiment analysis, and a minimum of social scientific domain knowledge to analyze the discourse about vaccinations in the light of the COVID-19 pandemic in the DACH region. We show that skepticism regarding the severity of the COVID-19 virus and towards efficacy and safety of vaccines were among the prevalent topics in the discourse on Twitter but that the most attention was given to debating the theme of freedom and civic liberties. Especially during later phases of the pandemic, when implemented policies restricted the freedom of unvaccinated citizens, increased vaccination uptake could be observed. At the same time, increasingly negative and polarized sentiments emerge in the discourse. This suggests that these policies might have effectively attenuated vaccination hesitancy but were not successfully dispersing citizens' doubts and concerns.
Complexity of 2D Snake Cube Puzzles
Venue: arXiv cs.CC 2024
Year: 2024

Given a chain of $HW$ cubes where each cube is marked "turn $90^\circ$" or "go straight", when can it fold into a $1 \times H \times W$ rectangular box? We prove several variants of this (still) open problem NP-hard: (1) allowing some cubes to be wildcard (can turn or go straight); (2) allowing a larger box with empty spaces (simplifying a proof from CCCG 2022); (3) growing the box (and the number of cubes) to $2 \times H \times W$ (improving a prior 3D result from height $8$ to $2$); (4) with hexagonal prisms rather than cubes, each specified as going straight, turning $60^\circ$, or turning $120^\circ$; and (5) allowing the cubes to be encoded implicitly to compress exponentially large repetitions.
An Interpretable Neural Network for Vegetation Phenotyping with Visualization of Trait-Based Spectral Features
Venue: arXiv cs.LG 2024
Year: 2024

Plant phenotyping is the assessment of a plant's traits and plant identification is the process of determining the category such as genus and species. In this paper we present an interpretable neural network trained on the UPWINS spectral library which contains spectra with rich metadata across variation in species, health, growth stage, annual variation, and environmental conditions for 13 selected indicator species and natural common background species. We show that the neurons in the network learn spectral indicators for chemical and physiological traits through visualization of the network weights, and we show how these traits are combined by the network for species identification with an accuracy around 90% on a test set. While neural networks are often perceived as `black box' classifiers, our work shows that they can be in fact more explainable and informative than other machine learning methods. We show that the neurons learn fundamental traits about the vegetation, for example the composition of different types of chlorophyll present which indicates species as well as response to illumination conditions. There is clear excess training capacity in our network, and we expect that as the UPWINS spectral library continues to grow the approach in this paper will provide further foundational insights in understanding plant traits. This provides a methodology for designing and interpreting neural networks on spectral data in general, and provides a framework for using neural networks with hyperspectral imagery for understanding vegetation that is extendable to other domains.
Towards Adapting Reinforcement Learning Agents to New Tasks: Insights from Q-Values
Venue: arXiv cs.LG 2024
Year: 2024

While contemporary reinforcement learning research and applications have embraced policy gradient methods as the panacea of solving learning problems, value-based methods can still be useful in many domains as long as we can wrangle with how to exploit them in a sample efficient way. In this paper, we explore the chaotic nature of DQNs in reinforcement learning, while understanding how the information that they retain when trained can be repurposed for adapting a model to different tasks. We start by designing a simple experiment in which we are able to observe the Q-values for each state and action in an environment. Then we train in eight different ways to explore how these training algorithms affect the way that accurate Q-values are learned (or not learned). We tested the adaptability of each trained model when retrained to accomplish a slightly modified task. We then scaled our setup to test the larger problem of an autonomous vehicle at an unprotected intersection. We observed that the model is able to adapt to new tasks quicker when the base model's Q-value estimates are closer to the true Q-values. The results provide some insights and guidelines into what algorithms are useful for sample efficient task adaptation.
Evolved Developmental Artificial Neural Networks for Multitasking with Advanced Activity Dependence
Venue: arXiv cs.NE 2024
Year: 2024

Recently, Cartesian Genetic Programming has been used to evolve developmental programs to guide the formation of artificial neural networks (ANNs). This approach has demonstrated success in enabling ANNs to perform multiple tasks while avoiding catastrophic forgetting. One unique aspect of this approach is the use of separate developmental programs evolved to regulate the development of separate soma and dendrite units. An opportunity afforded by this approach is the ability to incorporate Activity Dependence (AD) into the model such that environmental feedback can help to regulate the behavior of each type of unit. Previous work has shown a limited version of AD (influencing neural bias) to provide marginal improvements over non-AD ANNs. In this work, we present promising results from new extensions to AD. Specifically, we demonstrate a more significant improvement via AD on new neural parameters including health and position, as well as a combination of all of these along with bias. We report on the implications of this work and suggest several promising directions for future work.
A Robust Governance for the AI Act: AI Office, AI Board, Scientific Panel, and National Authorities
Venue: arXiv cs.CY 2024
Year: 2024

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU's new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA.
MPAT: Modular Petri Net Assembly Toolkit
Venue: arXiv cs.MS 2024
Year: 2024

We present a Python package called Modular Petri Net Assembly Toolkit (MPAT) that empowers users to easily create large-scale, modular Petri Nets for various spatial configurations, including extensive spatial grids or those derived from shape files, augmented with heterogeneous information layers. Petri Nets are powerful discrete event system modeling tools in computational biology and engineering. However, their utility for automated construction of large-scale spatial models has been limited by gaps in existing modeling software packages. MPAT addresses this gap by supporting the development of modular Petri Net models with flexible spatial geometries.
An Empirical Study of Mamba-based Pedestrian Attribute Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Current strong pedestrian attribute recognition models are developed based on Transformer networks, which are computationally heavy. Recently proposed models with linear complexity (e.g., Mamba) have garnered significant attention and have achieved a good balance between accuracy and computational cost across a variety of visual tasks. Relevant review articles also suggest that while these models can perform well on some pedestrian attribute recognition datasets, they are generally weaker than the corresponding Transformer models. To further tap into the potential of the novel Mamba architecture for PAR tasks, this paper designs and adapts Mamba into two typical PAR frameworks, i.e., the text-image fusion approach and pure vision Mamba multi-label recognition framework. It is found that interacting with attribute tags as additional input does not always lead to an improvement, specifically, Vim can be enhanced, but VMamba cannot. This paper further designs various hybrid Mamba-Transformer variants and conducts thorough experimental validations. These experimental results indicate that simply enhancing Mamba with a Transformer does not always lead to performance improvements but yields better results under certain settings. We hope this empirical study can further inspire research in Mamba for PAR, and even extend into the domain of multi-label recognition, through the design of these network structures and comprehensive experimentation. The source code of this work will be released at \url{https://github.com/Event-AHU/OpenPAR}
Comprehensive Review of Performance Optimization Strategies for Serverless Applications on AWS Lambda
Venue: arXiv cs.DC 2024
Year: 2024

This review paper synthesizes the latest research on performance optimization strategies for serverless applications deployed on AWS Lambda. By examining recent studies, we highlight the challenges, solutions, and best practices for enhancing the performance, cost efficiency, and scalability of serverless applications. The review covers a range of optimization techniques including resource management, runtime selection, observability improvements, and workload aware operations.
Distributed Scheduling for Throughput Maximization under Deadline Constraint in Wireless Mesh Networks
Venue: arXiv cs.NI 2024
Year: 2024

This paper studies the distributed scheduling of traffic flows with arbitrary deadlines that arrive at their source nodes and are transmitted to different destination nodes via multiple intermediate nodes in a wireless mesh network. When a flow is successfully delivered to its destination, a reward will be obtained, which is the embodiment of network performance and can be expressed by metrics such as throughput or network utility. The objective is to maximize the aggregate reward of all the deadline-constrained flows, which can be transformed into the constrained Markov decision process (CMDP). According to the transformation, a policy gradient-based distributed scheduling (PGDS) method is first proposed, where a primary reward and an auxiliary reward are designed to incentivize each node to independently schedule network resources such as power and subcarriers. The primary reward is generated when flows are successfully delivered to their destinations. The auxiliary reward, designed based on potential-based reward shaping (PBRS) using local information of data transmission, aims to accelerate the convergence speed. Inside this method, a reward feedback scheme is designed to let each node obtain the primary reward. Noting that each node selecting resources independently may cause interference and collision which leads to instability of data transmission, a policy gradient-based resource determination algorithm is proposed. Moreover, the optimality and convergence of the PGDS method are derived. Especially, when a policy obtained by the algorithm is not matched with the optimal policy but can better deal with the interference, an asymptotic optimum still exists and is further derived.
Assessing the Performance of Stateful Logic in 1-Selector-1-RRAM Crossbar Arrays
Venue: arXiv cs.AR 2024
Year: 2024

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO2-based selector and TiN/TiOx/HfOx/Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from $4\times4$ to $512\times512$. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.
Multi-source Knowledge Enhanced Graph Attention Networks for Multimodal Fact Verification
Venue: arXiv cs.MM 2024
Year: 2024

Multimodal fact verification is an under-explored and emerging field that has gained increasing attention in recent years. The goal is to assess the veracity of claims that involve multiple modalities by analyzing the retrieved evidence. The main challenge in this area is to effectively fuse features from different modalities to learn meaningful multimodal representations. To this end, we propose a novel model named Multi-Source Knowledge-enhanced Graph Attention Network (MultiKE-GAT). MultiKE-GAT introduces external multimodal knowledge from different sources and constructs a heterogeneous graph to capture complex cross-modal and cross-source interactions. We exploit a Knowledge-aware Graph Fusion (KGF) module to learn knowledge-enhanced representations for each claim and evidence and eliminate inconsistencies and noises introduced by redundant entities. Experiments on two public benchmark datasets demonstrate that our model outperforms other comparison methods, showing the effectiveness and superiority of the proposed model.
TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction
Venue: arXiv cs.CL 2024
Year: 2024

Traditional Chinese medicine (TCM) has relied on specific combinations of herbs in prescriptions to treat various symptoms and signs for thousands of years. Predicting TCM prescriptions poses a fascinating technical challenge with significant practical implications. However, this task faces limitations due to the scarcity of high-quality clinical datasets and the complex relationship between symptoms and herbs. To address these issues, we introduce \textit{DigestDS}, a novel dataset comprising practical medical records from experienced experts in digestive system diseases. We also propose a method, TCM-FTP (TCM Fine-Tuning Pre-trained), to leverage pre-trained large language models (LLMs) via supervised fine-tuning on \textit{DigestDS}. Additionally, we enhance computational efficiency using a low-rank adaptation technique. Moreover, TCM-FTP incorporates data augmentation by permuting herbs within prescriptions, exploiting their order-agnostic nature. Impressively, TCM-FTP achieves an F1-score of 0.8031, significantly outperforming previous methods. Furthermore, it demonstrates remarkable accuracy in dosage prediction, achieving a normalized mean square error of 0.0604. In contrast, LLMs without fine-tuning exhibit poor performance. Although LLMs have demonstrated wide-ranging capabilities, our work underscores the necessity of fine-tuning for TCM prescription prediction and presents an effective way to accomplish this.
9/7-Approximation for Two-Edge-Connectivity and Two-Vertex-Connectivity
Venue: arXiv cs.DS 2024
Year: 2024

We provide algorithms for the minimum 2-edge-connected spanning subgraph problem and the minimum 2-vertex-connected spanning subgraph problem with approximation ratio $\frac{9}{7}$. This improves upon a recent algorithm with ratio slightly smaller than $\frac{4}{3}$ for 2-edge-connectivity, and another one with ratio $\frac{4}{3}$ for 2-vertex-connectivity.
LIP-CAR: contrast agent reduction by a deep learned inverse problem
Venue: arXiv cs.CV 2024
Year: 2024

The adoption of contrast agents in medical imaging protocols is crucial for accurate and timely diagnosis. While highly effective and characterized by an excellent safety profile, the use of contrast agents has its limitation, including rare risk of allergic reactions, potential environmental impact and economic burdens on patients and healthcare systems. In this work, we address the contrast agent reduction (CAR) problem, which involves reducing the administered dosage of contrast agent while preserving the visual enhancement. The current literature on the CAR task is based on deep learning techniques within a fully image processing framework. These techniques digitally simulate high-dose images from images acquired with a low dose of contrast agent. We investigate the feasibility of a ``learned inverse problem'' (LIP) approach, as opposed to the end-to-end paradigm in the state-of-the-art literature. Specifically, we learn the image-to-image operator that maps high-dose images to their corresponding low-dose counterparts, and we frame the CAR task as an inverse problem. We then solve this problem through a regularized optimization reformulation. Regularization methods are well-established mathematical techniques that offer robustness and explainability. Our approach combines these rigorous techniques with cutting-edge deep learning tools. Numerical experiments performed on pre-clinical medical images confirm the effectiveness of this strategy, showing improved stability and accuracy in the simulated high-dose images.