Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-label Node Classification On Graph-Structured Data

Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-class classification scenario, a more general and realistic scenario in which each node could have multiple labels has so far received little attention. The first challenge in conducting focused studies on multi-label node classification is the limited number of publicly available multi-label graph datasets. Therefore, as our first contribution, we collect and release three real-world biological datasets and develop a multi-label graph generator to generate datasets with tunable properties. While high label similarity (high homophily) is usually attributed to the success of GNNs, we argue that a multi-label scenario does not follow the usual semantics of homophily and heterophily so far defined for a multi-class scenario. As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets. Finally, we perform a large-scale comparative study with $8$ methods and $9$ datasets and analyse the performances of the methods to assess the progress made by current state of the art in the multi-label node classification scenario. We release our benchmark at https://github.com/Tianqi-py/MLGNC.
Measuring the Hubble constant using strongly lensed gravitational wave signals

The measurement of the Hubble constant H 0 plays an important role in the study of cosmology. In this work, we propose a new method to constrain the Hubble constant using the strongly lensed gravitational wave (SLGW) signals. Through reparameterization, we find that the lensed waveform is sensitive to the H 0. Assuming the scenario that no electromagnetic counterpart of the GW source can be identified, our method can still give meaningful constraints on the H 0 with the information of the lens redshift. We then apply Fisher information matrix and Markov Chain Monte Carlo to evaluate the potential of this method. For the space-based GW detector, TianQin, the H 0 can be constrained within a relative error of ∼ 1% with a single SLGW event.
Application of the Marchenko method to a land seismic dataset

The Marchenko method is capable of estimating Green's functions between the surface of the Earth and arbitrary locations in the subsurface. This method enables the isolation of the response of a specific layer or package of layers, free from the influence of the overburden and underburden. In this study, we apply the Marchenko-based isolation technique to land S-wave seismic data acquired in the Groningen province, the Netherlands, even though applying the Marchenko method to land data is very challenging. We apply the technique for three scenarios: overburden elimination, underburden elimination, and combined elimination of the overburden and underburden. Our results indicate that this approach partly enhances the resolution of reflections and eliminates internal multiples. These enhanced reflections can be utilized for imaging and monitoring applications, where we expect improved results due to the elimination of the overburden and underburden.
Study of Robust Adaptive Beamforming with Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region

In this work, a simple and effective robust adaptive beamforming technique is proposed for uniform linear arrays, which is based on the power spectral estimation and uncertainty region (PSEUR) of the interference plus noise (IPN) components. In particular, two algorithms are presented to find the angular sector of interference in every snapshot based on the adopted spatial uncertainty region of the interference direction. Moreover, a power spectrum is introduced based on the estimation of the power of interference and noise components, which allows the development of a robust approach to IPN covariance matrix reconstruction. The proposed method has two main advantages. First, an angular region that contains the interference direction is updated based on the statistics of the array data. Secondly, the proposed IPN-PSEUR method avoids estimating the power spectrum of the whole range of possible directions of the interference sector. Simulation results show that the performance of the proposed IPN-PSEUR beamformer is almost always close to the optimal value across a wide range of signal-to-noise ratios.
Positive topological entropy for monotone recurrence relations

We associate the topological entropy of monotone recurrence relations with the Aubry–Mather theory. If there exists an interval $[{\it\rho}_{0},{\it\rho}_{1}]$ such that, for each ${\it\omega}\in ({\it\rho}_{0},{\it\rho}_{1})$, all Birkhoff minimizers with rotation number ${\it\omega}$ do not form a foliation, then the diffeomorphism on the high-dimensional cylinder defined via the monotone recurrence relation has positive topological entropy.
Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks

The release of ChatGPT has uncovered a range of possibilities whereby large language models (LLMs) can substitute human intelligence. In this paper, we seek to understand whether ChatGPT has the potential to reproduce human-generated label annotations in social computing tasks. Such an achievement could significantly reduce the cost and complexity of social computing research. As such, we use ChatGPT to relabel five seminal datasets covering stance detection (2x), sentiment analysis, hate speech, and bot detection. Our results highlight that ChatGPT does have the potential to handle these data annotation tasks, although a number of challenges remain. ChatGPT obtains an average accuracy 0.609. Performance is highest for the sentiment analysis dataset, with ChatGPT correctly annotating 64.9% of tweets. Yet, we show that performance varies substantially across individual labels. We believe this work can open up new lines of analysis and act as a basis for future research into the exploitation of ChatGPT for human annotation tasks.
Prediction of the evolution of the nuclear reactor core parameters using artificial neural network

A nuclear reactor based on MIT BEAVRS benchmark was used as a typical power generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used as a full-core reactor physics solver to emulate the operation of a reactor and to generate training, and validation data for the ANN. The ANN was implemented with dedicated Python 3.8 code with Google's TensorFlow 2.0 library. The effort was based to a large extent on the process of appropriate automatic transformation of data generated by PARCS simulator, which was later used in the process of the ANN development. Various methods that allow obtaining better accuracy of the ANN predicted results were studied, such as trying different ANN architectures to find the optimal number of neurons in the hidden layers of the network. Results were later compared with the architectures proposed in the literature. For the selected best architecture predictions were made for different core parameters and their dependence on core loading patterns. In this study, a special focus was put on the prediction of the fuel cycle length for a given core loading pattern, as it can be considered one of the targets for plant economic operation. For instance, the length of a single fuel cycle depending on the initial core loading pattern was predicted with very good accuracy (>99%). This work contributes to the exploration of the usefulness of neural networks in solving nuclear reactor design problems. Thanks to the application of ANN, designers can avoid using an excessive amount of core simulator runs and more rapidly explore the space of possible solutions before performing more detailed design considerations.
On the general linear group and Hochschild homology

Our main result here is a rational computation of the homology of the adjoint action of the infinite general linear group of an arbitrary ring. Before stating the result we establish some notation and conventions. Rings are associative and with unit. If A is a ring then GL(A)= Uk? 0GLk(A) is its infinite general linear group. An A-bimodule is an abelian group B which is both a left A-module and a right A-module and satisfies (alb)a2 = al(ba2) for ai eA, be B (for example B = A). If B is an A-bimodule, then M(B) = Uk? oMk(B) is the infinite additive group of matrices with entries in B. Conjugation defines an action (the adjoint action) of GL(A) on M(B). Note that an A ? Q-bimodule is just an A-bimodule which is also a rational vector space. If B is an A ? Q-bimodule, then HJ(A C) Q; B) denotes the Hochschild homology of A ? Q with coefficients in B. Our main result (it appears in slightly more detailed form as Theorem V.3) is:
Filter-Aware Model-Predictive Control

Partially-observable problems pose a trade-off between reducing costs and gathering information. They can be solved optimally by planning in belief space, but that is often prohibitively expensive. Model-predictive control (MPC) takes the alternative approach of using a state estimator to form a belief over the state, and then plan in state space. This ignores potential future observations during planning and, as a result, cannot actively increase or preserve the certainty of its own state estimate. We find a middle-ground between planning in belief space and completely ignoring its dynamics by only reasoning about its future accuracy. Our approach, filter-aware MPC, penalises the loss of information by what we call"trackability", the expected error of the state estimator. We show that model-based simulation allows condensing trackability into a neural network, which allows fast planning. In experiments involving visual navigation, realistic every-day environments and a two-link robot arm, we show that filter-aware MPC vastly improves regular MPC.
Speed Me up if You Can: Conditional Lower Bounds on Opacity Verification

Opacity is a property of privacy and security applications asking whether, given a system model, a passive intruder that makes online observations of system's behaviour can ascertain some"secret"information of the system. Deciding opacity is a PSpace-complete problem, and hence there are no polynomial-time algorithms to verify opacity under the assumption that PSpace differs from PTime. This assumption, however, gives rise to a question whether the existing exponential-time algorithms are the best possible or whether there are faster, sub-exponential-time algorithms. We show that under the (Strong) Exponential Time Hypothesis, there are no algorithms that would be significantly faster than the existing algorithms. As a by-product, we obtained a new conditional lower bound on the time complexity of deciding universality (and therefore also inclusion and equivalence) for nondeterministic finite automata.
Understanding Accelerated Gradient Methods: Lyapunov Analyses and Hamiltonian Assisted Interpretations

We formulate two classes of first-order algorithms more general than previously studied for minimizing smooth and strongly convex or, respectively, smooth and convex functions. We establish sufficient conditions, via new discrete Lyapunov analyses, for achieving accelerated convergence rates which match Nesterov's methods in the strongly and general convex settings. Next, we study the convergence of limiting ordinary differential equations (ODEs) and point out currently notable gaps between the convergence properties of the corresponding algorithms and ODEs. Finally, we propose a novel class of discrete algorithms, called the Hamiltonian assisted gradient method, directly based on a Hamiltonian function and several interpretable operations, and then demonstrate meaningful and unified interpretations of our acceleration conditions.
Two-Memory Reinforcement Learning

While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of parametric neural networks. Non-parametric episodic memory, on the other hand, provides a faster learning alternative that does not require representation learning and uses maximum episodic return as state-action values for action selection. Episodic memory and reinforcement learning both have their own strengths and weaknesses. Notably, humans can leverage multiple memory systems concurrently during learning and benefit from all of them. In this work, we propose a method called Two-Memory reinforcement learning agent (2M) that combines episodic memory and reinforcement learning to distill both of their strengths. The 2M agent exploits the speed of the episodic memory part and the optimality and the generalization capacity of the reinforcement learning part to complement each other. Our experiments demonstrate that the 2M agent is more data efficient and outperforms both pure episodic memory and pure reinforcement learning, as well as a state-of-the-art memory-augmented RL agent. Moreover, the proposed approach provides a general framework that can be used to combine any episodic memory agent with other off-policy reinforcement learning algorithms. 1
Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review

Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation. To achieve accurate and robust perception capabilities, autonomous vehicles are often equipped with multiple sensors, making sensor fusion a crucial part of the perception system. Among these fused sensors, radars and cameras enable a complementary and cost-effective perception of the surrounding environment regardless of lighting and weather conditions. This review aims to provide a comprehensive guideline for radar-camera fusion, particularly concentrating on perception tasks related to object detection and semantic segmentation. Based on the principles of the radar and camera sensors, we delve into the data processing process and representations, followed by an in-depth analysis and summary of radar-camera fusion datasets. In the review of methodologies in radar-camera fusion, we address interrogative questions, including “why to fuse”, “what to fuse”, “where to fuse”, “when to fuse”, and “how to fuse”, subsequently discussing various challenges and potential research directions within this domain. To ease the retrieval and comparison of datasets and fusion methods, we also provide an interactive website: https://radar-camera-fusion.github. io.
Maximize the Long-Term Average Revenue of Network Slice Provider via Admission Control Among Heterogeneous Slices

Network slicing endows 5G/B5G with differentiated and customized capabilities to cope with the proliferation of diversified services, whereas limited physical network resources may not be able to support all service requests. Slice admission control is regarded as an essential means to ensure service quality and service isolation when the network is under burden. Herein, the scenario where rational tenants coexist with partially competitive network slice providers is adopted. We aim to maximize the long-term average revenue of the network operators through slice admission control, with the feasibility of multidimensional resource requirements, the priority differences among heterogeneous slices, and the admission fairness within each slice taken into account concurrently. We prove the intractability of our problem by a reduction from the Multidimensional Knapsack Problem (MKP), and propose a two-stage algorithm called MPSAC to make a suboptimal solution efficiently. The principle of MPSAC is to split the original problem into two sub-problems; inter-slice decision-making and intra-slice quota allocation, which are solved using a heuristic method and a tailored auction mechanism respectively. Extensive simulations are carried out to demonstrate the efficacy of our algorithm, the results show that the long-term average revenue of ours is at least 9.6% higher than comparisons while maintaining better priority relations and achieving improved fairness performance.
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning

Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. The pre-training of previous methods needs to manually construct the meta-relation set, leading to numerous labor costs. Self-supervised learning (SSL) is treated as a solution to tackle the issue, but still at an early stage for FS-KGR task. Moreover, most of the existing methods ignore leveraging the beneficial information from aliasing relations (AR), i.e., data-rich relations with similar contextual semantics to the target data-poor relation. Therefore, we proposed a novel Self-Supervised Learning model by leveraging Aliasing Relations to assist FS-KGR, termed SARF. Concretely, four main components are designed in our model, i.e., SSL reasoning module, AR-assisted mechanism, fusion module, and scoring function. We first generate the representation of the co-occurrence patterns in a generative manner. Meanwhile, the representations of aliasing relations are learned to enhance reasoning in the AR-assist mechanism. Besides, multiple strategies, i.e., simple summation and learnable fusion, are offered for representation fusion. Finally, the generated representation is used for scoring. Extensive experiments on three few-shot benchmarks demonstrate that SARF achieves state-of-the-art performance compared with other methods in most cases.
Availability Model of a 5G-MEC System

Multi-access Edge Computing (MEC) is one of the enabling technologies of the fifth generation (5G) of mobile networks. MEC enables services with strict latency requirements by bringing computing capabilities close to the users. As with any new technology, the dependability of MEC is one of the aspects that need to be carefully studied. In this paper, we propose a twolevel model to compute the availability of a 5G-MEC system. We then use the model to evaluate the availability of a 5G-MEC system under various configurations. The results show that having a single redundancy of the 5G-MEC elements leads to an acceptable availability. To reach a high availability, the software failure intensity of the management elements of 5G and MEC should be reduced.
Architectures of Topological Deep Learning: A Survey on Topological Neural Networks

The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein. Topological Deep Learning (TDL) provides a comprehensive framework to process and extract knowledge from data associated with these systems, such as predicting the social community to which an individual belongs or predicting whether a protein can be a reasonable target for drug development. TDL has demonstrated theoretical and practical advantages that hold the promise of breaking ground in the applied sciences and beyond. However, the rapid growth of the TDL literature for relational systems has also led to a lack of unification in notation and language across message-passing Topological Neural Network (TNN) architectures. This presents a real obstacle for building upon existing works and for deploying message-passing TNNs to new real-world problems. To address this issue, we provide an accessible introduction to TDL for relational systems, and compare the recently published message-passing TNNs using a unified mathematical and graphical notation. Through an intuitive and critical review of the emerging field of TDL, we extract valuable insights into current challenges and exciting opportunities for future development.
Prediction under hypothetical interventions: evaluation of performance using longitudinal observational data

Prediction models provide risks of an adverse event occurring for an individual based on their characteristics. Some prediction models have been used to make treatment decisions, but this is not appropriate when the data on which the model was developed included a mix of individuals with some who did and others who did not initiate that treatment. By contrast, predictions under hypothetical interventions are estimates of what a person’s risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision making. Longitudinal observational data are increasingly used for developing models for prediction under interventions using causal inference methods. However, evaluating predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply to interventional prediction models, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different to those that are observed for some patients in the validation data set. This work describes methods for evaluating predictive performance of predictions under interventions using longitudinal observational data. We focus on time-to-event outcomes and predictions under treatment strategies that involve sustaining a particular treatment regime over time. We introduce a validation approach using artificial censoring and inverse probability weighting which involves creating a validation data set that mimics the particular treatment strategy under which predictions are made. We extend measures of calibration, discrimination (including c-index and cumulative/dynamic AUC) and overall prediction error (Brier score) to the interventional prediction setting. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. We also evaluate a previously applied approach that compares predictions under a specific treatment strategy with outcomes in the subset of individuals who were observed to sustain that treatment strategy. Results show that our proposed approach and corresponding measures of predictive performance correctly capture the true predictive performance and that the subset method can result in misleading assessments of predictive performance. The methods are applied to an illustrative example in the context of liver transplantation.
Mode-Dependent Phonon Transport Analysis of Silicon Crystal by Molecular Dynamics Method

In this work, we have investigated phonon transport in silicon crystal using molecular dynamics and lattice dynamics methods. The phonon relaxation time is derived from molecular phase space trajectories through two different analysis methods using normal mode projection and spectral energy density. By performing the calculations for wavevectors spanning the entire first Brillouin zone, we find that these two relaxation-time calculation methods give almost the same results despite the fundamental difference in the underlying theories. With the obtained phonon relaxation time and group velocity calculated by lattice dynamics method, we have quantified the contribution from each phonon mode to the overall thermal conductivity. In addition, by calculating the cumulative thermal conductivity, we have quantified contributions to thermal conductivity from phonons with different mean free paths to gain insight into the scale effect of heat conduction in nanoscale.