Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed-Point Centrality for Networks

This paper proposes a family of network centralities called fixed-point centralities. This centrality family is defined via the fixed point of permutation equivariant mappings related to the underlying network. Such a centrality notion is immediately extended to define fixed-point centralities for infinite graphs characterized by graphons. Variation bounds of such centralities with respect to the variations of the underlying graphs and graphons under mild assumptions are established. Fixed-point centralities connect with a variety of different models on networks including graph neural networks, static and dynamic games on networks, and Markov decision processes.
Earthquake Phase Association with Graph Neural Networks

Seismic phase association connects earthquake arrival-time measurements to their causative sources. Effective association must determine the number of discrete events, their location, and origin times, and it must differentiate real arrivals from measurement artifacts. The advent of deep-learning (DL) pickers, which provide high rates of picks from closely overlapping small-magnitude earthquakes, motivates revisiting the phase association problem and approaching it using the methods of DL. We have developed a graph neural network associator that simultaneously predicts both source space–time localization, and discrete source-arrival association likelihoods. The method is applicable to arbitrary geometry, time-varying seismic networks of hundreds of stations, and is robust to high rates of sources and input picks with variable noise and quality. Our Graph Earthquake Neural Interpretation Engine (GENIE) uses one graph to represent the station set and another to represent the spatial source region. GENIE learns relationships from data in this combined representation that enable it to determine robust source and source-arrival associations. We train on synthetic data, and test our method on real data from the northern California seismic network using input generated by the PhaseNet DL phase picker. We successfully re-detect ∼96% of all events M >1 reported by the U.S. Geological Survey (USGS) during 500 random days between 2000 and 2022. Over a 100-day continuous interval of processing in 2017–2018, we detect ∼4× the number of events reported by the USGS. Our new events have small-magnitude estimates below the magnitude of completeness of the USGS catalog, and are located close to the active faults and quarries in the region. Our results demonstrate that GENIE can effectively solve the association problem under complex seismic monitoring conditions.
Structure preservation via the Wasserstein distance

We show that under minimal assumptions on a random vector $X\in\mathbb{R}^d$ and with high probability, given $m$ independent copies of $X$, the coordinate distribution of each vector $(\langle X_i,\theta \rangle)_{i=1}^m$ is dictated by the distribution of the true marginal $\langle X,\theta \rangle$. Specifically, we show that with high probability, \[\sup_{\theta \in S^{d-1}} \left( \frac{1}{m}\sum_{i=1}^m \left|\langle X_i,\theta \rangle^\sharp - \lambda^\theta_i \right|^2 \right)^{1/2} \leq c \left( \frac{d}{m} \right)^{1/4},\] where $\lambda^{\theta}_i = m\int_{(\frac{i-1}{m}, \frac{i}{m}]} F_{ \langle X,\theta \rangle }^{-1}(u)\,du$ and $a^\sharp$ denotes the monotone non-decreasing rearrangement of $a$. Moreover, this estimate is optimal. The proof follows from a sharp estimate on the worst Wasserstein distance between a marginal of $X$ and its empirical counterpart, $\frac{1}{m} \sum_{i=1}^m \delta_{\langle X_i, \theta \rangle}$.
A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation

Event camera is an emerging bio-inspired vision sensors that report per-pixel brightness changes asynchronously. It holds noticeable advantage of high dynamic range, high speed response, and low power budget that enable it to best capture local motions in uncontrolled environments. This motivates us to unlock the potential of event cameras for human pose estimation, as the human pose estimation with event cameras is rarely explored. Due to the novel paradigm shift from conventional frame-based cameras, however, event signals in a time interval contain very limited information, as event cameras can only capture the moving body parts and ignores those static body parts, resulting in some parts to be incomplete or even disappeared in the time interval. This paper proposes a novel densely connected recurrent architecture to address the problem of incomplete information. By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data. Moreover, to better evaluate our model, we collect a large scale multimodal event-based dataset that comes with human pose annotations, which is by far the most challenging one to the best of our knowledge. The experimental results on two public datasets and our own dataset demonstrate the effectiveness and strength of our approach. Code can be available online for facilitating the future research.
Bipedal Robot Walking Control Using Human Whole-Body Dynamic Telelocomotion

For humanoids to be deployed in demanding situations, such as search and rescue, highly intelligent decision making and proficient sensorimotor skill is expected. A promising solution is to leverage human prowess by interconnecting robot and human via teleoperation. Towards creating seamless operation, this paper presents a dynamic telelocomotion framework that synchronizes the gait of a human pilot with the walking of a bipedal robot. First, we introduce a method to generate a virtual human walking model from the stepping behavior of a human pilot which serves as a reference for the robot to walk. Second, the dynamics of the walking reference and robot walking are synchronized by applying forces to the human pilot and the robot to achieve dynamic similarity between the two systems. This enables the human pilot to continuously perceive and cancel any asynchrony between the walking reference and robot. A consistent step placement strategy for the robot is derived to maintain dynamic similarity through step transitions. Using our human-machine-interface, we demonstrate that the human pilot can achieve stable and synchronous teleoperation of a simulated robot through stepping-in-place, walking, and disturbance rejection experiments. This work provides a fundamental step towards transferring human intelligence and reflexes to humanoid robots.
Learning to Exploit Elastic Actuators for Quadruped Locomotion

Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design. While previous work has focused on extensive modeling and simulation to find optimal controllers for such systems, we propose to learn model-free controllers directly on the real robot. In our approach, gaits are first synthesized by central pattern generators (CPGs), whose parameters are optimized to quickly obtain an open-loop controller that achieves efficient locomotion. Then, to make this controller more robust and further improve the performance, we use reinforcement learning to close the loop, to learn corrective actions on top of the CPGs. We evaluate the proposed approach on the DLR elastic quadruped bert. Our results in learning trotting and pronking gaits show that exploitation of the spring actuator dynamics emerges naturally from optimizing for dynamic motions, yielding high-performing locomotion, particularly the fastest walking gait recorded on bert, despite being model-free. The whole process takes no more than 1.5 hours on the real robot and results in natural-looking gaits.
ADAMANT: A Pipeline for Adaptable Manipulation Tasks

This paper presents ADAMANT, a set of software modules that provides grasp planning capabilities to an existing robot planning and control software framework. Our presented work allows a user to adapt a manipulation task to be used under widely different scenarios with minimal user input, thus reducing the operator's cognitive load. The developed tools include (1) plugin-based components that make it easy to extend default capabilities and to use third-party grasp libraries, (2) An object-centric way to define task constraints, (3) A user-friendly Rviz interface to use the grasp planner utilities, and (4) Interactive tools to use perception data to program a task. We tested our framework on a wide variety of robot simulations.
Vector time series modelling of turbidity in Dublin Bay

Turbidity is commonly monitored as an important water quality index. Human activities, such as dredging and dumping operations, can disrupt turbidity levels and should be monitored and analyzed for possible effects. In this paper, we model the variations of turbidity in Dublin Bay over space and time to investigate the effects of dumping and dredging while controlling for the effect of wind speed as a common atmospheric effect. We develop a novel Vector Auto-Regressive Conditional Heteroskedasticity (VARCH) approach to modelling the dynamical behaviour of turbidity over different locations and at different water depths. We use daily values of turbidity during the years 2017-2018 to fit the model. We show that the results of our fitted model are in line with the observed data and that the uncertainties, measured through Bayesian credible intervals, are well calibrated. Furthermore, we show that the daily effects of dredging and dumping on turbidity are negligible in comparison to that of wind speed.
CHIME Discovery of a Binary Pulsar with a Massive Nondegenerate Companion

Of the more than 3000 radio pulsars currently known, only ∼300 are in binary systems, and only five of these consist of young pulsars with massive nondegenerate companions. We present the discovery and initial timing, accomplished using the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope, of the sixth such binary pulsar, PSR J2108+4516, a 0.577 s radio pulsar in a 269 day orbit of eccentricity 0.09 with a companion of minimum mass 11 M ⊙. Notably, the pulsar undergoes periods of substantial eclipse, disappearing from the CHIME 400–800 MHz observing band for a large fraction of its orbit, and displays significant dispersion measure and scattering variations throughout its orbit, pointing to the possibility of a circumstellar disk or very dense stellar wind associated with the companion star. Subarcsecond resolution imaging with the Karl G. Jansky Very Large Array unambiguously demonstrates that the companion is a bright, V ≃ 11 OBe star, EM* UHA 138, located at a distance of 3.26(14) kpc. Archival optical observations of EM* UHA 138 approximately suggest a companion mass ranging from 17.5 M ⊙ < M c < 23 M ⊙, in turn constraining the orbital inclination angle to 50. °3 ≲ i ≲ 58. °3. With further multiwavelength follow-up, PSR J2108+4516 promises to serve as another rare laboratory for the exploration of companion winds, circumstellar disks, and short-term evolution through extended-body orbital dynamics.
Exploring Visual Interpretability for Contrastive Language-Image Pre-training

Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily available supervision of natural language. It improves the performance of downstream vision tasks, including but not limited to the zeroshot, long tail, segmentation, retrieval, caption, and video. However, the visual explainability of CLIP is rarely studied, especially for the raw feature map. To provide visual explanations of its predictions, we propose the Image-Text Similarity Map (ITSM). Based on it, we surprisingly find that CLIP prefers the background regions than the foregrounds, and shows erroneous visualization results against human understanding. This phenomenon is universal for both vision transformers and convolutional networks, which suggests this problem is unique and not owing to certain network. Experimentally, we find the devil is in the pooling part, where inappropriate pooling methods lead to a phenomenon called semantic shift. For this problem, we propose the Explainable Contrastive Language-Image Pretraining (ECLIP), which corrects the explainability via the Masked Max Pooling. Specifically, to avoid the semantic shift, we replace the original attention pooling by max pooling to focus on the confident foreground, with guidance from free attention during training. Experiments on three datasets suggest that ECLIP greatly improves the explainability of CLIP, and beyond previous explainability methods at large margins. The code will be released later.
Solving nonlinear Klein-Gordon equations on unbounded domains via the Finite Element Method

A large class of scalar-tensor theories of gravity exhibit a screening mechanism that dynamically suppresses ﬁfth forces in the Solar system and local laboratory experiments. Technically, at the scalar ﬁeld equation level, this usually translates into nonlinearities which strongly limit the scope of analytical approaches. This article presents femtoscope — a Python numerical tool based on the Finite Element Method (FEM) and Newton method for solving Klein-Gordon-like equations that arise in particular in the symmetron or chameleon models. Regarding the latter, the scalar ﬁeld behavior is generally only known inﬁnitely far away from the its sources. We thus investigate existing and new FEM-based techniques for dealing with asymptotic boundary conditions on ﬁnite-memory computers, whose convergence are assessed. Finally, femtoscope is showcased with a study of the chameleon ﬁfth force in Earth orbit.
Numerical approximations for Heston-Hull-White type models

We study a hybrid tree-finite difference method which permits to obtain efficient and accurate European and American option prices in the Heston Hull-White and Heston Hull-White2d models. Moreover, as a by-product, we provide a new simulation scheme to be used for Monte Carlo evaluations. Numerical results show the reliability and the efficiency of the proposed methods
Anderson localization transitions in disordered non-Hermitian systems with exceptional points

The critical exponents of continuous phase transitions of a Hermitian system depend on and only on its dimensionality and symmetries. This is the celebrated notion of the universality of continuous phase transitions. Here we report the superuniversality notion of Anderson localization transitions in non-Hermitian two-dimensional (2D) systems with exceptional points in which the critical exponents do not depend on the symmetries. The Anderson localization transitions are numerically studied by using the finite-size scaling analysis of the participation ratios. At the exceptional points of either second-order or fourth-order, two non-Hermitian systems with different symmetries have the same critical exponent $\nu\simeq 2$ of correlation lengths. This value differs from all known 2D disordered Hermitian and non-Hermitian systems. In the symmetry-preserved and symmetry-broken phases, the non-Hermitian models with time-reversal symmetry and without spin-rotational symmetry (without time-reversal and spin-rotational symmetries) are in the same universality class of 2D Hermitian electron systems of Gaussian symplectic (unitary) ensemble, where $\nu\simeq 2.7$ ($\nu\simeq 2.3$). The universality of the transition is further confirmed by showing that the critical exponent $\nu$ does not depend on the form of disorders and boundary conditions. Our results suggest that non-Hermitian systems of different symmetries around their exceptional points form a superuniversality class.
iFlipper: Label Flipping for Individual Fairness

As machine learning becomes prevalent, mitigating any unfairness present in the training data becomes critical. Among the various notions of fairness, this paper focuses on the well-known individual fairness, which states that similar individuals should be treated similarly. While individual fairness can be improved when training a model (in-processing), we contend that fixing the data before model training (pre-processing) is a more fundamental solution. In particular, we show that label flipping is an effective pre-processing technique for improving individual fairness. Our system iFlipper solves the optimization problem of minimally flipping labels given a limit to the individual fairness violations, where a violation occurs when two similar examples in the training data have different labels. We first prove that the problem is NP-hard. We then propose an approximate linear programming algorithm and provide theoretical guarantees on how close its result is to the optimal solution in terms of the number of label flips. We also propose techniques for making the linear programming solution more optimal without exceeding the violations limit. Experiments on real datasets show that iFlipper significantly outperforms other pre-processing baselines in terms of individual fairness and accuracy on unseen test sets. In addition, iFlipper can be combined with in-processing techniques for even better results.
AutoUpdate: Automatically Recommend Code Updates for Android Apps

Android has become the predominant smartphone operating system, with a rapidly evolving ecosystem that requires app developers to frequently update their apps to maintain quality, security, and compatibility. While deep learning has made significant strides in various software engineering tasks, including automated code updates, existing methods are not specifically tailored for Android apps, and the potential of pre-trained Language Models of Code (CodeLMs) for updating Android app code remains unexplored. In this paper, we present the first comprehensive evaluation of state-of-the-art CodeLMs, including CodeT5, CodeBERT, CodeGPT, and UniXcoder, for recommending code updates in Android applications. To facilitate this evaluation, we curate a unique dataset of paired updated methods from 3,195 Android apps published on Google Play and hosted on GitHub between 2008 and 2022. Our findings demonstrate that pre-trained CodeLMs outperform traditional approaches, achieving a higher accuracy ranging from 190% to 385% under a realistic time-wise evaluation scenario. Among the CodeLMs, CodeT5 consistently exhibits superior performance across most code update types. Furthermore, we examine the impact of update types, evaluation scenarios, method size, and update size on the performance of CodeLMs, revealing areas for future research to improve temporal adaptability and generalization capabilities.
Enhancements in cross-temporal forecast reconciliation, with an application to solar irradiance forecasts

In recent works by Yang et al. (2017a,b), and Yagli et al. (2019), geographical, temporal, and sequential deterministic reconciliation of hierarchical photovoltaic (PV) power generation have been considered for a simulated PV dataset in California. In the ﬁrst two cases, the reconciliations are carried out in spatial and temporal domains separately. To further improve forecasting accuracy, in the third case these two reconciliation approaches are sequentially applied. During the replication of the forecasting experiment, some issues emerged about non-negativity and coherence (in space and/or in time) of the sequentially reconciled forecasts. Furthermore, while the accuracy improvement of the considered approaches over the benchmark persistence forecasts is clearly visible at any data granularity, we argue that an even better performance may be obtained by a thorough exploitation of cross-temporal hierarchies. To this end, in this paper the cross-temporal point forecast reconciliation approach is applied to generate non-negative, fully coherent (both in space and time) forecasts. In particular, (i) some useful relationships between two-step, iterative and simultaneous cross-temporal reconciliation procedures are for the ﬁrst time established, (ii) non-negativity issues of the ﬁnal reconciled forecasts are discussed and correctly dealt with in a simple and eﬀective way, and (iii) the most recent cross-temporal reconciliation approaches proposed in literature are adopted. The normalised Root Mean Square Error is used to measure forecasting accuracy, and a statistical multiple comparison procedure is performed to rank the approaches. Besides assuring full coherence, and non-negativity of the reconciled forecasts, the results show that for the considered dataset, cross-temporal forecast reconciliation signiﬁcantly improves on the sequential procedures proposed by Yagli et al. (2019), at any cross-sectional level of the hierarchy and for any temporal granularity.
Design and Analysis of Polar Codes Based on Piecewise Gaussian Approximation

In this article, we propose the construction of polar codes based on piecewise Gaussian approximation (PGA) techniques. The PGA is first optimized and then compared to the Gaussian approximation (GA) construction method, showing performance gains for medium blocks and high precision for long blocks, in scenarios with successive cancellation (SC) decoding and additive white gaussian noise (AWGN) channel. Based on the PGA, we develop two approximations based on multi-segmented polynomials that are easy to implement. We present the Approximate PGA (APGA) that is optimized for medium blocks and provides a performance improvement without increasing complexity. Furthermore, we develop the simplified PGA (SPGA) as an alternative to the GA, which is optimized for long blocks and achieves high construction accuracy. Simulation results show that the APGA and SPGA construction methods outperform existing GA and competing approaches for medium and long block codes with notable performance improvement.
Literature Review of various Fuzzy Rule based Systems

: Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent the human understandable knowledge. They have been applied to various applications and areas throughout the literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extentions of FRBSs. In this paper, we present an overview and literature review for various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), Hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which uses cluster centroids as fuzzy rule, during the years 2010-2021. GFS uses genetic/evolutionary approaches to improve the learning ability of FRBSs, HFS solve the curse of dimensionality for FRBSs, NFS improves approximation ability of FRBSs using neural networks and dynamic systems for streaming data is considered in eFS. FRBSs are seen as good solutions for big data and imbalanced data, in the recent years the interpretability in FRBSs has gained popularity due to high dimensional and big data and rules are initialized with cluster centroids to limit the number of rules in FRBSs. This paper also highlights important contributions, publication statistics and current trends in the ﬁeld. The paper also addresses several open research areas which need further attention from the FRBSs research community.