Miracle-dz/UniFAR-FTUs huggingface dataset:
Pure Message Passing Can Estimate Common Neighbor for Link Prediction

Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto} standard in graph representation learning. However, when it comes to link prediction, they often struggle, surpassed by simple heuristics such as Common Neighbor (CN). This discrepancy stems from a fundamental limitation: while MPNNs excel in node-level representation, they stumble with encoding the joint structural features essential to link prediction, like CN. To bridge this gap, we posit that, by harnessing the orthogonality of input vectors, pure message-passing can indeed capture joint structural features. Specifically, we study the proficiency of MPNNs in approximating CN heuristics. Based on our findings, we introduce the Message Passing Link Predictor (MPLP), a novel link prediction model. MPLP taps into quasi-orthogonal vectors to estimate link-level structural features, all while preserving the node-level complexities. Moreover, our approach demonstrates that leveraging message-passing to capture structural features could offset MPNNs' expressiveness limitations at the expense of estimation variance. We conduct experiments on benchmark datasets from various domains, where our method consistently outperforms the baseline methods.
Federated cINN Clustering for Accurate Clustered Federated Learning

Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning and enables efficient crowd intelligence on a large scale. However, a significant challenge arises when coordinating FL with crowd intelligence which diverse client groups possess disparate objectives due to data heterogeneity or distinct tasks. To address this challenge, we propose the Federated cINN Clustering Algorithm (FCCA) to robustly cluster clients into different groups, avoiding mutual interference between clients with data heterogeneity, and thereby enhancing the performance of the global model. Specifically, FCCA utilizes a global encoder to transform each client's private data into multivariate Gaussian distributions. It then employs a generative model to learn encoded latent features through maximum likelihood estimation, which eases optimization and avoids mode collapse. Finally, the central server collects converged local models to approximate similarities between clients and thus partition them into distinct clusters. Extensive experimental results demonstrate FCCA's superiority over other state-of-the-art clustered federated learning algorithms, evaluated on various models and datasets. These results suggest that our approach has substantial potential to enhance the efficiency and accuracy of real-world federated learning tasks.
Using deep learning to predict matched signal-to-noise ratio of gravitational waves

The existing matched filtering method for gravitational wave (GW) search relies on a template bank. The computational efficiency of this method scales with the size of the templates within the bank. Higher-order modes and eccentricity will play an important role when third-generation detectors operate in the future. In this case, traditional GW search methods will hit computational limits. To speed up the computational efficiency of GW search, we propose the utilization of a deep learning (DL) model bank as a substitute for the template bank. This model bank predicts the latent templates embedded in the strain data. Combining an envelope extraction network and an astrophysical origin discrimination network, we realize a novel GW search framework. The framework can predict the GW signal's matched filtering signal-to-noise ratio (SNR). Unlike the end-to-end DL-based GW search method, our statistical SNR holds greater physical interpretability than the $p_{score}$ metric. Moreover, the intermediate results generated by our approach, including the predicted template, offer valuable assistance in subsequent GW data processing tasks such as parameter estimation and source localization. Compared to the traditional matched filtering method, the proposed method can realize real-time analysis. The minor improvements in the future, the proposed method may expand to other scopes of GW search, such as GW emitted by the supernova explosion.
ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via Graph Optimization

Accurately and reliably positioning pedestrians in satellite-denied conditions remains a significant challenge. Pedestrian dead reckoning (PDR) is commonly employed to estimate pedestrian location using a low-cost inertial sensor. However, PDR is susceptible to drift due to sensor noise, incorrect step detection, and inaccurate stride length estimation. This work proposes ReLoc-PDR, a fusion framework combining PDR and visual relocalization using graph optimization. ReLoc-PDR leverages time-correlated visual observations and learned descriptors to achieve robust positioning in visually degraded environments. A graph optimization-based fusion mechanism with the Tukey kernel effectively corrects cumulative errors and mitigates the impact of abnormal visual observations. Real-world experiments demonstrate that our ReLoc-PDR surpasses representative methods in accuracy and robustness, achieving accurate and robust pedestrian positioning results using only a smartphone in challenging environments such as less-textured corridors and dark nighttime scenarios.
Switch and Conquer: Efficient Algorithms By Switching Stochastic Gradient Oracles For Decentralized Saddle Point Problems

We consider a class of non-smooth strongly convex-strongly concave saddle point problems in a decentralized setting without a central server. To solve a consensus formulation of problems in this class, we develop an inexact primal dual hybrid gradient (inexact PDHG) procedure that allows generic gradient computation oracles to update the primal and dual variables. We first investigate the performance of inexact PDHG with stochastic variance reduction gradient (SVRG) oracle. Our numerical study uncovers a significant phenomenon of initial conservative progress of iterates of IPDHG with SVRG oracle. To tackle this, we develop a simple and effective switching idea, where a generalized stochastic gradient (GSG) computation oracle is employed to hasten the iterates' progress to a saddle point solution during the initial phase of updates, followed by a switch to the SVRG oracle at an appropriate juncture. The proposed algorithm is named Decentralized Proximal Switching Stochastic Gradient method with Compression (C-DPSSG), and is proven to converge to an $\epsilon$ -accurate saddle point solution with linear rate. Apart from delivering highly accurate solutions, our study reveals that utilizing the best convergence phases of GSG and SVRG oracles makes C-DPSSG well suited for obtaining solutions of low/medium accuracy faster, useful for certain applications. Numerical experiments on two benchmark machine learning applications show C-DPSSG's competitive performance which validate our theoretical findings. The codes used in the experiments can be found here.
Spectral analysis and phase transitions for long-range interactions in harmonic chains of oscillators

We consider chains of $N$ harmonic oscillators in two dimensions coupled to two Langevin heat reservoirs at different temperatures - a classical model for heat conduction introduced by Lebowitz, Lieb, and Rieder \cite{RLL67}. We extend our previous results \cite{BM20} significantly by providing a full spectral description of the full Fokker-Planck operator allowing also for the presence of a constant external magnetic field for charged oscillators. We then study oscillator chains with additional next-to-nearest-neighbor interactions and find that the spectral gap undergoes a phase transition if the next-to-nearest-neighbour interactions are sufficiently strong and may even cease to exist for oscillator chains of finite length.
Bayesian experimental design for linear elasticity

This work considers Bayesian experimental design for the inverse boundary value problem of linear elasticity in a two-dimensional setting. The aim is to optimize the positions of compactly supported pressure activations on the boundary of the examined body in order to maximize the value of the resulting boundary deformations as data for the inverse problem of reconstructing the Lam\'e parameters inside the object. We resort to a linearized measurement model and adopt the framework of Bayesian experimental design, under the assumption that the prior and measurement noise distributions are mutually independent Gaussians. This enables the use of the standard Bayesian A-optimality criterion for deducing optimal positions for the pressure activations. The (second) derivatives of the boundary measurements with respect to the Lam\'e parameters and the positions of the boundary pressure activations are deduced to allow minimizing the corresponding objective function, i.e., the trace of the covariance matrix of the posterior distribution, by a gradient-based optimization algorithm. Two-dimensional numerical experiments are performed to demonstrate the functionality of our approach.
Minimum entropy of a log-concave variable for fixed variance

We show that for log-concave real random variables with fixed variance the Shannon differential entropy is minimized for an exponential random variable. We apply this result to derive upper bounds on capacities of additive noise channels with log-concave noise. We also improve constants in the reverse entropy power inequalities for log-concave random variables.
Learning Shared Safety Constraints from Multi-task Demonstrations

Regardless of the particular task we want them to perform in an environment, there are often shared safety constraints we want our agents to respect. For example, regardless of whether it is making a sandwich or clearing the table, a kitchen robot should not break a plate. Manually specifying such a constraint can be both time-consuming and error-prone. We show how to learn constraints from expert demonstrations of safe task completion by extending inverse reinforcement learning (IRL) techniques to the space of constraints. Intuitively, we learn constraints that forbid highly rewarding behavior that the expert could have taken but chose not to. Unfortunately, the constraint learning problem is rather ill-posed and typically leads to overly conservative constraints that forbid all behavior that the expert did not take. We counter this by leveraging diverse demonstrations that naturally occur in multi-task settings to learn a tighter set of constraints. We validate our method with simulation experiments on high-dimensional continuous control tasks.
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models

The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension, leading to escalating computational complexity and parameter proliferation, thus posing challenges for modeling dynamical systems with high-dimensional latent states. To surmount this obstacle, we propose to integrate the efficient transformed Gaussian process (ETGP) into the GPSSM, which involves pushing a shared GP through multiple normalizing flows to efficiently model the transition function in high-dimensional latent state space. Additionally, we develop a corresponding variational inference algorithm that surpasses existing methods in terms of parameter count and computational complexity. Experimental results on diverse synthetic and real-world datasets corroborate the efficiency of the proposed method, while also demonstrating its ability to achieve similar inference performance compared to existing methods. Code is available at \url{https://github.com/zhidilin/gpssmProj}.
Image Compression: the Mathematics of JPEG 2000

We briefly review the mathematics in the coding engine of JPEG 2000, a state-of-the-art image compression system. We focus in depth on the transform, entropy coding and bitstream assembler modules. Our goal is to pass the readers a good understanding of the modern scalable image compression technologies without being swarmed by the details.
Challenges of the accelerated implementation of on-line learning in higher education in Paraguay

In Paraguay the accelerated context of change from face-to-face-learning to on-line learning was established by the National Council for Higher Education (Consejo Nacional de Educación Superior, CONES) in a situation which had led most higher education institutions in the world to apply emergency remote teaching (ERT). The fast implementation of this palliative modality generated challenges in terms of development of digital skills, pedagogical practices, educational policies, and investment in infrastructure and in breaching the digital gap. This research seeks to evaluate the impact of on-line learning implementation on Paraguayan higher education as adopted in an emergency context, and to assess its sustainability in Paraguay. Thus, 13 open interviews were made by video calls (n= 13) to professors working in 15 different institutions. The collected data was analyzed qualitatively with Atlas-ti. From the results emerged eight inter-related categories of different ways of influencing the relationships among the three main actors: institutions, students and professors. The main categories which emerged were adaptation and implementation, digital-skills deficiencies, access to infrastructure, self-regulation, skills development, lack of institutional policy, emotional issues, and socioeconomic problems. Some of these issues interact with others and they are of significance to enable, maintain or offer different types of on-line learning education methodologies, highlighting aspects that the Paraguayan educational society needs to strengthen according to its actors.
Towards a pedagogy of discomfort in culturally and linguistically diverse classrooms

This article explores initial thinking about a pedagogy of discomfort for teaching in the context of cultural and linguistic diversity. To foster inclusive and socially just cultures of participation, contemporary classrooms need to attend to the subtle ways that taken-for-granted teaching practices marginalise diverse cultural and linguistic knowledge. I draw from three critical episodes of teaching in culturally and linguistically diverse (CALD) classroom contexts to examine how educators’ feelings of discomforts shape their responses to students’ histories, cultural experiences and linguistic knowledge. Engaging with Bourdieu’s notion of habitus, I illuminate how these emotions are historically and socially shaped. In bringing habitus into conversation with a pedagogy of discomfort, I further reveal how the affective, specifically feelings of discomfort, can contribute to a transformative habitus. Such understandings about affect and teaching practice suggest the need to consider a pedagogy of discomfort for teaching in CALD contexts. To do so, entails acknowledging the challenges and ethical considerations involved in mobilising such a pedagogy in classroom teaching and
The conservation of European bats

Part 1 Natural history of bats, their conservation, problems and solutions - natural history of bats: including social organization, annual behaviour cycle, reproduction and longevity conservation problems and achievements: including population changes, causes of decline and continuing threats, climate, landscape, predation and disease, pollution, vandalism, legislation conservation needs: including education, site protection and management, creation of new roosts, co-ordination of research and advice. Part 2 Country accounts: Denmark Norway Sweden Finland Estonia SSR Latvia SSR Lithuania SSR Poland Czechoslovakia Democratic Republic of Germany Federal Republic of Germany Netherlands Belgium Luxembourg Great Britain Northern Ireland Eire Isle of Man Guernsey Jersey Portugal Gibraltar Spain France Switzerland and Liechtenstein Italy Austria Hungary Yugoslavia Romania Bulgaria Greece Albania Malta. Part 3: species accounts. (Part contents)
Congestion control and resource allocation

The HighSpeed TCP proposal, now an experimental RFC, makes TCP more aggressive only when it is clearly operating in a very high bandwidth-delay product environment and not competing with a lot of other traffic. In essence, when the congestion window gets very large, HighSpeed TCP starts to increase CongestionWindow by a larger amount that standard TCP. In the normal environment where CongestionWindow is relatively small (about 40 × MSS), HighSpeed TCP is indistinguishable from standard TCP. Many other proposals have been made in this vein, some of which are listed in the Further Reading section. Notably, the default TCP behavior in the Linux operating system is now based on a TCP variant called CUBIC, which also expands the congestion window aggressively in high bandwidth-delay product regimes, while maintaining compatibility with older TCP variants in more bandwidth-constrained environments. The Quick-Start proposal, which changes the start-up behavior of TCP, was mentioned above. Since it can enable a TCP connection to ramp up its sending rate more quickly, its effect on TCP performance is most noticeable when connections are short, or when an application periodically stops sending data and TCP would otherwise return to slow start. Yet another proposal, FAST TCP, takes an approach similar to TCP Vegas described in the next section. The basic idea is to anticipate the onset of congestion and avoid it, thereby not taking the performance hit associated with decreasing the congestion window. Several proposals that involve more dramatic changes to TCP or even replace it with a new protocol have been developed. These have considerable potential to fill the pipe quickly and fairly in high bandwidth-delay environments, but they also face higher deployment challenges. We refer the reader to the end of this chapter for references to ongoing work in this area. It is important to understand that TCP's strategy is to control congestion once it happens, as opposed to trying to avoid congestion in the first place. In fact, TCP repeatedly increases the load it imposes on the network in an effort to find the point at which congestion occurs, and then it backs off from this point. Said another way, TCP needs to create losses to find the available bandwidth of the connection. An appealing alternative, but one that has not yet been widely adopted, is to predict when congestion is about to happen and then to reduce the rate at which hosts send data …
Influence of Consolidation Shear Stresses and Relative Density on Threshold Strain and Pore Pressure during Cyclic Straining of Saturated Sand.

Abstract : A cyclic strain approach to pore pressure buildup and liquefaction during earthquakes of saturated choesionless soils has been proposed and developed. One of the key findings has been the existence, for every potentially liquefiable site and sand layer, of a 'threshold ground surface acceleration,' a sub t, below which there is no pore pressure buildup. The value of a sub t is mainly a function of two soil parameters: the 'threshold shear strain' of the sand, gamma sub t, and its maximum shear modulus, G sub max. The existence of a sub t has been detected by several researchers in a number of laboratory studies. Also, the existence of gamma sub t and its range of values for quartz sands has been predicted from simple particulate mechanics considerations. The work presented herein was performed at RPI and constitutes an extension of the experimental study by Ladd and focuses on the effects of anisotropic consolidation and of very low relative densities on gamma sub t and on the rate of pore pressure buildup at cyclic strains slightly above gamma sub t. Two sands, Montery No. 0 sand and Banding Sand, were tested and the results are reported herein. The effect of anisotropic consolidation is important because NC sands in the field are typically anisotropically consolidated, and also because any attempt to extend the cyclic strain approach to earth structures and slopes must necessarily consider the effect of consolidation shear stresses. Testing very loose sand, D sub r = 20 percent, is important because very loose sand deposits in the field are usually the most susceptible to pore pressure buildup and liquefaction during earthquakes.
QUALITY OF SERVICE ANALYSIS OF CLOUD STORAGE ON NEXTCLOUD AND PYDIO

--The development of information technology greatly influences the development of current storage media, such as cloud-based storage (cloud). Nextcloud and Pydio are part of an open-based cloud computing system, where both platforms have become popular in the domain of cloud storage and file collaboration. Nextcloud and Pydio both offer similar features, such as file storage, synchronization, collaborative sharing, encryption, and integration with third-party applications. Measurement of service quality can be seen from the aspects of throughput, delay, jitter, and packet loss. The purpose of this research is to compare upload and download speeds between Nextcloud servers and Pydio servers. The research method used is based on several methodologies to describe how the research flow process is carried out, including literature studies, system requirements analysis, system design, installation and configuration, application testing and analysis of application comparison results. The Nextcloud and Pydio services were each tested for upload and download 5 times with different file types and file sizes. The file types used consist of . doc files (1,377KB), . pdf files (1,256 KB), . exe files (75,647 KB), . jpeg files (85 KB), and . mp4 files (1,301 KB). The test results show that the nextcloud server is superior to the pydio server in terms of uploads and downloads. Overall, Nextcloud is the better choice for most users, as it offers better performance, security, and features. However, Pydio might be a better choice for users who need a lighter and more customizable
Human-AI Cooperation to Tackle Misinformation and Polarisation

A dominant narrative of the past decade is that algorithms contribute to a misinformed and segregated society. Perhaps paradoxically, algorithms are often sought as solutions to such problems. We describe a significant emerging trend away from this techno-solutionist approach that seeks to create and understand a new paradigm: a productive interplay between algorithms and people. Two relevant test cases are being explored in our region: the first addresses a new framework to tackle misinformation by assisting fact-checkers with computational methods; the second seeks new models to understand how search engines deliver personalised search results when little or no algorithmic personalisation exists.
Anthropogenically derived changes in the sedimentary flux of magnesium, chromium, nickel, copper, zinc, mercury, lead, and phosphorus in Lough Neagh, Northern Ireland

The concentration-depth behavior of Mg, Cr, Ni, Cu, Zn, Hg, Pb, and P in three sediment cores from a central site in Lough Neagh, Northern Ireland, was examined for changes in the sedimentary flux of these elements. Two main periods of change were found. A change in the catchment erosion-leaching regime in the 17th century, caused by widespread and comprehensive woodland clearance, produced increased sedimentary Mg, Cu, and Pb concentrations. A second and larger change occurred after about 1880 A.D. Cr, Cu, Zn, Hg, Pb, and P, and , to a lesser extent, Ni concentrations increase toward the sediment surface. Differing P and trace-metal profiles, a comparison of the estimated anthropogenic sedimentary flux with background atmospheric contributions, and a general comparison with other situations all suggest that background atmospheric sources make a substantial contribution to the more recent Cu, Zn, Hg, and Pb sedimentary contamination. The trace-metal contamination of Lough Neagh is part of a global pattern.