Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting Graph Neural Networks for Link Prediction

Graph neural networks (GNNs) have achieved great success in recent years. Three most common applications include node classification, link prediction, and graph classification. While there is rich literature on node classification and graph classification, GNNs for link prediction is relatively less studied and less understood. Two representative classes of methods exist: GAE and SEAL. GAE (Graph Autoencoder) first uses a GNN to learn node embeddings for all nodes, and then aggregates the embeddings of the source and target nodes as their link representation. SEAL extracts a subgraph around the source and target nodes, labels the nodes in the subgraph, and then uses a GNN to learn a link representation from the labeled subgraph. In this paper, we thoroughly discuss the differences between these two classes of methods, and conclude that simply aggregating node embeddings does not lead to effective link representations, while learning from properly labeled subgraphs around links provides highly expressive and generalizable link representations. Experiments on the recent large-scale OGB link prediction datasets show that SEAL has up to 195% performance gains over GAE methods, achieving new state-of-the-art results on 3 out of 4 datasets.
A Combinatorial Cut-Based Algorithm for Solving Laplacian Linear Systems

Over the last two decades, a significant line of work in theoretical algorithms has been progress in solving linear systems of the form $\mathbf{L}\mathbf{p} = \mathbf{b}$, where $\mathbf{L}$ is the Laplacian matrix of a weighted graph with weights $w(i,j)>0$ on the edges. The solution $\mathbf{p}$ of the linear system can be interpreted as the potentials of an electrical flow. Kelner, Orrechia, Sidford, and Zhu \cite{KOSZ13} give a combinatorial, near-linear time algorithm that maintains the Kirchoff Current Law, and gradually enforces the Kirchoff Potential Law. Here we consider a dual version of the algorithm that maintains the Kirchoff Potential Law, and gradually enforces the Kirchoff Current Law. We prove that this dual algorithm also runs in a near-linear number of iterations. Each iteration requires updating all potentials on one side of a fundamental cut of a spanning tree by a fixed amount. If this update step can be performed in polylogarithmic time, we can also obtain a near-linear time algorithm to solve $\mathbf{L}\mathbf{p} = \mathbf{b}$. However, if we abstract this update step as a natural data structure problem, we show that we can use the data structure to solve a problem that has been conjectured to be difficult for dynamic algorithms, the online vector-matrix-vector problem \cite{HKNS15}. The conjecture implies that the data structure does not have an $O(n^{1-\epsilon})$ time algorithm for any $\epsilon > 0$. Thus our dual algorithm cannot be near-linear time algorithm for solving $\mathbf{L}\mathbf{p} = \mathbf{b}$ unless we are able to take advantage of the structure of the particular update steps that our algorithm uses.
Interference Reduction in Virtual Cell Optimization

Virtual cell optimization clusters cells into neighborhoods and performs optimized resource allocation over each neighborhood. In prior works we proposed resource allocation schemes to mitigate the interference caused by transmissions in the same virtual cell. This work aims at mitigating both the interference caused by the transmissions of users in the same virtual cell and the interference between transmissions in different virtual cells. We propose a resource allocation technique that reduces the number of users that cannot achieve their constant guaranteed bit rate, i.e., the "unsatisfied users", in an uplink virtual cell system with cooperative decoding. The proposed scheme requires only the knowledge of the number of users each base station serves and relies on creating the interference graph between base stations at the edges of virtual cells. Allocation of frequency bands to users is based on the number of users each base station would serve in a non cooperative setup. We evaluate the performance of our scheme for a mmWave system. Our numerical results show that our scheme decreases the number of users in the system whose rate falls below the guaranteed rate, set to 128kbps, 256kbps or 512kbps, when compared with our previously proposed optimization methods.
A parallel tool for wavelet-adaptive simulations of 3D flows around flapping insects.

We present a wavelet-based adaptive method for computing 3D flows in complex, time-dependent geometries, implemented on massively parallel computers. While our focus is on simulations of flapping insects, it can be used for other flow problems as well. The incompressible fluid is modeled with an artificial compressibility approach in order to avoid solving elliptical problems. No-slip and in/outflow boundary conditions are imposed using volume penalization. The governing equations are discretized on a locally uniform Cartesian grid with centered finite differences, and integrated in time with a Runge--Kutta scheme, both of 4th order. The domain is partitioned into cubic blocks with equidistant grids with different resolution and, for each block, biorthogonal interpolating wavelets are used as refinement indicators and prediction operators. Thresholding the wavelet coefficients allows to generate dynamically evolving grids, and an adaption strategy tracks the solution in both space and scale. Blocks are distributed among MPI processes and the global topology of the grid is encoded using a tree-like data structure. Analyzing the different physical and numerical parameters allows balancing their individual error contributions and thus ensures optimal convergence while minimizing computational effort. Different validation tests score accuracy and performance of our new open source code, WABBIT (Wavelet Adaptive Block-Based solver for Interactions with Turbulence), on massively parallel computers using fully adaptive grids. Flow simulations of flapping insects demonstrate its applicability to complex, bio-inspired problems.
Study of COI sequences from endemic New Zealand aphids highlights high mitochondrial DNA diversity in Rhopalosiphina (Hemiptera: Aphididae)

ABSTRACT Focussed searches were made across New Zealand between 2013 and 2016, for endemic aphids from the Schizaphis (Rhopalosiphina) genus, which is currently represented by two putative, undescribed species from the endemic host plants Aciphylla and Dracophyllum. Cytochrome c oxidase I (COI) gene sequences (48 in total) from the Schizaphis were analysed together with those from a broader collection of New Zealand endemic aphids that has been assembled since the year 2000. The bulk of the Schizaphis belonged to two clusters corresponding to the host plant genera. Two aphids from central North Island Dracophyllum represented a much diverged lineage without clear affiliations to other New Zealand Schizaphis. Inter-population variation in the New Zealand Schizaphis was high compared with that seen in international studies of Aphidinae and among populations of other endemic New Zealand Aphidina. Within Schizaphis from Dracophyllum, geography played an apparent role in genetic structuring, with populations from Taranaki (North Island) and especially Mt Lyford (South Island) being divergent from those on the South Island main divide. Two distinct lineages of Schizaphis, which co-occurred at some sites, were found on Aciphylla. Our sequence comparisons, including GMYC analyses, indicated up to five New Zealand Schizaphis lineages, and two newly discovered endemic Aphis species from the host plants Clematis and Hebe.
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The primary focus of the paper is to present in detail a diverse arsenal of methods for doing so. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance.
Symplectic reduction of Yang-Mills theory with boundaries: from superselection sectors to edge modes, and back

I develop a theory of symplectic reduction that applies to bounded regions in Yang-Mills theory and electromagnetism. In this theory superselection sectors for the electric-flux play a central role. Within a given superselection sector, the symplectic structure of the reduced Yang-Mills theory can always be defined without inclusion of new degrees of freedom, but is a priori not unique. I then consider the possibility of defining a symplectic structure on the union of all superselection sectors. This in turn requires including additional boundary degrees of freedom aka "edge modes. " However, unless the new edge modes model a material physical system located at the boundary of the region, I argue that the the phase space extension by edge modes is also inherently ambiguous. In both the superselection and edge mode frameworks, the ambiguity can be understood as a residual gauge-fixing dependence due to the presence of boundaries -- a result that resonates with findings in QED with asymptotic boundaries. To conclude, I will compare and contrast the superselection and edge mode frameworks.
PIINET: A 360-degree Panoramic Image Inpainting Network Using a Cube Map

Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network. Since the cube map format is used, the correlation of the six sides of the cube map should be considered. Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.
Machine Learning (In) Security: A Stream of Problems

Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas. One of these challenges is the concept drift, which increases the existing arms race between attackers and defenders: malicious actors can always create novel threats to overcome the defense solutions, which may not consider them in some approaches. Due to this, it is essential to know how to properly build and evaluate an ML-based security solution. In this paper, we identify, detail, and discuss the main challenges in the correct application of ML techniques to cybersecurity data. We evaluate how concept drift, evolution, delayed labels, and adversarial ML impact the existing solutions. Moreover, we address how issues related to data collection affect the quality of the results presented in the security literature, showing that new strategies are needed to improve current solutions. Finally, we present how existing solutions may fail under certain circumstances, and propose mitigations to them, presenting a novel checklist to help the development of future ML solutions for cybersecurity.
Pushing run-and-tumble particles through a rugged channel

We analyze the case of run-and-tumble particles pushed through a rugged channel both in the continuum and on the lattice. The current characteristic is non-monotone in the external field with the appearance of a current and nontrivial density profile even at zero field for asymmetric obstacles. If an external field is exerted against the direction of that zero-field current, then the resulting current decreases with persistence at small field and increases with persistence at large field. Activity in terms of self-propulsion increases the maximal current and postpones dying. We give an effective theoretical description with wider validity.
Entanglement Induced Barren Plateaus

We argue that an excess in entanglement between the visible and hidden units in a Quantum Neural Network can hinder learning. In particular, we show that quantum neural networks that satisfy a volume-law in the entanglement entropy will give rise to models not suitable for learning with high probability. Using arguments from quantum thermodynamics, we then show that this volume law is typical and that there exists a barren plateau in the optimization landscape due to entanglement. More precisely, we show that for any bounded objective function on the visible layers, the Lipshitz constants of the expectation value of that objective function will scale inversely with the dimension of the hidden-subsystem with high probability. We show how this can cause both gradient descent and gradient-free methods to fail. We note that similar problems can happen with quantum Boltzmann machines, although stronger assumptions on the coupling between the hidden/visible subspaces are necessary. We highlight how pretraining such generative models may provide a way to navigate these barren plateaus.
The impact of mass-transfer physics on the observable properties of field binary black hole populations

We study the impact of mass-transfer physics on the observable properties of binary black hole populations that formed through isolated binary evolution. We used the POSYDON framework to combine detailed MESA binary simulations with the COSMIC population synthesis tool to obtain an accurate estimate of merging binary black hole observables with a specific focus on the spins of the black holes. We investigate the impact of mass-accretion efficiency onto compact objects and common-envelope efficiency on the observed distributions of the effective inspiral spin parameter χeff, chirp mass Mchirp, and binary mass ratio q. We find that low common envelope efficiency translates to tighter orbits following the common envelope and therefore more tidally spun up second-born black holes. However, these systems have short merger timescales and are only marginally detectable by current gravitational-wave detectors as they form and merge at high redshifts (z ∼ 2), outside current detector horizons. Assuming Eddington-limited accretion efficiency and that the first-born black hole is formed with a negligible spin, we find that all non-zero χeff systems in the detectable population can come only from the common envelope channel as the stable mass-transfer channel cannot shrink the orbits enough for efficient tidal spin-up to take place. We find that the local rate density (z ≃ 0.01) for the common envelope channel is in the range of ∼17–113 Gpc−3 yr−1, considering a range of αCE ∈ [0.2, 5.0], while for the stable mass transfer channel the rate density is ∼25 Gpc−3 yr−1. The latter drops by two orders of magnitude if the mass accretion onto the black hole is not Eddington limited because conservative mass transfer does not shrink the orbit as efficiently as non-conservative mass transfer does. Finally, using GWTC-2 events, we constrained the lower bound of branching fraction from other formation channels in the detected population to be ∼0.2. Assuming all remaining events to be formed through either stable mass transfer or common envelope channels, we find moderate to strong evidence in favour of models with inefficient common envelopes.
End-to-end Animal Image Matting

Extracting accurate foreground animals from natural animal images benefits many downstream applications such as film production and augmented reality. However, the various appearance and furry characteristics of animals challenge existing matting methods, which usually require extra user inputs such as trimap or scribbles. To resolve these problems, we study the distinct roles of semantics and details for image matting and decompose the task into two parallel sub-tasks: high-level semantic segmentation and low-level details matting. Specifically, we propose a novel Glance and Focus Matting network (GFM), which employs a shared encoder and two separate decoders to learn both tasks in a collaborative manner for end-to-end animal image matting. Besides, we establish a novel Animal Matting dataset (AM-2k) containing 2,000 high-resolution natural animal images from 20 categories along with manually labeled alpha mattes. Furthermore, we investigate the domain gap issue between composite images and natural images systematically by conducting comprehensive analyses of various discrepancies between foreground and background images. We find that a carefully designed composition route RSSN that aims to reduce the discrepancies can lead to a better model with remarkable generalization ability. Comprehensive empirical studies on AM-2k demonstrate that GFM outperforms state-of-the-art methods and effectively reduces the generalization error.
Adenoma detection rate as a quality indicator for colonoscopy: a descriptive cross-sectional study from a tertiary care hospital in Pakistan

Abstract Background and study aims Adenoma detection rate (ADR) is validated for measuring quality of colonoscopy, however there is lack of colorectal cancer (CRC) screening program in South Asia. The purpose of this study is to analyze and review the polyp detection rate (PDR) and ADR and provide insight into the factors that influence them in Pakistan. Patients and methods This retrospective, cross-sectional study was performed at the Aga Khan University Hospital, Karachi, Pakistan, on patients ≥ 18 years, who underwent colonoscopy between January 1, 2017 and June 30, 2018. Results Of 1985 patients, 59 % were male and 41 % female, with mean age of 47.8 ± 16.2 years. The most common indication for colonoscopy was bleeding-per-rectum (28.0 %) and overall PDR and ADR were 17.9 % and 9.9 %, respectively. There was no significant difference between genders for either PDR (P = 0.378) or ADR (P = 0.574). Significantly higher PDR and ADR were found for patients ≥ 50 years (P < 0.001), as well as for suboptimal bowel preparation [PDR (25.7 %; P = 0.007) and ADR (18.6 %; P = 0.014)]. Interestingly, endoscopists with < 500 colonoscopy-procedural-experience reported a higher PDR (21.6 %; P = 0.020) and ADR (14.4 %; P = 0.049), corresponding to a significantly higher PDR (20.6 %; P = 0.005) and ADR (11.7 %; P = 0.02) for endoscopists in practice for ≤ 10 years. Conclusions We have noticed low PDR and ADR, which require further investigation and research. In addition, we believe there should be a different baseline ADR and PDR as a quality indicator for colonoscopy in our region, where no internationally recommended colonoscopic screening programs have been implemented.
Aerobic Biodegradation of Fuel Oxygenates

Abstract : Methyl tert-Butyl Ether (MTBE) is the most commonly used fuel oxygenate in the world. Its recalcitrant nature as well as its chemical properties have led to widespread groundwater contamination. Questions regarding its toxicity have spurred a search for viable oxygenate alternatives. Since biodegradability is a key indicator of a chemical's environmental impact, this research used three different well-known methods, BOD5, respirometry, and GC analysis, to examine the extent and rates of aerobic biodegradation of MTBE along with tert-butyl alcohol (TBA). The common fuel component toluene was added to some of the samples to determine if the presence of a co-contaminant would effect aerobic microbial degradation of TBA or MTBE. This group of experiments used an acclimatized microbial consortium to enhance degradation of the oxygenates. BOD5 experiments were performed separately from the GC and respirometric analyses. The respirometry used 250ml microcosms containing a mix of microbial seed, BOD buffer, and varying concentrations of the oxygenates or oxygenate/toluene mixtures. The respirometer also maintained the microcosms in aerobic conditions for the duration of each experiment. For GC analysis, samples were drawn from the respirometer microcosms at predetermined intervals and first order degradation rate constants were calculated from established calibration curves. The oxygenates degraded much slower than toluene in all experiments. This degradation characteristic made BOD5 analysis impractical for MTBE or TBA. BOD5 did provide valid results for toluene. The respirometer data was not as good as gas chromatography to provide specific measurements of degradation. To facilitate comparison of degradation across experiments with differing seed, oxygenate degradation was compared to toluene. MTBE was effectively degraded under these experimental conditions and degraded more recalcitrant and only degraded at 1.37% of toluene.
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation

Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks. They train either an encoder-only Transformer mainly for understanding tasks, or an encoder-decoder Transformer specifically for generation tasks, ignoring the correlation between the two tasks and frameworks. In contrast, this paper presents a variable encoder-decoder (VECO) pre-training approach to unify the two mainstreams in both model architectures and pre-training tasks. VECO splits the standard Transformer block into several sub-modules trained with both inner-sequence and cross-sequence masked language modeling, and correspondingly reorganizes certain sub-modules for understanding and generation tasks during inference. Such a workflow not only ensures to train the most streamlined parameters necessary for two kinds of tasks, but also enables them to boost each other via sharing common sub-modules. As a result, VECO delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark covering text classification, sequence labeling, question answering, and sentence retrieval. For generation tasks, VECO also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1$\sim$2 BLEU.