Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of fault mitigation techniques for multi-core architectures

Fault tolerance in multi-core architecture has attracted attention of research community for the past 20 years. Rapid improvements in the CMOS technology resulted in exponential growth of transistor density. It resulted in increased challenges for designing resilient multi-core architecture at the same pace. The article presents a survey of fault tolerant methods like fault detection, recovery, re-configurability and repair techniques for multi-core architectures. Salvaging at micro-architectural and architectural level are also discussed. Gamut of fault tolerant approaches discussed in this article have tangible improvements on the reliability of the multi-core architectures. Every concept in the seminal articles is examined with respect to relevant metrics like performance cost, area overhead, fault coverage, level of protection, detection latency and Mean Time To Failure. The existing literature is critically examined. New research directions in the form of new fault tolerant design alternatives for both homogeneous and heterogeneous multi-core architectures are presented. Brief on an analytical approach for fault tolerating model is suggested for Intel and AMD based modern homogeneous multi-core architecture are presented to enhance the understanding of the readers about the architecture with respect to performance degradation, memory access time and execution time.
Binary Diffing as a Network Alignment Problem via Belief Propagation

In this paper, we address the problem of finding a correspondence, or matching, between the functions of two programs in binary form, which is one of the most common task in binary diffing. We introduce a new formulation of this problem as a particular instance of a graph edit problem over the call graphs of the programs. In this formulation, the quality of a mapping is evaluated simultaneously with respect to both function content and call graph similarities. We show that this formulation is equivalent to a network alignment problem. We propose a solving strategy for this problem based on max-product belief propagation. Finally, we implement a prototype of our method, called QBinDiff, and propose an extensive evaluation which shows that our approach outperforms state of the art diffing tools.
Simplifying Software Defect Prediction (via the “early bird” Heuristic)

Before researchers rush to reason across all available data or try complex meth-ods, perhaps it is prudent to ﬁrst check for simpler alternatives. Speciﬁcally, if the historical data has the most information in some small region, then perhaps a model learned from that region would sufﬁce for the rest of the project. To support this claim, we offer a case study with 240 GitHub projects, where we ﬁnd that the information in those projects “clumped” towards the earliest parts of the project. A defect prediction model learned from just the ﬁrst 150 commits works as well, or better than state-of-the-art alternatives. Using just this early life cycle data, we can build models very quickly, very early in the software project life cycle. Moreover, using this method, we have shown that a simple model (with just two features) generalizes to hundreds of software projects. Based on this experience, we doubt that prior work on generalizing software engineering defect prediction models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are online at https://github.com/snaraya7/simplifying-software-analytics
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution

Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable representation of degradations. To address it, this article proposes a transitional learning method for blind SR using an end-to-end network without any additional iterations in inference, and explores an effective representation for unknown degradation. To begin with, we analyze and demonstrate the transitionality of degradations as interpretable prior information to indirectly infer the unknown degradation model, including the widely used additive and convolutive degradations. We then propose a novel Transitional Learning method for blind Super-Resolution (TLSR), by adaptively inferring a transitional transformation function to solve the unknown degradations without any iterative operations in inference. Specifically, the end-to-end TLSR network consists of a degree of transitionality (DoT) estimation network, a homogeneous feature extraction network, and a transitional learning module. Quantitative and qualitative evaluations on blind SR tasks demonstrate that the proposed TLSR achieves superior performances and costs fewer complexities against the state-of-the-art blind SR methods. The code is available at github.com/YuanfeiHuang/TLSR.
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators

Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this work, we propose a novel privacy-preserving data Generative model based on the PATE framework (G-PATE), aiming to train a scalable differentially private data generator that preserves high generated data utility. Our approach leverages generative adversarial nets to generate data, combined with private aggregation among different discriminators to ensure strong privacy guarantees. Compared to existing approaches, G-PATE significantly improves the use of privacy budgets. In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator. In addition, with random projection and gradient discretization, the proposed gradient aggregation mechanism is able to effectively deal with high-dimensional gradient vectors. Theoretically, we prove that G-PATE ensures differential privacy for the data generator. Empirically, we demonstrate the superiority of G-PATE over prior work through extensive experiments. We show that G-PATE is the first work being able to generate high-dimensional image data with high data utility under limited privacy budgets ($\epsilon \le 1$). Our code is available at https://github.com/AI-secure/G-PATE.
C1-VEM for some variants of the Cahn-Hilliard equation: a numerical exploration

<p style='text-indent:20px;'>We consider the <inline-formula><tex-math id="M2">\begin{document}$ C^1 $\end{document}</tex-math></inline-formula>-Virtual Element Method (VEM) for the conforming numerical approximation of some variants of the Cahn-Hilliard equation on polygonal meshes. In particular, we focus on the discretization of the advective Cahn-Hilliard problem and the Cahn-Hilliard inpainting problem. We present the numerical approximation and several numerical results to assess the efficacy of the proposed methodology. </p>
TextRGNN: Residual Graph Neural Networks for Text Classification

Recently, text classification model based on graph neural network (GNN) has attracted more and more attention. Most of these models adopt a similar network paradigm, that is, using pre-training node embedding initialization and two-layer graph convolution. In this work, we propose TextRGNN, an improved GNN structure that introduces residual connection to deepen the convolution network depth. Our structure can obtain a wider node receptive field and effectively suppress the over-smoothing of node features. In addition, we integrate the probabilistic language model into the initialization of graph node embedding, so that the non-graph semantic information of can be better extracted. The experimental results show that our model is general and efficient. It can significantly improve the classification accuracy whether in corpus level or text level, and achieve SOTA performance on a wide range of text classification datasets.
Numerical analysis of the self-energy in covariant loop quantum gravity

We study numerically the first order radiative corrections to the self-energy, in covariant loop quantum gravity. We employ the recently developed 'sl2cfoam-next' spinfoam amplitudes library, and some original numerical methods. We analyze the scaling of the divergence with the infrared cutoff, for which previous analytical estimates provided widely different lower and upper bounds. Our findings suggest that the divergence is approximately linear in the cutoff. We also investigate the role of the Barbero-Immirzi parameter in the asymptotic behavior, the dependence of the scaling on some boundary data and the expectation values of boundary operators.
Channel Estimation for Hybrid Massive MIMO Systems With Adaptive-Resolution ADCs

Achieving high channel estimation accuracy and reducing hardware cost as well as power dissipation constitute substantial challenges in the design of massive multiple-input multiple-output (MIMO) systems. To resolve these difficulties, sophisticated pilot designs have been conceived for the family of energy-efficient hybrid analog-digital (HAD) beamforming architecture relying on adaptive-resolution analog-to-digital converters (RADCs). In this paper, we jointly optimize the pilot sequences, the number of RADC quantization bits and the hybrid receiver combiner in the uplink of multiuser massive MIMO systems. We solve the associated mean square error (MSE) minimization problem of channel estimation in the context of correlated Rayleigh fading channels subject to practical constraints. The associated mixed-integer problem is quite challenging due to the nonconvex nature of the objective function and of the constraints. By relying on advanced fractional programming (FP) techniques, we first recast the original problem into a more tractable yet equivalent form, which allows the decoupling of the fractional objective function. We then conceive a pair of novel algorithms for solving the resultant problems for codebook-based and codebook-free pilot schemes, respectively. To reduce the design complexity, we also propose a simplified algorithm for the codebook-based pilot scheme. Our simulation results confirm the superiority of the proposed algorithms over the relevant state-of-the-art benchmark schemes.
A Unified DRO View of Multi-class Loss Functions with top-N Consistency

Multi-class classiﬁcation is one of the most common tasks in machine learning applications, where data is labeled by one of many class labels. Many loss functions have been proposed for multi-class classiﬁcation including two well-known ones, namely the cross-entropy (CE) loss and the crammer-singer (CS) loss (aka. the SVM loss). While CS loss has been used widely for traditional machine learning tasks, CE loss is usually a default choice for multi-class deep learning tasks. There are also top-k variants of CS loss and CE loss that are proposed to promote the learning of a classiﬁer for achieving better top-k accuracy. Nevertheless, it still remains unclear the relationship between these different losses, which hinders our understanding of their expectations in different scenarios. In this paper, we present a uniﬁed view of the CS/CE losses and their smoothed top-k variants by proposing a new family of loss functions, which are arguably better than the CS/CE losses when the given label information is incomplete and noisy. The new family of smooth loss functions named label-distributionally robust (LDR) loss is deﬁned by leveraging the distributionally robust optimization (DRO) framework to model the uncertainty in the given label information, where the uncertainty over true class labels is captured by using distributional weights for each label regularized by a function.
Zero cycles on Prym varieties

In this text we prove that if an abelian variety A admits an embedding into the Jacobian of a smooth projective curve C, and if we consider ΘA to be the divisor ΘC ∩ A, where ΘC denotes the theta divisor of J(C), then the embedding of ΘA into A induces an injective push-forward homomorphism (under certain conditions) at the level of Chow groups. We show that this is the case for every Prym varietiy arising from an unramified double cover of smooth projective curves. As a consequence that there does not exist a universal codimension two cycle on the product of a very general cubic threefold and the Prym variety associated to it. Hence we conclude that a very general cubic threefold is stably irrational.
Data-Driven Optimal Control of Bilinear Systems

This letter develops a method to learn optimal controls from data for bilinear systems without a priori knowledge of the dynamics. Given an unknown bilinear system, we characterize when the available data is sufficiently informative to solve the optimal control problem. This characterization leads us to propose an online control experiment design procedure that guarantees that any input/state trajectory can be represented as a linear combination of collected input/state data matrices. Leveraging this representation, we transform the original optimal control problem into an equivalent data-based optimization problem with bilinear constraints. We solve the latter by iteratively employing a convex-concave procedure to find a locally optimal control sequence. Simulations show that the performance of the proposed data-based approach is comparable with model-based methods.
Approach to $K\to \ell\nu_\ell \ell'^+ \ell'^-$ decay width on lattice

Lattice QCD plays an increasing role in reducing theoretical uncertainties in kaon decays. Here we focus on kaon decay channels K → lνll′+l′−, which are closely related to radiative kaon decays since the lepton pairs l′+l′− come from virtual photon emission: K → lνlγ∗ → lνll′+l′−. Compared with real photon emission, these channels involve more complicated form factors due to the off-shell photon with possible large momentum transfer, which causes a challenge to latticeQCD studies. In this work, we introduce a lattice calculation procedure for their decay width, which can avoid parameterization of form factors. The systematic errors in our method are found to be controllable. Infinite volume reconstruction method is adopted to remove the temporal truncation effects and reduce finite volume effects from kaon intermediate state. This approach sets up a bridge between lattice QCD calculation and experimental measurement of decay width.
Characterization of rectifiability via Lusin type approximation

We prove that a Radon measure $\mu$ on $\mathbb{R}^n$ can be written as $\mu=\sum_{i=0}^n\mu_i$, where each of the $\mu_i$ is an $i$-dimensional rectifiable measure if and only if for every Lipschitz function $f:\mathbb{R}^n\to\mathbb{R}$ and every $\varepsilon>0$ there exists a function $g$ of class $C^1$ such that $\mu(\{x\in\mathbb{R}^n:g(x)\neq f(x)\})<\varepsilon$.
Materialized Knowledge Bases from Commonsense Transformers

Starting from the COMET methodology by Bosselut et al. (2019), generating commonsense knowledge directly from pre-trained language models has recently received significant attention. Surprisingly, up to now no materialized resource of commonsense knowledge generated this way is publicly available. This paper fills this gap, and uses the materialized resources to perform a detailed analysis of the potential of this approach in terms of precision and recall. Furthermore, we identify common problem cases, and outline use cases enabled by materialized resources. We posit that the availability of these resources is important for the advancement of the field, as it enables an off-the-shelf-use of the resulting knowledge, as well as further analyses on its strengths and weaknesses.
Effects of detergent use on water quality in Kathmandu, Nepal

The Guheshwori wastewater treatment plant in Kathmandu, Nepal, experiences foaming problems. Engineers from the facility attribute the foaming to synthetic detergent use, suspecting that the anionic surfactants used in local laundry detergents exhibit poor biodegradation properties. In particular, the use of the highly branched form of alkylbenzene sulfonate (ABS) is assumed. Eighteen synthetic laundry detergents were collected from around Kathmandu, representing 10 companies and 3 countries of origin. The detergent analysis followed the methylene blue active substance (MBAS) procedure. The representative total anionic surfactant concentration is estimated to be 6.6 weight percent. After two weeks of degradation, the average remaining surfactant concentration is 0.5 weight percent. The author suggests that this level is enough to play some, but not a significant role in the foaming at the Guheshwori WWTP. Potential major contributors to foaming at the Guheshwori facility are industrial detergents, especially those used by the textile and carpet industry and filamentous bacteria. The detergent samples were also examined for orthophosphates, with a representative concentration of 402 mg/kg detergent. This value was compared to monitoring data from the Bagmati River, as part of the South Asia Trans-boundary Water Quality Monitoring Program (SATWQM). The author concludes from the data that phosphates from synthetic detergents do not significantly contribute to eutrophication of the Bagmati River. Thesis Supervisor: Susan Murcott Title: Lecturer, Department of Civil and Environmental Engineering
Bayesian inference for fluid dynamics: A case study for the stochastic rotating shallow water model

In this work, we use a tempering-based adaptive particle filter to infer from a partially observed stochastic rotating shallow water (SRSW) model which has been derived using the Stochastic Advection by Lie Transport (SALT) approach. The methodology we present here validates the applicability of tempering and sample regeneration using a Metropolis-Hastings procedure to high-dimensional models appearing in geophysical fluid dynamics problems. The methodology is tested on the Lorenz 63 model with both full and partial observations. We then study the efficiency of the particle filter for the SRSW model in a configuration simulating the atmospheric Jetstream.
Deep Graph Clustering with Random-walk based Scalable Learning

Interactions between (social) entities can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits on accuracy as GCNs have been shown to suffer from over-smoothing issues. Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability. This paper addresses this open problem by relating the Laplacian smoothing to the Generalized PageRank, and applying a random-walk based algorithm as a scalable graph filter. This forms the basis for our scalable deep clustering algorithm, RwSL. Using 6 real-world datasets and 6 clustering metrics, we show that RwSL achieved improved results over several recent baselines. Most notably, by demonstrating execution of RwSL on a graph with 1.8 billion edges using only a single GPU. We show that RwSL can continue to scale, unlike other existing deep clustering frameworks.
Systematic search for extreme and singular behaviour in some fundamental models of fluid mechanics

This review article offers a survey of the research program focused on a systematic computational search for extreme and potentially singular behaviour in hydrodynamic models motivated by open questions concerning the possibility of a finite-time blow-up in the solutions of the Navier–Stokes system. Inspired by the seminal work of Lu & Doering (2008 Ind. Univ. Math. 57, 2693–2727), we sought such extreme behaviour by solving PDE optimization problems with objective functionals chosen based on certain conditional regularity results and a priori estimates available for different models. No evidence for singularity formation was found in extreme Navier–Stokes flows constructed in this manner in three dimensions. We also discuss the results obtained for one-dimensional Burgers and two-dimensional Navier–Stokes systems, and while singularities are ruled out in these flows, the results presented provide interesting insights about sharpness of different energy-type estimates known for these systems. Connections to other bounding techniques are also briefly discussed. This article is part of the theme issue ‘Mathematical problems in physical fluid dynamics (part 1)’.
The lightcurve intrinsic variability in 47 Kepler contact binary stars

This work studies the significance of the lightcurve intrinsic variability in the numerical modeling of contact binaries. Using synthetic light curves we are showing that the starspot-based intrinsic variability increases the apparent mass ratio by $\Delta q=5$%. For systems with orbital period $P>0.3 d$ the effect of intrinsic variability averaged over long time cancels each other out with the Kepler Mission-like phase smearing. Further, we analyse 47 totally eclipsing Kepler Mission contact binaries. We found a sharp cutoff of the intrinsic variability at P = 0.45 d. With the light curve numerical modeling and observational relations we derive physical parameters of the 47 systems. At least 53% of binaries have a possible third companion. 21 binaries show the O`Connell effect in the averaged phase curve. 19 of them have a primary maximum lower than the secondary, suggesting a stationary dark region on the trailing side. Using the P = 0.45 d cutoff we propose a new approach on the Period-Color relation. The only parameter correlating with the magnitude of the intrinsic variability is the apparent effective temperature ratio. We conclude that instead of describing the system parameters, the A/W-subtype division should be applicable only to the lightcurves, as a tentative phenomenon.