Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlled Update of Software Components using Concurrent Exection of Patched and Unpatched Versions

Software patching is a common method of removing vulnerabilities in software components to make IT systems more secure. However, there are many cases where software patching is not possible due to the critical nature of the application, especially when the vendor providing the application guarantees correct operation only in a specific configuration. In this paper, we propose a method to solve this problem. The idea is to run unpatched and patched application instances concurrently, with the unpatched one having complete control and the output of the patched one being used only for comparison, to watch for differences that are consequences of introduced bugs. To test this idea, we developed a system that allows us to run web applications in parallel and tested three web applications. The experiments have shown that the idea is promising for web applications from the technical side. Furthermore, we discuss the potential limitations of this system and the idea in general, how long two instances should run in order to be able to claim with some probability that the patched version has not introduced any new bugs, other potential use cases of the proposed system where two application instances run concurrently, and finally the potential uses of this system with different types of applications, such as SCADA systems.
Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations

As the computational footprint of modern NLP systems grows, it becomes increasingly important to arrive at more efficient models. We show that by employing graph convolutional document representation, we can arrive at a question answering system that performs comparably to, and in some cases exceeds the SOTA solutions, while using less than 5\% of their resources in terms of trainable parameters. As it currently stands, a major issue in applying GCNs to NLP is document representation. In this paper, we show that a GCN enriched document representation greatly improves the results seen in HotPotQA, even when using a trivial topology. Our model (gQA), performs admirably when compared to the current SOTA, and requires little to no preprocessing. In Shao et al. 2020, the authors suggest that graph networks are not necessary for good performance in multi-hop QA. In this paper, we suggest that large language models are not necessary for good performance by showing a na\"{i}ve implementation of a GCN performs comparably to SoTA models based on pretrained language models.
Discrete Cosine Transform as Universal Sentence Encoder

Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, sentences, or paragraphs. These kinds of representations are ideal for training a classifier for an end task such as sentiment analysis, question answering and text classification. Different models have been proposed to efficiently generate general purpose sentence representations to be used in pretraining protocols. While averaging is the most commonly used efficient sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an alternative that captures the underlying syntactic characteristics of a given text without compromising practical efficiency compared to averaging. However, as with most other sentence encoders, the DCT sentence encoder was only evaluated in English. To this end, we utilize DCT encoder to generate universal sentence representation for different languages such as German, French, Spanish and Russian. The experimental results clearly show the superior effectiveness of DCT encoding in which consistent performance improvements are achieved over strong baselines on multiple standardized datasets
Dynamics of three-dimensional turbulence from Navier-Stokes equations

〈(δru)〉, which acts as the coupling constant for scale-toscale interactions. This description can be generalized by introducing “structure functions” of order n, Sn = 〈(δru) 〉, which allow one to probe velocity increments including rare and extreme events, by considering δru(n) = O(S 1/n n ) for large and small n. If Sn ∝ r ζn , the theory for the exponents ζn in the entire allowable interval −1 < n < ∞ is one of the long-standing challenges in turbulence (one takes absolute values of δru for negative n), usually attacked by various qualitative cascade models. We accomplish two major tasks. First, we show that the turbulent motion at large scales obeys Gaussian statistics in the interval 0 < Rλ ≤ 8.8, where Rλ is the microscale Reynolds number, and that the Gaussian flow breaks down to yield place to anomalous scaling at the universal Reynolds number bounding the inequality above. In the inertial range of turbulence that emerges following the breakdown, the effective Reynolds number based on the turbulent viscosity, R λ, assumes this same constant value of about 9. This scenario works also for the emergence of turbulence from an initially non-turbulent state. Second, we derive expressions for the anomalous scaling exponents of structure functions and moments of spatial derivatives, by analyzing the Navier-Stokes equations in the form developed by Hopf. We present a novel procedure to close the Hopf equation, resulting in expressions for ζn in the entire range of allowable moment-order, n, and demonstrate that accounting for the temporal dynamics changes the scaling from normal to anomalous. For large n, the theory predicts the saturation of ζn with n, leading to two inferences: (a) the smallest length scale ηn = LRe ≪ LRe, where Re is the large-scale Reynolds number, and (b) velocity excursions across even the smallest length scales can sometimes be as large as the large scale velocity itself. Theoretical predictions for each of these aspects are shown to be in quantitative agreement with available experimental and numerical data.
Exploring Discourse Structures for Argument Impact Classification

Discourse relations among arguments reveal logical structures of a debate conversation. However, no prior work has explicitly studied how the sequence of discourse relations influence a claim’s impact. This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We further propose DisCOC to inject and fuse the sentence-level structural discourse information with contextualized features derived from large-scale language models. Experimental results and extensive analysis show that the attention and gate mechanisms that explicitly model contexts and texts can indeed help the argument impact classification task defined by Durmus et al. (2019), and discourse structures among the context path of the claim to be classified can further boost the performance.
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes

We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to large-scale data, focusing on the Nystr\"om method and the Sparse Variational Gaussian Processes (SVGP). While sparse approximation methods for GPs and kernel methods share some algebraic similarities, the literature lacks a deep understanding of how and why they are related. This may pose an obstacle to the communications between the GP and kernel communities, making it difficult to transfer results from one side to the other. Our motivation is to remove this obstacle, by clarifying the connections between the sparse approximations for GPs and kernel methods. In this work, we study the two popular approaches, the Nystr\"om and SVGP approximations, in the context of a regression problem, and establish various connections and equivalences between them. In particular, we provide an RKHS interpretation of the SVGP approximation, and show that the Evidence Lower Bound of the SVGP contains the objective function of the Nystr\"om approximation, revealing the origin of the algebraic equivalence between the two approaches. We also study recently established convergence results for the SVGP and how they are related to the approximation quality of the Nystr\"om method.
Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity

An improvement of Q-learning is proposed in this paper. It is different from classic Q-learning in that the similarity between different states and actions is considered in the proposed method. During the training, a new updating mechanism is used, in which the Q value of the similar state-action pairs are updated synchronously. The proposed method can be used in combination with both tabular Q-learning function and deep Q-learning. And the results of numerical examples illustrate that compared to the classic Q-learning, the proposed method has a significantly better performance.
Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments

Causal discovery, beyond the inference of a network as a collection of connected dots, offers a crucial functionality in scientific discovery using artificial intelligence. The questions that arise in multiple domains, such as physics, physiology, the strategic decision in uncertain environments with multiple agents, climatology, among many others, have roots in causality and reasoning. It became apparent that many real-world temporal observations are nonlinearly related to each other. While the number of observations can be as high as millions of points, the number of temporal samples can be minimal due to ethical or practical reasons, leading to the curse-of-dimensionality in large-scale systems. This paper proposes a novel method using kernel principal component analysis and pre-images to obtain nonlinear dependencies of multivariate time-series data. We show that our method outperforms state-of-the-art causal discovery methods when the observations are restricted by time and are nonlinearly related. Extensive simulations on both real-world and synthetic datasets with various topologies are provided to evaluate our proposed methods.
Retrospective Causal Inference Via Matrix Completion, with an Evaluation of the Effect of European Integration on Cross-Border Employment

We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always-treated units, but no never-treated units. We use the observed outcomes to impute the counterfactual outcomes of the later-treated using a matrix completion estimator. We propose a novel propensity-score and elapsed-time weighting of the estimator's objective function to correct for differences in the observed covariate and unobserved fixed effects distributions, and elapsed time since treatment between groups. Our methodology is motivated by studying the effect of two milestones of European integration -- the Free Movement of persons and the Schengen Agreement -- on the share of cross-border workers in sending border regions. We apply the proposed method to the European Labour Force Survey (ELFS) data and provide evidence that opening the border almost doubled the probability of working beyond the border in Eastern European regions.
Energy Injection Driven by Precessing Jets in Gamma-Ray Burst Afterglows

Jet precession is considered to universally exist in different-scale astronomical phenomena, including gamma-ray bursts (GRBs). For the long-lived GRB central engine, the relativistic precessing jets will periodically inject kinetic energy into the external shocks, then significantly modulate the shapes of the light curves (LCs) in GRB afterglows. In this paper, we adopt the standard external shock model to investigate the effects of jet precession on GRB X-ray afterglows in cases with different parameters, i.e., the steady or time-dependent jet powers, precession periods, precession angles, and viewing angles. In the case where the jet powers are in steady or slow decay and the jet can sweep across the line of sight, shallow decay (or plateau) segments should appear; otherwise, a giant bump will emerge in the GRB afterglow LCs. We show that jet precession is a new plausible mechanism of the energy injection in GRBs. Moreover, some observed X-ray transients without GRB associations might be powered by the precessing jets.
A Closer Look at Gluons

: Gluons are strong interaction gauge ﬁelds which interact between quarks, i.e. constituents of baryons and mesons. Interaction of matters is phenomenologically described by gauge theory of strong, electromagnetic, weak and gravitational interactions. In electro-weak theory, left handed leptons l L and neutrino ν L , right handed leptons l R and left handed quarks u L , d L and right handed quarks u R , d R follow SU (2) × U (1) symmetry. Charge of leptons and quarks deﬁne hypercharge Y , and via Higgs mechanism SU (2) L × U (1) Y symmetry forms U (1) em symmetry. of 4-dimensional space to 5-dimensional space was tried in lattice simulations, and in light front holographic QCD (LFHQCD) approach in which conformally symmetric light-front dynamics without ghost are embedded in AdS 5 , and a parameter that ﬁxes a mass scale was chosen from the Principle of Maximum Conformality. Coulomb or Landau gauge ﬁxed Faddeev-Popov Yang-Mills ﬁeld equation is known to have the Gribov ambiguity, and tunneling between vacua between different topological structures was proposed by van Baal and collaborators. The symmetry of three colors can be assigned three vectors of quaternion H , whose multiplication on 2 × 2 matrices of Dirac spinors on S 3 induces transformations. Instantons or sphalerons whose presence is expected from conformal equivalence of S 3 × R to R 4 are reviewed. An extension of the dynamics embedded in complex projective space is also discussed.
Improvement over Pinball Loss Support Vector Machine

Recently, there have been several papers that discuss the extension of the Pinball loss Support Vector Machine (Pin-SVM) model, originally proposed by Huang et al.,[1][2]. Pin-SVM classifier deals with the pinball loss function, which has been defined in terms of the parameter $\tau$. The parameter $\tau$ can take values in $[ -1,1]$. The existing Pin-SVM model requires to solve the same optimization problem for all values of $\tau$ in $[ -1,1]$. In this paper, we improve the existing Pin-SVM model for the binary classification task. At first, we note that there is major difficulty in Pin-SVM model (Huang et al. [1]) for $ -1 \leq \tau<0$. Specifically, we show that the Pin-SVM model requires the solution of different optimization problem for $ -1 \leq \tau<0$. We further propose a unified model termed as Unified Pin-SVM which results in a QPP valid for all $-1\leq \tau \leq 1$ and hence more convenient to use. The proposed Unified Pin-SVM model can obtain a significant improvement in accuracy over the existing Pin-SVM model which has also been empirically justified by extensive numerical experiments with real-world datasets.
Hydrodynamic theory of the Dyakonov-Shur instability in graphene transistors

We present a comprehensive theory of the Dyakonov-Shur (DS) plasma instability in currentbiased graphene transistors. Using the hydrodynamic approach, we derive equations describing the DS instability in the two-dimensional electron fluid in graphene at arbitrary values of electron drift velocity. These non-linear equations together with Maxwell’s equations are used for numerical analysis of the spatial and temporal evolution of the graphene electron system after the DS instability is triggered by random current fluctuations. We analyze conditions necessary for the onset of the DS instability and the properties of the final stationary state of the graphene electron system. We demonstrate that the instability results in the coherent anharmonic oscillatory state of the electron fluid and calculate both the spatial distribution and the power of the electromagnetic radiation generated by the graphene transistor in the DS instability regime.
Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels

In many classification tasks, the ground truth is either noisy or subjective. Examples include: which of two alternative paper titles is better? is this comment toxic? what is the political leaning of this news article? We refer to such tasks as survey settings because the ground truth is defined through a survey of one or more human raters. In survey settings, conventional measurements of classifier accuracy such as precision, recall, and cross-entropy confound the quality of the classifier with the level of agreement among human raters. Thus, they have no meaningful interpretation on their own. We describe a procedure that, given a dataset with predictions from a classifier and K ratings per item, rescales any accuracy measure into one that has an intuitive interpretation. The key insight is to score the classifier not against the best proxy for the ground truth, such as a majority vote of the raters, but against a single human rater at a time. That score can be compared to other predictors' scores, in particular predictors created by combining labels from several other human raters. The survey equivalence of any classifier is the minimum number of raters needed to produce the same expected score as that found for the classifier.
Improving low-resource ASR performance with untranscribed out-of-domain data

Semi-supervised training (SST) is a common approach to leverage untranscribed/unlabeled speech data to improve automatic speech recognition performance in low-resource languages. However, if the available unlabeled speech is mismatched to the target domain, SST is not as effective, and in many cases performs worse than the original system. In this paper, we address the issue of low-resource ASR when only untranscribed out-of-domain speech data is readily available in the target language. Specifically, we look to improve performance on conversational/telephony speech (target domain) using web resources, in particular YouTube data, which more closely resembles news/topical broadcast data. Leveraging SST, we show that while in some cases simply pooling the out-of-domain data with the training data lowers word error rate (WER), in all cases, we see improvements if we train first with the out-of-domain data and then fine-tune the resulting model with the original training data. Using 2000 hours of speed perturbed YouTube audio in each target language, with semi-supervised transcripts, we show improvements on multiple languages/data sets, of up to 16.3% relative improvement in WER over the baseline systems and up to 7.4% relative improvement in WER over a system that simply pools the out-of-domain data with the training data.
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?

Crowdsourcing is widely used to create data for common natural language understanding tasks. Despite the importance of these datasets for measuring and refining model understanding of language, there has been little focus on the crowdsourcing methods used for collecting the datasets. In this paper, we compare the efficacy of interventions that have been proposed in prior work as ways of improving data quality. We use multiple-choice question answering as a testbed and run a randomized trial by assigning crowdworkers to write questions under one of four different data collection protocols. We find that asking workers to write explanations for their examples is an ineffective stand-alone strategy for boosting NLU example difficulty. However, we find that training crowdworkers, and then using an iterative process of collecting data, sending feedback, and qualifying workers based on expert judgments is an effective means of collecting challenging data. But using crowdsourced, instead of expert judgments, to qualify workers and send feedback does not prove to be effective. We observe that the data from the iterative protocol with expert assessments is more challenging by several measures. Notably, the human–model gap on the unanimous agreement portion of this data is, on average, twice as large as the gap for the baseline protocol data.
Glass-like transition described by toppling of stability hierarchy

Building on the work of Fyodorov (2004) and Fyodorov and Nadal (2012) we examine the critical behaviour of population of saddles with fixed instability index k in high dimensional random energy landscapes. Such landscapes consist of a parabolic confining potential and a random part in N ≫ 1 dimensions. When the relative strength m of the parabolic part is decreasing below a critical value m c, the random energy landscapes exhibit a glass-like transition from a simple phase with very few critical points to a complex phase with the energy surface having exponentially many critical points. We obtain the annealed probability distribution of the instability index k by working out the mean size of the population of saddles with index k relative to the mean size of the entire population of critical points and observe toppling of stability hierarchy which accompanies the underlying glass-like transition. In the transition region m = m c + δN −1/2 the typical instability index scales as k = κN 1/4 and the toppling mechanism affects whole instability index distribution, in particular the most probable value of κ changes from κ = 0 in the simple phase (δ > 0) to a non-zero value κ max ∝ (−δ)3/2 in the complex phase (δ < 0). We also show that a similar phenomenon is observed in random landscapes with an additional fixed energy constraint and in the p-spin spherical model.
Polarization Doping - Ab Initio Verification of the Concept: Charge Conservation and Locality

In this work, we study the emergence of polarization doping in AlxGa1−xN layers with graded composition from a theoretical viewpoint. It is shown that bulk electric charge density emerges in the graded concentration region. The magnitude of the effect, i.e., the relation between the polarization bulk charge density and the concentration gradient is obtained. The appearance of mobile charge in the wurtzite structure grown along the polar direction was investigated using the combination of ab initio and drift-diffusion models. It was shown that the ab initio results can be recovered precisely by proper parameterization of drift-diffusion representation of the complex nitride system. It was shown that the mobile charge appears due to the increase of the distance between opposite polarization-induced charges. It was demonstrated that, for sufficiently large space distance between polarization charges, the opposite mobile charges are induced. We demonstrate that the charge conservation law applies for fixed and mobile charge separately, leading to nonlocal compensation phenomena involving (i) the bulk fixed and polarization sheet charge at the heterointerfaces and (ii) the mobile band and the defect charge. Therefore, two charge conservation laws are obeyed that induces nonlocality in the system. The magnitude of the effect allows obtaining technically viable mobile charge density for optoelectronic devices without impurity doping (donors or acceptors). Therefore, it provides an additional tool for the device designer, with the potential to attain high conductivities: high carrier concentrations can be obtained even in materials with high dopant ionization energies, and the mobility is not limited by scattering at ionized impurities.