Miracle-dz/UniFAR-FTUs huggingface dataset:
On randomized algorithms for numerical solution of applied Fredholm integral equations of the second kind

In this paper, the systematization of numerical (implemented on a computer) randomized functional algorithms for approximation of a solution of Fredholm integral equation of the second kind is carried out. Wherein, three types of such algorithms are distinguished: the projection, the mesh and the projection-mesh methods. The possibilities for usage of these algorithms for solution of practically important problems is investigated in detail. The disadvantages of the mesh algorithms, related to the necessity of calculation values of the kernels of integral equations in fixed points, are identified. On practice, these kernels have integrated singularities, and calculation of their values is impossible. Thus, for applied problems, related to solving Fredholm integral equation of the second kind, it is expedient to use not mesh, but the projection and the projection-mesh randomized algorithms.
The Variational Calculation of the 413 nm 4He Tune-out Wavelength

A tune-out wavelength of an atom is a wavelength of radiation such that the atom’s polarizability vanishes [1]. It can be used as an alternative to inducing energy shifts for the purpose of testing quantum electrodynamics (QED). This is done by incorporating perturbations to the polarizability and calculating a chosen root, i.e. a tune-out wavelength. Comparison with experiment can determine the effectiveness of the theory from which these perturbations arise. In this study, the calculated 23S 33P transition tune-out wavelength for 4He was compared with an interferometry experiment done by Kenneth Baldwin’s group at the Australian National University. The calculated result of 413.0858252(4) nm, which takes into account fine-structure effects and other relativistic corrections of order 1 c2 , differs from the experimental value of 413.0938(9stat)(20syst) nm [2] by about 1 part in 10, 000. This discrepancy is suspected to be due to QED effects such as the atom’s interaction with the vacuum, which is why QED perturbations should be tested as well in the future. The calculation of the 23S 33P transition tune-out wavelength basically served as an exercise in studying the perturbative effects of relativity to the order of 1 c2 .
How Effective Are Neural Networks for Fixing Security Vulnerabilities

Security vulnerability repair is a difficult task that is in dire need of automation. Two groups of techniques have shown promise: (1) large code language models (LLMs) that have been pre-trained on source code for tasks such as code completion, and (2) automated program repair (APR) techniques that use deep learning (DL) models to automatically fix software bugs. This paper is the first to study and compare Java vulnerability repair capabilities of LLMs and DL-based APR models. The contributions include that we (1) apply and evaluate five LLMs (Codex, CodeGen, CodeT5, PLBART and InCoder), four fine-tuned LLMs, and four DL-based APR techniques on two real-world Java vulnerability benchmarks (Vul4J and VJBench), (2) design code transformations to address the training and test data overlapping threat to Codex, (3) create a new Java vulnerability repair benchmark VJBench, and its transformed version VJBench-trans, to better evaluate LLMs and APR techniques, and (4) evaluate LLMs and APR techniques on the transformed vulnerabilities in VJBench-trans. Our findings include that (1) existing LLMs and APR models fix very few Java vulnerabilities. Codex fixes 10.2 (20.4%), the most number of vulnerabilities. Many of the generated patches are un-compilable patches. (2) Fine-tuning with general APR data improves LLMs’ vulnerability-fixing capabilities. (3) Our new VJBench reveals that LLMs and APR models fail to fix many Common Weakness Enumeration (CWE) types, such as CWE-325 Missing cryptographic step and CWE-444 HTTP request smuggling. (4) Codex still fixes 8.7 transformed vulnerabilities, outperforming all the other LLMs
Group sequential testing of homogeneity in finite mixture models.

The problem of testing wether a sample is from a homogeneous population has been investigated in the recent years by many authors. I t has been recognized that the lim iting d istribution of the likelihood ratio test for such homogeneity problems is very complex and d ifficu lt to implement. Therefore, recently, Chen et al (2001) proposed a modified likelihood ratio test for homogeneity in finite m ixture models w ith general parametric kernel d istribution families. In this thesis we provide a group sequential version of this modified likelihood ratio test. The group sequential tests are often used to reduce the sample size (number of observations) required for making decisions in statistical testing. In general, group sequential procedures require less sample than a fixed-sample (nonsequential) testing procedure w ith the same power and type I error. We used Monte Carlo simulations to illustrate the performance of the proposed group sequential procedures in the context of normal, binomial and Poisson mixtures. We apply the methods to a Poisson data set concerning the counts of number of accidents incurred by machinists.
JOURNAL BIBLIOMETRIC ANALYSIS: A CASE STUDY ON THE JASIST

The purpose of this study is to explore the relationship between Journal of the American Society for Information Science and Technology (JASIST), and other disciplines by citation analysis. The citation data were drawn from references of each article of JASIST in 1980, 1985, 1990, 1995, 2000 and 2004. The Ulrich’s Periodical Directory, Library of Congress Subject Heading, retrieved from the WorldCat and LISA database were used to identify the main class, subclass and subject of cited journals and books. The results of this study revealed that the production rate of JASIST literature doubles and the average number of references cited per paper is also increased 2 to 3 times in a period of about 25 years. Beginning in 1995, there has been a significant increase in the number of electronic resources and constitutes 5% of all document types in 2004. JASIST itself is the most highly cited, and is followed by four library and information science (LIS) journals, namely Information Processing and Management , Journal of Documentation , Annual Review of Information Science and Technology and Journal of Information Science . The number of countries publishing the cited journal increases from 9 to 26 within 25 years. The three main classes of journals that were cited by JASIST most are library science (50%), science (22.7%) and social sciences (6.3%). The three subclasses of LIS encompass general bibliography, machine methods of information & retrieval and mechanized bibliographic control and library & information science. The top five most cited books of JASIST are Smart Retrieval System—Experiments in Automatic Document Processing, Introduction to Modern Information Retrieval , Information Retrieval , Little Science and Big Science , Information Seeking in Electronic Environments , Information Retrieval: Data Structure and Algorithms . The most cited books of JASIST are quite dispersive and science is the most cited class followed by LIS, social sciences, philosophy/psychology/religion, and the most cited subject is computerized information retrieval and mechanized bibliographic control.
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution Supplementary Material

In this supplementary material, we first provide further details on the user study in Sec. 1. Secondly, we provide an analysis of the sampling temperature in Sec 2. Third, we present additional details about the minimally generalized L1 loss in Sec. 3. Finally, we provide a further qualitative and quantitative comparison of AdFlow with other state-of-the-art methods in Sec. 4. Additional visual results, used in our study, will be available on the project page git.io/AdFlow.
With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Online Regression Models

With the rise of third parties in the machine learning pipeline, the service provider in "Machine Learning as a Service'' (MLaaS), or external data contributors in online learning, or the retraining of existing models, the need to ensure the security of the resulting machine learning models has become an increasingly important topic. The security community has demonstrated that without transparency of the data and the resulting model, there exist many potential security risks, with new risks constantly being discovered. In this paper, we focus on one of these security risks -- poisoning attacks. Specifically, we analyze how attackers may interfere with the results of regression learning by poisoning the training datasets. To this end, we analyze and develop a new poisoning attack algorithm. Our attack, termed Nopt, in contrast with previous poisoning attack algorithms, can produce larger errors with the same proportion of poisoning data-points. Furthermore, we also significantly improve the state-of-the-art defense algorithm, termed TRIM, proposed by Jagielsk et al. (IEEE SP however, we deduce from their work that TRIM can take exponential time complexity in the worst-case scenario, in excess of Proda's logarithmic time. The performance of both our proposed attack and defense algorithms is extensively evaluated on four real-world datasets of housing prices, loans, health care, and bike sharing services. We hope that our work will inspire future research to develop more robust learning algorithms immune to poisoning attacks.
End-to-end interpretable learning of non-blind image deblurring-supplemental material

The results reported in our submission for non-uniform blur are only for 1% additive white Gaussian noise. We show below (Table 1) for completness the results for 1%, 3% and 5% noise levels. The experimental protocol and code used in these experiments are exactly the same as in the main paper. The test dataset is the non-uniform PASCAL benchmark presented in our submission. The proposed (L)CHQS models embed 5 outer HQS iterations and 2 inner CPCR iterations. The table shows that our method outperforms its competitors for all noise levels, with, in particular, a very significant margin of about 0.9dB with LCHQSF over HQS-CG and EPLL [9].
A novel fast target tracking method for UAV aerial image

Abstract Unmanned aerial vehicles (UAV) are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT) features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT) algorithm to track an object (target) in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1) Breaking each track set into tracklet at a specific time, 2) Estimating the association cost of each track set, 3) Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.
The Communication Complexity of Correlation

We examine the communication required for generating random variables remotely. One party Alice is given a distribution D, and she has to send a message to Bob, who is then required to generate a value with distribution exactly D. Alice and Bob are allowed to share random bits generated without the knowledge of D. There are two settings based on how the distribution D provided to Alice is chosen. If D is itself chosen randomly from some set (the set and distribution are known in advance) and we wish to minimize the expected communication in order for Alice to generate a value y, with distribution D, then we characterize the communication required in terms of the mutual information between the input to Alice and the output Bob is required to generate. If D is chosen from a set of distributions D, and we wish to devise a protocol so that the expected communication (the randomness comes from the shared random string and Alice's coin tosses) is small for each D isin D, then we characterize the communication required in this case in terms of the channel capacity associated with the set D. Our proofs are based on an improved rejection sampling procedure that relates the relative entropy between two distributions to the communication complexity of generating one distribution from the other. As an application of these results, we derive a direct sum theorem in communication complexity that substantially improves the previous such result shown by Jain et al. (2003).
Cascade-Free Fuzzy Finite-Control-Set Model Predictive Control for Nested Neutral Point-Clamped Converters With Low Switching Frequency

In this brief, a cascade-free fuzzy finite-control-set model predictive control (FCS-MPC) is proposed for nested neutral point-clamped converters with low switching frequency (SF). The main objective of the proposed method is to achieve a low SF operation. First, a new cost function is formulated to reduce the SF, and a fuzzy logic control (FLC) technique is employed to choose the weighting factors dynamically. As such, the FLC scheme has a good potential to avoid the constant of weighting factor for the conventional FCS-MPC strategy, especially in the presence of dynamic operation condition. Second, in order to enhance the dynamic response and improve the computationally efficient under low SF, a dynamic reference design with a simplified cascade-free strategy is embedded into the predictive controller. The novelty of this brief lies not only in combining the proposed cost function with the FLC technique for online determining the appropriate values of the weighting factors, but also in a compatible reference design that directly allows formulating the optimal control problem using a cascade-free structure. Finally, simulation results are presented for assessing the effectiveness of the proposed control strategy.
More for less: sampling strategies of plant functional traits across local environmental gradients

Summary Ecologists use approaches based on plant functional traits to tackle several fundamental and applied questions. Although a perfect characterization of functional trait structure requires the measurement of all the individuals in communities, this is prohibitively resource-consuming. Consequently, the general practice is to average the trait values of a reduced number of individuals per species. However, there are different alternatives regarding the number, identity and spatial location of the individuals chosen to calculate species-averaged trait values. In this study, we compared different strategies for sampling functional traits, using community-weighted mean trait values (CWM) and the Rao index of functional diversity (FD). We intensively sampled the functional trait structure along a topographical gradient in a Mediterranean grassland, obtaining accurate estimations of the ‘real’ values of these indices (CWMI and FDI) for three traits (height, leaf area and specific leaf area). We simulated three different sampling strategies differing in the spatial location of the individuals used to estimate species-mean trait: (i) average of the whole gradient (GLO), (ii) average of the sampling unit in which the abundances of species maximize (MAX) and (iii) average of a reduced number of individuals per species and sampling unit (LOC). For each strategy, we simulated different sampling intensities (number of individuals sampled). For each trait, we examined the ability of each strategy and sampling intensity to accurately estimate CWMI and FDI, as well as their ability to detect changes in functional trait structure along the topographical gradient. LOC outperformed the other strategies in terms of accuracy and bias, and was much more efficient to describe changes along the gradient, regardless of the traits and indicators considered. Furthermore, LOC was the only strategy that improved consistently as sampling intensity increased, especially at low levels of intensity. Our results indicate that the impact of considering intraspecific variability in trait values can be greater than commonly assumed. Strategies that neglect this source of variability can result in inaccurate or biased estimations of the functional trait structure of plant communities. Most importantly, we show that intraspecific variability can be taken into consideration without any increases in the total number of individuals measured.
Hardness of robust network design

The authors settle the complexity status of the robust network design problem in undirected graphs. The fact that the flow‐cut gap in general graphs can be large, poses some difficulty in establishing a hardness result. Instead, the authors introduce a single‐source version of the problem where the flow‐cut gap is known to be one. They then show that this restricted problem is coNP‐Hard. This version also captures, as special cases, the fractional relaxations of several problems including the spanning tree problem, the Steiner tree problem, and the shortest path problem. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(1), 50–54 2007
Overleaf Example

In this work we examine the impact of our motion with respect to the CMB rest frame on statistics of CMB maps by examining the one-, two-, threeand fourpoint statistics of simulated maps of the CMB and Sunyaev-Zeldovich (SZ) effects. We validate boosting codes by comparing their outcomes for temperature and polarization power spectra up to ` ' 6000. We derive and validate a new analytical formula for the computation of the boosted power spectrum of a signal with a generic frequency dependence. As an example we show how this increases the boosting correction to the power spectrum of CMB intensity measurements by ∼ 30% at 150 GHz. We examine the effect of boosting on thermal and kinetic SZ power spectra from semianalytical and hydrodynamical simulations; the boosting correction is generally small for both simulations, except when considering frequencies near the tSZ null. For the non-Gaussian statistics, in general we find that boosting has no impact with two exceptions. We find that, whilst the statistics of the CMB convergence field are unaffected, quadratic estimators that are used to measure this field can become biased at the O(1)% level by boosting effects. We present a simple modification to the standard estimators that removes this bias. Second, bispectrum estimators can receive a systematic bias from the Doppler induced quadrupole when there is anisotropy in the sky in practice this anisotropy comes from masking and inhomegenous noise. This effect is unobservable and already removed by existing analysis methods.
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce

Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks. However, BERT cannot well support E-commerce related tasks due to the lack of two levels of domain knowledge, i.e., phrase-level and product-level. On one hand, many E-commerce tasks require an accurate understanding of domain phrases, whereas such fine-grained phrase-level knowledge is not explicitly modeled by BERT's training objective. On the other hand, product-level knowledge like product associations can enhance the language modeling of E-commerce, but they are not factual knowledge thus using them indiscriminately may introduce noise. To tackle the problem, we propose a unified pre-training framework, namely, E-BERT. Specifically, to preserve phrase-level knowledge, we introduce Adaptive Hybrid Masking, which allows the model to adaptively switch from learning preliminary word knowledge to learning complex phrases, based on the fitting progress of two modes. To utilize product-level knowledge, we introduce Neighbor Product Reconstruction, which trains E-BERT to predict a product's associated neighbors with a denoising cross attention layer. Our investigation reveals promising results in four downstream tasks, i.e., review-based question answering, aspect extraction, aspect sentiment classification, and product classification.
Simulation Comparisons of Vehicle-based and Phase-based Traffic Control for Autonomous Vehicles at Isolated Intersections

With the advent of autonomous driving technologies, traffic control at intersections is expected to experience revolutionary changes. Various novel intersection control methods have been proposed in the existing literature, and they can be roughly divided into two categories: vehicle-based traffic control and phase-based traffic control. Phase-based traffic control can be treated as updated versions of the current intersection signal control with the incorporation of the performance of autonomous vehicle functions. Meanwhile, vehicle-based traffic control utilizes some brand-new methods, mostly in real-time fashion, to organize traffic at intersections for safe and efficient vehicle passages. However, to date, no systematic comparison between these two control categories has been performed to suggest their advantages and disadvantages. This paper conducts a series of numerical simulations under various traffic scenarios to perform a fair comparison of their performances. Specifically, we allow trajectory adjustments of incoming vehicles under phasebased traffic control, while for its vehicle-based counterpart, we implement two strategies, i.e., the first-come-first-serve strategy and the conflict-point based rolling-horizon optimization strategy. Overall, the simulation results show that vehicle-based traffic control generally incurs a negligible delay when traffic demand is low but lead to an excessive queuing time as the traffic volume becomes high. However, performance of vehicle-based traffic control may benefit from reduction in conflicting vehicle pairs. We also discovered that when autonomous driving technologies are not mature, the advantages of phase-based traffic control are much more distinct.
Improving the measurement accuracy of shadow moiré by three-step random phase-shifting algorithm

Abstract. Phase-shifting shadow moiré is an efficient technique for three-dimensional (3-D) shape measurement, but the technique is always suffered from the error of phase shifter. A three-frame random phase-shifting algorithm is proposed to relax the requirements of repeatability and accuracy of the phase shifter in a shadow moiré system. The proposed method can act as a bridge between the three phase-shifting analysis methods and two-shot phase-shifting methods. The value of introduced arbitrary and unknown phase shift can be calculated by a determinate formula. The algorithm has the advantages of being free from the disturbance of background elimination and less computation. In addition, the proposed method is immune to nonsinusoidal fringe patterns and can be implemented easily in many applications. Both simulation and optical experimental results verify the feasibility of this method.