Miracle-dz/UniFAR-FTUs huggingface dataset:
Bounding worst-case data cache performance

Tightly predicting worst-case execution times (WCETs) of programs on real-time systems with caches is difficult. Whether or not a particular reference is in cache depends on the program's previous dynamic behavior. While much progress has been accomplished recently on predicting instruction cache performance of programs, bounding worst-case data cache performance is significantly more challenging. Unlike instruction caching, many of the data addresses referenced by load and store instructions can change during the execution of a program. This dissertation describes an automatic tool-based approach for statically bounding the worst-case data cache performance of large code segments. It also presents the work done to verify the validity of the computed bounds. The given approach works on fully optimized code, performs the analysis over the entire control flow of a program, detects and exploits both spatial and temporal locality within data references, produces results typically within a few seconds, and produces, on average, 30% tighter WCET bounds than can be predicted without analyzing data cache behavior. The given method of timing analysis involves several steps. First, data flow analysis within an optimizing compiler is used to determine the bounded range of addresses of each data reference relative to a global symbol or activation record. Second, virtual address ranges are calculated from the relative address ranges by examining the order of the assembly data declarations and the call graph of the entire program. Third, the control flow of the program is analyzed to statically categorize the caching behavior of each data reference. Fourth, these categorizations are used when calculating the pipeline performance of sequences of instructions representing paths within the program. Finally, the pipeline path analysis is used to estimate the worst-case execution performance of each loop and function in the program. Overall, this dissertation presents a comprehensive report on methods and results of worst-case timing analysis of data cache behavior and shows that such an analysis can lead to a significantly tighter worst-case performance prediction. The given approach is unique and provides a considerable step towards realistic worst-case execution time prediction of contemporary architectures and its use in schedulability analysis for real-time systems.
Protocols for secure computations

The author investigates the following problem: Suppose m people wish to compute the value of a function f(x1, x2, x3, ., xm), which is an integer-valued function of m integer variables xi of bounded range. Assume initially person Pi knows the value of xi and no other x's. Is it possible for them to compute the value of f, by communicating among themselves, without unduly giving away any information about the values of their own variables? The author gives a precise formulation of this general problem and describe three ways of solving it by use of one-way functions (i.e., functions which are easy to evaluate but hard to invert). These results have applications to secret voting, private querying of database, oblivious negotiation, playing mental poker, etc. He also discusses the complexity question "How many bits need to be exchanged for the computation," and describes methods to prevent participants from cheating. Finally, he studies the question "What cannot be accomplished with one-way functions. "
Thesis and Dissertation Citations as Indicators of Faculty Research Use of University Library Journal Collections

Citation analysis is a long-standing collection-evaluation tool often undertaken to investigate one aspect of library collection use. Citations from theses and dissertations are much more easily and comprehensively gathered than are citations voluntarily supplied by faculty. Using four studies in geology and biology, the Kendall coefficient of rank correlation tests the degree of association between journals most heavily cited by graduate students and those titles most heavily cited in faculty publications. Positive associations are confirmed in three data sets. Additional descriptive analysis shows that the 40 titles most heavily cited in theses and dissertations consistently contained about 70% of the top 40 titles cited by faculty, including most of the 12-15 top titles, If results are replicated, thesis and dissertation citations can be reliably used as a surrogate for faculty publication citations in evaluations of the research portion of library collection use.
Teaching Writing in Five Canadian Provinces: A New Literacies Analysis

This article presents the results of the initial stage of research on grades 4–8 teachers' writing instruction within rural and urban contexts across Canada. Teachers' goals and their use of digital technologies and multimedia are examined within rural and urban schools in five eastern provinces. Through half-hour telephone interviews with 54 teachers, the authors found that perceptions of the value of writing within their communities and the kinds of support for writing that students were given outside school divided along teachers' perceptions of students' social class lines. Language spoken in students' homes also influenced teachers' goals for their students. Only 5 of the 54 teachers interviewed provide opportunities for students to compose on computers. In most teachers' classrooms, computers were used for retyping drafts, and often used at home, to create ‘good copies' that students handed in for grades.
Buffering Capacity of Fast-Growing Species and Curing Time of UF Resin Modified With Zinc Borate and Monoammonium Phosphate

Problem statement: Occupying a suitable hot pressing time for particleboard fabrication seems very tricky for manufacturers of the wood-based panel. Longer or shorter pressing times can affect physical and mechanical properties of the produced particleboards and that is why extra care should be given on this matter. Longer pressing time can cause resin in a particleboard to over-cure whereas shorter pressing time can cause insufficient curing of the resin. Determination of hot pressing time is influenced by the type of filler and chemical used. The acidity or alkalinity of a filler and chemical can lengthen or shorten the time taken by the resin to cure. These parameters are usually assessed using buffering capacity test and curing time test. In this study, the two analyses were occupied to assess acidity or alkalinity of kenaf, Shorea macrophylla and Acacia mangium as well as the effect of fire retardants (zinc borate and monoammonium phosphate) to the curing rate of urea formaldehyde resin. Determine acidity or alkalinity of the wood particles through buffering capacity test. The effect of the fire retardants to the curing of Urea Formaldehyde (UF) resin was also studied. Approach: For buffering capacity test, the wood particles were extracted. Then, the extracts were separated into two parts. One part was tested with sodium hydroxide (NaOH) and another part was tested with sulphuric acid (H2SO4). Sodium hydroxide was added into the extracts until they reached pH 11. The other part was added with sulphuric acid until they reached pH 3. The pH value was recorded each time 5 mL chemicals added. The acidity of the plants was observed by looking at the volume of sulphuric acid needed to reduce their pH to 3, whereas alkalinity was evaluated by looking at the volume of sodium hydroxide used to increase their pH to 11. Curing times of UF resin added or not added with fire retardants were calculated in second (sec). Results: Buffering capacity study revealed that kenaf, Shorea macrophylla and Acacia mangium behaved similarly in alkali and acidic conditions. All the materials had less resistance towards acid than towards alkali. Kenaf had the greatest resistance towards alkali compared with the other two species. As an important processing parameter in particleboard fabrication, the curing time of the adhesive mixture with added fire retardants was studied in comparison with a control mixture (without fire retardant with a curing time of 140 sec). Those two fire retardants showed different effect on the curing time of the mixture. Zinc borate was found to delay the curing time by more than 100% compared with control mixture whereas, monoammonium phosphate increased the curing time by almost 60%. The curing time for control mixture (without fire retardant) was 140 sec. Conclusion: Based on the testing results, some potential modifications to improve the curing time were discussed to avoid short curing time to improve the manufacturability of the material systems.
Bounds on WIMP dark matter from galaxy clusters at low redshift

In this work we study the cross-correlation between Fermi-LAT diffuse γ-ray maps and galaxy cluster catalogues in order to search for a diffuse dark matter signal. We employ an accurate estimation of the error covariance matrix and we select 4 galaxy cluster catalogues at low-redshift 0 < z < 0.2 with large-halo mass M500 > 1013M⊙ including infrared, optical and X-rays wavebands. We consider weakly interacting massive particle (WIMP) dark matter with 100% branching ratios into τ+τ−, $b\bar{b}$, W+W− and μ+μ−. No evidence for a WIMP dark matter signal is identified, and we derive competitive bounds. The WIMP thermally averaged cross section is excluded at 95% C.L. for dark matter masses below 20 GeV and annihilation in the τ+τ− channel.
Nonlinear filtering of stochastic differential equations driven by correlated L\'evy noises.

The work concerns nonlinear filtering problems of stochastic differential equations with correlated L\'evy noises. First, we establish the Kushner-Stratonovich and Zakai equations through martingale representation theorems and the Kallianpur-Striebel formula. Second, we show the pathwise uniqueness and uniqueness in joint law of weak solutions for the Zakai equation. Finally, we investigate the uniqueness in joint law of weak solutions to the Kushner-Stratonovich equation.
A stabilized total-Lagrangian SPH method for large deformation and failure in geomaterials

Conventional smoothed particle hydrodynamics based on Eulerian kernels (CESPH) is widely-used in large deformation analysis in geomaterials. Despite being popular, it suffers from tensile instability and rank-deficiency; thus, it needs several numerical treatments to be stable. In this work, we present a stabilized total-Lagrangian SPH method (TLSPH), which is inherently free of tensile instability. A stiffness-based hourglass control algorithm is employed to cure the hourglass mode caused by rank-deficiency. Periodic update of reference configuration is used in simulations to allow TLSPH to model large deformation and post-failure flow in geomaterials. Several numerical examples are presented to show the performance of the stabilized TLSPH method. The comparison between TLSPH and CESPH are discussed. The influences of hourglass control and configuration update are also discussed and shown in the numerical examples. It is found that the presented stabilized TLSPH is robust and can model large deformation and plastic flows in geomaterials. Particularly, the stabilized TLSPH delivers accurate and smooth stress results.
What’s the Ballyhoo about Blogs?

Abstract Ten librarians offer spontaneous, even off-the-cuff, opinions about the pros and cons of blogs and blogging. Are blogs a substitute for print communication or older electronic resources such as static Web pages and electronic discussion lists? What will the future hold for blogs and their content? The librarians reflect on these questions and describe their own use of blogs.
Random projections and sampling algorithms for clustering of high-dimensional polygonal curves

We study the $k$-median clustering problem for high-dimensional polygonal curves with finite but unbounded number of vertices. We tackle the computational issue that arises from the high number of dimensions by defining a Johnson-Lindenstrauss projection for polygonal curves. We analyze the resulting error in terms of the Fr\'echet distance, which is a tractable and natural dissimilarity measure for curves. Our clustering algorithms achieve sublinear dependency on the number of input curves via subsampling. Also, we show that the Fr\'echet distance can not be approximated within any factor of less than $\sqrt{2}$ by probabilistically reducing the dependency on the number of vertices of the curves. As a consequence we provide a fast, CUDA-parallelized version of the Alt and Godau algorithm for computing the Fr\'echet distance and use it to evaluate our results empirically.
Guided Learning for the combination of weakly-supervised and semi-supervised learning

We propose a simple but efficient method to combine semi-supervised learning with weakly-supervised learning for deep neural networks. Weakly-supervised learning is to solve the task which requires fine-level prediction with only coarse-level annotations available. Designing deep neural networks for weakly-supervised learning is always accompanied by a trade-off between fine-level information detection performance and coarse-level classification accuracy. While combining weakly-supervised learning with semi-supervised learning using unlabeled data, in contrast to seeking for this trade-off, we design two different models for different targets. One merely pursues finer information detection performance as the final target, while another one is more professional in achieving higher coarse-level classification accuracy so that it is regarded as a more professional teacher to teach the former model using unlabeled data. We present an end-to-end semi-supervised learning process termed Guided Learning for these two different models to improve the training efficiency. Our approach outperforms the first place result on DCASE2018 Task 4 which employs Mean Teacher with a well-design CRNN network from 32.4% to 38.9%, achieving state-of-the-art performance.
Cataloging accreted stars withinGaiaDR2 using deep learning

Aims.The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate theGaiaDR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion ofGaiaDR2.Methods. A method known as “transfer learning” is shown to be effective through extensive testing on a set of mockGaiacatalogs that are based on the FIREcosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matchedGaia/RAVE data set, which improves sensitivity to properties of the real Milky Way.Results. The result is a catalog that identifies ∼767 000 accreted stars withinGaiaDR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.
Fine-Tuned Spin-3/2 and the Hierarchy Problem

In the past, Kundu et al. and Chakraborty et al. used extra scalar fields to cancel the quadratic divergences in the Higgs mass squared and they determined the mass of the required scalar field. In this work, a spin-3/2 particle has been used in the same manner to nullify the power-law divergences, and it is determined that the mass of the spin-3/2 particle resides in the ball park of the GUT scale.
Discontinuous Galerkin Finite Element Methods for the Landau-de Gennes Minimization Problem of Liquid Crystals

We consider a system of second-order nonlinear elliptic partial differential equations that models the equilibrium configurations of a two-dimensional planar bistable nematic liquid crystal device. Discontinuous Galerkin (dG) finite element methods are used to approximate the solutions of this nonlinear problem with nonhomogeneous Dirichlet boundary conditions. A discrete inf–sup condition demonstrates the stability of the dG discretization of a well-posed linear problem. We then establish the existence and local uniqueness of the discrete solution of the nonlinear problem. A priori error estimates in the energy and $\mathbf{L}^2$ norms are derived and a best approximation property is demonstrated. Further, we prove the quadratic convergence of the Newton iterates along with complementary numerical experiments.
Light Multi-segment Activation for Model Compression

Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy but with strict tolerance to model complexity. Recently, Knowledge Distillation, which distills the knowledge from well-trained and highly complex teacher model into a compact student model, has been widely used for model compression. However, under the strict requirement on the resource cost, it is quite challenging to make student model achieve comparable performance with the teacher one, essentially due to the drastically-reduced expressiveness ability of the compact student model. Inspired by the nature of the expressiveness ability in NN, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model. Specifically, we propose a highly efficient multi-segment activation, called Light Multi-segment Activation (LMA), which can rapidly produce multiple linear regions with very few parameters by leveraging the statistical information. With using LMA, the compact student model is capable of achieving much better performance effectively and efficiently, than the ReLU-equipped one with same model complexity. Furthermore, the proposed method is compatible with other model compression techniques, such as quantization, which means they can be used jointly for better compression performance. Experiments on state-of-the-art NN architectures over the real-world tasks demonstrate the effectiveness and extensibility of the LMA.
Fundamental limits of caching with secure delivery

Caching is a procedure which allows popular files to be pre-fetched and stored partly in end users' memory. In modern wireless networks, caching is emerging to play a vital role in reducing peak data rates by storing popular content. In this paper, the concept of information theoretic security for caching is introduced. A novel caching scheme is proposed which leverages both the local and global caching gains to reduce peak rate while securely delivering requested content to the users. The analysis of the secure caching problem is presented which shows that the proposed scheme introduces security at negligible cost compared to insecure caching schemes, particularly for large number of files and users. It is also shown that the rate of the proposed scheme is within a constant multiplicative factor from the information-theoretic optimal rate for all feasible values of problem parameters.
Unifying Matrix Stability Concepts with a View to Applications

Multiplicative and additive $D$-stability, diagonal stability, Schur $D$-stability, $H$-stability are classical concepts which arise in studying linear dynamical systems. We unify these types of stability, as well as many others, in one concept of $({\mathfrak D}, {\mathcal G}, \circ)$-stability, which depends on a stability region ${\mathfrak D} \subset {\mathbb C}$, a matrix class ${\mathcal G}$ and a binary matrix operation $\circ$. This approach allows us to unite several well-known matrix problems and to consider common methods of their analysis. In order to collect these methods, we make a historical review, concentrating on diagonal and $D$-stability. We prove some elementary properties of $({\mathfrak D}, {\mathcal G}, \circ)$-stable matrices, uniting the facts that are common for many partial cases. Basing on the properties of a stability region $\mathfrak D$ which may be chosen to be a concrete subset of $\mathbb C$ (e.g. the unit disk) or to belong to a specified type of regions (e.g. LMI regions) we briefly describe the methods of further development of the theory of $({\mathfrak D}, {\mathcal G}, \circ)$-stability. We mention some applications of the theory of $({\mathfrak D}, {\mathcal G}, \circ)$-stability to the dynamical systems of different types.
Automated Deobfuscation of Android Native Binary Code

With the popularity of Android apps, different techniques have been proposed to enhance app protection. As an effective approach to prevent reverse engineering, obfuscation can be used to serve both benign and malicious purposes. In recent years, more and more sensitive logic or data have been implemented as obfuscated native code because of the limitations of Java bytecode. As a result, native code obfuscation becomes a great obstacle for security analysis to understand the complicated logic. In this paper, we propose DiANa, an automated system to facilitate the deobfuscation of native binary code in Android apps. Specifically, given a binary obfuscated by Obfuscator-LLVM (the most popular native code obfuscator), DiANa is capable of recovering the original Control Flow Graph. To the best of our knowledge, DiANa is the first system that aims to tackle the problem of Android native binary deobfuscation. We have applied DiANa in different scenarios, and the experimental results demonstrate the effectiveness of DiANa based on generic similarity comparison metrics.
Improved energy production of multi-rotor wind farms

The multi-rotor (MR) wind turbine concept can be used to upscale wind turbines without increasing the rotor diameter, which can be beneficial for transport, manufacture and design of wind turbines blades. The rotor interaction of a MR wind turbine leads to a faster wake recovery compared to an equivalent single-rotor (SR) wind turbine wake. In this article, the benefit of the faster wake recovery of MR wind turbines is quantified using Reynolds-averaged Navier-Stokes simulations of a 4}4 rectangular MR wind farm, for three different inter wind turbine spacings. The simulations predict an increase of 0.3-1.7% in annual energy production for the MR wind farm with respect to an equivalent SR wind farm, where the highest gain is obtained for the tightest inter wind turbine spacing. The gain in AEP is mainly caused by the aligned wind directions for the first downstream wind turbine in a wind turbine row of the MR wind farm, which is verified by an additional large-eddy simulation.
Coupling formalized knowledge bases with object-based image analysis

Object-based image analysis (OBIA) is a widely used method for knowledge-based interpretation of very high resolution imagery. It relies on expert knowledge to classify the desired classes from the imagery at hand. The definition of classes is subjective, usually project-specific and not shared with the community. Ontologies as a form of knowledge representation technique are acknowledged as solution to establish and document class definitions independently of an OBIA framework. However, ontologies have not yet been strongly integrated in this image analysis framework. This paper presents a method to automatically integrate ontologies in OBIA. The method has been implemented as a tool to be used with the eCognition® software (Trimble, Sunnyvale, CA, USA). A case study was conducted for classifying the land cover classes defined by the Environment Agency of Austria in the Land Information System Austria (LISA) project using WorldView-2 image. The strength of this approach is the direct integration of ontologies into the OBIA process, which reduces the effort necessary to define the classes for image analysis and simultaneously reduces its subjectivity.