Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing Linguistic Information For Logical Inference In Pre-trained Language Models

Progress in pre-trained language models has led to a surge of impressive results on downstream tasks for natural language understanding. Recent work on probing pre-trained language models uncovered a wide range of linguistic properties encoded in their contextualized representations. However, it is unclear whether they encode semantic knowledge that is crucial to symbolic inference methods. We propose a methodology for probing knowledge for inference that logical systems require but often lack in pre-trained language model representations. Our probing datasets cover a list of key types of knowledge used by many symbolic inference systems. We find that (i) pre-trained language models do encode several types of knowledge for inference, but there are also some types of knowledge for inference that are not encoded, (ii) language models can effectively learn missing knowledge for inference through fine-tuning. Overall, our findings provide insights into which aspects of knowledge for inference language models and their pre-training procedures capture. Moreover, we have demonstrated language models' potential as semantic and background knowledge bases for supporting symbolic inference methods.
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents

Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classification is the long-tailed distribution of labels. Many studies focus on improving the overall predictions of the model and thus do not prioritise tail-end labels. Improving the tail-end label predictions in multi-label classifications of medical text enables the potential to understand patients better and improve care. The knowledge gained by one or more infrequent labels can impact the cause of medical decisions and treatment plans. This research presents variations of concatenated domain-specific language models, including multi-BioMed-Transformers, to achieve two primary goals. First, to improve F1 scores of infrequent labels across multi-label problems, especially with long-tail labels; second, to handle long medical text and multi-sourced electronic health records (EHRs), a challenging task for standard transformers designed to work on short input sequences. A vital contribution of this research is new state-of-the-art (SOTA) results obtained using TransformerXL for predicting medical codes. A variety of experiments are performed on the Medical Information Mart for Intensive Care (MIMIC-III) database. Results show that concatenated BioMed-Transformers outperform standard transformers in terms of overall micro and macro F1 scores and individual F1 scores of tail-end labels, while incurring lower training times than existing transformer-based solutions for long input sequences.
Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, <inline-formula><tex-math notation="LaTeX">$(SGD)^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>S</mml:mi><mml:mi>G</mml:mi><mml:mi>D</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></

Readability criteria, such as distance or neighborhood preservation, are often used to optimize node-link representations of graphs to enable the comprehension of the underlying data. With few exceptions, graph drawing algorithms typically optimize one such criterion, usually at the expense of others. We propose a layout approach, Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, <inline-formula><tex-math notation="LaTeX">$(SGD)^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>S</mml:mi><mml:mi>G</mml:mi><mml:mi>D</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ahmed-ieq2-3155564.gif"/></alternatives></inline-formula>, that can handle multiple readability criteria. <inline-formula><tex-math notation="LaTeX">$(SGD)^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>S</mml:mi><mml:mi>G</mml:mi><mml:mi>D</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ahmed-ieq3-3155564.gif"/></alternatives></inline-formula> can optimize any criterion that can be described by a differentiable function. Our approach is flexible and can be used to optimize several criteria that have already been considered earlier (e.g., obtaining ideal edge lengths, stress, neighborhood preservation) as well as other criteria which have not yet been explicitly optimized in such fashion (e.g., node resolution, angular resolution, aspect ratio). The approach is scalable and can handle large graphs. A variation of the underlying approach can also be used to optimize many desirable properties in planar graphs, while maintaining planarity. Finally, we provide quantitative and qualitative evidence of the effectiveness of <inline-formula><tex-math notation="LaTeX">$(SGD)^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>S</mml:mi><mml:mi>G</mml:mi><mml:mi>D</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ahmed-ieq4-3155564.gif"/></alternatives></inline-formula>: we analyze the interactions between criteria, measure the quality of layouts generated from <inline-formula><tex-math notation="LaTeX">$(SGD)^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>S</mml:mi><mml:mi>G</mml:mi><mml:mi>D</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ahmed-ieq5-3155564.gif"/></alternatives></inline-formula> as well as the runtime behavior, and analyze the impact of sample sizes. The source code is available on github and we also provide an interactive demo for small graphs.
Introduction and Analysis of a Method for the Investigation of QCD-like Tree Data

The properties of decays that take place during jet formation cannot be easily deduced from the final distribution of particles in a detector. In this work, we first simulate a system of particles with well-defined masses, decay channels, and decay probabilities. This presents the “true system” for which we want to reproduce the decay probability distributions. Assuming we only have the data that this system produces in the detector, we decided to employ an iterative method which uses a neural network as a classifier between events produced in the detector by the “true system” and some arbitrary “test system”. In the end, we compare the distributions obtained with the iterative method to the “true” distributions.
Integrated production of semi-finished components in sawmills, part II: Management of internal operations

Abstract Recently, several Swedish sawmills have taken over production of components from customers in the furniture, joinery and house-manufacturing industries. The purpose of this study was to identify key factors in management of internal operations for sawmills with this strategy. A multiple case study design, based on face-to-face interviews with Swedish sawmill practitioners and on various forms of secondary data, produced comprehensive research information. Starting integrated production of components is a demanding strategy for sawmills. It requires deep knowledge about the customer and qualified skills in advanced further-processing of sawn wood. The complexity of quality management, production planning and cost calculation increases, and investments in processing equipment and customer relations reduce strategic flexibility. The diverging material flow complicates sawmills' possibilities to economize on scale, and they must instead economize on other factors, such as scope, combined operations and internal control. New tools for production follow-up and control, for production planning, for analysis of product profitability and for strategic partnership analysis are requested by practitioners. But, even if new analysis tools would facilitate better analysis and management of operations, the willingness and ability to innovate and learn among the personnel emerges as a key factor for success.
Comptes Rendus Physique

In an ensemble of two-level atoms that can be described in terms of a collective spin, entangled statescanbeusedtoenhancethesensitivityofinterferometricprecisionmeasurements.Whilenon-Gaussian spin states can produce larger quantum enhancements than spin-squeezed Gaussian states, their use requires the measurement of observables that are nonlinear functions of the three components of the collective spin. In this paper we develop strategies that achieve the optimal quantum enhancements using non-Gaussian states produced by a nonlinear one-axis-twisting Hamiltonian, and show that measurement-after-interaction techniques, known to amplify the output signals in quantum parameter estimation protocols, are e ﬀ ective in measuring nonlinear spin observables. Including the presence of the relevant decoherence processes from atomic experiments, we determine analytically the quantum enhancement of non-Gaussian over-squeezed states as a function of the noise parameters for arbitrary atom numbers. Résumé. Dans un ensemble d’atomes à
Systematically reviewing the layered architectural pattern principles and their use to reconstruct software architectures

Architectural reconstruction is a reverse engineering activity aiming at recovering the missing decisions on a system. It can help identify the components, within a legacy software application, according to the application's architectural pattern. It is useful to identify architectural technical debt. We are interested in identifying layers within a layered application since the layered pattern is one of the most used patterns to structure large systems. Earlier component reconstruction work focusing on that pattern relied on generic component identification criteria, such as cohesion and coupling. Recent work has identified architectural-pattern specific criteria to identify components within that pattern. However, the architectural-pattern specific criteria that the layered pattern embodies are loosely defined. In this paper, we present a first systematic literature review (SLR) of the literature aiming at inventorying such criteria for layers within legacy applications and grouping them under four principles that embody the fundamental design principles under-lying the architectural pattern. We identify six such criteria in the form of design rules. We also perform a second systematic literature review to synthesize the literature on software architecture reconstruction in the light of these criteria. We report those principles, the rules they encompass, their representation, and their usage in software architecture reconstruction.
Geometric Feature Learning for 3D Meshes

—Geometric feature learning for 3D meshes is central to computer graphics and highly important for numerous vision applications. However, deep learning currently lags in hierarchical modeling of heterogeneous 3D meshes due to the lack of required operations and/or their efﬁcient implementations. In this paper, we propose a series of modular operations for effective geometric deep learning over heterogeneous 3D meshes. These operations include mesh convolutions, (un)pooling and efﬁcient mesh decimation. We provide open source implementation of these operations, collectively termed Picasso . The mesh decimation module of Picasso is GPU-accelerated, which can process a batch of meshes on-the-ﬂy for deep learning. Our (un)pooling operations compute features for newly-created neurons across network layers of varying resolution. Our mesh convolutions include facet2vertex, vertex2facet, and facet2facet convolutions that exploit vMF mixture and Barycentric interpolation to incorporate fuzzy modelling. Leveraging the modular operations of Picasso, we contribute a novel hierarchical neural network, PicassoNet-II, to learn highly discriminative features from 3D meshes. PicassoNet-II accepts primitive geometrics and ﬁne textures of mesh facets as input features, while processing full scene meshes. Our network achieves highly competitive performance for shape analysis and scene parsing on a variety of benchmarks. We release Picasso and PicassoNet-II on Github.
Snake Robot Gait Decomposition and Gait Parameter Optimization

This paper proposes Gait Decomposition (G.D), a method of mathematically decomposing snake movements, and Gait Parameter Gradient (GPG), a method of optimizing decomposed gait parameters. G.D is a method that can express the snake gait mathematically and concisely from generating movement using the curve function to the motor control order when generating movement of snake robot. Through this method, the gait of the snake robot can be intuitively classified into a matrix, as well as flexibly adjusting the parameters of the curve function required for gait generation. This can solve the problem that parameter tuning, which is the reason why it is difficult for a snake robot to practical use, is difficult. Therefore, if this G.D is applied to snake robots, various gaits can be generated with a few of parameters, so snake robots can be used in many fields. We also implemented the GPG algorithm to optimize the gait curve function as well as define the gait of the snake robot through G.D.
Doubly Exotic Nth-Order Superintegrable Classical Systems Separating in Cartesian Coordinates

Superintegrable classical Hamiltonian systems in two-dimensional Euclidean space $E_2$ are explored. The study is restricted to Hamiltonians allowing separation of variables $V(x,y)=V_1(x)+V_2(y)$ in Cartesian coordinates. In particular, the Hamiltonian $\mathcal H$ admits a polynomial integral of order $N>2$. Only doubly exotic potentials are considered. These are potentials where none of their separated parts obey any linear ordinary differential equation. An improved procedure to calculate these higher-order superintegrable systems is described in detail. The two basic building blocks of the formalism are non-linear compatibility conditions and the algebra of the integrals of motion. The case $N=5$, where doubly exotic confining potentials appear for the first time, is completely solved to illustrate the present approach. The general case $N>2$ and a formulation of inverse problem in superintegrability are briefly discussed as well.
Flat lens antenna concept using aperture coupled microstrip patches

A planar lens antenna based on aperture coupled microstrip patch elements with stripline delay lines is described. Features of this design are discussed, and results for transmission coefficient magnitude against frequency are presented. The background for the development of this lens configuration is discussed in terms of a natural progression from frequency selective surfaces, and the inadequacies of simpler geometries are explained.
Optimal design for broadband quasi-phase-matched second-harmonic generation using simulated annealing

We propose a modulation-optimization method for achieving broadband quasi-phase matching in second-harmonic generation devices with domain-inverted structures. Using simulated annealing, we optimize phase-matching curves tailored to practical purposes by appropriately choosing a cost function in the annealing process. Both the acceptance bandwidth and the conversion efficiency are significantly improved in comparison with conventional periodically domain-inverted devices. >
Automatic throttle control for business and commuter aircraft

Honeywell has developed a digital autothrottle system for application on the Gulfstream IV business jet. This is the first true full-authority digital autothrottle designed specifically for the business and commuter market. A system description of the Gulfstream IV autothrottle is presented, and attention is given to modes and functions, design tradeoffs, and resulting performance. The autothrottle's modes are: takeoff, go around, flight level change, and speed hold. The primary design tradeoff during development was throttle activity versus control accuracy and dynamic response. The Honeywell design includes several techniques to yield the required accuracy with smooth response and minimal throttle activity. The result is an autothrottle system appropriate for business jets (where passenger comfort is very important). Some of the special considerations involved in designing a digital autothrottle system for use with FADEC (full authority digital engine controls)-controlled engines are discussed. <<ETX>>
A Grounded Theory Based Approach to Characterize Software Attack Surfaces

The notion of Attack Surface refers to the critical points on the boundary of a software system which are accessible from outside or contain valuable content for attackers. The ability to identify attack surface components of software system has a significant role in effectiveness of vulnerability analysis approaches. Most prior works focus on vulnerability techniques that use an approximation of attack surfaces and there have not been many attempts to create a comprehensive list of attack surface components. Although limited number of studies have focused on attack surface analysis, they defined attack surface components based on project specific hypotheses to evaluate security risk of specific types of software applications. In this study, we leverage a qualitative analysis approach to empirically identify an extensive list of attack surface components. To this end, we conduct a Grounded Theory (GT) analysis on 1444 previously published vulnerability reports and weaknesses with a team of three software developers and security experts. We extract vulnerability information from two publicly available repositories: 1) Common Vulnerabilities and Exposures (CVE) and 2) Common Weakness Enumeration (CWE). We ask three key questions: where the attacks come from, what they target, and how they emerge, and to help answer these questions we define three core categories for attack surface components: Entry points, Targets, and Mechanisms. We extract attack surface concepts related to each category from collected vulnerability information using the GT analysis and provide a comprehensive categorization that represents attack surface components of software systems from various perspectives. The paper introduces 254 new attack surface components that did not exist in the literature. The comparison of the proposed attack surface model with prior works indicates that only 6.7% of the identified Code level attack surface components are studied before.
Faster Multi-Goal Simulation-Based Testing Using DoLesS (Domination with Least Square Approximation)

For cyber-physical systems, finding a set of test cases with the least cost by exploring multiple goals is a complex task. For example, Arrieta et al. reported that state-of-the-art optimizers struggle to find minimal test suites for this task. To better manage this task, we propose DoLesS ( Do mination with Le a s t S quares Approximation) which uses a domination predicate to sort the space of possible goals to a small number of representative examples. Multi-objective domination then divides these examples into a “best” set and the remaining “rest” set. After that, DoLesS applies an inverted least squares approximation approach to learn a minimal set of tests that can distinguish best from rest in the reduced example space. DoLesS has been tested on four cyber-physical models: a tank flow model; a model of electric car windows; a safety feature of an AC engine; and a continuous PID controller combined with a discrete state machine. Comparing to the recent state-of-the-art paper attempted the same task, DoLesS performs as well or even better as state-of-the-art, while running 80-360 times faster on average (seconds instead of hours). Hence, we recommend DoLesS as a fast method to find minimal test suites for multi-goal cyber-physical systems. For replication purposes, all our code is on-line: https://github.com/hellonull123/Test_Selection_2021.
The Representation Jensen-Renýı Divergence

We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by infinitely divisible kernels. The empirical estimator of the divergence is computed using the eigenvalues of positive definite matrices that are obtained by evaluating the kernel over pairs of samples. The new measure shares similar properties to Jensen-Shannon divergence. Convergence of the proposed estimators follows from concentration results based on the difference between the ordered spectrum of the Gram matrices and the integral operators associated with the population quantities. The proposed measure of divergence avoids the estimation of the probability distribution underlying the data. Numerical experiments involving comparing distributions and applications to sampling unbalanced data for classification show that the proposed divergence can achieve state of the art results.
A Location Based Shopping List

People have always needed reminders, from tying a knot around the finger to writing a to-do list. The types of reminders have changed with the addition of modern technology, but the need is still the same. Technologies that use temporal events can be seen in many places, such as electronic calendars that remind users to go to a meeting a few minutes in advance. With the recent release of Google's Android platform, developers now have the opportunity to create applications that use spatial events, such as location reminders. This thesis explores how location reminders can be used, by implementing a location based shopping list for the Android platform. The primary and secondary use cases for a location based shopping list were identified, and the primary use cases were thouroughly described by studying how a paper shopping list works. Several user interface designs were described, and the best design was chosen by comparing the different designs' strengths and weaknesses. A prototype that implemented the primary use cases was implemented and tested on an HTC G1 smart phone. The tests included an acceptance test, unit tests, a usability test, and a field test. The results of the usability tests suggested that the prototype was easy to use and user friendly, but inconclusive in whether the implemented prototype was as fast to use as a paper shopping list. Field testing showed that location reminders only worked well using GPS and not with Cell-ID positioning on the G1 smart phone. The smart phone, however, became unusable because the GPS used too much power. The work done demonstrated that it is possible to implement applications that use location reminders for the Android platform, but the G1 hardware is still not capable to support location based applications.