Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons

The demand of many application domains for flexibility, stretchability, and porosity cannot be typically met by the silicon VLSI technologies. Printed Electronics (PE) has been introduced as a candidate solution that can satisfy those requirements and enable the integration of smart devices on consumer goods at ultra low-cost enabling also in situ and on-demand fabrication. However, the large features sizes in PE constraint those efforts and prohibit the design of complex ML circuits due to area and power limitations. Though, classification is mainly the core task in printed applications. In this work, we examine, for the first time, the impact of neural minimization techniques, in conjunction with bespoke circuit implementations, on the area-efficiency of printed Multilayer Perceptron classifiers. Results show that for up to 5 % accuracy loss up to 8× area reduction can be achieved.
RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks

The state-of-the-art predictive maintenance (PdM) techniques have shown great success in reducing maintenance costs and downtime of complicated machines while increasing overall productivity through extensive utilization of Internet-of-Things (IoT) and Deep Learning (DL). Unfortunately, IoT sensors and DL algorithms are both prone to cyber-attacks. For instance, DL algorithms are known for their susceptibility to adversarial examples. Such adversarial attacks are vastly under-explored in the PdM domain. This is because the adversarial attacks in the computer vision domain for classification tasks cannot be directly applied to the PdM domain for multivariate time series (MTS) regression tasks. In this work, we propose an end-to-end methodology to design adversarially robust PdM systems by extensively analyzing the effect of different types of adversarial attacks and proposing a novel adversarial defense technique for DL-enabled PdM models. First, we propose novel MTS Projected Gradient Descent (PGD) and MTS PGD with random restarts (PGD_r) attacks. Then, we evaluate the impact of MTS PGD and PGD_r along with MTS Fast Gradient Sign Method (FGSM) and MTS Basic Iterative Method (BIM) on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Bi-directional LSTM based PdM system. Our results using NASA's turbofan engine dataset show that adversarial attacks can cause a severe defect (up to 11X) in the RUL prediction, outperforming the effectiveness of the state-of-the-art PdM attacks by 3X. Furthermore, we present a novel approximate adversarial training method to defend against adversarial attacks. We observe that approximate adversarial training can significantly improve the robustness of PdM models (up to 54X) and outperforms the state-of-the-art PdM defense methods by offering 3X more robustness.
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming

In this paper, we study the predict-then-optimize problem where the output of a machine learning prediction task is used as the input of some downstream optimization problem, say, the objective coefficient vector of a linear program. The problem is also known as predictive analytics or contextual linear programming. The existing approaches largely suffer from either (i) optimization intractability (a non-convex objective function)/statistical inefficiency (a suboptimal generalization bound) or (ii) requiring strong condition(s) such as no constraint or loss calibration. We develop a new approach to the problem called \textit{maximum optimality margin} which designs the machine learning loss function by the optimality condition of the downstream optimization. The max-margin formulation enjoys both computational efficiency and good theoretical properties for the learning procedure. More importantly, our new approach only needs the observations of the optimal solution in the training data rather than the objective function, which makes it a new and natural approach to the inverse linear programming problem under both contextual and context-free settings; we also analyze the proposed method under both offline and online settings, and demonstrate its performance using numerical experiments.
Reynolds Equations for Common Generalized Newtonian Models and an Approximate Reynolds-Carreau Equation

Abstract Exact, closed-form, one-dimensional Reynolds equations are presented for the Ostwald-DeWaele model, the Ellis model, the Spriggs model, and the double-Newtonian Rabinowitsch and Ferry models. From numerical solutions for flowrate, an approximate Reynolds-Carreau equation is obtained. These models describe the wide range of constitutive shear responses that have been observed in lubricants at elevated pressure. It is hoped that the equations presented here will enable researchers to perform full elastohydrodynamic lubrication (EHL) simulations of the coupled elasticity and Reynolds equations for shear-thinning liquids.
Recursive deep learning framework for forecasting the decadal world economic outlook

Gross domestic product (GDP) is the most widely used indicator in macroeconomics and the main tool for measuring a country’s economic ouput. Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes such as pandemics and wars. Deep learning models are well suited for modeling temporal sequences have been applied for time series forecasting. In this paper, we develop a deep learning framework to forecast the GDP growth rate of the world economy over a decade. We use Penn World Table as the source of our data, taking data from 1980 to 2019, across 13 countries, such as Australia, China, India, the United States and so on. We test multiple deep learning models, LSTM, BD-LSTM, ED-LSTM and CNN, and compared their results with the traditional time series model (ARIMA,VAR). Our results indicate that ED-LSTM is the best performing model. We present a recursive deep learning framework to predict the GDP growth rate in the next ten years. We predict that most countries will experience economic growth slowdown, stagnation or even recession within ﬁve years; only China, France and India are predicted to experience stable, or increasing, GDP growth.
BERT-Embedding and Citation Network Analysis based Query Expansion Technique for Scholarly Search

The enormous growth of research publications has made it challenging for academic search engines to bring the most relevant papers against the given search query. Numerous solutions have been proposed over the years to improve the effectiveness of academic search, including exploiting query expansion and citation analysis. Query expansion techniques mitigate the mismatch between the language used in a query and indexed documents. However, these techniques can suffer from introducing non-relevant information while expanding the original query. Recently, contextualized model BERT to document retrieval has been quite successful in query expansion. Motivated by such issues and inspired by the success of BERT, this paper proposes a novel approach called QeBERT. QeBERT exploits BERT-based embedding and Citation Network Analysis (CNA) in query expansion for improving scholarly search. Specifically, we use the context-aware BERT-embedding and CNA for query expansion in Pseudo-Relevance Feedback (PRF) fashion. Initial experimental results on the ACL dataset show that BERT-embedding can provide a valuable augmentation to query expansion and improve search relevance when combined with CNA.
Low-Rank Winograd Transformation for 3D Convolutional Neural Networks

This paper focuses on Winograd transformation in 3D convolutional neural networks (CNNs) that are more over-parameterized compared with the 2D version. The over-increasing Winograd parameters not only exacerbate training complexity but also barricade the practical speedups due simply to the volume of element-wise products in the Winograd domain. We attempt to reduce trainable parameters by introducing a low-rank Winograd transformation, a novel training paradigm that decouples the original large tensor into two less storage-required trainable tensors, leading to a signiﬁcant complexity reduction. Built upon our low-rank Winograd transformation, we take one step ahead by propos-ing a low-rank oriented sparse granularity that mea-sures column-wise parameter importance. By simply involving the non-zero columns in the element-wise product, our sparse granularity is empowered with the ability to produce a very regular sparse pattern to acquire effectual Winograd speedups. To better understand the efﬁcacy of our method, we perform extensive experiments on 3D CNNs. Results manifest that our low-rank Winograd transformation well outperforms the vanilla Winograd transformation. We also show that our proposed low-rank oriented sparse granularity permits practical Winograd acceleration compared with the vanilla counterpart.
Gravitational-wave energy-momentum tensor and radiated power in a strongly curved background

Allowing for the possibility of extra dimensions, there are two paradigms: either the extra dimensions are hidden from observations by being compact and small as in Kaluza-Klein scenarios, or the extra dimensions are large/non-compact and undetectable due to a large warping as in the Randall-Sundrum scenario. In the latter case, the five-dimensional background has a large curvature, and Isaacson's construction of the gravitational energy-momentum tensor, which relies on the assumption that the wavelength of the metric fluctuations is much smaller than the curvature length of the background spacetime, cannot be used. In this paper, we construct the gravitational energy-momentum tensor in a strongly curved background such as Randall-Sundrum. We perform a scalar-vector-tensor decomposition of the metric fluctuations with respect to the $SO(1,3)$ background isometry and construct the covariantly-conserved gravitational energy-momentum tensor out of the gauge-invariant metric fluctuations. We give a formula for the power radiated by gravitational waves and verify it in known cases. In using the gauge-invariant metric fluctuations to construct the gravitational energy-momentum tensor we follow previous work done in cosmology. Our framework has applicability beyond the Randall-Sundrum model.
Conservative Safety Monitors of Stochastic Dynamical Systems

Generating accurate runtime safety estimates for autonomous systems is vital to ensuring their continued proliferation. However, exhaustive reasoning about future behaviors is generally too complex to do at runtime. To provide scalable and formal safety estimates, we propose a method for leveraging design-time model checking results at runtime. Specifically, we model the system as a probabilistic automaton (PA) and compute bounded-time reachability probabilities over the states of the PA at design time. At runtime, we combine distributions of state estimates with the model checking results to produce a bounded time safety estimate. We argue that our approach produces well-calibrated safety probabilities, assuming the estimated state distributions are well-calibrated. We evaluate our approach on simulated water tanks.
Linear congruence relations for exponents of Borcherds products

For all positive powers of primes p ≥ 5, we prove the existence of inﬁnitely many linear congruences between the exponents of twisted Borcherds products arising from a suitable scalar-valued weight 1 / 2 weakly holomorphic modular form or a suitable vector-valued harmonic Maaß form. To this end, we work with the logarithmic derivatives of these twisted Borcherds products, and oﬀer various numerical examples of non-trivial linear congruences between them modulo p = 11. In the case of positive powers of primes p = 2 , 3, we obtain similar results by multiplying the logarithmic derivative with a Hilbert class polynomial as well as a power of the modular discriminant function. Both results conﬁrm a speculation by Ono.
Semi-Automated Construction of Food Composition Knowledge Base

A food composition knowledge base, which stores the essential phyto-, micro-, and macro-nutrients of foods is useful for both research and industrial applications. Although many existing knowledge bases attempt to curate such information, they are often limited by time-consuming manual curation processes. Outside of the food science domain, natural language processing methods that utilize pre-trained language models have recently shown promising results for extracting knowledge from unstructured text. In this work, we propose a semi-automated framework for constructing a knowledge base of food composition from the scientific literature available online. To this end, we utilize a pre-trained BioBERT language model in an active learning setup that allows the optimal use of limited training data. Our work demonstrates how human-in-the-loop models are a step toward AI-assisted food systems that scale well to the ever-increasing big data.
Confidence Limits for the Ratio of Two Binomial Proportions Based on Likelihood Scores: Non-Iterative Method

The interval estimation of the ratio of two binomial proportions based on the score statistic is superior over other methods. Iterative algorithms for calculating the approximate confidence interval have been provided by, e.g., KOOPMAN (1984, Biometrics 40:513–517) and GART and NAM (1988a, Biometrics 44:323–338). This note presents the analytical solutions for upper and lower confidence limits in a closed form and gives examples for numerical illustration. The non-iterative method is generally more desirable than the iterative method.
Optimal ^{∞} estimates for the finite element method on irregular meshes

Uniform estimates for the error in the finite element method are derived for a model problem on a general triangular mesh in two dimensions. These are optimal if the degree of the piecewise polynomials is greater than one. Similar estimates of the error are also derived in L/sup p/. As an intermediate step, an L/sup 1/ estimate of the gradient of the error in the finite element approximation of the Green's function is proved that is optimal for all degrees.
Nonlinear multiparameter optimization using genetic algorithms; inversion of plane-wave seismograms

Seismic waveform inversion is one of many geophysical problems which can be identified as a nonlinear multiparameter optimization problem. Methods based on local linearization fail if the starting model is too far from the true model. We have investigated the applicability of “Genetic Algorithms” (GA) to the inversion of plane‐wave seismograms. Like simulated annealing, genetic algorithms use a random walk in model space and a transition probability rule to help guide their search. However, unlike a single simulated annealing run, the genetic algorithms search from a randomly chosen population of models (strings) and work with a binary coding of the model parameter set. Unlike a pure random search, such as in a “Monte Carlo” method, the search used in genetic algorithms is not directionless. Genetic algorithms essentially consist of three operations, selection, crossover, and mutation, which involve random number generation, string copies, and some partial string exchanges. The choice of the initial popul.
Elastoplastic Damaging Model for Adhesive Anchor Systems. II: Numerical and Experimental Validation

This paper presents the numerical and experimental validation of the analytical elastoplastic damaging model proposed in the companion paper (Part I). The validation was carried out by describing the pullout failure of epoxy adhesive anchors. Pullout tests were simulated numerically and performed experimentally. Several specimens made of a rebar embedded in a hardened concrete cylinder by means of polyester resin were tested. Conventional strain gauges and acoustic emission (AE) sensors were used to evaluate the structural response of the system and to monitor the onset and progression of structural damage, respectively. The parametric analysis and the moment tensor analysis of the AE data were used to discriminate among different sources of damage. The results show the ability of the model to predict the response of the anchors and the suitability of the AE method to monitor damage onset and propagation and to discriminate among different source of damage.
Open science and epistemic pluralism: A tale of many perils and some opportunities

Broadly, open science can be defined as “transparent and accessible knowledge that is shared and developed through collaborative networks” (Vicente-Saez & Martinez-Fuentes, 2018, p. 434). Hence, it refers to a broad range of practices aimed at detecting scientific fraud and enhancing transparency and replicability of research. In their focal article, Guzzo et al., (2022) highlighted several tensions between these values and applied research in organizations. In this commentary, we develop a slightly different argument: the open science movement, as a direct offspring of (post)positivist research paradigms1, has the potential to stifle epistemological and scientific pluralism and reproduce historical scientific hierarchies it purports to redress. In doing so, we distinguish between the spirit of open science (i.e., promoting participation, transparency, and access to science) and its implementations (e.g., OSF badges, TOP guidelines, and multi-laboratory research, but also sexist attacks on social media and podcasts by other scholars in the field [e.g., the Twitter pile-on in November 2021 regarding Roxanne Felig and her coauthors’ paper], and a general disregard of feminist epistemologies; Brabeck, 2021). In the first part of this commentary, we focus on open science’s ideals and examine a few unstated assumptions, advancing a set of equally valid assumptions based on constructionist thought, and then we discuss how unchecked implementations of open science practices can marginalize scholars that do not subscribe to its epistemic premises. We conclude with a few thoughts to improve the open science movement.
RETICULATE PHYLOGENY: A NEW TETRAPLOID PARTHENOGENETIC WHIPTAIL LIZARD DERIVED FROM HYBRIDIZATION AMONG FOUR BISEXUAL ANCESTRAL SPECIES OF ASPIDOSCELIS (REPTILIA: SQUAMATA: TEIIDAE)

Abstract. We describe the only known tetraploid parthenogenetic species of amniote that has haploid genomes from four distinct ancestral bisexual (gonochoristic) species. These genomes were brought together through three hybridization events that occurred over a time span of hundreds or thousands of years, the last of which occurred recently in captivity without any experimental manipulation. Inheritance of alleles at seven microsatellite deoxyribonucleic acid loci and DNA sequence data for adenosine deaminase intron 9 in the new tetraploid species confirms its parentage and, together with DNA quantification, demonstrates that tetraploidy and high heterozygosity are maintained parthenogenetically generation after generation. Comparisons of univariate and multivariate variation in scalation between the tetraploids and their parental taxa reveal a strong maternal similarity in morphological characters, with little to no significant differences, suggesting that such tetraploid females may exist in old museum samples misidentified as the maternal ancestor. This research on specimens of known parentage adds support to earlier papers that provide important insights for taxonomic treatment of many parthenogenetic clones of teiid lizards that occur in the Western Hemisphere.
Impurity Atoms/Ions Embedded in Metals

Impurity atoms/ions embedded in a metal are studied within a new nonlocal Hartree-Fock approach which utilizes the embedding scheme proposed by Inglesfield and the coupled Hartree-Fock calculational scheme proposed by Cohen and Roothaan. The method combines highly sophisticated calculational techniques common in separate fields of physics and can be used for embedded impurities being either in their ground or excited states. The range of applicability of the method is shown for the example of an excited Argon atom, Ar*(KL 3p5 4s), embedded in a metal, the latter being approximated within the jellium model.
Optical and equivalent paths in a stratified medium, treated from a wave standpoint

In a series of recent papers, Appleton has examined various methods of investigating tire structure of the Heaviside layer, lazing particular stress on the exact nature of the information which can be obtained from the experimental observations made by different methods. One particular point of importance in the interpretation of the observations emerges from this examination, namely the distinction between the "optical path" or "phase path" ∫μ ds = c ∫ ds / V and the "equivalent path" or "group path" c ∫ ds / U, where V, U are the wave and group velocities and the integrations are taken along the geometrical ray between transmitter and receiver. The analysis also shows clearly that the latter is the quantity which is observed by most methods, and in particular by the frequency-change method used by Appleton himself in his observational work.
Synthesis and characterization of MWCNTs/Al6082 nanocomposites through ultrasonic assisted stir casting technique

Abstract The objectives of this work are to investigate the microstructural characterization of multi-walled carbon nanotubes (MWCNTs) produced by chemical vapor deposition method and to prepare Al6082/MWCNT nanocomposites by stir casting method. MWCNTs were developed and then ball milled with aluminum powder to obtain its coating on MWCNTs to improve wettability. Similarly, ultrasonic mixing was used to de-agglomerate the bundled MWCNTs during the stir casting process. MWCNTs were added to molten Al6082 with varying weight percentages (0.3–1.2 wt. %) during the stirring process, and microstructural characterization was performed with field emission scanning electron microscope (FESEM) along with energy dispersive X-ray spectrometer (EDX) for evaluation of elements. EDX analysis of the developed and purified MWCNTs revealed the absence of nickel and other impurities after purification. The Results from the microstructure of nanocomposites indicate that MWCNTs were uniformly distributed for up to 0.9 wt. % of MWCNT and on further increment of MWCNTs, agglomeration was noticed.
Theory application in food waste behaviour programs: a systematic literature review

ABSTRACT Food waste is a significant environmental, economic, and social issue contributing to global warming. The benefits of theoretically informed programs have been verified in psychology, social science, and education, yet our understanding of theory application in food waste behaviour change is scarce. To understand how food waste can be reduced, we need to understand what works, when, where and why. Assessing the extent of theory use in the context of food waste behaviour is needed to advance progress. Following the approach employed in Willmott et al. (2019) this study sought to assess the extent of theory application in studies aiming to reduce household food waste. A systematic search and review of published studies concerning household food waste behaviour was conducted. Of 3,532 studies, nine studies met the inclusion criteria. Eight studies mentioned a theory, however, the association between program strategies implemented and the theory base reported was lacking. This review concludes there is considerable room for improvement in theory reporting to indicate clearly how theory is applied to reduce household food waste behaviour.