Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal clustering network for self-diagnosing faults from vibration measurements

There is a need to build intelligence in operating machinery and use data analysis on monitored signals in order to quantify the health of the operating system and self-diagnose any initiations of fault. Built-in control procedures can automatically take corrective actions in order to avoid catastrophic failure when a fault is diagnosed. This paper presents a Temporal Clustering Network (TCN) capability for processing acceleration measurement(s) made on the operating system (i.e. machinery foundation, machinery casing, etc.), or any other type of temporal signals, and determine based on the monitored signal when a fault is at its onset. The new capability uses: one-dimensional convolutional neural networks (1D-CNN) for processing the measurements; unsupervised learning (i.e. no labeled signals from the different operating conditions and no signals at pristine vs. damaged conditions are necessary for training the 1D-CNN); clustering (i.e. grouping signals in different clusters reflective of the operating conditions); and statistical analysis for identifying fault signals that are not members of any of the clusters associated with the pristine operating conditions. A case study demonstrating its operation is included in the paper. Finally topics for further research are identified.
MACRO-ECONOMIC MEASURES FOR A GLOBALIZED WORLD: GLOBAL GROWTH AND INFLATION

This paper offers a framework for measuring global growth and inflation, built on standard index number theory, national accounts principles, and the concepts and methods for international macro-economic comparisons. Our approach provides a sound basis for purchasing power parity (PPP)- and exchange rate (XR)-based global growth and inflation measures. The Sato–Vartia index number system advocated here offers very similar results to a Fisher system but has the added advantage of allowing a complete decomposition with PPP or XR effects. For illustrative purposes, we present estimates of global growth and inflation for 141 countries over the years 2005 and 2011. The contribution of movements in XRs and PPPs to global inflation are presented. The aggregation properties of the method are also discussed.
Barium Selective Chemosensing by Diazacrown Ether Naphthalimide Turn-on Fluorophores for Single Ion Barium Tagging

Single molecule fluorescence detection of barium is investigated for enhancing the sensitivity and robustness of a neutrinoless double beta decay ($0\nu\beta\beta$) search in $^{136}$Xe, the discovery of which would alter our understanding of the nature of neutrinos and the early history of the Universe. A key developmental step is the synthesis of barium-selective chemosensors capable of incorporation into ongoing experiments in high-pressure $^{136}$Xe gas. Here we report turn-on fluorescent naphthalimide chemosensors containing monoaza- and diaza-crown ethers as agents for single Ba$^{2+}$ detection. Monoaza-18-crown-6 ether naphthalimide sensors showed sensitivity primarily to Ba$^{2+}$ and Hg$^{2+}$, whereas two diaza-18-crown-6 ether naphthalimides revealed a desirable selectivity toward Ba$^{2+}$. Solution-phase fluorescence and NMR experiments support a photoinduced electron transfer mechanism enabling turn-on fluorescence sensing in the presence of barium ions. Changes in ion-receptor interactions enable effective selectivity between competitive barium, mercury, and potassium ions, with detailed calculations correctly predicting fluorescence responses. With these molecules, dry-phase single Ba$^{2+}$ ion imaging with turn-on fluorescence is realized using oil-free microscopy techniques. This represents a significant advance toward a practical method of single Ba$^{2+}$ detection within large volumes of $^{136}$Xe, plausibly enabling a background-free technique to search for the hypothetical process of $0\nu\beta\beta$.
Quota-Induced Discarding in Heterogeneous Fisheries

Abstract Individual quota (IQ) programs are a promising and increasingly common means of regulating fisheries. This paper examines how profit maximizing fishers respond to different types of IQ programs in fisheries where many types of fish are harvested simultaneously. This analysis shows that the most common types of individual quota programs can induce discarding, and that individual quota programs that regulate the value of harvests never induce discarding. Since discarded fish have a high mortality rate, “value-based” individual quota programs are superior to their more conventional counterparts in that they waste fewer fish. The disadvantages of value-based quotas are also examined. Results are driven by the fact that the harvest technology examined here does not satisfy a “free-disposal” assumption. Since this free-disposal assumption is ubiquitous in production theory, and not obviously true, the framework developed herein may be useful for analyzing a broad class of problems involving joint production.
Circuits: An abstract viewpoint

Our primary purpose is to isolate the abstract, mathematical properties of circuits -- both classical Boolean circuits and quantum circuits -- that are essential for their computational interpretation. A secondary purpose is to clarify the similarities and differences between the classical and quantum situations. The general philosophy in this note is to include the mathematically essential aspects of circuits but to omit any of the additional structures that are usually included for convenience. We shall, however, retain the assumption that circuits are finite; this assumption does no harm to the applicability of our approach and is necessary for some of our work.
Constructing Higher-Dimensional Exact Black Holes in Einstein-Maxwell-Scalar Theory

We construct higher-dimensional and exact black holes in Einstein-Maxwell-scalar theory. The strategy we adopted is to extend the known, static and spherically symmetric black holes in the Einstein-Maxwell dilaton gravity and Einstein-Maxwell-scalar theory. Then we investigate the black hole thermodynamics. Concretely, the generalized Smarr formula and the first law of thermodynamics are derived.
Uniqueness of a three-dimensional stochastic differential equation

In order to extend the study of uniqueness property of multi-dimensional systems of stochastic differential equations, in this paper, we look at the following three-dimensional system of equations, of which the two-dimensional case was well-studied before: $dX_t=Y_tdt\quad, dY_t=Z_tdt,\quad dZ_t=|X_t|^{\alpha}dB_t$. We proved that if $(X_0,Y_0,Z_0)\neq(0,0,0)$, and $\frac{3}{4}<\alpha<1$, then the system of equations has a unique solution in the strong sense.
Aspects of geometric inflation

We revisit the recently proposed mechanism of Geometric Inflation. On general grounds, we show that obtaining the right amount of inflation demands an exceedingly large initial energy density. We introduce a scalar field and study the combined action of both mechanisms. Besides fixing the aforementioned issue, a cascading process occurs whose last step seems undistinguishable from ordinary large field inflation. Strikingly, the scalar field remains approximately constant while Geometric Inflation rules the dynamics. This ultimately leads to the possibility of reducing the initial value of the scalar field and its excursion. We discuss the main features of this hybrid scenario.
Implications of CP invariants for the flavored hybrid neutrino mass matrix

In the present paper, I have re-examined the weak basis invariants at low energies, proposed by C. Jarlskog and Branco et. al, respectively, in their earlier analyses, after confronting them with the assumptions of two zeros and an equality between arbitrary non-zero elements in the Majorana neutrino mass matrix in the flavoured basis. This particular conjecture is found to be experimentally feasible as shown by S. Dev and D. Raj in their recent work. The present analysis attempts to find the necessary and sufficient condition for CP invariance for each experimentally viable ansatz, pertaining to the model along with some important implications.
Leadership in early childhood education: The case for pedagogical praxis

In this conceptual article, the authors examine the context of early childhood education and care in England and the underpinning predominant ideologies to explore how these impact on the framing of leadership. The English context entails several contradictions (antinomies) at ontological, epistemological and axiological levels, and is heavily influenced by an ideological struggle concerning the value of play within the sector as opposed to a climate of child performativity. Moreover, the predominately female workforce (a factor itself) has faced relentless changes in terms of qualifications and curriculum reforms in recent years. With the introduction of the graduate leader qualification (Early Years Teacher Status), a vast body of research has been seeking to conceptualise what leadership means for early childhood education and care. In this article, the authors argue that these attempts are helpful and contribute to this discourse of leadership, but it needs to be thought of not only abstractly, but also practically. Thus, the authors conclude, the (re)conceptualisation of leadership should locate it as pedagogical praxis after evaluating the inherent deep dispositions of leaders in conjunction with their history, surrounding culture and subjective perspectives/realities.
Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm

Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions. To overcome this problem, population-based metaheuristic algorithms are widely used to improve the searching capacity. In this paper, we improve a popular metaheuristic called cuckoo search using a ring topology based fully informed strategy. In this strategy, each individual in the population learns from its neighborhoods to improve the cooperation of the population and the learning efficiency. Best solution or best fitness value can be obtained from the initial random threshold values, whose quality is evaluated by the correlation function. Experimental results have been examined on various numbers of thresholds. The results demonstrate that the proposed algorithm is more accurate and efficient than other four popular methods.
The Increasing Value of Inexperience in Congressional Primaries

The dynamics of congressional primaries have changed dramatically in recent years. Recent examples, such as Majority Leader Eric Cantor losing to a political novice in the Republican primary in 2014 and Alexandria Ocasio-Cortez’s defeat of Democratic Caucus Chair Joe Crowley in 2018, demonstrate that traditional theories of candidate success may not translate to today’s primary electoral landscape. Utilizing new data on candidate experience in congressional primary elections we analyze what types of candidates are successful, how these trends have changed over time, and variation by party. As expected, we find evidence, especially in recent primaries, that candidates with prior experience in elective office may not be besting those without prior experience in the systematic way they once did. We find the driving force behind the success of inexperienced candidates to be the growth in outside money in primary elections. Our primary findings aid our understanding of electoral competition and the candidate choices voters are given in the general election. Perhaps most importantly, the results showcase the electoral consequences of early PAC donations and the types of candidates PAC money supports in congressional primaries.
Neural Anisotropy Directions

In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., classifying a class of linearly separable distributions, and show that, depending on the direction of the discriminative feature of the distribution, many state-of-the-art deep convolutional neural networks (CNNs) have a surprisingly hard time solving this simple task. We then define as neural anisotropy directions (NADs) the vectors that encapsulate the directional inductive bias of an architecture. These vectors, which are specific for each architecture and hence act as a signature, encode the preference of a network to separate the input data based on some particular features. We provide an efficient method to identify NADs for several CNN architectures and thus reveal their directional inductive biases. Furthermore, we show that, for the CIFAR-10 dataset, NADs characterize features used by CNNs to discriminate between different classes.
Transfer Payments and the Macroeconomy: The Effects of Social Security Benefit Increases, 1952-1991

This paper uses Social Security benefit increases from 1952 to 1991 to investigate the macroeconomic effects of changes in transfers. It finds a large, immediate, and significant positive response of consumption to permanent benefit increases. The response declines after about five months, and does not appear to spread to industrial production or employment. The effects of transfers are faster, but much less persistent and much smaller overall, than those of tax changes. Finally, monetary policy responds strongly to benefit increases but not to tax changes. This may account for the failure of the effects of transfers to persist or spread.
Diverse Rule Sets

While machine-learning models are flourishing and transforming many aspects of everyday life, the inability of humans to understand complex models poses difficulties for these models to be fully trusted and embraced. Thus, interpretability of models has been recognized as an equally important quality as their predictive power. In particular, rule-based systems are experiencing a renaissance owing to their intuitive if-then representation. However, simply being rule-based does not ensure interpretability. For example, overlapped rules spawn ambiguity and hinder interpretation. Here we propose a novel approach of inferring diverse rule sets, by optimizing small overlap among decision rules with a 2-approximation guarantee under the framework of Max-Sum diversification. We formulate the problem as maximizing a weighted sum of discriminative quality and diversity of a rule set. In order to overcome an exponential-size search space of association rules, we investigate several natural options for a small candidate set of high-quality rules, including frequent and accurate rules, and examine their hardness. Leveraging the special structure in our formulation, we then devise an efficient randomized algorithm, which samples rules that are highly discriminative and have small overlap. The proposed sampling algorithm analytically targets a distribution of rules that is tailored to our objective. We demonstrate the superior predictive power and interpretability of our model with a comprehensive empirical study against strong baselines.
Wannier quasi-classical approach to high harmonic generation in semiconductors

We develop a quasi-classical theory of high harmonic generation in semiconductors based on an interband current that has been transformed from Bloch to Wannier basis. The Wannier quasi-classical approach reveals a complete picture of the mechanisms shaping high harmonic generation, such that quantitative agreement with full quantum calculations is obtained. The intuitive picture revealed by quasi-classical wavepacket propagation will be helpful in the interpretation and design of high harmonic and attosecond experiments. Beyond that, the capacity to quantitatively model quantum dynamics with classical trajectories should prove useful for a wider spectrum of condensed matter research, including coherent control, transport theory, and strong field physics.
Structured Stochastic Quasi-Newton Methods for Large-Scale Optimization Problems

In this paper, we consider large-scale finite-sum nonconvex problems arising from machine learning. Since the Hessian is often a summation of a relative cheap and accessible part and an expensive or even inaccessible part, a stochastic quasi-Newton matrix is constructed using partial Hessian information as much as possible. By further exploiting the low-rank structures based on the Nystrom approximation, the computation of the quasi-Newton direction is affordable. To make full use of the gradient estimation, we also develop an extra-step strategy for this framework. Global convergence to stationary point in expectation and local suplinear convergence rate are established under some mild assumptions. Numerical experiments on logistic regression, deep autoencoder networks and deep learning problems show that the efficiency of our proposed method is at least comparable with the state-of-the-art methods.
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach

This paper compares two different types of data-driven control methods, representing model-based and model-free approaches. One is a recently proposed method - Deep Koopman Representation for Control (DKRC), which utilizes a deep neural network to map an unknown nonlinear dynamical system to a high-dimensional linear system, which allows for employing state-of-the-art control strategy. The other one is a classic model-free control method based on an actor-critic architecture - Deep Deterministic Policy Gradient (DDPG), which has been proved to be effective in various dynamical systems. The comparison is carried out in OpenAI Gym, which provides multiple control environments for benchmark purposes. Two examples are provided for comparison, i.e., classic Inverted Pendulum and Lunar Lander Continuous Control. From the results of the experiments, we compare these two methods in terms of control strategies and the effectiveness under various initialization conditions. We also examine the learned dynamic model from DKRC with the analytical model derived from the Euler-Lagrange Linearization method, which demonstrates the accuracy in the learned model for unknown dynamics from a data-driven sample-efficient approach.
I know why you like this movie: Interpretable Efficient Multimodal Recommender

Recently, the Efficient Manifold Density Estimator (EMDE) model has been introduced. The model exploits Local Sensitive Hashing and Count-Min Sketch algorithms, combining them with a neural network to achieve state-of-the-art results on multiple recommender datasets. However, this model ingests a compressed joint representation of all input items for each user/session, so calculating attributions for separate items via gradient-based methods seems not applicable. We prove that interpreting this model in a white-box setting is possible thanks to the properties of EMDE item retrieval method. By exploiting multimodal flexibility of this model, we obtain meaningful results showing the influence of multiple modalities: text, categorical features, and images, on movie recommendation output.
An Improved Traffic Crime Predictive System using Multinomial Naive Bayes Text Classification Algorithm

Traffic law enforcement agencies in Nigeria have faced a huge setback as they do not have records of offenders or criminals that have been persecuted in the past. In this paper, a system was developed that can predict the possible class of traffic crime together with the penalty attached to that class of criminal offence that a known traffic criminal offender is most likely to commit next. The likelihood and frequency table will be constructed from a dataset of traffic crime data, the likelihood of a user falling under a particular class of traffic crime will also be established. Also, proposed to be designed and developed is a predictive system that uses object-oriented analysis and design methodology (OOADM), improved naïve bayes text classification algorithm to solve these problems. This will be achieved by implementing the stated model with python model-view-controller (MVC) framework known as Django Framework. This improved system is implemented using a real-time, cloud-hosted NOSQL database called FireBase which guarantees scalability. From the results, it was found out that the speed and predictability of probability of any user falling under a class 1 crime type was 81.42% and 10.39%, 8.19% for class 2 and class 3 respectively.
Efficacy of antimicrobials against Campylobacter jejuni on chicken breast skin

SUMMARY Raw chicken is a significant source of the bacterial pathogen Campylobacter. The commercial processing of poultry for consumption presents many opportunities in which carcasses and other raw products can become newly contaminated or cross-contaminated with this microorganism. Increased use of chlorine and higher volumes of water during processing may not be effective for reducing Campylobacter populations. This study examined the efficacy of 10% trisodium phosphate (TSP), 0.1% acidified sodium chlorite (ASC), 0.1 and 0.5% cetylpyridinium chloride (CPC), 1% Tween 80, and water (50°C at application) for their ability to inactivate, reverse the attachment of, or inhibit the attachment of Campylobacter jejuni applied to chicken breast skin. Statistically significant (P ≤ 0.05) differences in the reduction of C. jejuni populations were observed across chemical treatments and contact times (30 s, 3 min, or 10 min). When bacteria were applied before treatment, a reduction of >1.0 log10 cfu/skin was achieved with 0.5% CPC (2.89), 10% TSP (1.63), 0.1% ASC (1.52), and 0.1% CPC (1.42). When bacteria were applied after chemical treatment, a reduction of >1.0 log10 cfu/skin was achieved with 0.5% CPC (4.67) and 10% TSP (1.28). The commercial use of an effective antimicrobial chemical spray may help to reduce the level of Campylobacter on raw poultry carcasses and reduce the volume of rinse water applied for carcass washing.
Self-Supervised Representation Learning for Visual Anomaly Detection

Self-supervised learning allows for better utilization of unlabelled data. The feature representation obtained by self-supervision can be used in downstream tasks such as classification, object detection, segmentation, and anomaly detection. While classification, object detection, and segmentation have been investigated with self-supervised learning, anomaly detection needs more attention. We consider the problem of anomaly detection in images and videos, and present a new visual anomaly detection technique for videos. Numerous seminal and state-of-the-art self-supervised methods are evaluated for anomaly detection on a variety of image datasets. The best performing image-based self-supervised representation learning method is then used for video anomaly detection to see the importance of spatial features in visual anomaly detection in videos. We also propose a simple self-supervision approach for learning temporal coherence across video frames without the use of any optical flow information. At its core, our method identifies the frame indices of a jumbled video sequence allowing it to learn the spatiotemporal features of the video. This intuitive approach shows superior performance of visual anomaly detection compared to numerous methods for images and videos on UCF101 and ILSVRC2015 video datasets.