Miracle-dz/UniFAR-FTUs huggingface dataset:
Bi-stability in a vertically excited rectangular tank with finite liquid depth

We discuss the bi - stability that is possibly exhibited by a liquid free surface in a parametrically - driven two-dimensional (2D) rectangular tank with finite liquid depth. Following the method of adaptive mode ordering, assuming two dominant modes and retaining polynomial nonlinearities up to third-order, a nonlinear finite-dimensional nonlinear modal system approximation is obtained. A "continuation method" of nonlinear dynamics is then used in order to elicit efficiently the instability boundary in parameters` space and to predict how steady surface elevation changes as the frequency and/or the amplitude of excitation are varied. Results are compared against those of the linear version of the system (that is a Mathieu-type model) and furthermore, against an intermediate model also derived with formal mode ordering, that is based on a second - order ordinary differential equation having nonlinearities due to products of elevation with elevation velocity or acceleration. The investigation verifies that, in parameters space, there must be a region, inside the quiescent region, where liquid surface instability is exhibited. There, behaviour depends on initial conditions and a wave form would be realised only if the free surface was substantially disturbed initially.
Solution to the Non-Monotonicity and Crossing Problems in Quantile Regression

This paper proposes a new method to address the long-standing problem of lack of monotonicity in estimation of the conditional and structural quantile function, also known as quantile crossing problem. Quantile regression is a very powerful tool in data science in general and econometrics in particular. Unfortunately, the crossing problem has been confounding researchers and practitioners alike for over 4 decades. Numerous attempts have been made to find a simple and general solution. This paper describes a unique and elegant solution to the problem based on a flexible check function that is easy to understand and implement in R and Python, while greatly reducing or even eliminating the crossing problem entirely. It will be very important in all areas where quantile regression is routinely used and may also find application in robust regression, especially in the context of machine learning. From this perspective, we also utilize the flexible check function to provide insights into the root causes of the crossing problem.
Creative Compensation (CC): Future of Jobs with Creative Works in 3D Printing

With the continuous growth of online 3D printing community and the democratization of 3D printers, growing number of users start sharing their own 3D designs on open platforms, enabling a wide audience to search, download, and 3D print models for free. Although sharing is mostly for altruistic reasons at first, open platforms had also created potential job opportunities to compensate creative labors. This paper analyzes new job opportunities emerged in online 3D printing social platforms and patterns of seeking compensations, and reveals various motivations for posting creative content online. We find that offering exclusive membership through subscriptions, selling final products or printing services through web stores, and using affiliate links are primary means of earning profits, while there exist gaps between creators' expectations and realities. We show that various socio-economic promises emerged, leading to a win-win situation for both creators to gain extra income and audiences to have access to more quality content. We also discuss future challenges that need to be addressed, such as ethical use of opensource content.
Learning Rates for Nonconvex Pairwise Learning

Pairwise learning is receiving increasing attention since it covers many important machine learning tasks, e.g., metric learning, AUC maximization, and ranking. Investigating the generalization behavior of pairwise learning is thus of great significance. However, existing generalization analysis mainly focuses on the convex objective functions, leaving the nonconvex pairwise learning far less explored. Moreover, the current learning rates of pairwise learning are mostly of slower order. Motivated by these problems, we study the generalization performance of nonconvex pairwise learning and provide improved learning rates. Specifically, we develop different uniform convergence of gradients for pairwise learning under different assumptions, based on which we characterize empirical risk minimizer, gradient descent, and stochastic gradient descent. We first establish learning rates for these algorithms in a general nonconvex setting, where the analysis sheds insights on the trade-off between optimization and generalization and the role of early-stopping. We then derive faster learning rates of order <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-3259324.gif"/></alternatives></inline-formula> for nonconvex pairwise learning with a gradient dominance curvature condition, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq2-3259324.gif"/></alternatives></inline-formula> is the sample size. Provided that the optimal population risk is small, we further improve the learning rates to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/n^{2})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq3-3259324.gif"/></alternatives></inline-formula>, which, to the best of our knowledge, are the first <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/n^{2})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq4-3259324.gif"/></alternatives></inline-formula> rates for pairwise learning.
Event Detection in Twitter: A Content and Time-Based Analysis

The detection of events from online social networks is a recent, evolving field that attracts researchers from across a spectrum of disciplines and domains. Here we report a time-series analysis for predicting events. In particular, we evaluated the frequency distribution of top n-grams of terms over time, focusing on two indicators: high-frequency n-grams over both short and long periods of time. Both indicators can refer to certain aspects of events as they evolve. To evaluate the models accuracy in detecting events, we built and used a Twitter dataset of the most popular hashtags that surrounded the well-documented protests that occurred at the University of Missouri (Mizzou) in late 2015.
Effect of fluctuations on the geodesic rule for topological defect formation

At finite temperatures, the field along a linear stretch of correlation length size is supposed to trace the shortest path in the field space given the two end point values, known as the geodesic rule. In this study, we compute the probability that, the field variations over distances of correlation length follow this rule in theories with [Formula: see text] global symmetry. We consider a simple ferromagnetic [Formula: see text]-spin model and a complex [Formula: see text] theory. The computations are carried out on an ensemble of equilibrium configurations, generated using Monte Carlo simulations. The numerical results suggest a significant deviation to the geodesic rule, relevant for the formation of topological defects during quench in second-order phase transition. Also for the case of [Formula: see text]-spins in two dimensions, the distribution and density of vortices, have been studied. It is found that, for quench temperatures close to the transition point, the Kibble–Zurek mechanism underestimates the equilibrium density of defects. The exponents corresponding to the width of the distributions are found to be smaller than Kibble mechanism estimates and match only when there is no deviation from the geodesic rule.
Bilinear pooling and metric learning network for early Alzheimer's disease identification with FDG-PET images

FDG-PET reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, the studies of identification of early MCI (EMCI) and late MCI (LMCI) with FDG-PET images are still insufficient. Compared with studies based on fMRI and DTI images, the researches of the inter-region representation features in FDG-PET images are insufficient. Moreover, considering the variability in different individuals, some hard samples which are very similar with both two classes limit the classification performance. To tackle these problems, in this paper, we propose a novel bilinear pooling and metric learning network (BMNet), which can extract the inter-region representation features and distinguish hard samples by constructing embedding space. To validate the proposed method, we collect 998 FDG-PET images from ADNI. Following the common preprocessing steps, 90 features are extracted from each FDG-PET image according to the automatic anatomical landmark (AAL) template and then sent into the proposed network. Extensive 5-fold cross-validation experiments are performed for multiple two-class classifications. Experiments show that most metrics are improved after adding the bilinear pooling module and metric losses to the Baseline model respectively. Specifically, in the classification task between EMCI and LMCI, the specificity improves 6.38% after adding the triple metric loss, and the negative predictive value (NPV) improves 3.45% after using the bilinear pooling module.
Universal lesion detection in CT scans using neural network ensembles

In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastatic lesions. A prerequisite for lesion sizing is their detection, as it promotes the downstream assessment of tumor spread. However, lesions vary in their size and appearance in CT scans, and radiologists often miss small lesions during a busy clinical day. To overcome these challenges, we propose the use of state-of-the-art detection neural networks to flag suspicious lesions present in the NIH DeepLesion dataset for sizing. Additionally, we incorporate a bounding box fusion technique to minimize false positives (FP) and improve detection accuracy. Finally, to resemble clinical usage, we constructed an ensemble of the best detection models to localize lesions for sizing with a precision of 65.17% and sensitivity of 91.67% at 4 FP per image. Our results improve upon or maintain the performance of current state-of-the-art methods for lesion detection in challenging CT scans.
Tackling Morphological Analogies Using Deep Learning - Extended Version

Analogical proportions are statements of the form"A is to B as C is to D". They constitute an inference tool that provides a logical framework to address learning, transfer, and explainability concerns and that finds useful applications in artificial intelligence and natural language processing. In this paper, we address two problems, namely, analogy detection and resolution in morphology. Multiple symbolic approaches tackle the problem of analogies in morphology and achieve competitive performance. We show that it is possible to use a data-driven strategy to outperform those models. We propose an approach using deep learning to detect and solve morphological analogies. It encodes structural properties of analogical proportions and relies on a specifically designed embedding model capturing morphological characteristics of words. We demonstrate our model's competitive performance on analogy detection and resolution over multiple languages. We provide an empirical study to analyze the impact of balancing training data and evaluate the robustness of our approach to input perturbation.
Ultra-Low Power Keyword Spotting at the Edge

Keyword spotting (KWS) has become an indispensable part of many intelligent devices surrounding us, as audio is one of the most efficient ways of interacting with these devices. The accuracy and performance of KWS solutions have been the main focus of the researchers, and thanks to deep learning, substantial progress has been made in this domain. However, as the use of KWS spreads into IoT devices, energy efficiency becomes a very critical requirement besides the performance. We believe KWS solutions that would seek power optimization both in the hardware and the neural network (NN) model architecture are advantageous over many solutions in the literature where mostly the architecture side of the problem is considered. In this work, we designed an optimized KWS CNN model by considering end-to-end energy efficiency for the deployment at MAX78000, an ultra-low-power CNN accelerator. With the combined hardware and model optimization approach, we achieve 96.3\% accuracy for 12 classes while only consuming 251 uJ per inference. We compare our results with other small-footprint neural network-based KWS solutions in the literature. Additionally, we share the energy consumption of our model in power-optimized ARM Cortex-M4F to depict the effectiveness of the chosen hardware for the sake of clarity.
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting

Time series forecasting is a significant problem in many applications, e.g., financial predictions and business optimization. Modern datasets can have multiple correlated time series, which are often generated with global (shared) regularities and local (specific) dynamics. In this paper, we seek to tackle such forecasting problems with DeepDGL, a deep forecasting model that disentangles dynamics into global and local temporal patterns. DeepDGL employs an encoder-decoder architecture, consisting of two encoders to learn global and local temporal patterns, respectively, and a decoder to make multi-step forecasting. Specifically, to model complicated global patterns, the vector quantization (VQ) module is introduced, allowing the global feature encoder to learn a shared codebook among all time series. To model diversified and heterogenous local patterns, an adaptive parameter generation module enhanced by the contrastive multi-horizon coding (CMC) is proposed to generate the parameters of the local feature encoder for each individual time series, which maximizes the mutual information between the series-specific context variable and the long/short-term representations of the corresponding time series. Our experiments on several real-world datasets show that DeepDGL outperforms existing state-of-the-art models.
Ethically aligned Deep Learning: Unbiased Facial Aesthetic Prediction

Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. In the past those results were highly correlated with human ratings, therefore also with their bias in annotating. As artificial intelligence can have racist and discriminatory tendencies, the cause of skews in the data must be identified. Development of training data and AI algorithms that are robust against biased information is a new challenge for scientists. As aesthetic judgement usually is biased, we want to take it one step further and propose an Unbiased Convolutional Neural Network for FBP. While it is possible to create network models that can rate attractiveness of faces on a high level, from an ethical point of view, it is equally important to make sure the model is unbiased. In this work, we introduce AestheticNet, a state-of-the-art attractiveness prediction network, which significantly outperforms competitors with a Pearson Correlation of 0.9601. Additionally, we propose a new approach for generating a bias-free CNN to improve fairness in machine learning.
PRINCIPLES OF ELECTROADHESION IN CLOTHING ROBOTICS

With the growing need of automation for garment assembly in the clothing industry comes the requirement for a compete range of textile handling devices. Robotics offers the flexibility needed to compete in a market where change in style and fashion can be both rapid and unpredictable. The actual physical mechanisms involved in the use of electrostatic attraction of clothing fabrics as a robot gripping technique are discussed. Following an outline of the underlying principles, experimental results are presented for a range of materials tested using electroadhesive surfaces. A selection of robot gripper configurations are provided together with a discussion of their relative merits.
Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation

We demonstrate that Domain Invariant Feature Learning (DIFL) can improve the out-of-domain generalizability of a deep learning Tuberculosis screening algorithm. It is well known that state of the art deep learning algorithms often have difficulty generalizing to unseen data distributions due to"domain shift". In the context of medical imaging, this could lead to unintended biases such as the inability to generalize from one patient population to another. We analyze the performance of a ResNet-50 classifier for the purposes of Tuberculosis screening using the four most popular public datasets with geographically diverse sources of imagery. We show that without domain adaptation, ResNet-50 has difficulty in generalizing between imaging distributions from a number of public Tuberculosis screening datasets with imagery from geographically distributed regions. However, with the incorporation of DIFL, the out-of-domain performance is greatly enhanced. Analysis criteria includes a comparison of accuracy, sensitivity, specificity and AUC over both the baseline, as well as the DIFL enhanced algorithms. We conclude that DIFL improves generalizability of Tuberculosis screening while maintaining acceptable accuracy over the source domain imagery when applied across a variety of public datasets.
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models

Stochastic gradient-based optimization for discrete latent variable models is challenging due to the high variance of gradients. We introduce a variance reduction technique for score function estimators that makes use of double control variates . These control variates act on top of a main control variate, and try to further reduce the variance of the overall estimator. We develop a double control variate for the REINFORCE leave-one-out estimator using Taylor expansions. For training discrete latent variable models, such as variational autoencoders with binary latent variables, our approach adds no extra computational cost compared to standard training with the REINFORCE leave-one-out estimator. We apply our method to challenging high-dimensional toy examples and for training variational autoencoders with binary latent variables. We show that our estimator can have lower variance compared to other state-of-the-art estimators.
An Examination of Sport Climbing Competition Format and Scoring System

Sport climbing, which made its Olympic debut at the 2020 Summer Games, generally consists of three separate disciplines: speed climbing, bouldering, and lead climbing. However, the International Olympic Committee (IOC) only allowed one set of medals each for men and women in sport climbing. As a result, the governing body of sport climbing, rather than choosing only one of the three disciplines to include in the Olympics, decided to create a competition combining all three disciplines. In order to determine a winner, a combined scoring system was created using the product of the ranks across the three disciplines to determine an overall score for each climber. In this work, the rank-product scoring system of sport climbing is evaluated through simulation to investigate its general features, specifically, the advancement probabilities and scores for climbers given certain placements. Additionally, analyses of historical climbing contest results are presented and real examples of violations of the independence of irrelevant alternatives are illustrated. Finally, this work finds evidence that the current competition format is putting speed climbers at a disadvantage.
“How Does It Detect A Malicious App?” Explaining the Predictions of AI-based Malware Detector

In this paper, we present a novel model-agnostic explanation method for AI-based malware detection models, to assure the trustworthiness from both cyber security and AI practitioners' perspectives. Our proposed method identifies and quantifies the data features relevant to the predictions by two steps: i) data perturbation that generates the synthetic data by manipulating features' values; and ii) optimization of features attribution values to seek significant changes of prediction scores on the perturbed data with minimal feature values changes. The proposed method is validated by three experiments. We firstly demonstrate that our proposed model explanation method can aid in discovering how AI models are evaded by adversarial samples quantitatively. In the following experiments, we compare the explainability and fidelity of our proposed method with state-of-the-arts. The results show that the proposed method can explain different AI models with robust explanations and high fidelity.
Bone Adaptation as a Geometric Flow

This paper presents bone adaptation as a geometric flow. The proposed method is based on two assumptions: first, that the bone surface is smooth (not fractal) permitting the definition of a tangent plane and, second, that the interface between marrow and bone tissue phases is orientable. This permits the analysis of bone adaptation using the well-developed mathematics of geometric flows and the numerical techniques of the level set method. Most importantly, topological changes such as holes forming in plates and rods disconnecting can be treated formally and simulated naturally. First, the relationship between biological theories of bone adaptation and the mathematical object describing geometric flow is described. This is termed the adaptation function, $F$, and is the multi-scale link described by Frost's Utah paradigm between cellular dynamics and bone structure. Second, a model of age-related bone loss termed curvature-based bone adaptation is presented. Using previous literature, it is shown that curvature-based bone adaptation is the limiting continuous equation of simulated bone atrophy, a discrete model of bone aging. Interestingly, the parameters of the model can be defined in such a way that the flow is volume-preserving. This implies that bone health can in principle change in ways that fundamentally cannot be measured by areal or volumetric bone mineral density, requiring structure-level imaging. Third, a numerical method is described and two in silico experiments are performed demonstrating the non-volume-preserving and volume-preserving cases. Taken together, recognition of bone adaptation as a geometric flow permits the recruitment of mathematical and numerical developments over the last 50 years to understanding and describing the complex surface of bone.