Miracle-dz/UniFAR-FTUs huggingface dataset:
IT’S FAKE, BUT I BUY IT! A STUDY ON THE CONSUMPTION OF PIRATED AND COUNTERFEIT PRODUCTS IN THE BRAZILIAN BORDER CITIES OF SANTANA DO LIVRAMENTO/BR AND RIVERA / UY

Purpose – This study seeks to identify the behavior of buying pirated and counterfeit products in the border towns of Santana do Livramento/RS and Rivera/UY. Design/methodology/approach – In order to achieve the proposed objective, a quantitative, descriptive research was undertaken using the survey method. The study sample comprised 562 observations, 274 of respondents from the city of Santana do Livramento/RS and 288 from residents of the city of Rivera/UY. Findings – The main results show that there are not many differences in the opinions of buyers and Uruguayan respondents in relation to the consumption of pirated and counterfeit products, and issues related to price and belonging to a social group do not seem to be the main motivations for the family of types of products. Research limitations/implications – The study was limited with regard to the sample, which did not have a probabilistic nature. Thus, it is suggested that new studies that seek to represent the population, and that are carried out in other borders of countries, in order to understand the way in which cultures can interact and spread habits among them. Practical implications – As practical implications, the need to develop marketing objectives with the intention of minimizing the interest and purchase of counterfeit and pirated products is highlighted. Social implications – Regarding the social implications, the potential effects of the purchase and consumption of counterfeit products for individuals are highlighted, since such behavior can reflect negative aspects both for the physical health of consumers and for the economy of the losing country. millions in taxes. Originality/value – The originality of the study lies in the fact that it strengthens the findings on the subject in academics, in addition to bringing a comparison of consumer professionals of two nationalities.
Parametric Study of the Covering Soil of Tunnels Constructed in Liquefiable Soil

Earthquake and its catastrophic failures is one of the disturbance worry of Civil Engineers. In geotechnical science, liquefaction is one of the most important phenomenon induced by earthquake. A change in pore water pressure follows a change in effective stress, and in a critical state in which pore-water pressure equals the total stress in soil particles, soil strength is suddenly lost and liquefaction occurs. This phenomenon causes destruction of structures constructed above ground surface or underground. In case of constructing underground structures or passing a tunnel through weak or compressible soils, with increase in forces and moments surrounding the structures leading to liquefaction, may impose damages to adjacent structures or soils. In this paper, a tunnel passing through a soil media with liquefaction potential is modeled. After static equilibrium, dynamic analyses are performed. Using liquefaction modeling and measurement of induced forces and moments of covering soil of the tunnel, parameters such as water bulk modulus, groundwater level, and porosity are investigated. The results showed that the above mentioned parameters have considerable effects on variation of effective stresses, induced forces, moments and surface dilation of the covering soil of the tunnel. doi: 10.5829/idosi.ije. 2012.25. 04a.05
Damped Online Newton Step for Portfolio Selection

We revisit the classic online portfolio selection problem, where at each round a learner selects a distribution over a set of portfolios to allocate its wealth. It is known that for this problem a logarithmic regret with respect to Cover's loss is achievable using the Universal Portfolio Selection algorithm, for example. However, all existing algorithms that achieve a logarithmic regret for this problem have per-round time and space complexities that scale polynomially with the total number of rounds, making them impractical. In this paper, we build on the recent work by Haipeng et al. 2018 and present the first practical online portfolio selection algorithm with a logarithmic regret and whose per-round time and space complexities depend only logarithmically on the horizon. Behind our approach are two key technical novelties of independent interest. We first show that the Damped Online Newton steps can approximate mirror descent iterates well, even when dealing with time-varying regularizers. Second, we present a new meta-algorithm that achieves an adaptive logarithmic regret (i.e. a logarithmic regret on any sub-interval) for mixable losses.
Shifting Trends of COVID-19 Tweet Sentiment with Respect to Voting Preferences in the 2020 Election Year of the United States

COVID-19 related policies were extensively politicized during the 2020 election year of the United States, resulting in polarizing viewpoints. Twitter users were particularly engaged during the 2020 election year. Here we investigated whether COVID-19 related tweets were associated with the overall election results at the state level during the period leading up to the election day. We observed weak correlations between the average sentiment of COVID-19 related tweets and popular votes in two-week intervals, and the trends gradually become opposite. We then compared the average sentiments of COVID-19 related tweets between states called in favor of Republican (red states) or Democratic parties (blue states). We found that at the beginning of lockdowns sentiments in the blue states were much more positive than those in the red states. However, sentiments in the red states gradually become more positive during the summer of 2020 and persisted until the election day.
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition

Label distributions in camera-trap images are highly imbalanced and long-tailed, resulting in neural networks tending to be biased towards head-classes that appear frequently. Although long-tail learning has been extremely explored to address data imbalances, few studies have been conducted to consider camera-trap characteristics, such as multi-domain and multi-frame setup. Here, we propose a unified framework and introduce two datasets for long-tailed camera-trap recognition. We first design domain experts, where each expert learns to balance imperfect decision boundaries caused by data imbalances and complement each other to generate domain-balanced decision boundaries. Also, we propose a flow consistency loss to focus on moving objects, expecting class activation maps of multi-frame matches the flow with optical flow maps for input images. Moreover, two long-tailed camera-trap datasets, WCS-LT and DMZ-LT, are introduced to validate our methods. Experimental results show the effectiveness of our framework, and proposed methods outperform previous methods on recessive domain samples.
Navigating Local Minima in Quantized Spiking Neural Networks

Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks face challenges when trained using error backpropagation, due to the absence of gradient signals when applying hard thresholds. The broadly accepted trick to overcoming this is through the use of biased gradient estimators: surrogate gradients which approximate thresholding in Spiking Neural Networks (SNNs), and Straight-Through Estimators (STEs), which completely by-pass thresholding in Quantized Neural Networks (QNNs). While noisy gradient feedback has enabled reasonable performance on simple supervised learning tasks, it is thought that such noise increases the difficulty of finding optima in loss landscapes, especially during the later stages of optimization. By periodically boosting the Learning Rate (LR) during training, we expect the network can navigate unexplored solution spaces that would otherwise be difficult to reach due to local minima, barriers, or flat surfaces. This paper presents a systematic evaluation of a cosine-annealed LR schedule coupled with weight-independent adaptive moment estimation as applied to Quantized SNNs (QSNNs). We provide a rigorous empirical evaluation of this technique on high precision and 4-bit quantized SNNs across three datasets, demonstrating state-of-the-art performance on the more complex datasets. Our source code is available at this link: https://github.com/jeshraghian/QSNNs.
Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data

We consider stochastic optimization under distributional uncertainty, where the unknown distributional parameter is estimated from streaming data that arrive sequentially over time. Moreover, data may depend on the decision of the time when they are generated. For both decision-independent and decision-dependent uncertainties, we propose an approach to jointly estimate the distributional parameter via Bayesian posterior distribution and update the decision by applying stochastic gradient descent on the Bayesian average of the objective function. Our approach converges asymptotically over time and achieves the convergence rates of classical SGD in the decision-independent case. We demonstrate the empirical performance of our approach on both synthetic test problems and a classical newsvendor problem.
Hardware-Efficient Entangled Measurements for Variational Quantum Algorithms

Variational algorithms have received significant attention in recent years due to their potential to solve practical problems using noisy intermediate-scale quantum (NISQ) devices. A fundamental step of these algorithms is the evaluation of the expected value of Hamiltonians, and hence efficient schemes to perform this task are required. The standard approach employs local measurements of Pauli operators and requires a large number of circuits. An alternative is to make use of entangled measurements, which might introduce additional gates between physically disconnected qubits that harm the performance. As a solution to this problem, we propose hardware-efficient entangled measurements (HEEM), that is, measurements that permit only entanglement between physically connected qubits. We show that this strategy enhances the evaluation of molecular Hamiltonians in NISQ devices by reducing the number of circuits required without increasing their depth. We provide quantitative metrics of how this approach offers better results than local measurements and arbitrarily entangled measurements. We estimate the ground-state energy of the H$_2$O molecule with classical simulators and quantum hardware using the variational quantum eigensolver with HEEM.
Using Multiple Emission Line Ratios to Constrain the Slope of the Dust Attenuation Law

We explore the possibility and practical limitations of using a three-line approach to measure both the slope and normalization of the dust attenuation law in individual galaxies. To do this, we focus on a sample of 11 galaxies with existing ground-based Balmer Hα and Hβ measurements from slit spectra, plus space-based grism constraints on Paschen-β. When accounting for observational uncertainties, we show that one galaxy has a well-constrained dust-law slope and normalization in the range expected from theoretical arguments; this galaxy therefore provides an example of what may be possible in the future. However, most of the galaxies are best fit by unusually steep or shallow slopes. We then explore whether additional astrophysical effects or observational biases could explain the elevated Paschen-β/Hα ratios driving these results. We find that galaxies with high Paschen-β/Hα ratios may be explained by slightly sub-unity covering fractions (>97%). Alternatively, differing slit losses for different lines can have a large impact on the results, emphasizing the importance of measuring all three lines with a consistent spectroscopic aperture. We conclude that, while the three-line approach to constraining the shape of the dust attenuation law in individual galaxies is promising, deep observations and a consistent observational strategy will be required to minimize observational biases and to disentangle the astrophysically interesting effect of differing covering fractions. The James Webb Space Telescope will provide more sensitive measurements of Balmer and Paschen lines for galaxies at z ≈ 0.3–2, enabling uniform constraints on the optical–infrared dust attenuation law and its intrinsic variation.
Velocity correlations in the high-density phase of active Brownian particles

The velocity correlation of active Brownian particles has been observed in the absence of white noise recently. However, this correlation will disappear if the noise is reconsidered. In our simulation, we find the velocity correlation still exists while using mean velocities instead of instantaneous ones. The cluster formed by particles whose local structures are symmetrical behaves like a rigid body. It translates or rotates leading to the velocity aligned or vortex domain. PACS numbers: 05.40. Jc, 05.70. Ln, 64.75. +g a E-Mail: yangcheng10@mails.ucas. ac.cn b E-Mail: xushun@sccas.cn
Towards hardware Implementation of WTA for CPG-based control of a Spiking Robotic Arm

Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in hardware for a deeper understanding and its possible application to solve complex problems in engineering and robotics. Central-Pattern-Generators (CPGs) are part of neuro-controllers, typically used at their last steps to produce rhythmic patterns for limbs movement. Different patterns and gaits typically compete through winner-take-all (WTA) circuits to produce the right movements. In this work we present a WTA circuit implemented in a Spiking-Neural-Network (SNN) processor to produce such patterns for controlling a robotic arm in real-time. The robot uses spike-based proportional-integrative-derivative (SPID) controllers to keep a commanded joint position from the winner population of neurons of the WTA circuit. Experiments demonstrate the feasibility of robotic control with spiking circuits following brain-inspiration.
Robust Multi-Objective Bayesian Optimization Under Input Noise

Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing processes, the design parameters are subject to random input noise, resulting in a product that is often less performant than expected. Although BO methods have been proposed for optimizing a single objective under input noise, no existing method addresses the practical scenario where there are multiple objectives that are sensitive to input perturbations. In this work, we propose the first multi-objective BO method that is robust to input noise. We formalize our goal as optimizing the multivariate value-at-risk (MVaR), a risk measure of the uncertain objectives. Since directly optimizing MVaR is computationally infeasible in many settings, we propose a scalable, theoretically-grounded approach for optimizing MVaR using random scalarizations. Empirically, we find that our approach significantly outperforms alternative methods and efficiently identifies optimal robust designs that will satisfy specifications across multiple metrics with high probability.
GAN-generated Faces Detection: A Survey and New Perspectives

Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts. Therefore, the corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. In this work, we aim to provide a comprehensive review of recent progress in GAN-face detection. We focus on methods that can detect face images that are generated or synthesized from GAN models. We classify the existing detection works into four categories: (1) deep learning-based, (2) physical-based, (3) physiological-based methods, and (4) evaluation and comparison against human visual performance. For each category, we summarize the key ideas and connect them with method implementations. We also discuss open problems and suggest future research directions.
Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can spark major concerns when they exhibit vast performance discrepancies between people from various demographic and social backgrounds. Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems. To initiate an effort towards standardized fairness audits, we propose three fairness indicators, which aim at quantifying harms and biases of visual systems. Our indicators use existing publicly available datasets collected for fairness evaluations, and focus on three main types of harms and bias identified in the literature, namely harmful label associations, disparity in learned representations of social and demographic traits, and biased performance on geographically diverse images from across the world. We define precise experimental protocols applicable to a wide range of computer vision models. These indicators are part of an ever-evolving suite of fairness probes and are not intended to be a substitute for a thorough analysis of the broader impact of the new computer vision technologies. Yet, we believe it is a necessary first step towards (1) facilitating the widespread adoption and mandate of the fairness assessments in computer vision research, and (2) tracking progress towards building socially responsible models. To study the practical effectiveness and broad applicability of our proposed indicators to any visual system, we apply them to “off-the-shelf” models built using widely adopted model training paradigms which vary in their ability to whether they can predict labels on a given image or only produce the embeddings. We also systematically study the effect of data domain and model size. The results of our fairness indicators on these systems suggest that blatant disparities still exist, which highlight the importance on the relationship between the context of the task and contents of a datasets. The code will be released to encourage the use of indicators.
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization

Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, diverse between each other, and that constitute good document representations for downstream tasks (e.g. document classification). In this paper, we conduct a multi-objective hyperparameter optimization of three well-known topic models. The obtained results reveal the conflicting nature of different objectives and that the training corpus characteristics are crucial for the hyperparameter selection, suggesting that it is possible to transfer the optimal hyperparameter configurations between datasets.
Box Supervised Video Segmentation Proposal Network

Bounding box supervision provides a balanced compromise between labeling effort and result quality for image segmentation. However, there exists no such work explicitly tailored for videos. Applying the image segmentation methods directly to videos produces sub-optimal solutions because they do not exploit the temporal information. In this work, we propose a box-supervised video segmentation proposal network. We take advantage of intrinsic video properties by introducing a novel box-guided motion calculation pipeline and a motion-aware affinity loss. As the motion is utilized only during training, the run-time remains fixed during inference time. We evaluate our model on Video Object Segmentation (VOS) challenge. The method outperforms the state-of-the-art self-supervised methods by 16.4% and 6.9% J&F score, and the majority of fully supervised ones on the DAVIS and Youtube-VOS dataset. Code is available at https://github.com/Tanveer81/BoxVOS.git.
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness

Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i. d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of the world between these extremes. In this work we establish novel regret bounds for online convex optimization in a setting that interpolates between stochastic i.i. d. and fully adversarial losses. By exploiting smoothness of the expected losses, these bounds replace a dependence on the maximum gradient length by the variance of the gradients, which was previously known only for linear losses. In addition, they weaken the i.i. d. assumption by allowing, for example, adversarially poisoned rounds, which were previously considered in the expert and bandit setting. Our results extend this to the online convex optimization framework. In the fully i.i. d. case, our bounds match the rates one would expect from results in stochastic acceleration, and in the fully adversarial case they gracefully deteriorate to match the minimax regret. We further provide lower bounds showing that our regret upper bounds are tight for all intermediate regimes in terms of the stochastic variance and the adversarial variation of the loss gradients.
Orthogonalising gradients to speed up neural network optimisation

The optimisation of neural networks can be sped up by orthogonalising the gradients before the optimisation step, ensuring the diversification of the learned representations. We orthogonalise the gradients of the layer's components/filters with respect to each other to separate out the intermediate representations. Our method of orthogonalisation allows the weights to be used more flexibly, in contrast to restricting the weights to an orthogonalised sub-space. We tested this method on ImageNet and CIFAR-10 resulting in a large decrease in learning time, and also obtain a speed-up on the semi-supervised learning BarlowTwins. We obtain similar accuracy to SGD without fine-tuning and better accuracy for na\"ively chosen hyper-parameters.
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods

Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent algorithms for solving min-max optimization and variational inequalities problems (VIP) appearing in various machine learning tasks. The success of the method led to several advanced extensions of the classical SGDA, including variants with arbitrary sampling, variance reduction, coordinate randomization, and distributed variants with compression, which were extensively studied in the literature, especially during the last few years. In this paper, we propose a unified convergence analysis that covers a large variety of stochastic gradient descent-ascent methods, which so far have required different intuitions, have different applications and have been developed separately in various communities. A key to our unified framework is a parametric assumption on the stochastic estimates. Via our general theoretical framework, we either recover the sharpest known rates for the known special cases or tighten them. Moreover, to illustrate the flexibility of our approach we develop several new variants of SGDA such as a new variance-reduced method (L-SVRGDA), new distributed methods with compression (QSGDA, DIANA-SGDA, VR-DIANA-SGDA), and a new method with coordinate randomization (SEGA-SGDA). Although variants of the new methods are known for solving minimization problems, they were never considered or analyzed for solving min-max problems and VIPs. We also demonstrate the most important properties of the new methods through extensive numerical experiments.