Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex-Valued Iris Recognition Network

In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode; however, the proposed method enables a new capability of automatic complex-valued feature learning that is tailored for iris recognition. We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition. We exploit visualization schemes to convey how the complex-valued network, when compared to standard real-valued networks, extracts fundamentally different features from the iris texture.
Phase transition of graph Laplacian of high dimensional noisy random point cloud

We systematically explore the spectral distribution of kernel-based graph Laplacian constructed from high dimensional and noisy random point cloud in the nonnull setup. An interesting phase transition phenomenon is reported, which is characterized by the signal-to-noise ratio (SNR). We quantify how the signal and noise interact over different SNR regimes; for example, how signal information pops out the Marchenko-Pastur bulk. Motivated by the analysis, an adaptive bandwidth selection algorithm is provided and proved, which coincides with the common practice in real data. Simulated data is provided to support the theoretical findings. Our results paves the way towards a foundation for statistical inference of various kernel-based unsupervised learning algorithms, like eigenmap, diffusion map and their variations, for real data analysis.
An iterative reconstruction algorithm for Faraday tomography

Faraday tomography offers crucial information on the magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters, by observing its magnetoionic media. The observed linear polarization spectrum is inverse Fourier transformed to obtain the Faraday dispersion function (FDF), providing us a tomographic distribution of the magnetoionic media along the line of sight. However, this transform gives a poor reconstruction of the FDF because of the instrument’s limited wavelength coverage. The current Faraday tomography techniques’ inability to reliably solve the above inverse problem has noticeably plagued cosmic magnetism studies. We propose a new algorithm inspired by the well-studied area of signal restoration, called the Constraining and Restoring iterative Algorithm for Faraday Tomography (craft). This iterative model-independent algorithm is computationally inexpensive and only requires weak physically motivated assumptions to produce high fidelity FDF reconstructions. We demonstrate an application for a realistic synthetic model FDF of the Milky Way, where craft shows greater potential over other popular model-independent techniques. The dependence of observational frequency coverage on the various techniques’ reconstruction performance is also demonstrated for a simpler FDF. craft exhibits improvements even over model-dependent techniques (i.e. QU-fitting) by capturing complex multiscale features of the FDF amplitude and polarization angle variations within a source. The proposed approach will be of utmost importance for future cosmic magnetism studies, especially with broad-band polarization data from the Square Kilometre Array and its precursors. We make the craft code publicly available†.
Exploring Contrastive Learning in Human Activity Recognition for Healthcare

Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring. Motivated by the limitations of labeled datasets in HAR, particularly when employed in healthcare-related applications, this work explores the adoption and adaptation of SimCLR, a contrastive learning technique for visual representations, to HAR. The use of contrastive learning objectives causes the representations of corresponding views to be more similar, and those of non-corresponding views to be more different. After an extensive evaluation exploring 81 combinations of different signal transformations for augmenting the data, we observed significant performance differences owing to the order and the function thereof. In particular, preliminary results indicated an improvement over supervised and unsupervised learning methods when using fine-tuning and random rotation for augmentation, however, future work should explore under which conditions SimCLR is beneficial for HAR systems and other healthcare-related applications.
Copernicus: Characterizing the Performance Implications of Compression Formats Used in Sparse Workloads

Sparse matrices are the key ingredients of several application domains, from scientific computing to machine learning. The primary challenge with sparse matrices has been efficiently storing and transferring data, for which many sparse formats have been proposed to significantly eliminate zero entries. Such formats, essentially designed to optimize memory footprint, may not be as successful in performing faster processing. In other words, although they allow faster data transfer and improve memory bandwidth utilization - the classic challenge of sparse problems - their decompression mechanism can potentially create a computation bottleneck. Not only is this challenge not resolved, but also it becomes more serious with the advent of domain-specific architectures (DSAs), as they intend to more aggressively improve performance. The performance implications of using various formats along with DSAs, however, has not been extensively studied by prior work. To fill this gap of knowledge, we characterize the impact of using seven frequently used compression formats on performance, based on a DSA for sparse matrix-vector multiplication (SpMV), implemented on an FPGA using high-level synthesis (HLS) tools, a growing and popular method for developing DSAs. Seeking a fair comparison, we tailor and well-optimize the HLS implementation of decompression for each format. We explore metrics, including decompression overhead, latency, balance ratio, throughput, memory bandwidth utilization, resource utilization, and power consumption, on a variety of real-world and synthetic sparse workloads.
3D Registration for Self-Occluded Objects in Context

While much progress has been made on the task of 3D point cloud registration, there still exists no learning-based method able to estimate the 6D pose of an object observed by a 2.5D sensor in a scene. The challenges of this scenario include the fact that most measurements are outliers depicting the object's surrounding context, and the mismatch between the complete 3D object model and its self-occluded observations. We introduce the first deep learning framework capable of effectively handling this scenario. Our method consists of an instance segmentation module followed by a pose estimation one. It allows us to perform 3D registration in a one-shot manner, without requiring an expensive iterative procedure. We further develop an on-the-fly rendering-based training strategy that is both time- and memory-efficient. Our experiments evidence the superiority of our approach over the state-of-the-art traditional and learning-based 3D registration methods.
Stable Weight Decay Regularization

Weight decay is a popular regularization technique for training of deep neural networks. Modern deep learning libraries mainly use $L_{2}$ regularization as the default implementation of weight decay. \citet{loshchilov2018decoupled} demonstrated that $L_{2}$ regularization is not identical to weight decay for adaptive gradient methods, such as Adaptive Momentum Estimation (Adam), and proposed Adam with Decoupled Weight Decay (AdamW). However, we found that the popular implementations of weight decay, including $L_{2}$ regularization and decoupled weight decay, in modern deep learning libraries usually damage performance. First, the $L_{2}$ regularization is unstable weight decay for all optimizers that use Momentum, such as stochastic gradient descent (SGD). Second, decoupled weight decay is highly unstable for all adaptive gradient methods. We further propose the Stable Weight Decay (SWD) method to fix the unstable weight decay problem from a dynamical perspective. The proposed SWD method makes significant improvements over $L_{2}$ regularization and decoupled weight decay in our experiments. Simply fixing weight decay in Adam by SWD, with no extra hyperparameter, can usually outperform complex Adam variants, which have more hyperparameters.
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for electroencephalogram (EEG), but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups, such as brain–computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this article could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.
CORAL: Colored structural representation for bi-modal place recognition

Place recognition is indispensable for a drift-free localization system. Due to the variations of the environment, place recognition using single-modality has limitations. In this paper, we propose a bi-modal place recognition method, which can extract a compound global descriptor from the two modalities, vision and LiDAR. Specifically, we first build the elevation image generated from 3D points as a structural representation. Then, we derive the correspondences between 3D points and image pixels that are further used in merging the pixel-wise visual features into the elevation map grids. In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic representation, namely CORAL. And the whole network is called CORAL-VLAD. Comparisons on the Oxford RobotCar show that CORAL-VLAD has superior performance against other state-of-the-art methods. We also demonstrate that our network can be generalized to other scenes and sensor configurations on cross-city datasets.
A Game-Theoretic Analysis of Cross-Chain Atomic Swaps with HTLCs

To achieve interoperability between unconnected ledgers, hash time lock contracts (HTLCs) are commonly used for cross-chain asset exchange. The solution tolerates transaction failure, and can “make the best out of worst” by allowing transacting agents to at least keep their original assets in case of an abort. Nonetheless, as an undesired outcome, reoccurring transaction failures prompt a critical and analytical examination of the protocol. In this study, we propose a game-theoretic framework to study the strategic behaviors of agents taking part in cross-chain atomic swaps implemented with HTLCs. We study the success rate of the transaction as a function of the exchange rate of the swap, the token price and its volatility, among other variables. We demonstrate that in an attempt to maximize one's own utility as asset price changes, either agent might withdraw from the swap. An extension of our model confirms that collateral deposits can improve the transaction success rate, motivating further research towards collateralization without a trusted third party. A second model variation suggests that a swap is more likely to succeed when agents dynamically adjust the exchange rate in response to price fluctuations.
Oral Health Knowledge, Attitudes, and Behaviors Among University Students in Jeddah, Saudi Arabia

Purpose This study aimed to evaluate oral health knowledge, attitudes, and behaviors among a sample of senior students at King Abdulaziz University (KAU), and to assess differences among these factors between genders. Methods A total of 1177 senior university students at KAU were included in this cross-sectional study. Participants were selected by stratified random sampling. KAU has three main streams categorized by field of study. One faculty was randomly selected from each stream. Information regarding oral health knowledge, attitudes, and behaviors was collected with a validated self-administered questionnaire. A knowledge score was calculated, and predictors of oral health knowledge were assessed by linear regression. Results In general, the mean oral health knowledge score of females (8.1 ± 1.8) was significantly higher than that of males (7.2 ± 2.1) out of 13. Relative to males, females reported a greater frequency of teeth cleaning and of using a toothbrush with proper technique (p < 0.001). Females were also more likely to have ever visited a dentist than males (95% and 86%, respectively). The linear regression model revealed that students in non-medical faculties and those who were not shown how to brush their teeth by a dentist had lower oral health knowledge scores, than their counterparts. Students older than 22 years and females were more likely to have higher oral health knowledge scores than were their counterparts. Conclusion Oral health knowledge was relatively low among the university students, but females showed better oral health knowledge and behaviors than males did.
Object Rearrangement Using Learned Implicit Collision Functions

Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict collisions in partial point clouds with occlusions, making generation of collision-free grasping or placement trajectories challenging. We propose a learned collision model that accepts scene and query object point clouds and predicts collisions for 6DOF object poses within the scene. We train the model on a synthetic set of 1 million scene/object point cloud pairs and 2 billion collision queries. We leverage the learned collision model as part of a model predictive path integral (MPPI) policy in a tabletop rearrangement task and show that the policy can plan collision-free grasps and placements for objects unseen in training in both simulated and physical cluttered scenes with a Franka Panda robot. The learned model outperforms both traditional pipelines and learned ablations by 9.8% in accuracy on a dataset of simulated collision queries and is 75x faster than the best-performing baseline. Videos and supplementary material are available at https://research.nvidia. com/publication/2021-03_Object-Rearrangement-Using.
Dense open-set recognition with synthetic outliers generated by Real NVP

Today's deep models are often unable to detect inputs which do not belong to the training distribution. This gives rise to confident incorrect predictions which could lead to devastating consequences in many important application fields such as healthcare and autonomous driving. Interestingly, both discriminative and generative models appear to be equally affected. Consequently, this vulnerability represents an important research challenge. We consider an outlier detection approach based on discriminative training with jointly learned synthetic outliers. We obtain the synthetic outliers by sampling an RNVP model which is jointly trained to generate datapoints at the border of the training distribution. We show that this approach can be adapted for simultaneous semantic segmentation and dense outlier detection. We present image classification experiments on CIFAR-10, as well as semantic segmentation experiments on three existing datasets (StreetHazards, WD-Pascal, Fishyscapes Lost & Found), and one contributed dataset. Our models perform competitively with respect to the state of the art despite producing predictions with only one forward pass.
Electron, hole and exciton effective g-factors in semiconductor nanocrystals

We review the existing and present the new results of $\bf kp$ calculations of the electron, hole, and exciton effective $g$-factors in semiconductor nanocrystals of different shape and symmetry. We propose a simple yet accurate method for calculation of electron $g$-factor size dependence in bare nanocrystals within the eight-band Kane model. Using the spherical approximation for Luttinger Hamiltonian we find the dependence of hole $g$-factor on light to heavy hole effective mass ratio in semiconductor nanostructures with spherical, axial, and cubically symmetric shape. We show that the non-equidistant Zeeman splitting of the four-fold degenerate hole state may take place in cube and spheroidal nanocrystals. We present a comparison of the calculated hole $g$-factors in nanostructures based on II-VI and III-V semiconductors for different sets of the Luttinger parameters and analyze the main effects contributing to the $g$-factor renormalization with the respect to the bulk value. We discuss different approaches to the definition of the hole and exciton $g$-factors which should be taken into account during the analysis of the experimental data and compare our results of $g$-factor calculations with the experimental data for semiconductor spherical nanocrystals and thin nanoplatelets available in the literature.
The Gaia-ESO Survey: Oxygen Abundance in the Galactic Thin and Thick Disks

We analyze the oxygen abundances of a stellar sample representative of the two major Galactic populations: the thin and thick disks. The aim is to investigate the differences between members of the Galactic disks and contribute to the understanding of the origin of oxygen chemical enrichment in the Galaxy. The analysis is based on the [O i] = 6300.30 Å oxygen line in high-resolution spectra (R ∼ 52,500) obtained from the Gaia-ESO public spectroscopic Survey (GES). By comparing the observed spectra with a theoretical data set computed in LTE with the SPECTRUM synthesis and ATLAS12 codes, we derive the oxygen abundances of 516 FGK dwarfs for which we have previously measured carbon abundances. Based on kinematic, chemical, and dynamical considerations, we identify 20 thin and 365 thick disk members. We study the potential trends of both subsamples in terms of their chemistry ([O/H], [O/Fe], [O/Mg], and [C/O] versus [Fe/H] and [Mg/H]), age, and position in the Galaxy. The main results are that (a) [O/H] and [O/Fe] ratios versus [Fe/H] show systematic differences between thin and thick disk stars with an enhanced O abundance of thick disk stars with respect to thin disk members and a monotonic decrement of [O/Fe] with increasing metallicity, even at metal-rich regime; (b) there is a smooth correlation of [O/Mg] with age in both populations, suggesting that this abundance ratio can be a good proxy of stellar ages within the Milky Way; and (c) thin disk members with [Fe/H] ≃ 0 display a [C/O] ratio smaller than the solar value, suggesting a possibly outward migration of the Sun from lower Galactocentric radii.
Knowledge and Attitude Towards Non-Pharmacological Pain Management and Associated Factors Among Nurses Working in Benishangul Gumuz Regional State Hospitals in Western Ethiopia, 2018

Background Millions of people worldwide are suffering from pain. Non-pharmacological therapy has an important role in the treatment of pain and is recognized as a valuable, simple help to lower the dosage of analgesic drugs needed, decreasing the side effects, reducing drug dependency and reducing health care costs; however, knowledge and attitude of nurses greatly affect the use of non-pharmacological pain management methods. Objective The study aimed to assess knowledge and attitude towards non-pharmacological pain management and associated factors among nurses working in Benishangul Gumuz Regional State Hospitals, western Ethiopia, 2018. Methods Institution-based cross-sectional study was conducted from April 1st to May 1st, 2017. Two hundred sixteen nurses were selected by using simple random sampling. Data were collected by using a pretested self-administered structured questionnaire. Collected data were checked, coded and entered to Epi-Info version 7 and exported to SPSS version 20 for further analysis. Bivariable and multivariable logistic regression was used. Results A total of 209 professional nurses participated in the study, a 96.7% response rate. This study shows that 51.2% (95% CI: 51.1–51.3) of nurses had adequate knowledge and 47% (95% CI: 46.9–47.06) of nurses had a favorable attitude towards non-pharmacological pain management. The findings reveal that level of qualification (AOR=12.2 (3.05, 48.4)), taking educational courses (AOR=7.5 (2.7, 21.24)), nurse to patient ratio (AOR=4.9 (1.64, 14.55)) and work experience were factors significantly associated with knowledge. Findings also show that nurse to patient ratio (AOR=10.36 (2.8, 38.4)), training (AOR=4.6 (1.4, 15.4)) and knowledge of non-pharmacological pain management (AOR=4.3 (1.74, 10.56)) were significantly associated with nurses' attitude to non-pharmacological pain management. Conclusion and Recommendations Nurses in Benishangul Gumuz regional state hospitals have unfavorable attitude, but they have relatively adequate knowledge about non-pharmacological pain management. Work experience, level of education, nurse to patient ratio and taking educational courses were associated with nurses’ knowledge, and nurse to patient ratio, training, and knowledge of non-pharmacological pain management were associated with nurses’ attitude. Therefore, efforts should focus on innovative educational strategies for nurses, training and achieving a 1:6 nurse to patient ratio.
Resistance scaling on 4N-carpets

Abstract The 4⁢N{4N}-carpets are a class of infinitely ramified self-similar fractals with a large group of symmetries. For a 4⁢N{4N}-carpet F, let {Fn}n≥0{\{F_{n}\}_{n\geq 0}} be the natural decreasing sequence of compact pre-fractal approximations with ⋂nFn=F{\bigcap_{n}F_{n}=F}. On each Fn{F_{n}}, let ℰ⁢(u,v)=∫FN∇⁡u⋅∇⁡v⁢d⁢x{\mathcal{E}(u,v)=\int_{F_{N}}\nabla u\cdot\nabla v\,dx} be the classical Dirichlet form and un{u_{n}} be the unique harmonic function on Fn{F_{n}} satisfying a mixed boundary value problem corresponding to assigning a constant potential between two specific subsets of the boundary. Using a method introduced by [M. T. Barlow and R. F. Bass, On the resistance of the Sierpiński carpet, Proc. Roy. Soc. Lond. Ser. A431 (1990), no. 1882, 345–360], we prove a resistance estimate of the following form: there is ρ=ρ⁢(N)>1{\rho=\rho(N)>1} such that ℰ⁢(un,un)⁢ρn{\mathcal{E}(u_{n},u_{n})\rho^{n}} is bounded above and below by constants independent of n. Such estimates have implications for the existence and scaling properties of Brownian motion on F.