Miracle-dz/UniFAR-FTUs huggingface dataset:
DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection

An iris presentation attack detection (IPAD) is essential for securing personal identity is widely used iris recognition systems. However, the existing IPAD algorithms do not generalize well to unseen and cross-domain scenarios because of capture in unconstrained environments and high visual correlation amongst bonafide and attack samples. These similarities in intricate textural and morphological patterns of iris ocular images contribute further to performance degradation. To alleviate these shortcomings, this paper proposes DFCANet: Dense Feature Calibration and Attention Guided Network which calibrates the locally spread iris patterns with the globally located ones. Uplifting advantages from feature calibration convolution and residual learning, DFCANet generates domain-specific iris feature representations. Since some channels in the calibrated feature maps contain more prominent information, we capitalize discriminative feature learning across the channels through the channel attention mechanism. In order to intensify the challenge for our proposed model, we make DFCANet operate over nonsegmented and non-normalized ocular iris images. Extensive experimentation conducted over challenging cross-domain and intra-domain scenarios highlights consistent outperforming results. Compared to state-of-the-art methods, DFCANet achieves significant gains in performance for the benchmark IIITD CLI, IIIT CSD and NDCLD13 databases respectively. Further, a novel incremental learning-based methodology has been introduced so as to overcome disentangled iris-data characteristics and data scarcity. This paper also pursues the challenging scenario that considers soft-lens under the attack category with evaluation performed under various cross-domain protocols. The code will be made publicly available.
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance loss

The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available. In this paper, we investigate three different approaches to improve DNNs in recognizing objects in OoD orientations and illuminations. Namely, these are (i) training much longer after convergence of the in-distribution (InD) validation accuracy, i.e., late-stopping, (ii) tuning the momentum parameter of the batch normalization layers, and (iii) enforcing invariance of the neural activity in an intermediate layer to orientation and illumination conditions. Each of these approaches substantially improves the DNN's OoD accuracy (more than 20% in some cases). We report results in four datasets: two datasets are modified from the MNIST and iLab datasets, and the other two are novel (one of 3D rendered cars and another of objects taken from various controlled orientations and illumination conditions). These datasets allow to study the effects of different amounts of bias and are challenging as DNNs perform poorly in OoD conditions. Finally, we demonstrate that even though the three approaches focus on different aspects of DNNs, they all tend to lead to the same underlying neural mechanism to enable OoD accuracy gains --individual neurons in the intermediate layers become more selective to a category and also invariant to OoD orientations and illuminations. We anticipate this study to be a basis for further improvement of deep neural networks' OoD generalization performance, which is highly demanded to achieve safe and fair AI applications.
Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding

For knowledge graph completion, two major types of prediction models exist: one based on graph embeddings, and the other based on relation path rule induction. They have different advantages and disadvantages. To take advantage of both types, hybrid models have been proposed recently. One of the hybrid models, UniKER, alternately augments training data by relation path rules and trains an embedding model. Despite its high prediction accuracy, it does not take full advantage of relation path rules, as it disregards low-confidence rules in order to maintain the quality of augmented data. To mitigate this limitation, we propose transductive data augmentation by relation path rules and confidence-based weighting of augmented data. The results and analysis show that our proposed method effectively improves the performance of the embedding model by augmenting data that include true answers or entities similar to them.
Living Literature Reviews

Literature reviews have long played a fundamental role in synthesizing the current state of a research field. However, in recent years, certain fields have evolved at such a rapid rate that literature reviews quickly lose their relevance as new work is published that renders them outdated. We should therefore rethink how to structure and publish such literature reviews with their highly valuable synthesized content. Here, we aim to determine if existing Linked Data technologies can be harnessed to prolong the relevance of literature reviews and whether researchers are comfortable with working with such a solution. We present here our approach of "living literature reviews'' where the core information is represented as Linked Data which can be amended with new findings after the publication of the literature review. We present a prototype implementation, which we use for a case study where we expose potential users to a concrete literature review modeled with our approach. We observe that our model is technically feasible and is received well by researchers, with our "living'' versions scoring higher than their traditional counterparts in our user study. In conclusion, we find that there are strong benefits to using a Linked Data solution to extend the effective lifetime of a literature review.
Energy-Efficient Cooperative Offloading for Edge Computing-Enabled Vehicular Networks

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, inappropriate task offloading to roadside units (RSUs) can lead to large energy consumption, which will result in negative economic, environmental, and performance impacts. Therefore, in this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks. We first establish a novel cooperative offloading model to multiple RSUs for given batch of moving vehicles, different from existing works that consider single vehicle only or static users. Then, we consider the total energy minimization by optimizing the task splitting ratio, computation resource, and communication resource, which is a challenging non-convex problem, and provide optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we extend our proposed scheme to the one for a more realistic scenario (i.e., online scenario), where batches of vehicles sequentially approach the RSUs. Finally, through numerical results, the impact of network parameters on the total energy consumption is analyzed, and we verify that our proposed solution consumes lower energy than baseline schemes.
Predicting Atlantic Multidecadal Variability

Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, therefore prediction of AMV, especially the extreme values, is of great societal utility for understanding and responding to regional climate change. This work tests multiple machine learning models to improve the state of AMV prediction from maps of sea surface temperature, salinity, and sea level pressure in the North Atlantic region. We use data from the Community Earth System Model 1 Large Ensemble Project, a state-of-the-art climate model with 3,440 years of data. Our results demonstrate that all of the models we use outperform the traditional persistence forecast baseline. Predicting the AMV is important for identifying future extreme temperatures and precipitation, as well as hurricane activity, in Europe and North America up to 25 years in advance.
Recent jet and jet substructure measurements at the LHC, and ML based tagging

Recent jet and jet substructure measurements at the LHC, and of machine-learning-based tagging techniques are presented using proton-proton collision data collected by the ATLAS and CMS experiments at CERN's Large Hadron Collider. These measurements are crucial for precise tests of electroweak and pQCD calculations and searches for physics beyond the Standard Model. The measurements are compared with several Monte Carlo event generators predictions which provide valuable input to the tuning of perturbative and non-perturbative models and to constraining model parameters of advanced parton-shower Monte Carlo programs.
My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music

Homophily describes the phenomenon that similarity breeds connection, i.e., individuals tend to form ties with other people who are similar to themselves in some aspect(s). The similarity in music taste can undoubtedly influence who we make friends with and shape our social circles. In this paper, we study homophily in an online music platform Last.fm regarding user preferences towards listening to mainstream (M), novel (N), or diverse (D) content. Furthermore, we draw comparisons with homophily based on listening profiles derived from artists users have listened to in the past, i.e., artist profiles. Finally, we explore the utility of users' artist profiles as well as features describing M, N, and D for the task of link prediction. Our study reveals that: (i) users with a friendship connection share similar music taste based on their artist profiles; (ii) on average, a measure of how diverse is the music two users listen to is a stronger predictor of friendship than measures of their preferences towards mainstream or novel content, i.e., homophily is stronger for D than for M and N; (iii) some user groups such as high-novelty-seekers (explorers) exhibit strong homophily, but lower than average artist profile similarity; (iv) using M, N and D achieves comparable results on link prediction accuracy compared with using artist profiles, but the combination of features yields the best accuracy results, and (v) using combined features does not add value if graph-based features such as common neighbors are available, making M, N, and D features primarily useful in a cold-start user recommendation setting for users with few friendship connections. The insights from this study will inform future work on social context-aware music recommendation, user modeling, and link prediction.
Conical Classification For Computationally Efficient One-Class Topic Determination

As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.
Asgl: A Python Package for Penalized Linear and Quantile Regression

Asg is a Python package that solves penalized linear regression and quantile regression models for simultaneous variable selection and prediction, for both high and low dimensional frameworks. It makes very easy to set up and solve different types of lasso-based penalizations among which the asgl (adaptive sparse group lasso, that gives name to the package) is remarked. This package is built on top of cvxpy, a Python-embedded modeling language for convex optimization problems and makes extensive use of multiprocessing, a Python module for parallel computing that significantly reduces computation times of asgl.
An Ordinary Rank-Two Case of Local-Global Compatibility for Automorphic Representations of Arbitrary Weight Over CM Fields

We prove a rank-two potential automorphy theorem for mod $l$ representations satisfying an ordinary condition. Combined with an ordinary automorphy lifting theorem, we prove a rank-two, $p \ne l$ case of local-global compatibility for regular algebraic cuspidal automorphic representations of arbitrary weight over CM fields that is $\iota$-ordinary for some $\iota: \overline{\mathbb{Q}}_l\xrightarrow{\sim}\mathbb{C}$.
A Systematic Search for Dual Active Galactic Nuclei in Merging Galaxies (ASTRO-DARING) II: First Results from Long-slit Spectroscopic Observations

Building a large sample of kiloparsec (kpc)-scale dual active galactic nuclei (AGNs) among merging galaxies is of vital importance to understand the coevolution between host galaxies and their central super massive black holes (SMBHs). Doing so, with just such a sample, we have developed an innovative method of systematically searching and identifying dual AGNs among kpc-scale merging galaxies and selected 222 candidates at redshifts ≤ 0.25. All the selected candidates have radio detection in the Faint Images of the Radio Sky at Twenty Centimeters survey and at least one of two cores previously revealed as AGN spectroscopically. We report the first results from a systematic search for dual AGNs in merging galaxies (ASTRO-DARING), which consist of spatially resolved long-slit spectroscopic observations of 41 targets selected from our merging galaxies sample carried out between 2014 November and 2017 February, using the Yunnan Faint Object Spectrograph and Camera mounted on the 2.4 meter telescope in Lijiang of Yunnan Observatories. Of these, 16 are likely dual AGNs, and 15 are newly identified. The efficiency of ASTRO-DARING is thus nearly 40%. With this method, we plan to build the first even sample of more than 50 dual AGNs constructed using a consistent approach. Further analysis of the dual AGN sample shall provide vital clues for understanding the coevolution of galaxies and SMBHs.
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring

Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount. This paper presents an end-to-end deep learning approach for removing defocus blur from a single image, so as to have an all-in-focus image for consequent vision tasks. First, a pixel-wise Gaussian kernel mixture (GKM) model is proposed for representing spatially variant defocus blur kernels in an efficient linear parametric form, with higher accuracy than existing models. Then, a deep neural network called GKMNet is developed by unrolling a fixed-point iteration of the GKM-based deblurring. The GKMNet is built on a lightweight scale-recurrent architecture, with a scale-recurrent attention module for estimating the mixing coefficients in GKM for defocus deblurring. Extensive experiments show that the GKMNet not only noticeably outperforms existing defocus deblurring methods, but also has its advantages in terms of model complexity and computational efficiency.
Changing the flow profile and resulting drying pattern of dispersion droplets via contact angle modification

Spilling tea or coffee leads to a tell-tale circular stain after the drying of the droplet. This phenomenon was termed after the latter example as the “coffee ring effect”. The evaporation of suspension droplets is a complex physical process, and prediction and control over particle deposit patterns obtained from sessile droplet evaporation are essential for many industrial processes such as ink-jet printing or crop-care applications. In this article, we present a systematic investigation of the effect of surface wettability on the evaporation dynamics of a particle-laden droplet, including the effect on the contact line stick-slip, the hydrodynamic flow of the suspended particles and the resulting particle deposit after evaporation. We tune the wettability of glass slides using silanisation and quantify the internal flow during the evaporation by tracking fluorescent tracer particles. We find that the internal flow shifts from a predominantly outward flow towards the contact line for low contact angles to an inward flow for large contact angles. Additionally, the corresponding deposit gradually changes from the typical coffee-ring to a central stain upon increasing the hydrophobicity of the substrate. Last, we corroborate these experimental findings with dynamic density functional theory, modelling the droplet evaporation process and stick-slip behaviour of the contact line. Our investigation suggests that the wettability of the substrate can substantially alter hydrodynamic flow within drying droplets and therefore the resulting particle deposit.
A Scalable Network Model for Electrically Tunable Ferroelectric Domain Structure in Twistronic Bilayers of Two-Dimensional Semiconductors

Moiré structures in small-angle-twisted bilayers of two-dimensional (2D) semiconductors with a broken-symmetry interface form arrays of ferroelectric (FE) domains with periodically alternating out-of-plane polarization. Here, we propose a network theory for the tunability of such FE domain structure by applying an electric field perpendicular to the 2D crystal. Using multiscale analysis, we derive a fully parametrized string-theory-like description of the domain wall network (DWN) and show that it undergoes a qualitative change, after the arcs of partial dislocation (PD) like domain walls merge (near the network nodes) into streaks of perfect screw dislocations (PSD), which happens at a threshold displacement field dependent on the DWN period.
Profit equitably: an investigation of market maker's impact on equitable outcomes

We look at discovering the impact of market microstructure on equitability for market participants at public exchanges such as the New York Stock Exchange or NASDAQ. Are these environments equitable venues for low-frequency participants (such as retail investors)? In particular, can market makers contribute to equitability for these agents? We use a simulator to assess the effect a market marker can have on equality of outcomes for consumer or retail traders by adjusting its parameters. Upon numerically quantifying market equitability by the entropy of the price returns distribution of consumer agents, we demonstrate that market makers indeed support equitability and that a negative correlation is observed between the profits of the market maker and equitability. We then use multi objective reinforcement learning to concurrently optimize for the two objectives of consumer agent equitability and market maker profitability, which leads us to learn policies that facilitate lower market volatility and tighter spreads for comparable profit levels.
Targeted Hardening of Electric Distribution System Components for Enhanced Resilience against Earthquakes

Securing the power system from catastrophic natural disasters is a rising problem in power system operation and planning. This paper particularly considers earthquake and aims to evaluate and improve the resilience of power distribution networks by developing a novel hardware hardening framework. In the proposed framework, fragility curves of the network equipment are used to represent equipment failure probabilities when facing an earthquake, and failure scenarios of the distribution network are obtained via the Monte Carlo method. Based on the distribution network topology and the locations of essential loads, various hardware hardening strategies are determined within the proposed framework. Through a series of resilience and economic analyses, the optimal hardening strategy is determined to improve the resilience and supply the essential loads during and after the earthquake. The efficacy of the proposed approach is examined through simulations on an IEEE 33-bus test feeder.
Statistical uncertainty estimation of higher-order cumulants with finite efficiency and its application in heavy-ion collisions

We derive the general analytical expressions for the statistical uncertainties of cumulants up to fourth order including an efficiency correction. The analytical expressions have been tested with a toy Monte Carlo model analysis. An application to the study of particle multiplicity fluctuations in heavy-ion collisions is investigated. In this derivation, a mathematical proof is given that the validity of an averaged efficiency correction and the fluctuations induced by the non-uniformity of efficiency can be eliminated. The estimation of statistical uncertainties using the analytical formulas is found to be significantly faster than the commonly used bootstrap method. The simplicity and efficiency of using the analytical formulas may be useful for massive data analysis in many fields.
Experimental Study on the Imitation of the Human Neck-and-Eye Pose Using the 3-DOF Agile Eye Parallel Robot Based on a Deep Neural Network Approach

—In this paper, a method to mimic a human face and eyes is proposed which can be regarded as a combination of computer vision techniques and neural network concepts. From a mechanical standpoint, a 3-DOF spherical parallel robot is used which imitates the human face movement. In what concerns eye movement, a 2-DOF mechanism is attached to the end-effector of the 3-DOF spherical parallel mechanism. In order to have robust and reliable results for the imitation, meaningful information should be extracted from the face mesh for obtaining the pose of a face, i.e., the roll, yaw, and pitch angles. To this end, two methods are proposed where each of them has its own pros and cons. The ﬁrst method consists in resorting to the so-called Mediapipe library which is a machine learning solution for high-ﬁdelity body pose tracking, introduced by Google. As the second method, a model is trained by a linear regression model for a gathered dataset of face pictures in different poses. In addition, a 3-DOF Agile Eye parallel robot is utilized to show the ability of this robot to be used as a system which is similar to a human neck for performing a 3-DOF rotational motion pattern. Furthermore, a 3D printed face and a 2-DOF eye mechanism are fabricated to display the whole system more stylish way. Experiments on this platform demonstrate the effectiveness of the proposed methods for tracking the human neck and eye movement.