Miracle-dz/UniFAR-FTUs huggingface dataset:
Fairness-Aware Online Personalization

Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluence of factors such as ubiquitous connectivity, ability to collect, aggregate, and process large amounts of fine-grained data using cloud computing, and ease of access to applying sophisticated machine learning models. Quite often, such applications are powered by search and recommendation systems, which in turn make use of personalized ranking algorithms. At the same time, there is increasing awareness about the ethical and legal challenges posed by the use of such data-driven systems. Researchers and practitioners from different disciplines have recently highlighted the potential for such systems to discriminate against certain population groups, due to biases in the datasets utilized for learning their underlying recommendation models. We present a study of fairness in online personalization settings involving the ranking of individuals. Starting from a fair warm-start machine-learned model, we first demonstrate that online personalization can cause the model to learn to act in an unfair manner if the user is biased in his/her responses. For this purpose, we construct a stylized model for generating training data with potentially biased features as well as potentially biased labels and quantify the extent of bias that is learned by the model when the user responds in a biased manner as in many real-world scenarios. We then formulate the problem of learning personalized models under fairness constraints and present a regularization based approach for mitigating biases in machine learning. We demonstrate the efficacy of our approach through extensive simulations with different parameter settings. Code: this https URL
Trade-offs in Top-k Classification Accuracies on Losses for Deep Learning

This paper presents an experimental analysis about trade-offs in top-k classification accuracies on losses for deep leaning and proposal of a novel top-k loss. Commonly-used cross entropy (CE) is not guaranteed to optimize top-k prediction without infinite training data and model complexities. The objective is to clarify when CE sacrifices top-k accuracies to optimize top-1 prediction, and to design loss that improve top-k accuracy under such conditions. Our novel loss is basically CE modified by grouping temporal top-k classes as a single class. To obtain a robust decision boundary, we introduce an adaptive transition from normal CE to our loss, and thus call it top-k transition loss. It is demonstrated that CE is not always the best choice to learn top-k prediction in our experiments. First, we explore trade-offs between top-1 and top-k (=2) accuracies on synthetic datasets, and find a failure of CE in optimizing top-k prediction when we have complex data distribution for a given model to represent optimal top-1 prediction. Second, we compare top-k accuracies on CIFAR-100 dataset targeting top-5 prediction in deep learning. While CE performs the best in top-1 accuracy, in top-5 accuracy our loss performs better than CE except using one experimental setup. Moreover, our loss has been found to provide better top-k accuracies compared to CE at k larger than 10. As a result, a ResNet18 model trained with our loss reaches 99 % accuracy with k=25 candidates, which is a smaller candidate number than that of CE by 8.
Comparison of local image descriptors for plant identification from leaf images

In this paper we present several descriptors used for the task of Plant recognition based on the images of leaves. The set of descriptors include texture based descriptors, fractal descriptors as well as some of the state of the art descriptors for image retrieval and object recognition in images. The descriptors are generated from the leaf images taken from single leaves on homogenous background. The descriptors are then used for training classifiers from a dataset of leaf images. The comparison of the obtained results will be presented in this paper.
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting

Forecasting of multivariate time-series is an important problem that has applications in traffic management, cellular network configuration, and quantitative finance. A special case of the problem arises when there is a graph available that captures the relationships between the time-series. In this paper we propose a novel learning architecture that achieves performance competitive with or better than the best existing algorithms, without requiring knowledge of the graph. The key element of our proposed architecture is the learnable fully connected hard graph gating mechanism that enables the use of the state-of-the-art and highly computationally efficient fully connected time-series forecasting architecture in traffic forecasting applications. Experimental results for two public traffic network datasets illustrate the value of our approach, and ablation studies confirm the importance of each element of the architecture. The code is available here: https://github.com/boreshkinai/fc-gaga.
A recommendation and risk classification system for connecting rough sleepers to essential outreach services

Abstract Rough sleeping is a chronic experience faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link (HL), a UK-based charity, in developing a data-driven approach to better connect people sleeping rough on the streets with outreach service providers. HL's platform has grown exponentially in recent years, leading to thousands of alerts per day during extreme weather events; this overwhelms the volunteer-based system they currently rely upon for the processing of alerts. In order to solve this problem, we propose a human-centered machine learning system to augment the volunteers' efforts by prioritizing alerts based on the likelihood of making a successful connection with a rough sleeper. This addresses capacity and resource limitations whilst allowing HL to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation using historical data shows that our approach increases the rate at which rough sleepers are found following a referral by at least 15% based on labeled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modeling process is done with careful considerations of ethics, transparency, and explainability due to the sensitive nature of the data involved and the vulnerability of the people that are affected.
Phylogenetic Relationships and Genetic Diversity among Orobanche cumana Wallr. and O. cernua L. (Orobanchaceae) Populations in the Iberian Peninsula

Abstract Orobanche cumana is found in the Iberian Peninsula as an allochthonous species parasitizing exclusively sunflower, in contrast to the closely related species Orobanche cernua, which is an autochthonous species that only parasitizes wild Asteraceae hosts. Ten O. cumana populations were collected in the two traditional areas of sunflower broomrape occurrence, the Guadalquivir Valley, Southern Spain (six populations) and Cuenca province, Central Spain (four populations). Twelve O. cernua populations were collected on wild hosts across its natural distribution area in Southeastern Spain. Genetic relationships within and between both sets of populations were studied using a set of 50 robust and co-dominant SSR markers from O. cumana. The results supported the taxonomic separation of the two species and the existence of two distant genetic groups for O. cumana, one in Guadalquivir Valley and another one in Cuenca province. The inter- and intra-population variability was extremely low for O. cumana, whereas the overall genetic diversity was much higher for O. cernua. The genetic structure of O. cumana populations probably reflects a founder effect, with the two genetically distant groups deriving from separate introduction events. The high degree of genetic differentiation observed in O. cernua is mainly explained on the basis of restricted gene flow due to ecological barriers together with the occurrence of a predominantly self-pollinating mating system. Complementary diversity studies on both species in its current distribution area are required for understanding global genetic variability and evolutionary characteristics of the parasitism.
A nonlocal optimization method for computational inverse design in nanophotonics

We propose a nonlocal optimization method for inverse design in nanophotonics. Existing local-gradient-based optimization approaches lack nonlocal exploration ability required for escaping from local minima in non-convex landscapes. A nonlocal gradient operator using directional Gaussian smoothing (DGS) is proposed to address this challenge in photonics device design. Compared with the local gradient, the DGS gradient allows for a large smoothing radius to capture the global structure of loss landscapes. A deterministic Gauss-Hermite quadrature is used to develop a DGS gradient estimator with higher accuracy than Monte Carlo estimators. The nonlocal optimization method using DGS gradient has its advantages on portability and flexibility to naturally incorporate the parameterization, physics simulation, and objective formulation together to builds up an effective inverse design workflow. An adaptive smoothing radius and learning rate with quadratic decay are proposed to improve robustness and convergence within the DGS gradient optimization. To enable a clear binarized design, a dynamic growth mechanism is imposed on the projection strength in parameterization. The proposed methodology is demonstrated by an example of wavelength demultiplexer. Our method shows superior performance with faster convergence compared with classical approaches. Given varying initialization with Gaussian random noise, the optimized design using the nonlocal DGS gradient demonstrates high robustness and low uncertainty. By incorporating volume constraints into optimization, the optimized design using the nonlocal method achieves an equivalently high performance but significantly reduces the amount of material usage in wavelength demultiplexer design.
An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction

Emotion Cause Extraction (ECE) aims to identify emotion causes from a document after annotating the emotion keywords. Some baselines have been proposed to address this problem, such as rule-based, commonsense based and machine learning methods. We show, however, that a simple random selection approach toward ECE that does not require observing the text achieves similar performance compared to the baselines. We utilized only position information relative to the emotion cause to accomplish this goal. Since position information alone without observing the text resulted in higher F-measure, we therefore uncovered a bias in the ECE single genre Sina-news benchmark. Further analysis showed that an imbalance of emotional cause location exists in the benchmark, with a majority of cause clauses immediately preceding the central emotion clause. We examine the bias from a linguistic perspective, and show that high accuracy rate of current state-of-art deep learning models that utilize location information is only evident in datasets that contain such position biases. The accuracy drastically reduced when a dataset with balanced location distribution is introduced. We therefore conclude that it is the innate bias in this benchmark that caused high accuracy rate of these deep learning models in ECE. We hope that the case study in this paper presents both a cautionary lesson, as well as a template for further studies, in interpreting the superior fit of deep learning models without checking for bias.
Data-efficient Hindsight Off-policy Option Learning

Solutions to most complex tasks can be decomposed into simpler, intermediate skills, reusable across wider ranges of problems. We follow this concept and introduce Hindsight Off-policy Options (HO2), a new algorithm for efficient and robust option learning. The algorithm relies on critic-weighted maximum likelihood estimation and an efficient dynamic programming inference procedure over off-policy trajectories. We can backpropagate through the inference procedure through time and the policy components for every time-step, making it possible to train all component's parameters off-policy, independently of the data-generating behavior policy. Experimentally, we demonstrate that HO2 outperforms competitive baselines and solves demanding robot stacking and ball-in-cup tasks from raw pixel inputs in simulation. We further compare autoregressive option policies with simple mixture policies, providing insights into the relative impact of two types of abstractions common in the options framework: action abstraction and temporal abstraction. Finally, we illustrate challenges caused by stale data in off-policy options learning and provide effective solutions.
χaroν: a tool for neutrino flux generation from WIMPs

Indirect searches for signatures of corpuscular dark matter have been performed using all cosmic messengers: gamma rays, cosmic rays, and neutrinos. The search for dark matter from neutrinos is of particular importance since they are the only courier that can reach detectors from dark matter processes in dense environments, such as the core of the Sun or Earth, or from the edge of the observable Universe. In this work, we introduce χaroν, a software package that, in the spirit of its mythological Greek namesake χ ́α ρ ω ν, bridges the dark sector and Standard Model by predicting neutrino fluxes from different celestial dark matter agglomerations. The flux at the point of production is either computed internally by χaroν or is taken from user supplied tables. χaroν then propagates this flux through vacuum or dense media and returns the expected neutrino yield at an observer's location. In developing χaroν, we have revisited and updated the production of neutrinos in dense media, updated the propagation of high-energy neutrinos, and studied the sources of uncertainty in neutrino transport. This package is coupled to a new calculation that includes electroweak corrections resulting in the most up-to-date and complete repository of neutrino fluxes from dark matter decay and annihilation over the energy range of 1 GeV to 10 PeV coming from the Earth, the Sun, and the Galactic halo.
Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling

High-level musical qualities (such as emotion) are often abstract, subjective, and hard to quantify. Given these difficulties, it is not easy to learn good feature representations with supervised learning techniques, either because of the insufficiency of labels, or the subjectiveness (and hence large variance) in human-annotated labels. In this paper, we present a framework that can learn high-level feature representations with a limited amount of data, by first modelling their corresponding quantifiable low-level attributes. We refer to our proposed framework as Music FaderNets, which is inspired by the fact that low-level attributes can be continuously manipulated by separate "sliding faders" through feature disentanglement and latent regularization techniques. High-level features are then inferred from the low-level representations through semi-supervised clustering using Gaussian Mixture Variational Autoencoders (GM-VAEs). Using arousal as an example of a high-level feature, we show that the "faders" of our model are disentangled and change linearly w.r. t. the modelled low-level attributes of the generated output music. Furthermore, we demonstrate that the model successfully learns the intrinsic relationship between arousal and its corresponding low-level attributes (rhythm and note density), with only 1% of the training set being labelled. Finally, using the learnt high-level feature representations, we explore the application of our framework in style transfer tasks across different arousal states. The effectiveness of this approach is verified through a subjective listening test.
Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in next-generation wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.
The Break-by-one Gamma Distribution: A Proper and Tractable Alternative to the Schechter Function for Modeling Cosmic Populations

The break-by-one gamma distribution has a probability density function resembling the Schechter function, but with the small-argument behavior modified so it is normalizable in commonly arising cases where the Schechter function is not. Its connection to the gamma distribution makes it straightforward to sample from. These properties make it useful for cosmic demographics.
The diffuse gamma-ray emission toward the Galactic mini starburst W43

In this paper we report the Fermi Large Area Telescope (Fermi-LAT) detection of the γ-ray emission toward the young star forming region W43. Using the latest source catalog and diffuse background models, the extended γ-ray excess is detected with a significance of ~16σ. The γ-ray emission has a spectrum with a photon index of 2.3 ± 0.1. We also performed a detailed analysis of the gas content in this region by taking into account the opacity correction to the HI gas column density. The total cosmic-ray (CR) proton energy is estimated to be on the order of 1048 erg, assuming the γ-rays are produced from the interaction of the accelerated protons and nuclei with the ambient gas. Comparing this region to the other star formation regions in our Galaxy, we find that the CR luminosity is better correlated with the wind power than the star formation rate (SFR). This result suggests that CRs are primarily accelerated by stellar wind in these systems.
One-dimensional electronic instabilities at the edges of 
MoS2

The one-dimensional (1D) metallic states that appear at the zigzag edges of semiconducting two-dimensional transition metal dichalcogenides (TMDCs) result from the intrinsic electric polarization in these materials, which for ${\mathrm{D}}_{3h}$ symmetry is a topological invariant. These 1D states are susceptible to electronic and structural perturbations that triple the period along the edge. In this paper we study possible spin-density waves (SDWs) and charge-density waves (CDWs) at the zigzag edges of ${\mathrm{MoS}}_{2}$, using first-principles density-functional theory calculations. Depending on the detailed structures and termination of the edges, we observe either combined SDW/CDWs or pure CDWs, along with structural distortions. In all cases the driving force is the opening of a band gap at the edge. The analysis should hold for all group VI TMDCs with the same basic structure as ${\mathrm{MoS}}_{2}$.
Prospects for improving biological control of olive fruit fly, Bactrocera oleae (Diptera: Tephritidae), with introduced parasitoids (Hymenoptera)

Abstract Olive fruit fly is a key pest of olive and consequently a serious threat to olive fruit and oil production throughout the Mediterranean region. With the establishment of Bactrocera oleae in California a decade ago, interest was renewed in classical (introduction) biological control of the pest. Here we discuss the prospects of identifying natural enemies of B. oleae in Africa and Asia that may help reduce B. oleae populations in California and elsewhere. Based on the current understanding of Bactrocera phylogenetics, early opinions that B. oleae originated in Africa or western Asia rather than the Mediterranean region or the Near East are taxonomically and ecologically supportable. Closely related to cultivated olive, the wild olive Olea europaea cuspidata is widely distributed in southern and eastern Africa, the Arabian Peninsula, and eastwards into Asia as far as southwestern China. Little is known regarding the biology and ecology of B. oleae in Africa and eastern Asia, especially in wild olives. While the diversity of parasitoids of B. oleae in the Mediterranean region is low and unspecialized, a diverse assemblage of parasitoids is known from B. oleae in Africa. Conversely, regions in Asia have remained largely unexplored for B. oleae and its natural enemies.
$L^{p}$ uniform random walk-type approximation for Fractional Brownian motion with Hurst exponent $0 < H < \frac {1}{2}$

In this note, we prove an $L^p$ uniform approximation of the fractional Brownian motion with Hurst exponent $0 < H < \frac{1}{2}$ by means of a family of continuous-time random walks imbedded on a given Brownian motion. The approximation is constructed via a pathwise representation of the fractional Brownian motion in terms of a standard Brownian motion. For an arbitrary choice $\epsilon_k$ for the size of the jumps of the family of random walks, the rate of convergence of the approximation scheme is $O(\epsilon_k^{p(1-2\lambda)+ 2(\delta-1)})$ whenever $\max\{0,1-\frac{pH}{2}\}< \delta < 1$, $\lambda \in \big(\frac{1-H}{2}, \frac{1}{2} + \frac{\delta-1}{p}\big)$.
Dark Matters on the Scale of Galaxies

The cold dark matter model successfully explains both the emergence and evolution of cosmic structures on large scales and, when we include a cosmological constant, the properties of the homogeneous and isotropic Universe. However, the cold dark matter model faces persistent challenges on the scales of galaxies. {Indeed,} N-body simulations predict some galaxy properties that are at odds with the observations. These discrepancies are primarily related to the dark matter distribution in the innermost regions of the halos of galaxies and to the dynamical properties of dwarf galaxies. They may have three different origins: (1) the baryonic physics affecting galaxy formation is still poorly understood and it is thus not properly included in the model; (2) the actual properties of dark matter differs from those of the conventional cold dark matter; (3) the theory of gravity departs from General Relativity. Solving these discrepancies is a rapidly evolving research field. We illustrate some of the solutions proposed} within the cold dark matter model, and solutions when including warm dark matter, self-interacting dark matter, axion-like particles, or fuzzy dark matter. { We also illustrate some modifications of the theory of gravity: Modified Newtonian Dynamics (MOND), MOdified Gravity (MOG), and $f(R)$ gravity.