Miracle-dz/UniFAR-FTUs huggingface dataset:
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages

Developing Named Entity Recognition (NER) systems for Indian languages has been a long-standing challenge, mainly owing to the requirement of a large amount of annotated clean training instances. This paper proposes an end-to-end framework for NER for Indian languages in a low-resource setting by exploiting parallel corpora of English and Indian languages and an English NER dataset. The proposed framework includes an annotation projection method that combines word alignment score and NER tag prediction confidence score on source language (English) data to generate weakly labeled data in a target Indian language. We employ a variant of the Teacher-Student model and optimize it jointly on the pseudo labels of the Teacher model and predictions on the generated weakly labeled data. We also present manually annotated test sets for three Indian languages: Hindi, Bengali, and Gujarati. We evaluate the performance of the proposed framework on the test sets of the three Indian languages. Empirical results show a minimum 10% performance improvement compared to the zero-shot transfer learning model on all languages. This indicates that weakly labeled data generated using the proposed annotation projection method in target Indian languages can complement well-annotated source language data to enhance performance. Our code is publicly available at https://github.com/aksh555/CL-NERIL.
Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19

COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus disease. Due to its contagious nature, the virus can easily infect an unprotected and exposed individual from mild to severe symptoms. The study of the virus’s effects on pregnant mothers and neonatal is now a concerning issue globally among civilians and public health workers considering how the virus will affect the mother and the neonate’s health. This paper aims to develop a predictive model to estimate the possibility of death for a COVID-diagnosed mother based on documented symptoms: dyspnea, cough, rhinorrhea, arthralgia, and the diagnosis of pneumonia. The machine learning models that have been used in our study are support vector machine, decision tree, random forest, gradient boosting, and artificial neural network. The models have provided impressive results and can accurately predict the mortality of pregnant mother’s with a given input. The precision rate for 3 models(ANN, Gradient Boost, Random Forest) is 100% The highest accuracy score(Gradient Boosting, ANN) is 95%, highest recall(Support Vector Machine) is 92.75% and highest f1 score(Gradient Boosting, ANN) is 94.66%. Due to the accuracy of the model, pregnant mother can expect immediate medical treatment based on their possibility of death due to the virus. The model can be utilized by health workers globally to list down emergency patients, which can ultimately reduce the death rate of COVID-19 diagnosed pregnant mothers.
Constructing the Emission-line Galaxy–Host Halo Connection through Auto and Cross Correlations

We investigate the [O ii] emission-line galaxy (ELG)-host halo connection via auto and cross correlations, and propose a concise and effective method to populate ELGs in dark matter halos without assuming a parameterized halo occupation distribution (HOD) model. Using the observational data from VIMOS Public Extragalactic Redshift Survey, we measure the auto and cross correlation functions between ELGs selected by [O ii] luminosity and normal galaxies selected by stellar mass. Combining the stellar–halo mass relation derived for the normal galaxies and the fraction of ELGs observed in the normal galaxy population, we demonstrate that we can establish an accurate ELG–halo connection. With the ELG–halo connection, we can accurately reproduce the auto and cross correlation functions of ELGs and normal galaxies both in real space and in redshift space, once the satellite fraction is properly reduced. Our method provides a novel strategy to generate ELG mock catalogs for ongoing and upcoming galaxy redshift surveys. We also provide a simple description for the HOD of ELGs.
The Impact of Data Distribution on Q-learning with Function Approximation

We study the interplay between the data distribution and Q-learning-based algorithms with function approximation. We provide a unified theoretical and empirical analysis as to how different properties of the data distribution influence the performance of Q-learning-based algorithms. We connect different lines of research, as well as validate and extend previous results. We start by reviewing theoretical bounds on the performance of approximate dynamic programming algorithms. We then introduce a novel four-state MDP specifically tailored to highlight the impact of the data distribution in the performance of Q-learning-based algorithms with function approximation, both online and offline. Finally, we experimentally assess the impact of the data distribution properties on the performance of two offline Q-learning-based algorithms under different environments. According to our results: (i) high entropy data distributions are well-suited for learning in an offline manner; and (ii) a certain degree of data diversity (data coverage) and data quality (closeness to optimal policy) are jointly desirable for offline learning.
Resonant dynamics of skyrmion lattices in thin film multilayers: Localised modes and spin wave emission

The spectral signatures of magnetic skyrmions under microwave field excitation are of fundamental interest and can be an asset for high frequency applications. These topological solitons can be tailored in multilayered thin films, but the experimental observation of their spin wave dynamics remains elusive, in particular due to large damping. Here, we study Pt/FeCoB/AlO$_x$ multilayers hosting dense and robust skyrmion lattices at room temperature with Gilbert damping of $\sim 0.02$. We use magnetic force microscopy to characterise their static magnetic phases and broadband ferromagnetic resonance to probe their high frequency response. Micromagnetic simulations reproduce the experiments with accuracy and allow us to identify distinct resonant modes detected in the skyrmion lattice phase. Low ($<$ 2 GHz) and intermediate frequency ($2-8$ GHz) modes involve excitations localised to skyrmion edges in conjunction with precession of the uniform background magnetisation, while a high frequency ($>$ 12 GHz) mode corresponds to in-phase skyrmion core precession emitting spin waves into uniform background with wavelengths in the 50--80 nm range commensurate with the lattice structure. These findings could be instrumental in the investigation of room temperature wave scattering and the implementation of novel microwave processing schemes in reconfigurable arrays of solitons.
No Free Lunch: Balancing Learning and Exploitation at the Network Edge

Over the last few years, the Deep Reinforcement Learning (DRL) paradigm has been widely adopted for 5G and beyond network optimization because of its extreme adaptability to many different scenarios. However, collecting and processing learning data entail a significant cost in terms of communication and computational resources, which is often disregarded in the networking literature. In this work, we analyze the cost of learning in a resource-constrained system, defining an optimization problem in which training a DRL agent makes it possible to improve the resource allocation strategy but also reduces the number of available resources. Our simulation results show that the cost of learning can be critical when evaluating DRL schemes on the network edge and that assuming a cost-free learning model can lead to significantly overestimating performance.
MFM-Net: Unpaired Shape Completion Network with Multi-stage Feature Matching

Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes during training. To build the correspondence between two data modalities, previous methods usually apply adversarial training to match the global shape features extracted by the encoder. However, this ignores the correspondence between multi-scaled geometric information embedded in the pyramidal hierarchy of the decoder, which makes previous methods struggle to generate high-quality complete shapes. To address this problem, we propose a novel unpaired shape completion network, named MFMNet, using multi-stage feature matching, which decomposes the learning of geometric correspondence into multi-stages throughout the hierarchical generation process in the point cloud decoder. Specifically, MFM-Net adopts a dual path architecture to establish multiple feature matching channels in different layers of the decoder, which is then combined with the adversarial learning to merge the distribution of features from complete and incomplete modalities. In addition, a refinement is applied to enhance the details. As a result, MFM-Net makes use of a more comprehensive understanding to establish the geometric correspondence between complete and incomplete shapes in a localto-global perspective, which enables more detailed geometric inference for generating high-quality complete shapes. We conduct comprehensive experiments on several datasets, and the results show that our method outperforms previous methods of unpaired point cloud completion with a large margin.
REPLICA: Enhanced Feature Pyramid Network by Local Image Translation and Conjunct Attention for High-Resolution Breast Tumor Detection

We introduce an improvement to the feature pyramid network of standard object detection models. We call our method enhanced featu RE P yramid network by L ocal I mage translation and C onjunct A ttention, or REPLICA. REPLICA improves object detection performance by simultaneously (1) generating realistic but fake images with simulated objects to mitigate the data-hungry problem of the attention mechanism, and (2) advancing the detection model architecture through a novel modiﬁcation of attention on image feature patches. Speciﬁcally, we use a convolutional autoencoder as a generator to create new images by injecting objects into images via local interpolation and reconstruction of their features extracted in hidden layers. Then due to the larger number of simulated images, we use a visual transformer to enhance outputs of each ResNet layer that serve as inputs to a feature pyramid network. We apply our methodology to the problem of detecting lesions in Digital Breast Tomosynthesis scans (DBT), a high-resolution medical imaging modality crucial in breast cancer screening. We demonstrate qualitatively and quantitatively that REPLICA can improve the accuracy of tumor detection us-ing our enhanced standard object detection framework via experimental results.
Wind speed and power forecasting based on spatial correlation models

Wind energy conversion systems (WECS) cannot be dispatched like conventional generators. This can pose problems for power system schedulers and dispatchers, especially if the schedule of wind power availability is not known in advance. However, if the wind speed can be reliably forecasted up to several hours ahead, the generating schedule can efficiently accommodate the wind generation. This paper illustrates a technique for forecasting wind speed and power output up to several hours ahead, based on cross correlation at neighboring sites. The authors develop an artificial neural network (ANN) that significantly improves forecasting accuracy comparing to the persistence forecasting model. The method is tested at different sites over a year.
Inducing Functions through Reinforcement Learning without Task Specification

We report a bio-inspired framework for training a neural network through reinforcement learning to induce high level functions within the network. Based on the interpretation that animals have gained their cognitive functions such as object recognition - without ever being specifically trained for - as a result of maximizing their fitness to the environment, we place our agent in an environment where developing certain functions may facilitate decision making. The experimental results show that high level functions, such as image classification and hidden variable estimation, can be naturally and simultaneously induced without any pre-training or specifying them.
PAM: Pose Attention Module for Pose-Invariant Face Recognition

Pose variation is one of the key challenges in face recognition. Conventional techniques mainly focus on face frontalization or face augmentation in image space. However, transforming face images in image space is not guaranteed to preserve the lossless identity features of the original image. Moreover, these methods suffer from more computational costs and memory requirements due to the additional models. We argue that it is more desirable to perform feature transformation in hierarchical feature space rather than image space, which can take advantage of different feature levels and benefit from joint learning with representation learning. To this end, we propose a lightweight and easy-to-implement attention block, named Pose Attention Module (PAM), for pose-invariant face recognition. Specifically, PAM performs frontal-profile feature transformation in hierarchical feature space by learning residuals between pose variations with a soft gate mechanism. We validated the effectiveness of PAM block design through extensive ablation studies and verified the performance on several popular benchmarks, including LFW, CFP-FP, AgeDB-30, CPLFW, and CALFW. Experimental results show that our method not only outperforms state-of-the-art methods but also effectively reduces memory requirements by more than 75 times. It is noteworthy that our method is not limited to face recognition with large pose variations. By adjusting the soft gate mechanism of PAM to a specific coefficient, such semantic attention block can easily extend to address other intra-class imbalance problems in face recognition, including large variations in age, illumination, expression, etc.
One or two frequencies? The Iterative Filtering answers

The Iterative Filtering method is a technique aimed at the decomposition of non-stationary and non-linear signals into simple oscillatory components. This method, proposed a decade ago as an alternative technique to the Empirical Mode Decomposition, has been used extensively in many applied fields of research and studied, from a mathematical point of view, in several papers published in the last few years. However, even if its convergence and stability are now established both in the continuous and discrete setting, it is still an open problem to understand up to what extent this approach can separate two close-by frequencies contained in a signal. In this paper, following the studies conducted on the Empirical Mode Decomposition and the Synchrosqueezing methods, we analyze in detail the abilities of the Iterative Filtering algorithm in extracting two stationary frequencies from a given signal. In particular, after reviewing the Iterative Filtering technique and its known properties, we present new theoretical results and numerical evidence showing the ability of this technique in addressing the fundamental"one or two frequencies"question.
Relating Business Goals to Architecturally Significant Requirements for Software Systems

Abstract : The primary purpose of the architecture for a software-reliant system is to satisfy the driving behavioral and quality attribute requirements. Quality attribute requirements tend to be poorly captured and poorly represented in requirements specifications, which focus on functionality. It is often up to the architect's own initiative to capture the actual quality attribute requirements for a system under development. Quality attributes come about because of the business goals behind the system being developed. Business goals drive the conception, creation, and evolution of software-reliant systems. This report examines business goals from the point of view of the software architect. It presents a wide survey of business goal categories from the business literature and uses that survey to produce a classification of business goals. It introduces the concept of goal-subject (the person or entity who owns the business goal) and goal-object (the person or entity that the goal is intended to benefit). Those concepts are essential to the structure of a business goal scenario -- a systematic way to elicit and express business goals. Using the concept of a business goal scenario drives the Pedigreed Attribute eLicitation Method (PALM), developed by the authors for eliciting architecturally significant business goals. The report illustrates how to use architecturally significant business goals to produce a set of derived quality attribute requirements that can then be vetted and elaborated with the appropriate goal-subject(s) and goal-object(s). This approach has been vetted in two workshops and the method piloted in an industrial setting.
A Shock-in-Jet Synchrotron Mirror Model for Blazars

Reinhard Schlickeiser has made groundbreaking contributions to various aspects of blazar physics, including diffusive shock acceleration, the theory of synchrotron radiation, the production of gamma-rays through Compton scattering in various astrophysical sources, etc. This paper, describing the development of a self-consistent shock-in-jet model for blazars with a synchrotron mirror feature, is therefore an appropriate contribution to a Special Issue in honor of Reinhard Schlickeiser’s 70th birthday. The model is based on our previous development of a self-consistent shock-in-jet model with relativistic thermal and non-thermal particle distributions evaluated via Monte-Carlo simulations of diffusive shock acceleration, and time-dependent radiative transport. This model has been very successful in modeling spectral variability patterns of several blazars, but has difficulties describing orphan flares, i.e., high-energy flares without a significant counterpart in the low-frequency (synchrotron) radiation component. As a solution, this paper investigates the possibility of a synchrotron mirror component within the shock-in-jet model. It is demonstrated that orphan flares result naturally in this scenario. The model’s applicability to a recently observed orphan gamma-ray flare in the blazar 3C279 is discussed and it is found that only orphan flares with mild (≲ a factor of 2–3) enhancements of the Compton dominance can be reproduced in a synchrotron-mirror scenario, if no additional parameter changes are invoked.
Imprints of decaying dark matter on cosmic voids

The Standard Cosmological Model assumes that more than 85% of matter is in the form of collisionless and pressureless dark matter. Unstable decaying dark matter has been proposed in the literature as an extension to the standard cold dark matter model. In this paper we investigate a scenario when dark matter decays and the resultant particle moves with respect to the dark matter. A covariant hydrodynamical model is developed in which the decay is modeled by the transfer of energy-momentum between two dark dust fluid components. We parameterise the model in terms of the decay rate Γ and injection velocity vi of the resultant dark matter particles. We apply the framework to study the evolution of cosmic voids which are environments with low content of baryonic matter. Thus, unlike baryon-rich environments, voids provide an opportunity to measure dark matter signals that are less contaminated by complex baryonic processes. We find that the growth of S-type voids is modified by the dark matter decay, leading to imprints at the present day. This paper serves as a proof-of-concept that cosmic voids can be used to study dark mater physics. We argue that future cosmological observations of voids should focus on signs of reported features to either confirm or rule out the decaying dark matter scenario. Lack of presence of reported features could put constraints of the decay of dark matter in terms of Γ > H 0 and vi < 10 km/s.
SACRIFICE: A Secure Road Condition Monitoring Scheme over Fog-based VANETs

With the rapid growth of Vehicular Ad-Hoc Networks (VANETs), huge amounts of road condition data are constantly being generated and sent to the cloud for processing. However, this introduces a significant load on the network bandwidth causing delay in the network and for a time-critical application like VANET such delay may have severe impact on real-time traffic management. This delay maybe reduced by offloading some computational tasks to devices as close as possible to the vehicles. Further, security and privacy of vehicles is another important concern in such applications. Thus, this paper proposes a secure road condition monitoring scheme for fog-based vehicular networks. It considers Road-Side Units (RSUs) with computational capabilities, which act as intermediate fog nodes in between vehicles and cloud to reduce delay in decision making and thereby increases scalability. Apart from fulfilling basic security features, our scheme also supports advanced features like unlinkability and untraceability. A detailed security analysis proves that the proposed scheme can handle both internal and external adversaries. We also justify the efficiency of our scheme through theoretical overhead analysis. Finally, the scheme is simulated using an integrated SUMO and NS-3 platform to show its feasibility for practical implementations while not compromising on the network performance. The results show an average 80.96% and 37.5% improvement in execution time and end-to-end delay respectively while maintaining at par results for packet delivery ratio over a state-of-the-art scheme.
Bridging scales in a multiscale pattern-forming system

Significance Biological processes operate in a spatially and temporally ordered manner to reliably fulfill their function. This is achieved by pattern formation, which generally involves many different spatial and temporal scales. The resulting multiscale patterns exhibit complex dynamics for which it is difficult to find a simplified description at large scales while preserving information about the patterns at small scales. Here, we introduce an approach for mass-conserving reaction–diffusion systems that is based on a linear theory and therefore conceptually simple to apply. We investigate multiscale patterns of the Min protein system and show that our approach enables us to explain and predict the intricate dynamics from the large-scale mass redistribution of the total protein densities.
Is Dynamic Rumor Detection on social media Viable? An Unsupervised Perspective

With the growing popularity and ease of access to the internet, the problem of online rumors is escalating. People are relying on social media to gain information readily but fall prey to false information. There is a lack of credibility assessment techniques for online posts to identify rumors as soon as they arrive. Existing studies have formulated several mechanisms to combat online rumors by developing machine learning and deep learning algorithms. The literature so far provides supervised frameworks for rumor classification that rely on huge training datasets. However, in the online scenario where supervised learning is exigent, dynamic rumor identification becomes difficult. Early detection of online rumors is a challenging task, and studies relating to them are relatively few. It is the need of the hour to identify rumors as soon as they appear online. This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques. The proposed architecture outperforms several existing baselines and performs better than several supervised techniques. The proposed method, being lightweight, simple, and robust, offers the suitability of being adopted as a tool for online rumor identification.