Miracle-dz/UniFAR-FTUs huggingface dataset:
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning

We present a reinforcement learning approach to goal based wealth management problems such as optimization of retirement plans or target dated funds. In such problems, an investor seeks to achieve a financial goal by making periodic investments in the portfolio while being employed, and periodically draws from the account when in retirement, in addition to the ability to re-balance the portfolio by selling and buying different assets (e.g. stocks). Instead of relying on a utility of consumption, we present G-Learner: a reinforcement learning algorithm that operates with explicitly defined one-step rewards, does not assume a data generation process, and is suitable for noisy data. Our approach is based on G-learning - a probabilistic extension of the Q-learning method of reinforcement learning. In this paper, we demonstrate how G-learning, when applied to a quadratic reward and Gaussian reference policy, gives an entropy-regulated Linear Quadratic Regulator (LQR). This critical insight provides a novel and computationally tractable tool for wealth management tasks which scales to high dimensional portfolios. In addition to the solution of the direct problem of G-learning, we also present a new algorithm, GIRL, that extends our goal-based G-learning approach to the setting of Inverse Reinforcement Learning (IRL) where rewards collected by the agent are not observed, and should instead be inferred. We demonstrate that GIRL can successfully learn the reward parameters of a G-Learner agent and thus imitate its behavior. Finally, we discuss potential applications of the G-Learner and GIRL algorithms for wealth management and robo-advising.
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction

Compressive sensing (CS) is widely used to reduce the acquisition time of magnetic resonance imaging (MRI). Although state-of-the-art deep learning based methods have been able to obtain fast, high-quality reconstruction of CS-MR images, their main drawback is that they treat complex-valued MRI data as real-valued entities. Most methods either extract the magnitude from the complex-valued entities or concatenate them as two real-valued channels. In both the cases, the phase content, which links the real and imaginary parts of the complex-valued entities, is discarded. In order to address the fundamental problem of real-valued deep networks, i.e. their inability to process complex-valued data, we propose a novel framework based on a complex-valued generative adversarial network (Co-VeGAN). Our model can process complex-valued input, which enables it to perform high-quality reconstruction of the CS-MR images. Further, considering that phase is a crucial component of complex-valued entities, we propose a novel complex-valued activation function, which is sensitive to the phase of the input. Extensive evaluation of the proposed approach on different datasets using various sampling masks demonstrates that the proposed model significantly outperforms the existing CS-MRI reconstruction techniques in terms of peak signal-to-noise ratio as well as structural similarity index. Further, it uses significantly fewer trainable parameters to do so, as compared to the real-valued deep learning based methods.
Staphylococcus Aureus Carriage among Healthcare Workers in Burn and Surgical Critical Care Units in Menoufia University Hospital, Egypt

Objectives: This study aimed to investigate Staphylococcal aureus (S. aureus) carriage among healthcare workers (HCWs) in Burn and Surgical Critical Care Units in Menoufia University Hospital, Egypt, following an outbreak of S. aureus wound infection in patients. Methods: The study involved 60 HCWs. They were 9 Doctors (15.0%) and 51 nurses and workers (85.0%). Each participant completed a questionnaire that covered demographic data, smoking habits, risk factors of S. aureus colonization and general infection control procedures. To detect S. aureus colonization, both nasal and hand swabs were collected from HCWs. Testing for sensitivity to methicillin was performed by cefoxitin disc diffusion method (30μg). Results: S. aureus was isolated from 53.3% of HCWs and 68.8% of them were colonized with MRSA strains. The overall MRSA carriage rate was 36.7%. MRSA was confined only in Surgical intensive care units (ICUs). MRSA isolates were sensitive to amikacin, chloramphenicol, vancomycin and ceftriaxone. There was no correlation between infection control training, antibiotic intake in the preceding month, duration of work, diabetes mellitus and smoking of HCWs and carriage rate of MRSA (P >0.5). Conclusion: HCWs who had contact with patients were at risk of acquisition and colonization with S. aureus and antimicrobial resistant bacteria especially MRSA . Training of HCWs on hygienic measures especially proper hand hygiene is the key to overcome S. aureus infection in Surgical ICUs and Burn Units. Key wards: S. aureus , MRSA , HCWs, Surgical ICU, Burn, Egypt
Lithium-rotation connection in the newly discovered young stellar stream Psc–Eri (Meingast 1)

Context. As a fragile element, lithium is a sensitive probe of physical processes occurring in stellar interiors. Aims. We aim to investigate the relationship between lithium abundance and rotation rate in low-mass members of the newly discovered 125 Myr-old Psc–Eri stellar stream. Methods. We obtained high-resolution optical spectra and measured the equivalent width of the 607.8 nm LiI line for 40 members of the Psc–Eri stream, whose rotational periods have been previously derived. Results. We show that a tight correlation exists between the lithium content and rotation rate among the late-G to early-K-type stars of the Psc–Eri stream. Fast rotators are systematically Li rich, while slow rotators are Li depleted. This trend mimics that previously reported for the similar age Pleiades cluster. Conclusions. The lithium-rotation connection thus seems to be universal over a restricted effective temperature range for low-mass stars at or close to the zero-age main sequence, and does not depend on environmental conditions.
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction

Motivation: Drug combination is a sensible strategy for disease treatment by improving the efficacy and reducing concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a plenty of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in performance and scalability. Results: In this paper, we proposed a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide quantitative importance of each network. To explain the feasibility of NEMN, we applied our framework to the data of drug-drug interactions, on which it showed better performance in terms of AUPR and ROC. For Drug combination prediction, we found seven novel drug combinations which have been validated by external sources among the top-ranked predictions of our model.
Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation

The lack of transparency of neural networks stays a major break for their use. The Layerwise Relevance Propagation technique builds heat-maps representing the relevance of each input in the model s decision. The relevance spreads backward from the last to the first layer of the Deep Neural Network. Layer-wise Relevance Propagation does not manage normalization layers, in this work we suggest a method to include normalization layers. Specifically, we build an equivalent network fusing normalization layers and convolutional or fully connected layers. Heatmaps obtained with our method on MNIST and CIFAR 10 datasets are more accurate for convolutional layers. Our study also prevents from using Layerwise Relevance Propagation with networks including a combination of connected layers and normalization layer.
Event Detection with Relation-Aware Graph Convolutional Neural Networks

Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific types of events in text. Recently, graph convolutional networks (GCNs) over dependency trees have been widely used to capture syntactic structure information and get convincing performances in event detection. However, these works ignore the syntactic relation labels on the tree, which convey rich and useful linguistic knowledge for event detection. In this paper, we investigate a novel architecture named Relation-Aware GCN (RA-GCN), which efficiently exploits syntactic relation labels and models the relation between words specifically. We first propose a relation-aware aggregation module to produce expressive word representation by aggregating syntactically connected words through specific relation. Furthermore, a context-aware relation update module is designed to explicitly update the relation representation between words, and these two modules work in the mutual promotion way. Experimental results on the ACE2005 dataset show that our model achieves a new state-of-the-art performance for event detection.
SNIFF: Reverse Engineering of Neural Networks With Fault Attacks

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a change of value that is currently being computed. They can be realized by various techniques, ranging from clock/voltage glitching to application of lasers to rowhammer. Previous works have mostly explored fault attacks for output misclassification, thus affecting the reliability of neural networks. In this article, we investigate the possibility to reverse engineer neural networks with fault attacks. Sign bit flip fault attack enables the reverse engineering by changing the sign of intermediate values. We develop the first exact extraction method on deep-layer feature extractor networks that provably allows the recovery of proprietary model parameters. Our experiments with Keras library show that the precision error for the parameter recovery for the tested networks is less than $10^{-13}$ with the usage of 64-bit floats, which improves the current state of the art by six orders of magnitude.
Carbon- and Oxygen-rich Asymptotic Giant Branch (AGB) Stars in the Bulge Asymmetries and Dynamical Evolution (BAaDE) Survey

Detections of SiO masers from the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey more tightly define the region where Oxygen-rich (O) Asymptotic Giant Branch (AGB) stars reside in multiple infrared (IR) color–color diagrams. Using Midcourse Space eXperiment (MSX) and Two Micron All Sky Survey (2MASS) data along with radio spectra from the BAaDE survey, we find that three main populations were observed in the BAaDE survey: O-rich AGB stars of which about 73% host SiO masers, Carbon-rich (C) AGB stars that do not host these masers, and a small contaminating set of possible Young Stellar Objects (YSOs). The distinction between YSOs and AGB stars can be drawn using only MSX data, specifically the [D]−[E] color, while the shorter wavelengths provided by 2MASS are necessary to divide potential C- and O-rich AGB stars. Divisions similar to these have been seen in multiple earlier IR-studies, but BAaDE currently provides a sample of ∼15,000 sources, which far exceeds previous studies in sample size, and, therefore, provides much more distinct divisions. With these IR distinctions in place, we discuss the sources that are exceptions in either their molecular detections or IR colors, as well as the distribution of the three populations in Galactic coordinates.
Renormalization scale setting for heavy quark pair production in 
e+e−
 annihilation near the threshold region

Heavy fermion pair production in $e^+e^-$ annihilation is a fundamental process in hadron physics and is of considerable interest for various phenomena. In this paper, we will apply the Principle of Maximum Conformality (PMC) to provide a comprehensive analysis of these processes. The PMC provides a systematic, unambiguous method for determining the renormalization scales of the QCD coupling constant for single-scale and multiple-scale applications. The resulting predictions eliminate any renormalization scheme-and-scale ambiguities, eliminate the factorial renormalon divergences, and are consistent with the requirements of the renormalization group. It is remarkable that two distinctly different scales are determined by using the PMC for heavy fermion pair production near the threshold region. One scale is the order of the fermion mass $m_f$, which enters the hard virtual corrections, and the other scale is of order $ v\,m_f$, where $v$ is the quark velocity, which enters the Coulomb rescattering amplitude. The PMC scales yield the correct physical behavior and reflect the virtuality of the propagating gluons (photons) for the QCD (QED) processes. Moreover, we demonstrate the consistency of PMC scale setting from QCD to QED. Perfect agreement between the Abelian unambiguous Gell-Mann-Low and the PMC scale-setting methods in the limit of zero number of colors is demonstrated.
Uncovering ecological state dynamics with hidden Markov models

Abstract Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or ‘hidden’. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.
Learning the mapping x 7 → ∑ di = 1 x 2 i : the cost of finding the needle in a haystack A Preprint

The task of using machine learning to approximate the mapping x 7→ ∑d i=1 x 2 i with xi ∈ [−1, 1] seems to be a trivial one. Given the knowledge of the separable structure of the function, one can design a sparse network to represent the function very accurately, or even exactly. When such structural information is not available, and we may only use a dense neural network, the optimization procedure to find the sparse network embedded in the dense network is similar to finding the needle in a haystack, using a given number of samples of the function. We demonstrate that the cost (measured by sample complexity) of finding the needle is directly related to the Barron norm of the function. While only a small number of samples is needed to train a sparse network, the dense network trained with the same number of samples exhibits large test loss and a large generalization gap. In order to control the size of the generalization gap, we find that the use of explicit regularization becomes increasingly more important as d increases. The numerically observed sample complexity with explicit regularization scales as O(d2.5), which is in fact better than the theoretically predicted sample complexity that scales as O(d4). Without explicit regularization (also called implicit regularization), the numerically observed sample complexity is significantly higher and is close to O(d4.5). ar X iv :2 00 2. 10 56 1v 1 [ cs . L G ] 2 4 Fe b 20 20 Learning the mapping x 7→ ∑d i=1 x 2 i : the cost of finding the needle in a haystack A Preprint
Virtual-Link Representation for Link Prediction

Link prediction predicts the likelihood of a future association between two nodes in a network. It plays an important role in mining and analyzing the evolution of networks and it is the fundament of many applications, including bioinformatics, e-commerce, security domain and co-authorship networks. The past few decades has witnessed the development of link prediction. In this paper, we propose a link prediction method VirtualLink2vec, which learns features of target link by concatenating representations from two different aspects as the link’s full representation. Comprehensive experiments are conducted on six networks. Compared with heuristics and latent feature methods, our results show that VirtualLink2vec has the ability to reserve rich information related to positive link and negative link with less consumption of time and computing resources. In the meantime, our method consistently performs well across networks with different structures and characteristics.
Correlations Between the Scaling Factor and Fitness Values in Differential Evolution

Designing fitness-based adaptive scaling factor (<inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>) is an effective method to enhance the performance of differential evolution (DE) algorithms. This paper investigates the correlations between <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> and fitness values of target vectors, base vectors and difference vectors. The correlations are described by the notations of monotonicity and nonlinearity. Monotonicity is used to examine whether the optimization performance of DE and the fitness values of certain vectors have positive or negative correlation. Nonlinearity denotes the operation in which nonlinear mappings are used to redistribute the values of <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> in [0, 1] so as to boost the optimization performance. These two aspects of correlations are empirically tested on the Numerical Optimization Competition benchmark functions in IEEE Congress on Evolutionary Computation. Simulation results reveal different qualitative and quantitative correlations between <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> and fitness values of different vectors. Then, a new <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> that combines these relations is designed. Its strength is numerically verified by testing different CEC Benchmark functions.
Finding out the needy one from Tweets : An analysis using #kerelafloods

Natural disasters are difficult to predict and when it happens it is very difficult for government and other agencies to get the information about the effected people or properties. To save the life and properties first step to know where it happens and what is the current scenario of that place. To get these information social media plays a key role. In this article, we explores a detailed overview on Kerala flood situation which happened in late July 2018, severe flooding affected the Kerala very badly. In this article, we proposed a system to detection of real-time help needed to people based on different tweets related to Kerala flood. This article provided a complete detailed overview on the situation in Kerala and collects needed information through tweets as twitter is one of the best way of spreading awareness about the worsening situation in Kerala. To alleviate this problem, tweets, which are largely available,can be exploited to extract the required data.
Number of TCP Connections to Saturate Bandwidth of Wireless Networks

Wireless LANs have been used all over the world as an access line with high throughput. In addition, Internet users can connect to servers through multiple TCP connections. However, too many TCP connections will degrade the total throughput on a wireless network. In this paper, we evaluate the conditions in terms of the number of TCP connections to obtain the highest total throughput on an IEEE 802.11g network. Besides, the direction of data flow and TCP versions, CUBIC, Compound, and NewReno are taken into account. The results will offer reference data to develop dynamic adjusting method of the number of TCP connections on wireless networks.
Diagnosis of Different Eccentricity Faults in Induction Motors Based on Electrical and Magnetic Signatures and Unbalanced Magnetic Pull

In induction motors, eccentricity faults are very common. If it is not detected at early stages, these faults would develop more serious problems. In this paper static, dynamic and mixed eccentricity are modeled for understanding the effects of this type of faults. The effects are examined in terms of alterations in stator current, torque characteristic, radial flux, and magnetic pull. Results are compared with each other and with a healthy machine.
IOT Based Precision Horticulture in North India

Horticulture incorporates a major impact on economy of the country. It is a subdivision of agriculture which deals with plant gardening under controlled environment. Heap of analysis has been dole out in automating the irrigation system by using wireless device and mobile computing. Conjointly analysis has been done in applying machine learning in Horticultural system too. Machine to Machine (M2M) communication is a growing technology that permits devices, objects to speak among one another and send knowledge to Server or Cloud through the Core Network. Therefore, consequently we tend to have developed a Brainy IOT primarily based machine-controlled Irrigation system. Wherever device knowledge is touching wet soil, temperature is captured and consequently machine learning algorithmic is deployed for analysing the device knowledge for prediction towards irrigating the soil with water or switching ON/OFF fan to control temperature. This can be a totally machine-controlled wherever devices communicate among themselves and apply the intelligence in real time monitoring & analysis. Making data available online through Cloud to Scientists to remotely make smart decisions on Precision Horticulture. System has been developed using low value embedded devices like Raspberry Pi3, Arduino and successfully implemented in Delhi.