Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case Study: Using Genetic Algorithm for Job Scheduling Problem

Nowadays, DevOps pipelines of huge projects are getting more and more complex. Each job in the pipeline might need different requirements including specific hardware specifications and dependencies. To achieve minimal makespan, developers always apply as much machines as possible. Consequently, others may be stalled for waiting resource released. Minimizing the makespan of each job using a few resource is a challenging problem. In this study, it is aimed to 1) automatically determine the priority of jobs to reduce the waiting time in the line, 2) automatically allocate the machine resource to each job. In this work, the problem is formulated as a multi-objective optimization problem. We use GA algorithm to automatically determine job priorities and resource demand for minimizing individual makespan and resource usage. Finally, the experimental results show that our proposed priority list generation algorithm is more effective than current priority list producing method in the aspects of makespan and allocated machine count. Keywords—Genetic algorithm, Job shop scheduling, Optimization.
Element-specific electronic and structural dynamics using transient X-ray spectroscopy

Transient X-ray absorption techniques can measure ultrafast dynamics of the elemental edges in a material or multiple layer junction, giving them immense potential for deconvoluting concurrent processes. However, the interpretation of the photoexcited changes to an X-ray edge is not as simple as directly probing a transition with optical or infrared wavelengths. The core hole left by the core-level transition distorts the measured absorption and reflection spectra, both hiding and revealing different aspects of a photo-induced process. In this perspective, we describe the implementation and interpretation of transient X-ray experiments. This description includes a guide of how to choose the best wavelength and corresponding X-ray sources when designing an experiment. As an example, we focus on the rising use of extreme ultraviolet (XUV) spectroscopy for understanding performance limiting behaviors in solar energy materials, such as measurements of polaron formation, electron and hole kinetics, and charge transport in each layer of a metal-oxide-semiconductor junction. The ability of measuring photoexcited carriers in each layer of a multilayer junction could prove particularly impactful in the study of molecules, materials, and their combinations that lead to functional devices in photochemistry and photoelectrochemistry.
Ultra High Dimensional Change Point Detection

Structural breaks have been commonly seen in applications. Specifically for detection of change points in time, research gap still remains on the setting in ultra high dimension, where the covariates may bear spurious correlations. In this paper, we propose a two-stage approach to detect change points in ultra high dimension, by firstly proposing the dynamic titled current correlation screening method to reduce the input dimension, and then detecting possible change points in the framework of group variable selection. Not only the spurious correlation between ultra-high dimensional covariates is taken into consideration in variable screening, but non-convex penalties are studied in change point detection in the ultra high dimension. Asymptotic properties are derived to guarantee the asymptotic consistency of the selection procedure, and the numerical investigations show the promising performance of the proposed approach. 1 ar X iv :2 10 6. 04 86 9v 1 [ st at . M E ] 9 J un 2 02 1
Quantum simulation of lattice gauge theories in more than one space dimension—requirements, challenges and methods

Over recent years, the relatively young field of quantum simulation of lattice gauge theories, aiming at implementing simulators of gauge theories with quantum platforms, has gone through a rapid development process. Nowadays, it is not only of interest to the quantum information and technology communities. It is also seen as a valid tool for tackling hard, non-perturbative gauge theory problems by particle and nuclear physicists. Along the theoretical progress, nowadays more and more experiments implementing such simulators are being reported, manifesting beautiful results, but mostly on 1+1 dimensional physics. In this article, we review the essential ingredients and requirements of lattice gauge theories in more dimensions and discuss their meanings, the challenges they pose and how they could be dealt with, potentially aiming at the next steps of this field towards simulating challenging physical problems in analogue, or analogue-digital ways. This article is part of the theme issue ‘Quantum technologies in particle physics’.
HyCA: A Hybrid Computing Architecture for Fault-Tolerant Deep Learning

Hardware faults on the regular 2-D computing array of a typical deep learning accelerator (DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches typically have each homogeneous redundant processing element (PE) to mitigate faulty PEs for a limited region of the 2-D computing array rather than the entire computing array to avoid the excessive hardware overhead. However, they fail to recover the computing array when the number of faulty PEs in any region exceeds the number of redundant PEs in the same region. The mismatch problem deteriorates when the fault injection rate rises and the faults are unevenly distributed. To address the problem, we propose a hybrid computing architecture (HyCA) for fault-tolerant DLAs. It has a set of dot-production processing units (DPPUs) to recompute all the operations that are mapped to the faulty PEs despite the faulty PE locations. According to our experiments, HyCA shows significantly higher reliability, scalability, and performance with less chip area penalty when compared to the conventional redundancy approaches. Moreover, by taking advantage of the flexible recomputing, HyCA can also be utilized to scan the entire 2-D computing array and detect the faulty PEs effectively at runtime.
Crosslingual Embeddings are Essential in UNMT for distant languages: An English to IndoAryan Case Study

Recent advances in Unsupervised Neural Machine Translation (UNMT) has minimized the gap between supervised and unsupervised machine translation performance for closely related language-pairs. However and the situation is very different for distant language pairs. Lack of overlap in lexicon and low syntactic similarity such as between English and IndoAryan languages leads to poor translation quality in existing UNMT systems. In this paper and we show that initialising the embedding layer of UNMT models with cross-lingual embeddings leads to significant BLEU score improvements over existing UNMT models where the embedding layer weights are randomly initialized. Further and freezing the embedding layer weights leads to better gains compared to updating the embedding layer weights during training. We experimented using Masked Sequence to Sequence (MASS) and Denoising Autoencoder (DAE) UNMT approaches for three distant language pairs. The proposed cross-lingual embedding initialization yields BLEU score improvement of as much as ten times over the baseline for English-Hindi and English-Bengali and English-Gujarati. Our analysis shows that initialising embedding layer with static cross-lingual embedding mapping is essential for training of UNMT models for distant language-pairs.
Detection of a Multiphase Ultrafast Wind in the Narrow-line Seyfert 1 Galaxy Mrk 1044

We present a detailed analysis of XMM-Newton X-ray spectra of the Narrow-Line Seyfert 1 galaxy Mrk 1044. We find robust evidence for a multiphase, ultrafast outflow, traced by four separate components in the grating spectrum. One component has high column density and ionization state, and is outflowing at ∼0.15c. The other three wind components have lower temperature, lower column density, and have outflow velocities ∼0.08c. This wind structure is strikingly similar to that found in IRAS 17020+4544, suggesting that stratified winds may be a common feature of ultrafast outflows. Such structure is likely produced by fluid instabilities that form when the nuclear wind shocks the ambient medium. We show that, in an energy-driven wind scenario, the wind in Mrk 1044 might carry enough energy to produce significant feedback on its host galaxy. We further discuss the implications of the presence of a fast wind in yet another NLS1 galaxy with high Eddington ratio.
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction

Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document. Mainstream KP methods can be categorized into purely generative approaches and integrated models with extraction and generation. However, these methods either ignore the diversity among keyphrases or only weakly capture the relation across tasks implicitly. In this paper, we propose UniKeyphrase, a novel end-to-end learning framework that jointly learns to extract and generate keyphrases. In UniKeyphrase, stacked relation layer and bag-of-words constraint are proposed to fully exploit the latent semantic relation between extraction and generation in the view of model structure and training process, respectively. Experiments on KP benchmarks demonstrate that our joint approach outperforms mainstream methods by a large margin.
On the sensitivity analysis of spread options using Malliavin calculus

In this paper we derive tractable formulae for price sensitivities of two-dimensional spread options using Malliavin calculus. In particular, we consider spread options with asset dynamics driven by geometric Brownian motion and stochastic volatility models. Unlike the fast Fourier transform approach, the Malliavin calculus approach does not require the joint characteristic function of underlying assets to be known and is applicable to spread options with discontinuous payoff functions. The results obtained reveal that the Malliavin calculus approach gives the price sensitivities in terms of the expectation of spread option payoff functional multiplied with some random variables (Malliavin weights) which are independent of the payoff functional. This is consistent with results in Fournié et al. [1]. The results also show the flexibility of Mallavin calculus approach when applied to spread options.
On the Analysis of the Second Order Time Filtered Backward Euler Method for the EMAC formulation of Navier-Stokes Equations

This paper considers the backward Euler based linear time filtering method for the EMAC formulation of the incompressible Navier-Stokes equations. The time filtering is added as a modular step to the standard backward Euler code leading to a 2-step, unconditionally stable, second order linear method. Despite its success in conserving important physical quantities when the divergence constraint is only weakly enforced, the EMAC formulation is unable to improve solutions of backward Euler discretized NSE. The combination of the time filtering with the backward Euler discretized EMAC formulation of NSE greatly increases numerical accuracy of solutions and still conserves energy, momentum and angular momentum as EMAC does. Several numerical experiments are provided that both verify the theoretical fidings and demonstrate superiority of the proposed method over the unfiltered case.
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling

Image classification is considered, and a hierarchical max-pooling model with additional local pooling is introduced. Here the additional local pooling enables the hierachical model to combine parts of the image which have a variable relative distance towards each other. Various convolutional neural network image classifiers are introduced and compared in view of their rate of convergence. The finite sample size performance of the estimates is analyzed by applying them to simulated and real data.
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks

Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction logs in an offline reinforcement learning (RL) setting is a promising approach. However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise. We present a concise overview of these challenges and discuss possible solutions.
Vulnerability of rural households to climate-induced shocks in Lokka Abaya district, Sidama zone, southern Ethiopia

This study was conducted in rural communities of Lokka Abaya district, Sidama zone, southern Ethiopia to assess vulnerability status of men and women households to climate-induced shocks and stress. This article is based on household survey, focus group discussion and key informant interviews. A total of 258 smallholder farmers were selected from three villages using stratified random sampling. A combination of social, economic and environmental indicators was employed to develop the vulnerability index of each household head and estimate quantitatively that is vulnerability is estimated as a function of adaptive capacity, exposure and sensitivity of households. The results indicated that farmers had poor access to public services including access to affordable credit, market, health services and climate information. The survey revealed that droughts, floods, soil erosion, pests and diseases were climate-related challenges in the study area. Regarding vulnerability of households to climate variability, results indicated that around 8.5% and 18.2% of male- and female-headed households, respectively, were highly vulnerable whilst 41% and 45.5% of male- and female-headed households, respectively, were moderately vulnerable. The results confirmed that 37.7% and 27.3% of male- and female-headed households, respectively, were less vulnerable. The rest 12.8% men and 9% women were not vulnerable. Therefore, there is a need to enhance access to affordable credit, market, climate information, health, income diversification of farmers, soil and water conservation and afforestation of hilly areas if farmers need to be climate resilient.
Thermodynamic Analysis on the Post-crystallization for Calcium Titanium Phosphate Glass Ceramics

Porous glass ceramics with CaTi4(PO4)6 as the main crystalline phase could be produced by heat-treating the 5Na2O-20TiO2-32P2O5-43CaO glass system at 690℃ and 735℃ separatively and then by acid-leaching the crystallization product Ca3(PO4)2. In this paper, a method of enlarging the size of pores was introduced. A high temperature heat-treatment (880℃), that called post-crystallization, could be inserted into the processes between crystallization and acid-leaching. In the post-crystallization, there was a solid reaction where the CaTi4(PO4)6 and 5Ca3(P04)2 reacted and then products TiO2 and Ca2P2O7 produced. The later phase could be easily leached from the glass-ceramic. The pores became larger, since more Ca and P were leached. In the present study, the change in free enthalpy of the solid reaction was estimated as -333Kcal/mol by applying the approximate first thermodynamic equation. The phase analysis, XRD before and after post-crystallization, confirmed that this solid reaction could happen spontaneously. The dynamic parameters for crystallization activation energy and reaction index were calculated as 584.7kJ/mol and 3.2 respectively by applying Jormson-Arirami-Mehl equation while implied that the sample was crystallized in bulk. This calculation was also confirmed by the observation under SEM, that the pore size enlarged in the whole area of the sample after post-crystallization.
Experience of midwives in providing care to labouring women in varied healthcare settings: A qualitative study

Background Midwives are essential to timely, effective, family-centred care. In South Africa, patients have often expressed dissatisfaction with the quality of midwifery care. Negative interpersonal relationships with caregivers, lack of information, neglect and abandonment were consistent complaints. Less is known about how midwives experience providing care. Aim This research explored and described the experiences of midwives in providing care to labouring women in varied healthcare settings. Setting Midwives practicing in the Gauteng province, South Africa, in one of three settings: private hospitals, public hospitals or independent maternity hospital. Methods A convenience sample of midwives (N = 10) were interviewed. An exploratory and descriptive design, with individual semi-structured interviews conducted, asked a primary question: ‘How is it for you to be a midwife in South Africa? ’ Transcribed interviews were analysed using thematic coding. Results Five themes were found: proud to be a midwife, regulations and independent function, resource availability, work burden and image of the midwife. Conclusion Midwives struggle within systems that fail to allow independent functioning, disallowing a voice in making decisions and creating change. Regardless of practice setting, midwives expressed frustration with policies that prevented utilisation consistent with scope of practice, as well as an inability to practice the midwifery model of care. Those in public settings expressed concern with restricted resource appropriation. Similarly, there is clear need to upscale midwifery education and to establish care competencies to be met in providing clinical services. Contribution This research provides evidence of the midwifery experience with implications for needed health policy change.
Counselling preparedness and responsiveness of industrial psychologists in the face of COVID-19

Orientation The coronavirus disease 2019 (COVID-19) pandemic has brought to the forefront the need for industrial-organisational psychologists (IOPs) and organisations to place an emphasis on employees’ mental and physical health at all times. Research purpose The purpose of the research was to determine how prepared IOPs are to counsel employees during the pandemic and how responsive they are to provide counselling. Motivation for the study It is not clear to what extent such counselling is being practised by IOPs in the workplace during the COVID-19 pandemic. Research approach/design and method A qualitative approach was used to gain an understanding of registered South African IOPs’ experiences of workplace counselling, particularly during the time of the COVID-19 pandemic. Main findings Regarding preparedness, we found that IOPs are ill-prepared to counsel in the workplace. Preparedness was influenced by participants’ counselling education, skills and knowledge; experience; convictions about counselling; and psychological and organisational preparedness. Whilst some IOPs did engage in more counselling during the COVID-19 pandemic, most reverted to mitigating actions such as referrals, wellness management, equipping managers and change initiatives. Practical/managerial implications The results of this study indicate that, under pandemic conditions, there is an increased need for counselling practices within the workplace and that IOPs should explore the ways in which they could play a more active role in such counselling. Contribution/value-add Although we found that IOPs generally responded to employees’ mental health needs in a positive manner, there was a lack of counselling preparedness and responsiveness during the COVID-19 pandemic.
Single-Incision Laparoscopic Cholecystectomy: Initial Report From the Turks and Caicos Islands

There has been no prior report of single-incision laparoscopic surgery (SILS) from the Caribbean island of Turks and Caicos. We report our initial experience with SILS cholecystectomy to show that SILS in this environment is feasible with minimal change to the existing hardware. It is a safe alternative to conventional multi-trocar laparoscopic cholecystectomy in this setting.
Antibiotic use amongst pregnant women in a public hospital in KwaZulu-Natal

Background Antibiotics are amongst the more frequently prescribed medicines in pregnant women and the use of antibiotics is increasing. However, with limited studies available in this population, the safe use of antibiotics in pregnancy remains a concern. Aim To evaluate the use of antibiotics amongst pregnant women attending a public health care facility. The main objective of this study was to quantify the types of antibiotics used in pregnant women. Setting A public hospital classified as a referral hospital located in Durban, KwaZulu-Natal. Methods Demographic and treatment information of women were collected retrospectively from January 2019 to July 2019. A total of 184 pregnant patients, who received antibiotic therapy, were included in this study. Descriptive and analytical measures were used to analyse both patient demographics and treatment variables. Results A total of 416 antibiotic prescriptions, issued to 184 patients, were reviewed. Penicillins (39.7%), macrolides (13.0%) and combination penicillin- and beta-lactam inhibitors (12.3%) were reported as the most commonly prescribed antibiotics. Rifamycin (2.9%), hydrazides (2.2%) and aminoglycosides (1.9%) were less frequently prescribed. Most antibiotics were prescribed for diseases of the circulatory system (36.1%). Conclusion Several classes of antibiotics were used in pregnancy despite the lack of available safety data and clinical evidence. Informing women of the potential side effects and keeping abreast with new information played an important role in the safe, rational and effective use of medicines that contributed to improving maternal health.