Miracle-dz/UniFAR-FTUs huggingface dataset:
Suicidal Phenotype of Proofreading-Deficient Herpes Simplex Virus 1 Polymerase Mutants

Recovery of fully exonuclease-deficient herpes simplex virus 1 (HSV-1) DNA polymerase mutants has been so far unsuccessful. However, exonuclease activity is not known to be directly essential for virus replication, and the lethal phenotype of certain HSV-1 polymerase mutants is thus attributed to factors other than exonuclease activity. ABSTRACT Herpes simplex virus 1 (HSV-1) encodes a family B DNA polymerase (Pol) capable of exonucleolytic proofreading whose functions have been extensively studied in the past. Early studies on the in vitro activity of purified Pol protein found that the enzymatic functions of the holoenzyme are largely separate. Consequently, exonuclease activity can be reduced or abolished by certain point mutations within catalytically important regions, with no or only minor effects on polymerase activity. Despite unimpaired polymerase activity, the recovery of HSV-1 mutants with a catalytically inactive exonuclease has been so far unsuccessful. Hence, mutations such as D368A, which abolish exonuclease activity, are believed to be lethal. Here, we show that HSV-1 can be recovered in the absence of Pol intrinsic exonuclease activity and demonstrate that a lack of proofreading causes the rapid accumulation of likely detrimental mutations. Although mutations that abolish exonuclease activity do not appear to be lethal, the lack of proofreading yields viruses with a suicidal phenotype that cease to replicate within few passages following reconstitution. Hence, we conclude that high replication fidelity conferred by proofreading is essential to maintain HSV-1 genome integrity and that a lack of exonuclease activity produces an initially viable but rapidly suicidal phenotype. However, stably replicating viruses with reduced exonuclease activity and therefore elevated mutation rates can be generated by mutating a catalytically less important site located within a conserved exonuclease domain. IMPORTANCE Recovery of fully exonuclease-deficient herpes simplex virus 1 (HSV-1) DNA polymerase mutants has been so far unsuccessful. However, exonuclease activity is not known to be directly essential for virus replication, and the lethal phenotype of certain HSV-1 polymerase mutants is thus attributed to factors other than exonuclease activity. Here, we showed that the recovery of a variety of exonuclease-deficient HSV-1 polymerase mutants is possible and that these mutants are initially replication competent. We, however, observed a progressive loss of mutant viability upon cell culture passaging, which coincided with the rapid accumulation of mutations in exonuclease-deficient viruses. We thus concluded that a lack of DNA proofreading in exonuclease-deficient viruses causes an initially viable but rapidly suicidal hypermutator phenotype and, consequently, the extinction of mutant viruses within few generations following recovery. This would make the absence of exonuclease activity the primary reason for the long-reported difficulties in culturing exonuclease-deficient HSV-1 mutants.
Millennial entrepreneurial persistence under harsh contextual environments in Iran

Millennial Iranian entrepreneurs, as an emerging business class, face new realities rarely seen in other countries due to the harsh and uncertain contextual environment. As young and dynamic entrepreneurs, they want to build independent careers by developing new start-up businesses to address the social and economic needs of the masses and, at the same time, to earn an income to ensure their economic survival. However, given the difficult business environment with domestic challenges and international embargos, these entrepreneurs find themselves faced with many problematic issues. Therefore, this research is based on the concept of entrepreneurial persistence for survival and aims to investigate how Iranian millennial entrepreneurs develop businesses in a difficult contextual environment. A new conceptual framework and a number of propositions for future research have been offered in this study on the basis of the findings. Implications for policy and practices are also discussed with the focus on possible policy reform, better entrepreneurial education as well as other incentives to support millennial entrepreneurs.
Use of clinical tolerance limits for assessing agreement

In this study, we have further extended the methodology proposed, first, by Lin et al. (2002) and, later, extended by Stevens et al. (2017, 2018), on the coverage probability/probability of agreement, by relaxing the strong parametric assumptions regarding the distribution of the latent trait and developing inference methods allowing to compute both pointwise and simultaneous confidence bands. The methodology requires repeated measurements by at least one of the two measurement methods and accommodates heteroscedastic measurement errors. It performs often very well even when one has only one measurement by one of the two measurement methods and at least five repeated measurements from the other. It circumvents some of the deficiencies of the Bland & Altman limits of agreement method and provides a more direct assessment of the agreement level.
Prediction of High-Resolution Atmospheric CO2 Concentration from OCO-2 using Machine Learning

Carbon Dioxide (CO2) is a greenhouse gas (GHG) emitted by human anthropogenic activities. The satellite measurement of atmospheric column-averaged CO2 concentration (XCO2) provides an excellent opportunity to understand the global carbon cycle for a large comprehensive temporal range. Orbiting Carbon Observatory-2 (OCO-2) satellite provides highly accurate data with a spatial resolution of approximately 3 km2. However, OCO-2 measures one location on the Earth’s surface almost fortnightly. Also, the clouds and aerosols cause missing data. In this work, the OCO-2 measurements, along with Open-Source Data Inventory for Anthropogenic CO2 (ODIAC) emission estimate, are considered. A spatial upscaling, followed by different machine learning methods are used to predict high-resolution, continuous mapping of XCO2. The prediction models are evaluated using the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) for Germany, considering a temporal range of November 2018 to December 2019. The least error is attained by monthly model, achieving a MAE and RMSE of 0.707 ppm and 1.187 ppm, respectively, using the extremely randomized trees (ERT) method. The predictions are externally validated using Total Carbon Column Observing Network (TCCON) ground-based measurements as well.
Generalized Structured Low-Rank Tensor Learning

We consider the problem of learning tensors from partial observations with structural constraints, under the low-rank assumption. To this end, we propose a general convex low-rank regularizer, parameterized by linear maps, that extends the existing regularizers. Different choices of the linear maps lead to different convex low-rank regularizers. The resulting problem is called the generalized structured low-rank tensor learning problem. To solve this problem, we use duality theory to reformulate it into simpler sub-problems. The dual problem contains a rich geometric structure, which we exploit to develop first-order and second-order Riemannian optimization algorithms. The associated duality gap is derived, and it is shown to be zero. Moreover, we experimentally verify the correctness of our algorithm on several special cases of the proposed general framework.
Paradigm shift in Nutritional Science: Using Machine Learning to Predict Macronutrient Requirements

Dietary interventions are necessary to lead a healthy life. Most of these interventions are done by adjusting daily macronutrient intake. Assessing daily macronutrient intake for users based on their health profile is a tedious task. In this paper, a supervised learning-based system to predict macronutrient categories is presented. The system is built using synthetic data generated from live user profiles. The generated dataset is validated and annotated with macronutrient category labels. Along with the category labels, the system also presents reasons for the prediction. Various experiments pertaining to the efficacy and trustworthiness of the models are performed and presented. Results suggest that macronutrient category prediction and its interpretability are highly reliable and could be easily adopted by the healthcare industry.
Relation Classification Model Performance as a Function of Dataset Label Noise

The central question that this paper address is: How does the performance of a supervised model vary with respect to the label noise in the testing and training dataset? Answering this question is crucial for many real-world applications for two main reasons. First, most datasets used for training large supervised models such as Deep Neural Networks (DNNs) are crowd-sourced. Such datasets are known to have significant label noise due to multiple factors such as the complexity of the annotation task and low wages for crowd-sourced annotators. Second, these crowd-sourced datasets are large. Reannotating such large datasets is a time-consuming and costly process. Our work aims to understand the relation between the cost of data reannotation and the performance of supervised models. Understanding this relationship can be helpful while planning for data reannotation. We focus on a specific supervised learning task of Relation Classification (RC). It is the task of predicting a relation label between a pair of real-world entities in a given natural language sentence. It is an important NLP task with applications in multiple domains such as knowledge graph completion, and question answering. All the recent and state-of-the-art RC models are based on the Deep Learning paradigm. Recent studies have shown that the quality of the dataset is a significant bottleneck in improving the performance of supervised machine learning models for RC [1, 2]. One possible solution for improving the data quality is to reannotate the data to reduce the label noise. Existing works on RC dataset reannotation follow two extremes. They either select only a tiny fraction for reannotation [1] or go for reannotation of the complete data [2]. To overcome this rigidity, we introduce the concept of a reannotation budget. It provides flexibility about what fraction of the dataset to reannotate. For a given reannotation budget, we also have to decide which subset of the data to reannotate. We explore four strategies to select sentences for reannotation. We perform extensive experiments using a popular RC dataset TACRED[3] and a set of recent Deep Learning-based RC models. While varying the reannotation budget, we track the performance of RC models through the F1 score. Our work has two specific research contributions. First, this is the first work that analyzes RC model performance as a function of the amount of label noise in the data. Such analysis will be helpful while planning the data reannotation process. We observe that a significant improvement can be achieved in the RC model performance by reannotating only a part of the training data (Figure 1a). Second, we show that the reported performance of RC models on noisy datasets is inflated. The F1 score of these models drops from the reported range of 60%-70% to as low as below 50% when tested on clean test data (Figure 1b). This drop in performance should be considered while deploying the RC models for real-world applications.
Surface alloying characteristics of WS2/Cu composite electrodes deposited on an aluminum alloy by electrical discharge coating

Abstract Electrical discharge machining (EDM) is a widely used non machining technique for machining difficult-to-machine materials. Electro discharge coating (EDC) has been utilized to convert the surface of workpieces using standard electro discharge equipment by transferring electrode material to the workpiece using reverse polarity. Nowadays there is an increasing demand for lightweight materials such as aluminum (Al) alloys are widely used in aerospace as well as in automobile components due to reduction in weight to improve performance and efficiency. Nevertheless, the surface qualities such as hardness and wear resistance, of aluminium alloy are insufficient to meet the needs of a wide range of applications. As a result, the current research looked into the EDC of aluminium alloy utilizing a green compact WS2/Cu electrode to alter the surface properties. Experiments were designed utilizing a response surface approach and a central composite rotatable design. The following significant EDC variables current, pulse on time, and pulse off time, determine the coating characteristics such as deposition rate (DR) and electrode wear rate (EWR). The most significant variables were determined using analysis of variance. A scanning electron microscope was used to explore various surface morphology that occurred during EDC. Energy-dispersive spectroscopy measurements were taken to validate the successful deposition of several elements on the workpiece surface.
Intelligent Robotic Process Automation: Generating Executable RPA Scripts from Unsegmented UI Logs

Robotic Process Automation (RPA) is an automation technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI) that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (or simply routines) performed by human users in their applications’ user interfaces (UIs). RPA tools are able to capture in dedicated UI logs the execution of many routines of interest. A UI log consists of user actions that are mixed in some order that reflects the particular order of their execution by the user, thus potentially belonging to different routines. When considering state-of-the-art RPA technology in the BPM domain, it becomes apparent that the current generation of RPA tools is driven by predefined rules and manual configurations made by expert users rather than intelligent techniques. In this paper, we discuss our research targeted at injecting intelligence into RPA practices. Specifically, we present an approach to: (i) automatically understand which user actions contribute to which routines inside a UI log (this issue is known as segmentation) and (ii) automatically generate executable RPA scripts directly from the UI logs that record the user interactions with the SW applications involved in a routine execution, thus skipping completely the (manual) modeling activity of the flowchart diagrams.
Assessing Drought for the Period 1955-2021 in Heraklion-Crete (S. Greece) Urban Environment

The urban environment is characterized by unique climatic characteristics that can highly affect the growth and survival of vegetation. This is critical for cities located in the Mediterranean basin, as it is considered a climate change hot-spot with frequent and long lasting drought episodes. In this work, a drought assessment was performed for the city of Heraklion, by investigating the values of four drought indices (SPI, RDI, aSPI and eRDI) for the period 1955-2022. The results confirm the good performance of all examined indices to detect extreme and severe droughts, however there were differences for the identification of moderate droughts. In 2015-2016 the most severe drought in the city occurred, whereas a significant decreasing trend in the indices values was identified for the dry semester of the year, implying that more frequent and severe droughts are expected to occur in the future, with severe impacts on urban green infrastructures and on the local urban climate dynamics.
The Dynamic Window Approach to Collision Avoidance - IEEE Robotics & Automation Magazine

ne of the ultimate goals of indoor mobile robotics 0 research is to build robots that can safely carry out missions in hazardous and populated environments. For example, a service-robot that assists humans in indoor office environments should be able to react rapidly to unforeseen changes, and perform its tasks under a wide variety of external circumstances. Most of today’s commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often cease to function if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments] react to unforeseen circumstances, and (re)plan dynamically in order to achieve their missions. This paper focuses on one particular aspect of the design of such a robot: the reactive avoidance of collisions with obstacles, The dynamic window approach proposed in this paper is especially designed to deal with the constraints imposed by limited velocities and accelerations, because it is derived directly from the motion dynamics of synchro-drive mobile robots. In a nutshell, our approach considers periodically only a short time interval when computing the next steering command to avoid the enormous complexity of the general motion planning problem. The approximation of trajectories during such a time interval by circular curvatures results in admissible velocities allowing the robot to stop safely. Due to the limited accelerations of the motors a further restriction is imposed on the velocities: the robot only considers velocities that can be reached within the next time interval. These velocities form the dynamic window which is centered around the current velocities of the robot in the velocity space. Among the admissible velocities within the dynamic window the combination of translational and rotational velocity is chosen by maximizing an objective function. The objective function includes a measure of progress towards a goal location, the forward velocity of the robot, and the distance to the next obstacle on the trajectoy. By combining these, the robot trades off its desire to move fast towards the goal and its desire to ship around obstacles (which decrease the free space). The combination of all objectives leads to a very robust and elegant collision avoidance strategy. The dynamic window approach has been implemented and tested on a B21 robot manufactured by Real World Interface Inc. (see Figure l ) , and other synchro-drive robots. In extensive experimental evaluations using ultrasonic proximity sensors for the construction of local world models (obstacle line fields), the method has proven to avoid collisions reliably with speeds of up to 95 cm/sec on several robots in several indoor environments (University of Bonn, Carnegie Mellon University, 1994 AAA1 robot competition. 1995 Figure 1. The robot RHINO, an RWIB21.
Assessing some formula of spam probability and applying in spam classification

Spam classification is a problem that has been studied for a long time in the world. The spam classification feature is integrated into the Mail Server or Mail Client. Traditional methods still have certain weaknesses, the content-based classification method proved effective with the use of techniques in statistical machine learning. In particular, spam classification based on Bayes with the advantages of simplicity, ease of use and fast speed. This article presents an assessment of some methods to calculate the probability of being Spam of Tokens through spam classification application.
Coupled Electromagnetic and Heat Transfer Model for Grain Seeds Drying in a Hybrid Dryer

A multiphysics model that coupled electric fields and heat transfer was developed to simulate microwave heating of maize seeds in a dryer. The hybrid dryer with the maize seeds inside was simulated using COMSOL Multiphysics software. The dryer was equipped with three magnetrons, with a power of 800 W each, operating at a frequency of 2.45 GHz. These magnetrons create an electric field that is absorbed by the heating maize seeds. In order to increase the accuracy of the results, a study regarding the independence of the discretization grid in the field of the dryer calculation was performed. By the action of the electric field in the dryer, the temperature in the seed layer is evenly distributed, with a maximum value of 44°C after a period of 9-12 seconds. By losing moisture, the seeds become lighter, being transported around the top of the seed layer, and are discharged from the dryer every 3-4 seconds when the feed rate of maize seed is 500 kg/h. The spiral movement of the seeds in the first half of the dryer in the regions where the distribution of the electric field that alternates from maximum to minimum, makes the temperature of the seeds to be uniform in the whole volume, achieving a uniform drying.
A NEW RADIAL-VELOCITY CURVE FOR THE CEPHEID ETA AQL.

A new radial velocity curve is presented for the bright Cepheid Eta Aquilae. The curve was derived from 21 red spectrograms taken with the 1.2-m coude spectrograph at the Dominion Astrophysical Observatory at a dispersion of 10 A/mm. The shape of the curve is found to be unchanged since 1923, with a possible small increase in amplitude since 1898 and an apparent 0.74 km/sec decrease in systematic velocity, possibly associated with orbital motion about a recently discovered companion. The previously noted increase in period since 1825 is also confirmed. Comparison of the behavior of the H-alpha curve with that of the metallic line curves indicates the H-alpha to be formed at an optical depth of 0.01 if the metallic lines are assumed to be formed at a depth of 0.10, and the presence of infalling material high in the atmosphere at the time when the velocity of the metallic absorbing layer has been reversed.
Robust computation of optimal transport by β-potential regularization

Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions. For instance, OT is a popular loss function that quantifies the discrepancy between an empirical distribution and a parametric model. Recently, an entropic penalty term and the celebrated Sinkhorn algorithm have been commonly used to approximate the original OT in a computationally efficient way. However, since the Sinkhorn algorithm runs a projection associated with the Kullback-Leibler divergence, it is often vulnerable to outliers. To overcome this problem, we propose regularizing OT with the \beta-potential term associated with the so-called $\beta$-divergence, which was developed in robust statistics. Our theoretical analysis reveals that the $\beta$-potential can prevent the mass from being transported to outliers. We experimentally demonstrate that the transport matrix computed with our algorithm helps estimate a probability distribution robustly even in the presence of outliers. In addition, our proposed method can successfully detect outliers from a contaminated dataset
Parkes radio sources in the directions of southern rich clusters

We have compiled a listing of all Parkes radio sources coincident within 0.3 cluster radii of southern rich clusters of galaxies [Abell et al. ApJS, 70, 1 (1989)]. A pronounced peak of radio sources very near the cluster centers was found. Bautz Morgan type I clusters tend to have more powerful radio sources and are radio loud ≃5 times more often than clusters without dominant central galaxies. There is no strong correlation between radio emission and richness class.
The UoSAT-5 Earth Imaging System-in-orbit results

For the first time, the Earth Imaging System (EIS) on the UoSAT-5 spacecraft has demonstrated the capability of remote sensing from microsatellites. In addition to the meteorological-scale CCD imager, the EIS incorporates a transputer-based processing unit that provides substantial processing power to the module. This transforms the EIS into a unique platform for research into sophisticated on-board image analysis, processing and compression. The use of these techniques is essential for full exploitation of sensing microsatellites, by maximizing the throughput and turnaround of image data on the narrow communications channels.