Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalised hardness of approximation and the SCI hierarchy -- On determining the boundaries of training algorithms in AI

A BSTRACT . Hardness of approximation (HA) – the phenomenon that, assuming P 6 = NP, one can easily compute an ǫ -approximation to the solution of a discrete computational problem for ǫ > ǫ 0 > 0 , but for ǫ < ǫ 0 it suddenly becomes intractable – is a core phenomenon in the foundations of computations that has transformed computer science. In this paper we study the newly discovered phenomenon in the foundations of computational mathematics: generalised hardness of approximation (GHA) – which in spirit is close to classical HA in computer science. However, GHA is typically independent of the P vs. NP question in many cases. Thus, it requires a new mathematical framework that we initiate in this paper. We demonstrate the hitherto undiscovered phenomenon that GHA happens when using AI techniques in order to train optimal neural networks (NNs). In particular, for any non-zero underdetermined linear problem the following phase transition may occur: One can prove the existence of optimal NNs for solving the problem but they can only be computed to a certain accuracy ǫ 0 > 0 . Below the approximation threshold ǫ 0 – not only does it become intractable to compute the NN – it becomes impossible regardless of computing power, and no randomised algorithm can solve the problem with probability better than 1/2. In other cases, despite the existence of a stable optimal NN, any attempts of computing it below the approximation threshold ǫ 0 will yield an unstable NN. Our results use and extend the current mathematical framework of the Solvability Complexity Index (SCI) hierarchy and facilitate a program for detecting the GHA phenomenon throughout computational mathematics and AI.
The effect of executives' education and compensation on firm value: evidence from China

ABSTRACT In this study, we empirically investigate the relation between firm executives’ education and compensation levels and firm value in non-state-owned Chinese listed firms. We closely examine whether executive compensation moderates the relationship between executives’ education and firm value. Using a sample of 6,674 firm-year observations from 2005–2016, we find that firms with more educated executives have better values. Furthermore, our results show that the executive’s compensation level enhances this positive association. Our findings lend credence to similar studies that suggest that education and compensation levels can enhance firm value.
Environmental consequences of fossil fuel in Spain amidst renewable energy consumption: a new insights from the wavelet-based Granger causality approach

ABSTRACT By comparing ecological footprint and biocapacity, the load capacity factor follows a given ecological threshold, thus allowing for a thorough study of environmental deterioration. As a result, this research uses the load capacity factor (LCF) as a distinct proxy for ecological deterioration, resulting in a detailed environmental evaluation tool that considers both ecological footprint and biocapacity at the same time. Based on this knowledge, the current research investigates the relationship between load capacity factor (LCF) and fossil fuel (FF), renewable energy (REC), economic complexity (ECI) and foreign direct investment (FDI) in Spain utilizing a dataset covering the period between 1970Q1 and 2017Q4. We applied the novel wavelet coherence technique to assess the connection and/or causality interrelationship between LCF and the exogenous variables at various frequencies and different timeframes. The outcomes of the wavelet correlation disclosed that: (i) In the short and medium term, REC enhances the quality of the environment; (ii) In the short and medium term, fossil fuels worsen the quality of the environment; (iii) At all frequencies, FDI inflows enhance the quality of the environment; and (iv) In the short, medium and long term, economic complexity hinders the quality of the environment. In addition, the MWC and PWC outcomes are consistent with the wavelet coherence outcomes. Moreover, the wavelet-based Granger causality revealed that all the variables can predict each other at various frequencies.
ON THE VIRIALIZATION OF DISK WINDS: IMPLICATIONS FOR THE BLACK HOLE MASS ESTIMATES IN ACTIVE GALACTIC NUCLEI

Estimating the mass of a supermassive black hole in an active galactic nucleus usually relies on the assumption that the broad line region (BLR) is virialized. However, this assumption seems to be invalid in BLR models that consist of an accretion disk and its wind. The disk is likely Keplerian and therefore virialized. However, beyond a certain point, the wind material must be dominated by an outward force that is stronger than gravity. Here, we analyze hydrodynamic simulations of four different disk winds: an isothermal wind, a thermal wind from an X-ray-heated disk, and two line-driven winds, one with and the other without X-ray heating and cooling. For each model, we determine whether gravity governs the flow properties by computing and analyzing the volume-integrated quantities that appear in the virial theorem: internal, kinetic, and gravitational energies. We find that in the first two models, the winds are non-virialized, whereas the two line-driven disk winds are virialized up to a relatively large distance. The line-driven winds are virialized because they accelerate slowly so that the rotational velocity is dominant and the wind base is very dense. For the two virialized winds, the so-called projected virial factor scales with inclination angle as 1/sin 2i. Finally, we demonstrate that an outflow from a Keplerian disk becomes unvirialized more slowly when it conserves the gas specific angular momentum, as in the models considered here, than when it conserves the angular velocity, as in the so-called magneto-centrifugal winds.
Application of metagenomic next-generation sequencing technique for diagnosing a specific case of necrotizing meningoencephalitis caused by human herpesvirus 2

Abstract Reactivation of latent human herpesvirus 2 (HHV-2) can cause spontaneous recovering aseptic meningitis and recurrent meningitis in adults, but it rarely affects the brain parenchyma to cause encephalitis. Here, we report the case of a 37-year-old male patient admitted to our hospital due to fever with a progressive headache for 3 days and paroxysmal episodes of unconsciousness for 1 day. Brain magnetic resonance imaging (MRI) revealed viral meningoencephalitis. Then, metagenomics next-generation sequencing (mNGS) was applied, which detected 12,024 unique sequences of HHV-2 in cerebrospinal fluid (2022), indicating HHV-2 encephalitis. After antiviral treatment, the patient’s symptoms improved, and he was discharged. During the 1-month follow-up, the patient recovered without any new symptoms, but a brain MRI revealed significant atrophy of the original foci. The patient was finally diagnosed with HHV-2 necrotizing meningoencephalitis, which is extremely rare. mNGS helped with the clinical diagnosis and strengthened our understanding of HHV-2 infections in the central nervous system.
How did discourse shift among Afghan citizens during the fall of republic: Early insights using conversational AI platform

Abstract. Facing the increasing demand for conversational informatics to facilitate scalable meaningful online discussion, many government organizations in war-ravaged countries as Afghanistan recently adopted these tools to strengthen deliberative democracy. However, a comparative study on its social impact in view of latest development in Afghanistan is yet to be clearly articulated. Guided by a comparative case study approach to trace the people's thoughts and attitudes during a tumultuous time on the withdrawal of US troops from Afghanistan using conversational AI platform, which included over 150 participants in the Kabul area using open call. The first study conducted during the final 15 days of the republic, and the second study conducted during the first 15 days following the republic's fall using same tool. There was a statistically difference in responsiveness, concerns and uncertainty while comparing both studies. We found that people more likely talking about their concerns and future uncertainty about their future in Afghanistan, while seeking or suggesting help for each other during second study. The output of this research can be used for policy making.
Balancing the Digital Portfolio: Empirical evidence of an ambidextrous bias in digital government

The public sector is currently undergoing significant transformation in striving for digital government. With digital government involving a dilemma between continuous improvements and radical innovation, this places new demands on the manner through which digital initiatives are prioritized and managed. Previous research has called for both increased agility and adaptability in the governing of digital, yet there is to date a dearth of studies into public sector portfolio management. In this study, we address the issue of how digital initiatives are prioritized and managed in organizations, i.e., the practice of portfolio management. Through a clinical inquiry of three public sector organizations (one agency, one healthcare region, and one municipality), we analyze and compare existing portfolio management practices and portfolio compositions using previous findings from the field of organizational ambidexterity. Based on this, we identify pathologies in the current practice, elaborate on the role of balancing in portfolio management, and offer concrete recommendations to practice.
Temperature and dietary energy content influence female maturation age and egg nutritional content in Atlantic salmon

The environment experienced by a female influences reproductive traits in many species of fish. Environmental factors such as temperature and diet are not only important mediators of female maturation and reproduction but also of egg traits and offspring fitness through maternal provisioning. In this study, we use three-year-old, tank-reared, Atlantic salmon from two Finnish populations to investigate the effect of temperature and diet on maturation and egg traits. We show that a temperature difference of 2°C is sufficient to delay maturation in female Atlantic salmon whereas a 22% reduction in dietary energy content had no effect on maturation. Diet did not influence the body size, condition, or fecundity of the mature females or the size or protein content of the eggs. However, a higher energy diet increased egg lipid content. Neither female body size nor condition were associated with egg size or fat/protein composition. Our results indicate that female salmon that have a poorer diet in terms of energy content may have a reproductive disadvantage due to lower energy provisioning of eggs. This disadvantage has the potential to translate into fitness consequences for their offspring.
Haloperidol-Induced Immediate Early Genes in Striatopallidal Neurons Requires the Converging Action of cAMP/PKA/DARPP-32 and mTOR Pathways

Antipsychotics share the common pharmacological feature of antagonizing the dopamine 2 receptor (D2R) which is abundant in the striatum and involved in both the therapeutic and side effects of this drugs class. Pharmacological blockade of striatal D2R, by disinhibiting the D2R-containing medium-size spiny neurons (MSNs), leads to a plethora of molecular, cellular and behavioral adaptations which are central in the action of antipsychotics. Here, we focused on the cell type-specific (D2R-MSNs) regulation of some striatal immediate early genes (IEGs), such as cFos, Arc and Zif268. Taking advantage of transgenic mouse models, pharmacological approaches and immunofluorescence analyses, we found that haloperidol-induced IEGs in the striatum required the synergistic activation of A2a (adenosine) and NMDA (glutamate) receptors. At the intracellular signaling level, we found that the PKA/DARPP-32 and mTOR pathways synergistically cooperate to control the induction of IEGs by haloperidol. By confirming and further expanding previous observations, our results provide novel insights into the regulatory mechanisms underlying the molecular/cellular action of antipsychotics in the striatum.
Selective attention reduces responses to relevant sounds in human auditory cortex

Selective attention enables the preferential processing of relevant stimulus aspects. Invasive animal studies have shown that attending a sound feature rapidly modifies neuronal tuning throughout the auditory cortex. Human neuroimaging studies have reported enhanced auditory cortical responses with selective attention. To date, it remains unclear how the results obtained with functional magnetic resonance imaging (fMRI) in humans relate to the electrophysiological findings in animal models. Here we aim to close the gap between animal and human research by combining a selective attention task similar in design to those used in animal electrophysiology with high spatial resolution ultra-high field fMRI at 7 Tesla. Specifically, human participants perform a detection task, while the probability of target occurrence varies with sound frequency. Contrary to previous fMRI studies, we show that selective attention reduces responses to the attended frequencies in those neuronal populations preferring the attended frequency. Through population receptive field (pRF) mapping, we furthermore show that these response reductions are at least partially driven by frequency-induced pRF narrowing. The difference between our results to those of previous fMRI studies supports the notion that the influence of selective attention on auditory cortex is diverse and may depend on context, task, and auditory processing stage.
Phenotypic plasticity and the anthropause: an urban bird becomes less aggressive

Urban areas often impose strong, novel selection pressures on wildlife. Phenotypic plasticity is an important mechanism helping organisms establish populations in novel environments. Phenotypic plasticity can be difficult to study in urban wildlife because many urban environmental variables are challenging to isolate and manipulate experimentally. We took advantage of the COVID-19 lockdowns to assess whether urban birds expressed aggression differently when relieved from frequent encounters with humans. We measured the territorial aggression responses of resident dark-eyed juncos (Junco hyemalis) on an urban college campus in Los Angeles, USA. We found that the population overall displayed significantly reduced aggression in pandemic year 2021 compared to the typical year 2019. Furthermore, individuals measured in both 2019 and 2021 showed significantly reduced aggression during 2021, demonstrating that individual birds maintain phenotypic plasticity in this trait. Our results show that human disturbance likely has a significant effect on the aggressive behavior of urban birds.
Offloading Dependent Tasks with Mobility in Mobile Edge Computing

With the rise in computation-intensive and delay-sensitive applications, the limited resource of the user device has become a significant challenge. Mobile Edge Computing(MEC) has emerged as a paradigm to compensate for the resource shortage. When offloading application’s tasks to the Mobile Edge Network(MEN) of the MEC with consideration of task dependency and resource usage, the offloading process also requires consideration of user device’s mobility. However, most existing research that consider user mobility or task dependency among several factors assume that the offloaded tasks in the MEC are parallelizable or that the user device is immobile. Nev-ertheless, real-world applications have sequential dependencies and user devices are frequently mobile. In this situation, the previous methods no longer apply. In this paper, we propose a novel algorithm that jointly considers task dependency and user mobility to minimize application completion time in MEN. We show the improved performance of our proposed algorithm over the existing research.
Using Visualization to Facilitate Knowledge Acquisition and Dissemination for M&A

Acquisition researchers pointed out that to understand real world complexity it is necessary to look at mergers and acquisitions (M&A) from different perspectives. Thus, we investigated the presentation of M&A research in a dynamic and illustrative way that provides rare insights into the potential outcomes of acquisitions. Through an iterative process and much trial and error, it was found that large amounts of information can be combined in a single page to facilitate overall understanding and enable instantaneous comprehension of complex data. In the context of this research, using such illustrations enhanced both the seller’s perspective and that of the potential buyers in the M&A process. Visualization techniques have been accepted as a key tool to manage knowledge for strategic purposes and clearly the addition of the visualization diagrams enhanced this case-based acquisition research, in addition to being found useful and relevant in the decision-making process for those players in industry considering entering into the M&A process.
Harnessing Keypoints with Deep Metric Learning for Lane Line Instance Segmentation

The paper mainly studies methods on lane line detection in autonomous driving applications. The specific aim of paper is to experiment on the instance-based segmentation technique for a lane line detection in images. We incorporate the process of selecting portions or patches of lane line pixels as keypoints. We then improve the proposed method with deep metric learning techniques, more specifically, with an associative embedding having an angular loss function, to ensure that, keypoints with similar features belongs to the same instance of lane line. Next, the instance detection of the lane line is completed by cluster fitting using Kmeans. We also experiment our modified lane line detection model on TuSimple and CULane datasets. To confirm the important applications of our proposed methods, we evaluated it and achieved some competitive accuracies. The experimental results show the feasibility of the proposed method
IT Governance Mechanisms, IT Governance Domains, and Their Influence on IT Governance Effectiveness: Empirical Analysis in Colombia

The pervasive use of IT and its increasing management and governance complexity calls for a better attention about the topic of IT governance. Even though the topic is gaining more attention between practitioners and academics, there is a gap about what mechanisms improve IT governance effectiveness and how they are linked to IT governance domains. This study uses structural equation modelling to evaluate 215 valid surveys that were collected from members of the respective local chapters in Bogotá- Colombia of the following institutions: ISACA and PMI. In addition, members of the Colombian computer science engineers association - ACIS also participate answering our survey instrument. This study uses the variable ITG relevant knowledge to divide the dataset in two groups. The first group was labelled “High ITG relevant knowledge” and the second one “Low ITG relevant knowledge In the first group processes are perceived as the most influential ones on ITG domains. In the second group were processes and relational mechanisms. In the first group, all IT governance domains influence IT governance effectiveness. In the second group, value delivery and resource management were not found as they influence IT governance effectiveness.
An Ensemble of CapsNet and KSVM Using Feature Fusion: Application to COVID-19 Detection

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural Networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images.
Mechanisms that Influence IT Governance Effectiveness: Empirical Analysis in Private and Public Sector in Colombia

The influence of information technology (IT) is visible everywhere. The evolution of IT encourages firms to have more concerns about the role that IT is playing for achieving strategic goals. Worldwide is known that companies are making huge investments in IT acquisitions and also in how govern its complexity. As a result, IT governance gains attention among organizations. This study reviews individual IT governance mechanisms and their influence on IT governance effectiveness. The study examines how IT Intensity use by firms and IT governance experience influence IT governance effectiveness. The IT governance mechanisms selected were IT strategy committee, IT steering committee, involvement of senior management in IT, corporate performance measurement systems, ethics and culture of compliance, and corporate communication Systems. This study uses structural equation modelling to evaluate 293 valid surveys collected from members of the ISACA and PMI in Bogotá as well as the Colombian Association of Computer Science Engineers (ACIS). This study found that corporate communication systems and IT strategy committee have a positive and significant influence on the overall IT governance effectiveness. In firms with less than 10 years of IT governance experience, the influence of IT strategy committee is higher.
Recommendation Algorithm of Product Configuration based on Knowledge Graph Optimization

To satisfy the demand for automated product configuration in manufacturing enterprises, a product configuration recommendation algorithm based on knowledge graph optimization is proposed. Firstly, the historical configuration information of the same product, material and component is mined through FP-Tree, and the inventory data of the same configuration relationship is compressed into a single normalized weight data. Secondly, combined with the weight data, the knowledge graph is constructed by using the a priori relationships of the same product, material and component stored in the structured database. On this basis, the recall of associated materials and components is completed by using the graph query of product entity, and then the FM algorithm is used to achieve detailed sorting recommendations based on the normalized weight data. Validated using production data, the accuracy rate of product configuration recommendation reaches 74.54%.