Miracle-dz/UniFAR-FTUs huggingface dataset:
To be a Knight-errant Novel Master: Knight-errant Style Transfer via Contrastive Learning

Knight-errant style writing is a challenging task 001 for novice writers due to the highly condensed 002 terminology and highly literary language cul-003 ture of the knight-errant works. To tackle 004 this problem, in this paper, we propose a new 005 large-scale parallel knight-errant dataset and 006 model the knight-errant writing as a text style 007 transfer (TST) task between modern style and 008 knight-errant style. We establish the bench-009 mark performance of six current SOTA models 010 for knight-errant style transfer. Empirical re-011 sults demonstrate that the existing SOTA TST 012 models are unable to accurately identify and 013 generate knight-errant style sentences. There-014 fore, we propose Knight, a TST framework 015 based on contrastive learning. Knight uses mul-016 tiple strategies to construct positive and neg-017 ative samples, making it significantly better 018 than existing SOTA models in terms of content 019 fluency, style transfer accuracy, and factuality. 020 The data and code are publicly available 1 . 021
Multilayered Electroactive Polyaniline-ZnO Modified GCE for Electrochemical Detection of Paracetamol

Paracetamol (PC) is widely used for defensive and medicinal purposes for human beings. But their deposition in the human body will persuade thoughtful health risks. So, the enhancement of sensitive and selective techniques for the easy and fast detection of PC is extremely fundamental. Multilayered electroactive polyaniline-ZnO nanocomposite (NC) film was developed for PC detection which was based on the oxidation of PC with polyaniline doped zinc oxide on a glassy carbon electrode (PANI/ZnO/GCE). PANI and PANI/ZnO NCs were fabricated by both electrochemical and in-situ oxidative chemical polymerization methods. The morphology of PANI/ZnO shows that the polymer was distributed on the surface of ZnO, and the XRD pattern confirms that the average size calculated was 7.48 nm for PANI/ZnO. From UV-Vis measurements, the PANI/ZnO composite shows a redshift relative to PANI and ZnO. Moreover, FT-IR spectra show that chemical bonds and stretching are assigned to different peaks. Using differential pulse voltammetry, the fabricated PANI/ZnO/GCE electrochemical sensor displays good analytical performance for PC with detection limits of 4 × 10−8 M and a sensitivity of 2.571 mA/1 × 10−8 M with a linear concentration of PC within the range of 1.0 to 7.0 × 10−8 M.
Control False Negative Instances In Contrastive Learning To Improve Long-tailed Item Categorization

Item categorization (IC) is an important core 001 technology in e-commerce natural language 002 processing (NLP). Given category labels’ long-003 tailed distribution, IC performances on tail la-004 bels tend to be poor due to sporadic supervision. 005 To address the long-tail issue in classification, 006 an increasing number of methods have been 007 proposed in the computer vision domain. In 008 this paper, we adopted a new method, which 009 consists of decoupling the entire classification 010 task into (a) learning representations in a k-011 positive contrastive learning (KCL) way and (b) 012 training a classifier on balanced data set, into 013 IC tasks. Using SimCSE to be our self-learning 014 backbone, we demonstrated that the proposed 015 method works on the IC text classification task. 016 In addition, we spotted a shortcoming in the 017 KCL: false negative instances (FN) may harm 018 the representation learning step. After eliminat-019 ing FN instances, IC performance (measured 020 by macro-F1) has been further improved. 021
Active Relation Discovery: Towards General and Label-aware OpenRE

Open Relation Extraction (OpenRE) aims to 001 discover and label novel relations from open 002 domains. Previous methods mainly suffer from 003 two problems: (1) Insufficient capacity to dis-004 criminate between known and novel relations. 005 When extending conventional test settings to 006 a more general setting where test data might 007 also come from seen classes, existing OpenRE 008 approaches have a significant performance de-009 cline. (2) Secondary labeling must be per-010 formed before practical application. Exist-011 ing methods cannot label human-readable and 012 meaningful types for novel relations, which is 013 urgently required by the downstream tasks. To 014 address these issues, we propose the A ctive 015 R elation D iscovery ( ARD ) framework, which 016 utilizes relational outlier detection for discrimi-017 nating known and novel relations and involves 018 active learning for labeling novel relations. 019 Extensive experiments 1 on three real-world 020 datasets show that ARD significantly outper-021 forms state-of-the-art methods on both conven-022 tional and our proposed general OpenRE set-023 tings. 024
Melting, compaction and reactive flow: Controls on melt fraction and composition change in crustal mush reservoirs

Changes in melt fraction and local bulk composition in high-crystallinity, crustal mush reservoirs are essential to produce the large volumes of low-crystallinity, silicic magma that are emplaced to form plutons, or erupted to surface. Heating (and cooling) is well understood and widely invoked in driving melt fraction change, but does not cause chemical differentiation because there is no separation of melt and crystals. Fractional crystallisation at high melt fraction is widely assumed to explain differentiation, but is inconsistent with the evidence that large-scale, long-term magma storage and evolution occurs in high-crystallinity mush reservoirs. Compaction has been suggested to explain melt fraction change and differentiation at low melt fraction, but compaction (and decompaction) causes simple unmixing (and mixing) of melt and solid crystals: to produce very refractory bulk composition by compaction, melt fraction must be driven down to very low values. Yet microstructural evidence demonstrating widespread compaction in crustal mush reservoirs at low melt fraction is lacking. Here we show that melt fraction change can be expressed in terms of heating/cooling and compaction, plus an additional term that we term ’reactive flow’. Similarly, composition change can be expressed in terms of compaction and reactive flow. Reactive flow changes the local bulk composition, which causes ‘chemical’ melting (dissolution) and freezing (precipitation), distinct from ‘thermal’ melting/freezing caused by changes in enthalpy. We use numerical modelling to show that the contributions of compaction and reactive flow in a crustal magma reservoir are similar in magnitude. However, reactive flow opposes melt fraction and composition changes caused by compaction when compaction occurs in a temperature gradient that increases upwards at, for example, the base of a sill intrusion, or decompaction occurs in a temperature gradient that decreases upwards at, for example, the top of a sill intrusion. If compaction causes melt fraction decrease and creates a more refractory bulk composition, then reactive flow causes melt fraction increase and a more evolved bulk composition, and vice-versa. Reactive flow means that very small melt fraction is not required to produce very re-fractory composition in a crustal magma reservoir, consistent with the relatively scarce microstructural evidence for widespread compaction. The apparent lack of compaction in crustal magma reservoirs, as compared to other natural and engineered systems in which reaction does not occur, is also explained by the contribution of reactive flow. Reactive flow also means that melt loss in compacting regions of a crustal magma reservoir may instead be accompanied by evidence for mineral dissolution, which facilitates ongoing melt fraction loss by preserving connected melt flow paths through the mush pore-space. Reactive flow in decompacting regions can also explain why interstitial mineral phases display textures that mimic those of interstitial melt.
Subframes Discrimination and Time Synchronization Methods in LTE-V2X System

L TE- V2X system is developed based on L TE system and their synchronization methods are similar. In this paper, the content of physical layer of L TE sidelink system is analyzed. Based on the characteristics of channels and symbols, a method to identify subframe types by symbol periodicity is proposed, which can effectively distinguish data frames from broadcast frames. On the basis of traditional L TE synchronization algorithms, two new synchronization algorithms are proposed for L TE sidelink system. One is subframe synchronization method based on cyclic prefix autocorrelation and the other is PSSS synchronization method for sidelink broadcast frames. The simulation results show that the synchronization performance of the subframe synchronization algorithm, which integrates multiple symbols' correlation results, is significantly better than that of the traditional auto-correlation algorithm. The synchronization miss detected ratio is reduced to 0% when SNR is more than 4dB, which is barely affected by frequency offset. PSSS synchronization algorithm is applied to broadcast frames and its synchronization RMSE value is only 0.1 % of the subframe synchronization algorithm, when the SNR is less than 2dB. Even under the influence of frequency offset, the miss detected ratio of this algorithm is 50% lower than that of subframe synchronization algorithm.
High-Gain All-Metal 3-D Printed Lens–Horn Antenna for Millimeter-Wave Applications

In this letter, a high-gain all-metal lens–horn antenna for millimeter-wave applications is proposed. Unlike the conventional metal lens horn, the proposed lens horn can theoretically achieve an arbitrary focal-to-diameter (F/D) ratio, which can further shorten the length of the horn antenna. Considering the design difficulty of the feed, in practice, the F/D ratio generally needs to be greater than 0.4. To further reduce the overall height of the antenna, the lens can be kept at a certain thickness according to the Fresnel lens principle. To demonstrate the proposed theory, a lens–horn antenna with an F/D ratio of 0.7 and an aperture area of π×93.752 mm2 is designed, fabricated, and tested. In order to reduce the processing costs and difficulties, the antenna is processed by metal 3-D printed technology. The test results indicate that the antenna achieves a peak gain of 30.8 dBi and 3-dB gain bandwidth of 22.5% (28.4–34.5 GHz). The length of the proposed antenna can be reduced by about 68% compared with the horn antennas without lenses. The proposed antenna has the characteristics of high gain, compact structure, and low cost, which is suitable for high-power millimeter-wave applications.
The impact of mobile device usage on lottery behavior: evidence from China

ABSTRACT This paper examines how mobile device usage affect retail investors’ lottery behaviour. Using data from the Chinese stock market, which is dominated by retail investors, we observe a structural change in mobile investing trends in 2015. By employing a structural shift model, we find that higher mobile device usage in the market disproportionately increases the trading volume of lottery stocks and amplifies the lottery-related pricing anomaly. Our results suggest that the use of mobile devices increases retail investors’ demand for lottery stocks by attention-induced trading, but fail to find evidence that mobile devices influence retail investors’ inherent skewness preferences.
Security in Internet of Things: A Review

Internet of Things (IoT) is the paramount virtual network that enables remote users to access connected multimedia devices. It has dragged the attention of the community because it encompasses real-world scenarios with implicit environs. Despite several beneficial aspects, IoT is surrounded by provocations for successful implementation, as data travels in different layers. One of the critical challenges is the security of the data in these layers. Researchers conducted numerous studies focusing on the level of security at a single technique, creating loopholes to address the entire scenario of securing an IoT network. This study aims to comprehensively review current security issues, wireless communication techniques, and technologies for securing IoT. This work’s utmost significance is addressing all the security perspectives at a glance. For this purpose, research contributions from the previous years are investigated for better understanding. Some countermeasures and snags from security perspectives have also been analyzed in detail concerning the current industry trends. Blockchain, machine learning, fog, and edge computing are possible solutions to secure IoT. After studying these techniques and their immunity to attacks, machine learning can become a hope if incorporated with end-to-end security. This comprehensive review will provide adequate understanding and knowledge in defining security lines of action for the successful implementation of IoT.
Mechanism of Chemical Bath Deposition of CdS Thin Films: Influence of Sulphur Precursor Concentration on Microstructural and Optoelectronic Characterizations

In this study, we aimed to improve our understanding of the response mechanisms associated with the formation of CdS thin films. CdS thin film remains the most valuable option for many researchers, since it has shown to be an effective buffer material for film-based polycrystalline solar cells (CdTe, CIGSe, CZTS). We performed experimental and numerical simulations to investigate the effect of different thiourea concentrations on the characteristics of the CdS buffer layer. The experimental results reveal that an increase in thiourea concentrations had a direct effect on the optical results, with bandgap values ranging from (2.32 to 2.43) eV. XRD analysis confirmed that all deposited films were polycrystalline, except for [1/0.75], where there is no CdS formation. Electrical studies indicated that CdS with a molar ratio of [Cd]/[S] of 1 had the maximum carrier concentration (3.21 × 1014 cm−3) and lowest resistivity (1843.9 Ω·cm). Based on the proposed mechanism, three kinds of mechanisms are involved in the formation of CdS layers. Among them, the ion-by-ion mechanism has a significant effect on the formation of CdS films. Besides, modelling studies reveal that the optic-electrical properties of the buffer layer play a crucial role in influencing the performance of a CIGS solar cell.
Filling in CMB map missing data using constrained Gaussian realizations

For analyzing maps of the cosmic microwave background sky, it is necessary to mask out the region around the Galactic equator where the parasitic foreground emission is strongest as well as the brightest compact sources. Since many of the analyses of the data, particularly those searching for non-Gaussianity of a primordial origin, are most straightforwardly carried out on full-sky maps, it is of great interest to develop ecient algorithms for lling in the missing information in a plausible way. In this paper we explore algorithms for lling in based on constrained Gaussian realizations. Although carrying out such realizations is in principle straightforward, for nely pixelized maps as will be required for the Planck analysis a direct brute force method is not numerically tractable. Here we present some concrete solutions to this problem, both on a spatially at sky with periodic boundary conditions and on the pixelized sphere. One approach is to solve the linear system with an appropriately preconditioned conjugate gradient method. While this approach was successfully implemented on a rectangular domain with periodic boundary conditions and worked even for very wide masked regions, we found that the method failed on the pixelized sphere for reasons that we explain here. We present an approach that works for full-sky pixelized maps on the sphere involving a kernel-based multi-resolution Laplace solver followed by a series of conjugate gradient corrections near the boundary of the mask.
Virtual Gait Visualization based on Geometric Kinematics

Biomechanical modeling of the human body and scientific visualization are widely used tools for gait analysis. In this study, we propose a framework for the scientific visualization of the human gait cycle based on the geometric inverse kinematics of position (GIK) of an 8 degrees of freedom (DoF). The coordinates of the joint references in gait space used as input were obtained in a previous work using quaternions algebra (QFK). Subsequently, the joint angles obtained by the GIK method were used to make a 3D animation of the normal gait cycle in Maya Autodesk. To validate the inverse kinematics and the animation, the well-known OpenSim platform was used as a reference. The results obtained are of special interest to the end-user in clinical settings, since they can be used to improve the diagnoses of gait diseases and their inherent treatments.
Post-COVID Pain Is Not Associated with Inflammatory Polymorphisms in People Who Had Been Hospitalized by COVID-19

Our aim was to assess the association between four inflammatory polymorphisms with the development of post-COVID pain and to associate these polymorphisms with the clinical pain phenotype in individuals who had been hospitalized by COVID-19. Three potential genotypes of IL-6 (rs1800796), IL-10 (rs1800896), TNF-α (rs1800629), and IFITM3 (rs12252) single nucleotide polymorphisms (SNPs) were obtained from no-stimulated saliva samples from 293 (49.5% female, mean age: 55.6 ± 12.9 years) previously hospitalized COVID-19 survivors by polymerase chain reactions. Pain phenotyping consisted of the evaluation of pain features, sensitization-associated symptoms, anxiety levels, depressive levels, sleep quality, catastrophizing, and kinesiophobia levels in patients with post-COVID pain. Analyses were conducted to associate clinical features with genotypes. One hundred and seventeen (39.9%) patients experienced post-COVID pain 17.8 ± 5.2 months after hospital discharge. No significant differences in the distribution of the genotype variants of any SNPs were identified between COVID-19 survivors with and without post-COVID pain (all, p > 0.47). Similarly, the clinical pain phenotype was not significantly different between patients with and without post-COVID pain since no differences in any variable were observed for any SNPs. In conclusion, four SNPs associated with inflammatory and immune responses did not appear to be associated with post-COVID pain in previously hospitalized COVID-19 survivors. Further, neither of the SNPs were involved in the phenotyping features of post-COVID pain.
Jet noise generated by large-scale coherent motion

The noise generated by large scale turbulence structures and instability waves of jets is discussed. Emphasis is placed on supersonic jets with moderate to high Reynolds numbers. This is because it is in these jets that unambiguous experimental and theoretical evidence is found indicating that large turbulence structures and instability waves are directly responsible for generating the dominant part of the noise. For subsonic jets similar large turbulence structures and instability waves do play a crucial role in the dynamics, spread, and mixing of the jet fluid. However, at subsonic convection speeds, they do not appear to be efficient noise generators. Many investigators believe that the dominant noise source of subsonic jets is, in fact, the small scale turbulence. As yet, this belief has not yet received universal acceptance. The issues involved are complicated and are not easy to resolve.
Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient’s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.
Modelling of imaged ellipse intensity profiles using Euclidean geometry

Abstract To model the evolution in intensity of an ellipse image along randomly oriented profiles, this paper starts with the derivation of an analytical solution involving infinite summations of products of Hermite polynomials, which is consequently argued to be impractical. Generation of profiles adopting Fourier analyses hampers the extraction of arbitrarily located intensity distributions as a direct result of equidistant spatial sampling, and an alternative procedure is therefore presented to calculate the sought for intensity at isolated spatial locations. The algorithm introduced is based on the overlap area between the ellipse and concentric circles, which can be performed using basic trigonometric properties. The simplicity of the proposed method allows, contrary to Fourier based analyses, the modelling of intensity profiles with high resolution while demanding minimal computational memory. The proficiency of the method is demonstrated numerically, and its generality and applicability to real images in terms of motion induced image blurring and digitisation are discussed.
Loose rock detection methods for automating the scaling process

For the scaling process to be successful, it is important to first detect the loose rock. Even today, this task is mainly performed by experienced personnel. This leads to opportunities for increased potential to use sensor driven digital assistance systems. This paper presents a review and analysis of sensor-based loose rock detection methods considering the specific conditions of testing and use. The investigations can be classified into three categories based on their respective sensor technology approach. The opportunities for automating the scaling process highly depend on the method used. If scaling is conducted manually, the pre-entry examination could be automated. For the automation of the entire process, the use of machinery is more beneficial. Long wave infrared (LWIR) cameras or visual techniques have a great potential. However, in the case of LWIR, innovative methods for generating a sufficient heat flow between air and rock need to be developed in future.
On the slope and geography of fibred surfaces and threefolds

In this tesis we study numerical propieties of surfaces and threefolds, mainly fibred over curves, the so called "slope" of the fibration. We prove partially a conjecture of Fujita on the semiampleness of the direct image of the relative dualizing sheaf of a fibration. We give new lower bounds of the slope of a fibred surface depending on data of the general fibre (existence of involutions) and on data of the hole surface (the fibration not being the Albanese morphism, for example). We study the case of threefolds over curves. We prove that, in general, the relative algebraic Euler characteristic is nonnegative and give lower bound for the slope. We classify the lowest cases of the invariants.