Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetohydrodynamic wave propagation from the subphotosphere to the corona in an arcade-shaped magnetic field with a null point

The aim of this work is to study the energy transport by means of MHD waves propagating in quiet Sun magnetic topology from layers below the surface to the corona. Upward propagating waves find obstacles, such as the equipartition layer with plasma b=1 and the transition region, and get converted, reflected and refracted. Understanding the mechanisms by which MHD waves can reach the corona can give us information about the solar atmosphere and the magnetic structures. We carry out two-dimensional numerical simulations of wave propagation in a magnetic field structure that consists of two vertical flux tubes separated by an arcade shaped magnetic field. This configuration contains a null point in the corona, that significantly modifies the behaviour of the waves. We describe in detail the wave propagation through the atmosphere under different driving conditions. We also present the spatial distribution of the mean acoustic and magnetic energy fluxes and the spatial distribution of the dominant frequencies in the whole domain. We conclude that the energy reaches the corona preferably along vertical magnetic fields, inside the flux tubes, and it has an acoustic nature. Most of the magnetic energy keeps concentrated below the transition region due to the refraction of the magnetic waves and the continuous conversion of acoustic-like waves into fast magnetic waves in the equipartition layer located in the photosphere. However, part of the magnetic energy reaches the low corona when propagating in the region where the arcades are located, but waves are sent back downwards to the lower atmosphere at the null point surroundings. This phenomenon, together with the reflection and refraction of waves in the TR and the lower turning point, act as a re-feeding of the atmosphere. In the frequency distribution, we find that high frequency waves can reach the corona outside the vertical flux tubes.
EARNING MANAGEMENT IN BANKING SECTOR ON INDONESIA STOCK EXCHANGE

The practice of earnings management could not be separated from the process of preparing financial statements. It could occur in all business sectors including banking. This study aims to analyze earnings management in financial statements of banks on Indonesian Stock Exchange. This research is a descriptive study of banks listed on Indonesia Stock Exchange in 2020. Earnings management in bank financial statements is proxied by discretionary accruals. The population and sample of this research are 42 banks. The results show that 31 banks have a negative Discretionary Accruals and 11 banks have a positive Discretionary Accruals. A negative discretionary accruals indicates that the company performs earnings management by decreasing earnings while a positive discretionary accruals indicates that the company performs earnings management by increasing the earnings in the financial statements.
A Socially-Aware Radio Map Framework for Improving QoS of UAV-Assisted MEC Networks

The expeditious growth of the Internet of Things (IoT) has accelerated the evolution of multi-access edge computing (MEC). MEC alleviates the challenges of conventional cloud computing, such as high data latency, poor data gathering reliability, increased network cost, and lack of network robustness. The primary objective of MEC is to facilitate a hierarchy of edge servers to address these quality-of-service (QoS) challenges, especially the information propagation issue due to the mobility of IoT devices (IoD). Further, social-relationship among mobile IoD is a critical parameter used to reduce the data transmission delay and queue size at the MEC. Specifically, in this work, a novel socially-aware radio map generation method is proposed to compute the fine-grained and accurate locations of QoS-deprived areas. Firstly, a novel method to compute the social relationship index (SRI) factor is proposed on the basis of current and future encounters among moving IoDs. Then the obtained SRI factor is used to form clusters of mobile IoD. The clusters’ signal to interference plus noise ratio (SINR) is then used to generate the socially-aware radio map. Following that, unmanned aerial vehicles (UAV) use this radio map, which contains rich and serviceable channel information, for 3D beamforming towards the mobile clusters. Using the obtained radio map, Kalman filter-based offline path planning of UAVs is proposed to minimize the UAVs flying distance from the initial to final locations. Furthermore, an optimization problem is formulated to assess the performance of the proposed method. Finally, the performance of the proposed method is compared with the existing methods, taking into account various network parameters such as optimum number of UAVs needed to cover the deployed area, data transmission delay, and received SINR.
High-pressure transport properties of TiS2 and TiSe2

Measurements of the pressure dependence of the Hall coefficient and conductivity of TiS2 indicate that TiS2 is an extrinsic semiconductor. The strong temperature dependence of the conductivity is matched by a strong pressure dependence that is consistent with electron-phonon scattering. In contrast, TiSe2 is a semimetal, with a strongly pressure dependent p-d band overlap. The transition temperature of the structural phase transition in TiSe2 falls at -1.2K kbar-1.
Strong field gravitational lensing by a Kerr black hole

We consider a Kerr black hole acting as a gravitational deflector within the geometrical optics, and point source approximations. The Kerr black hole gravitational lens geometry consisting of an observer and a source located far away and placed at arbitrary inclinations with respect to the black hole's equatorial plane is studied in the strong field regime. For this geometry the null geodesics equations of our interest can go around the black hole several times before reaching the observer. Such photon trajectories are written in terms of the angular positions in the observer's sky and therefore become "lens equations". As a consequence, we found for any image a simple classification scheme based in two integers numbers: the number of turning points in the polar coordinate $\theta$, and the number of windings around the black hole's rotation axis. As an application, and to make contact with the literature, we consider a supermassive Kerr black hole at the Galactic center as a gravitational deflector. In this case, we show that our proposed computational scheme works successfully by computing the positions and magnifications of the relativistic images for different source-observer geometries. In fact, it is shown that our general procedure and results for the positions and magnifications of the images off the black hole's equatorial plane, reduce and agree with well known cases found in the literature.
Icing Conditions Encountered by a Research Aircraft

Abstract The characteristics of clouds which have led to airframe icing on an instrumented Beechcraft Super King Air are summarized. The icing encounters occurred at altitudes from 0–8000 m MSL, in summer and winter, in stratiform and cumuliform clouds, and at temperatures from 0 to −30°C. The characteristics of icing encounters in different areas and in different seasons are compared. The fraction of measurements exceeding various threshold values of liquid water content, average liquid water content over a given distance, volume-median droplet diameter, droplet concentration, ice crystal concentration, and potential ice accumulation are given. The effects of these cloud characteristics on aircraft performance were measured by comparing the rate of climb of the aircraft with ice to the rate of climb for the clean aircraft under the same conditions. Most icing encounters led to a reduction in the rate of climb that increased linearly with the path integral of the supercooled liquid water content. The volu.
Substituting Fat with Olive Oil, Mash Potato, or a Gelatin Matrix in Low-Salt-Content Dry-Fermented Sausages

It was investigated whether physicochemical properties and consumer perception of dry-fermented sausages were affected by the partial replacement of fat and salt by other compounds. A control batch and nine experimental batches, following 3 × 3 factorial design, were manufactured. The NaCl was replaced with of calcium lactate, magnesium chloride, or a blend 85% NaCl (sodium chloride) and 15% KCl (potassium chloride). The fat was partially substituted by olive oil, potato puree, or commercial gelatin. The pH, dry matter, fat content, free fatty acid content, peroxide index, microbial analysis, and tasting, were measured. Both fat type and slat type were affected to measured variables. In terms of moisture, gelatin would be the most recommendable substitute for fat, whereas KCL would be the better substitute for salt. The three oil batches and the potato-magnesium batches presented lower fat content than the control batch. Both the free fatty acid content and the peroxide values increased over the ripening time. At the end of the ripening, the three oil batches presented the highest values for free fatty acids, but there were no differences among the batches of peroxide index. Regarding bacterial counts, the potato-KCL batch was the most like the control batch. In visual appraisal, none of the scores of the measured variables were affected by the batch. Nevertheless, the batch of oil-magnesium would be purchased less than expected. The tasting quality was affected only by the salt type, but all of the batches were different from the control. Neither the gender (p > 0.05) nor the age (p > 0.05) of the respondents affected the taste scores, visual appraisal, or purchase intent. Results shows that the partial substitution of fat and salt in dry-fermented sausages is possible, even in greater percentages than those stated in the literature, without a detriment of sensory properties. Nevertheless, further experiments would be necessary to adjust the formulation, as well as to explore other possibilities.
Exact Algorithms for Optimization and parameterized versions of some graph theoretic problems[HBNI Th 6]

An important goal of the theory of algorithms is to design efficient algorithms that solve computationally difficult problems. When considering the class of NP-hard combinatorial optimization problems, the goal “appears” beyond our reach. As these problems need to be solved exactly for various applications in theory and practice routinely, exact algorithms become unavoidable. Parameterized Complexity is an exact algorithmic approach to deal with intractable computational problems having some small parameters. For decision problems with input size n, and a parameter k (which typically, and in all the problems we consider in this paper, is the solution size), the goal here is to design an algorithm with runtime f(k)n where f is a function of k alone, against a trivial n algorithm. Problems having such an algorithm are said to be fixed parameter tractable (FPT), and such algorithms are practical when small parameters cover practical ranges. In recent years, there has been a growing interest in designing exact algorithms for optimization versions of NP-complete problems. This has led to the development of fast exponential-time algorithms for various problems including Satisfiability, Graph Coloring, Maximum Independent Set and many others. These exponential-time algorithms lead to practical algorithms for at least moderate instance sizes. In this thesis we look at various problems from the exact algorithm paradigm for both parameterized and optimization versions of the problems. The first part of the thesis consists of FPT algorithms for various graph problems and the other part consists of exact algorithms for optimization versions of a few graph problems. We first give FPT algorithms for Feedback Vertex Set in undirected graphs, Feedback Set Problems in Tournaments and duals of Feedback Set problems in directed graphs. Then we show that several problems like Dominating Set, Independent Set that are hard for various “parameterized complexity classes” on general graphs, become fixed parameter tractable on graphs with no small cycles. As our last algorithmic result we give an FPT algorithm for the Directed Maximum Leaf Out-Tree problem which is the problem of finding a directed out-tree with at least k leaves in directed graphs. Finally, we give W[1]-hardness results for variations of coloring problems like List Coloring and Equitable
The Metaphysics of Words in Context

To determine the meaning of these sentences from lexical items, we need at least a notion of composition on which combining the meanings of two words may result in a change to those meanings themselves. That is, we need at the very least a context-sensitive notion of composition, which is not what we find in the standard theory of composition found, e.g., in Montague ∗We would like to thank Sheila Asher, Tim Fernando, Ivan Sag, Stan Peters, David Israel Alex Lascarides, Ann Copestake, and Johanna Seibt for helpful comments on earlier drafts.
Stock Market Prediction using Natural Language Processing - A Survey

The stock market is a network which provides a platform for almost all major economic transactions. While investing in the stock market is a good idea, investing in individual stocks may not be, especially for the casual investor. Smart stock-picking requires in-depth research and plenty of dedication. Predicting this stock value offers enormous arbitrage profit opportunities. This attractiveness of finding a solution has prompted researchers to find a way past problems like volatility, seasonality, and dependence on time. This paper surveys recent literature in the domain of natural language processing and machine learning techniques used to predict stock market movements. The main contributions of this paper include the sophisticated categorizations of many recent articles and the illustration of the recent trends of research in stock market prediction and its related areas.
Polarization-anisotropy induced spatial anisotropy of polariton amplification in planar semiconductor microcavities

Based on a microscopic many-particle theory we investigate the amplification of polaritons in planar semiconductor microcavities. We study a spatially perfectly isotropic microcavity system and excitation geometry. For this system, our analysis shows that a pump-induced vectorial polarization anisotropy can lead to a spatial anisotropy in the stimulated amplification of polaritons. This effect is brought about by the interplay of the longitudinal-transverse cavity mode splitting (TE-TM splitting) and the spin-dependence of the polariton-polariton scattering processes.
Storing, learning and retrieving biased patterns. (English)

Summary: The formal equivalence between the Hopﬁeld network (HN) and the Boltzmann Machine (BM) has been well established in the context of random, unstructured and unbiased patterns to be retrieved and recognised. Here we extend this equivalence to the case of “biased” patterns, that is patterns which display an unbalanced count of positive neurons/pixels: starting from previous results of the bias paradigm for the HN, we construct the BM’s equivalent Hamiltonian introducing a constraint parameter for the bias correction. We show analytically and numerically that the parameters suggested by equivalence are ﬁxed points under contrastive divergence evolution when exposed to a dataset of blurred examples of each pattern, also enjoying large basins of attraction when the model suﬀers of a noisy initialisation. These results are also shown to be robust against increasing storage of the models
Wavelet-based denoising: comparisons between orthogonal and non-orthogonal decompositions

In this study we present a denoising scheme applied to a non-orthogonal wavelet transform and compare the performances obtained with those based on orthogonal decompositions. We investigate the effect due to small sample sizes, specific thresholding levels, and initial thresholding scales, and compare results obtained for orthogonal and non-orthogonal transforms.
[10]-Parameters Predicting Poor Blood Pressure Control among Male Hypertensives in A Primary Care Setting, Kwara State Nigeria

Background: Poor blood pressure control is a global challenge and male hypertensives constitute an important group of patients that should be treated with utmost priority because previous studies have associated male gender with high prevalence of hypertension and poor blood control. Aim: This was to determine the level of blood pressure (BP) control and to find the relationship between the demographic, clinical and laboratory parameters that can predict uncontrolled BP among male hypertensives. Material and Method: This study was a hospital-based descriptive cross-sectional descriptive study done in Family Medicine department of the University of Teaching Hospital, Ilorin, Kwara State, Nigeria. The study population was male hypertensives. Interviewers’ questionnaires were used to obtain information and clinical measurements such as BP and body mass index were taken with appropriate instruments. Laboratory measurement including fasting blood glucose and fasting lipid profile were also carried out. Data was collected using the Version 20 software packages of the Statistical Package for Social Sciences (SPSS-20). Result: About half (48%) of the respondents had control atBP ≤140/90 mmHg, 34.9% at ≤130/80 and only 15.7% had optimum BP control. The diastolic BP control 154(67.2%) was higher than those with systolic BP control 107(46.2%). Multiple regression analysis shows that only increasing age (P value = 0.003) and low level of education (P value = 0.009) can predict the development of uncontrolled hypertension. Conclusion: This study shows that there is poor BP control among male hypertensives and increasing age and low level of education can predict uncontrolled hypertension.
Short-Term Evaluation of the Effectiveness of Shock Wave Therapy (Diamagnetic Shock Waves) Versus Physio Kinesiotherapy in Non-Calcific Tendinopathy of the Shoulder: A Preliminary Comparative Study

Citation: Short-Term Evaluation of the Effectiveness of Shock Wave Therapy (Diamagnetic Shock Waves) Versus Physio Kinesiotherapy in Non-Calcific Tendinopathy of the Shoulder: A Preliminary Comparative Study. J syndrome, is the most common tendinopathy of the upper limb. Its management must consider the anatomic deep location of the rotator tendons and the fact that multiple shoulder joint structures can contribute to the origin of pain [3]. The first-choice line of treatment for calcific and non-calcific shoulder tendinopathy includes conservative approaches, and they consist of different modalities: strengthening and stretching exercises, corticosteroids, platelet-rich plasma or blood injections, acupuncture, laser therapy, and many more [4]. However, there is still uncertainty as to which is the best non-surgical intervention, and a variable percentage of patients, between 4% and 11%, do not benefit from any conservative therapy and must undergo surgery [5]. ESWT is a well-established conservative for tendinopathies The rationale for lies observational to evaluate the effects, in the short of a of shock in
Toward Grammar Inference via Refinement Types

1 Motivation Ad hoc parsers are pieces of code that use common string functions like split, trim, or slice to effectively perfom parsing—that is, “the process of structuring a linear representation in accordance with a given grammar” [13]—without employing any formal parsing techniques or frameworks; the “given grammar” remains entirely implicit. As an example, here is a Python expression that turns a string of comma-separated numbers into a list of integers:
A Deep Neural Differential Distinguisher for ARX based Block Cipher

Over the last few years, deep learning is becoming the most trending topic for the classical cryptanalysis of block ciphers. Differential cryptanalysis is one of the primary and potent attacks on block ciphers. Here we apply deep learning techniques to model differential cryptanalysis more easily. In this paper, we report a generic tool using deep neural classifier that assists to find differential distinguishers for block ciphers with reduced round. We apply this approach for the differential cryptanalysis of ARX-based encryption schemes HIGHT, LEA, and SPARX. The result shows that our deep learning based distinguishers work with high accuracy for 14-round HIGHT, 13-Round LEA and 11-round SPARX. We also achieve an improvement of the lower bound of data complexity for these three ARX based ciphers.
Joint Learning of Practical Dialogue Systems and User Simulators

A key difficulty in training task-oriented dialogue (TOD) systems is a lack of training data. We explore the possibility of creating new dialogue data through the interaction of an end-to-end (E2E) Transformer-based TOD system, and an E2E Transformer-based User Simulator. We refer to the generation of new data through conversations between the two systems as self-play. Our goal is to develop such a TOD system and User Simulator using supervised learning that a) can operate on unseen dialogues, and b) are good enough to drive self-play experiments with the goal of refining their performance. We develop UBAR-E, an E2E TOD system that extends an influential model; UBAR, enabling it to work on unseen dialogues by using Inferred Turn Domains as opposed to ground-truth turn domains. We show this approach produces similar performance to UBAR in both model Score and a range of lexical richness metrics on the MultiWOZ dataset. We also show that UBAR-E sets a very high baseline performance. Along with UBAR-E, we develop LSP-US (Large-Scale Pre-trained User Simulator); the first E2E Transformer-based User Simulator. LSP-US uses a novel design and is able to drive conversations based on any goal that follows the MultiWOZ schema. LSP-US demonstrates comparable or greater lexical richness than UBAR-E. You can interact with UBAR-E and LSP-US using our web application at https://huggingface.co/spaces/alistairmcleay/cambridgemasters-project. We demonstrate that our two E2E Transformer-based models provide a strong foundation for future self-play experiments, achieving our primary research goal. Further to this, we perform a number of experiments using synthetic data generated from self-play between the two systems. We analyse the MultiWOZ evaluation metrics and propose a number of improvements. We develop a Reinforcement Learning (RL) regime using Proximal Policy Optimisation, PPO Transformer RL, to refine our two models, and show promising preliminary results. Finally, we suggest a framework to leverage the two models we have developed for joint optimisation in future research.
Towards Learners that Plan: Integrating Trainable Planning Modules for Data-Efficient Learning

Learning is one of the most important abilities of intelligent adaptive agents. The generalization capability and training efficiency of learning algorithms depend heavily on the abstract representations acquired. Planning, on the other hand, allows agents to anticipate the future consequences of their actions so as to act optimally at the now. The action-contingent predictive features generated by planning modules thereby provide a good abstract representation constituting the current state of the agent. From this insight, this thesis aims to integrate trainable planning modules for data-efficient learning in sequential decision making and manipulation problems, ranging from Go game to real-world robotic AI. Specifically, this thesis will investigate the effectiveness of such approach by trying to solve the key questions of (1) how to integrate planning modules into deep learning frameworks so as to train the whole system from data, and (2) how to exploit predictive, but possibly inaccurate, abstract features from planning modules to guide the learning process. The main contributions of this thesis are to answer these questions within a broad literature survey and incorporate the ideas in an algorithm that can be applied to learn to plan in visual navigation tasks in a completely unsupervised manner.