Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Magnetic Field Emissions From the Human Body: Sources, Sensors, and Uses

It has long been common practice to capture the electric fields emanated by the human body as a means of detecting and/or monitoring diverse health conditions. However, these electric fields are strongly impacted by the complex permittivity of biological tissues which deteriorates their waveforms and limits their diagnostic capabilities. As an alternative, recent progress has been made in the measurement of bio-magnetic fields occur from the natural currents flowing through the body. The advantage in this case is, since tissues are non-magnetic, magnetic fields propagate in an uninterrupted manner towards the skin surface where they are eventually collected. This unveils game-changing opportunities for future medical diagnostics. Nevertheless, a major challenge associated with sensing these naturally emanated magnetic fields is that they are extremely weak, and in fact orders of magnitude smaller than those generated by the Earth. To this end, extensive efforts have been pursued to realize sensing technology that is sensitive enough to collect bio-magnetic fields. Example fields of use include magnetomyography (MMG), magnetocardiography (MCG), magnetoencephalography (MEG), and Magnetoneurography (MNG) (including magnetospinography (MSG)). This review will provide an overview of technologies used to sense bio-magnetic fields, list their merits and limits in a critical manner, and discuss clinical applications.
Efficient design of axially corrugated coaxial-type multi-band horns for reflector antennas

A highly efficient design procedure for dielectric-loading multi-band coaxial-type feed antennas is proposed in this paper. Cylindrical modal matching method is employed for generalized scattering matrix calculation inside the feed, while spherical wave expansion is adopted for the feed aperture discontinuity in consideration of the outer shape. Closed-form scattering matrices with respect to different junctions help realize higher computational efficiency than the full-wave simulation solvers, and thus enable rapid optimization in terms of various electrical functional requirements across multiple frequency bands simultaneously. A Ku/Ka multi-band feed is investigated as a numerical example. Excellent radiation performances, including the low cross-polarization level, constant beamwidth, stable phase center, high return loss, and high isolation between bands of interest are achieved by employing multi-scalar rings, dielectric loading, and a three-port hybrid junction. The design is validated by experimental results from a manufactured prototype. Good consistency confirms the accuracy of our numerical approach, and demonstrates the proposed feed a good candidate for Ku/Ka-band satellite communication antenna systems.
Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing

Advancement in technology has led to an increase in data. Consequently, techniques such as deep learning and artificial intelligence which are used in deciphering data are increasingly becoming popular. Further, advancement in technology does increase user expectations on devices, including consumer interfaces such as mobile apps, virtual environments, or popular software systems. As a result, power from the battery is consumed fast as it is used in providing high definition display as well as in charging the sensors of the devices. Low latency requires more power consumption in certain conditions. Cloud computing improves the computational difficulties of smart devices with offloading. By optimizing the device's parameters to make it easier to find optimal decisions for offloading tasks, using a metaheuristic algorithm to transfer the data or offload the task, cloud computing makes it easier. In cloud servers, we offload the tasks and limit their resources by simulating them in a virtual environment. Then we check resource parameters and compare them using metaheuristic algorithms. When comparing the default algorithm FCFS to ACO or PSO, we find that PSO has less battery or makespan time compared to FCFS or ACO. The energy consumption of devices is reduced if their resources are offloaded, so we compare the results of metaheuristic algorithms to find less battery usage or makespan time, resulting in the PSO increasing battery life or making the system more efficient.
Leakage Current Mitigation Technique in Solar PV Array System Using Passive Filter

The parasitic capacitances in the transformerless solar photovoltaic (PV) array system develop the closed-loop path and generate the leakage current. As a result, it hampers the system performance with the presence of the leakage current such as electromagnetic interference, lessened the lifespan of PV panel, etc. To solve these issues, the passive filter is designed herein for the solar PV array system to suppress the leakage current. The frequency-domain analysis of the system is performed herein to carry out the optimum filter parameters. In this design approach, there is not any need for additional semiconductor switches, unlike the state-of-art systems. The adaptive controller is adopted herein to extract the fundamental component of the load current in order to provide effective harmonic compensation. Simulation results demonstrate that the presented passive filter effectively suppresses the leakage current within limits based on the DIN and NB/T grid codes. Additionally, several comparative analyses are performed to validate the practical feasibility and effectiveness of the presented system. The real-time hardware-in-loop (HIL) performance is analyzed for manifold operating cases. The total harmonic distortions (THDs) of the grid currents are accomplished within 5% as per the revised IEEE standard 519 and IEC standard 61727.
Towards Reliable Spike Sorting Evaluation Without Ground Truth Data

How do brains work? Computational neuroscience sits at the heart of this very question. Cognitive processes are underpinned by complex dynamics of networks of neurons in the brain. Recent recording technology now enables researchers to detect activity from thousands of neurons with immense precision. Whilst recording technologies evolve rapidly, analysis techniques struggle to keep up with the increasing quantity and complexity of the resultant data. This discrepancy, between the potential of these recording devices and their accompanying data processing techniques needs to be bridged. Spike sorting algorithms have been developed as an attempt to satisfy this need. These algorithms must successfully detect ﬁring behaviour from signals in raw data, then assign each signal to the cell which produced it. Many spike sorting algorithms exist, but show little similarity in their results for one dataset [17, 1, 20, 27, 8, 7, 6]. Simultaneously, many studies which rely on extracellular recordings make use of only one such algorithm. Conﬁrming the results of spike sorting algorithms requires use of complicated and expensive experimental procedures. So called “paired recordings” can provide ground truth data, which are rarely available in practice. Evaluating these algorithms in the absence of paired recordings remains a challenge. Whilst various methods have been suggested, there remains little consensus on the best way to assess the accuracy of results. Without established methods by which to evaluate spike sorting results, experiments which use extracellular data are forced to rely on ill-validated algorithms. This data is used to draw ambitious conclusions about the role of the brain in cognitive processes. As the validity of these claims hinges on the correctness of spike sorting algorithms, it is imperative (to the reliability of such studies) that robust evaluation techniques are developed. This project examines existing evaluation methods and results in the ﬁeld of electro-physiology in order to suggest a more holistic approach to evaluation. This project demonstrates that many evaluation methods are not consistently indicative of accuracy. By evaluating the usefulness of eleven common performance metrics, a predictive model was designed and implemented to demonstrate one possible method by which to evaluate individual spike sorting outputs without requiring the use of multiple sorters. This model combines the analysis of metrics with a promising existent evaluation technique, the “consensus based method”. In
Saldırı Tespit Sistemlerinde Yapay Sinir Ağlarının Kullanılması

Yapay Sinir Ağları (YSA) kullanarak bir ağ üzerinde akan paketlerin hangi saldırı yöntemi kullandığının bulunması bu çalışmanın konusudur. Bu nedenle öncelikle saldırı tespit sistemlerinin ne şekilde yapılandıkları, hangi yöntemleri kullandıkları incelenmiştir. Ardından genel olarak saldırı tipleri ve özellikleri ele alınmıştır. Bu saldırılardan ağ da anormalik yaratan saldırılardan “Neptune” ve “the ping of death” „ın bulunması için Çok Katmanlı Algılayıcı (Multi Layer Perceptron (MLP)) yapay sinir ağ modeli kullanılarak bir yapay sinir ağı oluşturulmuştur. Ağ kurulmasında MATLAB programı kullanılmıştır. Yapay sinir ağında kullanılacak veri setleri DARPA veri setlerini örnek alarak, bir kamu ağından toplanan ağ paketlerinden oluşturulmuştur. Oluşturulan veri setleri ile MATLAB programına uygulanmıştır.
Ultrasound-triggered oxygen-loaded nanodroplets enhance and monitor cerebral damage from sonodynamic therapy

In sonodynamic therapy, cellular toxicity from sonosensitizer drugs, such as 5-aminolevulinic acid hydrochloride (5-ALA), may be triggered with focused ultrasound through the production of reactive oxygen species (ROS). Here we show that by increasing local oxygen during treatment, using oxygen-loaded perfluorocarbon nanodroplets (250 +/- 8 nm), we can increase the damage induced by 5-ALA, and monitor the severity by recording acoustic emissions in the brain. To achieve this, we sonicated the right striatum of 16 healthy rats after an intravenous dose of 5-ALA (200 mg/kg), followed by saline, nanodroplets, or oxygen-loaded nanodroplets. We assessed haemorrhage, edema and cell apoptosis immediately following, 24 hr, and 48 hr after focused ultrasound treatment. The localized volume of damaged tissue was significantly enhanced by the presence of oxygen-loaded nanodroplets, compared to ultrasound with unloaded nanodroplets (3-fold increase), and ultrasound alone (40-fold increase). Sonicating 1 hr following 5-ALA injection was found to be more potent than 2 hr following 5-ALA injection (2-fold increase), and the severity of tissue damage corresponded to the acoustic emissions from droplet vaporization. Enhancing the local damage from 5-ALA with monitored cavitation activity and additional oxygen could have significant implications in the treatment of atherosclerosis and non-invasive ablative surgeries.
Influência da cor de pele auto-referida na prevalência da síndrome metabólica numa população urbana do Brasil

BACKGROUND: The metabolic syndrome (MS) has a high prevalence in different parts of the world, with variations between different ethnic groups. OBJECTIVE: This study aims at exploring the influence of the self-reported skin color on the prevalence of MS Methods: Cross-sectional study, carried out in a population subgroup (n=1,439 adults) in Salvador, Brazil. The self-reported skin color (white, mulatto or black) was used as well as the MS criterion of ATP-III. The Chi-square test for tendency was used to analyze the prevalence gradient between the groups and logistic regression, for association analysis. RESULTS: The general prevalence of MS, adjusted for potentially confounder variables, did not differ among whites (23.3%), mulattos (23.3%) and blacks (23.4%). The analysis by sex showed, among men, a reduction in the MS prevalence of whites (26.2%, 95%CI: 20.7-31.7), in comparison to blacks (17.5%, 95%CI: 12.3-22.8) and an intermediate prevalence among mulattos, 21.9%, 95%CI: 18.6-25.1, p tend. = 0.002. Among the women, the tendency was the opposite, being higher among the blacks, 27.0%, 95%CI: 22.2-31.8, and lower among the whites, 20.5%, 95%CI: 15.6-25.4, p tend. = 0.02. The multivariate analysis of the association between skin color and MS (white = group of reference) showed that the black color of the skin was a protective factor among black men, with a prevalence ratio (PR) = 0.60 (0.36-0.97), whereas it tended to be a risk factor among black women, with a PR = 1.33 (0.94-1.78). CONCLUSION: The prevalence of MA presented an inverse variation according to the color of skin between men and women. To be black was a protective factor among men and a risk factor among women.
Impact of carbonic anhydrase 9 gene polymorphism on the progression of colorectal cancer

Colorectal cancer (CRC) is a commonly occurring tumor type worldwide, and its development is governed by a connection between genetic variations and acquired factors. Carbonic anhydrase 9 (CA9) is a cell-surface pH modulator that has been demonstrated to contribute to key steps of cancer progression. Here, we attempted to interrogate the effect of CA9 gene polymorphisms on the development of CRC in 470 cases and 470 gender- and age-matched non-cancer controls. We found that none of three CA9 single-nucleotide polymorphisms (SNPs) tested, including rs2071676, rs3829078, and rs1048638, was significantly associated with the occurrence of CRC. Yet, while evaluating the clinicopathological variables, cases carrying at least one reference allele (G allele) of rs2071676 tended to develop poorly differentiated tumors less frequently than those who are homozygous for the alternative allele (A allele) of rs2071676 (GA+GG vs AA; OR, 0.483; 95% CI, 0.242-0.963; p=0.036). Further stratification revealed that as compared to homozygous carriers of the alternative allele (AA), cases of colon cancer bearing at least one reference allele of rs2071676 (GA+GG) less frequently developed poorly differentiated tumors (OR, 0.449; 95% CI, 0.221-0.911; p=0.024) and lymphovascular invasion (OR, 0.570; 95% CI, 0.361-0.900; p=0.015). Such genetic effect was exclusively observed in colon cancer but not in rectal cancer. Our results indicate an anatomical site-specific impact of CA9 gene polymorphisms on modulating the progression of colorectal malignancies.
Enhanced Entity Annotations for Multilingual Corpora

Modern approaches in Natural Language Processing (NLP) require, ideally, large amounts of labelled data for model training. However, new language resources, for example, for Named Entity Recognition (NER), Co-reference Resolution (CR), Entity Linking (EL) and Relation Extraction (RE), naming a few of the most popular tasks in NLP, have always been challenging to create since manual text annotations can be very time-consuming to acquire. While there may be an acceptable amount of labelled data available for some of these tasks in one language, there may be a lack of datasets in another. WEXEA is a tool to exhaustively annotate entities in the English Wikipedia. Guidelines for editors of Wikipedia articles result, on the one hand, in only a few annotations through hyperlinks, but on the other hand, make it easier to exhaustively annotate the rest of these articles with entities than starting from scratch. We propose the following main improvements to WEXEA: Creating multi-lingual corpora, improved entity annotations using a proven NER system, annotating dates and times. A brief evaluation of the annotation quality of WEXEA is added.
OF FINANCIAL AND QUANTITATIVE ANALYSIS Proceedings Issue Volume XV , No . 4 , November 1980 DISCUSSION : SIGNALING , INFORMATION CONTENT , AND THE RELUCTANCE TO CUT DIVIDENDS Lemma

Avner Kalay makes a point that managerial reluctance to cut dividends is a necessary condition for the existence of a signaling equilibrium in which dividends are employed as a signaling device. He argues, however, that the available empirical evidence is unable to confirm the reluctance to cut dividends. In particular, the well-known Lintner's (1956) partial adjustment model, which is often cited in support of the reluctance to cut dividends, is empirically indistinguishable from an alternative model which is devoid of such a reluctance. Kalay then conducts an alternative test, but it too leads to an inconclusive result on the issue. To me, the alternative empirical test is a worthwhile effort. It is the first of its kind in an area of finance which is receiving a growing attention. However, the empirical design is not powerful. It is unclear as to how one is able to conduct a meaningful test of significance in Kalay's empirical framework. The attempt here is to obtain a substantial number of cases where dividends are reduced by force imposed by bond covenants and other restrictions. These restrictions dictate the availability of funds for dividend payments, including share repurchases, so as to mitigate a conflict of interest among bondholders and stockholders in the event that dividends are financed by reduction in planned investment or new issue of debt. Since forced reductions are not outcomes of managerial discretion, they cast doubt on the reliability of dividends to serve as a signaling device. Kalay finds in his sample that only 5 percent are forced reductions, and hence he is unable to refute the "reluctance" hypothesis. I am curious what his conclusion would have been if he had observed, instead, 25 percent forced reductions! How does one go about conducting a meaningful significance test in his empirical framework?
Decentralized Crowdsourcing for Human Intelligence Tasks with Efficient On-Chain Cost

Crowdsourcing for Human Intelligence Tasks (HIT) has been widely used to crowdsource human knowledge, such as image annotation for machine learning. We use a public blockchain to play the role of traditional centralized HIT systems, such that the blockchain deals with cryptocurrency payments and acts as a trustworthy judge to resolve disputes between a worker and a requester in a decentralized setting, preventing false-reporting and free-riding. Our approach neither uses expensive cryptographic tools, such as zero-knowledge proofs, nor sends the worker's answers to the blockchain. Compared with prior works, our approach significantly reduces on-chain cost: it only requires O(1) on-chain storage and O(log N ) smart contract computation, where N is the question number of a HIT. Additionally, our approach uses known answers or gold standards to determine the worker's answer quality. To motivate the requester to use honest known answers, the requester cannot learn the worker's answers if the answer quality does not meet the requirement. We further provide formal security definitions for our decentralized HIT and prove security of our construction.
The central-peripheral dichotomy and metacontrast masking

According to the central-peripheral dichotomy (CPD), feedback from higher to lower cortical areas along the visual pathway to aid recognition is weaker in the more peripheral visual field. Metacontrast masking is predominantly a reduced visibility of a brief target by a brief and spatially adjacent mask when the mask succeeds rather than precedes or coincides with the target. If this masking works mainly by interfering with the feedback mechanisms for target recognition, then, by the CPD, this masking should be weaker at more peripheral visual locations. We extended the metacontrast masking at fovea by Enns and Di Lollo to visual field eccentricities 1 ∘ , 3 ∘ , and 9 ∘ . Relative to the target’s onset, the mask appeared at a stimulus onset asynchrony (SOA) of − 50 , 0, 50, 92, or 142 milliseconds (ms). Enlarged stimuli were used for larger eccentricities to equalize target discrimination performance across eccentricities as best as possible for zero SOA and when SOA was too long for substantial masking. At each eccentricity, the masking was weakest at 0 or − 50 ms SOA, strongest at 50 ms SOA, and weakened with larger (positive) SOAs. Consistent with the CPD, larger eccentricities presented weaker maskings at all nonzero, and particularly the positive, SOAs.
Putting Context in SNACS: A 5-Way Classification of Adpositional Pragmatic Markers

The SNACS framework provides a network of semantic labels called supersenses for annotating adpositional semantics in corpora. In this work, we consider English prepositions (and prepositional phrases) that are chiefly pragmatic, contributing extra-propositional contextual information such as speaker attitudes and discourse structure. We introduce a preliminary taxonomy of pragmatic meanings to supplement the semantic SNACS supersenses, with guidelines for the annotation of coherence connectives, commentary markers, and topic and focus markers. We also examine annotation disagreements, delve into the trickiest boundary cases, and offer a discussion of future improvements.
My Case, For an Adposition: Lexical Polysemy of Adpositions and Case Markers in Finnish and Latin

Adpositions and case markers contain a high degree of polysemy and participate in unique semantic role configurations. We present a novel application of the SNACS supersense hierarchy to Finnish and Latin data by manually annotating adposition and case marker tokens in Finnish and Latin translations of Chapters IV-V of Le Petit Prince (The Little Prince). We evaluate the computational validity of the semantic role annotation categories by grouping raw, contextualized Multilingual BERT embeddings using k-means clustering.
I NTERNATIONAL C APITAL M ARKET I NTEGRATION , E DUCATIONAL C HOICE AND E CONOMIC G ROWTH

This paper examines the impact of capital market integration (CMI) on higher education and economic growth. We take into account that participation in higher education is non-compulsory and depends on individual choice. Integration increases (decreases) the incentives to participate in higher education in capital-importing (-exporting) economies, all other things equal. Increased participation in higher education enhances productivity progress and is accompanied by rising wage inequality. From a national policy point of view, education expenditure should increase after integration of similar economies. Using foreign direct investment (FDI) as a measure for capital flows, we present empirical evidence which largely confirms our main hypothesis: An increase in net capital inflows in response to CMI raises participation in higher education and thereby fosters economic growth. We apply a structural estimation approach to fully track the endogenous mechanisms of the model.
Emerging Mechanisms and Targeted Therapy of Ferroptosis in Neurological Diseases and Neuro-oncology

Ferroptosis is a novel type of cell death characterized by iron-dependent lipid peroxidation that involves a variety of biological processes, such as iron metabolism, lipid metabolism, and oxidative stress. A growing body of research suggests that ferroptosis is associated with cancer and neurodegenerative diseases, such as glioblastoma, Alzheimer's disease, Parkinson's disease, and stroke. Building on these findings, we can selectively induce ferroptosis for the treatment of certain cancers, or we can treat neurodegenerative diseases by inhibiting ferroptosis. This review summarizes the relevant advances in ferroptosis, the regulatory mechanisms of ferroptosis, the participation of ferroptosis in brain tumors and neurodegenerative diseases, and the corresponding drug therapies to provide new potential targets for its treatment.