Miracle-dz/UniFAR-FTUs huggingface dataset:
Crustal Anatexis and Initiation of the Continental‐Scale Chongshan Strike‐Slip Shear Zone on the Southeastern Tibetan Plateau

Continental‐scale strike‐slip shear zones often record significant tectono‐magmatism and dynamic deformation processes of the crustal lithosphere. However, the genetic relationships and timing among the anatexis, deformation, and initial shearing along a strike‐slip shear zone have not been well defined. Here, we carried out detailed field, microstructural, zircon U–Pb geochronology, geochemical and EBSD texture analyses of leucogranites and migmatites in the Chongshan shear zone (CS‐SZ). The results indicate that most migmatites and leucogranites exhibit strong shear deformation and well‐developed high‐temperature mylonitic microstructures. The quartz aggregated from foliated leucogranites developed dominant high‐temperature prism and prism slip systems. The pre‐ and syn‐kinematic crustal anatexis and localized weak zone mainly occurred from 35 to 29 Ma along the CS‐SZ, which is closely related to the post‐collisional extension and collapse of overthickened crust. Biotite dehydration melting formed pre‐ and syn‐kinematic melts and leucogranites that further experienced fractional crystallization of plagioclase and K‐feldspar during melting and subsequent melt migration and emplacement upward along the tectonic weak zone. The thinning and weakening of lithospheric crust further facilitated the initial and formation strike‐slip displacement along the CS‐SZ, which occurred from 29 to 20 Ma or much later to 18 Ma. Finally, we propose that crustal anatexis and upward migrating melts play a key role in controlling the thermal state and rheological strength of the crust, resulting in nucleation and initiation of the localized deep‐seated shear zone that accommodates significant displacement for the India–Asia continental forward collision and intracontinental deformation.
On the Advantages of Microwave Photonic Interrogation of Fiber-Based Sensors: A Noise Analysis

Although microwave photonic approaches have been used for fiber sensing applications before, most contributions in the past dealt with evaluating the sensor signal’s amplitude. Carrying this topic on, the authors previously presented a scheme for the interrogation of fiber sensors that was based on a fiber Bragg grating’s phase response for the electrical signal. However, neither has the measurement setup been analyzed nor have the amplitude and phase-based approaches been compared in detail before. Hence, this paper picks up the previously proposed setup, which relies on an amplitude modulation of the optical signal and investigates for sources of signal degradation, an aspect that has not been considered before. Following the incorporation of the microwave signal, the setup is suitable not only for an amplitude-based evaluation of fiber Bragg gratings but also for a phase-based evaluation. In this context, the signal-to-noise ratios are studied for the conventional amplitude-based evaluation approach and for the recently developed phase-based approach. The findings indicate a strong advantage for the signal-to-noise ratio of the phase response evaluation; an 11 dB improvement at the least has been found for the examined setup. Further studies may investigate the consequences and additional benefits of this approach for radio-over-fiber sensing systems or general performance aspects such as achievable sensitivity and sampling rates.
Recherche du boson de Higgs du Modèle Standard produit par fusion de bosons vecteurs et se désintégrant en deux photons dans l’expérience ATLAS auprès du LHC

In this thesis, the search for the Standard Model Higgs boson produced through Vector Boson Fusion and decaying to two photons, using the ATLAS detector at the LHC, is presented. After introducing the theoretical context relevant to Higgs boson searches at the LHC, a description of the ATLAS detector design and performance is given. Key experimental aspects of photon physics, such as the reconstruction and identification, are discussed. A special accent is put on photon trigger studies. Global improvements to the Higgs boson to di-photon analysis strategy and categorization of events are then presented. In particular, we describe the design and implementation of a cut-based search for the Higgs boson produced through the Vector Boson Fusion (VBF) process. These improvements had a decisive impact in the discovery of a new resonance by the ATLAS Collaboration in 2012. An alternative inclusive search for H → γγ, fully based on a multivariate technique (MVA), is also reported. Finally, an improved MVA is used to search for the VBF production of the Higgs boson in the di-photon channel, and is applied to the full LHC Run I dataset. This analysis has enhanced the sensitivity to VBF by about 30%. It contributed greatly to the first experimental evidence for the VBF production, which is one of the most important results published by ATLAS in Higgs boson physics.
Hepatic, Muscle and Intestinal Oxidative Status and Plasmatic Parameters of Greater Amberjack (Seriola dumerili, Risso, 1810) Fed Diets with Fish Oil Replacement and Probiotic Addition

The present study was conducted to investigate the effects of dietary fish oil replacement with a mixture of vegetable oils and probiotic supplementation on plasma biochemical parameters, oxidative stress, and antioxidant ability of Seriola dumerili. Specimens with an initial weight of 175 g were used. Four feeds were formulated with 0% (FO-100), 75% (FO-25), and 100% (FO-0 and FO-0+ with the addition of Lactobacillus probiotics) substitution of fish oil with a mixture of linseed, sunflower, and palm oils. After 109 days, no significant differences were observed in the activity of antioxidant enzymes in the liver, foregut, and hindgut, only glucose-6-phosphate dehydrogenase activity in the liver was higher in the fish fed the FO-100 diet than in those fed the FO-0 diet. No significant differences were observed in the total, reduced, and oxidized glutathione and the oxidative stress index in the liver. In addition, lipid peroxidation in the liver and red muscle values were higher in the fish fed the FO-100 diet than in the fish fed the FO-0+ diet, however, the foregut of the fish fed the FO-100 diet presented lower values than that of the fish fed the FO replacement diet, with and without probiotics. There were significant differences in cholesterol levels in the FO-100 group; they were significantly higher than those observed with the fish diets without fish oil. To sum up, fish oil can be replaced by up to 25% with vegetable oils in diets for Seriola dumerili juveniles, but total fish oil substitution is not feasible because it causes poor survival. The inclusion of probiotics in the FO-0+ diet had no effects on the parameters measured.
The effect of teacher leadership on students’ purposeful learning

Abstract Teaching leadership is a key factor for students’ learning and flourishing, and it occurs in an asymmetrical and interpersonal relationship. This research seeks to characterize the teaching leadership of high school teachers through the influence they exert on their students. It is not based on the teacher’s self-perception but on the experience that the students have had. For this purpose, a qualitative study was carried out in Spain with 200 first-year university students in which they answered four questions about the teacher who most influenced them during their high school. The results point to five leadership traits or styles that are not mutually exclusive: empathetic leadership, comforting leadership, motivating leadership, exemplary leadership, and wise leadership. In addition, moments of weakness and vulnerability in students are those that most favour the influence of the teacher in their lives, encouraging vital learning in them. This study highlights the importance of teacher leadership for student character education.
Power, Performance, Area, and Cost Analysis of Face-to-Face-Bonded 3-D ICs

This article presents a power, performance, area, and cost (PPAC) analysis for large-scale 3-D processor designs based on face-to-face wafer-to-wafer (W2W) and collective die-to-wafer (Co-D2W) bonding technologies. From evaluating our cost model on a comprehensive portfolio of technology nodes, we investigate a typically disregarded opportunity in 3-D through diverse computer-age statistical methods: area savings due to buffer savings and better routability, offering unforeseen, and considerable cost savings. We explore the viability of this factor with the feedback of a state-of-the-art 3-D memory-on-logic implementation flow. We show how this affects the PPAC of full-chip GDS implementations of a large-scale manycore processor design. Experiments show that our memory-on-logic 3-D implementation offers 7% silicon area savings, resulting in a 53.5% footprint reduction. We also obtained a 40% power-performance-cost improvement compared with the 2-D counterparts.
High Temperature and Width Influence on the GIDL of Nanowire and Nanosheet SOI nMOSFETs

In this work, an experimental evaluation of Gate-Induce Drain Leakage (GIDL) current is presented for nanowire and nanosheet-based SOI transistors. The effects of fin width and temperature increase are studied. Obtained results indicate that the increase in device width makes the GIDL current more sensitive to temperature increase. Three-dimensional numerical simulations have shown that despite the reverse junction leakage increase with temperature, leakage current in nanosheet and nanowire transistors is composed predominantly of GIDL current. The change in valence and conduction bands caused by temperature increase favors the band-to-band tunneling, which is responsible for the worsening of GIDL at high temperatures.
Design Optimization of Combined Function Accelerator Magnet Using Truncated Singular Value Decomposition

The Japan Proton Accelerator Research Complex (J-PARC) is a world-class accelerator facility for particle and nuclear physics experiments such as the rare kaon decays experiment (KOTO), the T2K long-baseline neutrino experiment, and future projects such as COMET. A new stretcher ring (SR) comprising a combined function superconducting magnet is proposed to run all of the facilities at the same time, which can improve beam efficiency and increase operating time. In order to achieve the field quality requirements on the magnet aperture, it is critical to identify an optimum pole shape. Dipole, quadrupole, and sextupole fields are required for the combined function magnet. To find the best design parameters for this analysis, we employed singular value decomposition. The singular value decomposition (SVD) technique resulted in a solution to the desired parameters and a quick process to obtain a 2D pole profile.
Sparsity-Aware Medium-Density Parity-Check Decoder for McEliece Cryptosystems

McEliece cryptosystem based on medium-density parity-check (MDPC) codes is one of the finalists for the post-quantum cryptography standard. Although decoder design for low-density parity-check (LDPC) codes used for digital communications is well-investigated, the design of MDPC decoders faces many new challenges due to the different structure in the parity-check matrix. Even though the parity-check matrices of MDPC codes have relatively higher density, they are still very sparse. Previous decoder designs did not explore such sparsity and derive the columns of the parity check matrix one after the other by cyclic shifting. This brief proposes a low-complexity MDPC decoder design by exploiting the sparsity of the parity-check matrix. The processing corresponding to zero segments of each parity check matrix column is skipped to substantially reduce the latency. Moreover, the columns are processed in a novel non-consecutive order to significantly reduce the number of memory writes for deriving all the columns and accordingly the power consumption. For an example MDPC code considered for the standard, the proposed design can reduce both the decoding latency and the number of memory writes by 70% with 35% area saving.
Borrowing from Yourself: 401(K) Loans and Household Balance Sheets

We examine 401(k) borrowing since 1992 and identify a puzzle: despite potential gains from borrowing against 401(k) assets instead of from other sources, most eligible households eschew 401(k) loans, including many who carry relatively expensive balances on credit cards and auto loans. We estimate that households with access to 401(k) loans could have saved about $3.3 billion in 2004--about $200 per household--by shifting debt to 401(k) loans. We find that liquidity constrained households are most likely to borrow against their accounts; however, the fastest growth has been among higher income, less liquidity constrained households. From 1992 to 2004, we do not find significantly different growth in wealth between households eligible for loans and those ineligible for loans. The recent tightening of terms and standards in mortgage and consumer lending has likely increased 401(k) borrowing, which could improve household balance sheets, if handled correctly. However, the improvement could be short-lived if the economic downturn leads to reduced contributions or significantly higher 401(k) loan defaults.
Resolving impact of technical and biological variability on the convolutional neural networks: evaluating chest x-ray scans

Large chest x-ray datasets accumulate technical and biological variations. Technical variations are often caused by differences in scanner types (e.g. x-ray energy) whereas biological variations refer to those resulting from morphological differences among populations. Such variations decrease the generalizability of convolutional neural networks (CNNs) and could lead to disparities within specific patient subgroups. We sought to disentangle the sources of variations for a CNN in the context of technical and biological variability. The primary goal of this study was to understand whether tailored and specific AI models might need to be created (i.e., gender-specific or machine-specific AI models).
What Do TIPS Say about Real Interest Rates and Required Returns?

Abstract An arbitrage-free model decomposes yields on Treasury Inflation-Protected Securities (TIPS) into expected real rates, real frictionless term premiums, and liquidity premiums. Estimation eschews non-market information, incorporates a novel observable liquidity factor, and addresses factor persistence and sample biases, including real-time estimation. Results include a modest secular decline in equilibrium real rates and a much larger drop in frictionless required excess real returns, on net, from July 1999 to September 2022. Real term premiums appear to be pro-cyclical, which implies that the default risk-free asset is a hedge, and some evidence suggests that TIPS liquidity premiums are counter-cyclical.
Current Trends in Volume and Surgical Outcomes in Gastric Cancer

Gastric cancer is ranked as the fifth most frequently diagnosed type of cancer. Complete resection with adequate lymphadenectomy represents the goal of treatment with curative intent. Quality assurance is a crucial factor in the evaluation of oncological surgical care, and centralization of healthcare in referral hospitals has been proposed in several countries. However, an international agreement about the setting of “high-volume hospitals” as well as “minimum volume standards” has not yet been clearly established. Despite the clear postoperative mortality benefits that have been described for gastric cancer surgery conducted by high-volume surgeons in high-volume hospitals, many authors have highlighted the limitations of a non-composite variable to define the ideal postoperative period. The textbook outcome represents a multidimensional measure assessing the quality of care for cancer patients. Transparent and easily available hospital data will increase patients’ awareness, providing suitable elements for a more informed hospital choice.
Larvicidal efficacy of methanol leaf extracts of Hippocratea africana WILD and Lasianthera africana P. BEAUV against Anopheles gambiae (Diptera:Culicidae)

Developing potent adulticidal and larvicidal products for mosquito control remains a major control strategy for several mosquito-borne parasitic disease. The bio-efficacy of Hippocratea africana and Lasianthera africana methanol leaf extracts were assessed against the larvae of Anopheles gambiae. Plant extracts were shade dried at room temperature and powdered coarsely. Extracts concentrations used for larvicidal bioassays were 0.15, 0.30, 0.45, 0.60 and 0.75 w/v. Observations were made after 24, 48 and 72 h of exposure. The LC50 and LC90 of L. africana and H. africana against the larval of Anopheles species were determined. The larvicidal activities of both plant extracts increased as their concentrations and duration of exposure increased. The highest concentrations (0.75 w/v) of the extract of H. africana resulted in the highest mortality (85%) at 72 h. The highest concentrations (0.75 w/v) of the extract of L. africana resulted in 70% mortality. No death of larvae A. gambiae occurred in the control experiment. Mortality of A. gambiae larvae increased significantly as the concentration of the extracts were increased (r = 0.648, p < 0.0001). Increase in the mosquito larval mortality was significant for H. africana (r = 0.634, p = 0.005), and L. africana (r = 0.854, p < 0.0001), but H. africana was a more potent larvicide. This study has shown that leaf extract of H. africana and L. africana could be incorporated in the formulation of potent larvicides against Anopheles gambiae.
Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis

In the realm of remote sensing image classification and detection, deep learning has emerged as a highly effective approach, owing to the remarkable advancements in object perception models and the availability of abundant annotated data. Nevertheless, for specific remote sensing image scene classification tasks, obtaining diverse and large amounts of data remains a daunting challenge, leading to limitations in the applicability of trained models. Consequently, researchers are increasingly focusing on optimal data utilization and interpretability of learning. Drawing inspiration from brain neural perception research, researchers have proposed novel approaches for deeper interpretation and optimization of deep learning models from diverse perspectives. In this paper, we present a brain-inspired network optimization model for remote sensing image scene classification, which considers both shape and texture features and reconstructs feature scaling of data through feature bias estimation. The model achieves greater robustness through complementary training. We evaluate our optimized model on general datasets by integrating it into an existing benchmark method and compare its performance with the original approach. Our results demonstrate that the proposed model is highly effective, with dynamically reconstructed data leading to a significant enhancement of model learning.
Automatically Identifying CVE Affected Versions With Patches and Developer Logs

While vulnerability databases are important sources of information for software security, it is known that information in these databases is inconsistent. How to rectify these incorrect data is a challenging issue. In this article, we employ developer logs and patches to automatically identify vulnerable source code versions that each CVE really affects. Our tool organizes all versions of a piece of software into a version tree, and identifies the first vulnerable version, and the last vulnerable versions in the version tree trunk and branches. For evaluation, we took Linux Kernel as the case study and quantified the error rate of the vulnerable versions reported by the NVD. The total number of vulnerable Linux Kernel versions reported by the NVD was 43,727 (as of September 2020), of which the total number of false positives reached 2,497 and the total number of false negatives reached 9,330, accounting for 5.7% and 21.34%, respectively. In addition, we compare our tool with two vulnerability detection tools and show that our tool could achieve high detection accuracy.
EVALUATING THE IMPACT OF HEAD MANAGEMENT SKILLS ON THE SCHOOL WORKING CLIMATE AT SECONDARY EDUCATION LEVEL

Primary purpose of this research is to examine impact of head management skills on the working climate of secondary schools in district Bannu. A survey approach is used to gather data from 292 respondents using the convenient sampling technique. 108 secondary schools registered with district education board in Bannu, Pakistan. Thus, different validity and reliability tests were applied using SPSS. Based upon “structural equation modeling” technique using smart partial least squares (smart-PLS), findings indicate that head management skills have an impact on school working climate. This research contributes to existing literature on management skills and provide valuable insights into role of the head management skills for HR scholars, practitioners, and regulators interested in the head management skills in the context of Pakistan, particularly in district Bannu. This is, to best of author knowledge, study focuses on head management skills and school working environment in the context of Pakistan, particularly in district Bannu. The results provide significant information regarding research issues in reaching conclusion and offering suggestions.
Finding Maximum Rank Moment Matrices by Facial Reduction and Douglas-Rachford Method on Primal Form

Recent breakthroughs have been made in the use of semi-definite programming and its application to real polynomial solving. For example, the real radical of a zero dimensional ideal, can be determined by such approaches as shown by Lasserre and collaborators. Some progress has been made on the determination of the real radical in positive dimension by Ma, Wang and Zhi. Such work involves the determination of maximal rank semidefinite moment matrices. Existing methods are computationally expensive and have poorer accuracy on larger examples. In previous work we showed that regularity in the form of the Slater constraint qualification (strict feasibility) fails for the moment matrix in the SDP feasibility problem. We used facial reduction to obtain a smaller regularized problem for which strict feasibility holds. However we did not give a theoretical guarantee that our methods, based on facial reduction and Douglas-Rachford iteration ensured the satisfaction of the maximum rank condition to possibly approximate the real radical characterizing all real roots. Our paper is motivated by the problems above. We discuss how to compute the moment matrix and its kernel using facial reduction techniques where the maximum rank property can be guaranteed by solving the dual problem. The facial reduction algorithms on the primal form is presented. We give examples that exhibit for the first time additional facial reductions beyond the first which can be computed in practice.
Time Resolution of the St. Petersburg Paradox: A Rebuttal

Peters (2011) claims to provide a resolution of the three century old St Petersburg paradox by using time averages and thereby avoiding the use of utility theory completely. Peters also claims to have found an error in Menger (1934, 1967) who established the vulnerability of any unbounded utility function to the St Petersburg paradox. This paper argues that both these claims in Peters (2011) are incorrect. The time average argument can be circumvented by using a single random number (between zero and one) to represent the entire infinite sequence of coin tosses, or alternatively by applying a time reversal to the coin tossing. Menger’s proof can be reinstated by comparing the utility of playing the Super St Petersburg game to the utility of an arbitrarily large sure payoff.