Miracle-dz/UniFAR-FTUs huggingface dataset:
Quick Response Mobile Payment Adoption Using Unified Theory of Acceptance and Use of The Technology Model Development

This paper serves as an initial study for an ongoing research project that focuses on user intentions regarding mobile payment using QR codes. The model employed is UTAUT, with additional variables of risk, cost, and security. The objective of this research is to analyze the user intentions towards the mobile payment system through QR codes, considering the increasing population of mobile payment users. The sampling technique uses purposive sampling with the data consists of information from 235 users across various regions in Jakarta, analyzed using the Partial Least Squares Structural Equation Modeling (PLSSEM) approach. The results indicate that 3 out of 8 hypotheses were rejected. Overall, the accepted hypotheses provide valuable insights to researchers regarding the importance of utilizing mobile payment via QR codes while considering aspects of risk, cost, and security.
Detection of Thoracic Diseases Using Chest X-Ray: A Comparative Study of Binary Class and Multiclass Classification Using Deep Learning

Identifying thoracic ailments stands pivotal in medical imaging, offering insights for disease recognition and treatment. Chest X-ray (CXR) imaging emerges as a prevalent non-invasive and cost-effective technique for thoracic disease detection [1–4]. This research delves into a comparative exploration between binary class and multiclass classification methodologies in identifying thoracic diseases through CXR images. Our approach involves training and assessing deep learning models leveraging a publicly accessible CXR image dataset, juxtaposing the performance of binary and multiclass models using diverse performance metrics. We formulated 17 distinct binary class models and orchestrated their ensemble, alongside crafting a solitary multiclass classification model [25–26]. The assessment of these models encompassed various performance metrics, including F1 score, accuracy, precision, and recall. The outcomes advocate for binary classification's pragmatic suitability, attributed to its heightened accuracy and simplified implementation. This inquiry strives to refine the precision and efficacy of CXR image analysis in clinical practice.
EEG-Based Emotion Classification with Per Class Feature Importance Using Multi-Domain Features

Emotion dysfunction, characterized by an elevated level of negative emotions compared to positive emotions, can often be indicative of neurological disorders such as depression. This paper introduces an efficient approach for emotion recognition using the electroencephalogram (EEG) signals. Experiments are conducted using the DEAP [1] dataset. This study basically focused on finding the most significant features from multiple domains and then select necessary emotion classes based on the per-class feature importance function (PCFIF) to reduce computational complexity and enhance recognition accuracy. The Support Vector Machine (SVM) classifier with leave-one-out cross-validation (LOOCV) and 10-fold cross-validation methods are applied to achieve an average accuracy of 72.72 %.
Visual Sentinel: Data Analytics for Missing Subject Identification

The goal of the “Visual Sentinel: Video Analytics for Missing Subject Identification” project is to automate the process of identifying missing subjects from CCTV video by utilizing cutting-edge machine learning and computer vision techniques. One of the project's goals is to create a real-time surveillance system that integrates face recognition technology for increased precision and accuracy. The technology has the ability to save lives and bring families back together by speeding up the search and recovery procedure. To effectively locate missing subjects, the system uses de-blurring methods and machine learning algorithms. The technology effectively matches people in real-time against a specified data collection, giving authorities access to timely information, according to key results. To sum up, Visual Sentinel provides a strong and practical solution that enables security and law enforcement professionals.
Coordinated Planning of Electricity-Hydrogen Integrated Energy System Considering Lifecycle Carbon Emissions

Hydrogen energy is a promising solution to reaching carbon emissions peak and carbon neutrality. With the development of hydrogen energy devices, coupling hydrogen energy with renewable energy for power generation can compensate for the intermittency and instability of renewable energy. Furthermore, the two-stage coordinated planning of integrated energy system (IES) and electricity-hydrogen integrated energy system (EHIES), compared to independent planning, can help generate additional benefits and reduce carbon emissions. Therefore, this paper proposes a coordinated planning of EHIES considering lifecycle carbon emissions. In the first stage, aiming to minimize investment costs, network losses, voltage deviations, and lifecycle carbon emissions, the equipment capacity and location of EHIES is determined. The life cycle assessment (LCA) method is used to quantify the lifecycle carbon emissions of the EHIES, preparing for the coordinated operation in the second stage. Subsequently, in the second stage, using carbon emissions flow to allocate users’ responsibility for emissions, the carbon emissions per unit electricity of the IES system are calculated. With the optimal cost of independent operation for IES and EHIES as the coordinated operation objective, the alternating direction method of multipliers (ADMM) is employed for sequential solution, obtaining the carbon emissions reduction amount, and iteratively updating until convergence. Finally, the effectiveness of the proposed method is verified by using the IEEE 33-bus power distribution network (PDN) and natural gas 6 node systems. The results show that compared to independent planning, the two-stage coordinated planning method reduces the overall operating cost of IES by 3.81% and decreases carbon emissions by 15.89%. This method is effective in reducing the carbon emissions of IES.
RT-SCALER: Adaptive Resource Allocation Framework for Real-Time Containers

—Container-based virtualization has emerged as an advantageous deployment model in fog computing platforms since it enables the seamless co-location of applications in a heterogeneous environment with minimal overhead. For some application domains requiring a certain degree of predictability in the time domain (e.g., industrial automation), the adoption of container-based virtualization is not straightforward since the technology is not built to support real-time properties. In this paper, we propose RT-SCALER, which is a framework for adaptive resource allocation and dimensioning for real-time containers. RT-SCALER dynamically adapts the resource reservation of real-time enabled containers in order to improve the temporal predictability of the real-time applications running within the containers. We discuss the high-level orchestration approach, relating the different control levels, and give some practical insights into node-level container adaptation.
Impact of ITCZ width on global climate: ITCZ-MIP

The width of the ITCZ affects tropical rainfall, Earth’s albedo, large-scale circulation and climate sensitivity; yet we do not understand what controls it. To better understand the ITCZ width and its effects on global climate, we present a protocol to force changes in ITCZ width in climate models. Starting from an aquaplanet configuration with a slab ocean, adding surface heat fluxes in the deep tropics forces the ITCZ to narrow, and subtracting them causes it to widen. The protocol successfully generates changes in ITCZ width in four climate models; within each model, ITCZ width responds linearly to 5 forcing magnitude and sign. Comparing across the four climate models, a response to varying ITCZ width that is remarkably consistent among models is the ITCZ strength, which is greater the narrower the ITCZ. On the other hand, the effect of varying ITCZ width on climate sensitivity is divergent among our four models, varying even in sign. These results indicate that idealized model experiments have the potential to increase our understanding of ITCZ width.
Depth from Asymmetric Frame-Event Stereo: A Divide-and-Conquer Approach

Event cameras asynchronously measure brightness changes in a scene without motion blur or saturation, while frame cameras capture images with dense intensity and ﬁne details at a ﬁxed rate. The exclusive advantages of the two modalities make depth estimation from Stereo Asymmetric Frame-Event (SAFE) systems appealing. However, due to the inevitable information absence of one modality in certain challenging regions, existing stereo matching meth-ods lose efﬁcacy for asymmetric inputs from SAFE systems. In this paper, we propose a divide-and-conquer approach that decomposes depth estimation from SAFE systems into three sub-tasks, i.e., frame-event stereo matching, frame-based Structure-from-Motion (SfM), and event-based SfM. In this way, the above challenging regions are addressed by monocular SfM, which estimates robust depth with two views belonging to the same functioning modality. More-over, we propose a dual sampling strategy to construct cost volumes with identical spatial locations and depth hypotheses for different sub-tasks, which enables sub-task fusion at the cost volume level. To tackle the occlusion issue raised by the sampling strategy, we further introduce a temporal fusion scheme to utilize long-term sequential inputs with multi-view information. Experimental results validate the superior performance of our method over existing solutions.
DE LA FACULTÉ DES SCIENCES Mathématiques

A BSTRACT. — Fractional Brownian motion (fBm) is a centered self-similar Gaussian process with stationary increments, which depends on a parameter H ∈ (0 , 1) called the Hurst index. In this conference we will survey some recent advances in the stochastic calculus with respect to fBm. In the particular case H = 1 / 2, the process is an ordinary Brownian motion, but otherwise it is not a semimartingale and Itˆo calculus cannot be used. Diﬀerent approaches have been introduced to construct stochastic integrals with respect to fBm: pathwise techniques, Malliavin calculus, approximation by Riemann sums. We will describe these methods and present the corresponding change of variable formulas. Some applications will be discussed.
CHARACTERISTIC CLASSES IN TMF OF LEVEL Γ 1 (3)

Let TMF 1 ( n ) be the spectrum of topological modular forms equipped with a Γ 1 ( n )-structure. We compute the K (2)-local TMF 1 (3)- cohomology of B S tring and B S pin : both are power series rings freely generated by classes that we explicitly construct and which generalize the classical Pontryagin classes. As a ﬁrst application of this computation, we show how to construct TMF (3 n )-cohomology classes from stable positive energy representations of the loop groups L S pin .
Ligações entre as "Micro, Small, Mid e Large Caps" da zona euro

This paper examines short-term and long-term linkag es among stock markets within EMU, taking into account the business capitalization. With this goal, micro, small, mid and large cap segments were considered, for the period between November 20 07 and December 2013. To analyze shortterm linkages, the vector error correction model, t he Granger causality concept and impulseresponse functions were considered, to conclude tha t Large Cap describe relatively autonomous movements, and which contains information that help s to explain changes in other indices. To analyze long-term linkages, we resorted to the usua l cointegration tests, which allowed to realize that Large Cap describe a different behavior compar ed to smaller capitalizations. This proves to be particularly important for an international portfol io diversification strategy.
Atmospheric histories and growth trends of C4F10, C5F12,

Atmospheric observations and trends are presented for the high molecular weight perﬂuorocarbons (PFCs): decaﬂuorobutane (C 4 F 10 ), dodecaﬂuoropentane (C 5 F 12 ), tetradecaﬂuorohexane (C 6 F 14 ), hexadecaﬂuorohep-tane (C 7 F 16 ) and octadecaﬂuorooctane (C 8 F 18 ). Their atmospheric histories are based on measurements of 36 Northern Hemisphere and 46 Southern Hemisphere
Inter-Sectoral Linkage and External Trade Analysis for Virtual Water and Embodied Carbon Emissions in China

In the globalized world, water utilization and carbon emissions are two important indicators for water and energy resources evaluation. This paper investigates the inter-sectoral linkage and external trade of virtual water (water embodied in products) and embodied carbon emissions in China based on input–output tables during 1997–2015. Results indicate that: inside China, agriculture, the electric and water industry are major virtual water suppliers, while heavy industrial sectors including the metal products industry, the petrochemical industry, other nonmetallic mineral products industry, and the mining industry are major embodied carbon emissions suppliers. China is the net exporter of virtual water (137.15 × 109 m3) and embodied carbon emissions (16.05 × 108 t). From the perspective of industrial chain, about 81% of virtual water export come from agriculture, the electric and water industry ultimately, and about 85% of embodied carbon emissions export come from the mining industry, the petrochemical industry, other nonmetallic mineral products industry, the metal products industry, and the electric and water industry ultimately.
Investigation on the Different Needs of Practicing Doctors for Continuing Medical Education under the Three-Level Medical System in China

Background : To explore the needs of obstetricians and gynecologists (OB-GYNs) for training methods and contents, under China’s three-level diagnosis and treatment system. Methods : Epidemiological investigation was adopted, and network questionnaires were distributed in the largest academic training platform of obstetrics and gynecology in China, from April 2020 to May 2020. The investigation contents mainly included training methods and contents of continuing medical education (CME), as well as the mastery of diseases by doctors from hospitals at different levels of training. Results : The questionnaire received a total of 16,400 cumulative page views. 4458 questionnaires were collected in total, and 3954 questionnaires were included in the research. Doctors from hospitals at different levels chose the professional direction of gynecological endocrine diseases as the subject requiring the most strengthening of training, with ratios of 80.47%, 81.60% and 82.10%, respectively. Abnormal uterine bleeding (AUB) was the most desirable training content for doctors from primary, secondary, and other hospitals, while training of polycystic ovary syndrome (PCOS) was mostly needed by doctors from tertiary hospitals. According to the investigation, network education was the major training form favored by doctors, as well as a preferential way to acquire professional resources. The number of doctors selecting site meeting report only took up 4.70%. Based on the single-factor chi-square ( χ 2 ) analysis of the degree of mastery of 19 diseases, significant statistical differences were found among doctors from hospitals at each level, except for birth control ( p < 0.001). After related factors were corrected, the multivariate regression analysis indicated that the degree of mastery of diseases was positively correlated to hospital levels. Conclusions : Doctors from hospitals at each level failed to effectively master gynecological endocrine diseases, and AUB, menopausal syndrome, and PCOS were confirmed as diseases for which the doctors had the greatest need of continuing education.
THREE–DIMENSIONAL ACOUSTIC IMAGING USING ASYNCHRONOUS MICROPHONE ARRAY MEASUREMENTS

Abstract Complex test models in aeroacoustic experiments often present an arrangement of noise sources within a three–dimensional space. Planar microphone array normally have difficulties in separating sound sources in the direction normal to the array plane due to their poorer spatial resolution in this direction. This paper evaluates the benefits of combining asynchronous microphone array measurements for three–dimensional acoustic source. An experimental setup consisting of three out–of–plane speakers was considered. A planar microphone array was employed for the acoustic measurements in a baseline position and then displaced around the speakers to provide different points of view. The acoustic source maps obtained from each array position were combined using the geometric mean of their source autopowers. The performance of this approach in combination with the following acoustic imaging methods was investigated: conventional frequency domain beamforming (as baseline), functional beamforming, orthogonal beamforming, robust adaptive beam-forming, CLEAN–SC, Richardson–Lucy deconvolution, and global optimization methods. For each case, the performance is evaluated in terms of accuracy in source position localization and spectral quantification in sound pressure level. In general, it was determined that combining additional views considerably improved the accuracy in terms of position localization (especially in the depth direction).
A Radiofrequency Threshold Sensing System Using a Hf 0.5 Zr 0.5 O 2 Device and a Microacoustic Piezoelectric Resonant Sensor

—This article presents the first prototype of a novel threshold sensing system operating in the radiofrequency (RF) range, capable of passively detecting and recording instances of temperature violations with high sensitivity and without the need of DC-biased memory devices. The system comprises a microfabricated hafnium zirconium oxide (HZO) ferroelectric varactor, a microfabricated lithium niobate (LiNbO 3 ) shear-horizontal (SH0) Lamb wave microacoustic resonator used as a temperature sensor, and an inductor. All components have been soldered onto a printed circuit board for testing and characterization. Through this threshold sensing system, we also provide the first demonstration of ferroelectric switching in a HZO varactor triggered exclusively by an RF signal without requiring any DC voltages. The findings reported in this paper offer a promising pathway for future RF passive tags leveraging micro-and nanosystems to implement a programmable threshold sensing functionality with high sensitivity and with embedded memory capabilities.
COMBINING DATA SOURCES IN ESTIMA TlON

The problem of simultaneous estimation of a set of normal means is considered. The properties of the proposed estimator are assessed. It is demon strated analytically and numerically that the pro posed shrinkage preliminary test estimator provides a wider range than the usual preliminary test esti mator in which it dominates the classical estimator. An optimal rule for the size of the preliminary test is presented. It is found that the size of the prelim inary test for the proposed shrinkage preliminary test estimator is reasonable.
Munich Papers in Political Economy

Corporate research and development constitutes one of the main sources of innovation. Recent research, however, discusses a decline in corporate research and its implications for technological progress. The contribution of this study is to model research & development (R&D) decisions in an R&D investment model that allows the analysis of ﬁrms’ engagement in research (R) as compared to development (D) activities. The model predicts higher investments in both activities for larger ﬁrms, but it also shows that research intensity, i.e. the R-share in R&D, declines with ﬁrm size. We test these propositions using data of R&D-active ﬁrms over the period from 2000 to 2015. Results from panel model estimations that account for unobserved heterogeneity across ﬁrms show that larger ﬁrms invest indeed more in both research and development whereas the relative focus on research decreases with ﬁrm size. In addition, the empirical results suggest that, since the returns to research decline with ﬁrm size, specialization maximizes productivity. We discuss policy implications based on these ﬁndings.
The Characterization of the Roles of Ipl1 and Cdc5 in Spindle Pole Body Duplication in Yeast Meiosis

The centrosome is the microtubule organizing center (MTOC) in higher eukaryotes. During meiosis, proper duplication and separation of the centrosomes are necessary for accurate chromosome segregation and leads to the production of gametes containing half of the parental genome. During meiotic interphase I, centrosomes are duplicated when chromosomes replicate. A pair of centrosomes establish a bipolar microtubule spindle that facilitates the segregation of homologs during meiosis I. Centrosomes duplicate once more at interphase II, when DNA duplication is absent, and form two independent spindles for sister chromatid separation during meiosis II. The centrosome in yeast is called the spindle pole body (SPB). Here we show that the Aurora Kinase Ipl1, which protects sister chromatid cohesion, is also required for the maintenance of a tight association between duplicated sister SPBs, referred to here as SPB cohesion. Premature loss of cohesion leads to SPB over-duplication and the formation of multipolar spindles during meiosis II. The Polo-like kinase Cdc5 is a licensing factor for SPB duplication at interphase II and promotes SPB separation during meiosis II. Our data suggests Ipl1 and Cdc5 interact antagonistically at the SPB to maintain proper duplication and separation of SPBs during meiosis.
Modular Indexer: Fully User-Verified Execution Layer for Meta-Protocols on Bitcoin

Before the emergence of inscriptions and ordinal protocols, Bitcoin was limited in its applications due to the Turing-incompleteness of its script language. Fortunately, with recent advances in techniques, Turing-complete off-chain execution layers are established via Bitcoin indexers. Yet, existing indexers have their data integrity and availability strongly dependent on the honesty of indexers. This violated the trustlessness and decentralization principle of the cryptocurrency literature. To provide an alternative Bitcoin indexer scheme and overcome the above limitations, we have reallocated the roles of committee index-ers (for heavy computations), normal indexers, and light indexers (the client end), and established a fully user-verified execution layer based on our modular indexer protocol. For the trustless relay of data, we have adopted Verkle trees to store and prove the states. Thus, data integrity and availability are guaranteed even in the case of a majority of malicious committee indexers. Ideally, our modular indexer would safely bridge the gap between the Bitcoin layer-1 and applications from BRC-20, and contribute to the further prosperity of the Bitcoin ecosystem.