Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Birmingham Relationships Among Values, Achievement Orientations, and Attitudes in Youth Sport

This research examines the value-expressive function of attitudes and achievement goal theory in predicting moral attitudes. In Study 1, the Youth Sport Values Questionnaire (YSVQ; Lee, Whitehead, & Balchin, 2000) was modified to measure moral, competence, and status values. In Study 2, structural equation modeling on data from 549 competitors (317 males, 232 females) aged 12–15 years showed that moral and competence values predicted prosocial attitudes, whereas moral (negatively) and status values (positively) predicted antisocial attitudes. Competence and status values predicted task and ego orientation, respectively, and task and ego orientation partially mediated the effect of competence values on prosocial attitudes and of status values on antisocial attitudes, respectively. The role of sport values is discussed, and new research directions are proposed.
PENGARUH PENGETAHUAN PAJAK, KEMUDAHAN PAJAK DAN KEADILAN PAJAK TERHADAP KEPATUHAN FORMAL WAJIB PAJAK UMKM PP NOMOR 46 TAHUN 2013 DI KPP PRATAMA SURABAYA GUBENG

Penelitian ini bertujuan untuk menguji pengaruh dari pengetahuan pajak, kemudahan pajak, dan keadilan pajak terhadap kepatuhan formal Wajib Pajak UMKM yang berlandaskan pada Peraturan Pemerintah Nomor 46 Tahun 2013 tentang Pajak Penghasilan atas Penghasilan dari usaha yang diterima atau diperoleh Wajib Pajak yang memiliki perederan bruto tertentu yaitu yang omzetnya tidak lebih dari Rp 4,8 Milyar. Penelitian ini menggunakan pendekatan kuantitatif. Variabel independen yang digunakan adalah pengetahuan pajak, kemudahan pajak, dan keadilan pajak. Sedangkan variabel dependen dalam penelitian ini adalah kepatuhan Wajib Pajak UMKM. Penelitian ini menggunakan metode survei kuisoner pada 100 responden Wajib Pajak UMKM efektif yang omzetnya tidak melebihi dari Rp 4,8 Milyar yang terdaftar di Kantor Pelayanan Pajak Pratama Surabaya Gubeng. Kuisoner yang terkumpul dianalisis menggunakan PASW Statistik 18. Penelitian ini menggunakan analisis regresi linier berganda. Hasil penelitian menunjukan bahwa pengetahuan pajak dan kemudahan pajak berpengaruh terhadap kepatuhan Wajib Pajak UMKM. Sedangkan keadilan pajak tidak berpengaruh terhadap kepatuhan Wajib Pajak UMKM.
KEEFEKTIFAN MODEL PEMBELAJARAN DARING DALAM PERKULIAHAN BAHASA INDONESIA DI PERGURUAN TINGGI

Sejalan dengan kemajuan teknologi informasi dan komunikasi, kini pendekatan pembelajaran telah berubah ke arah pembelajaran abad pengetahuan. Orang dapat belajar di mana saja, kapan saja, dengan siapa saja. Itulah ciri pembelajaran abad pengetahuan yang dikenal sebagai berbasis komputer. Penelitian ini bertujuan untuk mengembangkan model pembelajaran daring atau Online Learning Model (OLM), sekaligus menerapkannya untuk meningkatkan keefektifan perkuliahan Bahasa Indonesia di Perguruan Tinggi. Pengembangan OLM menggunakan teknik Online Interactive Learning Model (OILM) dengan memanfaatkan media sosial berbasis internet serta telepon seluler atau ponsel sebagai sarananya. Subjek penelitian ini adalah mahasiswa program S-1 peserta mata kuliah wajib umum Bahasa Indonesia di tiga fakultas, yaitu Fakultas Keguruan dan Ilmu Pendidikan (FKIP), Fakultas Sains dan Teknologi (FST), dan Fakultas Kedokteran (FK). Penelitian ini menggunakan pendekatan campuran atau mix methods. Data dikumpulkan dengan menggunakan kuesioner, angket, dan tes. Hasil penelitian ini menunjukkan: (1) OILM efektif digunakan dalam perkuliahan Bahasa Indonesia di program S-1; (2) model pembelajaran telah mampu meningkatkan penyerapan mahasiswa terhadap materi kuliah, dengan peningkatan mencapai lebih dari 81% dibandingkan dengan hanya menggunakan model pembelajaran tatap-muka; (3) berdasarkan hasil kuesioner, subjek berpendapat bahwa OLM telah memberikan sebuah pengalaman baru yang lebih menantang daripada model pembelajaran konvensional atau tatap-muka. In line with advances in information and communication technology, learning approaches have now shifted toward the learning of the century of knowledge. People can learn anywhere, anytime, with anyone. That is the hallmark of learning the century of knowledge known as computer-based. This study aims to develop online learning model (OLM), as well as apply it to improve the effectiveness of Indonesian language courses in Higher Education. OLM development uses the technique of Online Interactive Learning Model (OILM) by utilizing internet-based social media as well as mobile phone or mobile phone as its ingredients. The subjects of this research are the students of the S-1 program of the compulsory subjects of Bahasa Indonesia in three faculties, namely Faculty of Teacher Training and Education (FKIP), Faculty of Science and Technology (FST), and Faculty of Medicine (FK). This research uses the mixed approach or mix methods. Data were collected using questionnaires, questionnaires, and tests. The results of this study show: (1) OILM is effectively used in Indonesian language courses in S-1 programs; (2) the learning model has been able to increase the students' absorption of the lecture material, with an increase of more than 81% compared to using only face-to-face learning model; (3) based on the results of the questionnaire, the subject argues that OLM has provided a more challenging new experience than the conventional or face-to-face learning model.
On the relation between supersoft X-ray sources and VY Scl stars: The cases of V504 Cen and VY Scl†

We summarise our optical monitoring program of VY Scl stars with the SMARTS telescopes, and triggered X-ray as well as optical observations after/during state transitions of V504 Cen and VY Scl (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Fourier restriction to polynomial curves I: a geometric inequality

We prove a Fourier restriction result for general polynomial curves in ${\Bbb R}^d$. Measuring the Fourier restriction with respect to the affine arclength measure of the curve, we obtain a universal estimate for the class of all polynomial curves of bounded degree. Our method relies on establishing a geometric inequality for general polynomial curves which is of interest in its own right. Applications of this geometric inequality to other problems in euclidean harmonic analysis have recently been established.
Modeling of coplanar waveguide for buffered clock tree

Owing to inductive effect, coplanar waveguide (CPW) is widely used to achieve signal integrity in high performance clock designs. We first propose a piece-wise linear (PWL) model for the far-end response of a CPW considering ramp input and capacitive loading. The PWL model has a high accuracy but uses at least l000x less time compared to SPICE. We then apply the PWL model to synthesize the CPW geometry for clock trees considering constrains of rising time and oscillation at sinks. We obtain a spectrum of solutions with smooth tradeoff between area and power.
Sociology of the contemporary family

The sociologic definition of marriage is given and the various family structures and functions explained. Attention is paid to the social changes in relation to family structure and the impact that cultural and especially economic development has had on the traditional structure of the family. Marriage is stressed as the only institution able to guarantee reproduction and this function is examined in various economic classes especially in the most mobile. The quickly changing position of women inside the family and inside society is discussed and how this changing position has weakened the stability of marraige and increased the rate of divorces all over the world. The problem of contraception is then taken into consideration considering as well how it has been tackled by several countries and which laws have been promulgated especially in Europe to protect the family. The well-being of the family and of its individual members is seen as reflecting the well being and psychological adjustment of society-at-large.
Effects of Swedish Massage on the Improvement of Mood Disorders in Women with Breast Cancer undergoing Radiotherapy

Background Breast cancer is the most common cancer in women, and the second leading cause of cancer deaths. The detection and treatment of this cancer may create mental pressure and lower mood levels, causing anxiety, depression, stress, and pain for the patients. Objectives The aim of this study was to determine the effects of Swedish massage on mood disorders in breast cancer patients undergoing radiotherapy at the cancer institute of the Imam Khomeini hospital at the Tehran University of Medical Sciences. Patients and Methods This study consisted of a clinical trial including 100 patients with breast cancer. The participants were chosen randomly, with their consent, by the use of polling, to be included in the intervention group (which received a Swedish massage three times a week, for 30 minutes, over five weeks) and control group (which received routine care). At the beginning of the intervention and after 5 weeks, the mood disorders of the patients, including anger, anxiety, depression, and any positive affect, were assessed using the affective control scale (ACS) questionnaire. Results Before the intervention, there was no significant difference in the average of the overall scale between the intervention and control groups in the subscales of anger, anxiety, depression, and positive affect (P = 0.469). The average of the overall scale in the Swedish massage group decreased from 3.52 ± 0.65 to 2.42 ± 0.76 when compared to the pre-intervention conditions, and to (P < 0.001) after the intervention. Moreover, the values for the control group were 3.41 ± 0.94 for the pre-intervention and 3.38 ± 0.9 after the intervention (P = 0.620). Conclusions When compared to the control group, the Swedish massage showed an improvement in the mood disorders of women with breast cancer.
People detection in crowded scenes via regional-based convolutional network

The detection of people in crowded scenes is a challenging task owing to both the severe occlusion among people and various changes in posture. We propose a regional-based convolutional network to address the tasks of people detection in the crowded scenes. Unlike the traditional methods, we propose an end-to-end framework that uses the convolutional network for feature representation, which generates candidate proposals online and performs the location optimization simultaneously. By separately detecting the heads of people, our system gets rid of the problems caused by occlusion and achieves robust detection results in the case that no matter whether the head is towards or backwards to the camera. Experimental results show that it greatly outperforms traditional methods.
Osteopontin is a hematopoietic stem cell niche component that negatively regulates stem cell pool size

Stem cells reside in a specialized niche that regulates their abundance and fate. Components of the niche have generally been defined in terms of cells and signaling pathways. We define a role for a matrix glycoprotein, osteopontin (OPN), as a constraining factor on hematopoietic stem cells within the bone marrow microenvironment. Osteoblasts that participate in the niche produce varying amounts of OPN in response to stimulation. Using studies that combine OPN-deficient mice and exogenous OPN, we demonstrate that OPN modifies primitive hematopoietic cell number and function in a stem cell–nonautonomous manner. The OPN-null microenvironment was sufficient to increase the number of stem cells associated with increased stromal Jagged1 and Angiopoietin-1 expression and reduced primitive hematopoietic cell apoptosis. The activation of the stem cell microenvironment with parathyroid hormone induced a superphysiologic increase in stem cells in the absence of OPN. Therefore, OPN is a negative regulatory element of the stem cell niche that limits the size of the stem cell pool and may provide a mechanism for restricting excess stem cell expansion under conditions of niche stimulation.
Data Efficient Deep Reinforcement Learning through Model-Based Intrinsic Motivation

In the last few years we have experienced great advances in the field of reinforcement learning (RL), much thanks to deep learning. By introducing deep neural networks in RL it is possible to have agents learn complex behaviors by just observing a game screen, just like humans learn to play games. Even though this is great, there is one limitation that makes the transition to real world problems tough, and that is data efficiency. One way to go about improving the data efficiency of RL is to approximate a model of the environment, called model-based RL. Even though model-based agents can be more data efficient, they are usually computationally heavy and often end up being too inaccurate. In this thesis, we explore the use of deep dynamics models (DDM) trained dynamically in environments with high-dimensional state representations. Furthermore, we study four different ways of calculating curiosity-based intrinsic motivation extracted from the DDM to achieve more efficient exploration. Having the DDM made up of an autoencoder (AE) and a transition prediction model that operate in the latent space generated by the AE, we introduce the first intrinsic bonus as the AE reconstruction error. The second one is based on the prediction error from the DDM. The third bonus introduce a novel idea of using MC dropout, presented in (Gal & Ghahramani 2015), to extract the uncertainty of the DDM. The last type of intrinsic bonus extract the uncertainty by MC dropout from a bootstrapped DDM. Interestingly, the proposed bonus based on MC dropout outperforms the more commonly used bonus based on dynamics prediction errors in both data efficiency and final performance in the Atari 2600 domain. Additionally, we manage to have agents learn by only receiving intrinsic reward and no any extrinsic rewards from the environment.
The Potential Impact of Improving Appropriate Treatment for Fever on Malaria and Non-Malarial Febrile Illness Management in Under-5s: A Decision-Tree Modelling Approach

Background As international funding for malaria programmes plateaus, limited resources must be rationally managed for malaria and non-malarial febrile illnesses (NMFI). Given widespread unnecessary treatment of NMFI with first-line antimalarial Artemisinin Combination Therapies (ACTs), our aim was to estimate the effect of health-systems factors on rates of appropriate treatment for fever and on use of ACTs. Methods A decision-tree tool was developed to investigate the impact of improving aspects of the fever care-pathway and also evaluate the impact in Tanzania of the revised WHO malaria guidelines advocating diagnostic-led management Results Model outputs using baseline parameters suggest 49% malaria cases attending a clinic would receive ACTs (95% Uncertainty Interval:40.6–59.2%) but that 44% (95% UI:35–54.8%) NMFI cases would also receive ACTs. Provision of 100% ACT stock predicted a 28.9% increase in malaria cases treated with ACT, but also an increase in overtreatment of NMFI, with 70% NMFI cases (95% UI:56.4–79.2%) projected to receive ACTs, and thus an overall 13% reduction (95% UI:5–21.6%) in correct management of febrile cases. Modelling increased availability or use of diagnostics had little effect on malaria management outputs, but may significantly reduce NMFI overtreatment. The model predicts the early rollout of revised WHO guidelines in Tanzania may have led to a 35% decrease (95% UI:31.2–39.8%) in NMFI overtreatment, but also a 19.5% reduction (95% UI:11–27.2%), in malaria cases receiving ACTs, due to a potential fourfold decrease in cases that were untested or tested false-negative (42.5% vs.8.9%) and so untreated. Discussion Modelling multi-pronged intervention strategies proved most effective to improve malaria treatment without increasing NMFI overtreatment. As malaria transmission declines, health system interventions must be guided by whether the management priority is an increase in malaria cases receiving ACTs (reducing the treatment gap), reducing ACT waste through unnecessary treatment of NMFI or expanding appropriate treatment of all febrile illness.
High-Accuracy Classification of Parkinson's Disease Through Shape Analysis and Surface Fitting in 123I-Ioflupane SPECT Imaging

Early and accurate identification of Parkinsonian syndromes (PS) involving presynaptic degeneration from nondegenerative variants such as scans without evidence of dopaminergic deficit (SWEDD) and tremor disorders is important for effective patient management as the course, therapy, and prognosis differ substantially between the two groups. In this study, we use single photon emission computed tomography (SPECT) images from healthy normal, early PD, and SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, and process them to compute shape- and surface-fitting-based features. We use these features to develop and compare various classification models that can discriminate between scans showing dopaminergic deficit, as in PD, from scans without the deficit, as in healthy normal or SWEDD. Along with it, we also compare these features with striatal binding ratio (SBR)-based features, which are well established and clinically used, by computing a feature-importance score using random forests technique. We observe that the support vector machine (SVM) classifier gives the best performance with an accuracy of 97.29%. These features also show higher importance than the SBR-based features. We infer from the study that shape analysis and surface fitting are useful and promising methods for extracting discriminatory features that can be used to develop diagnostic models that might have the potential to help clinicians in the diagnostic process.
Maximising expected value under axiological uncertainty : an axiomatic approach

The topic of this thesis is axiological uncertainty – the question of how you should evaluate your options if you are uncertain about which axiology is true. As an answer, I defend Expected Value Maximisation (EVM), the view that one option is better than another if and only if it has the greater expected value across axiologies. More precisely, I explore the axiomatic foundations of this view. I employ results from state-dependent utility theory, extend them in various ways and interpret them accordingly, and thus provide axiomatisations of EVM as a theory of axiological uncertainty. Chapter 1 defends the importance of the problem of axiological uncertainty. Chapter 2 introduces the most basic theorem of this thesis, the Expected Value Theorem. This theorem says that EVM is true if the betterness relation under axiological uncertainty satisfies the von NeumannMorgenstern axioms and a Pareto condition. I argue that, given certain simplifications and modulo the problem of intertheoretic comparisons, this theorem presents a powerful means to formulate and defend EVM. Chapter 3 then examines the problem of intertheoretic comparisons. I argue that intertheoretic comparisons are generally possible, but that some plausible axiologies may not be comparable in a precise way. The Expected Value Theorem presupposes that all axiologies are comparable in a precise way. So this motivates extending the Expected Value Theorem to make it cover less than fully comparable axiologies. Chapter 4 then examines the concept of a probability distribution over axiologies. In the Expected Value Theorem,
Resolving Scale Ambiguity via XSlit Aspect Ratio Analysis

In perspective cameras, images of a frontal-parallel 3D object preserve its aspect ratio invariant to its depth. Such an invariance is useful in photography but is unique to perspective projection. In this paper, we show that alternative non-perspective cameras such as the crossed-slit or XSlit cameras exhibit a different depth-dependent aspect ratio (DDAR) property that can be used to 3D recovery. We first conduct a comprehensive analysis to characterize DDAR, infer object depth from its AR, and model recoverable depth range, sensitivity, and error. We show that repeated shape patterns in real Manhattan World scenes can be used for 3D reconstruction using a single XSlit image. We also extend our analysis to model slopes of lines. Specifically, parallel 3D lines exhibit depth-dependent slopes (DDS) on their images which can also be used to infer their depths. We validate our analyses using real XSlit cameras, XSlit panoramas, and catadioptric mirrors. Experiments show that DDAR and DDS provide important depth cues and enable effective single-image scene reconstruction.
Exploiting Reducibility in Unsupervised Dependency Parsing

The possibility of deleting a word from a sentence without violating its syntactic correctness belongs to traditionally known manifestations of syntactic dependency. We introduce a novel unsupervised parsing approach that is based on a new n-gram reducibility measure. We perform experiments across 18 languages available in CoNLL data and we show that our approach achieves better accuracy for the majority of the languages then previously reported results.
Mental Models and Normal Errors in Naturalistic Decision Making

“On a beautiful night in October 1978, in the Chesapeake Bay, two vessels sighted one another visually and on radar. On one of them, the Cost Guard cutter training vessel Cuyahoga, the captain (a chief warrant officer) saw the other ship up ahead as a small object on the radar, and visually he saw two lights, indicating that it was proceeding in the same direction as his own ship [Figure 1(t1), top panel]. He thought [emphasis added] it possibly was a fishing vessel. The first mate saw the lights, but saw three, and estimated (correctly) that it was a ship proceeding toward them [Figure 1(t1), bottom panel]. He had no responsibility to inform the captain, nor did he think he needed to. Since the two ships drew together so rapidly, the captain decided [emphasis added] that it must be a very slow fishing boat that he was about to overtake [Figure 1(t2), top panel]. This reinforced his incorrect interpretation. The lookout knew the captain was aware of the ship, so did not comment further as it got quite close and seemed to be nearly on a collision course. Since both ships were traveling full speed, the closing came fast. The other ship, a large cargo ship, did not establish any bridge-to-bridge communication, because the passing was routine. But at the last moment the captain of the Cuyahoga realized [emphasis added] that in overtaking the supposed fishing boat, which he assumed was on a near-parallel course, he would cut off that boat’s ability to turn as both of them approached the Potomac River [Figure 1(t3), top panel]. So he ordered a turn to the port [Figure 1(t4), top panel]. This brought him directly in the path of the oncoming freighter, which hit the cutter [Figure 1(t4), bottom panel]. Eleven coastguardsmen perished. ”