Miracle-dz/UniFAR-FTUs huggingface dataset:
Nudging Credit Scores in the Field: The Effect of Text Reminders on Creditworthiness in the United States

Given the fundamental role that credit scores play in day-to-day life in the United States, it is very important to understand what can be done to help individuals improve their credit scores. This question is important in general, and especially important for the low-to-moderate-income (LMI) individuals who likely have a greater need for access to liquidity than higher-income individuals. In this paper the authors report results from a field experiment conducted between early 2013 and early 2014 in Boston, Massachusetts, with LMI taxpayers who were offered credit advising services. Taxpayers who opted into the advising sessions were randomized as to whether they received extra information on credit scores and the average APR (annual payment rate) on basic credit cards in their area (the "information" condition), and, independently, as to whether or not they received monthly text reminders (the "text" condition). These reminders included the individual's financial goal and credit score range, reminders to pay bills on time and to pay at least the minimum amount, and updated interest rate information on basic credit cards.
Efficient context adaptive entropy coding for real-time applications

Context based entropy coding has the potential to provide higher gain over memoryless entropy coding. However serious difficulties arise regarding the practical implementation in real-time applications due to its very high memory requirements. This paper presents an efficient method for designing context adaptive entropy coding while fulfilling low memory requirements. From a study of coding gain scalability as a function of context size, new context design and validation procedures are derived. Further, supervised clustering and mapping optimization are introduced to model efficiently the context. The resulting context modelling associated with an arithmetic coder was successfully implemented in a transform-based audio coder for real-time processing. It shows significant improvement over the entropy coding used in MPEG-4 AAC.
Scientific Ontology Construction Based on Interval Valued Fuzzy Theory Under Web 2.0

Constructi on of the unified and shared domain ontology is significant for effective knowledge management. For the acquisition and sharing of scientific research knowledge under Web2.0, a novel approach of building Interval Valued Fuzzy Ontology ( IVFO ) in scientific research domain is presented. Through interval valued fuzzy theory, the definition and constructing framework of IVFO is proposed. Then IVFO is applied to semi-automatic extraction of information retrieval research domain. The preliminary constructing of research domain ontology is an essential base for the knowledge management system of scientific research. It can be effective methods for enhancing the efficiency and productivity of researching.
Rain versus Snow in the Sierra Nevada, California: Comparing Doppler Profiling Radar and Surface Observations of Melting Level

Abstract The maritime mountain ranges of western North America span a wide range of elevations and are extremely sensitive to flooding from warm winter storms, primarily because rain falls at higher elevations and over a much greater fraction of a basin’s contributing area than during a typical storm. Accurate predictions of this rain–snow line are crucial to hydrologic forecasting. This study examines how remotely sensed atmospheric snow levels measured upstream of a mountain range (specifically, the bright band measured above radar wind profilers) can be used to accurately portray the altitude of the surface transition from snow to rain along the mountain’s windward slopes, focusing on measurements in the Sierra Nevada, California, from 2001 to 2005. Snow accumulation varies with respect to surface temperature, diurnal cycles in solar radiation, and fluctuations in the free-tropospheric melting level. At 1.5°C, 50% of precipitation events fall as rain and 50% as snow, and on average, 50% of measured pre.
A DNA Network as an Information Processing System

Biomolecular systems that can process information are sought for computational applications, because of their potential for parallelism and miniaturization and because their biocompatibility also makes them suitable for future biomedical applications. DNA has been used to design machines, motors, finite automata, logic gates, reaction networks and logic programs, amongst many other structures and dynamic behaviours. Here we design and program a synthetic DNA network to implement computational paradigms abstracted from cellular regulatory networks. These show information processing properties that are desirable in artificial, engineered molecular systems, including robustness of the output in relation to different sources of variation. We show the results of numerical simulations of the dynamic behaviour of the network and preliminary experimental analysis of its main components.
Season's Greetings: An Analysis of Christmas Card Use

Christmas is the time of year when people reaffirm social connections through the medium of Christmas cards. Although much communication in the modern age is conducted via electronic means, many people continue to send and receive paper-based cards during the festive season. With a view to understanding practices surrounding the use of digital and paper-based media, this paper explores the use of paper-based and electronic Christmas cards among a sample of university students. We describe students' practices regarding Christmas cards, examining motivations of use and perceptions of value associated with paper and electronic cards. Our analysis leads to a number of potential opportunities for enhancing the perception of electronic alternatives to paper-based cards.
OMNIX: A Topology-Independent P2P Middleware

Peer-to-Peer (P2P) computing has been increasingly gaining interest. P2P architectures are no longer just used for sharing music files over the Internet. P2P middleware systems attempt to provide an abstraction between the application and the underlying P2P infrastructure by providing higher-level services such as distributed P2P searches and direct communication among peers. Such systems often provide a pre-defined topology that is suitable for a certain task (e.g., for exchanging files). This topology influences the performance and scalability of the applications that are built on top of it. The problem is that it is difficult to change a topology-dependent architecture once requirements change or problems related to the topology are identified. In this paper, we present Omnix, a topology-independent P2P middleware. Omnix has a pluggable architecture that allows different topology descriptions to be plugged in based on the changing requirements of an application. Applications that use Omnix are provided a consistent API that is not affected whenever the underlying topology has to be changed or adapted.
Disruption of Vps4 and JNK Function in Drosophila Causes Tumour Growth

Several regulators of endocytic trafficking have recently been identified as tumour suppressors in Drosophila. These include components of the endosomal sorting complex required for transport (ESCRT) machinery. Disruption of subunits of ESCRT-I and –II leads to cell-autonomous endosomal accumulation of ubiquitinated receptors, loss of apicobasal polarity and epithelial integrity, and increased cell death. Here we report that disruption of the ATPase dVps4, the most downstream component of the ESCRT machinery, causes the same array of cellular phenotypes. We find that loss of epithelial integrity and increased apoptosis, but not loss of cell polarity, require the activation of JNK signalling. Abrogation of JNK signalling prevents apoptosis in dVps4 deficient cells. Indeed double deficiency in dVps4 and JNK signalling leads to the formation of neoplastic tumours. We conclude that dvps4 is a tumour suppressor in Drosophila and that JNK is central to the cell-autonomous phenotypes of ESCRT-deficient cells.
Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multi-Parametric MRI in Patients with Stroke

In ischemic stroke, the extent of ischemic lesion recovery is one of the most important correlate of functional recovery in brain. Using a set of acute phase MR images (Diffusion-Weighted - DWI, T1-Weighted - T1WI, T2-Weighted T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a pixel-by-pixel forecast of the chronic T2WI. The ANN was trained and tested using 14 slices from 3 subjects using a K-Folding Cross-Validation (KFCV) method with 14 folds. The Area Under the Receiver Operator Characteristic Curve (AUROC) for 14 folds was used for training, testing and optimization of the ANN. After training and optimization, the ANN produced a map that was well correlated (r = 0.88, p ≪ 0.0001) with the T2WI at 3 months. To confirm that the trained ANN performed well against a new dataset, 13 slices from 4 other patients were shown to the trained ANN. The prediction made by the ANN had an excellent overall performance (AUROC = 0.82), and was very well correlated to the 3-month ischemic lesion on T2-Weighted image.
Effectiveness of The Combination of an Educational Package of "Legitimacy in Midwifery" and Student-Centered Learning in Order to Improve the Quality of Law and Forensics Course in Midwifery

Background and Aims: Practical and proportional education of legal issues in the midwifery is important as a result of covering a wide range of health care services. This study aimed to present and integrate the student-centered participatory teaching method and provide an educational package in order to improve the quality of the legal and medical law course in midwifery. Methods: This was a case-control study. Statistical population consists of 90 midwifery students. Three researcher made questionnaires: "evaluation the unit of rights and forensic in midwifery, knowledge of students about of forensic medicines issues in midwifery and student satisfaction of the workshop" was used for collecting data. Finally, to describe the data SPSS-20 software, descriptive statistical methods, t-test and Wilcox on tests were used. Results: According to the results the average score of student's knowledge in forensic issues in midwifery had increased significantly (P<0.005) after the workshop (14.05 of 18) compared to before the workshop (4.8 of 18). About the educational package the 80% of students considered the contents of the curriculum to be appropriate for training and expressed that the package could be used as a source of educational assistance available to enhance their legal activities. Conclusions: According to the results, using combination learnin method, holding a participatory student-centered workshop and presenting a supplementary educational package can improve the quality of law and forensics course in midwifery.
Flexible Copula Density Estimation with Penalized Hierarchical B‐splines

The paper introduces a new method for flexible spline fitting for copula density estimation. Spline coefficients are penalized to achieve a smooth fit. To weaken the curse of dimensionality, instead of a full tensor spline basis, a reduced tensor product based on so called sparse grids (Notes Numer. Fluid Mech. Multidiscip. Des., 31, 1991, 241‐251) is used. To achieve uniform margins of the copula density, linear constraints are placed on the spline coefficients, and quadratic programming is used to fit the model. Simulations and practical examples accompany the presentation.
On the largest component of a random graph with a subpower-law degree sequence in a subcritical phase

A uniformly random graph on $n$ vertices with a fixed degree sequence, obeying a $\gamma$ subpower law, is studied. It is shown that, for $\gamma>3$, in a subcritical phase with high probability the largest component size does not exceed $n^{1/\gamma+\varepsilon_n}$, $\varepsilon_n=O(\ln\ln n/\ln n)$, $1/\gamma$ being the best power for this random graph. This is similar to the best possible $n^{1/(\gamma-1)}$ bound for a different model of the random graph, one with independent vertex degrees, conjectured by Durrett, and proved recently by Janson.
Analysis of polyphonic audio using source-filter model and non-negative matrix factorization

This paper proposes a method for analysing polyphonic audio signals based on a signal model where an input spectrogram is modelle d as a linear sum of basis functions with time-varying gains. Each basis is further represented as a product of a “source” spectrum and t he magnitude response of a “filter”. This formulation reduces the n umber of free parameters needed to represent realistic audio signal a d leads to a more reliable parameter estimation. Two novel estimation algorithms are proposed, one extended from non-negative matrix factor iza ion and the other from non-negative matrix deconvolution. In preli minary experiments with singing signals, both algorithms have been found to converge towards meaningful analysis results.
Generalized PWM algorithm for Direct Torque Controlled Induction Motor Drives using the only Sampled Voltages

This paper presents a simple generalized pulse width modulation (GPWM) algorithm for direct torque controlled induction motor drives. Though the classical direct torque control (DTC) algorithm gives fast dynamic response, it gives large steady state ripples and variable switching frequency operation of the inverter. To overcome these problems, space vector pulse width modulation (SVPWM) algorithm is used for DTC drives. But, as the SVPWM algorithm requires angle and sector information the complexity involved is more. Also, it gives more switching losses of the inverter. To reduce the complexity involved the proposed algorithm uses the instantaneous phase voltages only. Moreover, the proposed approach gives the realization of various carrier based PWM algorithms that include both SVPWM and various discontinuous PWM (DPWM) algorithms by using a generalized control algorithm and is obtained via unequal sharing of zero states. In the proposed approach, by varying a constant   k value from zero to one, various DPWM algorithms can be generated along with the SVPWM algorithm. To validate the proposed GPWM algorithm based DTC drive, several numerical simulation studies have been carried out and the results have been presented. The simulation results show the effectiveness of the proposed algorithm.
Microcontroller Compatible Sealed Lead Acid Battery Remaining Energy Prediction Using Adaptive Neural Fuzzy Inference System

All over the world, many portable devices need battery to run. Every expert has to use efficient hardware and software documentation to make battery last longer and make a correlation between microcontrollers’ duties and the remaining energy of batteries. In order to make battery last longer, battery information must be evaluated continuously. In many devices, fluctuating current is used due to its own load so alternating current makes it hard to compute the remaining battery level. For many devices, there could be battery level indicator as solution. This solution gives clue about the remaining time for user but it does not give any hint for microcontroller about battery situation. For low cost devices, it could be very difficult to estimate the remaining storage energy in the battery. In this study, microcontroller compatible sealed lead acid battery remaining energy predictor based on adaptive neural fuzzy inference system has been designed and proposed. In order to test proposed method, mean absolute error and leave one out have been used to measure proposed system performance. The obtained mean absolute error results for leave one out is 10.55, epoch error is 11.72. Through the study, low adaptive neural fuzzy inference system rules and low microcontroller memory consumption were aimed.
Synergy and Anti-Synergy between Palladium and Gold in Nanoparticles Dispersed on a Reducible Support

Highly active and stable bimetallic Au–Pd catalysts have been extensively studied for several liquid-phase oxidation reactions in recent years, but there are far fewer reports on the use of these catalysts for low-temperature gas-phase reactions. Here we initially established the presence of a synergistic effect in a range of bimetallic Au–Pd/CeZrO4 catalysts, by measuring their activity for selective oxidation of benzyl alcohol. The catalysts were then evaluated for low-temperature WGS, CO oxidation, and formic acid decomposition, all of which are believed to be mechanistically related. A strong anti-synergy between Au and Pd was observed for these reactions, whereby the introduction of Pd to a monometallic Au catalyst resulted in a significant decrease in catalytic activity. Furthermore, monometallic Pd was more active than Pd-rich bimetallic catalysts. The nature of the anti-synergy was probed by several ex situ techniques, which all indicated a growth in metal nanoparticle size with Pd addition. However, the most definitive information was provided by in situ CO-DRIFTS, in which CO adsorption associated with interfacial sites was found to vary with the molar ratio of the metals and could be correlated with the catalytic activity of each reaction. As a similar correlation was observed between activity and the presence of Au0* (as detected by XPS), it is proposed that peripheral Au0* species form part of the active centers in the most active catalysts for the three gas-phase reactions. In contrast, the active sites for the selective oxidation of benzyl alcohol are generally thought to be electronically modified gold atoms at the surface of the nanoparticles.
An analytical study of a tradeoff between transmission power and FEC for TCP optimization in wireless networks

It is well known that TCP has performance problems when wireless links are involved in the end-to-end connection. This is due to the high bit error rate characterizing wireless links. Appropriate power management and error correction can improve the link reliability observed by TCP and increase the throughput performance accordingly. In the literature, the effects of transmission power and error correction capability on TCP performance have been considered separately, so far. We study the tradeoff between power management and error correction. To this end, an analytical framework to maximize a user satisfaction function, defined as the ratio between the TCP throughput and a cost function, is introduced. The proposed analytical framework does not depend on the specific wireless system and does not rely on any TCP throughput approximation formula. The benefits of joint power management and error control are demonstrated in several relevant case studies.
Folate-targeted polymeric micelles loaded with ultrasmall superparamagnetic iron oxide: combined small size and high MRI sensitivity

Targeted delivery of contrast agents is a highly desirable strategy for enhancing diagnostic efficiency and reducing side effects and toxicity. Water-soluble and tumor-targeting superparamagnetic iron oxide nanoparticles (SPIONs) were synthesized by loading hydrophobic SPIONs into micelles assembled from an amphiphilic block copolymer poly(ethylene glycol)- poly(ɛ-caprolactone) (PEG-PCL) bearing folate in the distal ends of PEG chains. Compared to the water-soluble SPIONs obtained by small molecular surfactant coating, ultrasmall SPION encapsulation with PEG-PCL micelles (PEG-PCL-SPIONs) simultaneously increases transverse (r2) and decreases longitudinal (r1) magnetic resonance (MR) relaxivities of water proton in micelle solution, leading to a notably high r2/r1 ratio up to 78, which makes the PEG-PCL-SPIONs a highly sensitive MR imaging (MRI) T2 contrast agent. The mean size of folate-attached SPION micelles (Fa-PEG-PCL-SPIONs) is 44 ± 3 nm on average, ideal for in vivo MRI applications in which long circulation is greatly determined by small particle size and is highly desirable. Prussian blue staining of BEL-7402 cells over-expressing folate receptors, after incubation with micelle-containing medium, demonstrated that folate functionalization of the magnetic particles significantly enhanced their cell uptake. The potential of Fa-PEG-PCL-SPIONs as a potent MRI probe for in vivo tumor detection was assessed. At 3 hours after intravenous injection of the Fa-PEG-PCL-SPION solution into mice bearing subcutaneous xenografts of human BEL-7402 hepatoma, a 41.2% signal intensity decrease was detected in the T2-weighted MR images of the tumor, indicating the efficient accumulation of Fa-PEG-PCL-SPIONs in the tumor tissue.
A Real-Time Foveated Sensor with Overlapping Receptive Fields

Vision systems for autonomous robots sometimes require high resolution, sometimes a wide field-of-view, and always fast processing. To achieve these same goals, the primate retina performs non-linear `image? data reduction which ultimately produces a relatively small output. Such a data reduction scheme provides a compromise between the requirements of field of view, resolution, and speed of processing. An overlapping receptive field (RF) data reduction model, as proposed in the system presented here, is based on the retina, and offers flexibility in the selection of RF averaging masks. This flexibility is illustrated with a set of outputs produced using uniform, Gaussian, difference-of-Gaussians, and edge detection masks. However, such models are more computationally expensive than their non-overlapping counterparts. To compute the requisite mapping, an adapted scan-line algorithm is used due to its efficiency with respect to memory and speed. To achieve at least 10 frames/s, we employed MIMD parallel processing using six TMS320C40 digital signal processors. An image was captured by one processor, and the data was distributed line by line to up to four other mapping nodes. Each of the mapping nodes produced partial results that were combined by the last node. Throughput measurements showed that the minimum median throughput was 11.9 frames/s for useful model parameter combinations. Measurements using one to four mappings nodes showed that the speedup was linear with respect to the number of mapping nodes. The output of this data reduction system has been employed to compute points of interest in the field-of-view, which were then used to alter the camera gaze.
Subcarrier clustering for MISO-OFDM channels with quantized beamforming

We consider a transmit beamforming for an OFDM channel with multiple transmit antennas and single receive antenna. With channel information, a receiver selects and quantizes transmit beamforming vector for each subcarrier. The quantized beamformers are then relayed to the transmitter via a rate-limited feedback channel. We propose to reduce the required number of feedback bits by applying a common transmit beamformer for a cluster of adjacent subcarriers. The sum capacity over all subcarriers depends on a cluster size, the number of feedback bits, and the number of channel taps. Approximation of the optimal cluster size that maximizes the sum rates is derived and is shown to predict simulation results very well. Numerical results show that operating with the optimal cluster size can achieve significant performance gain.