Miracle-dz/UniFAR-FTUs huggingface dataset:
Responsiveness, Minimal Clinically Important Difference, and Validity of the MoCA in Stroke Rehabilitation

Objective Persons with stroke frequently suffer from cognitive impairment. The Montreal Cognitive Assessment (MoCA), a recently developed screening tool, is sensitive to poststroke cognitive deficits. The present study assessed its psychometric and clinimetric properties (i.e., responsiveness, minimal clinically important difference (MCID), and criterion validity) in stroke survivors receiving rehabilitative therapy. Method The MoCA and the Stroke Impact Scale (SIS) were administered to 65 stroke survivors before and after 4 to 5 weeks of therapy. The effect size and standardized response mean (SRM) were calculated for responsiveness. Anchor- and distribution-based methods were used to estimate the MCID. Criterion validity was measured with the Spearman correlation coefficient. Results The responsiveness of the MoCA was moderate (SRM = 0.67). Participants exceeding the MCID according to the anchor- and distribution-based approaches were 33 (50.77%) and 20 (30.77%), respectively. Fair to good concurrent validity was reported between the MoCA and the SIS communication subscale. The MoCA had satisfactory predictive validity with the SIS communication and memory subscales. Conclusion This study may support the responsiveness, MCID, and criterion validity of the MoCA in stroke populations. Future studies with larger sample sizes are needed to validate the current findings.
Beyond Word-level to Sentence-level Sentiment Analysis for Financial Reports

This paper attempts to conduct a sentence-level sentiment analysis with respect to financial risk on a collection of financial reports. Specifically, we first propose a simple yet efficient algorithm to generate financial sentiment phrases (senti-phrases), and then with the obtained senti-phrases, we utilize multiple sentence embedding models for better learning the representations of financial risk sentences. In order to verify the performance of the proposed approach, we conduct a risk classification task of financial sentences on a sentence-level labeled dataset of finance reports. Experimental results show that incorporating the obtained senti-phrases into the embedding-based models improves the classification performance.
Modelling protection behaviour towards micronutrient deficiencies: Case of iodine biofortified vegetable legumes as health intervention for school-going children

BACKGROUND/OBJECTIVES Despite successes recorded in combating iodine deficiency, more than 2 billion people are still at risk of iodine deficiency disorders. Rural landlocked and mountainous areas of developing countries are the hardest hit, hence the need to explore and advance novel strategies such as biofortification. SUBJECTS/METHODS We evaluated adoption, purchase, and consumption of iodine biofortified vegetable legumes (IBVL) using the theory of protection motivations (PMT) integrated with an economic valuation technique. A total of 1,200 participants from three land-locked locations in East Africa were recruited via multi-stage cluster sampling, and data were collected using two, slightly distinct, questionnaires incorporating PMT constructs. The survey also elicited preferences for iodine biofortified foods when offered at a premium or discount. Determinants of protection motivations and preferences for iodine biofortified foods were assessed using path analysis modelling and two-limit Tobit regression, respectively. RESULTS Knowledge of iodine, iodine-health link, salt iodization, and biofortification was very low, albeit lower at the household level. Iodine and biofortification were not recognized as nutrient and novel approaches, respectively. On the other hand, severity, fear, occupation, knowledge, iodine status, household composition, and self-efficacy predicted the intention to consume biofortified foods at the household level; only vulnerability, self-efficacy, and location were the most crucial elements at the school level. In addition, results demonstrated a positive willingness-to-pay a premium or acceptance of a lesser discount for biofortification. Furthermore, preference towards iodine biofortified foods was a function of protection motivations, severity, vulnerability, fear, response efficacy, response cost, knowledge, iodine status, gender, age. and household head. CONCLUSIONS Results lend support for prevention of iodine deficiency in unprotected populations through biofortification; however 'threat' appraisal and socio-economic predictors are decisive in designing nutrition interventions and stimulating uptake of biofortification. In principle, the contribution is threefold: 1) Successful application of the integrated model to guide policy formulation; 2) Offer guidance to stakeholders to identify and tap niche markets; 3) stimulation of rural economic growth around school feeding programmes.
Improving Handwritten Signature-Based Identity Prediction through the Integration of Fuzzy Soft-Biometric Data

Automated identification of individuals using biometric technologies is finding increasing application in diverse areas, yet designing practical systems can still present significant challenges. Choice of the modality to adopt, the classification/matching techniques best suited to the application, the most effective sensors to use, and so on, are all important considerations, and can help to ameliorate factors which might detract from optimal performance. Less well researched, however, is how to optimise performance by means of exploiting broader-based information often available in a specific task and, in particular, the exploitation of so-called "soft" biometric data is often overlooked. This paper proposes a novel approach to the integration of soft biometric data into an effective processing structure for an identification task by adopting a fuzzy representation of information which is inherently continuous, using subject age as a typical example. Our results show this to be a promising methodology with possible benefits in a number of potentially difficult practical scenarios.
Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study

Summary A stochastic discrete‐time susceptible‐exposed‐infectious‐recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.
Sum Throughput Maximization for Multi-tag MISO Backscattering

Backscatter communication (BSC) is emerging as the core technology for pervasive sustainable internet-of-things applications. However, owing to the resource-limitations of passive tags, this work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the full-duplex multiantenna reader and backscattering coefficients (BC) at the single antenna tags. Despite this joint optimization problem being non-convex, we present low-complexity joint TRX-BC designs by exploring the asymptotically-optimal solutions in low and high signal-to-noise-ratio regimes. We discourse that with precoder and detector designs at the reader respectively targeting downlink energy beam-forming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Selected computer simulations are presented to validate the analytical claims, shed optimal-design insights, and demonstrate the throughput enhancement of around 20% over the relevant benchmark schemes.
The Impact of Publicly Funded Managed Care on Adolescent Substance Abuse Treatment Outcomes

This study compares the 12-month changes in substance use following admission to substance abuse treatment in Massachusetts between adolescents enrolled in Medicaid managed care and other publicly funded adolescents. Two hundred and fifty-five adolescents were interviewed as they entered substance abuse treatment and at 6 and 12 month follow-ups. Medicaid enrollment data were used to determine the managed care enrollment status. One hundred forty two (56%) adolescents were in the managed care group and 113 (44%) comprise the comparison group. Substance use outcomes include a count of negative consequences of substance use, days of alcohol use, days of cannabis use, and days of any substance use in the previous 30 days. Repeated measures analysis of covariance (ANCOVA) was used to assess change with time of measurement and managed care status as main effects and the interaction of time and managed care included to measure differences between the groups over time. Although several changes across time were detected for all four outcomes, we found no evidence of an impact of managed care for any of the outcomes. The results of our study do not support the fears that behavioral managed care, by imposing limits on services provided, would substantially reduce the effectiveness of substance abuse treatment for adolescents. At the same time, the results do not support those who believe that the continuity of care and improved resource utilization claimed for managed care would improve outcomes.
A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models

In this paper, a new algorithm for sensitivity analysis of discrete hidden Markov models (HMMs) is proposed. Sensitivity analysis is a general technique for investigating the robustness of the output of a system model. Sensitivity analysis of probabilistic networks has recently been studied extensively. This has resulted in the development of mathematical relations between a parameter and an output probability of interest and also methods for establishing the effects of parameter variations on decisions. Sensitivity analysis in HMMs has usually been performed by taking small perturbations in parameter values and re-computing the output probability of interest. As recent studies show, the sensitivity analysis of an HMM can be performed using a functional relationship that describes how an output probability varies as the network’s parameters of interest change. To derive this sensitivity function, existing Bayesian network algorithms have been employed for HMMs. These algorithms are computationally inefficient as the length of the observation sequence and the number of parameters increases. In this study, a simplified efficient matrix-based algorithm for computing the coefficients of the sensitivity function for all hidden states and all time steps is proposed and an example is presented.
UMD / BBN at MSE 2005

We implemented an initial application of a sentence-trimming approach (Trimmer) to the problem of multi-document summarization in the MSE2005 task. Sentence trimming was incorporated into a feature-based summarization system, called Multi-Document Trimmer (MDT), by using sentence trimming as both a pre-processing stage and a feature for sentence ranking. We demonstrate that we were able to port Trimmer easily to this new problem, although the impact of sentence trimming was minimal compared to other features used in the system. The performance of our system in the official MSE2005 task was around the middle of the pack (16 out of 27). After some minor bug fixes and a simple correction (dateline removal) we obtained an improvement on a post-hoc run on the test data.
Smartphone biosensor for Salmonella and Amitriptyline

The proposed application has been developed to detect CRP, salmonella, and amitriptyline (AMT) on the basis of a structured lateral flow chip. It is combined with a read-out system using the camera of a smartphone and software such as ImageJ or a specially programmed app to evaluate color depth proportional to concentration. These strips are structured for multi-channel applications for either multi-analyte or for replica measurements. Calibration curves are provided for analytes, structuring and read-out will be discussed. The results allow the application in the area of POCT (point-of-care diagnostics), Anywhere care, or citizen science.
A 5.8-GHz 1-V low-noise amplifier in SiGe bipolar technology

A 5.8-GHz monolithic low-noise amplifier (LNA) with a minimum noise figure of 1.65 dB and an associated gain of 15 dB is implemented in a standard SiGe bipolar technology. It dissipates 13 mW from a 1-V supply (with only 9 mW in the gain stages). The measured transducer gain is 13 dB with a noise figure of 2.1 dB at 5.8 GHz. This is believed to be the lowest noise figure reported for a 5.8-GHz LNA in any production-level technology. The transducer gain is above 10 dB from 3.8 to 8 GHz. At 5.8 GHz, the input return loss and reverse isolation are 6 and 28 dB, respectively.
Production of plasminogen activator by established cell lines of mouse origin

The correlation between malignant transformation and increased plasminogen activator synthesis has been studied in a variety of established cell lines. In contrast to the behavior of secondary mouse embryo cultures, which always show increased fibrinolytic activity when transformed, no such correlation was found within the BALB/c 3T3 line and its transformed derivatives. Cell lines were established from tumors initiated in BALB/c mice by several transformed cell lines. These lines were generally found to contain no more plasminogen activator than the cells used for inoculation. A correlation was found between transformation and plasminogen activator synthesis within Swiss 3T3 cell lines. However, the correlation was not maintained by serum revertants of transformed Swiss 3T3 cells.
Antiviral Susceptibility of Variant Influenza A(H3N2)v Viruses Isolated in the United States from 2011 to 2013

ABSTRACT Since 2011, outbreaks caused by influenza A(H3N2) variant [A(H3N2)v] viruses have become a public health concern in the United States. The A(H3N2)v viruses share the A(H1N1)pdm09 M gene containing the marker of M2 blocker resistance, S31N, but do not contain any known molecular markers associated with resistance to neuraminidase (NA) inhibitors (NAIs). Using a fluorescent NA inhibition (NI) assay, the susceptibilities of recovered A(H3N2)v viruses (n = 168) to FDA-approved (oseltamivir and zanamivir) and other (peramivir, laninamivir, and A-315675) NAIs were assessed. All A(H3N2)v viruses tested, with the exception of a single virus strain, A/Ohio/88/2012, isolated from an untreated patient, were susceptible to the NAIs tested. The A/Ohio/88/2012 virus contained two rare substitutions, S245N and S247P, in the NA and demonstrated reduced inhibition by oseltamivir (31-fold) and zanamivir (66-fold) in the NI assay. Using recombinant NA (recNA) proteins, S247P was shown to be responsible for the observed altered NAI susceptibility, in addition to an approximately 60% reduction in NA enzymatic activity. The S247P substitution has not been previously reported as a molecular marker of reduced susceptibility to the NAIs. Using cell culture assays, the investigational antiviral drugs nitazoxanide, favipiravir, and fludase were shown to inhibit the replication of A(H3N2)v viruses, including the virus with the S247P substitution in the NA. This report demonstrates the importance of continuous monitoring of susceptibility of zoonotic influenza viruses to available and investigational antiviral drugs.
Some Analytic Results for the Study of Broadband Noise Radiation from Wings, Propellers and Jets in Uniform Motion

Alan Powell has made significant contributions to the understanding of many aeroacoustic problems, in particular, the problems of broadband noise from jets and boundary layers. In this paper, some analytic results are presented for the calculation of the correlation function of the broadband noise radiated from a wing, a propeller, and a jet in uniform forward motion. It is shown that, when the observer (or microphone) motion is suitably chosen, the geometric terms of the radiation formula become time independent. The time independence of these terms leads to a significant simplification of the statistical analysis of the radiated noise, even when the near field terms are included. For a wing in forward motion, if the observer is in the moving reference frame, then the correlation function of the near and far field noise can be related to a space-time cross-correlation function of the pressure on the wing surface. A similar result holds for a propeller in forward flight if the observer is in a reference frame that is attached to the propeller and rotates at the shaft speed. For a jet in motion, it is shown that the correlation function of the radiated noise can be related to the space-time cross-correlation of the Lighthill stress tensor in the jet. Exact analytical results are derived for all three cases. For the cases under present consideration, the inclusion of the near field terms does not introduce additional complexity, as compared to existing formulations that are limited to the far field.
Dynamic fair scheduling with QoS constraints in multimedia wideband CDMA cellular networks

A class of dynamic fair scheduling schemes based on the generalized processor sharing (GPS) fair service discipline, under the generic name code-division GPS (CDGPS), is proposed for a wideband direct-sequence code-division multiple-access (CDMA) cellular network to support multimedia traffic. The CDGPS scheduler makes use of both the traffic characteristics in the link layer and the adaptivity of the wideband CDMA physical layer to perform fair scheduling on a time-slot by time-slot basis, by using a dynamic rate-scheduling approach rather than the conventional time-scheduling approach. Soft uplink capacity is characterized for designing an efficient CDGPS resource allocation procedure. A credit-based CDGPS (C-CDGPS) scheme is proposed to further improve the utilization of the soft capacity by trading off the short-term fairness. Theoretical analysis shows that, with the C-CDGPS scheme, tight delay bounds can be provided to delay-sensitive traffic, and short-term unfairness can be bounded so that long-term weighted fairness for all users can still be satisfied. Simulation results show that bounded delays, increased throughput, and long-term fairness can be achieved for both homogeneous and heterogeneous traffic.
Stability in Force-Production Tasks

Abstract Exerting a force on a mechanical system can induce mechanical instability. To overcome that instability, humans may take advantage of their upper limb mechanical impedance (e.g., hand stiffness). The authors investigated what stiffness is required to maintain static stability and how humans can achieve that stiffness in the context of the task of pushing on a pivoting stick. Results showed that the stiffness required is in the range of measured human upper limb stiffness. To avoid an ill-posed problem, one can better express the requirements for stability as a simple geometrical criterion related to the curvature of the potential energy field at the hand. A planar model of the upper limb revealed that individuals can use both hand rotational and translational stiffness to stabilize a stick. Although hand rotational stiffness does not participate in producing the axial force on the stick, it can significantly contribute to achieving a limb stiffness appropriate for maintaining static stability. Hand rotational stiffness can be important for the design of hand tools, because humans can increase it only by augmenting grip force, a biomechanical factor associated with cumulative trauma injuries of the upper extremities.
Mechanical Performance of Asphalt Mortar Containing Hydrated Lime and EAFSS at Low and High Temperatures

In this paper, the possibility of improving the global response of asphalt materials for pavement applications through the use of hydrated lime and Electric Arc-Furnace Steel Slag (EAFSS) was investigated. For this purpose, a set of asphalt mortars was prepared by mixing two different asphalt binders with fine granite aggregate together with hydrated lime or EAFSS at three different percentages. Bending Beam Rheometer (BBR) creep tests and Dynamic Shear Rheometer (DSR) complex modulus tests were performed to evaluate the material response both at low and high temperature. Then, the rheological Huet model was fitted to the BBR creep results for estimating the impact of filler content on the model parameters. It was found that an addition of hydrated lime and EAFSS up to 10% and 5%, respectively, results in satisfactory low-temperature performance with a substantial improvement of the high-temperature behavior.
A new robust context-based dense CRF model for image labeling

Fully-connected conditional random fields (CRF) models have recently been developed for image labeling task to incorporate interactions of all pairs of pixels in the image. Efficient inference in fully-connected models is very sensitive to initialization of the unary potentials. In this paper, we propose a new robust context-based fully-connected CRF model which alleviates initialization sensitivity of inference in dense CRFs. The new model integrates an extra hidden node that accounts for the overall context of the image and is connected to all other pixel nodes. By incorporating the new context node in CRF graph, we maximize probability of labeling configuration jointly with the image's context. Therefore, wrong initializations of objects that contradict the overal context could be refined. We define the context-based unary and pairwise potentials and further derive the inference algorithm for the proposed model based on the mean field approximation method. We run experiments over the benchmark MSRC image database and demonstrate that the new model improves object recognition accuracy by about 21%. We show where the conventional CRF model is impeded by wrong initialization of unary potentials, the proposed model identifies the labels correctly.
Conditioning of Census 2000 Data Collected in Accuracy and Coverage Evaluation Block Clusters

To measure the overall and differential coverage of the U.S. population in Census 2000, the U.S. Census Bureau conducted the Accuracy and Coverage Evaluation (A.C.E.). The A.C. E. included various stages of sampling. An initial A.C. E. sample of block clusters was drawn, and housing units within the sampled block clusters were listed. Then, the A.C. E. sample was reduced through subsampling operations (Childers and Fenstermaker, 2000).