Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.
Effect of Iron Chelators on Labile Iron and Oxidative Status of Thalassaemic Erythroid Cells

Background/Aims: Iron accumulation in vital organs such as heart and liver is a major pathology in β-thalassaemia. It may also affect mature RBCs and developing erythroid precursors. The cellular damage is mainly caused by the labile iron pool (LIP) and is mediated by reactive oxygen species (ROS). We have previously shown that thalassaemic RBCs and their precursors have more LIP and ROS than their normal counterparts. We now report the effect of clinically relevant iron chelators on these parameters. Methods: RBCs, reticulocytes and cultured erythroid precursors derived from patients with β-thalassaemia were studied for LIP and oxidative stress parameters by flow-cytometry. Results: In vitro treatment with deferiprone, deferasirox and deferoxamine reduced the cytosolic LIP in RBCs and reticulocytes, and both the cytosolic and mitochondrial LIP in cultured erythroid precursors. This was associated with reduced oxidative stress (ROS and external phosphatidylserine). While the effect of deferiprone and deferasirox was fast (within 10 min), deferoxamine affected these parameters after 24 h, suggesting a slower rate of entry. Conclusion: The chelators studied reduce the LIP and the oxidative status of thalassaemic RBC and their precursors. Whether these effects directly improve ineffective erythropoiesis and RBC survival remains to be shown.
GALACTIC ULTRACOMPACT X-RAY BINARIES: DISK STABILITY AND EVOLUTION

We study the mass-transfer rates and disk stability conditions of ultracompact X-ray binaries (UCXBs) using empirical time-averaged X-ray luminosities from Paper I and compiled information from the literature. The majority of UCXBs are consistent with evolutionary tracks for white dwarf donors. Three UCXBs with orbital periods longer than 40 minutes have mass-transfer rates above 10−10 M☉ yr−1, inconsistent with white dwarf donor tracks. We show that if helium star donors can retain their initial high entropy, they can explain the observed mass-transfer rates of these UCXBs. Several UCXBs show persistent luminosities apparently below the disk instability limit for irradiated He accretion disks. We point out that a predominantly C and/or O disk (as observed in the optical spectra of several) lowers the disk instability limit, explaining this disagreement. The orbital period and low time-averaged mass-transfer rate of 2S 0918-549 provide evidence that the donor star is a low-entropy C/O white dwarf, consistent with optical spectra. We combine existing information to constrain the masses of the donors in 4U 1916-053 (0.064 ± 0.010 M☉) and 4U 1626-67 (<0.036 M☉ for a 1.4 M☉ neutron star). We show that 4U 1626-67 is indeed persistent, and not undergoing a transient outburst, leaving He star models as the best explanation for the donor.
The role of ANS acuity and numeracy for the calibration and the coherence of subjective probability judgments

The purpose of the study was to investigate how numeracy and acuity of the approximate number system (ANS) relate to the calibration and coherence of probability judgments. Based on the literature on number cognition, a first hypothesis was that those with lower numeracy would maintain a less linear use of the probability scale, contributing to overconfidence and nonlinear calibration curves. A second hypothesis was that also poorer acuity of the ANS would be associated with overconfidence and non-linearity. A third hypothesis, in line with dual-systems theory (e.g., Kahneman and Frederick, 2002) was that people higher in numeracy should have better access to the normative probability rules, allowing them to decrease the rate of conjunction fallacies. Data from 213 participants sampled from the Swedish population showed that: (i) in line with the first hypothesis, overconfidence and the linearity of the calibration curves were related to numeracy, where people higher in numeracy were well calibrated with zero overconfidence. (ii) ANS was not associated with overconfidence and non-linearity, disconfirming the second hypothesis. (iii) The rate of conjunction fallacies was slightly, but to a statistically significant degree decreased by numeracy, but still high at all numeracy levels. An unexpected finding was that participants with better ANS acuity gave more realistic estimates of their performance relative to others.
Information-Theoretic Characterization of Uncertainty in Manual Control

We present a novel approach for quantifying the impact of uncertainty in manual control, based on information and control theories and utilizing the information-theoretic capacity of empowerment, a task-independent universal utility measure. Empowerment measures, for agent-environment systems with stochastic transitions, how much influence, which can be sensed by the agent sensors, an agent has on its environment. It enables combining different types of disturbances, arising in human-machine systems (i.e. noise, delays, errors, etc.), into one single measure. We expand empowerment to manual control, demonstrate its application in the field of HCI and evaluate it in a user study. Results showed that empowerment is strictly monotonic in relation to the means of standard performance metrics total time off-target, perceived uncertainty, perceived performance and frustration, which suggests its potential in making theoretical predictions of other measures. Loss of empowerment implicated interesting trends in activity levels, which open a new area for future work. Results suggest the potential empowerment has in providing better theoretical foundations for the science of HCI.
A unified approach to the velocity-free consensus algorithms design for double integrator dynamics with input saturations

This paper considers the consensus problem of double integrator multi-agent systems where each agent is subject to input saturations, and the velocity (second state) of each agent is not available for feedback. We present a unified approach to the consensus algorithms design that extends most of the existent consensus algorithms developed for double integrator multi-agents in ideal situations to handel these two problems simultaneously. To illustrate the effectiveness of the proposed approach, we present solutions to three different second order consensus problems and provide simulation results.
Improved synapse detection for mGRASP-assisted brain connectivity mapping

Motivation: A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy. Results: We developed a fully automatic method for detecting mGRASP-labeled synapse puncta. By modeling each punctum as a Gaussian distribution, our method enables accurate detection even when puncta of varying size and shape partially overlap. The method consists of three stages: blob detection by global thresholding; blob separation by watershed; and punctum modeling by a variational Bayesian Gaussian mixture models. Extensive testing shows that the three-stage method improved detection accuracy markedly, and especially reduces under-segmentation. The method provides a goodness-of-fit score for each detected punctum, allowing efficient error detection. We applied this advantage to also develop an efficient interactive method for correcting errors. Availability: The software is available on http://jinny.kist.re.kr Contact: tingzhao@gmail.com; kimj@kist.re.kr
Capital inflows and capital outflows: measurement, determinants, consequences

This paper develops new estimates of capital outflows and is the first, to our knowledge, to analyze the determinants, consequences and inter-relationship between inflows and outflows. Given the dynamics and individual country effects, we use a panel-VAR and find that inflows and outflows are inter-related, that lower inflows/higher outflows lead to lower growth, and among other effects to a higher fiscal deficit, which feeds back to lower inflows/higher outflows. These results provide evidence of vicious and virtuous cycles. We find no strong evidence that official flows crowd-in private ones. We conclude it is particularly important for developing countries to maintain prudent policies, and especially adequate fiscal discipline, to avoid vicious and reinforce virtuous cycles.
A comparative study of different frequent pattern mining algorithm for uncertain data: A survey

Most of the data mining algorithms were designed to mine the frequent pattern from precise data. However, uncertainty exists in many real life situations such as sensor network and privacy preserving applications. To extract meaningful information from uncertain data a number of frequent pattern mining algorithms have been proposed. While dealing with uncertain data U-Apriori, UF-growth, UFP-growth, UH-mine, PUF-growth, TPC-growth algorithm are examples of existing frequent pattern mining algorithms, which utilize different approaches to mine frequent pattern. One important observation is that algorithms behave completely different in the uncertain database as compared to the precise database due of the inclusion of probability value. In this survey paper, a number of algorithms have been analyzed for finding the frequent pattern from uncertain database. The analysis is represented in the form of comparative study of following algorithm: U-Apriori, UF-growth, UFP-growth, UH-mine, and PUF-growth, TPC-growth algorithm on the basis on various parameters such as database scan, running time, memory utilization and storage structure. The survey paper also focuses on the advantage and limitation of each algorithm.
Crowd analysis with target tracking, K-means clustering and hidden Markov models

The paper presents a framework for crowd analysis that can handle both sparse and dense crowds, by combining micro- and macroscopic crowd analysis approaches. The paper focuses on detection, tracking and behaviour of dense crowds. We use multiple target tracking (MTT), group tracking, K-means clustering and hidden Markov models (HMM). K-means clustering is used to decide if micro- or macroscopic approaches should be used. A first evaluation, based on recorded and simulated data sets, has been done. The evaluation shows that MTT works well when the crowd is relatively sparse. When the crowd becomes dense track identities are easily switched between tracks. For dense crowds centroid-based group tracking is proposed. The algorithms for dense crowd detection and behavior recognition show promising results. The accuracies of the algorithms range from 84 % and above. Increased internal crowd activities will, however, temporarily reduce the accuracy of the centroid-based group tracking.
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification

In this paper, we provide a solution to multilingual sentiment classification using deep learning. Given input text in a language, we use word translation into English and then the embeddings of these English words to train a classifier. This projection into the English space plus word embeddings gives a simple and uniform framework for multilingual sentiment analysis. A novel idea is augmentation of the training data with polar words, appearing in these sentences, along with their polarities. This approach leads to a performance gain of 7-10% over traditional classifiers on many languages, irrespective of text genre, despite the scarcity of resources in most languages.
Aggressive Debridement Improves Outcome of Fungal Keratitis

PurposeTo study surgical debridement in the management of fungal keratitis in 44 patients. DesignA prospective randomized study. MethodsPatients with presumed fungal keratitis were randomized into 2 groups. The first group had diagnostic corneal scraping with a Kimura spatula while the second group underwent surgical debridement utilizing a motorized drill with attached diamond burr. Material obtained from both methods was plated on Sabaurauds medium, blood agar and stained for bacteria and fungi. Topical and systemic antifungal therapy was instituted, and patients were examined until resolution. ResultsPathogens were identified to a greater degree in the aggressive debridement group than in the Kimura spatula group. Patients who had aggressive debridement had a significant reduction in time to re-epithelialization and keratitis resolution (9.4 days) compared with the scraping group (17.1 days, P < 0.001). Mean uncorrected visual acuity (UCVA) in the surgical group was 1.02 logarithm of the minimal angle of resolution (logMAR) preoperatively and 0.43 logMAR postoperatively (P < 0.001). Mean UCVA in the Kimura spatula group was 1.20 logMAR before and 0.58 logMAR after scraping (P = 0.03). A single incidence of perforation occurred in the scraping group with the diagnosis of Fusarium made from the corneal button. ConclusionsAggressive debridement and debulking of fungal keratitis provides a faster clinical resolution with no reduction in visual potential. The acquisition of pathogens is more accurate with surgical debridement as compared with conventional scraping. Aggressive surgical debridement of keratitis not only aids in diagnosis but may also be therapeutic.
Improving Dementia Screening Tests with Machine Learning Methods

We applied Machine Learning (ML) methods to test whether their application on data derived from two simple tests of cognitive and functional skills can improve dementia screening. DSM-IV criteria for dementia were used to categorize patients into normal, cognitively impaired but not demented, and demented groups. The two tests are the Functional Activities Questionnaire (FAQ) and the Six-Item Blessed, Orientation, Memory and Concentration Exam (BOMC); both are recommended for screening by the Agency For Health Care Policy Research (AHCPR). The sample consisted of the initial visits of 609 normal, cognitively impaired or demented subjects evaluated at the UC Irvine Alzheimer's Disease Research Center (ADRC). Using published cuto criteria for the FAQ (> 8 is demented) and the BOMC (> 10 is demented) tests gave classi cation accuracies of 73% and 64% respectively. Combining the cuto criteria gave a 60% classi cation accuracy. On this same sample, we applied four ML algorithms using the FAQ and BOMC test response results. ML methods obtained 86% overall classi cation accuracy, an increase of 13-24% over conventional cuto criteria. For the cognitively impaired group, classi cation accuracy was 24% using cuto criteria, and 61-64% using ML methods, representing a 37-40% improvement. ML methods extract more information from these AHCPR-recommended tests to increase substantially their accuracy in detecting cognitive impairment and dementia, and to implement their combined use in a simple, descriptive way.
Prediction of Surface Roughness in Wire Electric Discharge Machining (WEDM) Process based on Response Surface Methodology

This paper presents an investigation on WEDM of pure titanium (grade-2). An attempt has been made to model the response variable i.e. surface roughness in WEDM process using response surface methodology. The experimental plan is based on Box-Behnken design. Six parameters i.e. pulse on time, pulse off time, peak current, spark gap voltage, wire feed and wire tension has been varied to investigate their effect on surface roughness. The surface roughness has been optimized using multi-response optimization through desirability. The ANOVA has been applied to identify the significance of developed model. The test results confirm the validity and adequacy of the developed RSM model. Finally, the optimum parametric setting has been designed for the optimization of process.
From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).
Joint source channel coding optimization for heterogeneous access networks in multiuser scenario

In a multiple access technologies scenario, aimed at providing the “always best connected” (ABC) service, the future fourth-generation wireless networks will be characterized by an increasing heterogeneity. When a multimedia transmission is considered, the ABC can be improved by the optimization of the source coding. In this work, we propose a joint source channel coding (JSCC) optimization technique, able to adapt the system parameters to channel variations, providing at the same time a high level of scalability of the encoded video bitstream. With the proposed JSCC method, we address the ABC issue in multi-user scenario affected by multiple interference. The results show that, despite the simplicity of the algorithm, the adaptive system experiences an improvement of the performance compared to fixed coding schemes.
Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods

Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 cells that can be occupied. We show that our method can correctly estimate the occupied cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.
Unaligned Profile Monitoring Using Penalized Methods

In some manufacturing processes, complex profiles are collected to characterize quality status. However, some of these profiles may have unequal lengths, which makes the attempt of directly comparing them difficult. In addition, when a shift occurs in a profile, it usually affects a segment of continuously connected observations. That is, local shifts instead of global shifts are frequently seen. As shift signals are easily mixed with allowable mean trends, statistical monitoring of such unaligned profiles becomes a challenging task. In this paper, we propose a framework for monitoring profiles with unequal lengths. The profiles are first aligned using a modified robust dynamic time warping algorithm, which is insensitive to local mean shifts. Penalization‐based methods are then used to estimate profile means. Finally, mean estimates are utilized in a likelihood ratio test statistic for effective monitoring. Both simulation studies and a real example are used to demonstrate the effectiveness of the proposed monitoring procedure. Copyright © 2016 John Wiley & Sons, Ltd.