Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.
Chinese Handwritten Writer Identification based on Structure Features and Extreme Learning Machine

In this paper,we propose a new approach for writer identification of Chinese handwritten.In our method, we deal with writer identification of Chinese handwritten using Chinese character structure features(CSF) and extreme learning machine(ELM). To extract the features embedded in Chinese handwriting characters, special structures have been explored according to the trait of Chinese handwriting characters,where 20 features are extracted from the structures, these features constitute patterns of writer handwriting. We also combine structure features with extreme learning machine (ELM) as a new scheme for writer recognition, ELM is single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN. This algorithm learns much faster than traditional popular learning algorithms. Experimental results demonstrate CSF/ELM method can achieve better performance than other traditional schemes for writer identification.
FLOOD: A Planning Framework for Reasoning with Linguistic Data

Open domain question answering is slowly broadening its horizons, expanding from simple factoid questions to encompass broader and more complex queries. Recent areas of interest include scenario-based question answering, incorporating existing domain knowledge during question analysis, various types of inference, and intelligently processing subquestions. FLOOD (standing for Fluent, Linguistic, ObjectOriented, and Dynamic) provides a system within which these problems may be explored. It consists of three parts: A Text Processor which integrates existing tools to provide an end-to-end semantic parse. This begins with simple sentence breaking and tokenization and ends with semantic role filling. The Text Processor is organized as a separate module applicable to other tasks outside of FLOOD. The FLOOD Planning Platform, an environment for authoring and utilizing simple linguistic planning domains and algorithms. The platform handles routine tasks such as domain and state space management while leaving specifics up to individual reasoning modules; the primary goals of the platform are to provide a convenient and flexible interface for reasoner and domain development. A Question Answering Reasoner operating within the context of the Planning Platform. The current implementation is based on the FLECS algorithm (Veloso and Stone, 1995) and performs simple inference and subquerying tasks based on the output of the text processor.
Minor Head Injury Symptoms and Recovery From Whiplash Injury: A 1-Year Prospective Study

Aim: To examine concussion-related disability in neck injuries, the Rivermead Head Injury Follow-Up Questionnaire (RHFUQ) was applied. Furthermore, we wanted to investigate symptoms found in post-concussion syndrome (PCS) and global pain, neck pain intensities obtained from acute whiplash patients within 1 week and at 6 months after injury in a prospective study on 1-year work disability. Methods: A total of 143 consecutive acute whiplash-injured patients were admitted to the study from the Emergency Unit (Aarhus University Hospital). Patients with direct head trauma or reported retro- or anterograde amnesia were excluded from the study. Average neck pain and global pain intensity were measured on a Visual Analogue Scale (VAS 0-10). The RHFUQ (10 items, score from 0 to 4, total score from 0 to 40) was fulfilled after 1 week and 6 months. Patients underwent neurological examination within 1 week after injury. Recovery (return to work) was assessed 1 year post-injury. Results: In total, 97% completed the study, and 9% (12/138) did not recover. Non-recovered patients reported more neck pain and global pain after 1 week (P < .003) and 6 months (P < .008) and higher PCS symptom score after 1 week (P < .001) and 6 months (P < .002). Using the RHFUQ total score as a predictive test, a receiver-operating characteristic curve (ROC) area of 0.77 (0.61-0.92) and a cut-off at 10 points revealed a sensitivity of 75% and a specificity of 67.2%. At 1 week, 8 of 10 items reached higher scores among non-recovered and 10 of 10 items after 6 months post-injury. Conclusions: RHFUQ is useful in acute whiplash patients for predicting 1-year work disability. PCS-related symptoms along with neck pain and global pain are more burdensome in the non-recovered group. This emphasizes that post-concussion symptoms are not a sign of brain injury alone, but are found in other types of mishaps like whiplash injuries.
Increasing efficiency of M-out-of-N redundancy

In industrial automation, unexpected failures often generate high direct and indirect costs. To achieve high availability, the critical parts of the system are typically redundant. M-out-of-N (MooN) redundancy is a widespread redundancy pattern because it offers low failover times. This pattern has however two major disadvantages. First, the voter, an essential entity in MooN patterns, is itself a single point of failure. Second, it introduces delay to the control process and significant network load when deployed with multiple voters. This article proposes new MooN redundancy patterns for distributed control systems. These eliminate the voter as a single point of failure and reduce the network load and delay of the MooN redundancy. For each of these patterns we introduce an algorithm, which computes a near-minimum instance for the MooN problem. We validate our solution against minimum values and present a case study giving the delays introduced through our redundancy patterns and the induced network load.
Contributed chapter Title : Model-based statistical analysis with examples from cancer and stem-cell biology

1 Summary Scientists are able to measure biological systems at incredible levels of detail and also to deploy interventions whose measured effects provide new knowledge about these systems. The variety of systems studied, interventions considered, and effects measured are equally incredible, and one might be surprised to know that a single intellectual framework provides the scientist a guide for how to infer new biological knowledge from the measured data. What we know is that the particular measurements (their numerical values as arranged in whatever Excel spreadsheet, database, or cloud-computing system), while in a way central, are in fact secondary during the intellectual generaton of new insights. Rather, what is primary is the process by which these data have emerged. The numbers are meaningless when stripped of information about their context and details of their generation. In statistical analysis, we achieve understanding by viewing our particular measurements as the realization of a random process. Of course, this is not to say that data are completely unpredictable. But if they were completely predictable, then we would not have bothered to do the necessary experiments to generate them! Indeed, statistics thrives in the grey zone between perfect knowledge and complete uncertainty. Research in this area rewards those who enjoy mathematical and computational thinking, who recognize the primacy of the biological problems, and who are keen to address these problems in order to advance the state of knowledge. Depending on the nature of the data and the type of system in focus, the precise way in which statistical analyses are deployed and the role of the calculations also vary greatly. In many cases, analysis centers on the construction, use, and assessment of probability models, and a great deal of current research in statistics aims to improve these activities. Very often the investigator has indirect measurements of the objects of primary scientific interest, and the statistical analysis seeks to infer plausible properties of these objects by accounting somehow for signal and noise characteristics of the system and measurement process. This situation is often addressed by constructing probability models of the data and by developing statistical methods based on these models. Two recent examples from my own collaborative work are discussed in the following sections. Statistical concepts employed include maximum likelihood estimation, hypothesis testing, bootstrap sampling, latent variables, nonparamet-2 ric modeling, and mixture modeling, among others. The examples aim to shed some light on the complex …
Experimental study of vascular reactivity in the fingertip: An infrared thermography method

Vascular reactivity (VR) test has been proved to be an effective method to predict the risk of future cardiovascular disease. A most common technique used to study VR is to analyze the hyperemic response after an ischemia through an arterial occlusion. Current techniques to monitor hyperemic response rely on monitoring blood flow parameters. As tissue temperature is directly impacted by blood flow, in this paper, an infrared thermograph method was used to investigate the variation of fingertip temperature during VR test. Subsequently, an image processing algorithm was developed to extract the temperature of special parts of the sequential thermal images. The measured results of temporal temperature variation clearly characterized the thermal state of a subject (vasoconstriction, vasodilation, and thermoneutral) as well as perfusion. Finally, a lumped parameter thermal model was employed to explain the experimental results.
License Plate Automatic Recognition based on edge detection

In this paper, we present an Automatic License Plate Recognition System (ALPRS) to identify license plates which is an application of image processing. The main process of ALPRS is divided into four steps: The noise in the image is removed by using FMH filter. A simple algorithm is used for background subtraction. Canny edge detection is used to localize the license plate location. Finally, letters and digits are extracted through template matching technique. The proposed algorithms have two advantages: First, the method has strong robustness against noise. Second, it can deal with license plates with different colors. The performance of the algorithm is tested in a real-time video stream. Based on the result, our algorithm shows the missing rate is almost 16% from 70 vehicle images.
High throughput implementation of the new secure hash algorithm through partial unrolling

A design approach to create small-sized high-speed implementation of the new version of secure hash algorithm is proposed. The resulted design can be easily embedded to operate in HMAC IP cores, providing a high degree of security. The proposed implementation does not introduce significant area penalty, compared to other competitive designs. However the achieved throughput presents an increase compared to commercially available IP cores that range from 43%-1830%.
Crossing the Finish Line: A Review Article

Human capital production in the U.S. economy is no longer what it used to be. Graduation rates for high school and college have fallen (Card and Lemieux 2001; Heckman and Lafontaine 2007). As a result, the rate of growth of the stock of human capital has slowed considerably (Goldin and Katz 2008). At the same time, other countries are improving their educational attainment. Although the United States still has the highest rate of tertiary attainment (Ehrlich 2007), this accomplishment should not be taken for granted. As recently as 1998, the United States ranked third in tertiary graduation rate among the OECD countries. By 2006, it barely made it into the top 10 (OECD 2008). Quantity is only one part of the problem because the human capital produced is also of lower quality. Out of the 30 OECD countries that participated in the 2006 Program for International Student Assessment, U.S. students ranked twenty-fifth (OECD 2007). Human capital is an important source of long-run growth (e.g., Lucas 1988; Becker, Murphy, and Tamura 1990; Romer 1990; Ehrlich and Lui 1991), so it is necessary to reverse this situation. Following up on the seminal contributions by Schultz (1959), Becker (1964), and Ben-Porath (1967), recent research has shown that the production of human capital starts before birth and continues over the entire life cycle (e.g., Cunha and Heckman 2007). It is produced by families (e.g., Cunha, Heckman, and Schennach, forthcoming) and schools (Rivkin, Hanushek, and Kain 2005; Todd and Wolpin 2007). In the late adolescent and early adulthood years, human capital investments are made via formal schooling (a college or post-baccalaureate education) or in the labor market via learning by doing and on-the-job training (e.g., Keane and Wolpin 1997, 2001, 2006; Cameron and Heckman 1998, 2001; Heckman, Lochner, and Taber 1998; Heckman, Lochner, and Cossa 2002). The market for higher education has been the focus of much research by economists (e.g., Clotfelter and Rothschild 1993; Hoxby 2004). A
Collaborating Across the Miles

In today's scientific environment scientists can no longer work in isolated laboratories. The problems being studied require experts from several fields to combine their talents. The instruments used to gather scientific data are expensive and diff icult to build so there are only a few in the world. The Internet connectivity and the powerful desktop workstations available today provide the potential to create collaboration tools and allow scientists to collaborate across the miles and access instruments remotely. The DOE 2000 program sponsored by the US Department of Energy (DOE) is developing new technologies and integrating existing technologies to create a software environment that allows collaboratories to become a reality. The DOE 2000 program is composed of two pilot programs putting collaboratories into practice and five research and development projects developing some of the tools required to complete the collaboratory environment that were not already available. These technologies include videoconferencing over the Internet, electronic notebooks, shared visualization, security infrastructure, floor control, and session management. Much of this work is in conjunction with other DOE and non-DOE development efforts. This paper describes the tools and technologies available today and some of the tools being developed to provide collaboration capabiliti es. Through collaboratories we are changing the way scientific experiments are carried out and the way scientists work together.
Patterns of interactions at grade 5 classroom in learning the topic of statistics viewed from cognitive load theory

The nature of interactions that occurs among teacher, students, learning sources, and learning environment creates different settings to enhance learning. Any setting created by a teacher is affected by 3 (three) types of cognitive load: intrinsic cognitive load, extraneous cognitive load, and germane cognitive load. This study is qualitative in nature, aims to analyse the patterns of interaction that are constituted in mathematics instructions by taking into account the cognitive load theory. The subjects of this study are 21 fifth-grade students who learn mathematics in small groups and whole-class interactive lessons. The data were collected through classroom observations which were videotaped, while field notes were also taken. The data analysis revealed that students engaged in productive interaction and inquiry while they were learning mathematics in small groups or in whole class setting, in which there was a different type of cognitive load that dominantly affecting the learning processes at each setting. During learning mathematics in whole class setting, the most frequently found interaction patterns were to discuss and compare solution based on self-developed models, followed by expressing opinions. This is consistent with the principles of mathematics learning, which gives students wide opportunities to construct mathematical knowledge through individual learning, learning in small groups as well as learning in whole class settings. It means that by participating in interactive learning, the students are habitually engaged in productive interactions and high level of mathematical thinking.
Hands-free telecommunication for elderly persons suffering from hearing deficiencies

Speech communication is the most natural form of human interaction. Communication by means of telephones, mobile phones or video-conference systems is common nowadays especially amongst younger persons. In the past years, also a growing amount of elderly people has started to extensively use communication systems since more and more people live apart from their relatives, friends or acquaintances. However, especially elderly people suffer from hearing loss, which often prevents them from using acoustic communication devices. While approximately every second European adult of age 65+ has a hearing loss that requires treatment, only the minority actually wears hearing aids for different reasons. To tackle this problem, this contribution deals with a personalized and adaptable communication system that enhances the acoustic signal and incorporates the individual hearing loss of a hearing-impaired person. By this, the typical elderly user is enabled to take part in natural communication again.
A Quantitative And Qualitative Approach To Knowledge Management – Episodic Extraction , Enhancement And Creation Of Tacit Knowledge

Capturing employee’s knowledge is of prime importance to any organization. If the knowledge is captured after a completion of entire task, quality and quantity of knowledge obtained from the expert decreases as it is human tendency to forget after a long interval of time. We, in this paper have developed a model emphasizing the need for episodic extraction, enhancement and creation of the tacit knowledge which will increase the quality and the quantity of the tapped tacit knowledge. With this model, the organizations will be able to capture the knowledge of employees in phased manner so that when an individual leaves the organization, the loss incurred by the organization is minimized to a great extent.
Gemcitabine in metastatic nasopharyngeal carcinoma of the undifferentiated type.

BACKGROUND We conducted two parallel phase II trials in chemonaïve and previously treated patients with metastatic nasopharyngeal carcinoma (NPC) to evaluate the tumour response, progression-free and overall survival, and toxicity of gemcitabine. PATIENTS AND METHODS Gemcitabine 1250 mg/m2 was given on days 1 and 8 of a 21-day cycle. Patients with an Eastern Cooperative Oncology Group performance status <2, adequate renal, hepatic and bone marrow function, and radiologically measurable NPC were eligible. RESULTS Twenty-five chemonaïve and 27 previously treated patients were enrolled. The overall response rate was 28% [95% confidence interval (CI) 14% to 48%] for the chemonaïve and 48% (95% CI 31% to 66%) for previously treated patients. Toxicities greater than or equal to grade 3 occurred in 15 (60%) chemonaïve and 13 (48%) previously treated patients. Neutropenia was uncommon in chemonaïve patients, but occurred in 37% of previously treated patients. The median time to progression was 3.6 months (range 0.9-7.9) for chemonaïve and 5.1 months (0.9-13.1) for previously treated patients. Median overall survival time was 7.2 months (1.4-15.6) and 10.5 months (2.4-15.0) for chemonaïve and previously treated patients, respectively. CONCLUSIONS Gemcitabine has moderate activity in NPC with minimal toxicity, and is also an effective salvage agent for patients who have failed or progressed after treatment with other agents.
A D-pseudomanifold with Fo Vertices Has at Least Dfo-(d-1 )(d+2) D-simplices

Barnette was the first o prove that if fk is the number of k-faces of a simple (d+l)-polytope P then (*) fo • dfd' (d-1)(d+2). He later extended (*) to a graph-theoretic setting and was thereby enabled to prove the dual inequality for triangulated d-manifolds. Here his methods are used to provide a different graph-theoretic extension of (*) and thus extend the dual inequality to simplicial d-pseudomanifolds.
DISCOVERIES FROM A NEAR-INFRARED PROPER MOTION SURVEY USING MULTI-EPOCH TWO MICRON ALL-SKY SURVEY DATA

We have conducted a 4030 deg2 near-infrared proper motion survey using multi-epoch data from the Two Micron All-Sky Survey (2MASS). We find 2778 proper motion candidates, 647 of which are not listed in SIMBAD. After comparison to Digitized Sky Survey images, we find that 107 of our proper motion candidates lack counterparts at B, R, and I bands and are thus 2MASS-only detections. We present results of spectroscopic follow-up of 188 targets that include the infrared-only sources along with selected optical-counterpart sources with faint reduced proper motions or interesting colors. We also establish a set of near-infrared spectroscopic standards with which to anchor near-infrared classifications for our objects. Among the discoveries are six young field brown dwarfs, five “red L” dwarfs, three L-type subdwarfs, twelve M-type subdwarfs, eight “blue L” dwarfs, and several T dwarfs. We further refine the definitions of these exotic classes to aid future identification of similar objects. We examine their kinematics and find that both the “blue L” and “red L” dwarfs appear to be drawn from a relatively old population. This survey provides a glimpse of the kinds of research that will be possible through time-domain infrared projects such as the UKIDSS Large Area Survey, various VISTA surveys, and WISE, and also through z- or y-band enabled, multi-epoch surveys such as Pan-STARRS and LSST.