Miracle-dz/UniFAR-FTUs huggingface dataset:
A Design Framework and Its Applications for Tangible Panel Display

Various information displays are becoming available for implementing new kinds of human computer interaction (HCI) methods. Among many types and models, touch panel displays have been used in wide range of applications and are proven to be a useful infrastructure for creating intuitive HCI. In spite of their popularity, there are some weak points. The most serious drawback is their hardness to operate especially for the weak in information technology such as elderly and blind users. A tactile feedback function has a potential ability for enabling them to make full use of the devices. We propose an approach for effectively designing user-friendly HCI based on the tactile feedback. We exemplify our approach through the design and development of a few practical applications, an electronic voting system and a disaster information exploration system. The applications are targeted at supporting the weak, but the touch interactions quite are useful for general public for improving the stability and the degree of satisfaction in operations. These systems use a touch panel haptic display for helping the users to operate with straightforward touch sensations. It allows them to easily confirm, select, and activate their desired functions. We also conducted some evaluations to verify the effectiveness of the proposed framework.
Learning context-free grammars from stochasticstructural information

We consider the problem of learning context-free grammars from stochastic structural data. For this purpose, we have developed an algorithm (tlips) which identiies any rational tree set from stochastic samples and approximates the probability distribution of the trees in the language. The procedure identiies equivalent subtrees in the sample and outputs the hypothesis in linear time with the number of examples.
Design of a Hamming-distance classifier for ECG biometrics

In existing ECG-based biometric recognition systems, the feature extraction and matching are performed in Euclidean spaces. However, there are many scenarios (e.g., biometric template encryption for privacy protection, or low-complexity classification in an identification mode of operation) in which it is useful to binarize the feature vectors. The main contribution of this paper is a Hamming-distance classifier for ECG biometrics based on SPEC-Hashing. The proposed system was evaluated over a database of ECG signals from 52 different subjects that were collected at the Biometrics Security Laboratory of the University of Toronto. The EER of the Hamming-distance classifier was found to be 5.5% for closed-set matching and 14.82% for open set matching.
Shifting Regret, Mirror Descent, and Matrices

We consider the problem of online prediction in changing environments. In this framework the performance of a predictor is evaluated as the loss relative to an arbitrarily changing predictor, whose individual components come from a base class of predictors. Typical results in the literature consider different base classes (experts, linear predictors on the simplex, etc.) separately. Introducing an arbitrary mapping inside the mirror decent algorithm, we provide a framework that unifies and extends existing results. As an example, we prove new shifting regret bounds for matrix prediction problems.
Neurogenesis, Neurodegeneration, Interneuron Vulnerability, and Amyloid-β in the Olfactory Bulb of APP/PS1 Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, mostly idiopathic and with palliative treatment. Neuropathologically, it is characterized by intracellular neurofibrillary tangles of tau protein and extracellular plaques of amyloid β peptides. The relationship between AD and neurogenesis is unknown, but two facts are particularly relevant. First, early aggregation sites of both proteinopathies include the hippocampal formation and the olfactory bulb (OB), which have been correlated to memory and olfactory deficits, respectively. These areas are well-recognized integration zones of newly-born neurons in the adult brain. Second, molecules, such as amyloid precursor protein (APP) and presenilin-1 are common to both AD etiology and neurogenic development. Adult neurogenesis in AD models has been studied in the hippocampus, but only occasionally addressed in the OB and results are contradictory. To gain insight on the relationship between adult neurogenesis and AD, this work analyzes neurogenesis, neurodegeneration, interneuron vulnerability, and amyloid-β involvement in the OB of an AD model. Control and double-transgenic mice carrying the APP and the presenilin-1 genes, which give rise amyloid β plaques have been used. BrdU-treated animals have been studied at 16, 30, 43, and 56 weeks of age. New-born cell survival (BrdU), neuronal loss (using neuronal markers NeuN and PGP9.5), differential interneuron (calbindin-, parvalbumin-, calretinin- and somatostatin-expressing populations) vulnerability, and involvement by amyloid β have been analyzed. Neurogenesis increases with aging in the granule cell layer of control animals from 16 to 43 weeks. No neuronal loss has been observed after quantifying NeuN or PGP9.5. Regarding interneuron population vulnerability: calbindin-expressing neurons remains unchanged; parvalbumin-expressing neurons trend to increase with aging in transgenic animals; calretinin-expressing neurons increase with aging in transgenic mice and decrease in control animals and neurogenesis is higher in control as compared to transgenic animals at given ages, finally; somatostatin-expressing neurons of transgenic mice decrease with aging and as compared to controls. Amyloid β aggregates with aging in the granule cell layer, which may be related to the particular involvement of somatostatin-expressing cells.
Driving Forces of Arable Land Conversion in China

This paper examines the major driving forces behind the conversion of arable land to non-agricultural uses in china during the reform years (1978-1995). Both graphic techniques, based on a Geographic Information System (GIS), and an econometric modeling approach are employed. It is found that industrialization and land losses due lo land degradation have played an equally important role in reducing the gross quantity of arable land. The findings suggest that strengthening protection measures for arable land against natural hazards will significantly contribute to maintaining the gross scale of arable land. In order to save high-quality farmland it is necessary to increase the intensity of non-agricultural land uses through both economic incentives and administrative measures.
Accounting Disclosure in Companies Listed on the Egyptian Stock Exchange

Knowledge can be transferred and imported across international borders. However, recent research shows that local cultures may affect the acceptance and application of such imported knowledge content. Egypt's adoption of IAS in 1996 provides an excellent opportunity to examine how conflicts between imported accounting standards and a nation's cultural values affect implementation of the new standards. This manuscript focuses on one cultural variable; secrecy, that was found to be high in the Egyptian market and its effect on the actual disclosures as applied by companies that are listed in the Egyptian stock market. The research is conducted through detailed analysis of the disclosures of the financial statements for the year 2004 of the population of actively traded companies in the Egyptian Stock Market as reported by the Egyptian Capital Market Authority. The findings of this research indicate that the disclosure level for the studied companies averaged 61% of the required level that is mandated by the Egyptian Capital Market Authority requirements that are based on IAS. Also, most of the non-compliance could be explained by cultural reasons. The companies were selective in their choice of what to comply with and what not to.
The diffusion of docetaxel in patients with metastatic prostate cancer.

BACKGROUND Diffusion of new cancer treatments can be both inefficient and incomplete. The uptake of new treatments over time (diffusion) has not been well studied. We analyzed the diffusion of docetaxel in metastatic prostate cancer. METHODS We identified metastatic prostate cancer patients diagnosed from 1995 to 2007 using the Surveillance, Epidemiology, and End Results Program (SEER)-Medicare database. Medicare claims through 2008 were analyzed. We assessed cumulative incidence of docetaxel by socioeconomic, demographic, and comorbidity variables, and compared diffusion patterns to landmark events including release of phase III results and FDA approval dates. We compared docetaxel diffusion patterns in prostate cancer to those in metastatic breast, lung, ovarian, and gastric cancers. To model docetaxel use over time, we used the classic "mixed influence" deterministic diffusion model. All statistical tests were two-sided. RESULTS We identified 6561 metastatic prostate cancer patients; 1350 subsequently received chemotherapy. Among patients who received chemotherapy, docetaxel use was 95% by 2008. Docetaxel uptake was statistically significantly slower (P < .01) for patients older than 65 years, blacks, patients in lower income areas, and those who experienced poverty. Eighty percent of docetaxel diffusion occurred prior to the May, 2004 release of phase III results showing superiority of docetaxel over standard-of-care. The maximum increase in the rate of use of docetaxel occurred nearly simultaneously for prostate cancer as for all other cancers combined (in 2000). CONCLUSION Efforts to increase the diffusion of treatments with proven survival benefits among disadvantaged populations could lead to cancer population survival gains. Docetaxel diffusion mostly preceded phase III evidence for its efficacy in castration-resistant prostate cancer, and appeared to be a cancer-wide-rather than a disease-specific-phenomenon. Diffusion prior to definitive evidence indicates the prevalence of off-label chemotherapy use.
A Global Arnoldi Method for Large Non-Hermitian Eigenproblems with Special Applications to Multiple Eigenproblems

Global projection methods have been used for solving numerous large matrix equations, but nothing has been known on if and how this method can be proposed for solving large eigenproblems. In this paper, a global Arnold method is proposed for large eigenproblems. It computes certain F-Ritz pairs that are used to approximate some eigenpairs. The global Arnoldi method inherits convergence properties of the standard Arnoldi method applied to a larger matrix whose distinct eigenvalues are the eigenvalues of the original given matrix. As an application, assuming that A is diagonalizable, we show that the global Arnoldi method is able to solve multiple eigenvalue problems. To be practical, we develop an implicitly restarted global Arnoldi algorithm with certain F-shifts suggested. In particular, this algorithm can be adaptively used to solve multiple eigenvalue problems. Numerical experiments show that the algorithm is efficient for the eigenproblem and is reliable for quite ill-conditioned multiple eigenproblems.
A novel classifier using random sampling and expert knowledge

In this work we investigate several issues in order to improve the performance of decision trees. Firstly, we introduced or adopt a new composite splitting criterion aimed to improve classification accuracy. Secondly, we derive a new pruning technique using expert knowledge, which is able to significantly reduce the size of tree without degrading the classification accuracy. Finally, we implemented our new splitting criterion and pruning technique to form a new decision tree model; Classification Using Randomization and Expert knowledge (CURE). Carried out experiments using 40 UCI datasets on four existing algorithms showed empirical effectiveness of the devised approach.
Optimal switching function discrete sliding-mode control based on unmatched uncertainties states observer

For a class of discrete unmatched uncertain systems with incompletely measured states, a stable closed-loop sliding-mode control method based on the unmatched uncertain states observer is proposed. The output error feedback matrix is obtained by transformation of uncertainties state equation and solving the linear matrix inequalities (LMI). The stability of the states observer is proved. A reaching-law sliding-mode controller is presented for this discrete system by employing the results of the observer. To enhance the stability of the reaching-law sliding-mode controller, the stability margin is defined by using the min-max eigenvalue ration method. By deducing the min-max Lyapunov difference function, solving linear programming and optimizing the switching function, the closed-loop sliding-mode controller can get the maximum stability margin. The simulation results show the effectiveness of the proposed control method.
Examining listener reaction time as a measure of speech disorder

Historically, speech intelligibility has been the key measure of dysarthria severity, and both rating scales and orthographic transcription have been used as the primary forms of assessment. However, due to limitations in these measures, such as ceiling effects in transcriptions and subjective biases in rating scales, different forms of assessment are required to accurately assess speech severity in people with dysarthria. This study seeks to address these limitations by examining a new form of dysarthria assessment: listener reaction time. Listeners (one group of 16 younger adults, one of 16 older adults) performed three measures of speech intelligibility: a sentence transcription task, a rating scale task and reaction time task to veracity statements. These three measures were significantly correlated to one another, with reaction time accounting for 88% of the variance in transcription scores and 84% of the variance in the ratings of dysarthria provided by the younger listener group. Overall, the younger listeners produced significantly different reaction times when listening to speakers with dysarthria as compared to healthy control speakers. Reaction times between pairs of speakers (one with dysarthria, one control) were significantly different for all but the two speakers with the mildest dysarthria. In examining the older listener data, similar associations between assessment scores were found. However, the older listeners produced significantly longer reaction times than the younger group, and age and hearing loss were identified as significant factors for both reaction time and orthographic transcription. REACTION TIME TO DYSARTHRIA III
Exploitation of Regression Line Potentiality to Track the Object through Color Optical Flow

Normally gray images are less potential with the optical flow, especially to emulate relevant information. Since very long time the color optical flow strategy had been ignored. In this work, we are proposing a strategy which attempts to separate out the optical flow for each channels such as R, G and B through Horn-Schunk with Barren, Fleet and Beuchemin (BFB) kernel. Subsequently, obtained upshots are overlaid to get the rich information of the motion to detach the moving objects. Consequently the histograms of the moving objectss are employed to create the regression lines and extract product-moment correlation of each moving object. This coefficient utilized to match between the template and the candidate templates. Hence the template is updated with the best match. Further the bounding box is enclosed over the object based on spatial information of updated template.
A Knowledge-Light Nonlinear Case-Based Reasoning Approach to Radiotherapy Planning

Radiation is often used in prostate cancer treatments. Radiotherapy treatment planning is a complex decision making process. In order to make a patient well, the oncologist has to make a trade-off between the benefit of radiation i.e. delivering a high enough dose to fight the cancer cells, and risk that refers to the side effects of the treatment. In this research, a case based reasoning system is developed for dose planning in prostate cancer treatment. A case similar to the new patient is retrieved. An adaptation of the suggested plan is performed taking into account trade-offs made in the past to determine the extent to which the maximum dose limits of the new patient (case) can be violated. The performance of the proposed method is validated on the real data sets obtained from the City Hospital Nottingham, University Hospital, NHS Trust, UK.
CAM-BRAIN Growing an Artificial Brain with a Million Neural Net Modules Inside a Trillion Cell Cellular Automata Machine

This paper reports on a research project which aims to build (i.e. grow/evolve) an artificial brain by the year 2001. This artificial brain should contain thousands of interconnected artificial neural network modules, and be capable of controlling approximately 1000 "behaviors" in a "robot kitten". The name given to this research project is "CAM-Brain", because the neural networks (based on cellular automata) will be grown inside special hardware called Cellular Automata Machines (CAMs). Using a family of CAMs, each with its own processor to measure the performance quality or fitness of the evolved neural circuits, will allow the neural modules and their interconnections to be grown/evolved at electronic speeds. State of the art in CAM design is about 10 to the power 9 or 10 cells. Since a neural module of about 15 connected neurons can fit inside a cube of 100 cells on a side (1 million cells), a CAM which is specially adapted for CAM-Brain could contain thousands of interconnected modules, i.e. an artificial brain.
Validation of a clinical score for assessing the risk of resistant pathogens in patients with pneumonia presenting to the emergency department.

BACKGROUND Resistant organisms (ROs) are increasingly implicated in pneumonia in patients presenting to the emergency department (ED). The concept of healthcare-associated pneumonia (HCAP) exists to help identify patients infected with ROs but may be overly broad. We sought to validate a previously developed score for determining the risk for an RO and to compare it with the HCAP definition. METHODS We evaluated adult patients admitted via the ED with bacterial pneumonia (January-December 2010). We defined methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa, and extended-spectrum β-lactamases as ROs. The risk score was as follows: 4, recent hospitalization; 3, nursing home; 2, chronic hemodialysis; 1, critically ill. We evaluated the screening value of the score and of HCAP by determining their areas under the receiver-operating characteristic (AUROC) curves for predicting ROs. RESULTS The cohort included 977 patients, and ROs were isolated in 46.7%. The most common organisms included MRSA (22.7%), P. aeruginosa (19.1%), and Streptococcus pneumoniae (19.1%). The risk score was higher in those with an RO (median score, 4 vs 1; P < .001). The AUROC for HCAP equaled 0.62 (95% confidence interval [CI], .58-.65) versus 0.71 (95% CI, .66-.73) for the risk score. As a screening test for ROs, a score > 0 had a high negative predictive value (84.5%) and could lead to fewer patients unnecessarily receiving broad-spectrum antibiotics. CONCLUSIONS ROs are common in patients presenting to the ED with pneumonia. A simple clinical risk score performs moderately well at classifying patients regarding their risk for an RO.
Gender recognition based on multiple scale textural feature

Traditional gender recognition technologies with single feature cannot express an image completely and are limited by their recognition speed and accuracy. In this paper, we explored a way of fulfilling this task by combing the characteristics of both Haar-like and textural feature and proposed the approach to construct a multiple scale textural feature (MST), meanwhile, in order to achieve height recognition accuracy, we further improved the Adaboost algorithm improved by Freidman et al. Data from experiments based on MIT database show that our MST feature working together with the improved Adaboost algorithm can obtain a recognition rate of 86%.
Microbial culturomics unravels the halophilic microbiota repertoire of table salt: description of Gracilibacillus massiliensis sp. nov.

Background Microbial culturomics represents an ongoing revolution in the characterization of environmental and human microbiome. Methods By using three media containing high salt concentration (100, 150, and 200 g/L), the halophilic microbial culturome of a commercial table salt was determined. Results Eighteen species belonging to the Terrabacteria group were isolated including eight moderate halophilic and 10 halotolerant bacteria. Gracilibacillus massiliensis sp. nov., type strain Awa-1T (=CSUR P1441=DSM 29726), is a moderately halophilic gram-positive, non-spore-forming rod, and is motile by using a flagellum. Strain Awa-1T shows catalase activity but no oxidase activity. It is not only an aerobic bacterium but also able to grow in anaerobic and microaerophilic atmospheres. The draft genome of G. massiliensis is 4,207,226 bp long, composed of 13 scaffolds with 36.05% of G+C content. It contains 3,908 genes (3,839 protein-coding and 69 RNA genes). At least 1,983 (52%) orthologous proteins were not shared with the closest phylogenetic species. Hundred twenty-six genes (3.3%) were identified as ORFans. Conclusions Microbial culturomics can dramatically improve the characterization of the food and environmental microbiota repertoire, deciphering new bacterial species and new genes. Further studies will clarify the geographic specificity and the putative role of these new microbes and their related functional genetic content in environment, health, and disease.
Partially Improper Gaussian Priors forNonparametric Logistic

A \partially improper" Gaussian prior is considered for Bayesian inference in logistic regression. This includes generalized smoothing spline priors that are used for nonparametric inference about the logit, and also priors that correspond to generalized random e ect models. Necessary and su cient conditions are given for the posterior to be a proper probability measure, and bounds are given for the tails of the posterior density. These results are applied to investigate Monte Carlo methods for approximating the posterior.
Phonetic Change and Phonology

I will propose that a model of phonetic change which adequately captures these effects, describes it as a change in language specific phonetic implementation of phonological objects. Phonetic change, then, can be localized to the implementation of particular phonological features, or bundle of features. I will conclude with some remarks about the consequences of this model for the nature of phonological representation.
From keywords to keyqueries: content descriptors for the web

We introduce the concept of keyqueries as dynamic content descriptors for documents. Keyqueries are defined implicitly by the index and the retrieval model of a reference search engine: keyqueries for a document are the minimal queries that return the document in the top result ranks. Besides applications in the fields of information retrieval and data mining, keyqueries have the potential to form the basis of a dynamic classification system for future digital libraries---the modern version of keywords for content description. To determine the keyqueries for a document, we present an exhaustive search algorithm along with effective pruning strategies. For applications where a small number of diverse keyqueries is sufficient, two tailored search strategies are proposed. Our experiments emphasize the role of the reference search engine and show the potential of keyqueries as innovative document descriptors for large, fast evolving bodies of digital content such as the web.