Miracle-dz/UniFAR-FTUs huggingface dataset:
IARMMD: A Novel System for Incremental Association Rules Mining from Medical Documents

This paper presents a novel system for Incremental Association Rules Mining from Medical Documents (IARMMD). The system concerns with maintenance of the discovered association rules and avoids redoing the mining process on whole documents during the updating process. The design of the system is based on concepts representation. It designed to develop our previous D-EART system. The IARMMD improves the updating process, and will lead to decrease the number of scanning and the execution time. The system consists of three phases that are Text Preprocessing, Incremental Association Rule Mining, and Visualization phase. Hash-based Incremental Association Rule Mining Algorithm (HIARM) is introduced in the mining phase. The algorithm employs the power of data structure called Hash Table. The performance of the algorithm is compared with both Apriori and FUP algorithms for the execution time and the evaluation of the extracted association rules. The results reveal that the number of extracted association rules in the IARMMD system is always less than that in Apriori-based and FUP-based systems. Furthermore, the execution time of HIARM algorithm is much better than Apriori and FUP algorithms in the updating process in all experimental cases.
Three-dimensional mapping of gyral shape and cortical surface asymmetries in schizophrenia: gender effects.

OBJECTIVE People with schizophrenia exhibit abnormalities in brain structure, often in the left hemisphere. Disturbed structural lateralization is controversial, however, and effects appear mediated by gender. The authors mapped differences between schizophrenic and normal subjects in gyral asymmetries, complexity, and variability across the entire cortex. METHOD Asymmetry and shape profiles for 25 schizophrenic patients (15 men) and 28 demographically similar normal subjects (15 men) were obtained for 38 gyral regions, including the sylvian fissure and temporal and postcentral gyri, by using magnetic resonance data and a novel surface-based mesh-modeling approach. Cortical complexity was examined for sex and diagnosis effects in lobar regions. Intragroup variability was quantified and visualized to assess regional group abnormalities at the cortical surface. RESULTS The patients showed greater variability in frontal areas than the comparison subjects. They also had significant deviations in gyral complexity asymmetry in the superior frontal cortex. In temporoparietal regions, significant gyral asymmetries were present in both groups. Sex differences were apparent in superior temporal gyral measures, and cortical complexity in inferior frontal regions was significantly greater in men. CONCLUSIONS Cortical variability and complexity show regional abnormalities in the frontal cortex potentially specific to schizophrenia. The results indicate highly significant temporoparietal gyral asymmetries in both diagnostic groups, contrary to reports of less lateralization in schizophrenia. Substantially larger study groups are necessary to isolate smaller deviations in surface asymmetries, if present in schizophrenia, suggesting their diagnostic value is minimal.
Revealing infrared populations of nearby galaxies using the Spitzer Space Telescope

Abstract Due to their brightness in infrared, asymptotic giant branch (AGB) stars are in important evolutionary stage to be understood at this wavelength. In particular, in next decades, when the infrared optimised telescopes, such as the JWST and the ELT are in operation, it will be essential to include the AGB phase more precisely into the population synthesis models. However, the AGB phase is still one of the remaining major problems in the stellar evolution. This is because the AGB stellar evolution is strongly affected by the mass-loss process from the stars. It is important to describe mass loss more accurately so as to incorporate it into stellar evolutionary models. Recent observations using the Spitzer Space Telescope (SST) enabled us to make a significant progress in understanding the mass loss from AGB stars. Moreover, the SST large surveys contributed to our understanding of the role of AGB stars in chemical enrichment process in galaxies. Here we present the summary of our recent progress.
Evaluative Pattern Extraction for Automated Text Generation

Getting travel tips from the experienced blo g-gers and online forums has been one of the important supplements to the travel guidebook in the web society. In this paper we present a novel approach by identifying and extracting evaluative patterns, providing a different li n-guistically - motivated framework for aut o-mated evaluative text generation. We target at domain - specific observation in online travel blogs in Chinese. Results suggest that the s e-mantic prosody accompanying the patterns demonstrates that online travel bloggers prefer to employ tacit pragmatic strategy in pr e-senting their sentiment polarity in comments. The extracted patterns and their differentiation can be beneficial to identifying and characte r-izing evaluative language for further aut o-mated opinion summarization and ma c-ro/micro planning in natural language generation ( NLG ) as well.
Does α‐synuclein modulate dopaminergic synaptic content and tone at the synapse?

α‐Synuclein is a key component of the pathological process of neurodegeneration in Parkinson's disease. Although its contributions to normal physiological conditions remain elusive, converging observations suggest that a primary function of this protein in dopaminergic neurons may be the regulation of dopamine content and synaptic tone at the synapse. We review here cumulative evidence that demonstrates the participation of α‐synuclein in the life cycle of dopamine from its synthesis, storage, release, and reuptake. The regulatory role of α‐synuclein on dopamine metabolism is assessed by discussing the experimental evidence supporting each of these observations in the healthy physiological maintenance of dopaminergic neurons, as well as showing how disruption of these events can initiate the observed neurotoxicity of α‐synuclein and the genesis of the degenerative processes associated with Parkinson's disease. —Sidhu, A. , Wersinger, C. , Vernier, P. Does α‐synuclein modulate dopaminergic synaptic content and tone at the synapse?
Comparative study of invariant descriptors for face recognition

We present in this paper a comparative study of invariant descriptors for face recognition. Despite of their multiple advantages (pose and scale invariant characterization), few methods from the state of the art exploit this kind of features. A support vector machine is used for the training and recognition steps. Experimental results show the efficiency of such descriptors on the AR face database. With the proposed method, face recognition is correctly achieved in 97.4% cases
Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis

Learning and recognizing the activities of daily living (ADLs) of an individual is vital when providing an individual with context-aware at-home healthcare. In this work, unobtrusive detection of inhabitants’ activities in the home environment is implemented through the smartphone and wearable wireless sensor belt solution. A hybrid classifier is developed by combining threshold-based methods and machine learning mechanisms. Features extracted from the raw inertial sensor data are collected from a Body Area Network (BAN) (consisting of the Zephyr BioHarness sensor and an Android smartphone), and are used to build classification models using different machine learning algorithms. A cloud-based data analytics framework is developed to process different classification models in parallel and to select the most suitable model for each user. The evolving machine learning mechanism makes the model become customizable and self-adaptive by utilizing a cloud infrastructure which also overcomes the limitation of the computing power and storage of a smartphone. Furthermore, we investigate methods for adapting a universal model, which is trained using the data set of all users, to an individual user through an unsupervised learning scheme. The evaluation results of the experiments conducted on eight participants indicate that the proposed approach can robustly identify activities in real-time across multiple individuals: the highest recognition rate achieved 98% after a few runs.
A temporal model of text periodicities using Gaussian Processes

Temporal variations of text are usually ignored in NLP applications. However, text use changes with time, which can affect many applications. In this paper we model periodic distributions of words over time. Focusing on hashtag frequency in Twitter, we first automatically identify the periodic patterns. We use this for regression in order to forecast the volume of a hashtag based on past data. We use Gaussian Processes, a state-ofthe-art bayesian non-parametric model, with a novel periodic kernel. We demonstrate this in a text classification setting, assigning the tweet hashtag based on the rest of its text. This method shows significant improvements over competitive baselines.
Time-Domain Optoelectronic Vector Network Analysis on Coplanar Waveguides

The development of a one-port laser-based vector network analyzer (VNA) is described. Measurements are carried out in the time domain using a femtosecond laser for the generation and detection of ultrashort voltage pulses on a coplanar waveguide (CPW). The accurate separation of voltage signals propagating in forward and backward directions, being one of the key tasks of vector network analysis, is achieved by measuring voltage pulses at different positions on the CPW. This separation technique even works in the case of temporally overlapping forward and backward propagating signals. With this laser-based VNA, a frequency spacing of 500 MHz, a measurement bandwidth of approximately 500 GHz, and a dynamic range of more than 40 dB are obtained. As an application example, the laser-based VNA is used to realize an ultra-broadband voltage pulse standard.
The effects of ezetimibe on surrogate markers of cholesterol absorption and synthesis in Japanese patients with dyslipidemia.

AIM To demonstrate the clinical benefit of inhibiting intestinal cholesterol absorption, we evaluated the effects of ezetimibe on surrogate markers of cholesterol absorption and synthesis, lipid and glucose metabolism, and markers of obesity and inflammation. METHODS A total of 120 patients with dyslipidemia (46 men; mean age 66.5 years), who had not achieved the low density lipoprotein cholesterol (LDL-C) goal recommended by the Japan Atherosclerosis Society Guideline despite diet and exercise or any statin therapy, were enrolled and additionally treated with ezetimibe (10 mg/day) for 12 weeks. RESULTS Compared to the baseline, LDL-C was reduced by 19.2% (p<0.001) after ezetimibe monotherapy and by 24.7% (p<0.001) after co-administration with ezetimibe and any statin. Ezetimibe therapy decreased cholesterol absorption markers and increased a cholesterol synthesis marker. Treatment with ezetimibe reduced the fasting serum insulin level (p<0.05) and HbA1c (p<0.05), increased serum adiponectin (p<0.01), and showed a significant decrease of high-sensitive C-reactive protein (hsCRP, p<0.01). No adverse events occurred during the study. CONCLUSION Thus, cholesterol absorption inhibition by ezetimibe is an important therapeutic strategy since LDL-C and cholesterol absorption markers had a positive correlation. Ezetimibe not only reduced the serum LDL-C level but also improved glucose metabolism as well as obesity and inflammation markers. These findings support the benefit of ezetimibe as a new option for the treatment of dyslipidemia.
Determinants of the (un)employment intensity of growth: Evidence from a cross section of EU regions

The responsiveness of labor demand and unemployment to growth is an issue of ongoing political and academic interest. Economic growth is supposed to be the key to increase labor demand and reduce unemployment. Departing from Okun’s and Verdoorn’s law, most research on the (un)employment intensity of growth has focused on national disparities and the role of labor market institutions. Empirical evidence on the regional level is scarce. We investigate difierences in regional labor market responsiveness and their potential determinants for a cross section of European regions. The data set covers the NUTS 2 regions in the EU15 for the period 1980 to 2002. Following a spatial modeling approach estimation of the (un)employment intensity of growth takes interaction among neighboring labor markets into account. The flndings point to substantial difierences in labor market effects of output growth among European regions. Labor market institutions explain a signiflcant part of these disparities. However, apart from such national factors there are important regional determinants. The sectoral specialization of the regional economy plays a prominent role, and the responsiveness of employment to growth in urban and rural regions also difiers signiflcantly.
Design and Locomotion Control of a Soft Robot Using Friction Manipulation and Motor–Tendon Actuation

Robots built from soft materials can alter their shape and size in a particular profile. This shape-changing ability could be extremely helpful for rescue robots and those operating in unknown terrains and environments. In changing shape, soft materials also store and release elastic energy, a feature that can be exploited for effective robot movement. However, design and control of these moving soft robots are nontrivial. This paper presents design methodology for a 3-D printed motor-tendon actuated soft robot that is capable of locomotion. The modular design of the robot facilitates rapid fabrication, deployment, and repair. In addition to shape change, the robot uses friction-manipulation mechanisms to effect locomotion. The motor-tendon actuators are comprised of nylon tendons embedded inside the soft body structure along a given path with one end fixed on the body and the other attached to a motor. These actuators directly control the deformation of the soft body, which influences the robot locomotion behavior. Static stress analysis is used as a tool for designing the shape of the paths of these tendons embedded inside the body. This paper also presents a novel model-free learning-based control approach for soft robots, which interact with the environment at discrete contact points. This approach involves discretization of factors dominating robot-environment interactions as states, learning the results as robot transitions between these robot states, and evaluation of desired periodic state control sequences optimizing a cost function corresponding to a locomotion task (rotation or translation). The clever discretization allows the framework to exist in a robot's task space, hence facilitating calculation of control sequences without modeling the actuator, body material, or details of the friction mechanisms. The flexibility of the framework is experimentally explored by applying it to robots with different friction mechanisms and different shapes of tendon paths.
Asymmetries in Responses to Attitude Statements: The Example of “Zero-Sum” Beliefs

While much has been written about the consequences of zero-sum (or fixed-pie) beliefs, their measurement has received almost no systematic attention. No researchers, to our awareness, have examined the question of whether the endorsement of a zero-sum-like proposition depends on how the proposition is formed. This paper focuses on this issue, which may also apply to the measurement of other attitudes. Zero-sum statements have a form such as “The more of resource X for consumer A, the less of resource Y for consumer B. ” X and Y may be the same resource (such as time), but they can be different (e.g., “The more people commute by bicycle, the less revenue for the city from car parking payments”). These statements have four permutations, and a strict zero-sum believer should regard these four statements as equally valid and therefore should endorse them equally. We find, however, that three asymmetric patterns routinely occur in people's endorsement levels, i.e., clear framing effects, whereby endorsement of one permutation substantially differs from endorsement of another. The patterns seem to arise from beliefs about asymmetric resource flows and power relations between rival consumers. We report three studies, with adult samples representative of populations in two Western and two non-Western cultures, demonstrating that most of the asymmetric belief patterns are consistent across these samples. We conclude with a discussion of the implications of this kind of “order-effect” for attitude measurement.
Nickle: Language Principles and Pragmatics

Nickle is a vaguely C-like programming language for numerical applications, useful both as a desk calculator and as a prototyping and implementation language for numerical and semi-numerical algorithms. Nickle abstracts a number of useful features from a wide variety of other programming languages, particularly functional languages. Nickle’s design principles and implementation pragmatics mesh nicely to form a language filling a useful niche in the UNIX software environment. The history of Nickle is also an instructive example of the migration of an application from an idea to a freely-available piece of software.
Software Library Usage Pattern Extraction Using a Software Model Checker

The need to manually specify temporal properties of software systems is a major barrier to wider adoption of software model checking, because the specification of software temporal properties is a difficult, time-consuming, and error-prone process. To address this problem, we propose to automatically extract software library usage patterns, which are one type of temporal specifications. Our approach uses a model checker to check a set of software library usage pattern candidates against existing programs using that library, and identifies valid patterns based on model checking results. These valid patterns can help programmers learn about common software library usage. They can also be used to check new programs using the same library. We applied our approach to C programs using the OpenSSL library and the C standard library, and extracted valid usage patterns using BLAST. We also successfully used the extracted valid usage patterns to detect an error in an open source project hosted by SourceForge.net
xOMB: Extensible Open MiddleBoxes with commodity servers

This paper presents the design and implementation of an incrementally scalable architecture for middleboxes based on commodity servers and operating systems. xOMB, the eXtensible Open MiddleBox, employs general programmable network processing pipelines, with user-defined C++ modules responsible for parsing, transforming, and forwarding network flows. We implement three processing pipelines in xOMB, demonstrating good performance for load balancing, protocol acceleration, and application integration. In particular, our xOMB load balancing switch is able to match or outperform a commercial programmable switch and popular open-source reverse proxy while still providing a more flexible programming model.
Effect of Neutrophils on Nitric Oxide Production from Stimulated Macrophages.

BACKGROUND During the initial phase of an infection, there is an upregulation of inducible nitric oxide synthase in the macrophages for the production of nitric oxide. This is followed by the recruitment of polymorphonuclear leukocytes (neutrophils) which release arginase. Arginase competes with inducible nitric oxide synthase for a common substrate L-arginine. OBJECTIVE To investigate whether the entry of neutrophils and release of arginase can interfere with nitric oxide production from stimulated mouse macrophages. METHODS Neutrophils were isolated from human blood and stimulated with cytodex-3 beads. Cultured macrophages were stimulated with lipopolysaccharide and interferon gamma with or without N (G)-nitro-L-arginine methyl ester or N (omega)-hydroxy-nor-L-arginine. Measurement of NO2-/NO3- and urea were done using the spectrophotometer. RESULTS A significantly higher level of nitric oxide production from stimulated macrophages was observed compared to control. There was a decrease in nitric oxide production when stimulated macrophages were treated with the supernatant from activated neutrophils (p<0.05). CONCLUSION Arginase from neutrophils can modulate nitric oxide production from activated macrophages which may affect the course of infection by intracellular bacteria.
Bifidobacterium lactis in Treatment of Children with Acute Diarrhea. A Randomized Double Blind Controlled Trial

BACKGROUND: Probiotics are becoming increasingly popular treatment for children diarrhea. Although there are several probiotic strains potentially useful, researches were often limited to certain strains. AIM: To test Bifidobacterium lactis on morbidity of acute diarrhea in children less than 2 years. SUBJECTS AND METHODS: A randomized double-blind controlled clinical trial was conducted in 50 children (1 - 23 months) admitted with acute diarrhea to the Pediatric Hospital, Cairo University and were randomly assigned to receive in addition to usual treatment of diarrhea according to WHO guidelines; one of two treatments either milk formula non-supplemented (n = 25) or supplemented (n = 25) with Bifidobacterium lactis 14.5 × 106 CFU/100 ml daily for one week. Primary outcomes were frequency and duration of diarrhea and hospital stay. Secondary outcomes were duration of fever and vomiting episodes. Safety and tolerance were also recorded. RESULTS: On admission, patients’ characteristics of both groups (50 cases) were similar. For children who received the probiotics for one week; mean duration of diarrhoea was shorter than in controls (3.12 ± 0.92 vs. 4.10 ± 0.94 days) (P = 0.02), number of motions per day was less than in controls (3.96 ± 0.62 vs. 4.46 ± 0.85) (P = 0.04) and discharge from hospital <2 days was more frequent than in controls (72% vs. 44%) (P = 0.048). There was no effect on fever (P = 0.63) or vomiting (P = 0.54). CONCLUSION: Bifidobacterium lactis probiotics in supplemented milk formula decreased significantly frequency, duration of diarrhea, and hospital stay than usual treatment alone in children with acute diarrhea. Additional researches on other uncommon local probiotic species should be encouraged.