Opinion Mining on Non-English Short Text

As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We propose a new method projects the text into dense and low dimensional feature vectors according to the sentiment strength of the words. We detect the mixture of positive and negative sentiments on a multi-variant scale. Empirical evaluation of the proposed framework on Turkish tweets shows that our approach gets good results for opinion mining

EEG Classification based on Image Configuration in Social Anxiety Disorder

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs

The inner circumstellar disk of the UX Ori star V1026 Sco

The UX Ori type variables (named after the prototype of their class) are intermediate-mass pre-main sequence objects. One of the most likely causes of their variability is the obscuration of the central star by orbiting dust clouds. We investigate the structure of the circumstellar environment of the UX~Ori star V1026 Sco (HD 142666) and test whether the disk inclination is large enough to explain the UX Ori variability. We observed the object in the low-resolution mode of the near-infrared interferometric VLTI/AMBER instrument and derived H- and K-band visibilities and closure phases. We modeled our AMBER observations, published Keck Interferometer observations, archival MIDI/VLTI visibilities, and the spectral energy distribution using geometric and temperature-gradient models. Employing a geometric inclined-ring disk model, we find a ring radius of 0.15 +- 0.06 AU in the H band and 0.18 +- 0.06 AU in the K band. The best-fit temperature-gradient model consists of a star and two concentric, ring-shaped disks. The inner disk has a temperature of 1257^{+133}_{-53} K at the inner rim and extends from 0.19 +- 0.01 AU to 0.23 +- 0.02 AU. The outer disk begins at 1.35^{+0.19}_{-0.20} AU and has an inner temperature of 334^{+35}_{-17} K. The derived inclination of 48.6^{+2.9}_{-3.6}deg approximately agrees with the inclination derived with the geometric model (49 +- 5deg in the K band and 50 +- 11deg in the H band). The position angle of the fitted geometric and temperature-gradient models are 163 +- 9deg (K band; 179 +- 17deg in the H band) and 169.3^{+4.2}_{-6.7}deg, respectively. The narrow width of the inner ring-shaped model disk and the disk gap might be an indication for a puffed-up inner rim shadowing outer parts of the disk. The intermediate inclination of ~50deg is consistent with models of UX Ori objects where dust clouds in the inclined disk obscure the central star

Inactivation of Aurora kinases and Cyclin-dependent kinases 4/6 allows cancers to adopt an endoreplication and form polyploid/polyaneuploid giant cancer cells (PGCCs/PACCs) that resist antimitotic drugs

View full abstracthttps://openworks.mdanderson.org/leading-edge/1044/thumbnail.jp

MBotCS: A mobile botnet detection system based on machine learning

As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices

Analyzing Complex Problem Solving by Dynamic Brain Networks

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution

Large strain mechanical behavior of 1018 cold-rolled steel over a wide range of strain rates

The large-strain constitutive behavior of cold-rolled 1018 steel has been characterized at strain rates ranging from ε = 10^(−3) to 5 × 10^4 s^(−1) using a newly developed shear compression specimen (SCS). The SCS technique allows for a seamless characterization of the constitutive behavior of materials over a large range of strain rates. The comparison of results with those obtained by cylindrical specimens shows an excellent correlation up to strain rates of 10^4 s^(−1). The study also shows a marked strain rate sensitivity of the steel at rates exceeding 100 s^(−1). With increasing strain rate, the apparent average strain hardening of the material decreases and becomes negative at rates exceeding 5000 s^(−1). This observation corroborates recent results obtained in torsion tests, while the strain softening was not clearly observed during dynamic compression of cylindrical specimens. A possible evolution scheme for shear localization is discussed, based on the detailed characterization of deformed microstructures. The Johnson-Cook constitutive model has been modified to represent the experimental data over a wide range of strain rates as well as to include heat-transfer effects, and model parameters have been determined for 1018 cold-rolled steel

Engineering of Aspergillus niger for the production of secondary metabolites

Background: Filamentous fungi can each produce dozens of secondary metabolites which are attractive as therapeutics, drugs, antimicrobials, flavour compounds and other high-value chemicals. Furthermore, they can be used as an expression system for eukaryotic proteins. Application of most fungal secondary metabolites is, however, so far hampered by the lack of suitable fermentation protocols for the producing strain and/or by low product titers. To overcome these limitations, we report here the engineering of the industrial fungus Aspergillus niger to produce high titers (up to 4,500 mg • l-1) of secondary metabolites belonging to the class of nonribosomal peptides. Results: For a proof-of-concept study, we heterologously expressed the 351 kDa nonribosomal peptide synthetase ESYN from Fusarium oxysporum in A. niger. ESYN catalyzes the formation of cyclic depsipeptides of the enniatin family, which exhibit antimicrobial, antiviral and anticancer activities. The encoding gene esyn1 was put under control of a tunable bacterial-fungal hybrid promoter (Tet-on) which was switched on during early-exponential growth phase of A. niger cultures. The enniatins were isolated and purified by means of reverse phase chromatography and their identity and purity proven by tandem MS, NMR spectroscopy and X-ray crystallography. The initial yields of 1 mg • l-1 of enniatin were increased about 950 fold by optimizing feeding conditions and the morphology of A. niger in liquid shake flask cultures. Further yield optimization (about 4.5 fold) was accomplished by cultivating A. niger in 5 l fed batch fermentations. Finally, an autonomous A. niger expression host was established, which was independent from feeding with the enniatin precursor D-2-hydroxyvaleric acid D-Hiv. This was achieved by constitutively expressing a fungal D-Hiv dehydrogenase in the esyn1-expressing A. niger strain, which used the intracellular ɑ-ketovaleric acid pool to generate D-Hiv. Conclusions: This is the first report demonstrating that A. niger is a potent and promising expression host for nonribosomal peptides with titers high enough to become industrially attractive. Application of the Tet-on system in A. niger allows precise control on the timing of product formation, thereby ensuring high yields and purity of the peptides produced.EC/FP7/607332/EU/Quantitative Biology for Fungal Secondary Metabolite Producers/QuantFungDFG, EXC 314, Unifying Concepts in Catalysi

When Models Interact with their Subjects: The Dynamics of Model Aware Systems

A scientific model need not be a passive and static descriptor of its subject. If the subject is affected by the model, the model must be updated to explain its affected subject. In this study, two models regarding the dynamics of model aware systems are presented. The first explores the behavior of "prediction seeking" (PSP) and "prediction avoiding" (PAP) populations under the influence of a model that describes them. The second explores the publishing behavior of a group of experimentalists coupled to a model by means of confirmation bias. It is found that model aware systems can exhibit convergent random or oscillatory behavior and display universal 1/f noise. A numerical simulation of the physical experimentalists is compared with actual publications of neutron life time and {\Lambda} mass measurements and is in good quantitative agreement.Comment: Accepted for publication in PLoS-ON