Miracle-dz/UniFAR-FTUs huggingface dataset:
Suction measurements using fiber optic sensors

Soil suction is important in explaining the behaviour of unsaturated soils. Unsaturated soil refers to soils with solid, liquid and gaseous phases in it (i.e. soil particles, soil water and air), and these kind of soils are the most commonly encountered in the field. Knowing the suction of a particular soil would allow the shear strength, volume change and hydraulic behaviour of the soil to be characterised. Having instrumentation that is simple to use and effective in the field that can measure the various suction components of soil would be ideal, but unfortunately this is not presently the case. Soil is not a man-made material; the difficulties involved in measuring soil parameters are well known as soil does not behave in a linear fashion. Experiments done in the laboratory often do not match the soil conditions on the site where the soil was sampled. It is important to know the suction of soil as the knowledge will, for example, enable safety standards to be improved on construction sites, or improve crop yield by providing perfect soil conditions for agriculture. Soil suction is commonly referred to as the free energy state of soil water and is measured in kilopascals. There exists a thermodynamic relationship between soil suction and the partial pressure of the water vapor in the soil. Several techniques using fiber optic sensors were investigated to determine which was most suitable for soil suction measurements. For direct measurements of water pressure within the pores of soil (matric suction), the methods explored were: 3 dimensional (3D) shape measurement of meniscus of capillary water, followed by hydrostatic pressure measurements using fiber Bragg grating (FBG) sensors and long-period grating (LPG) sensors. For indirect measurements of matric suction using relative humidity (RH) of soil (total suction), the methods investigated were: the use of Fresnel reflection in cleaved single mode fibers (SMF) and gold tip-coated fibers to determine the refractive index change of air when humidity changes, followed by using absorption spectroscopy to monitor water absorption lines to see how they change as humidity changes, followed by using plastic optical fibers (POF) and LPGs coated with humidity sensitive films to form chemical sensors which can sense the humidity in soil. iii ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Static optimization in PHP 7

PHP is a dynamically typed programming language commonly used for the server-side implementation of web applications. Approachability and ease of deployment have made PHP one of the most widely used scripting languages for the web, powering important web applications such as WordPress, Wikipedia, and Facebook. PHP's highly dynamic nature, while providing useful language features, also makes it hard to optimize statically. This paper reports on the implementation of purely static bytecode optimizations for PHP 7, the last major version of PHP. We discuss the challenge of integrating classical compiler optimizations, which have been developed in the context of statically-typed languages, into a programming language that is dynamically and weakly typed, and supports a plethora of dynamic language features. Based on a careful analysis of language semantics, we adapt static single assignment (SSA) form for use in PHP. Combined with type inference, this allows type-based specialization of instructions, as well as the application of various classical SSA-enabled compiler optimizations such as constant propagation or dead code elimination. We evaluate the impact of the proposed static optimizations on a wide collection of programs, including micro-benchmarks, libraries and web frameworks. Despite the dynamic nature of PHP, our approach achieves an average speedup of 50% on micro-benchmarks, 13% on computationally intensive libraries, as well as 1.1% (MediaWiki) and 3.5% (WordPress) on web applications.
Using Re-Sampling Methods in Mortality Studies

Traditional methods of computing standardized mortality ratios (SMR) in mortality studies rely upon a number of conventional statistical propositions to estimate confidence intervals for obtained values. Those propositions include a common but arbitrary choice of the confidence level and the assumption that observed number of deaths in the test sample is a purely random quantity. The latter assumption may not be fully justified for a series of periodic “overlapping” studies. We propose a new approach to evaluating the SMR, along with its confidence interval, based on a simple re-sampling technique. The proposed method is most straightforward and requires neither the use of above assumptions nor any rigorous technique, employed by modern re-sampling theory, for selection of a sample set. Instead, we include all possible samples that correspond to the specified time window of the study in the re-sampling analysis. As a result, directly obtained confidence intervals for repeated overlapping studies may be tighter than those yielded by conventional methods. The proposed method is illustrated by evaluating mortality due to a hypothetical risk factor in a life insurance cohort. With this method used, the SMR values can be forecast more precisely than when using the traditional approach. As a result, the appropriate risk assessment would have smaller uncertainties.
Hazy: Making it Easier to Build and Maintain Big-data Analytics

The rise of big data presents both big opportunities and big challenges in domains ranging from enterprises to sciences. The opportunities include better-informed business decisions, more efficient supply-chain management and resource allocation, more effective targeting of products and advertisements, better ways to "organize the world’s information," faster turnaround of scientific discoveries, etc.
Sensorimotor control of gait: a novel approach for the study of the interplay of visual and proprioceptive feedback

Sensorimotor control theories propose that the central nervous system exploits expected sensory consequences generated by motor commands for movement planning, as well as online sensory feedback for comparison with expected sensory feedback for monitoring and correcting, if needed, ongoing motor output. In our study, we tested this theoretical framework by quantifying the functional role of expected vs. actual proprioceptive feedback for planning and regulation of gait in humans. We addressed this question by using a novel methodological approach to deliver fast perturbations of the walking surface stiffness, in conjunction with a virtual reality system that provided visual feedback of upcoming changes of surface stiffness. In the "predictable" experimental condition, we asked subjects to learn associating visual feedback of changes in floor stiffness (sand patch) during locomotion to quantify kinematic and kinetic changes in gait prior to and during the gait cycle. In the "unpredictable" experimental condition, we perturbed floor stiffness at unpredictable instances during the gait to characterize the gait-phase dependent strategies in recovering the locomotor cycle. For the "unpredictable" conditions, visual feedback of changes in floor stiffness was absent or inconsistent with tactile and proprioceptive feedback. The investigation of these perturbation-induced effects on contralateral leg kinematics revealed that visual feedback of upcoming changes in floor stiffness allows for both early (preparatory) and late (post-perturbation) changes in leg kinematics. However, when proprioceptive feedback is not available, the early responses in leg kinematics do not occur while the late responses are preserved although in a, slightly attenuated form. The methods proposed in this study and the preliminary results of the kinematic response of the contralateral leg open new directions for the investigation of the relative role of visual, tactile, and proprioceptive feedback on gait control, with potential implications for designing novel robot-assisted gait rehabilitation approaches.
Panoramic Image Stitching

Faculty of Computer Science by Kai Wang Pengbo Li Panoramic image stitching is an active research topic and lots of methods was developed to achieve a nice stitched panorama under different limitation of raw pictures. For example, pictures need to be aligned properly, pictures need to has same exposure level, or pictures need to be horizontal or vertical in space. In this project, we followed the pipeline proposed by Matthew Brown and David G. Lowe[1] to implement construct a feature based method using SIFT feature, which is much less sensitive to rotation and scale. We will also implement two methods of blending methods: Laplacian pyramid method and Poisson blending to deal with the seam problem due to exposure difference. Compared with the method without blending technique, blending stitching images apparently shows better performance on the pictures with different exposure level than version without blending. Last but not least, we will provide a set of pictures with various transform, various exposure level, and try to create a nice panoramic image.
Research and Application of an improved Support Vector Clustering Algorithm on Anomaly Detection

Anomaly detection approaches build models of normal data and detect deviations from the normal model in observed data. Anomaly detection applied to intrusion detection and computer security has been an active area of research. The major benefit of anomaly detection algorithms is their ability to potentially detect unforeseen attacks. In this paper, a novel weighted support vector clustering algorithm for anomaly detection is proposed. The weight to each input point is defined according to the position of samples in sphere space. The results of experiment demonstrate that the algorithm has excellent capability and applying it in intrusion detection system can be an effective way via using the data sets of KDD cup 99.
Running OpenMP applications efficiently on an everything-shared SDSM

Summary form only given. Traditional software distributed shared memory (SDSM) systems modify the semantics of a real hardware shared memory system by relaxing the coherence semantic and by limiting the memory regions that are actually shared. These semantic modifications are done to improve performance of the applications using it. We show that a SDSM system that behaves like a real shared memory system (without the afore mentioned relaxations) can also be used to execute OpenMP applications and achieve similar speedups as the ones obtained by traditional SDSM systems. This performance can be achieved by encouraging the cooperation between the SDSM and the OpenMP runtime instead of relaxing the semantics of the shared memory. In addition, techniques like boundaries alignment and page presend are demonstrated as very useful to overcome the limitations of the current SDSM systems.
Device physics of organic light-emitting diodes : interplay between charges and excitons

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Human Stiff-Person Syndrome IgG Induces Anxious Behavior in Rats

Background Anxiety is a heterogeneous behavioral domain playing a role in a variety of neuropsychiatric diseases. While anxiety is the cardinal symptom in disorders such as panic disorder, co-morbid anxious behavior can occur in a variety of diseases. Stiff person syndrome (SPS) is a CNS disorder characterized by increased muscle tone and prominent agoraphobia and anxiety. Most patients have high-titer antibodies against glutamate decarboxylase (GAD) 65. The pathogenic role of these autoantibodies is unclear. Methodology/Principal Findings We re-investigated a 53 year old woman with SPS and profound anxiety for GABA-A receptor binding in the amygdala with (11)C-flumazenil PET scan and studied the potential pathogenic role of purified IgG from her plasma filtrates containing high-titer antibodies against GAD 65. We passively transferred the IgG fraction intrathecally into rats and analyzed the effects using behavioral and in vivo electrophysiological methods. In cell culture, we measured the effect of patient IgG on GABA release from hippocampal neurons. Repetitive intrathecal application of purified patient IgG in rats resulted in an anxious phenotype resembling the core symptoms of the patient. Patient IgG selectively bound to rat amygdala, hippocampus, and frontal cortical areas. In cultured rat hippocampal neurons, patient IgG inhibited GABA release. In line with these experimental results, the GABA-A receptor binding potential was reduced in the patient's amygdala/hippocampus complex. No motor abnormalities were found in recipient rats. Conclusion/Significance The observations in rats after passive transfer lead us to propose that anxiety-like behavior can be induced in rats by passive transfer of IgG from a SPS patient positive for anti-GAD 65 antibodies. Anxiety, in this case, thus may be an antibody-mediated phenomenon with consecutive disturbance of GABAergic signaling in the amygdala region.
Miniatures Spontaneous Peripheral T-cell Responses toward the Tumor-Associated Antigen Cyclin D 1 in Patients with Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a heterogeneous groupof kidney cancerswith clear cell RCC (ccRCC) as themajor subgroup. To expand the number of clinically relevant tumor-associated antigens (TAA) that can be targeted by immunotherapy, we analyzed samples from 23 patients with primary ccRCC for the expression and immunogenicity of various TAAs. We found high-frequency expression of MAGE-A9 and NY-ESO-1 in 36% and 55% of samples, respectively, and overexpression of PRAME, RAGE-1, CA-IX, Cyclin D1, ADFP, C-MET, and RGS-5 in many of the tumor samples. We analyzed the blood of patients with HLA-A2þ ccRCC for the presence of CD8þ T cells specific for TAA-derived HLA-A2–restricted peptides and found spontaneous responses to cyclin D1 in 5 of 6 patients with Cyclin D1–positive tumors. Cyclin D1–specific CD8þ T cells secreted TNF-a, IFN-g , and interleukin2 (IL-2), and degranulated, indicating the presence of polyfunctional tumor-specific CD8þ T cells in the blood of these patients with ccRCC. The high frequency (43%) of Cyclin D1 overexpression and the presence of functional cyclin D1–specific T cells in 83% of these patients with ccRCC suggest that cyclin D1 may be a target for immunotherapeutic strategies. Cancer Immunol Res; 1(5); 288–95. 2013 AACR.
Optimized Protocol for Protein Extraction from the Breast Tissue that is Compatible with Two-Dimensional Gel Electrophoresis

Proteomics is a highly informative approach to analyze cancer-associated transformation in tissues. The main challenge to use a tissue for proteomics studies is the small sample size and difficulties to extract and preserve proteins. The choice of a buffer compatible with proteomics applications is also a challenge. Here we describe a protocol optimized for the most efficient extraction of proteins from the human breast tissue in a buffer compatible with two-dimensional gel electrophoresis (2D-GE). This protocol is based on mechanically assisted disintegration of tissues directly in the 2D-GE buffer. Our method is simple, robust and easy to apply in clinical practice. We demonstrate high quality of separation of proteins prepared according to the reported here protocol.
The role of lysine-rich histone in the maintenance of chromatin structure in metaphase chromosomes.

Dense masses of chromatin are present in the nuclei of many cells during interphase; dense chromatin is also present in the chromosomes of cells during mitosis. Relatively little RNA synthesis takes place in the dense chromatin of interphase cells,(1,2) and apparently none at all in the dense chromatin of metaphase chromosomes. It has been shown that chromatin threads of thymus nuclei are bound together into dense masses by lysine-rich histones. (3) In this paper we present evidence that in metaphase chromosomes, too, chromatin threads are held together by lysine-rich histones.
Multiclass Sentiment Classification of Online Health Forums using Both Domain-independent and Domain-specific Features

Online health-related discussion provides a rich source of information for both informing the public and providing feedback to health professionals to detect trends and inform policy. However, there are few studies that focus on analysing sentiment in medical forum discourse. Online health communities devoted to specific medical conditions and health-related problems support people with similar conditions, enabling them to exchange personal experiences. Analysing sentiment expressed by members of a health community in medical forum discourse can be valuable for identifying a particular aspect of the information space. In this paper, we identify sentiments expressed on online medical forums discussing Lyme disease. There are two goals in our research. First, to identify a set of categories that can represent a comprehensive connotation of emotions expressed in the discussions, while also being adequately distinct for the purposes of machine learning. Second, to identify the sentiments expressed by participants in individual posts. Three types of feature (content-free, content-specific and meta-level) are extracted and inductive learning algorithms utilized to build a feature-based classification model for an automated multi-class classification model. The experimental results demonstrate the effectiveness of our approach.
Novel self balancing single phase 7 level inverter with coupled inductors

This paper proposes a novel self balancing 7 level inverter with two coupled inductors. The proposed inverter can generate 7 levels with only one DC source. By series charging and parallel discharging of capacitors, it can self balance the voltage across the two capacitors. The proposed topology incurs a lower part count compared to existing (conventional) topologies and can self balance the voltage across capacitors without any extra balancing circuit with simple control strategy. Because of high frequency coupled inductors, the dead band between some of the power switches can be eliminated and output voltage distortion is low. A 1200W power rating system has been simulated to verify the viability of the proposed topology and the relevant simulation waveforms are included.
Face detection algorithm based on a cascade of ensembles of decision trees

Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. To test the applicability of the algorithm in the real world have been conducted research on a robustness. Robustness research shown that the algorithm based on the CEDT show that Gaussian noise, impulsive “salt-and-pepper” noise exert a strong influence on the algorithm (in the worst case decrease in the area under the ROC-curve of 21.2% with a decrease in PSNR metric to 17.99 dB). At the same time blurring, JPEG-compression and JPEG2000 algorithms distortion have little effect on the proposed face detection algorithm (reduction of the area under the ROC-curve by 3.5% while reducing PSNR metric to 21.58 dB).
Methodology for analysis of the coverage probability of WLAN using the padé approximant

This paper proposes a methodology for analysis of the signal reception probability in a 2.4GHz Wireless Local Area Network (WLAN). For validating this methodology a measurement campaign was carried out in an indoor environment using an access point as signal transmitter, and a notebook computer, equipped with a wireless board, as receiver. Twenty five average power received values were collected in this environment. Considering that the number of samples was insufficient for statistically characterize the environment, the colleted data was expanded using an Artificial Neural Network (ANN). The propagation loss was calculated using the expanded data in an adapted Pade Approximant model. It was included also, in the model, the number of obstacles intersected by the signal, as well as the loss due to each kind of obstacles. The obtained results were compared to a classical literature model.
Some Results on g-Frame Operator in Hilbert Space

The g-frame operator for g-frame in Hilbert space is introduced. The results of g-frame operator are presented. A sequence of operators Λ̄j∈J(where Λ̄ = ΛSg −1 ) is a g-frame for the Hilbert space H with frame bounds 1 B and 1 A . Frame identities for g-normalized tight frames are established. Results on direct sum of gframe operators on direct sum of Hilbert spaces are presented. Mathematics Subject Classification: 42C15, 41A58
Bayesian Comparison of Neurovascular Coupling Models Using EEG-fMRI

Functional magnetic resonance imaging (fMRI), with blood oxygenation level-dependent (BOLD) contrast, is a widely used technique for studying the human brain. However, it is an indirect measure of underlying neuronal activity and the processes that link this activity to BOLD signals are still a topic of much debate. In order to relate findings from fMRI research to other measures of neuronal activity it is vital to understand the underlying neurovascular coupling mechanism. Currently, there is no consensus on the relative roles of synaptic and spiking activity in the generation of the BOLD response. Here we designed a modelling framework to investigate different neurovascular coupling mechanisms. We use Electroencephalographic (EEG) and fMRI data from a visual stimulation task together with biophysically informed mathematical models describing how neuronal activity generates the BOLD signals. These models allow us to non-invasively infer the degree of local synaptic and spiking activity in the healthy human brain. In addition, we use Bayesian model comparison to decide between neurovascular coupling mechanisms. We show that the BOLD signal is dependent upon both the synaptic and spiking activity but that the relative contributions of these two inputs are dependent upon the underlying neuronal firing rate. When the underlying neuronal firing is low then the BOLD response is best explained by synaptic activity. However, when the neuronal firing rate is high then both synaptic and spiking activity are required to explain the BOLD signal.
Improved Parallel Algorithms for Spanners and Hopsets

We use exponential start time clustering to design faster parallel graph algorithms involving distances. Previous algorithms usually rely on graph decomposition routines with strict restrictions on the diameters of the decomposed pieces. We weaken these bounds in favor of stronger local probabilistic guarantees. This allows more direct analyses of the overall process, giving: Linear work parallel algorithms that construct spanners with O(k) stretch and size O(n1+1/k) in unweighted graphs, and size O(n1+1/k log k) in weighted graphs. Hopsets that lead to the first parallel algorithm for approximating shortest paths in undirected graphs with O(m poly log n) work.