Miracle-dz/UniFAR-FTUs huggingface dataset:
Properties and Architecture of Drugs and Natural Products Revisited

Computer-based analysis revealed that natural products exhibit a remarkable structural diversity of molecular frameworks and scaffolds that could be systematically exploited for combinatorial synthesis. Natural products offer a rich pool of unique molecular frameworks that complement "drug space". They possess desirable druglike properties rendering them ideal starting points for molecular design considerations. This review provides an overview of chemotype diversity and molecular properties of collections of drugs and druglike molecules, pure natural products, and natural product- derived compounds. Compared to druglike molecules, pure natural products contain more oxygen atoms and chiral cen- ters, and have less aromatic atoms on average. Among the natural product library we identified more than one thousand scaffolds that were not contained in any other compound set analyzed. This outcome provides a basis for the design of new natural product-derived compound libraries. Our study demonstrates that computational chemical biology can assist in finding suitable molecular entities in collections of natural products for drug discovery.
Long-term outcomes of a multidisciplinary cognitive behavioural programme for coping with chronic neuropathic spinal cord injury pain.

OBJECTIVE To explore the long-term outcomes of CONECSI (COping with NEuropathiC Spinal cord Injury pain), a multidisciplinary cognitive behavioural treatment programme in persons with spinal cord injury. DESIGN Long-term follow-up pre-post-intervention design. SUBJECTS A total of 29 subjects with a spinal cord injury and chronic neuropathic pain from 4 Dutch rehabilitation centres. METHODS Primary outcomes were pain intensity and pain-related disability (Chronic Pain Grade questionnaire). Secondary outcomes were mood (Hospital Anxiety and Depression Scale), participation in activities (Utrecht Activities List), and life satisfaction (Life Satisfaction Questionnaire). Random coefficient analysis was used for the analyses of measurements before (t1), immediate post-intervention (t2), and 6 (t3), 9 (t4), and 12 (t5) months follow-up. RESULTS The analyses showed significant improvements on pain intensity (t1-t2 and t1-t5) and pain-related disability (t1-t2, t1-t4, and t1-t5), anxiety and participation in activities (t1-t2, t1-t3, and t1-t5). CONCLUSION This exploratory study suggests that a multidisciplinary cognitive behavioural programme might have lasting improvements on pain intensity, pain-related disability, anxiety, and participation in activities in people with chronic neuropathic spinal cord injury pain and highlights the potential of such programmes.
Data-Driven Feature Analysis in Control Design for Series-Compensated Transmission Systems

One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. In this study, data-driven feature analysis is performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross correlation, multinomial logistical regression, and classification and regression trees, are integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrates the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.
AudioGene: Computer-based prediction of genetic factors involved in non-syndromic hearing impairment

AudioGene is a software system developed at the University of Iowa to classify and predict gene mutations that indicate causal or increased risk factors of disease. We focus on a concise example — the most likely genetic causes of a particular form of inherited hearing loss — ADNSHL. Whereas the cost and throughput involved in the collection of genomic data have advanced dramatically during the past decade, gathering and interpreting clinical information regarding disease diagnosis remains slow, costly and error-prone. AudioGene employs machine-learning techniques in an iterative procedure to prioritize probable genetic risk factors of disease, which are then verified with a molecular (wet lab) assay. In our current implementation AudioGene achieves 67% first-choice accuracy (versus 23% using a majority classifier). When the top three choices are considered, accuracy increases to 83%. This has numerous implications for reducing the cost of genetic screening as well as increasing the power of novel gene discovery efforts. While AudioGene is focused on hearing loss, the design and underlying mechanisms are generalizable to many other diseases including heart disease, cancer and mental illness.
Source imaging of simple finger movements captured during auditory response tasks: Estimation of variation in neural information flow under varying vigilance levels

Neural Information Flow during simple cognitive or motor actions is very recent research topic. One of the simple movements to be studied is a simple finger movement. In this paper, Electro-encephalograph (EEG) Source Imaging has been used to model the Neural Information flow, during performance of a simple Auditory Response Task (ART). The estimated sources are firstly divided into separate scouts according to the Destrieux Atlas. Then, relative power carried in each scout at each instant has been used to create a neural information flow map. Another key feature of this paper lies in the fact that the database corresponded of subjects performing a group of tasks repetitively until significant drop in vigilance. This provides a unique point of view, as to how drop in vigilance levels affect the Neural Information flow. The results show a correlation between trends reflected in EEG parameters and performance scores of neuropsychological tests.
Fourier ptychographic reconstruction using weighted replacement in the fourier domain

Fourier ptychographic microscopy (FPM) is an attractive method to extend the resolution beyond the conventional limit defined by a microscope optics, sharing properties with ptychographic, synthetic aperture imaging and phase retrieval. The algorithm uses a sequence of low-resolution (LR) images acquired under angularly varying illumination to reconstruct a high-resolution (HR) image. However, traditional FPM may trap to the sub-optimal solution, since brute-force replacement in the Fourier domain is applied. To address this problem, we propose here a weighted replacement for Fourier ptychographic microscopy (WFPM). We employ the weighted average of spectrums corresponding to different illumination angle to replace the overlapped regions in the Fourier domain. A series of experimental results demonstrate that the reconstructed image using the proposed WFPM shows a better quality and a faster convergence compared with the results obtained by FPM.
Designing Kernel Functions Using the Karhunen-Lo`eve Expansion

In recent years, a number of kernel-based learning algorithms such as the regularization networks [1], the support vector machines [7, 4, 5], and the Gaussian process regression [8] have been investigated. These kernel machines are shown to work very well on real-world problems, given appropriate kernel functions. For general purposes, the Gaussian kernel is widely used and seems to work well [5]. On the other hand, a lot of attention have been paid recently to designing kernel functions using the problem-dependent prior knowledge. Various methods for constructing suitable kernels have been proposed [7, 3, 9, 6, 4]. In this contribution, we propose a framework for designing kernel functions for regression.
Identification of dynamic economic models from reduced form VECM structures: an application of covariance restrictions

This analysis is a straightforward implementation of both long-run and short-run identifying or overidentifying restrictions on a vector error correction model in the "structural VAR" framework. The framework utilizes covariance restrictions, long-run multiplier restrictions, error correction coefficient restrictions, and restrictions on slope coefficients of the stimultaneous interactions in the "economic model. " The framework is general enough to incorporate restrictions on impact multipliers. Two examples are provided. The first example is a dynamic M2 demand specification with a comparison to previous results that are constructed using restrictions on distributed lag coefficients to achieve identification. The second example illustrates the identification of equilibrium short-term and long-term interest responses of money demand using error-correction coefficient restrictions when the individual coefficients are underidentified in the cointegrating vectors in the presence of stationary interest rate spreads.
Criticality, universality, and isoradiality

Critical points and singularities are encountered in the study of critical phenomena in proba- bility and physics. We present recent results concerning the values of such critical points and the nature of the singularities for two prominent probabilistic models, namely percolation and the more general random-cluster model. The main topic is the statement and proof of the criticality and universality of the canonical measure of bond percolation on isoradial graphs (due to the author and Ioan Manolescu). The key technique used in this work is the star-triangle transformation, known also as the Yang-Baxter equation. The second topic reported here is the identification of the critical point of the random-cluster model on the square lattice (due to Be ara and Duminil-Copin), and of the criticality of the canonical measure of the random-cluster model with q 4 on periodic isoradial graphs (by the same authors with Smirnov). The proof of universality for percolation is expected to extend to the random-cluster model on isoradial graphs.
Leading faith schools in a secular society: Challenges facing head teachers of Muslim schools in the United Kingdom

Muslim faith schools have been existent in the UK since the 1980s and their number has been rising ever since. These schools are established specifically to provide Muslim children with a learning environment based on Islamic values. However, fulfilling such a mission would not be an easy task in a globalised context characterised by secularism and materialism. The study reported in this paper sought to explore the leadership experiences and challenges as perceived by the head teachers of Muslim schools in the UK. The study employed a qualitative research design using semi-structured interviews with a sample of head teachers from four Muslim schools in London and Nottingham. The findings revealed that although the participating head teachers were proud of their role as leaders of Muslim schools, they faced significant challenges in their practice of leadership within British secular society. While some of these challenges were general in nature such as parental expectations and lack of support from Muslim community, other challenges were more practical and mission related such as negative perceptions among the wider society, issues in developing Muslim identity among students in a secular society, recruitment of qualified Muslim teachers and financial challenges.
Software Versioning in the Cloud - Towards Automatic Source Code Management

With the introduction of cloud computing and Web2.0, many applications are moving to the cloud environment. Version control systems have also taken a first step towards this direction. Nevertheless, existing systems are either client-server oriented or completely distributed, and they don’t match exactly the nature of the cloud. In this paper we propose a new cloud version control system focusing on the requirements imposed by cloud computing, that we identified as: concurrent editing, history rewrite, accountability, scalability, security, and fault tolerance. Our plan is to tackle these issues in a systematic way, and we present in this paper an overview of the solutions organized in three separate layers: access API, logical structure, and physical storage. External Posting Date: March 6, 2012 [Fulltext] Approved for External Publication Internal Posting Date: March 6, 2012 [Fulltext] Additional Publication Information: Filippo Gioachin, Qianhui Liang, Yuxia Yao, Bu-Sung Lee. Software Versioning in the Cloud Towards Automatic Source Code Management. In Proceedings of ICSOFT (1)'2011. pp.160~165  Copyright ICSOFT 2011. SOFTWARE VERSIONING IN THE CLOUD Towards Automatic Source Code Management Filippo Gioachin, Qianhui Liang, Yuxia Yao and Bu-Sung Lee ∗† ∗Hewlett-Packard Laboratories Singapore, Singapore, Republic of Singapore †Nanyang Technological University, Singapore, Republic of Singapore
Novel Approaches for Generating Video Textures

Video texture, a new type of medium, can produce a new video with a continuously varying stream of images from a recorded video. It is synthesized by reordering the input video frames in a way which can be played without any visual discontinuity. However, video texture still experiences few unappealing drawbacks. For instance, video texture techniques can only generate new videos by simply rearranging the order of frames in original videos. Therefore, all the individual frames are the same as before and the result would suffer from "dead-ends" if the current frame could not discover similar frames to make a transition. In this thesis, we propose several new approaches for synthesizing video textures. These approaches adopt dimensionality reduction and regression techniques to generate video textures. Not only the frames in the resulted video textures are new, but also the "Dead end" problem is avoided. First, we have extended die work of applying principal components analysis (PCA) and autoregressive (AR) process to generate video textures by replacing PCA with five other dimension reduction techniques. Based on our experiments, using these dimensionality reduction techniques has improved the quality of video textures compared with extraction of frame signatures using PCA. The synthesized video textures may contain similar motions as the input video and will never be repeated exactly. All frames synthesized have never appeared before. We also propose a new approach for generating video textures using probabilistic principal components analysis (PPCA) and Gaussian process dynamical model (GPDM). GPDM is a nonparametric model for learning high-dimensional nonlinear dynamical data sets. We adopt PPCA and GPDM on several movie clips to synthesize video textures which contain frames that never appeared before and with similar motions as original videos. Furthermore, we have proposed two ways of generating real-time video textures by applying the incremental Isomap and incremental Spati04emporal Isomap (IST-Isomap). Both approaches can produce good real-time video texture results. In particular, IST-Isomap, that we propose, is more suitable for sparse video data (e.g. cartoon)
Knowledge-based approach to video content classification

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.
Understanding the Success or Failure of Organizational ICT Integration: The Criticality of Managerial Involvement

ABSTRACT This study explores how managerial involvement affects the success or failure of ICT-based change. Employees’ use and non-use of new ICT were indicators for change success and failure, respectively. Based on the thematic analysis, the findings accentuate the criticality of adequate managerial involvement with affected employees during ICT-based organizational change. This proved vital for the successful integration of new ICT and the development of new work processes and practices. The combination of absence of managerial involvement and expert control prevented employees who did not have previous ICT experience from developing new work practices that integrated tasks, work processes and the new technology. ICT experts were authorized to control the change process by a combination of managerial involvement and delegated managerial authority. Closeness to the technology and to managers/decision-makers combined with high levels of mutual involvement and ICT experience seem to be important factors for the success of ICT-based organizational change.
Study on tracking method based on infrared random point

A new tracking method based on infrared random point is proposed, which has an advantage of tracking accuracy and nature. It can track and position any indoor flat object. An infrared laser projection device is designed to generate infrared random point. Infrared camera is used to get infrared image and LLAH algorithm is adopted in recognition. Experimental platform is set up. The stability of the tracking method based on infrared random points is verified by the actual test. Results show that the method in the process of tracking is hardly affected by shade and is not sensitive to the change of visible brightness. Even if the camera moves rapidly, object can be accurately identified.
Experimental Study of Register Saturation in Basic Blocks and Super-Blocks: Optimality and heuristics

A computer user interface generation system and method is disclosed, wherein computer user interfaces can be generated dynamically during activation of the computer application for which the generated user interface provides user access to the functional features of the application. The generated user interface may be a graphical user interface (GUI) that uses instances of various user interaction techniques. A user interface specification is provided in a configuration database for generating the user interface, and by changing the user interface specification in the configuration database, the user interface for the computer application can be changed during activation of the application.
An industrial case study of performance and cost design space exploration

Determining the trade-off between performance and costs of a distributed software system is important as it enables fulfilling performance requirements in a cost-efficient way. The large amount of design alternatives for such systems often leads software architects to select a suboptimal solution, which may either waste resources or cannot cope with future workloads. Recently, several approaches have appeared to assist software architects with this design task. In this paper, we present a case study applying one of these approaches, i.e. PerOpteryx, to explore the design space of an existing industrial distributed software system from ABB. To facilitate the design exploration, we created a highly detailed performance and cost model, which was instrumental in determining a cost-efficient architecture solution using an evolutionary algorithm. The case study demonstrates the capabilities of various modern performance modeling tools and a design space exploration tool in an industrial setting,provides lessons learned, and helps other software architects in solving similar problems.
Deep Neural Architectures for Algorithms and Sequential Data

The first part of the dissertation describes two deep neural architectures with external memories: Neural Random-Access Machine (NRAM) and Hierarchical Attentive Memory (HAM). The NRAM architecture is inspired by Neural Turing Machines, but the crucial difference is that it can manipulate and dereference pointers to its randomaccess memory. This allows it to learn concepts that require pointers chasing, such as “linked list” or “binary tree”. The HAM architecture is based on a binary tree with leaves corresponding to memory cells. This enables the memory access in Θ(log n), which is a significant improvement over Θ(n) access used in the standard attention mechanism. We show that Long Short-Term Memory (LSTM) augmented with HAM can successfully learn to solve a number of challenging algorithmic problems. In particular, it is the first architecture that learns from pure input/output examples to sort n numbers in time Θ(n log n) and the solution generalizes well to longer sequences. We also show that HAM is very generic and can be trained to act like classic data structures: a stack, a FIFO queue and a priority queue. The second part of the dissertation describes three novel systems based on deep recurrent neural networks (RNNs). The first one is a framework for finding computationally efficient versions of symbolic math expressions. By using RNNs it can efficiently search the state space and quickly find identities with significantly better time complexity (e.g., Θ(n) instead of exponential time). Then, we present an RNN-based system for predicting dangerous events from multivariate, non-stationary time series data. It requires almost no feature engineering and achieved very good results in two machine learning competitions. Finally, we describe Smart Reply – an RNN-based end-to-end system for suggesting automatic responses to e-mails. The system is capable of handling hundreds of millions messages daily. Smart Reply was successfully deployed in Google Inbox and currently generates 10% of responses on mobile devices.
FPGA Architecture of Generalized Laguerre-Volterra MIMO Model for Neural Population Activities

We present a full-parallelized and pipelined architecture for a generalized Laguerre-Volterra MIMO system to identify the time-varying neural dynamics underlying spike activities. The proposed architecture consists of a first stage containing a vector convolution and MAC (Multiply and Accumulation) component, a second stage containing a prethreshold potential updating unit with an error approximation function component, and a third stage consisting of a gradient calculation unit. A flexible and efficient architecture that can accommodate different design speed requirements is generated. The design runs on a Xilinx Virtex-6 FPGA and the processing core produces data samples at a speed of 1.33×10^6 data frames/sec, which is 3.1×10^3 times faster than the corresponding C model running on an Intel i7-860 Quad Core Processor.
Extension of Nonlinear DMC for MIMO Systems

In this paper an extended approach to the nonlinear DMC algorithm is proposed, which can handle constrained and MIMO systems under some defined conditions. This extension also admits higher control, M, and prediction, P, horizons that are required to increase the performance of the controller in most practical applications. Simulation results of the proposed method in the control of a stirred tank reactor with 2 inputs and 2 outputs nonlinear model are presented to illustrate effectiveness of the method and its justification.