Miracle-dz/UniFAR-FTUs huggingface dataset:
Research report h-hexosaminidase lentiviral vectors: transfer into the CNS via systemic administration

Brain inflammation in GM2 gangliosidosis has been recently realized as a key factor in disease development. The aim of this study was to investigate the effects of a FIV h-hexosaminidase vector in the brain of HexB-deficient (Sandhoff disease) mice following intraperitoneal administration to pups of neonatal age. Since brain inflammation, lysosomal storage and neuromuscular dysfunction are characteristics of HexB deficiency, these parameters were employed as experimental outcomes in our study. The ability of the lentiviral vector FIV(HEX) to infect murine cells was initially demonstrated with success in normal mouse fibroblasts and human Tay-Sachs cells in vitro. Furthermore, systemic transfer of FIV(HEX) to P2 HexB / knockout pups lead to transduction of peripheral and central nervous system tissues. Specifically, h-hexosaminidase expressing cells were immunolocalized in periventricular areas of the cerebrum as well as in the cerebellar cortex. FIV(HEX) neonatal treatment resulted in reduction of GM2 storage along with attenuation of the brain inflammation and amelioration of the attendant neuromuscular deterioration. In conclusion, these results demonstrate the effective transfer of a h-hexosaminidase lentiviral vector to the brain of Sandhoff mice and resolution of the GM2 gangliosidosis after neonatal intraperitoneal administration. D 2004 Elsevier B.V. All rights reserved. Theme: Disorders of the nervous system Topic: Degenerative disease: other
A Deep Learning Example: the Generic Vector Projection Model, Interpolation and Generalization Analysis

The simplest deep learning models for functions are compositions of non-linear activation functions applied to afﬁne linear functions. The Universal Approximation Theorem for Neural Networks implies that any Borel measurable function can be approximated using this class of models.This powerful class of functions is proving to be remarkably useful and underpins the explosion of artiﬁcial intelligence applications. Mathematically one is seeking a representation computed by a composition of all but the ﬁnal function that accurately predicts the function values by the ﬁnal linear function. In this paper we mathematically analyze a simple, but general two-layer model where the ﬁrst layer function represents the data by applying the biased projections of the data onto a single vector generic for the training data set.The Relu activation function ensures that the solution for the the second layer solution may easily be computed exactly by solving an upper triangular matrix. This model gives us a huge family of models which exactly interpolate the training data. We can minimize the generalization error for a sample over this family and minimize the average generalization error for a set of ‘leave out k samples’ . Properties of the model are proved exploiting the geometry of the model parameters and data. A number of current research questions can be answered for this simple model. The model exploits recent theoretical research shown to be effective: adaptation of the architecture to the data, positive homogeneity, parallel architectures, unbounded width, zero training error, and data dependent kernels. The paper does not assume deep learning knowledge and the proofs are self-contained.
ENHANCED MODELLING OF FINE GRAINED KNOWLEDGE SHARING USING TAG SUPPRESSIONS

In collaborative filtering of item combinations with fine grained knowledge that acquires data from various domains and software develops independent features. The data that connects and shares fine grain knowledge for analysing and searching from data that abridges information framework with searching based network models. In our proposed model the privacy of user with tags that categorizes users profile in web technology. Data clustering by various processes that generates parameters of infinite models during search methodologies using proper sharing of data collected with expected results. The integrated techniques along with significant accuracy of searching with proficient methods controlling the semantics towards certain degree of protected information. Tag suppression is a technique that classifies the hierarchy based automated structure with bookmark tags. They access various scenario of resource that collaborate the knowledge sharing of distributed systems. Expressions with privacy protection tag for relevant tag filters user content that enhances the performance for contents specific of users. Integrated discrimination of results associates generative clustering models for proximal tool context. Computation is complex for machine learning for automated classification that potentially affects the complex and malicious activities. Offensive and complex activities for preventing profile attackers with tag suppression language processing tool.
An error-controlled pre-partitioned adaptive chemistry methodology for accelerating particle PDF methods

Abstract: Turbulent combustion computations can be accelerated significantly without loss of accuracy by utilizing reduced models tailored to the local compositions. Such reduced models can be determined in an offline preprocessing stage or on-the-fly. Liang et al. (Combustion and Flame, 2015) proposed a pre-partitioned adaptive chemistry methodology (PPAC), which belongs to the former category and generates a set of reduced models using a database of compositions. The set of reduced models are then dynamically utilized at runtime to efficiently integrate particle compositions. PPAC assumes that the database of compositions samples the likely accessed region of composition space encountered at runtime. Consequently, the accuracy of PPAC is directly linked to similarity of compositions seen at runtime to those utilized for the generation of reduced models. We propose an approach based on in-situ adaptive tabulation (ISAT) capable of retaining the accuracy of PPAC for cases where the database is not completely representative of the compositions encountered at runtime. The ability of this approach to efficiently identify compositions that lead to errors bounded by a user-specified threshold is demonstrated through a-priori tests. This paves the way for further development of algorithms based on this technique that can control errors incurred at runtime below a pre-specified threshold, even when the database used for generating the reduced models is not fully representative of compositions encountered at runtime.
Violation of energy-momentum conservation Laws

The math in the paper proves the existence of new force field (it might be called Dark Force, but there is nothing demonic about it). And the math proves the violation of the conservation laws (including energy violation). All this is in favour of the Flat Earth Society, because without conservation laws any model of Reality is safe and sound.
Restoration of Images using only noisy data A Deep Learning Approach using U-NET

: Digital Images have played a vital role in the research and development of technology. With increasing demand in quality for better visualization of the subject in the image, the main challenge in Image Processing is to remove the noise from the original image. There are many examples where images captured contains a lot of noise, for example, Satellite image, MRI, Mobile Captured images. The task of image restoration i.e. taking a corrupted image and estimating the cleaned original image is classical as well studied problem in Computer Vision and Image Processing. In this work, we propose to use Deep Learning, a unique algorithm can be developed that can fix the bad images by learning from noisy images only. Deep Learning models help in solving complex, classical problems which have not been solved for decades. We use U-Net [3] , a fully convolutional network which is meant for segmentation of images. We aim to apply this neural network as an image restoration technique to remove Gaussian Noise. This approach can restore bad images to clean images without feeding clean high-quality images to the Neural Network. It will take milliseconds to render the image and produce the resulting clean image.
Analyzing the Consumer`s Buying Behavior towards Green Products

Environment is basic and most important thing we have in our life if it is being ruined then it is our responsibility to prevent it. Since the leftover (wrappers, bottles and plastic packaging etc.) of non-green products cannot be disposed after the products are consumed; in that case they are buried, burnt and thrown into the sea; in all the cases, they pollute the environment, harm earth or poison our water. These three things (water, earth and air) are necessary for healthy life on earth. In order to prevent this the consumers are needed to buy the products which can be easily disposed with any harm. In this regard, this study has been conducted to analyze the buying behavior of consumers towards the green products. Data was collected by the help of questionnaire adopted from the literature. Data was collected online (by sending the link of Google form in email), at superstores, marts, supermarkets and shopping malls of Hyderabad Pakistan. The number of valid responses collected from the respondents was 200. Data analysis was conducted in the statistical package for social sciences (SPSS) version 22. Internal consistency of data was check by using the cronbach`s alpha test. Frequency distribution of demographic characteristics of the respondents was taken out along with descriptive statistics (mean, standard deviation, skewness and kurtosis). Skewness and kurtosis were calculated to indicate the normality and when it was revealed that the data was normal, the developed hypothesis were tested by Analysis of variance (ANOVA) test. Furthermore, the Pearson correlation of demographic characteristics (age, income and qualification) was calculated with Consumer`s interest, will and buying behavior towards the green products. On the same time Pearson correlation between the various constructs was also calculated. In last regression analysis was conducted on the various constructs. It was concluded that demographics were not in any association with the consumer`s interest, will and buying behavior but consumers` will, interest and buying behavior towards green products were weakly correlated with one another. Keywords— Green products, consumer, purchasing.
PACIFIC: LHCb Scintillator Fibre Tracker Readout ASIC

The LHCb detector will undergo a major upgrade during the Long Shutdown 2 (LS2), with the full replacement of the main tracking system. The new tracker will use scintillating fibres to cover the complete area of the detector. An array of silicon photomultipliers (SiPMs) will be used to readout these fibres. Each array will provide 128 channels, to be readout by the electronics. The low Power ASIC for the sCIntillating FIbres traCker (PACIFIC) is the readout ASIC designed specifically to cope with the readout of the Scintillating Fibre Tracker (SciFi). The 64 channel ASIC comprises for every channel analog processing, digitization, slow control and digital output at a rate of 40MHz. The analog processing includes preamplifier, shaper and integrator. The integrator is formed by an interleaved double gated integrator and a track and hold to avoid dead time (one integrator is in reset while the other collects the signal and the track and hold merges the two integrators output to give a continuous measurement). The output of the track and hold is digitized using 3 comparators (non-linear flash ADC). The three bits output is then encoded into two bits and serialized. Some auxiliary blocks are also needed to produce a fully functional device and include voltage references, current references, control DACs, power on reset (POR) circuitry and serializers. The slow control digital block consists in a 10 bit addressing I2C slave and a register bank for holding the configuration values. PACIFIC has been designed using TSMC 130nm technology and several prototypes have been validated. PACIFICr5 is expected to be the final prototype version with minor changes in the analog channel, some features added and a differential data link serializing the channels output at 320MHz.
Consonant confusions provide further evidence that time-reversed rooms disturb compensation for reverberation

Human listeners routinely and unconsciously attune themselves to the reverberation characteristics of their acoustic environment. Experiments by Watkins [J. Acoust. Soc. Am. 118, 249-262 (2005)] and Longworth-Reed et al. [J. Acoust. Soc. Am. 125, EL13-EL19 (2009)] have suggested, however, that this process breaks down when the time-direction of reverberation is reversed. This finding is of interest since it is not predicted by intelligibility standards based on the room’s modulation transfer characteristic. While Longworth-Reed and colleagues measured a relatively slow room adaptation effect, Watkins found fast-acting compensatory effects within individual utterances. The generality of Watkins’ finding remains to be tested, however, since it involved categorical perception of words distinguished by a single consonant cue. The current paper generalises Watkins’ experiment by analysing confusions among unvoiced stop-consonants in read speech from multiple talkers, using semantically unpredictable test syllables embedded in short sentences. When the speech precursor was reverberated at a consistent distance with the test item, the accuracy of consonant identification was governed by the time-direction of reverberation in the precursor. The forward condition promoted compensation for the effects of reverberation, indicating a rapid monaural calibration of the listener to the room condition experienced. Under time-reversed reverberation, however, listeners no longer appeared to derive any benefit from a consistently reverberated speech precursor. Finally, we describe a computational model in which sensitivity to the room condition was achieved via an efferent auditory pathway that regulated auditory nerve firing based on an estimate of reverberant decay. The model reproduced the essential features of the human listener data.
SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities

We present SaSTL—a novel Spatial Aggregation Signal Temporal Logic—for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
A New Collaborative Filtering Model based on Robust Graph Coloring for generation of communities

Collaborative Filtering is one of the most important models that have been implemented to support the retrieval of relevant information for users of recommendation systems. This paper proposes a Collaborative Filtering model based on the Pearson correlation index and the Robust Graph Coloring model. According to the results obtained from three algorithms (Greedy, and two versions of GRASP) we can aﬃrm that it is possible to implement this model in a collaborative ﬁltering recommendation system that have thousands of users.
Link Functions and the Generalized Linear Model The Logit Link Function Logistic regression can be thought of as consisting of a mathematical transformation of a standard regression

Link Functions and the Generalized Linear Model The Logit Link Function Logistic regression can be thought of as consisting of a mathematical transformation of a standard regression model. Remember that one solution to outliers or heteroscedasticity problems is to transform X or Y or both by taking the square root or the log etc. The transformation used in logistic regression is a transformation of the predicted scores ( Ŷ ), which is different from transforming the dependent variable (Y). The transformation in logistic regression is called the logit transformation (so sometimes logistic is referred to as a logit model if there is a binary independent variable). Instead of using Ŷ , the natural log of the probabilities is used.
Multi-source Least-squares Migration of Gulf of Mexico Data

It has been shown that least-squares migration (LSM) (Nemeth et al., 1999; Duquet et al., 2000) can improve the resolution of the migration images and suppress the migration artifacts. However, one of the drawbacks of LSM is its high computational cost. Romero et al. (2000) proposed a blended source method by encoding and stacking different shot gathers into a supergather. The blended source data was formed by phase encoding each shot gather and stacking the shot gathers together to get a supergather. Dai et al. (2011) adapted LSM to blended source data, which I now define as the multisource least-squares migration (MSLSM) procedure. This algorithm is applicable to Kirchhoff migration (Dai et al., 2011), wave-equation migration (Huang and Schuster, 2011) and reverse time migration (Dai et al., 2010). Schuster et al. (2011) provide rigorous formulas for predicting the level of crosstalk noise as a function of the encoding parameters . Application of MSLSM to marine streamer data is hampered by the mismatch of receiver position and receiver number for different shots for wave-equation migration or reverse time migration. Huang and Schuster (2011) proposed a frequency division multiplexing strategy with multisource least-squares wave-equation migration. However, this procedure is not required for Kirchhoff based MSLSM, which can be applied to marine streamer data without any restrictions on the acquisition geometry. This paper is organized into the following three sections. The first part presents the theory of MSLSM. The next section presents synthetic and field data results that demonstrate the efficiency and effectiveness of the MSLSM algorithm. Finally, a summary is provided.
Influence of Information Capital on Competitive Advantage of Public Universities

Presentation of information capital today has become one of the principal mechanisms by which public universities gain competitive advantage. Given inadequate government funding which has forced institutions of higher learning in Kenya to attract Self Sponsored Programme (SSP) students in order to meet the short-fall. This paper aims to review current literature and contributes a set of empirical evidence that capture the current state of information capital and competitive advantage of public universities. Pragmatism philosophical paradigm and explanatory survey research design was utilized. Target population of 450 was considered, out of which a sample of 212 employees drawn from 28 universities were obtained using Slovin’s sample size formula. Simple random sampling technique was adopted. Questionnaires were then administered to these employees. Multiple regression analysis was used to analyze data collected. The findings revealed positive significant relationship between competitive advantage of public universities and reporting tools, between competitive advantage of public universities and techniques and between competitive advantage of public universities and procedures. F-test as an overall test indicated high significance effect of information capital on public universities competitive advantage. It was concluded that the findings extended the use of competitive advantage and resource based view theories. Subsequently, it extended the literature on the match between information capital on public universities competitive advantage. The findings clarified the alignment of information capital on public universities competitive advantage which will help practitioners utilize more attentively information capital resources for decision-making and competitive advantage.
413-422 Hamdan A.pmd

The life table of the Chrysomya rufifacies (Macquart) (Diptera: Calliphoridae) were studied under the laboratory conditions at 28 ± 2.5oC and 75 ± 10% relative humidity using field strain from Penang, Malaysia. The raw data collected were analysed based on the age-stage, two-sex life table theory in order to consider both sexes and the variable developmental rate among individuals and between sexes. The population parameters; intrinsic rate of natural increase (r) was 0.2361 d-1, the finite rate of increase (λ) was 1.2663 d-1, the net reproduction rate (R0) was 97.2 and the mean generation time (T) was 19.4. The two-sex life table analysis provides a comprehensive description of the changes in structure stage of C. rufifacies which is potentially useful for forensic investigations in the country.
International Drivers of Policy Uncertainty in Emerging Economies

We investigate whether the international context can affect the economic policy uncertainty in a group of seven emerging market countries. Using a variety of global variables and a Bayesian Panel Vector Autoregression that connects domestic and international variables, we find that the conditions of the global goods market are essential drivers of policy uncertainty. Especially, a central role is played by global uncertainty, with significant and persistent effects coming from both global policy uncertainty and the volatility of global financial markets.
Categorization & Recognition of Lung Tumor Using Machine Learning Representations.

BACKGROUND Lung Cancer is the disease spreading around the world nowadays. Early recognition of lung disease is a difficult task as the cells which cause tumor will grow quickly and the majority of these cells are enclosed with each other. From the beginning of the treatment, tumor detection handling systems which are generally utilized for the diagnosis of lung cancer, recognizable proof of hereditary and ecological elements is imperative in creating a novel technique for lung tumor detection. In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images. METHODS In this proposed framework, GLCM (Gray Level Co-event Matrix) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM (Gray Level Co-event Matrix) features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increment survival rate. RESULTS & CONCLUSION The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In this manuscript, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features. In this manner, early discovery and probability of lung cancer should assume a crucial task in finding a procedure and furthermore, an increment in the survival rate of the patient. This exploration investigates machine learning systems which consider quality articulation, to perceive cancer or to identify lung cancer.