Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-analysis of Gene Expression in Neurodegenerative Diseases Reveals Patterns in GABA Synthesis and Heat Stress Pathways

Neurodegenerative diseases are characterized as the progressive loss of neural cells, e.g. neurons, glial cells. Ageing, monogenic variations, viral infections, and many other factors are determined and speculated as causes for them. While many individual genes, such as APP for Alzheimer disease and HTT for Huntington disease, and biological pathways are studied for neurodegenerative diseases, system-wide pathogenesis studies are limited. In this study, we carried out a meta-analysis of RNA-Seq studies for three neurodegenerative diseases, namely Alzheimer's disease, Parkinson's disease and Amyotrophic Lateral Sclerosis (ALS) to minimize the batch effect derived differences and identify the similarly altered factors among studies. Our main assumption is that these three diseases share some pathological pathway pattern. For this purpose, we downloaded publicly available Alzheimer's disease (84 patients + 33 controls = 117 individuals), Parkinson's disease (28 patients + 43 controls = 71 individuals) and ALS (2 studies: 46 patients + 25 control = 71 individuals) RNA-Seq data from Sequence Read Archive (SRA) database. The significantly differentially expressed genes common to these studies were first identified and analyzed for the patterns in their pathways and variations. Our meta-analysis revealed the shared nature of differential gene expression and mutation load of the cellular heat stress response and GABA synthesis in neurodegenerative diseases. The downregulated GABA synthesis-related genes (e.g. GAD1 and GAD2) and the upregulated cellular heat stress response-related genes (e.g. DNAJB6 and HSP90AA1), in addition to their expression patterns, contain unique variations in samples from patients with neurodegenerative diseases. The significance of genes and pathways we identified in this study corroborated by the recent literature on neurodegenerative diseases.
A Multi-Class Fisher Linear Discriminant Approach for the Improvement in the Accuracy of Complex Texture Discrimination

Texture segmentation has a wide spectrum of applications in diverse fields. This paper presents an elaborated Fisher Linear Discriminant (FLD) based semi-supervised approach for improving the accuracy of segmentation of multi-class complex fine textures. Gabor filter and local statistics (local variance) are used for feature extraction of texture images. Texture segments in the image are separated using K-means clustering. The results obtained using K-means clustering are refined by multi-class Fisher Linear Discriminant (MFLD). The algorithm is tested on wide varieties of several hundred homogenous and complex textures from five texture databases viz. Outex texture database, vision texture database (Vistex), Brodatz textures, Prague textures and Pertex texture database. Fisher distance (FD) is a measure of texture separability. Segmentation of complex textures is relatively a difficult task. The improvement in the segmentation accuracy of complex textures is achieved simply by the termination of MFLD based algorithm when Fisher distance (FD) ceases to increase with the increasing iterations of MFLD. After a quantitative analysis of the experimentation, it is concluded that the segmentation accuracy of complex textures and the combination of complex and homogeneous fine textures (with small texture primitives) increases as high as 29.83% with the increasing iterations of MFLD resulting in a significant improvement at the boundaries. Detailed results are provided in the experimentation and results section of the paper. The results achieve the second rank for 21 benchmark images among the ten state-of-the-art algorithms.
NPP VIIRS and Aqua MODIS RSB Comparison Using Observations from Simultaneous Nadir Overpasses (SNO)

Suomi NPP (National Polar-orbiting Partnership) satellite (http://npp.gsfc.nasa.gov/viirs.html) began to daily collect global data following its successful launch on October 28, 2011. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key NPP sensor. Similar to the design of the OLS, SeaWiFS and MODIS instruments, VIIRS has on-board calibration components including a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) for the reflective solar bands (RSB), a V-groove blackbody for the thermal emissive bands (TEB), and a space view (SV) port for background subtraction. Immediately after the VIIRS nadir door’s opening on November 21, 2011, anomalously large degradation in the SD response was identified in the near-IR wavelength region, which was unexpected as decreases in the SD reflectance usually occur gradually in the blue (~0.4 μm) wavelength region based on past experience. In this study, we use a well-calibrated Aqua MODIS as reference to track and evaluate VIIRS RSB stability and performance. Reflectances observed by both sensors from simultaneous nadir overpasses (SNO) are used to determine VIIRS to MODIS reflectance ratios for their spectral matching bands. Results of this study provide an immediate post-launch assessment, independent validation of the anomalous degradation observed in SD measurements at near-IR wavelengths and initial analysis of calibration stability and consistency.
– Special issue : Imaging in Coronary Artery Disease Echocardiography in coronary artery disease

Coronary artery disease (CAD) is one of the major causes of morbidity and mortality. Imaging techniques represent the key method for disease extent and severity assessment and evaluation of hemodynamic complications. In skilled hands the method provides useful information for clinical management and prognosis assessment. Complex evaluation brings information about global and regional myocardial function, myocardial viability, ischemic mitral regurgitation, and about development of complications such as left ventricular thrombus formation, myocardial rupture and pericardial effusion. The main drawback of echocardiography is the limited echogenicity of many patients and its undeniable operator-dependence. However, the possibility of bringing the echocardiographic imaging to the bedside of our patients makes the method essential and its knowledge indispensable for all cardiologists. # 2015 The Czech Society of Cardiology. Published by Elsevier Sp. z o.o. All rights
Energy Efficient Resource Allocation for Hybrid Services with Future Channel Gains Changyang She

In this paper, we propose a framework to maximize energy efficiency (EE) of a system supporting real-time (RT) and non-real-time services by exploiting future average channel gains of mobile users, which change in the timescale of seconds and are reported predictable within a minute-long time window. To demonstrate the potential of improving EE by jointly optimizing resource allocation for both services by harnessing both future average channel gains and current instantaneous channel gains, we optimize a two-timescale policy with perfect prediction, by taking orthogonal frequency division multiple access system serving RT and video-on-demand (VoD) users as an example. Considering that fine-grained prediction for every user is with high cost, we propose a heuristic policy that only needs to predict the median of average channel gains of VoD users. Simulation results show that the optimal policy outperforms relevant counterparts, indicating the necessity of the joint optimization for both services and for two timescales. Besides, the heuristic policy performs closely to the optimal policy with perfect prediction while becomes superior with large prediction errors. This suggests that the EE gain over non-predictive policies can be captured with coarse-grained prediction. Index Terms Energy efficiency, predictive resource allocation, future information, VoD services, real-time services
Cloud Storage Servers Clients Third Party Auditor Cloud Service Provider Data Auditing Delegation Public Data Auditing Outsource Data

Cloud Computing moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. In this work, we study the problem of ensuring the integrity of data storage in Cloud Computing. To reduce the computational cost at user side during the integrity verification of their data, the notion of public verifiability has been proposed. However, the challenge is that the computational burden is too huge for the users with resource-constrained devices to compute the public authentication tags of file blocks. To tackle the challenge, we propose OPoR, a new cloud storage scheme involving a cloud storage server and a cloud audit server, where the latter is assumed to be semi-honest. In particular, we consider the task of allowing the cloud audit server, on behalf of the cloud users, to pre-process the data before uploading to the cloud storage server and later verifying the data integrity. OPoR outsources the heavy computation of the tag generation to the cloud audit server and eliminates the involvement of user in the auditing and in the preprocessing phases. Furthermore, we strengthen the Proof of Retrievabiliy (PoR) model to support dynamic data operations, as well as ensure security against reset attacks launched by the cloud storage server in the upload phase.
Face detection based on feature similarity clustering and random forest regression ensemble learning algorithm

A comprehensive color model (CCM) extraction method is proposed for the shortcomings such as poor extraction effect of the common color model extraction method and less removal of the background. The Adaboost classifier algorithm is used for face detection. This method provides better effect of complex background removal, reduces the Haar-like feature number of the classifier and accelerates the detection rate by adjusting the amplification factor of the search window. The experimental results show that this method effectively improves the detection ratio, reduce the detection time, provides high adaptability and has higher practicality in face detection.
Evaluation of forest inventory processes in a forest under concession in the southwestern Brazilian Amazon

ABSTRACT Forest inventory procedures are of utmost importance to studies of wood volume stocks, and forest structure and diversity, which provide relevant information to public policies, management plans and ecological research. The present work focused on the performance of inventory techniques in the Amazon region to evaluate wood volume stocks with higher levels of accuracy while maintaining sampling intensity fixed. Two sampling processes were assessed: simple random sampling and two-stage cluster sampling. The processes were evaluated through the allocation of sampling units with different dimensions, and the effectiveness of the generated estimators was analyzed as a function of stand density and basal area. Simple random sampling resulted in the smallest errors, reaching 9% when all species were sampled together. The method depicted forest phytosociological parameters with greater sensitivity, whereas two-stage cluster sampling produced the least accurate estimators and presented slower responses to variation in phytosociological parameters.
Care for Grandchildren and Psychological Well-being of Older Parents in Rural China

Using data from the baseline and follow-up surveys of “Well-being of Elderly in Anhui Province, China” conducted in 2001 and in 2003 respectively by the Institute for Population and Development Studies of Xi’an Jiaotong University, this paper used multiple regression in order to estimate the effects of care for grandchildren on psychological well-being of the grandparents. The results showed that care for grandchildren directly improved grandparents’ psychological well-being, and among which the gender of grandparents or children played important roles. Controlling the out-migration of children and intergenerational supports, we found grandparents providing grandchild care to sons had better psychological well-being, which suggested that traditional “son preference” norm had an effect on psychological well-being of the grandparents. Care for daughters’ grandchildren improved the psychological well-being of grandfathers, but not of grandmothers, which suggested that altruistic support had a protective effect on the grandfathers’ psychological well-being, while grandmother benefited more from reciprocity between generations.
ANALYSIS OF DIFFERENT INVERTER TOPOLOGIES FOR A POWER CONDITIONING UNIT OF A PHOTOVOLTAIC GENERATING SYSTEM

This paper presents different inverter topologies for a power conditioning unit of a photovoltaic generating system and performance of different topologies are compared in terms of efficiency, regulation and DC current injection. A model of power conditioning unit is simulated in MATLAB / SIMULINK and its performance is analyzed. Phase deposition and maximum boost control method is used for multilevel inverter and Z source inverter. The effect of modulation index is also studied. The simulated results are presented in this paper.
Draft : Simplification on Learning Model by Using PROC GENMOD

Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of the statistical analysis of such data is to describe the marginal expectation of the outcome variable as a function of the covariates, while accounting for the correlation among the repeated observations for a given subject. In this paper, we apply the generalized estimating equation (GEEs) approach to fit one of the class of models, learning model, with repeated binary outcomes which is common in the biological and social sciences. Our intention was to discuss what we consider to be a recently widely applicable methodology for longitudinal data, discuss its advantages and disadvantages, and illustrated by SAS system. The method is demonstrated with an analysis of data obtained from a dog-shock experiment and a delay of reward experiment between alcohol preferring and non-preferring rats.
Event Extraction from Online Text using a Generative Adversarial Network

To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a document are generated from a single event. While this may be true for short text such as tweets, such an assumption does not generally hold for long text such as news articles. Moreover, Bayesian graphical models often rely on Gibbs sampling for parameter inference which may take long time to converge. To address these limitations, we propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM). AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events. A discriminator is used to distinguish documents reconstructed from the latent events and the original documents. A byproduct of the discriminator is that the features generated by the learned discriminator network allow the visualization of the extracted events. Our model has been evaluated on two Twitter datasets and a news article dataset. Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15% is observed in F-measure.
New evidence of proto-urban seffle-ments in the Lake Chad area

In the last three decades subjects related to the development of socio-economic complexity in sub-Saharan Africa have become a major focus for African archaeology. Concerning the factors which started this process, there has been a general consensus that agricultural surplus, functional specialisation and finally trade definitely promoted the growth of complex communities and contributed to the rise of precolonial urban centres and states. Furthermore, scholars are now of the opinion that local and regional trade networks are much older than long-distance ones and that they played a fundamental and pioneering role in the development of complex social systems of African societies (Connah 1987; Mclntosh 1995; Mclntosh 1998). Recent archaeological research and on-going studies in the sahel-savanna belt of West Africa have been partly responsible for the understanding of the transformation processes experienced by those communities.
Microstrip Antenna with Split Circular Ring Shaped DGS Structure for WLAN Applications

– Paper is outcome of DGS inspired antenna which was proposed for the WLAN applications of S band. Proposed antenna is designed at 1.8GHz frequency. DGS of split ring shaped structure was implemented later to modify the antenna characteristics. Modified results increases the return loss up to - 37dB from -12dB and increased the bandwidth to 41.4MHz from 38MMHz.
Bid-Separation in Asymmetric Auctions ∗

The theoretical literature on asymmetric first-price auctions has focused mainly on settings with either (1) exactly two bidders or (2) an arbitrary number of bidders with types in a common support. Here, we explore the complications that arise when moving beyond these settings. The structure of the problem changes significantly as bidders may now tender bids over different intervals, a property we term bid-separation. We present a number of comparative statics results that characterize when this is likely to occur. Bid-separation is more prevalent the more bidders there are at auction and the “stronger” bidders are on their respective supports. Stretching the supports may or may not lead to more bid-separation; a complete characterization is provided for the influential uniform-distribution model. Importantly, bid-separation invalidates existing numerical solution methods. However, we propose a method that can be used to modify these to take bid-separation into account. JEL Classification Number: D44
(A)symmetry × (Non)monotonicity: Towards a Deeper Understanding of Key Cognitive Di/Trichotomies and the Common Model of Cognition

A range of dichotomies from across the cognitive sciences are reduced to either (a)symmetry or (non)monotonicity. Taking the cross-product of these two elemental dichotomies then yields a deeper understanding of both two key trichotomies – based on control and content hierarchies – and the Common Model of Cognition, with results that bear on the structure of integrative cognitive architectures, models and systems, and on their commonalities, differences and gaps.
Author ' s personal copy Co-registration of lunar topographic models derived from Chang ’ E-1 , SELENE , and LRO laser altimeter data based on a novel surface matching method

Various lunar digital topographic models (DTMs) have been generated from the data collected from earlier and recent lunar missions. There are usually inconsistencies among them due to differences in sensor configurations, data acquisition periods, and production techniques. To obtain maximum value for science and exploration, the multi-source lunar topographic datasets must be co-registered in a common reference frame. Only such an effort will ensure the proper calibration, registration, and analysis of the datasets, which in turn will permit the full comparative and synergistic use of them. This study presents a multi-feature-based surface matching method for the co-registration of multiple lunar DTMs that incorporates feature points, lines, and surface patches in surface matching to guarantee robust surface correspondence. A combined adjustment model is developed for the determination of seven transformation parameters (one scale factor, three rotations, and three translations), from which the multiple DTMs could be co-registered. The lunar DTMs derived from the Chang’E-1, SELENE, and LRO laser altimeter data in the Apollo 15 landing area and the Sinus Iridum area are examined in this study. Small offsets were found among the Chang’E-1, SELENE, and LRO DTMs. Through experimental analysis, the developed multi-feature-based method was proven able to effectively co-register multiple lunar DTMs. The performances of the multi-feature-based surface matching method were compared with the point-based method, and the former was proven to be superior to the latter. & 2012 Elsevier B.V. All rights reserved.
The Effects of Human Resource Management Practices on Employee Service Quality in Thailand ’ s Chained-Brand Hotels

This research investigates the determining effects of human resource management (HRM) practices on employee service quality in Thailand’s chained-brand hotels located in Bangkok, Thailand. Eleven hypotheses have been developed that point to possible positive and negative relationships between HRM practices, mediating variables and employee service quality as employee performance. A total of 450 managers, consisting of top management, HR leader, and Line Hotel Operation manager that include Restaurant manager, Engineering & Service Maintenance manager, Executive Chef were examined and then empirically tested using SPSS AMOS for Path Analysis. The results show that HRM practices have no direct relationship with Employee service quality as employee performance. But they have an indirect association, meaning employee service quality cannot be determined solely by the use of HRM practices. Out of the five mediating variables in this study, the one that has the most substantial effect on employee performance is employee motivation.
Characterizing and Detecting Livestreaming Chatbots

Livestreaming platforms enable content producers, or streamers, to broadcast creative content to a potentially large viewer base. Chatrooms form an integral part of such platforms, enabling viewers to interact both with the streamer, and amongst themselves. Streams with high engagement (many viewers and active chatters) are typically considered engaging, and often promoted to end users by means of recommendation algorithms, and exposed to better monetization opportunities via revenue share from platform advertising, viewer donations, and third-party sponsorships. Given such incentives, some streamers make use of fraudulent means to increase perceived engagement by simulating chatter via fake “chatbots” which can be purchased from shady online marketplaces. This inauthentic engagement can negatively influence recommendation, hurt streamer and viewer trust in the platform, and harm monetization for honest streamers. In this paper, we tackle the novel problem of automating detection of chatbots on livestreaming platforms. To this end, we first formalize the livestreaming chatbot detection problem and characterize differences between botted and genuine chatter behavior observed from a real-world livestreaming chatter dataset collected from Twitch.tv. We then propose Sherlock, which posits a two-stage approach of detecting chatbotted streams, and subsequently detecting the constituent chatbots. Finally, we demonstrate effectiveness on both real and synthetic data: to this end, we propose a novel strategy for collecting labeled, synthetic chatter dataset (typically unavailable) from such platforms, enabling evaluation of proposed detection approaches against chatbot behaviors with varying signatures. Our approach achieves . 97 precision/recall on the real-world dataset, and . 80+ F1 scores across most simulated attack settings.