Miracle-dz/UniFAR-FTUs huggingface dataset:
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

Generative pre-trained models have achieved remarkable success in various domains such as natural language processing and computer vision. Specifically, the combination of large-scale diverse datasets and pre-trained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between linguistic constructs and cellular biology — where texts comprise words, similarly, cells are defined by genes — our study probes the applicability of foundation models to advance cellular biology and genetics research. Utilizing the burgeoning single-cell sequencing data, we have pioneered the construction of a foundation model for single-cell biology, scGPT, which is based on generative pre-trained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT, a generative pre-trained transformer, effectively distills critical biological insights concerning genes and cells. Through the further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell-type annotation, multi-batch integration, multi-omic integration, genetic perturbation prediction, and gene network inference. The scGPT codebase is publicly available at https://github.com/bowang-lab/scGPT.
Urumaco and Venezuelan Paleontology: The Fossil Record of the Northern Neotropics

Urumaco and Venezuelan Paleontology offers a synthesis of the paleontological record of Venezuela, including new discoveries on stratigraphy, paleobotany, fossil invertebrates, and vertebrates. Besides providing a critical summary of the record of decapods, fishes, crocodiles, turtles, rodents, armadillos, and ungulates, several chapters introduce new information on the distribution and paleobiology of groups not previously studied in this part of the world. Given its position in the northern neotropics, close to the Panamanian land bridge, Venezuela is a key location for understanding faunal exchanges between the Americas in the recent geological past. The book reviews the recent paleobotanical and vertebrate fossil record of the region, provides an understanding of Pleistocene climatic change and biogeography for the last few thousand years, and integrates new information with summaries of Spanish language works on Venezuelan geology and paleontology.
Correcting Standard Errors in Two-Stage Estimation Procedures with Generated Regressands

Feenstra and Hanson [NBER Working Paper No. 6052 (1997)] propose a procedure to correct the standard errors in a two-stage regression with generated dependent variables. Their method has subsequently been used in two-stage mandated wage models [Feenstra and Hanson, "Quarterly Journal of Economics" (1999) Vol. 114, pp. 907-940; Haskel and Slaughter, "The Economic Journal" (2001) Vol. 111, pp. 163-187; "Review of International Economics" (2003) Vol. 11, pp. 630-650] and for the estimation of the sector bias of skill-biased technological change [Haskel and Slaughter, "European Economic Review" (2002) Vol. 46, pp. 1757-1783]. Unfortunately, the proposed correction is negatively biased (sometimes even resulting in negative estimated variances) and therefore leads to overestimation of the inferred significance. We present an unbiased correction procedure and apply it to the models reported by Feenstra and Hanson (1999)and Haskel and Slaughter (2002). Copyright 2005 Blackwell Publishing Ltd.
The diversity of why A meta-analytical study of usage motivation in enterprise social networks

In times of demographic change, skill short-age, and disruptive innovations, organizational knowledge management and innovative capacity are the key to a company’s success. But how can knowledge be retained with fast staﬀ turnover, global project-based work and parental leaves? Using enterprise social media to improve knowledge dissemination at work seems promising, when looking at the success of private social networking sites. In this article we combine ten diﬀerent empirical studies which investigated diﬀerent aspects of how user diversity inﬂuences the motivation to use social media at work. The emerging meta study using the DeSimonian Laird Method (total sample size N=522) analyzes diﬀerent aspects of user diversity and their correlation with eight motives for SNS usage: information, importance, contact, self presentation, autonomy, social comparison, and power & control. We found that that the individual achievement motivation correlates positively with the motives importance, power, information and self presentation. The need for autonomy correlates with openness to new experiences and the need for social comparison with gender and neuroticism. From our ﬁndings we derive practical implications for designing a social networking site for work which fulﬁll the users’ needs and function along their motivation.
A SMART AIR POLLUTION MONITORING SYSTEM

Air pollution affects our day to day activities and quality of life. It poses a threat to the ecosystem and the quality of life on the planet. The dire need to monitor air quality is very glaring, owing to increased industrial activities over the past years. People need to know the extent to which their activities affect air quality. This project proposes an air pollution monitoring system. The system was developed using the Arduino microcontroller. The air pollution monitoring system was designed to monitor and analyze air quality in real-time and log data to a remote server, keeping the data updated over the internet. Air quality measurements were taken based on the Parts per Million (PPM) metrics and analyzed using Microsoft Excel. The air quality measurements taken by the designed system was accurate. The result was displayed on the designed hardware’s display interface and could be accessed via the cloud on any smart mobile device.
AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

2 New model formulations, optimal design and control, and model reduction, with application to nanomanufacturing 1 2.1 SCFT and phase field theory and algorithms . 1 2.2 Nonlocal Cahn-Hilliard-based optimal design . 1 2.3 Model reduction for nonlocal Cahn-Hilliard . 3 2.4 Model inadequacy . 4
Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation

The radiative forcing caused by a volcanic eruption is dependent on several eruption source parameters such as the mass of sulfur dioxide (SO2) emitted, the eruption column height, and the eruption latitude. General circulation models with prognostic aerosol and chemistry schemes can be used to investigate how each parameter influences the volcanic forcing. However, the range of multidimensional parameter space that can be explored is restricted because such simulations are computationally expensive. Here we use statistical emulation to explore the radiative impact of eruptions over a wide covarying range of SO2 emission magnitudes, injection heights, and eruption latitudes based on only 30 simulations. We use the emulators to build response surfaces to visualize and predict the sulfate aerosol e‐folding decay time, the stratospheric aerosol optical depth, and net radiative forcing of thousands of different eruptions. We find that the volcanic stratospheric aerosol optical depth and net radiative forcing are primarily determined by the mass of SO2 emitted, but eruption latitude is the most important parameter in determining the sulfate aerosol e‐folding decay time. The response surfaces reveal joint effects of the eruption source parameters in influencing the net radiative forcing, such as a stronger influence of injection height for tropical eruptions than high‐latitude eruptions. We also demonstrate how the emulated response surfaces can be used to find all combinations of eruption source parameters that produce a particular volcanic response, often revealing multiple solutions.
the Composition of the Training Set

This chapter aims to define the characteristics of an optimal training set for the automated segmentation of short-axis left ventricular magnetic resonance (MR) images in clinical practice, using an Active Appearance Model (AAM). We investigated the segmentation accuracy by varying the size and composition of the training set (i.e., the ratio between pathologic and normal ventricle images, and the vendor dependence). The accuracy was assessed using the degree of similarity and the difference in ejection fraction between automatically detected and manually drawn contours. Including more images in the training set results in a better accuracy of the detected contours, with optimum results achieved when including 200 to 250 images. Using AAM-based contour detection with a mixed model of 80% normal and 20% pathologic cases provides good segmentation accuracy in clinical routine. Finally, this work shows that it is essential to define different AAM models for images from different MRI systems. A model defined on a sufficient number of images with the correct distribution of image characteristics achieves good results in clinical routine.
Fast Searchable Encryption With Tunable Locality

Searchable encryption (SE) allows a client to outsource a dataset to an untrusted server while enabling the server to answer keyword queries in a private manner. SE can be used as a building block to support more expressive private queries such as range/point and boolean queries, while providing formal security guarantees. To scale SE to big data using external memory, new schemes with small locality have been proposed, where locality is defined as the number of non-continuous reads that the server makes for each query. Previous space-efficient SE schemes achieve optimal locality by increasing the read efficiency-the number of additional memory locations (false positives) that the server reads per result item. This can hurt practical performance. In this work, we design, formally prove secure, and evaluate the first SE scheme with tunable locality and linear space. Our first scheme has optimal locality and outperforms existing approaches (that have a slightly different leakage profile) by up to 2.5 orders of magnitude in terms of read efficiency, for all practical database sizes. Another version of our construction with the same leakage as previous works can be tuned to have bounded locality, optimal read efficiency and up to 60x more efficient end-to-end search time. We demonstrate that our schemes work fast in in-memory as well, leading to search time savings of up to 1 order of magnitude when compared to the most practical in-memory SE schemes. Finally, our construction can be tuned to achieve trade-offs between space, read efficiency, locality, parallelism and communication overhead.
Can We Trust Online Physician Ratings? Evidence from Cardiac Surgeons in Florida

Despite heated debate about the pros and cons of online physician ratings, little systematic work has examined the correlation between physicians’ online ratings and their actual medical quality. Using the ratings of cardiac surgeons at RateMDs and the patient outcomes of coronary artery bypass graft surgeries in the 2013 Florida Hospital Inpatient Discharge Data, we investigate whether online ratings are informative about physicians’ medical quality. To account for potentially nonrandom matchings of patients of different severity levels to surgeons of different rating categories, we focus on patients who arrived through the emergency department and explicitly consider how observed and unobserved patient health conditions jointly affect the matching arrangements and surgical outcomes. Both reduced form and two-stage estimation results show that, compared with surgeons rated four stars or higher, or those without rating information, lower rated surgeons are associated with significantly higher in-hospital .
Chapter # Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory

We describe our experience in developing a discourse -annotated corpus for community -wide use. Working in the framework of Rhetorical Structure Theory, we were able to create a large annotated resource with very high consistency, using a well -defined methodology and protocol. This resource is made publicly available through the Linguistic Data Consortium to enable researchers to develop empirically grounded, discourse specific applications.
Southeast Europe Journal of Soft Computing Available online:

: For the taxonomic classification of microbes, 16S ribosomal RNA (rRNA) gene sequences are widely used in environmental microbiology as reliable markers. Although the massive sequencing of 16S rRNA gene amplicons encompassing the full length of genes is not easy, because of the limitations of the current sequencing techniques, in databases Greengenes, RDP
A game control

The invention relates to a device for controlling games ends of the blades of a rotor 19 in a gas turbine engine 10 is described. The device comprises means for supplying hot air and means for supplying cold air to the bore of the rotor. A valve controls the flow of the heating air. Applications gas turbine engines.
Finance or Demand: What drives the Responses of Young and Small Firms to Financial Crises?

Using a large dataset of Greek ﬁrms over the period 1998-2014, we investigate the excess sensitivity of small and young ﬁrms to the Greek ﬁnancial crisis, along with the potential sources and aggregate implications. Controlling for size eﬀects, the decline in sales growth rate during the crisis was about 6.6 percentage points larger in young ﬁrms than in their mature counterparts. Controlling for age eﬀects, the decline in sales growth rate during the crisis was about 9.8 percentage points larger in small ﬁrms than in their large counterparts. Although the excess decline in small ﬁrms’ growth rates is driven by both credit constraints and unexpected demand shocks, the excess decline in young ﬁrms is driven by ﬁnancing constraints but not by unexpected demand shocks. The excess sensitivity of small ﬁrms accounts for about 13% of the drop in total output of the Greek economy during the crisis, with credit constraints and ﬁrm-level unexpected demand shocks contributing almost equally to this. The excess sensitivity of young ﬁrms accounts for about 14% of the aggregate output drop, with more than the half of this eﬀect stemming from ﬁnancing constraints.
from Data

This paper empirically tests the effects of Anti-Dumping (AD) protection on the price-cost margin of firms. To this end, we use a rich panel data set of 1,666 EU producers that were involved in AD cases initiated in 1996. Our findings indicate that price-cost margins in most cases significantly increase in the period of protection compared to a period before protection. In industries where competition is very tough before protection, we fail to find an increase in price-cost margins, while in industries with positive markups before protection, trade policy raises market power between 3% points and 15 % points, depending on the sector. Our results are robust to alternative specifications and estimation techniques, controlling for unobservable fixed effects and potential endogeneity of the regressors. Our findings are also consistent with recent theoretical models that deal with the economic effects on price behaviour in response to AD protection. as the panel progresses, which increases the efficiency of the estimates. To test whether our instruments are valid we report a Sargan test of over-identifying restrictions, which is c 2 distributed. We also report a test of second order serial correlation, which is standard Normal distributed. This test is useful to detect serial correlation, in which case a static model would not be valid. Since we estimate equation (7) in this case in first differences, what matters is the absence of second order serial correlation in order to have no first order serial correlation in the levels equation of (7). All the parameters in that case are then consistently estimated.
Application-Driven Data Analysis

An improved semiconductor device and method of manufacturing employs interconnecting films on film circuit as ground lines which extend to the periphery of the film circuit where there is a further connection to a conductive reinforcing plate 25. Advantageously, the conductive reinforcing plate reduces electrical noise from interfering with the semiconductor device and prevents the semiconductor device from radiating undesired signals. The interconnecting films also reduce cross-talk between signal lines of the semiconductor device.
URBAN SPRAWL DI KOTA SEMARANG: KARAKTERISTIK DAN EVALUASINYA TERHADAP RENCANA DETAIL TATA RUANG KOTA

Perkembangan kota berkecenderungan merembet ke daerah pinggiran merembetkan sifat kekotaan. Penelitian ini bertujuan mengidentifikasi perkembangan Kota Semarang secara umum 1992-2000; identifikasi karakteristik urban sprawl (tingkat, arah dan tipe urban sprawl) 2000-2009, mengkaji perkembangan perubahan karakteristik kekotaan pada wilayah dengan tingkat urban sprawl tinggi dan tingkat kesesuaian terhadap RDTRK rendah tahun 2000-2009; mengevaluasi penyimpangan landuse terhadap RDTRK. Metode yang digunakan dalam penelitian ini adalah klasifikasi tingkat urban sprawl, overlay data spasial dan pembuatan tipologi sebagai purposive sampling. Hasil menunjukkan perkembangan makro Kota Semarang tahun 1992-2000 mengarah ke Barat, Timur dan Selatan, penambahan terbesar pada zona 10-15 Km dari CBD. Identifikasi tingkat urban sprawl tahun 2000-2009, kelas tinggi tersebar mengelilingi kota, terdapat 118 kelas tinggi. Kelurahan Tipologi A perubahan landuse paling luas adalah dari kebun menjadi tegalan, Tipologi B adalah sawah irigasi menjadi permukiman, sedangkan Tipologi C adalah kebun menjadi tegalan. Secara keseluruhan, ketidaksesuaian landuse terhadap RDTRK adalah 18,69 %.
Direct and Underspecified Interpretations of LFG f-structures

We describe an approach to interpreting LFG f-structures (Kaplan & Bresnan, 1982) truth-conditionally as underspecified quasi-logical forms. F-structures are either interpreted indirectly in terms of a homomorphic embedding into Quasi Logical Form (QLF) (Alshawi, 1992; Alshawi & Crouch, 1992; Cooper et al., 1994a) representations or directly in terms of adapting QLF interpretation clauses to f-structure representations. We provide a reverse mapping from QLFs to f-structures and establish isomorphic subsets of the QLF and LFG formalism. A simple mapping which switches off QLF contextual resolution can be shown to be truth preserving with respect to an independently given semantics (Dalrymple et al., 1995). We compare our proposal with approaches discussed in the literature.
Pattern and mechanisms of atrophy progression in individuals with a family history of Alzheimer's disease: a comparative study

Alzheimer's disease (AD) includes a long period of presymptomatic brain changes. Different risk factors are associated with AD development, including having a family history of AD (FHAD). The Braak scheme suggests that tau pathology, in synergy with amyloid-beta (A{beta}), spreads along structural connections in AD, eventually leading to atrophy. Studying the pathways in which atrophy spreads early on, as well as the factors underpinning this pathway, is crucial for improving diagnostic accuracy and early interventions. However, the pattern of atrophy progression in people with a FHAD and the biological factors associated with this progression remain unclear. Here we used structural MRI from three databases (ADNI, PREVENT-AD and Montreal Adult Lifespan Study) to map the atrophy progression in FHAD and AD and assess the constraining effects of structural connectivity on atrophy progression. Cross-sectional and longitudinal data up to 4 years were used to perform atrophy progression analysis in FHAD and AD compared to controls. Positron emission tomography (PET) radiotracers were also used to quantify the distribution of tau and A{beta} proteins at baseline. We first derived cortical atrophy progression maps using deformation-based morphometry from 153 FHAD, 156 AD, and 116 controls with similar age, education, and sex at baseline. We next examined the spatial relationship between atrophy progression and spatial patterns of tau and A{beta} deposition, structural connectivity, and neurotransmitter receptor and transporter distributions. Our results show that there were similar patterns of atrophy progression in FHAD and AD, notably in the cingulate, temporal and parietal cortices, with more widespread and severe atrophy in AD. Both tau and A{beta} pathology tended to accumulate in regions that were structurally connected in FHAD and AD. The pattern of atrophy and its progression also aligned with existing structural connectivity in FHAD. In AD, our findings suggest that atrophy progression results from propagating pathology that occurred much earlier, on an intact connectome. Moreover, a relationship was found between the serotonin 5-HT6 receptors spatial distribution and atrophy progression in AD, supporting an important role of these receptors in neurodegeneration. The current study demonstrates that regions showing atrophy progression in FHAD and AD present with specific connectivity and cellular characteristics, uncovering certain of the mechanisms involved in preclinical and clinical neurodegeneration.
The Effects of Noise : a Simulation

Denoising autoencoders (DAEs) have proven useful for unsupervised representation learning, but a thorough theoretical understanding is still lacking of how the input noise influences learning. Here we develop theory for how noise influences learning in DAEs. By focusing on linear DAEs, we are able to derive analytic expressions that exactly describe their learning dynamics. We verify our theoretical predictions with simulations as well as experiments on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise allows DAEs to ignore low variance directions in the inputs while learning to reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs to standard regularised autoencoders, we show that noise has a similar regularisation effect to weight decay, but with faster training dynamics. We also show that our theoretical predictions approximate learning dynamics on real-world data and qualitatively match observed dynamics in nonlinear DAEs. *
PM10 CONCENTRATION PREDICTION FOR AREAS WITH NO UPDATING MONITORING SYSTEM USING AUTO – REGRESSIVEGROUP METHOD OF DATA HANDLING NEURAL NETWORK

Introduction: Predicting PM10 concentration as a significant risk factor for anumber of pollution related diseases has been recently inevitable task for areas with high population density particularly for areas with no updating monitoring systems. This study aims to illustrate how PM10 concentration level can be predicted by the prior information of the air pollutants and the meteorologicalfactors in urban areas. Materials and methods: The data we used are measured from four monitoringstations in the city of Tehran between January 2012 and December 2014. We use the Auto-regressive group method of data handling (AR - GMDH) neuralnetwork approach which employees the prior stationary time series data setting. Results: Our results demonstrate that PM10 concentration level for a specific dayis more likely to be predictable by sulfur dioxide (SO2) and nitrogen dioxide (NO2) than the carbon monoxide (CO) concentrations, and also show thatPM10 concentration is positively associated with precipitation and wind speedand with high temperature. The accuracy of the predicted values of the PM10 concentration is evaluated by inspecting the coefficient of determination, meansquared error, the square root of mean squared error, mean absolute deviation, and index of agreement. Conclusions: The AR - GMDH algorithm can be proposedin comparison with the chemical and physical approaches due to its accuracyand simplicity, and its cost efficiency.