Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse block signal detection and identification for shared cross-trait association analysis

Genome-wide association studies (GWAS) have identiﬁed thousands of single nucleotide polymorphisms (SNPs) that are associated with complex traits. GWAS data allows us to investigate the shared genetic etiologies among different traits. However, linkage disequilibrium (LD) between the SNPs complicates the detection and identiﬁcation of shared genetic effects. In this paper, we model the LD by dividing the genome into LD blocks and linking the genetic variants within a block to a possible latent causal variant. An eigenvector-projected score statistic that leverages the set of variants in LD and a max-type test statistic (Max-block) are proposed to detect the existence of cross-trait genetic association. The Max-block is easy to calculate and is shown to control the genome-wide error rate. After the detection, a step-wise procedure is proposed to identify the signiﬁcant blocks that explain the genetic sharing between two traits. Simulation experiments show that Max-block is more powerful than standard approaches in the sparse settings and is robust to different signal strengths or levels of sparsity. The method is applied to study shared cross-trait associations in 10 pediatric autoimmune diseases and identiﬁed several regions that explain the genetic sharing between juvenile idiopathic arthritis (JIA) and ulcerative colitis (UC) and between UC and Crohn’s disease (CD). In addition, our analysis also indicates the ge-netic sharing in the MHC region among systemic lupus (SLE), celiac disease (CEL) and common variable immunodeﬁciency (CVID). Results from real data and simulation studies show that Max-block provides an important alternative to commonly used genetic correlation estimation in understanding genetic correlation among complex diseases.
Re: Distributed Distillation for On-Device Learning

—In this project, we reproduce the work introduced in the paper Distributed Distillation for On-Device Learning [1] as part of the 2020 NeurIPS Reproducibility Challenge. In this paper, Ilai Bistritz et. al [1] introduce a distributed distillation algorithm where devices communicate and learn from soft-decision outputs that are inherently architecture-agnostic and scale only with the number of classes. This algorithm provides performance improvement while requiring only a little communication overhead compared to traditional distributed training. During the reproducing experiments, we mainly focus on the following topics: 1) if the baselines chosen in this paper are appropriate and state-of-the-art. 2) if the performance improvement and communication reduction are as good as described in this paper. 3) if this training algorithm is compatible with different network compression strategies. We also ﬁnd plausible typos or problems in this paper, which would also be discussed. Overall, we think Distributed Distillation is a good training strategy with reliability and compatibility.
Predicting Topic Change and Emoji Usage from Twitter Data

The research on human social interaction on twitter has become very active in the recent years, especially in the area of computational linguistics. Most research works so far focused on the tweets’ content, sentiment and the associated topic. However, an important part of this messaging schema had remained mostly unnoticed: the emojis. The extensive use of these Unicode characters only recently gained the attention of researchers, leading to the ﬁrst publications on the association of the message topic to certain emoji. In our work, we study, whether emojis play an important role in topic change in turn-taking twitter conversations. We perform the emoji classiﬁ-cation task to show, how the use of certain emojis is connected to the written message context. We create a new twitter conversation dataset with a subset annotated for topic change.Finally, we test supervised and unsupervised machine learning methods for topic change detection to evaluate, how important are the emoji for triggering a topic change.
Characterization of Monte Carlo Dynamic/Kinetic Properties of Local Structure in Bond Fluctuation Model of Polymer System

We report the results of the characterization of local Monte Carlo (MC) dynamics of an equilibrium bond fluctuation model polymer matrix (BFM), in time interval typical for MC simulations of non-linear optical phenomena in host-guest systems. The study contributes to the physical picture of the dynamical aspects of quasi-binary mosaic states characterized previously in the static regime. The polymer dynamics was studied at three temperatures (below, above and close to the glass transition), using time-dependent generalization of the static parameters which characterize local free volume and local mobility of the matrix. Those parameters play the central role in the kinetic MC model of host-guest systems. The analysis was done in terms of the probability distributions of instantaneous and time-averaged local parameters. The main result is the characterization of time scales characteristic of various local structural processes. Slowing down effects close to the glass transition are clearly marked. The approach yields an elegant geometric criterion for the glass transition temperature. A simplified quantitative physical picture of the dynamics of guest molecules dispersed in BFM matrix at low temperatures offers a starting point for stochastic modeling of host-guest systems.
EEG SIGNAL PROCESSING FOR ABNORMALITY DETECTION USING DWT, SE AND KNN TECHNIQUES

: The EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). The nonlinear method Shannon entropy, which measure complexity and chronicity in the EEG recording, is used to extract the features. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design technique: The University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and many more. The proposed method could potentially be used to assist epilepsy and ASD diagnosis. The combination of DWT, Shannon entropy and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem.
Magneto Resistive-Based Biosensors and Biochips

Protein biochips are at the heart of many medical and bioanalytical processes applications, please. Increased interest was focused on surface activation and associated functionalization methods for the activation of these biomolecules. Over the past five years, magneto electronics has emerged as a promising new platform technology for biosensor and biochip production. The techniques are based on the identification of the magnetic fringe field of a magnetically labelled biomolecule interacting with a complementary biomolecule attached to a magnetic-field sensor. Magneto resistive-based devices, easily used as read heads in hard disk drives, were used to demonstrate the identification of molecular recognition in tandem with biologically usable magnetic marks. Real-world bio-applications are also being studied, allowing for tailor-made product architecture based on sensor and mark functionality. This detection platform offers a reliable, affordable sensing technique with high sensitivity and substantial scope for quantitative signal results, allowing magneto resistive biochips to address unique diagnostic needs that are not fulfilled by current technologies.
Trends in Explanations

Humans reason about the world around them by seeking to understand why and how something occurs. The same principle extends to the technology that so many of human activities increasingly rely on. Issues of trust, transparency, and understandability are critical in promoting adoption and proper use of systems. However, with increasing complexity of the systems and technologies we use, it is hard or even impossible to comprehend their function and behavior, and justify surprising observations through manual investigation alone. Explanation support can ease humans’ interactions with technology: explanations can help users understand a system’s function, justify system results, and increase their trust in automated decisions. Our goal in this article is to provide an overview of existing work in explanation support for data-driven processes, through a lens that identifies commonalities across varied problem settings and solutions. We suggest a classification of explainability requirements across three dimensions: the target of the explanation (“What”), the audience of the explanation (“Who”), and the purpose of the explanation (“Why”). We identify dominant themes across these dimensions and the high-level desiderata each implies, accompaBoris Glavic, Alexandra Meliou and Sudeepa Roy (2021), “Trends in Explanations”, Foundations and Trends® in Databases: Vol. xx, No. xx, pp 1–18. DOI: 10.1561/XXXXXXXXX.
Value Creation and Persistence in Private Equity

We study how private equity (PE) firms generate returns for their investors, by estimating the effects of PE funding on portfolio companies’ operational efficiency and market power. We confirm prior findings that PE funding leads to operational efficiency: both labor productivity and total factor productivity improve as PE-backed companies ramp up investment, employment, and sales. We find no evidence that PE-backed companies increase their market power. In fact, the PE-backed companies in our sample reduce their price markups by 6%, which allows them to gain substantial market shares. Using detailed confidential information obtained from inside PE firms, we show that the PE firms in our sample push for operational improvements and that these improvements are the main drivers of the returns investors receive from PE funds. We find that the majority of the operational improvements instigated by PE firms persist even after they fully exit their investments. These findings are consistent with PE firms’ ability to create long-lasting value as opposed to maximizing short-term returns at the expense of portfolio companies. JEL Classification: D24, G24, G32, G34, L11, L25
Ascorbic Acid and Polyphenols Mediated Green Synthesis of Silver Nanoparticles from Tagetes erecta L. Aqueous Leaf Extract and Studied Their Antioxidant Properties

Silver nanoparticle synthesis of the leaf extract Tagetes erecta L. enriched with ascorbic acid and polyphenols has been investigated. The color of the golden yellow extract has changed to pinkish-brown due to the reduction of Ag+ to the colloidal solution of AgNPs and a sharp absorption peak at 420 nm under the UV-Vis spectrophotometer. In addition, the Fourier Transfer Infrared Spectroscopy (FTIR) estimation was completed in order to recognize and identify the biomolecules present in the extract acting as a reducing and capping agent for the AgNPs. The X-ray diffraction (XRD) peaks at (111), (201), (220), and (311) confirm the presence of monoclinic crystals in the solution. The morphology and size of the particles were provided by transmission electron microscopy (TEM) images of AgNPs. At a scale of 100 nm, synthesized AgNPs were predominantly spherical with a size range of 7-35 nm. In comparison to 7.39 mg/100 g in AgNPs, aqueous leaf extract was 55.14 mg/100 g higher in ascorbic acid. The phenolic and flavonoid content of extract was 52.54 ± 2.15 mg (GAE/100 g) and 15.43 ± 0.34 mg (QE/mL), and the colloidal AgNP solution was 21.45 ± 1.15 mg (GAE/100 g) and 8.05 ± 2.42 mg (QE/mL), respectively. Phenolic and flavonoid contents play a major role as a reducing agent and reduce the precursor AgNO3 into AgNPs. The DPPH scavenging assay also assessed the antioxidant properties of extract and its derived AgNPs. As compared antioxidant value to aqueous leaf extract (mg/mL), higher percentage inhibition (PI) was found in AgNPs and free-radical scavenging activity of extract and AgNPs were directly linked to their concentrations. Results of this research have discovered a higher potential for free-radical scavenging AgNPs and will help to develop new and more potent antioxidants for the treatment of different diseases caused by oxidative stress; the higher antioxidant properties bearing AgNPs might be used.
Green Strategy to Reduced Nanographene Oxide through Microwave Assisted Transformation of Cellulose

A green strategy for fabrication of biobased reduced nanographene oxide (r-nGO) was developed. Cellulose derived nanographene oxide (nGO) type carbon nanodots were reduced by microwave assisted hydrothermal treatment with superheated water alone or in the presence of caffeic acid (CA), a green reducing agent. The carbon nanodots, r-nGO and r-nGO-CA, obtained through the two different reaction routes without or with the added reducing agent, were characterized by multiple analytical techniques including FTIR, XPS, Raman, XRD, TGA, TEM, AFM, UV–vis, and DLS to confirm and evaluate the efficiency of the reduction reactions. A significant decrease in oxygen content accompanied by increased number of sp2 hybridized functional groups was confirmed in both cases. The synergistic effect of superheated water and reducing agent resulted in the highest C/O ratio and thermal stability, which also supported a more efficient reduction. Interesting optical properties were detected by fluorescence spectroscopy where nGO,.
Migration of Escherichia coli and Klebsiella pneumoniae Carbapenemase (KPC)-Producing Enterobacter cloacae through Wastewater Pipework and Establishment in Hospital Sink Waste Traps in a Laboratory Model System

Sink waste traps and drains are a reservoir for multi-drug resistant Gram-negative bacteria in the hospital environment. It has been suggested that these bacteria can migrate through hospital plumbing. Hospital waste traps were installed in a laboratory model system where sinks were connected through a common wastewater pipe. Enterobacterales populations were monitored using selective culture, MALDI-TOF identification and antibiotic resistance profiling before and after a wastewater backflow event. When transfer between sinks was suspected, isolates were compared using whole-genome sequencing. Immediately after the wastewater backflow, two KPC-producing Enterobacter cloacae were recovered from a waste trap in which Carbapenemase-producing Enterobacterales (CPE) had not been detected previously. The isolates belonged to ST501 and ST31 and were genetically indistinguishable to those colonising sinks elsewhere in the system. Following inter-sink transfer, KPC-producing E. cloacae ST501 successfully integrated into the microbiome of the recipient sink and was detected in the waste trap water at least five months after the backflow event. Seven weeks and three months after the backflow, other inter-sink transfers involving Escherichia coli ST5295 and KPC-producing E. cloacae ST501 were also observed.
Exogenous gut microbiota influence fiber size and energy metabolism characteristics in skeletal muscle of mice

The objective of this study was to investigate the effects of exogenous gut microbiota influence fiber size and energy metabolism characteristics in skeletal muscle of mice. Firstly, we compared the differences in muscle fiber diameter, fiber density, and gut microbiota composition between Erhualian pigs (EH) and Landrace×Yorkshire crossed pigs (LY). It is found that EH exhibited thinner fiber diameters and higher fiber densities (P < 0.05) of skeletal muscle compared with that of LY. Moreover, 8 gut microbial phyla and 35 genera were significantly different (P < 0.05) between EH and LY. Correlation analysis revealed all those significantly different microbial genera were significantly correlated with skeletal muscle fiber diameters and densities. Next, 24 newborn mice were divided into 2 groups (CG and AG) treated with sterilized saline and fecal microbiota suspensions from EH, respectively, for 90 days. It is revealed that the relative abundance of Parabacteroides, Sutterlla, and Dehalobacterium were significantly increased in the fecal of AG compared with that of CG. These microbiota contributing to improved functions related to carbohydrate, lipid, and vitamin metabolism. The weight, the fiber densities, and gene expression related to the growth, differentiation, fiber types, mitochondrial function, and carbohydrate metabolism were significantly increased, while the fiber diameters were significantly decreased in the skeletal muscle of AG compared with that of CG. These results suggested that exogenous gut microbiota influence the weight, fiber size and energy metabolism in skeletal muscle of mice via reshaped their gut microbiota composition.
Extending Pillai Scores to Fricative Mergers: Advancing a Gradient Analysis of a Split-in-Progress in Andalusian Spanish

While vocalic mergers and splits have been analyzed acoustically since the inception of variationist sociolinguistics (Labov 1994, Labov et al. 1972, Labov et al. 1991), consonant mergers and splits have principally been analyzed impressionistically. Andalusian Spanish presents such a case wherein the fricative mergers ceceo and seseo, and their ongoing split into distinción, have been extensively documented via impressionistic analysis documenting the role of social and linguistic factors (GarcíaAmaya 2008, Melguizo 2007, Moya Corral and García-Wiedemann 1995, Regan 2017a, Santana 2016, 2016-207, Villena 1996), but there has been a lack of studies that acoustically analyze the gradience of this consonant demerger. In order to fill this gap, the current study utilizes a Pillai score analysis on a fricative split-in-progress in Andalusian Spanish, building on previous acoustic studies (Lasarte Cervantes 2010, Regan 2017b, in press a, in press b). The aims were two-fold: (i) to provide researchers a gradient sociophonetic approach to analyze the demerger of ceceo (or seseo) into distinción that can complement previous acoustic analyses; and (ii) to extend the use of Pillai scores to fricatives in order to incorporate consonant mergers and splits into the larger variationist discussion of mergers and splits as it is heavily biased towards English vowels (Gordon 2013). The study, based on read speech includes 19,420 tokens from 80 speakers, ages 18-87 (M: 43.7, SD: 17.2), balanced for gender (40 male, 40 female) and origin (40 Huelva, 40 Lepe). Independent variables included gender, age, education, occupation, origin, style, orthography, and following phonological context with speaker as a random factor. The acoustic measures considered in the creation of the Pillai scores were center of gravity (Hz), variance (Hz), skewness, and mean intensity (dB). The best explanation of the data was the Pillai score that incorporated only center of gravity and mean intensity, taking into consideration following phonological context. A mixed-effects linear regression found that this apparent time (Labov 1994:45) change in progress of ceceo into distinción is led by those with more formal education (secondary or university education), those employed in service or professionally oriented occupations, females, and in more formal styles. The current paper therefore extends the use of the Pillai score into a fricative split-in-progress, simultaneously advancing the sociophonetic analysis of Andalusian fricatives as well as providing a non-English and nonvocalic example to diversify the variationist discussion of mergers and splits. This working paper is available in University of Pennsylvania Working Papers in Linguistics: https://repository.upenn. edu/pwpl/vol26/iss2/13
Automatic detection of weak cipher usage in aircraft communications

—The Aircraft Communications Addressing and Reporting System (ACARS) allows aircraft to communicate with entities on the ground via short messages. To provide conﬁdentiality for sensitive information communicated via the ACARS network, some operators started to deploy proprietary cryptography to encrypt message contents. This is highly problematic, however, as all of the observed approaches in practice offer next to no communication security but give a false sense of it. Authorities hence would like to ﬁlter out weak ciphers at the network level to alert operators of their risks. This project explored the use of deep convolutional neural networks (CNN) for automatic detection of weakly encrypted message contents on the ACARS data link network. We constructed a labelled dataset of plaintext and ciphertext messages and experimentally evaluated the performance of a deep CNN for message classiﬁcation. We ﬁnd that a model trained on a sufﬁciently diverse set of ciphertexts is able to detect weakly encrypted messages with high accuracy.
Measurement of methylamines in seawater using solid phase microextraction and gas chromatography

The methylamines form part of the marine organic nitrogen pool. Although they are ubiquitous in marine systems, little is known about their distribution, production, and fate in the marine environment. Analytical methods have been developed for their analysis in marine waters, but these have employed custom‐made apparatus, limiting data to a few studies. Here, we report a method developed for the determination of methylamines in seawater which combines headspace solid phase microextraction (SPME) and gas chromatography with selective nitrogen detection. Gaseous methylamines were pre‐concentrated from water samples (∼ 1 L) heated to 60 °C, onto a polydimethylsiloxane/divinylbenzene SPME fiber located in the headspace of the extraction flask. The analytes were then thermally desorbed from the fiber in a gas chromatograph containing a CP‐Volamine column and a nitrogen–phosphorus detector. The method was routinely calibrated down to 1 nM for all three analytes using matrix‐matched external standards. Limits of detection were determined operationally and ranged from 0.4 nM to 2.9 nM during two sampling campaigns. The analytes were detected in seawater samples from the English Channel and Southern Ocean where concentrations up to 20 nM and 5.6 nM were measured, respectively, consistent with previously reported levels. We propose this method for the analysis of methylamines, and other low‐molecular weight amines, as it is sensitive, robust, and the necessary equipment is widely available.
62. The Return of Educational Radio?

This paper examines one of the traditional technologies of distance education, radio, and presents examples of educational and community radio usage in Asia and Africa. Instead of merely transposing western approaches to distance education in developing countries, it is suggested that the developed world can learn from uses of radio in developing countries, and that the medium deserves greater attention as a means of giving educational opportunity to rural, isolated peoples worldwide.
A Performance-based Assessment Platform for Developing Computational Thinking Concepts and Practices: EasyCode

 Abstract —Computational Thinking (CT) concepts and practices involve higher-order cognitive skills. Higher-order cognitive skills can be assessed using a performance-based assessment of programming processes. This study presents EasyCode, a performance-based assessment platform for developing CT in students. Distinct from existing methods that rely on the static code analysis of students’ artifacts, EasyCode conducts a dynamic code analysis, enabling the assessment of CT practices throughout the programming process. EasyCode aids student learning by setting performance tasks and providing an online judgment service, which tests the functionality of students’ submitted code and provides immediate feedback. For students with needs, EasyCode provides a support mode with tailor-made toolboxes and decomposed subtasks to help them develop CT concepts and practices. The performance tasks on EasyCode are categorized into three difficulty levels to provide a progressing ladder for students. A sample CT performance task is provided to illustrate how EasyCode facilitates students’ learning of CT concepts and practices.