Miracle-dz/UniFAR-FTUs huggingface dataset:
CORRELATIONS BETWEEN CHORUS SIZE AND GENETIC DIVERSITY IN HYLA ARBOREA IN THE REUSS VALLEY , SWITZERLAND

I investigated the relationship between genetic diversity and chorus size estimates in the European treefrog (Hyla arborea). The European Tree frog is one of the most endangered amphibian species in Switzerland and only occurs in a few geographically distinct regions. For the metapopulation in the Reuss valley, comprehensive genetic and male chorus size data exists. For genetic parameters, I used the allelic richness, the number of alleles, the expected and the observed heterozygosity and the inbreeding coefficient of each of the 33 known subpopulations. For chorus parameters, I used the chorus size, the subpopulation trend and the coefficient of variation of the chorus size. Chorus size parameters were computed for the individual subpopulations and for genetically differentiated clusters of subpopulations. Furthermore I calculated the connectivity for each subpopulation. I then built models for the genetic parameters described by chorus count parameters and connectivity conducting model selection and model averaging. It appears that different genetic parameters are correlated to different parameters used in this study. Strong correlation was found for allelic richness and geographical connectivity. The number of alleles was related to male chorus size and the proportional variation in chorus size. The expected heterozygosity seems to be influenced solely by the proportional variation in chorus size. For the inbreeding coefficient, I found a correlation with the proportional variation in chorus size, but theoretical support for the resulting model is lacking and it even appears that the model does not make sense. The results of the present study are generally congruent with expectations based on theory. There exist biological explanations for most of my findings. Based on the results I recommend not only to use the population size but also the variation of the population size over time when looking for the influence on genetic parameters or when assessing the condition of populations.
Special Topics in Complexity Theory

This document collects the Fall 2017 lectures given by the instructor. Compared to the lectures on the class website, I have made some minor edits to harmonize the various lectures. In addition we had two guest lectures and presentations by students, which can also be found on the class website. Many thanks to Matthew Dippel, Xuangui Huang, Chin Ho Lee, Biswaroop Maiti, Tanay Mehta, Willy Quach, and Giorgos Zirdelis for doing an excellent job scribing these lectures. Many thanks also to all the students, postdocs, and faculty who attended the class and created a great atmosphere.
An adaptive predictive financial fraud detection approach using deep learning methods on a Big Data platform

Fraud, waste, and abuse in many financial systems are estimated to result in significant losses annually. Predictive analytics offer government and private financial institutions the opportunity to identify, prevent or recover such losses. This work proposed a novel Big Data driven approach for fraud detection based on Deep Learning methods. A supervised Deep Learning solution leveraging Big Data was shown to be an effective Fraud predictor. Additionally, an unsupervised method based on anomaly detection using deep autoencoders was proposed for when there is few or no labelled data. The two methods presented offered adaptive and predictive Fraud detection through improved Analytics. Future work will look into how the two methods can be integrated into an effective tool for enhanced Fraud detection.
IDENTIFICATION OF RAPD MARKERS LINKED TO A FERTILITY RESTORER GENE FOR PET 1 CYTOPLASM OF SUNFLOWER ( HELIANTHUS ANNUUS )

Fertility restoration ability of a restorer was controlled by a single dominant gene. Bulked segregant analysis (BSA) was applied to identify molecular markers linked to a major restorer gene (Rf) using the F2 population of CMS 234 A RHA 272. A total of 144 random oligonucleotide primers were surveyed. The primer OPAM 06-1800 was found to produce putative markers, which differentiate fertile parent and bulk from sterile parent and sterile bulk. The cosegregation analysis of the putative marker on the F2 population confirmed the association of OPAM 06-1800 produced by the primer OPAM 06 with the fertility restoration gene. This will help in transfer of fertility restorer genes to the inbreds if they lack restoration genes.
Tuberculosis and Molecular Diagnosis Corresponding

The incidence of Tuberculosis varies considerably around the world and most Mycobacterial infections in developing nations are still being caused by Mycobacterium tuberculosis members. A quick and correct diagnosis is of great importance because of the high morbidity. Unfortunately, conventional bacteriological methods are time consuming, their sensitivity is low, and so treatment occasionally becomes empirical. PCR method has high specificity in identifying M. tuberculosis in various specimens. Molecular diagnostic tools for Tuberculosis (TB) have evolved quickly with new innovations which can provide unprecedented opportunities for the rapid, sensitive and specific diagnosis of M. tuberculosis in clinical specimens and the status of its drug sensitivity. Microscopy and culture methods can not be replaced but the molecular assays can be applied in parallel with any new molecular tests for the diagnosis of TB. For extra pulmonary specimens, the use of the amplification methods is advocated, since rapid and accurate laboratory diagnosis is critical. Customization of the diagnostic usefulness of a molecular assay, according to the ease, reliability and need for health care sector is of immense value in a modern clinical Mycobacteriology laboratory.
Generative Adversarial Networks for Model Based Reinforcement Learning with Tree Search

Recent advances in Generative Adversarial Networks (GANs) have shown much promise in the domain of image generation, especially using deep multi-scale architectures. Tree-search methods, on the other hand, have been successful in offline domains but not online learning. This work utilizes a GAN to learn a dynamics model, which is then used for online tree search. We propose a model-based reinforcement learning framework leveraging a combination of video prediction with GANs and online tree-search methods. Our method attempts to combine the near-horizon benefits of a learned dynamics model with the farhorizon benefits of a value network. Experimental results show that the proposed architecture is generally able to produce visually-realistic predictions and achieve performance results somewhat comparable to state-of-the-art such as DQN.
1 Self-propelling slugs in a tube

Surface coating is generally achieved by an active operation: painting with a brush, withdrawing from a bath. Porous imbibition constitutes a more passive way: a porous material just put in contact with a reservoir containing a wetting fluid is spontaneously invaded. In this case, the material can eventually be filled by the fluid. If the aim is to coat only the surface of the pores, the liquid excess must be actively removed. We present an experiment in which a liquid train spontaneously moves in a capillary tube because of the trail it leaves behind. From a practical point of view, this system realizes a coating of the tube. The condition required for this motion and a model for its dynamics are presented. We also show how these trains can drive extremely viscous liquid slugs (or even solid bodies) in narrow tubes. We discuss the thickness of the deposited films. Extensions and limits of this system in more complex geometries are finally described, together with special cases such as the deposition of solid films.
Characterization of local grain size variation Summary

The most commonly reported microstructural measure in literature is the average grain, even though there is a large variety of e.g. ASTM grain size measurement methods available [2]. In addition to measurement of average grain size, other material specific factors such as differences in phase structure and grain size distribution need to be considered. For heterogeneous materials the grain size distribution is of particular interest since it has been shown to influence the mechanical properties [3–9]. Improved grain size measurement methods are thus required to enhance the understanding between grain size distribution and mechanical properties [2]. Accurate description of the microstructural heterogeneity is also required for the mesoscale modelling of material behaviour [10]. Welds are an extreme case of heterogeneity since it is present both in macroscopic scale across the joint and in microscopic scale within a single zone; see Figure 1.
Analysis Based on Improved K-means Algorithm

In the network public opinion analysis, the K-means algorithm is sensitive to the initial cluster center and outliers. In order to solve this problem, an improved K-means algorithm for optimizing the initial cluster center is proposed. In the improved K-means algorithm, firstly, the outliers of each data object in the dataset are calculated. And the sampling factor  is put forward to obtain the candidate initial center point set. Then, according to the idea of the maximum and minimum distance, k data objects are selected as initial clustering centers from the candidate initial center point set. Finally, a case based on the actual network text data is studied. The case study results show that the new proposed improved K-means algorithm is better than K-means algorithm and K-means++ algorithm in clustering effect. And it is more suitable for network public opinion analysis.
The Effect of IFN-β 1 a on Biochemical Factors in Multiple Sclerosis Patients

Background: Multiple sclerosis (MS) is known as a progressive and demyelinating disease, which involves biochemical changes. Objectives: To evaluate the effect of IFNβ-1a therapy on the biochemical factors in the MS patients. Methods: In this descriptive study, 30 MS patients and 30 healthy controls were included. The study was conducted in the Neurology Center at the Shefa hospital, Kerman, Iran from September 2013 to July 2015. The patients’ blood test was taken before and after six months of IFNβ-1a therapy and the biochemical factors (LDH, AST, ALT, Creatinine, Uric acid, Malondialdehyde (MDA) and nitric oxide (NO) were measured in all the samples. The participants were divided to three groups, namely main group (30 patients), females (22 patients) and males (8 patients). Before taking the medicine, the groups were compared to the control group. After six months of taking the medicine, each group was compared to its former state before taking the drug. Results: In the patients group (30 patients), a significant difference was observed in their measured biochemical factors in comparison to the control group (P = 0.001), however, after six months of using IFNβ-1a, only MDA was shown in the main (P = 0.003) and female group (P = 0.003), and the ALT that was shown in the female group had a significant reduction in comparison to that before receiving IFNβ-1a. Conclusions: This study showed that IFNβ-1a decreased oxidant impacts in MS patients, but had no influence in improving mitochondrial dysfunction.
econstor Make Your Publications Visible . A Service of zbw

Firms can communicate private information about their own product quality through a combination of pricing and direct disclosure where disclosure may be deliberately false. We examine the effect of regulation that penalizes false disclosure by firms in a competitive setting. The cost of false disclosure influences the mix of direct, costly information provision and price signaling in the market, and thereby market outcomes. Stronger regulation reduces the reliance on pricing as the means of credibly conveying quality, reducing prices, market power and the consumption distortion associated with price signaling. Despite this, the gain in social surplus is always outweighed by the cost of direct disclosure unless disclosure itself is relatively inexpensive and the penalty for false disclosure is sufficiently high. Weak regulation of false disclosure is always worse than no regulation. Even high quality firms may suffer from regulation of false disclosure (and may lobby against it). JEL Classification: L13, L15, D82, D43. Key-words: Price Signaling; Disclosure; Product Quality; Oligopoly. ∗Earlier versions of this paper have been circulated under other titles. We have gained from comments made by members of the audience during presentations at the University of Vienna, the University of Kansas, the Workshop on the Economics of Advertising (Vilnius, July 2016), the Spring 2016 Midwest Economic Theory Conference, the 2016 EARIE Conference in Lisbon and the 2016 Texas Economic Theory Camp. We also thank Andrew Rhodes and Chris Wilson for helpful suggestions.
Nitrogen cluster doping for high-mobility/conductivity graphene films with millimeter-sized domains

Clusterization of dopants in graphene would minimize carrier scattering, achieving a remarkably high carrier mobility. Directly incorporating heteroatoms into the hexagonal lattice of graphene during growth has been widely used to tune its electrical properties with superior doping stability, uniformity, and scalability. However the introduction of scattering centers limits this technique because of reduced carrier mobilities and conductivities of the resulting material. Here, we demonstrate a rapid growth of graphitic nitrogen cluster–doped monolayer graphene single crystals on Cu foil with remarkable carrier mobility of 13,000 cm2 V−1 s−1 and a greatly reduced sheet resistance of only 130 ohms square−1. The exceedingly large carrier mobility with high n-doping level was realized by (i) incorporation of nitrogen-terminated carbon clusters to suppress the carrier scattering and (ii) elimination of all defective pyridinic nitrogen centers by oxygen etching. Our study opens up an avenue for the growth of high-mobility/conductivity doped graphene with tunable work functions for scalable graphene-based electronic and device applications.
Reward Responsiveness and Inhibition Traits Differentially Predict Economic Biases in Gain and Loss Contexts

Research on economic decision making has revealed specific biases in gain versus loss domains such that risky choice options are overvalued in gain conditions, implying optimism, but undervalued in loss conditions, implying pessimism. Individual differences in motivational traits and affective states have been shown to predict beliefs and behavior in risky decision making, but it is presently unclear which personal characteristics are most predictive of domain-specific biases. To address this gap in the literature, we investigated the relative influence of positive and negative motivational traits (general sensitivity to rewards and punishments) versus affective states (current levels of positive and negative emotions) on beliefs and choice behavior during a risky economic decision task. We also expanded on previous research by examining how the valence of one’s judgment context (positive context tested in Experiment 1, negative context tested in Experiment 2) may determine whether risky choice behavior is more strongly influenced by positive versus negative characteristics. Biases in belief were calculated using an economic decision task that involved estimating the value of risky “stocks” relative to safe “bonds” from experienced outcomes. Experiment 1 used a positive judgment context (likelihood of a “good stock”) while Experiment 2 used a negative judgment context (likelihood of a “bad stock”). Consistent with previous findings, we observed a domain-based bias in beliefs about stock values across experiments, such that participants exhibited optimism in gain domain and pessimism in the loss domain. Experiment 1 further revealed that domain-based bias and suboptimal choice behavior was predicted by trait-level reward sensitivity, while positive affective state (PAS) had a more limited influence on belief bias alone. Under the negative judgment context of Experiment 2, there was a similar relationship between reward sensitivity and choice behavior; however, results revealed a slightly stronger influence of negative affective state (NAS). A subsequent cross-study analysis found sensitivity to rewards was most predictive of domain-based biases. These results suggest that motivational traits – particularly those relating to reward sensitivity – are more consistent predictors of domain-based biases and risky choice behavior than affective states, but their predictive power depends the valence of the decision context.
A hybrid traffic responsive intersection control algorithm using global positioning system and inductive loop data: (paper no 18-02779)

This paper compares the performance of a traffic responsive intersection controller which combines vehicle Global Positioning System (GPS) data and inductive loop information, to fixed-time, inductive loop, and GPS based controllers. The INRIX Global Traffic Scorecard reports that vehicles spent up to 42% of their travel time in congested traffic in 2016. Inefficient signal timing choices by isolated intersection controllers contribute to traffic delays, causing severe negative impacts on the economy and environment. Signal timings can be improved using vehicles’ GPS information combined with vehicle flow information from inductive loops to overcome the control action deficit at isolated intersections. This proposed new signal control algorithm is beneficial for traffic engineers and governmental agencies, as optimised traffic flow can reduce fuel consumption and emissions. The proposed traffic responsive Hybrid Vehicle Actuation (HVA) algorithm uses position and heading data from vehicle status broadcasts, and inferred velocity information to determine vehicle queue lengths and detect vehicles passing through the intersection to actuate intersection signal timings. When vehicle broadcast data are unavailable, HVA uses inductive loop data. Microscopic simulations comparing HVA to fixed-time control, inductive Loop Based Vehicle Actuation (Loop-VA) and GPS Based Vehicle Actuation (GPS-VA) on four urban road networks were carried out to see how the proposed HVA algorithm performs compared to existing control strategies. The results show that HVA is an effective alternative to traditional intersection control strategies, offering delay reductions of up to 32% over Loop-VA, for networks with 0 − 100% connected vehicle presence.
An Attention-augmented Deep Architecture for Hard Drive Status Monitoring in Large-scale Storage Systems

Data centers equipped with large-scale storage systems are critical infrastructures in the era of big data. The enormous amount of hard drives in storage systems magnify the failure probability, which may cause tremendous loss for both data service users and providers. Despite a set of reactive fault-tolerant measures such as RAID, it is still a tough issue to enhance the reliability of large-scale storage systems. Proactive prediction is an effective method to avoid possible hard-drive failures in advance. A series of models based on the SMART statistics have been proposed to predict impending hard-drive failures. Nonetheless, there remain some serious yet unsolved challenges like the lack of explainability of prediction results. To address these issues, we carefully analyze a dataset collected from a real-world large-scale storage system and then design an attention-augmented deep architecture for hard-drive health status assessment and failure prediction. The deep architecture, composed of a feature integration layer, a temporal dependency extraction layer, an attention layer, and a classification layer, cannot only monitor the status of hard drives but also assist in failure cause diagnoses. The experiments based on real-world datasets show that the proposed deep architecture is able to assess the hard-drive status and predict the impending failures accurately. In addition, the experimental results demonstrate that the attention-augmented deep architecture can reveal the degradation progression of hard drives automatically and assist administrators in tracing the cause of hard drive failures.
LATENCY EFFICIENT MAPPING IN 3D MESH NOC INTERCONNECTION NETWORK ARCHITECTURE

The progresses and developments in the domain of semiconductor technology is about to take us into the era where there will be thousands of cores available on a System on Chip. The onset of 3D integration technologies has unlocked the doors of novel prospects for design of on-chip networks in SoCs attaining the higher efficiency in contrast to 2D integration by aptly adjusting the increased path lengths of 2D on chip archetypes. The intelligent mapping of the applications to core on a given NoC architecture can result into the improved system’s dynamic latency. The paper presents a heuristic based on branch and bound methodology for intelligent mapping of applications to cores in 3D Mesh NoC architecture. The experimental outcomes illustrate that average latency has been immensely decreased in the optimized 3D-Mesh on chip networks when compared against optimized 2D-Mesh network of the same size.
Design and Development of the “POD Adventures” Smartphone Game: A Blended Problem-Solving Intervention for Adolescent Mental Health in India

Introduction: Digital technology platforms offer unparalleled opportunities to reach vulnerable adolescents at scale and overcome many barriers that exist around conventional service provision. This paper describes the design and development of POD Adventures, a blended problem-solving game-based intervention for adolescents with or at risk of anxiety, depression and conduct difficulties in India. This intervention was developed as part of the PRemIum for ADolEscents (PRIDE) research programme, which aims to establish a suite of transdiagnostic psychological interventions organized around a stepped care system in Indian secondary schools. Methods and Materials: Intervention development followed a person-centered approach consisting of four iterative activities: (i) review of recent context-specific evidence on mental health needs and preferences for the target population of school-going Indian adolescents, including a multiple stakeholder analysis of school counseling priorities and pilot studies of a brief problem-solving intervention; (ii) new focus group discussions with N = 46 student participants and N = 8 service providers; (iii) co-design workshops with N = 22 student participants and N = 8 service providers; and (iv) user-testing with N = 50 student participants. Participants were aged 12–17 years and recruited from local schools in New Delhi and Goa, including a subgroup with self-identified mental health needs (N = 6). Results: Formative data from existing primary sources, new focus groups and co-design workshops supported a blended format for delivering a brief problem-solving intervention, with counselors supporting use of a game-based app on “offline” smartphones. User-testing with prototypes identified a need for simplification of language, use of concrete examples of concepts and practice elements to enhance engagement. There were also indications that participants most valued relatability and interactivity within real-world stories with judicious support from an in-app guide. The final prototype comprised a set of interactive and gamified vignettes and a structured set of problem-solving questions to consolidate and generalize learning while encouraging real-world application. Discussion: Findings shaped the design of POD Adventures and its delivery as an open-access blended intervention for secondary school students with a felt need for psychological support, consistent with an early intervention paradigm. A randomized controlled trial is planned to evaluate processes and impacts of POD Adventures when delivered for help-seeking students in low-resource school settings.