Miracle-dz/UniFAR-FTUs huggingface dataset:
Blood Rubber*

We examine the legacy of one of the most extreme examples of colonial extraction, the rubber concessions granted to private companies under King Leopold II in the Congo Free State, the present-day Democratic Republic of Congo. The companies used violent tactics to force villagers to collect rubber. Village chiefs were co-opted into supporting the rubber regime, and villagers were severely punished if they did not meet the rubber quotas. We use a regression discontinuity design along the well-defined boundaries of the ABIR and Anversoise concessions to show that historical exposure to the rubber concessions causes significantly worse education, wealth, and health outcomes. We then use survey and experimental data collected along a former concession boundary to examine effects on local institutions and culture. We find a negative effect on local institutional quality and a positive effect on culture. Consistent with the historical co-option of chiefs by the concession companies, village chiefs within the former concessions are more likely to be hereditary, rather than elected, and they provide fewer public goods. However, individuals within the concessions are more trusting, more cohesive, and more supportive of sharing income. The results suggest that colonial extraction may have different effects on institutions and culture.
Percolation and Phase Transition in SAT

Erd\"os and R\'enyi proved in 1960 that a drastic change occurs in a large random graph when the number of edges is half the number of nodes: a giant connected component surges. This was the origin of percolation theory, where statistic mechanics and mean field techniques are applied to study the behavior of graphs and other structures when we remove edges randomly. In the 90's the study of random SAT instances started. It was proved that in large 2-SAT random instances also a drastic change occurs when the number of clauses is equal to the number of variables: the formula almost surely changes from satisfiable to unsatisfiable. The same effect, but at distinct clause/variable ratios, was detected in k-SAT and other random models. In this paper we study the relation between both phenomena, and establish a condition that allows us to easily find the phase transition threshold in several models of 2-SAT formulas. In particular, we prove the existence of a phase transition threshold in scale-free random 2-SAT formulas.
Auditory brain stem of the ferret: some effects of rearing with a unilateral ear plug on the cochlea, cochlear nucleus, and projections to the inferior colliculus

To examine the influence of acoustic experience on the development of the mammalian auditory brain stem, darkly pigmented ferrets were reared with a plug inserted in the right outer ear. The plugs were first inserted on postnatal day 23–34 and produced a variable, frequency- dependent attenuation of up to 60 dB. Between 3–15 months after the ear plug was begun, animals were prepared for physiological recording and injection of wheat germ agglutinin-HRP (WGA-HRP) in the left inferior colliculus (IC). The plug was removed and the condition of the right ear was assessed by pure-tone stimulation and recordings from neurons in the left IC. Neural audiograms for each animal showed a residual deficit in most cases. Following 24–60 hr survival, the animals were perfused and the right ear was examined. Brain-stem sections were reacted with tetramethylbenzidine. Outer and/or middle ear pathology was present in over half of the animals. However, the cochleas appeared to be normal and the spiral ganglion cells were normal by several quantitative criteria: number, area, and nucleolar eccentricity. The volume of each division of the cochlear nuclei (CN) and the areas of individual neurons in the anteroventral CN were the same on the right and left sides. The number of CN neurons retrogradely labeled from the left IC injection of WGA-HRP was found to be significantly increased in the left CN, relative to normal animals, when expressed as a ratio of the number labeled in the right CN. We conclude that the residual hearing loss in the previously plugged ears was predominantly or exclusively conductive. Neonatal, unilateral conductive hearing loss in the ferret does not lead to degeneration of the CN on the side of the loss, but it does lead to at least one rearrangement of auditory brain- stem connectivity. We suggest that the extent to which the brain stem is modified by early auditory deprivation is dependent on the type, degree, and symmetry of the hearing loss.
Reliable and Optimal Location System for Monitoring Geolocation Objects

Location systems for remote objects (vehicle, person, commodity, etc.) are increasingly used today by several enterprises. These systems use positioning devices such as GPS and communication devices to send remote location data and monitor these objects. The constraint of GPS is that it does not provide a precise position at any time. The second constraint is to sending remote location data. Often, the communication devices used for this purpose are GSM modules that send data on the GSM network. The use of GSM network is expensive to send data periodically. To overcome these drawbacks, monitoring prototype is proposed to create a reliable and optimal location system using radio and GPS technologies.
Associations of coffee genetic risk scores with consumption of coffee, tea and other beverages in the UK Biobank

Abstract Aims To evaluate the utility of coffee‐related genetic variants as proxies for coffee consumption in Mendelian randomization studies, by examining their association with non‐alcoholic beverage consumption (including subtypes of coffee and tea) and a range of socio‐demographic and life‐style factors. Design Observational study of the association of genetic risk scores for coffee consumption with different types of non‐alcoholic beverage consumption. Setting UK general population. Participants Individuals of European ancestry aged 40–73 years from the UK Biobank between 2006 and 2010 (n = 114 316). Measurements Genetic risk scores were constructed using two, four and eight independent single nucleotide polymorphisms (SNPs) identified in genome‐wide association studies (GWAS) of coffee consumption. Drinks were self‐reported in a baseline questionnaire (all participants) and in detailed 24 dietary recall questionnaires in a subset (n = 48 692). Findings Genetic risk scores explained up to 0.38, 0.19 and 0.76% of the variance in coffee, tea and combined coffee and tea consumption, respectively. Genetic risk scores demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption, and with most subtypes of coffee consumption, but only with standard tea consumption. There was no clear evidence for positive associations with most other non‐alcoholic beverages, but higher genetic risk for coffee consumption was associated with lower daily water consumption. The genetic risk scores were associated with increased alcohol consumption, but not consistently with other socio‐demographic and life‐style factors. Conclusions Coffee‐related genetic risk scores could be used as instruments for combined coffee and tea consumption in Mendelian randomization studies. However, associations observed with alcohol consumption require further investigation to determine whether these are due to causal effects of coffee and tea consumption or biological pleiotropy.
Handwritten character recognition using piecewise linear two-dimensional warping

The effectiveness of piecewise linear 2D warping, a dynamic programming-based elastic image matching technique, in handwritten character recognition is investigated. The technique presented is capable of providing compensation for most variations in character patterns with tractable computation. The superiority of the present technique over several conventional 2D warping techniques in variation compensation is experimentally justified. Another comparison with monotonic and continuous 2D warping, a more flexible matching technique, reveals that the method presented takes far less computation than the latter, yet provides almost the same recognition accuracy for most categories.
UvA-DARE ( Digital Academic Repository ) Design decisions of the UvA Rescue 2007 Team on the Challenges of the Virtual Robot competition

This discussion paper highlights the design decisions of the UvA Rescue Team on the challenges imposed by the 2007 competition. Consistent with the approach of the previous year, the UvA Rescue Team research focus is dedicated to perceptional issues, to allow later extensions towards complex multi-agent exploration. The Simultaneous Localization and Mapping (SLAM) approach is extended with additional scan matching techniques. Victim detection is made possible with Histogram based Skin Detection.
An essential novel component of the noncanonical mitochondrial outer membrane protein import system of trypanosomatids

The mitochondrial outer membrane protein Tom40 is the entry gate for imported proteins in essentially all eukaryotes. Trypanosomatids lack a conventional Tom40 and use instead a protein termed ATOM. pATOM36 is a novel essential component of the trypanosomal outer membrane protein import system that interacts with ATOM.
Cache memories

A computer has three logical systems. The central processing unit (CPU) that processes data according to instructions. The memory and storage system that stores instructions and data (it can be a volatile temporary memory or a permanent storage system like hard disks). Lastly, the input/output system that is responsible for communication between the computer and external entities. The memory and storage system is the primary concern of this article, specifically cache memory. It stores frequently accessed instructions and data for the CPU. Cache is from the French word "cachet" which means "to hide. " This is a reference to early cache implementations where the cache was invisible to the user and CPU. Cache memory is important; it bridges the gap in capabilities between the CPU and main memory. CPUs have been rapidly getting faster. Cache memory has advanced considerably since Wilkes in 1965 proposed a two-level main store: one conventional and the other unconventional "slave" memory. It has now become a conventional component of high speed computing, with increasing sophistication and size. The performance of cache is critical to overall system processing ability and the ongoing research in this area is attempting to reduce as much as possible the speed gap between the CPU and the memory.
Response-Time Analysis for Mixed Criticality Systems

Many safety-critical embedded systems are subject to certification requirements. However, only a subset of the functionality of the system may be safety-critical and hence subject to certification, the rest of the functionality is non safety-critical and does not need to be certified, or is certified to a lower level. The resulting mixed criticality system offers challenges both for static schedulability analysis and run-time monitoring. This paper considers a novel implementation scheme for fixed priority uniprocessor scheduling of mixed criticality systems. The scheme requires that jobs have their execution times monitored (as is usually the case in high integrity systems). An optimal priority assignment scheme is derived and sufficient response-time analysis is provided. The new scheme formally dominates those previously published. Evaluations illustrate the benefits of the scheme.
Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV

MOTIVATION The ability to detect copy-number variation (CNV) and loss of heterozygosity (LOH) from exome sequencing data extends the utility of this powerful approach that has mainly been used for point or small insertion/deletion detection. RESULTS We present ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads, and we assess both the method's power and the effects of confounding variables. We apply our method to a cancer exome resequencing dataset. As expected, accuracy and resolution are dependent on depth-of-coverage and capture probe design. AVAILABILITY CRAN package 'ExomeCNV'. CONTACT fsathira@fas.harvard. edu; snelson@ucla.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
A Fuzzy Approach for Robust Reference-Tracking-Control Design of Nonlinear Distributed Parameter Time-Delayed Systems and Its Application

This paper addresses the robust reference-tracking-control problem for nonlinear distributed parameter systems (NDPSs) with time delays, external disturbances, and measurement noises. The NDPS is measured at several sensor locations for output-feedback tracking control. A fuzzy-spatial state-space model derived via finite-difference approach is introduced to represent the nonlinear distributed parameter time-delayed system. Thus, we use a fuzzy interpolation method with several local linear systems to approximate the nonlinear system and employ the finite-difference method to approximate the partial differential operators in fuzzy-spatial state-space model. Based on this model, a robust fuzzy-observer-based reference-tracking controller is proposed to control the NDPS to track a desired reference trajectory. First, a 2-D tracking performance in a spatiotemporal domain is proposed for robust tracking design of nonlinear distributed parameter time-delayed systems. Then, an equivalent 1-D reference-tracking design is developed to simplify the design procedure, and the linear-matrix-inequality (LMI) technique is applied to solve the control gains and observer gains for the robust tracking-design problem via a systematic control-design procedure. Finally, a tracking-control-design example for the nervous system is given to confirm the proposed reference-tracking-control scheme of nonlinear distributed parameter time-delayed systems.
An Importin β Protein Negatively Regulates MicroRNA Activity in Arabidopsis[W]

As an important mechanism of gene regulation, the microRNA pathway itself requires fine regulation. This work shows that an Importin β protein modulates the activity of microRNAs by influencing their loading into the ARGONAUTE1 complex. As key components in the eukaryotic gene regulatory network, microRNAs (miRNAs) themselves are regulated at the level of both metabolism and activity. To identify factors that modulate miRNA activity, we used an Arabidopsis thaliana transgenic line expressing an artificial miRNA that causes trichome clustering and performed a screen for mutants with compromised miRNA activity (cma mutants) or enhanced miRNA activity (ema mutants). From this screen, we identified two novel mutant alleles of SERRATE, which is known to be required for miRNA biogenesis and dozens of other cma and ema mutants. In this study, we analyzed ema1. SAD2/EMA1 encodes an Importin β protein. The ema1 mutation had no effects on the accumulation of miRNAs and ARGONAUTE1 (AGO1) or on their cytoplasmic and nuclear distributions. Intriguingly, we found that the miRNA effector complexes purified from ema1 contained a larger amount of miRNAs and displayed elevated mRNA cleavage activities, indicating that EMA1 modulates miRNA activity by influencing the loading of miRNAs into AGO1 complexes. These results implicate EMA1 as a negative regulator of the miRNA pathway and reveal a novel layer of miRNA activity modulation.
Application of Convolutional Recurrent Neural Network for Individual Recognition Based on Resting State fMRI Data

In most task and resting state fMRI studies, a group consensus is often sought, where individual variability is considered a nuisance. None the less, biological variability is an important factor that cannot be ignored and is gaining more attention in the field. One recent development is the individual identification based on static functional connectome. While the original work was based on the static connectome, subsequent efforts using recurrent neural networks (RNN) demonstrated that the inclusion of temporal features greatly improved identification accuracy. Given that convolutional RNN (ConvRNN) seamlessly integrates spatial and temporal features, the present work applied ConvRNN for individual identification with resting state fMRI data. Our result demonstrates ConvRNN achieving a higher identification accuracy than conventional RNN, likely due to better extraction of local features between neighboring ROIs. Furthermore, given that each convolutional output assembles in-place features, they provide a natural way for us to visualize the informative spatial pattern and temporal information, opening up a promising new avenue for analyzing fMRI data.
EFindBugs: Effective Error Ranking for FindBugs

Static analysis tools have been widely used to detect potential defects without executing programs. It helps programmers raise the awareness about subtle correctness issues in the early stage. However, static defect detection tools face the high false positive rate problem. Therefore, programmers have to spend a considerable amount of time on screening out real bugs from a large number of reported warnings, which is time-consuming and inefficient. To alleviate the above problem during the report inspection process, we present EFindBugs to employ an effective two-stage error ranking strategy that suppresses the false positives and ranks the true error reports on top, so that real bugs existing in the programs could be more easily found and fixed by the programmers. In the first stage, EFindBugs initializes the ranking by assigning predefined defect likelihood for each bug pattern and sorting the error reports by the defect likelihood in descending order. In the second stage, EFindbugs optimizes the initial ranking self-adaptively through the feedback from users. This optimization process is executed automatically and based on the correlations among error reports with the same bug pattern. Our experiment on three widely-used Java projects (AspectJ, Tomcat, and Axis) shows that our ranking strategy outperforms the original ranking in Find Bugs in terms of precision, recall and F1-score.
Error Moderation in Low-Cost Machine-Learning-Based Analog/RF Testing

Machine-learning-based test methods for analog/RF devices have been the subject of intense investigation over the last decade. However, despite the significant cost benefits that these methods promise, they have seen a limited success in replacing the traditional specification testing, mainly due to the incurred test error which, albeit small, cannot meet industrial standards. To address this problem, we introduce a neural system that is trained not only to predict the pass/fail labels of devices based on a set of low-cost measurements, as aimed by the previous machine-learning-based test methods, but also to assess the confidence in this prediction. Devices for which this confidence is insufficient are then retested through the more expensive specification testing in order to reach an accurate test decision. Thus, this two-tier test approach sustains the high accuracy of specification testing while leveraging the low cost of machine-learning-based testing. In addition, by varying the desired level of confidence, it enables the exploration of the tradeoff between test cost and test accuracy and facilitates the development of cost-effective test plans. We discuss the structure and the training algorithm of an ontogenic neural network which is embodied in the neural system in the first tier, as well as the extraction of appropriate measurements such that only a small fraction of devices are funneled to the second tier. The proposed test-error-moderation method is demonstrated on a switched-capacitor filter and an ultrahigh-frequency receiver front end.
Figure 1. Effects of Estrus Synchronization on Reproductive Performance during a Single Week of Breeding. Additional Benefits for Beef Producers Estrus Synchronization: a Reproductive Management Tool

33 65 21 75 65 49 Detected in estrus Conception rate Pregnancy rate 0 20 40 60 80 100 Percent With Synchronization Without Synchronization istorically, estrus synchronization has been promoted as a labor saving tool for those producers who want to capitalize on the superior genetics available through use of AI. However, the labor saving aspect is peanuts compared to the economic returns available when estrus synchronization is used as a " reproductive management tool ". Before we delve too deeply into discussion, it's important to get dairy and beef producers speaking the same language. Beef producers typically breed cows during " breeding seasons " while dairy producers attempt to get cows pregnant shortly after a " voluntary waiting period. " Breeding season and voluntary waiting period are different ways of saying the same thing. In each case, we want all animals to conceive within a reasonable amount of time after calving (45 to 90 days). This is essential to maintenance of a short (12 to 13 month) calving interval and is a primary factor affecting the profitability of any cattle breeding enterprise. In order to lend some unbiased consistency, we'll use the term " breeding period " to refer to that window of time within which you begin your attempts to achieve pregnancies in your cows. Because the estrous cycle is 21 days long, you (or the herd bull) can only expect to catch about 1/3 of the cycling animals in heat during the first week of the breeding period if you don't use estrus synchronization. Regardless of whether the animals are inseminated naturally or artificially, you can only expect 65 to 70% of them to conceive to a given insemination. Thus, after a week of breeding to natural heats, only 21% of the eligible animals could possibly be pregnant (33% in heat x 65% conception). Because many animals may not have resumed normal cycling activity, the actual pregnancy rate during the first week of the breeding period will likely be considerably less. Many estrus synchronization protocols can induce 75 to 90% of the cycling animals to display estrus within a 5 day period. Additionally, many protocols can induce a fertile heat in as much as 50% of the anestrous cows. Thus, it is typical for many of these synchronization protocols to result in 45 to 55% of the animals being pregnant by the end of the first week …
Peer Effects in the Classroom: Learning from Gender and Race Variation

Peer effects are potentially important for understanding the optimal organization of schools, jobs, and neighborhoods, but finding evidence is difficult because people are selected into peer groups based, in part, on their unobservable characteristics. I identify the effects of peers whom a child encounters in the classroom using sources of variation that are credibly idiosyncratic, such as changes in the gender and racial composition of a grade in a school in adjacent years. I use specification tests, including one based on randomizing the order of years, to confirm that the variation I use is not generated by time trends or other non-idiosyncratic forces. I find that students are affected by the achievement level of their peers: a credibly exogenous change of 1 point in peers' reading scores raises a student's own score between 0.15 and 0.4 points, depending on the specification. Although I find little evidence that peer effects are generally non-linear, I do find that peer effects are stronger intra-race and that some effects do not operate through peers' achievement. For instance, both males and females perform better in math in classrooms that are more female despite the fact that females' math performance is about the same as that of males.