Miracle-dz/UniFAR-FTUs huggingface dataset:
The alpha 6 subunit of the GABAA receptor is concentrated in both inhibitory and excitatory synapses on cerebellar granule cells

Although three distinct subunits seem to be sufficient to form a functional pentameric GABAA receptor channel, cerebellar granule cells express nRNA for nine subunits. They receive GABAergic input from a relatively homogenous population of Golgi cells. It is not known whether all subunits are distributed similarly on the surface of granule cells or whether some of them have differential subcellular distribution resulting in distinct types of synaptic and/or extrasynaptic channels. Antibodies to different parts of the alpha 6 and alpha 1 subunits of the GABAA receptor and electron microscopic immunogold localization were used to determine the precise subcellular distribution of these subunits in relation to specific synaptic inputs. Both subunits were present in the extrasynaptic dendritic and somatic membranes at lower densities than in synaptic junctions. The alpha 6 and alpha 1 subunits were colocalized in many GABAergic Golgi synapses, demonstrating that both subunits are involved in synaptic transmission in the same synapse. Synapses immunopositive for only one of the alpha subunits were also found. The alpha 6, but not the alpha 1, subunit was also concentrated in glutamatergic mossy fiber synapses, indicating that the alpha 6 subunit may have several roles depending on its different locations. The results demonstrate a partially differential synaptic targeting of two distinct GABAA receptor subunits on the surface of the same type of neuron.
CorporAl: a Method and Tool for Handling Overlapping Parallel Corpora

CorporAl: a Method and Tool for Handling Overlapping Parallel Corpora This work introduces a method and tool for handling overlapping parallel corpora — i.e. corpora that are based on the same source material. The method is insensitive to minor changes in the text, different segmentation levels of the corpora and omitted material from either corpora. The aim is to detect matching sentence pairs and either produce combinations of the overlapping corpora or compare them and assess their quality in comparison to each other. The introduced tool enables the user to define the desired behavior when combining corpora pairs, resulting in pure comparison, maximum-size or maximum-quality versions of the combinations. We test the tool on two cases of overlapping parallel corpora and five language pairs. We also evaluate the impact of using the method on two translation systems — a phrase-based and a parsing-based one.
The Transmission of Legal Precedent Across the Australian State Supreme Courts Over the Twentieth Century

This article considers several possible determinants of the transmission of legal precedent across Australian state supreme courts over the course of the twentieth century. The study finds that that the transmission of legal precedent is higher between State supreme courts that are more physically proximate and between state supreme courts in which a majority of judges in both courts are appointed by conservative governments. The study further finds that having an intermediate trial court and providing appointments to the High Court of Australia are correlated with whether a state is a source of interstate citations or a cue sender.
THE EFFECT OF COMPUTER-ASSISTED SCIENCE INSTRUCTION ATTITUDE TOWARDS SCIENCE AND THE COMPUTER

The aim of the study was to investigate the effect of the use of the computer-assisted instruction (CAI) program prepared according to the constructivist approach in the primary school 6 th grade level on students’ attitude toward science and the computer. The randomized pretest-posttest control group design based on the split-pilot model was used. The study was conducted on 6 th grade primary school children who were assigned to experimental and control groups in a primary school in Buca, Izmir, Turkey. In this study the “Attitude Scale toward Science” and the “Attitude Scale toward the Computer” were used as a means of collecting data. T-test, paired-samples t-test, Man-Whitney U test and Wilcoxon Signed-Rank test for paired sample were used for analyses. It was determined that computer-assisted science instruction based on the constructivist method positively affected the attitude of students toward science and the computer and that there was a statistically significant difference in favor of the experimental group. Key Words: computer-assisted instruction, constructivism, science, computer, attitude, primary school pupil
An Introduction to Reactive Oxygen Species Measurement of ROS in cells

Reactive oxygen species (ROS) have long been known to be a component of the killing response of immune cells to microbial invasion. Recent evidence has shown that ROS play a key role as a messenger in normal cell signal transduction and cell cycling. These reactive molecules are formed by a number of different mechanisms and can be detected by various techniques. This white paper briefly describes the biology behind some of these molecules and the means for their detection.
Publishing Location Dataset Differential Privately with Isotonic Regression

We consider the problem of publishing location datasets, in particular 2D spatial pointsets, in a differentially private manner. Many existing mechanisms focus on frequency counts of the points in some a priori partition of the domain that is difficult to determine. We propose an approach that adds noise directly to the point, or to a group of neighboring points. Our approach is based on the observation that, the sensitivity of sorting, as a function on sets of real numbers, can be bounded. Together with isotonic regression, the dataset can be accurately reconstructed. To extend the mechanism to higher dimension, we employ locality preserving function to map the dataset to a bounded interval. Although there are fundamental limits on the performance of locality preserving functions, fortunately, our problem only requires distance preservation in the "easier" direction, and the well-known Hilbert space-filling curve suffices to provide high accuracy. The publishing process is simple from the publisher's point of view: the publisher just needs to map the data, sort them, group them, add Laplace noise and publish the dataset. The only parameter to determine is the group size which can be chosen based on predicted generalization errors. Empirical study shows that the published dataset can also exploited to answer other queries, for example, range query and median query, accurately.
Stability regions and spectra of discrete third-order autoregressive time-series

This paper presents an analysis of third order autoregressive time-series. The parameter regions, in the three dimensional parameter space, that produce the six separate types of power spectral density are analyzed. The study reveals that when a particular two dimensional cross section of the three dimensional parameter space is taken, the region of stability is always triangular. Within each triangular stability region in this two dimensional space, subregions which produce the six possible types of spectral shape are indicated. From these subregions it is possible to approximately choose the parameters necessary to model a process whose power spectral density contains at most two critical frequencies (maxima and minima).
Quantifying the Complexity of Superscalar Processors

To characterize future performance limitations of superscalar processors, the delays of key pipeline structures in superscalar processors are studied. First, a generic superscalar pipeline is defined. Then the specific areas of register renaming, instruction window wakeup and selection logic, and operand bypassing are analyzed. Each is modeled and Spice simulated for feature sizes of 0:8 m, 0:35 m, and 0:18 m. Performance (delay) results and trends are expressed in terms of issue width and window size. This analysis indicates that window (wakeup and select) logic and operand bypass logic are likely to be the most critical in the future.
From Sky to Strategy: Construction Activity Index and Stage Estimation From Drone-Captured Imagery

In the rapidly evolving construction industry, timely and accurate monitoring of construction activities is paramount. This paper introduces a novel approach to quantifying construction activity using high-resolution drone imagery and advanced object detection models. We present a unique, high-quality dataset of construction site images captured from drones, which is distinct from the commonly used open-source or lower-quality datasets. Leveraging this dataset, we fine-tuned pre-trained object detection models to achieve remarkable accuracy in detecting construction materials from images. Building on this, we introduce the Composite Construction Activity Index (CAI), a metric formulated by combining the Material Diversity Ratio (MDR) and the Percentage of Image Covered by Construction Materials (PIC). This metric offers a comprehensive view of construction activity, capturing both its diversity and intensity. Additionally, we propose a methodology to map construction stages based on the detected materials, categorizing them into distinct phases of construction, providing insights into the project's lifecycle. The paper culminates in the visualization of CAI values through heatmaps, offering a spatial representation of construction activity across cities or countries. Our findings and methodologies pave the way for more informed decision-making by stakeholders, from urban planners to construction managers, emphasizing the potential of drone technology and machine learning in revolutionizing construction monitoring.
Forecasting Elections Comparing Prediction Markets, Polls, and Their Biases

Using the 2008 elections, I explore the accuracy and informational content of forecasts derived from two different types of data: polls and prediction markets. Both types of data suffer from inherent biases, and this is the first analysis to compare the accuracy of these forecasts adjusting for these biases. Moreover, the analysis expands on previous research by evaluating state-level forecasts in Presidential and Senatorial races, rather than just the national popular vote. Utilizing several different estimation strategies, I demonstrate that early in the cycle and in not-certain races debiased prediction market-based forecasts provide more accurate probabilities of victory and more information than debiased poll-based forecasts. These results are significant because accurately documenting the underlying probabilities, at any given day before the election, is critical for enabling academics to determine the impact of shocks to the campaign, for the public to invest wisely and for practitioners to spend efficiently.
Travel Motivation and Travel Career Pattern

The purpose of this study lies in theoretical assessment of the Travel Career Patterns theory, which is a theoretical motivation framework originating from the Travel Career Ladder theory. As a replicate study from the previous one, the study followed same methodological steps using different data. This study examined the relationship between patterns of travel motivation and their development in relation to accumulation of travel career. Fourteen travel motivation factors were identified and different travel career levels were measured by analysing travel experience, life-stage, and age. Overall results suggested travel motivation patterns could be understood in three layers according to their importance; the core, middle, and outer layer. The travel motives in the core and the outer layer is common regardless of one's travel career level and the motives in the middle layer changes from internal (e.g. self-development) to external oriented motives (e.g. experiencing different culture).
Nursing and private philanthropy.

It has now been a year since Health Affairs , in collaboration with Grantmakers in Healthy inaugurated GrantWatch. Since that time we have been very pleased with the response of grantmakers and nonfoundation readers alike. Beginning with this issue, we will be publishing an occasional short foundation profile to introduce readers to the context of foundation grant-making activities. For our first profile, we have chosen the Carnegie Corporation of New York. GrantWatch is not intended to be exhaustive; rather, the information it contains represents trends in health philanthropy. We encourage all grantmakers interested in spreading the word about their work to participate.
Mapping Mindscapes as a Diagnostic Tool to Understanding Cultivation Processes in Emerging Economic Landscapes

This paper reports on a longitudinal case study of the interplay between two partners collaborating in an international joint venture. The paper develops a diagnostic tool for investigating, conceptualizing and discussing the collaboration process in a parternship and alliance. It draws on the Industrial Network Approach (Håkansson 1987) and I Ching (Wilhelm and Baynes 1967) in developing an analytical framework that is dynamic, holistic and internally consistent, where RAA configurations are used as conceptual tools for characterizing core elements of corporate culture. The diagnostic tool – the hexagram – endeavors to allow for dynamic dimensions to play a greater role in management research by pointing to the significance of mapping mindscapes as a tool to understanding cultivation processes in emerging economic landscapes. The core ideas presented herein comes form a creative and unique mindscape, attempting to bridge old and new, but mindful that the receptive is also required if these ideas are to gain hold and grow. It follows that I shall look forward to receiving your comments and ideas. As this is work in progress please not quote or use without approval of the author Mapping Mindscapes as a Tool to Understanding Cultivation Processes in Emerging Economic Landscapes
Efficient Computation of Relationship-Centrality in Large Entity-Relationship Graphs

Given two sets of entities - potentially the results of two queries on a knowledge-graph like YAGO or DBpedia- characterizing the relationship between these sets in the form of important people, events and organizations is an analytics task useful in many domains. In this paper, we present an intuitive and efficiently computable vertex centrality measure that captures the importance of a node with respect to the explanation of the relationship between the pair of query sets. Using a weighted link graph of entities contained in the English Wikipedia, we demonstrate the usefulness of the proposed measure.
Quantum transport theory for semiconductor nanostructures: A density-matrix formulation

A general density-matrix formulation of quantum transport phenomena in semiconductor nanostructures is presented. More specifically, contrary to the conventional single-particle correlation expansion, we shall investigate separately the effects of the adiabatic or Markov limit and of the reduction procedure. Our fully operatorial approach allows us to better identify the general properties of the scattering superoperators entering our effective quantum transport theory at various description levels, e.g., $N$ electrons-plus-quasiparticles, $N$ electrons only, and single-particle picture. In addition to coherent transport phenomena characterizing the transient response of the system, the proposed theoretical description allows us to study scattering induced phase coherence in steady-state conditions. As a prototypical example, we shall investigate polaronic effects in strongly biased semiconductor superlattices.
Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations

Abstract During the recent decades, many niching methods have been proposed and empirically verified on some available test problems. They often rely on some particular assumptions associated with the distribution, shape, and size of the basins, which can seldom be made in practical optimization problems. This study utilizes several existing concepts and techniques, such as taboo points, normalized Mahalanobis distance, and the Ursem’s hill-valley function in order to develop a new tool for multimodal optimization, which does not make any of these assumptions. In the proposed method, several subpopulations explore the search space in parallel. Offspring of a subpopulation are forced to maintain a sufficient distance to the center of fitter subpopulations and the previously identified basins, which are marked as taboo points. The taboo points repel the subpopulation to prevent convergence to the same basin. A strategy to update the repelling power of the taboo points is proposed to address the challenge of basins of dissimilar size. The local shape of a basin is also approximated by the distribution of the subpopulation members converging to that basin. The proposed niching strategy is incorporated into the covariance matrix self-adaptation evolution strategy (CMSA-ES), a potent global optimization method. The resultant method, called the covariance matrix self-adaptation with repelling subpopulations (RS-CMSA), is assessed and compared to several state-of-the-art niching methods on a standard test suite for multimodal optimization. An organized procedure for parameter setting is followed which assumes a rough estimation of the desired/expected number of minima available. Performance sensitivity to the accuracy of this estimation is also studied by introducing the concept of robust mean peak ratio. Based on the numerical results using the available and the introduced performance measures, RS-CMSA emerges as the most successful method when robustness and efficiency are considered at the same time.
Analysis of Influence Factors of Model Predictive Control for Wave Energy Converters

Wave energy generation is an important renewable energy technology with great potential. In wave power generation systems, model predictive control is widely used to improve the efficiency and stability of the power converter system. The aim of this paper is to analyse the factors affecting the performance of the model predictive control of wave energy convetvers, and on this basis, to study in depth the optimisation of the model predictive control, which is of great significance to improve the control performance and power generation efficiency of wave energy converters.
Generation of a safe Salmonella Gallinarum vaccine candidate that secretes an adjuvant protein with immunogenicity and protective efficacy against fowl typhoid

We constructed a live, attenuated Salmonella Gallinarum (SG) that secretes heat-labile enterotoxin B subunit protein (LTB), and evaluated this strain as a new vaccine candidate by assessing its safety, immunogenicity and protective efficacy against fowl typhoid. An asd+ p15A ori low-copy plasmid containing eltB encoding LTB was transformed into a ΔlonΔcpxRΔasd SG (JOL967) to construct the candidate, JOL1355. In Experiments 1 and 2, birds were orally immunized with JOL1355 at 4 weeks of age, while control birds were inoculated with sterile phosphate-buffered saline. In Experiment 2, the birds of both groups were orally challenged with a virulent SG at 8 weeks of age. In Experiment 1, examination for safety revealed that the immunized group did not show any bacterial counts of the vaccine candidate in the liver and spleen. Birds immunized with the vaccine candidate showed a significant increase in systemic IgG and mucosal secretory IgA levels in Experiment 2. In addition, the lymphocyte proliferation response and the numbers of CD3+CD4+ and CD3+CD8+ T cells were also significantly elevated in the immunized group, which indicated that the candidate also induced cellular immune responses. In the protection assay, efficient protection with only 16% mortality in the immunized group was observed against challenge compared with 76% mortality in the control group. These results indicate that the live, attenuated SG secreting LTB can be a safe vaccine candidate. In addition, it can induce humoral and cellular immune responses and can efficiently reduce mortality of birds exposed to fowl typhoid.
Intelligent Information System for Monitoring the Road Pavement Conditions

Good pavement condition is an important requirement for the safety and continuity of traffic. The architecture of an intelligent information system for monitoring road pavement conditions is developed. Common types of road pavement defects and methods of their detection are analyzed. The main stages of system design and subsystems in its composition are considered: core subsystem, data processing subsystem and analytical subsystem. Photos and videos from smartphones, data from car DVRs, accelerometer data, etc., accompanied by geolocation tags can be used as input data of the information system for monitoring. The purpose of this paper is to create an intelligent information system for timely informing vehicle users about the deterioration of road pavement condition to prevent road accidents.
Deep Learning to Classify Bacterial Species in the same Genus

Bacterial strains in the same genus share highly similar morphology, gram-staining characteristics, colony sizes, and spatial arrangements. Therefore, identifying them by deep learning can be quite challenging. This study aimed to assess the classification of 7 species of bacteria from 2 genera of Bacillus and Vibrio by using 8 Convolutional Neural Network (CNN) models. We implemented Python programming along with Keras API within the Jupyter Notebook. The models were constructed and evaluated under unbalanced and balanced datasets by augmentation (rotation, flip, etc.). Transfer learning with fine-tuning, and pre-processing of mixup and label smoothing were also applied to reduce overfitting and enhance generalization. Based on the experimental results on private dataset, the results of InceptionResNetV2 emerged as the top-performing model with a notable accuracy of 82.8%, 88.6% precision, 78.4% recall, and 78.0% F1-score when label smoothing was applied at 0.5 on balanced dataset.
Improved channel assignment scheme with access control for the common packet channel in WCDMA systems

We propose a channel assignment scheme with access control for obtaining better system performance within the common packet channel (CPCH) access procedure of wideband code-division multiple-access (WCDMA) systems. The proposed scheme is analyzed taking multiple access interference into consideration and is compared with other schemes in the 3rd Generation Partnership Project (3GPP): the basic scheme, the channel monitoring scheme, and the channel assignment scheme without access control. The results show that the proposed scheme provides high throughput, even under high-loading conditions, since the access control algorithm based on channel load prohibits excessive interference.
A Link to the Past: Constructing Historical Social Networks

To assist in the research of social networks in history, we develop machine-learning-based tools for the identification and classification of personal relationships. Our case study focuses on the Dutch social movement between 1870 and 1940, and is based on biographical texts describing the lives of notable people in this movement. We treat the identification and the labeling of relations between two persons into positive, neutral, and negative both as a sequence of two tasks and as a single task. We observe that our machine-learning classifiers, support vector machines, produce better generalization performance on the single task. We show how a complete social network can be built from these classifications, and provide a qualitative analysis of the induced network using expert judgements on samples of the network.