Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change

This paper proposes the multiple swarm optimization method composed of some numbers of populations, each of which is optimized by the different swarm optimization algorithm to adapt to dynamically change environment. To investigates the effectiveness of the proposed method, we apply it into the complex environment, where the objective function changes in a certain interval. The intensive experiments have revealed that the performance of the proposed method is better than the other conventional algorithms (i.e., particle swarm optimization (PSO), cuckoo search (CS), differential evolution (DE)) in terms of convergence and fitness.
Study on Motivating Physical Activity in Children with Personalized Gamified Feedback

Gamified interfaces have the potential to motivate children's everyday physical activity. An activity model should allow for unobtrusive data input and offer interpretable output for gamified feedback. We define a physical activity user model that uses smartphone sensor data to learn the children's activity level according to classes of MET scores. We conduct a user study using a pre-/post-test design with 61 children to measure the accuracy of the the predicted activity classes compared to accelerometer data. 19 of these children receive gamified feedback on their activity level using a mobile application. We analyze how the effect of the motivational interface compares to influence factors. This work combines user activity modelling with motivational interfaces and offers insights into the limits and chances of applications designed for children.
LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.
Multi-population genetic programming with adaptively weighted building blocks for symbolic regression

Genetic programming(GP) is a powerful tool to solve Symbolic Regression that requires finding mathematic formula to fit the given observed data. However, existing GPs construct solutions based on building blocks (i.e., the terminal and function set) defined by users in an ad-hoc manner. The search efficacy of GP could be degraded significantly when the size of the building blocks increases. To solve the above problem, this paper proposes a multi-population GP framework with adaptively weighted building blocks. The key idea is to divide the whole population into multiple sub-populations with building blocks with different weights. During the evolution, the weights of building blocks in the sub-populations are adaptively adjusted so that important building blocks can have larger weights and higher selection probabilities to construct solutions. The proposed framework is tested on a set of benchmark problems, and the experimental results have demonstrated the efficacy of the proposed method.
Narrative Plot Comparison Based on a Bag-of-actors Document Model

Comparing documents based on their semantic plot structure or narrative is an important problem in several application areas. Approaches based on information retrieval methods, latent semantic indexing, sentence embedding, and topic modeling are inadequate to capture the structural elements of the narrative. In this work, we present an abstract "bag-of-actors" document model, meant for comparing, indexing and retrieving documents based on their narrative structures. This model is based on resolving the main entities or actors in the plot, and the corresponding actions associated with them. We use this to compare movie plot summaries from IMDB (Internet Movie Database) to identify movie plots that are remakes or were inspired by one another. Evaluation over a wide range of movie plots from different genres, show encouraging results.
A historical interdependency based differential grouping algorithm for large scale global optimization

Cooperative co-evolution (CC) is a powerful evolutionary computation framework for solving large scale global optimization (LSGO) problems via the strategy of "divide-and-conquer", but its efficiency highly relies on the decomposition result. Existing decomposition algorithms either cannot obtain correct decomposition results or require a large number of fitness evaluations (FEs). To alleviate these limitations, this paper proposes a new decomposition algorithm named historical interdependency based differential grouping (HIDG). HIDG detects interdependency from the perspective of vectors. By utilizing historical interdependency information, it develops a novel criterion which can directly deduce the interdependencies among some vectors without consuming extra FEs. Coupled with an existing vector-based decomposition framework, HIDG further significantly reduces the total number of FEs for decomposition. Experiments on two sets of LSGO benchmark functions verified the effectiveness and efficiency of HIDG.
Increasing accuracy of automated essay grading by grouping similar graders

Automated essay evaluation is a widely used practical solution for replacing time-consuming manual grading of student essays. Automated systems are used in combination with human graders in different high-stake assessments, where grading models are learned on essays datasets scored by different graders. Despite the unified grading rules, human graders can unintentionally introduce subjective bias into scores. Consequently, a grading model has to learn from a data that represents a noisy relationship between essay attributes and its grade. We propose an approach for separating a set of essays into subsets that represent similar graders, which uses an explanation methodology and clustering. The results confirm our assumption that learning from the ensemble of separated models can significantly improve the average prediction accuracy on artificial and real-world datasets.
Explorations in Homeomorphic Variational Auto-Encoding

The manifold hypothesis states that many kinds of high-dimensional data are concentrated near a low-dimensional manifold. If the topology of this data manifold is non-trivial, a continuous en-coder network cannot embed it in a one-to-one manner without creating holes of low density in the latent space. This is at odds with the Gaussian prior assumption typically made in Variational Auto-Encoders (VAEs), because the density of a Gaussian concentrates near a blob-like manifold. In this paper we investigate the use of manifold-valued latent variables. Specifically, we focus on the important case of continuously differen-tiable symmetry groups (Lie groups), such as the group of 3D rotations SO(3). We show how a VAE with SO(3)-valued latent variables can be constructed, by extending the reparameterization trick to compact connected Lie groups. Our exper-iments show that choosing manifold-valued latent variables that match the topology of the latent data manifold, is crucial to preserve the topological structure and learn a well-behaved latent space.
XCSR based on compressed input by deep neural network for high dimensional data

This paper proposes the novel Learning Classifier System (LCS) which can solve high-dimensional problems, and obtain human-readable knowledge by integrating deep neural networks as a compressor. In the proposed system named DCAXCSR, deep neural network called Deep Classification Autoencoder (DCA) compresses (encodes) input to lower dimension information which LCS can deal with, and decompresses (decodes) output of LCS to the original dimension information. DCA is hybrid network of classification network and autoencoder towards increasing compression rate. If the learning is insufficient due to lost information by compression, by using decoded information as an initial value for narrowing down state space, LCS can solve high dimensional problems directly. As LCS of the proposed system, we employs XCSR which is LCS for real value in this paper since DCA compresses input to real values. In order to investigate the effectiveness of the proposed system, this paper conducts experiments on the benchmark classification problem of MNIST database and Multiplexer problems. The result of the experiments shows that the proposed system can solve high-dimensional problems which conventional XCSR cannot solve, and can obtain human-readable knowledge.
Analysis of Annotated Social and Information Networks: Methods and Applications

In order to understand, control or improve a complex system composed out of a large number of inter-related parts it is necessary to quantify, characterize and comprehend the structure and evolution of underlying complex networks. The last two decades have witnessed rapid growth in the number of studies analyzing networks representing complex real-world systems, leading to the emergence of network science, i.e. an interdisciplinary research field focused on metrics, algorithms and models that can reveal, reproduce and explain frequently observed structural and evolutionary characteristics of real-world networks. This companion paper briefly summarizes the content of a tutorial on methods for analyzing social and information networks in which nodes are augmented with various kinds of attributes. Special emphasis is given to methods for analyzing subgraphs in annotated networks, constructing their block models and mining attachment preferences. The presented methods are demonstrated on two case studies related to the evaluation of modularized semantic web ontologies and research collaboration.
A New Hybrid Cascaded H-Bridge Inverter for Photovoltaic-Wind Energy System

Abstract— This paper deals with electricity from photovoltaic-wind system for cascaded multilevel inverter. The objective of this paper is to propose a novel multilevel inverter using hybrid Photo Voltaic (PV)/Wind power system in order to simplify the power system and reduce harmonics and the cost effect . This inverter has advantageous of industrial application. The conventional use of converters will increase losses and cost in conventional methods. The proposed nine level multilevel inverter with solar-wind energy using Pulse Width Modulation Technique (PWM) of providing high switching frequency will highly reduce harmonics confirmed by MATLAB as well hardware results.
Fully human agonist antibodies to TrkB using autocrine cell-based selection from a combinatorial antibody library

Significance Neurotrophin receptors are a class of receptor tyrosine kinases that couple to signaling pathways critical for neuronal survival and growth. One member, TrkB, is particularly interesting because it plays a role in many severe degenerative neurological diseases. The TrkB natural ligand brain-derived neurotrophic factor (BDNF) is not suitable to be developed as a drug or therapy as proved by previous unsuccessful clinical trials. Here we report a selection method that produced potent full agonist antibodies that mimic BDNF function, yet with better biophysical properties. This study paves the road for the development of agonist antibodies for other receptor tyrosine kinases. The diverse physiological roles of the neurotrophin family have long prompted exploration of their potential as therapeutic agents for nerve injury and neurodegenerative diseases. To date, clinical trials of one family member, brain-derived neurotrophic factor (BDNF), have disappointingly failed to meet desired endpoints. Contributing to these failures is the fact that BDNF is pharmaceutically a nonideal biologic drug candidate. It is a highly charged, yet is a net hydrophobic molecule with a low molecular weight that confers a short t1/2 in man. To circumvent these shortcomings of BDNF as a drug candidate, we have employed a function-based cellular screening assay to select activating antibodies of the BDNF receptor TrkB from a combinatorial human short-chain variable fragment antibody library. We report here the successful selection of several potent TrkB agonist antibodies and detailed biochemical and physiological characterization of one such antibody, ZEB85. By using a human TrkB reporter cell line and BDNF-responsive GABAergic neurons derived from human ES cells, we demonstrate that ZEB85 is a full agonist of TrkB, comparable in potency to BDNF toward human neurons in activation of TrkB phosphorylation, canonical signal transduction, and mRNA transcriptional regulation.
Sleuthing Wormhole Attacks Using Hidden Markov Model in Wireless Networks

----Wireless networks are vulnerable to many attacks such as attacks like eavesdropping, man-in-the-middle, etc. Therefore, security assailability should be identified and safeguard against wireless networks.We bring out the wormhole attack, a severe attack in wireless networks that is particularly challenging to protect against. The wormhole attack is very powerful, and preventing the attack has proven to be very difficult. In the wormhole attack, an attacker captures packet at one location in the network, tunnels them to another location, and retransmits them there into the network. To struggle against wormhole attacks, we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs). The MSs, by sniffing the traffic, extract features for detecting these attacks and construct normal usage behavior profiles. We have used a model called, Hidden Markov Model (HMM), to compute behavioral distance in order to compare the normal usage behavioral profiles to detect intrusions. The monitoring stubs produces sound alarm on the sender side when data gets attacked.
An Asynchronous Pipelined Time-to-Digital Converter Using Time-Domain Subtraction

This paper presents the design of a low-power asynchronous pipelined time-to-digital converter (AP-TDC) to be employed in a time-domain signal processing system. The presented AP-TDC utilizes two novel concepts, namely time-domain subtraction and absolute value based algorithmic conversion. The design and simulation of the AP-TDC is done using a standard CMOS 65 nm process. The least-significant-bit resolution of the AP-TDC is designed to be 200 ps and the AP-TDC outputs 7-bit digital words with an ENOB of 6.2 bits. The dynamic range of the TDC is 25.4 ns and the TDC core consumes 38 μW from a supply voltage of 1V and has a total area of 1275 μm2. When compared to a Flash TDC implementation using the same delay elements, power consumption, total area, and conversion time are reduced by 28.3%, 31.5%, and 24.6%, respectively. The AP-TDC has a figure-of-merit of 9.9-fJ/conversion step.
A textual Taylor rule: estimating central bank preferences combining topic and scaling methods

Abstract Scholars often use voting data to estimate central bankers’ policy preferences but consensus voting is commonplace. To get around this, we combine topic-based text analysis and scaling methods to generate theoretically motivated comparative measures of central bank preferences on the US Federal Open Market Committee (FOMC) leading up to the financial crisis in a way that does not depend on voting behavior. We apply these measures to a number of applications in the literature. For example, we find that FOMC members that are Federal Reserve Bank Presidents from districts experiencing higher unemployment are also more likely to emphasize unemployment in their speech. We also confirm that committee members on schedule to vote are more likely to express consensus opinion than their off schedule voting counterparts.
Optimization of the critical loop in Renormalization CABAC decoder

Context-based adaptive binary arithmetic coding (CABAC) is needed in the present days for high speed H.264/AVC decoder. The high speed is achieved by decoding one symbol per clock cycle using parallelism and pipelining techniques. In this paper we present an innovative hardware implementation of the renormalization which is a part of CABAC binary arithmetic decoder. The renormalization of range and value is specified as a sequential loop process that shifts only one bit per cycle until the range and value are renormalized. To speed up this process, a special hardware technique is used. The hardware will take one clock cycle to shift n bit data. The proposed hardware is coded using HDL language and synthesized using Xilinx CAD tool.
Mikronährstoffe bei prämenstruellem Syndrom

Zusammenfassung Die Häufigkeit sowohl des prämenstruellen Syndroms (PMS) und des prämenstruellen dysphorischen Syndroms (PMDS) hat in den letzten Jahren zugenommen. Bei der Behandlung spielt die Mikronährstofftherapie neben weiteren Maßnahmen eine bedeutende Rolle. Die Verabreichung von Kalziumkarbonat, verschiedenen Vitaminen und Mineralstoffen sowie L-Tryptophan und 5-Hydroxytryptophan haben sich hier als hilfreich erwiesen.
Empty gaps? Depleting annular regions in debris discs by secular resonance with a two-planet system

We investigate the evolution on secular time-scales of a radially extended debris disc under the influence of a system of two coplanar planets interior to the disc, showing that the secular resonances of the system can produce a depleted region in the disc by exciting the eccentricities of planetesimals. Using Laplace-Lagrange theory, we consider how the two exterior secular resonance locations, time-scales and widths depend on the masses, semi-major axes and eccentricities of the planets. In particular, we find that unless the resonances are very close to each other, one of them is very narrow and therefore unimportant for determining the observable structure of the disc. We apply these considerations to the debris disc of HD 107146, identifying combinations of the parameters of a possible unobserved two-planet system that could configure the secular resonances appropriately to reproduce the depletion observed in the disc. By performing N-body simulations of such systems, we find that planetesimal eccentricities do indeed become large near the theoretical secular resonance locations. The N-body output is post-processed to set the initial surface density profile of the disc, and to include the possible effects of collisional depletion. We find that it is possible to obtain a double-ringed disc in these simulations but not an axisymmetric one, with the inner ring having an offset whose magnitude depends on the eccentricities of the planets, and the outer ring showing spiral structure.
Validated Approaches for Quantification of Bone Mineral Crystallinity Using Transmission Fourier Transform Infrared (FT-IR), Attenuated Total Reflection (ATR) FT-IR, and Raman Spectroscopy

Bone mineral crystallinity is an important factor determining bone quality and strength. The gold standard method to quantify crystallinity is X-ray diffraction (XRD), but vibrational spectroscopic methods present powerful alternatives to evaluate a greater variety of sample types. We describe original approaches by which transmission Fourier transform infrared (FT-IR), attenuated total reflection (ATR) FT-IR, and Raman spectroscopy can be confidently used to quantify bone mineral crystallinity. We analyzed a range of biological and synthetic apatite nanocrystals (10–25 nm) and found strong correlations between different spectral factors and the XRD determination of crystallinity. We highlight striking differences between FT-IR spectra obtained by transmission and ATR. In particular, we show for the first time the absence of the 1030 cm−1 crystalline apatite peak in ATR FT-IR spectra, which excludes its use for analyzing crystallinity using the traditional 1030/1020 cm−1 ratio. The ν4PO4 splitting ratio was also not adequate to evaluate crystallinity using ATR FT-IR. However, we established original approaches by which ATR FT-IR can be used to determine apatite crystallinity, such as the 1095/1115 and 960/1115 cm−1 peak ratios in the second derivative spectra. Moreover, we found a simple unified approach that can be applied for all three vibrational spectroscopy modalities: evaluation of the ν1PO4 peak position. Our results allow the recommendation of the most reliable analytical methods to estimate bone mineral crystallinity by vibrational spectroscopy, which can be readily implemented in many biomineralization, archeological and orthopedic studies. In particular, we present a step forward in advancing the use of the increasingly utilized ATR FT-IR modality for mineral research.
Future Trends and the Economic Burden of Dementia in Manitoba: Comparison with the Rest of Canada and the World

Dementia is a growing public health concern in Canada. This epidemic is linked to huge human and economic costs. The number of Manitobans (65+) with dementia in 2045 (47,021), representing 2.58% of the Manitoban population, will be 2.3 times that of the year 2015 (20,235). The number of cases of dementia in Manitoba grew by 20.7% from 2015 to 2025, 68.16% from 2015 to 2035 and at an alarming rate of 125% from 2015 to 2045. Importantly, the total economic burden of dementia in Manitoba is close to one billion USD and is expected to grow more than 28 billion USD during the year 2038. The focus of this review is to compare dementia rates and the financial burden of dementia in Manitoba with the rest of Canada and the world from 2012 to 2048.
Efficacy of zofenopril in combination with amlodipine in patients with acute myocardial infarction: a pooled individual patient data analysis of four randomized, double-blind, controlled, prospective studies

Abstract Objective: In the four SMILE (Survival of Myocardial Infarction Long-Term Evaluation) studies, early administration of zofenopril in acute myocardial infarction (AMI) showed beneficial effects as compared to placebo and other angiotensin converting enzyme inhibitors (ACEIs). This study investigated whether the concomitant administration of the dihydropyridine calcium channel-blocker amlodipine may improve zofenopril efficacy to prevent cardiovascular events in post-AMI patients. Methods: This was a post-hoc analysis of pooled individual patient data from the four large randomized SMILE studies. The primary endpoint was the 1-year combined occurrence of death or hospitalization for cardiovascular causes. Results: In total, 3488 patients were considered, 303 (8.7%) treated with concomitant amlodipine. Baseline systolic blood pressure and prevalence of metabolic syndrome were higher in amlodipine treated patients. The 1-year occurrence of major cardiovascular outcomes was significantly reduced in patients receiving concomitant treatment with amlodipine (hazard ratio, HR = 0.66; and 95% confidence interval, CI = 0.44–0.98; p = .039). After accounting for treatment with amlodipine, the risk of cardiovascular events was significantly reduced with zofenopril compared to placebo (HR = 0.78; 95% CI = 0.63–0.97; p = .026]. Among ACEI-treated patients, the zofenopril plus amlodipine combination reduced the risk of cardiovascular events by 38%, compared to the combination of other ACEIs plus amlodipine [HR = 0.76; 95% CI = 0.61–0.94); p = . 013). The prognostic benefit of concomitant treatment with zofenopril plus amlodipine was independent from blood pressure lowering. Conclusions: Zofenopril had a positive impact on prognosis in post-AMI patients, compared to other ACEIs. Concomitant administration of amlodipine may help to reduce the risk of cardiovascular events at 1 year.