Miracle-dz/UniFAR-FTUs huggingface dataset:
WeisoEvent: A Ming-Weiso Event Analytics Tool with Named Entity Markup and Spatial-Temporal Information Linking

Weiso(衛所制), which means "guardhouse", is one of the military units of the barracks used by the Chinese dynasty Ming (1368-1644) to maintain peace throughout its empire. WeisoEvent is a web-based digital humanity research tool targeting Ming Weiso events recorded in Ming Shilu, which contains the imperial annals of the Ming emperors. WeisoEvent is composed of two parts: (1) an event type classifier that categorizes paragraphs according to their event types; (2) an analytics tool that shows (1)’s result, markups named entities, links guard mentions to Academia Sinica’s Chinese Civilization in Time and Space (CCTS) spatial-temporal platform, and provides four visualization functions. Historians can use this tool to search for specific event types and gain insight into the relationship between particular guards and those event types, not only improving the efficiency but still maintaining the quality of research.
Temperature management system to prevent heat shock in households for elderly people

This paper presents a temperature management system (TMS) to prevent heat shock in a house using Internet of Things technology. Heat shock is a serious problem for elderly people in Japan, particularly those with heart disease or a cerebral infarction. To prevent heat shock in the home, stable temperature control is necessary. Therefore, the proposed TMS implements two main functions: one is controlling the heat, ventilation, and air conditioning (HVAC) system according to the collected environmental data using networked sensors, and the other is providing information through a webpage instructing how to prevent the effects of heat shock. To test the applicability of this system, a feasibility study was conducted using two households for two weeks during the winter. To evaluate the system, the data and impressions of the users were collected. The results show that the system is able to maintain stable temperatures between different rooms in a home and provide useful information.
Maximum Efficiency Tracking of an Integrated Two-Staged AC–DC Converter Using Variable DC-Link Voltage

This paper presents an approach to achieve maximum conversion efficiency of a two-staged-cascaded ac–dc converter. In this study, the integrated ac–dc converter consists of a three-phase boost-type power factor correction rectifier and a phase-shifted full-bridge dc–dc converter. The detailed loss modeling of the integrated stage implies that the total power loss varies in a nonmonotonic fashion with intermediate dc-link voltage. In order to ensure maximum conversion efficiency, an optimum dc-link voltage level can be set depending on the load power level. A comparison between the efficiency variation under two different cases is carried out with extensive mathematical analyses: 1) fixed dc bus and 2) variable dc-bus voltage control. In addition, the experimental results obtained from a 6-kW-integrated ac–dc hardware prototype show a unity power factor along with a peak efficiency of 97.2%, which is 2.5% more than the fixed dc-link control approach.
Combining encoded structured light and photometric stereo for underwater 3D reconstruction

Surface normal maps created by photometric stereo allow for high-quality rendering from certain viewpoints, even when the resolution of original images is low. However, the lack of constraints between multiple disconnected patches, the frequent presence of low-frequency distortion, and some actual conditions often lead to a bias during the photometric stereo reconstruction using direct integration. In this paper, we therefore present a hybrid method, which exploits the depth information that the encoded structured light system produces, in order to correct the photometric stereo bias. On the other hand, this method retains the high-precision normal information. Our experimental results show that the proposed method can not only recover high-frequency details but also avoid, or at least reduce, the low-frequency bias. In particular, the error that our method generates in the underwater environment is tolerant, even in the case that high turbidity values occur.
Working Paper No . 12 November 2007 Evolution in Economic Geography : Institutions , Regional Adaptation and Political Economy

Economic geography has, over the last decade or so, drawn upon ideas from evolutionary economics in trying to understand processes of regional growth and change, with the concept of path dependence assuming particular prominence. Recently, some prominent researchers have sought to delimit and develop an evolutionary economic geography (EEG) as a distinct approach, aiming to create a more coherent and systematic theoretical framework for research. This paper contributes to debates on the nature and development of EEG. It has two main aims. First, we seek to restore a broader conception of social institutions and agency to EEG, informed by the recent writings of institutional economists like Geoffrey Hodgson. Second, we link evolutionary concepts to political economy approaches, arguing that the evolution of the economic landscape must be related to the broader dynamics of capital accumulation, centred upon the creation, realisation and geographical transfer of value. As such, we favour the utilisation of evolutionary and institutional concepts within a geographical political economy approach rather than the construction of a separate and theoretically ‘pure’ EEG; evolution in economic geography, not an evolutionary economic geography.
Human Cortical Pyramidal Neurons: From Spines to Spikes via Models

We present the first-ever detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA-and somatic/axonal- Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and post mortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA- conductances per synaptic contact (0.88 nS and 1.31nS, respectively) and a steep dependence of the NMDA-conductance on voltage. These estimates were based on intracellular recordings from synaptically-connected HL2/L3 pairs, combined with extra-cellular current injections and use of synaptic blockers. A large dataset of high-resolution reconstructed HL2/L3 dendritic spines provided estimates for the EPSPs at the spine head (12.7 ± 4.6 mV), spine base (9.7 ± 5.0 mV) and soma (0.3 ± 0.1 mV), and for the spine neck resistance (50 – 80 MΩ). Matching the shape and firing pattern of experimental somatic Na+ -spikes provided estimates for the density of the somatic/axonal excitable membrane ion channels, predicting that 134 ± 28 simultaneously activated HL2/L3- HL2/L3 synapses are required for generating (with 50% probability) a somatic Na+ spike. Dendritic NMDA spikes were triggered in the model when 20 ± 10 excitatory spinous synapses were simultaneously activated on individual dendritic branches. The particularly large number of basal dendrites in HL2/L3 PCs and the distinctive cable elongation of their terminals imply that ~25 NMDA-spikes could be generated independently and simultaneously in these cells, as compared to ~14 in L2/3 PCs from the rat temporal cortex. These multi-sites nonlinear signals, together with the large (~30,000) excitatory synapses/cell, equip human L2/L3 PCs with enhanced computational capabilities. Our study provides the most comprehensive model of any human neuron to-date demonstrating the biophysical and computational distinctiveness of human cortical neurons.
Evaluation of the nonlinear response of plasmonic metasurfaces: Miller’s rule, nonlinear effective susceptibility method, and full-wave computation

In this article, the second-harmonic generation (SHG) from gold split-ring resonators is investigated using different theoretical methods, namely, Miller’s rule, the nonlinear effective susceptibility method, and full-wave computation based on a surface integral equation method. The results confirm that Miller’s rule is, in general, not well suited for the description of SHG from plasmonic metasurfaces. On the other hand, the comparison of the nonlinear effective susceptibility method with full-wave computations shows that this method permits us to evaluate second-harmonic (SH) emission patterns from noncentrosymmetric nanoparticles with good accuracy. However, the nonlinear effective susceptibility method fails to reproduce the multipolar nature of the SH emission from centrosymmetric nanoparticles. This shortcoming is attributed to the intrinsic nature of the nonlinear effective susceptibility method, which neglects the exact positions of the nonlinear sources. The numerical implementations of these two methods are also discussed in detail, revealing that the main limitation of the nonlinear effective susceptibility method, aside from the inaccuracy observed in specific cases, is its higher numerical requirements when several emitting directions need to be considered. This limitation stands for most of the numerical methods used for solving Maxwell’s equations at the nanoscale. This work provides clear insight into the limitations and advantages of the different methods available for evaluation of SHG from plasmonic metasurfaces.
An evaluation of convolutional neural networks on material recognition

Material recognition from a single image is a challenging problem in the computer vision field due to the lack of reliable and discriminative features. Previous approaches employ off-the-shelf features such as SIFT descriptors or filter bank response to build material recognition systems. The recent success of deep convolutional neural networks (DCNNs) in object recognition motivated us to evaluate their performance in material recognition tasks. In this paper, we tested the generality of several CNN architectures, including VGGNet [31], GoogLeNet [32], Inception V3 [33] and ResNet [10], on two commonly used material datasets: Flickr Material Database (FMD) and Materials IN Context database (MINC). The results show that the best performing CNN architecture, i.e., Inception V3, achieves at least 5% boost on FMD compared with the other networks and almost reaches human's performance. The results on MINC-2500 also exhibit the state-of-the-art level.
Design and control of a low-cost bipedal robot

This paper addresses the development and control of a low-cost bipedal robot. The main goal is to evaluate different control strategies on a robot built with economical components. We describe the mechanical design, electronics and computer hardware of the biped, and introduce a simple trajectory generation method for a statically stable walk. The stability of the given trajectory is validated by simulated walk with the Simmechanics toolbox of Matlab and by manually calculating ZMP trajectories. This is followed by examining the effect of certain simplifications on torques and ZMP trajectories, such as using the Inverted Pendulum Mode or neglecting centripetal and Coriolis forces, gyroscopic effect or even the moment of inertia.
Effects of Design Parameters on Performance of Brushless Electrically Excited Synchronous Reluctance Generator

Permanent magnet synchronous generators, doubly fed induction generators, and traditional electrically excited synchronous generators are widely used for wind power applications, especially large offshore installations. In order to eliminate brushes and slip rings for improved reliability and maintenance-free operation, as well as to avoid costly permanent magnets, a novel brushless electrically excited synchronous reluctance generator having many outstanding advantages has been proposed in this paper. The fundamental operating principles, finite-element analysis design studies, and performance optimization aspects have been thoroughly investigated by simulations and experimentally under different loading conditions. The effects of different pole combinations and rotor dimensions on the magnetic coupling capacity of this machine have been specifically addressed and fully verified by offline testing of the 6/2 pole and 8/4 pole prototypes with magnetic barrier reluctance rotor and a new hybrid cage rotor offering superior performance.
Histogram modification and DCT based contrast enhancement

In this paper, a new contrast image enhancement algorithm is proposed. The algorithm is based on new approach for histogram modification, and a DCT based enhancement algorithm. Most of the histogram based approaches suffers from the problem of over enhancement and under enhancement. This works addresses the said issue by proposing a new approach for histogram modification. The algorithm modifies the histogram by devices a sigmoid based function. The resulting images are further improved by applying an DCT based algorithm for local contrast enhancement. The algorithm is test on large set of images and compared with existing algorithms. The quantitative and visual analysis of the resulting images shows the proposed algorithm performs comparatively better than many existing algorithms.
Research on text sentiment analysis based on CNNs and SVM

Sentiment analysis of Internet reviews is a hot research topic in Web information mining. The traditional text sentiment analysis method is mainly based on emotion dictionary or machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolutional Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM.
Can We Find Any Sustained Neurofunctional Alteration in Remitted Depressive Patients with a History of Modified Electroconvulsive Therapy

Background and Aim: Recent magnetic resonance imaging (MRI) studies have revealed structural and functional differences in the human brain before and after an electroconvulsive therapy (ECT) series in patients with depression. The hippocampus is one of the well-documented subcortical areas that show an increase in cortical volume following an ECT series. Moreover, some resting-state functional MRI studies have shown that an ECT series alters the connectivities of the hippocampus with several brain areas. However, it remains unknown whether ECT can induce some sustained neurofunctional alterations in the human brain. Methods: In this study of the depressive patients with and without a history of modified ECT (N = 5 and 9, respectively) using a cross-sectional design, we investigated the functional connectivity between the left and right hippocampi in terms of blood-oxygenation-level dependent signal changes with time by resting-state functional MRI. Results: Voxel-based morphometry showed no significant structural differences in hippocampal volume between the ECT and non-ECT groups. The ECT group showed a significantly lower functional connectivity between the same brain areas than the non-ECT group (p < 0.01). Conclusions: Although their clinical significance remains unclear, our results suggest that an ECT series induces a sustained neurofunctional alteration in the human brain.
Short term load forecasting for power exchange between Brasil and Paraguay

This work presents a case study of short term load forecasting to assist in the power exchange real time dispatch operation between Brazil and Paraguay at Itaipu Dam. A classical method with statistical approach, Seasonal Autoregressive Moving Average, is compared with an artificial intelligence method based on Artificial Neural Networks. The methods are tested using a time series representing the average hourly power exchange. The results were compared with the current forecast methods used to define the daily program of operation of Itaipu using the Mean Absolute Percentage Error method. The results of the analysis showed that the model based on the Seasonal Autoregressive Moving Average present a lower error index among the methods tested.
A financial statement fraud detection model based on hybrid data mining methods

Financial statement fraud has been a difficult problem for both the public and government regulators, so various data mining methods have been used for financial statement fraud detection to provide decision support for stakeholders. The purpose of this study is to propose an optimized financial fraud detection model combining feature selection and machine learning classification. The study indicated that random forest outperformed the other four methods. As to two feature selection methods, Xgboost performed better. And according to our research, 2 or 5 variables are more acceptable for models in this paper.
Benefits of Pavement Markings

One of the most important aspects of a safe and efficient roadway is the uniform application of pavement markings to delineate the roadway path and specific traffic travel lanes. Pavement markings can communicate information to road users as no other traffic control device can. They provide continuous information to road users related to roadway alignment, vehicle positioning, and other important driving-related tasks. It is estimated that in the United States alone, approximately $2 billion is spent annually on pavement markings. Despite these expenditures, there is a general void in terms of a consolidated effort to quantify proven benefits of pavement markings, although many research projects have focused on specific elements of pavement markings. This paper was developed to bring together many of the recent and ongoing research efforts to demonstrate a renewed perspective regarding the benefits of pavement markings and, where information is available, describe the benefits of various aspects of pavement markings. This paper presents areas where conclusive findings are available, and describes areas where findings are available but show inconsistent and sometimes conflicting results.
Implementation of Low Power Programmable Flash ADC Using IDUDGMOSFET

In this brief, a novel programmable low power analog-to-digital converter (ADC) circuit design is proposed using independently driven underlap double gate MOSFET (IDUDGMOS) to operate in radio frequency domain and its performance is studied. The primary motivation of the design is to eliminate the need for a resistor ladder for reference voltage selection and to minimize the number of transistor used in the flash ADCs. The proposed design utilizes the back gate voltage controllability of IDUDGMOS for varying reference voltage of the comparators in the ADCs. The parameters of the designed ADC analyzed are the bandwidth, the linearity, and the power consumption of the circuit. The proposed circuit offers wider bandwidth and lower power consumption compared to earlier ADC designs.
Sequential Monte Carlo algorithm for source release estimation

A key problem is that of source localization of a chemical release. A distributed set of sensors samples the surrounding air and, based on the concentration of particulates, may signal a detection event. We are developing a source localization algorithm, based on sequential Monte Carlo (SMC) methods, that estimates the location of the release, the size of the release, and the time of the release. The algorithm employs a transport and dispersion model to predict concentrations in the surveillance region for many different hypothesized release parameters, which, combined with a sensor model, predicts which sensors will signal detections and which will not. The algorithm considers both sensor detection events and, importantly, sensor non-detection events to select which of the source release hypotheses are most consistent with the observed sensor data and, ultimately, to estimate the source release parameters. Simulation results are given for different scenarios and conditions.
Fight Recognition in Video Using Hough Forests and 2D Convolutional Neural Network

While action recognition has become an important line of research in computer vision, the recognition of particular events, such as aggressive behaviors, or fights, has been relatively less studied. These tasks may be extremely useful in several video surveillance scenarios, such as psychiatric wards, prisons, or even in personal camera smartphones. Their potential usability has led to a surge of interest in developing fight or violence detectors. One of the key aspects in this case is efficiency, that is, these methods should be computationally fast. “Handcrafted” spatio-temporal features that account for both motion and appearance information can achieve high accuracy rates, albeit the computational cost of extracting some of those features is still prohibitive for practical applications. The deep learning paradigm has been recently applied for the first time to this task too, in the form of a 3D convolutional neural network that processes the whole video sequence as input. However, results in human perception of other’s actions suggest that, in this specific task, motion features are crucial. This means that using the whole video as input may add both redundancy and noise in the learning process. In this paper, we propose a hybrid “handcrafted/learned” feature framework which provides better accuracy than the previous feature learning method, with similar computational efficiency. The proposed method is compared to three related benchmark data sets. The method outperforms the different state-of-the-art methods in two of the three considered benchmark data sets.
Efficient distributed machine learning for large-scale models by reducing redundant communication

Distributed machine learning is used to train large-scale models within a moderate amount of time. To accelerate the training, all nodes have to exchange calculation results frequently. However, the communication of the updated parameters is a large overhead affecting the total execution time. This paper proposes a communication method to decrease redundant transmissions of parameters without changing the semantics of the algorithm. Before the training, we identify the parts of the collective communication that can be omitted and replace them with direct communication between the nodes requesting the intermediate results. We implemented this algorithm and evaluated it on a cluster of five nodes connected with 10-Gbps Ethernet. The evaluation using a real dataset showed that our method reduced the number of elements exchanged between nodes and shortened the communication time.
Performance analysis of Intel Ivy Bridge and Intel Broadwell microarchitectures using Intel VTune amplifier software

In recent years the thread level parallelism has emerged as more competent technique for enhancing the processor's performance. As, it was not possible to exploit the instruction level parallelism any further and the heat dissipation problem associated with complex larger processors had become a serious issue. Thus, the multithreading approach was encapsulated in the processor architectures to exploit the thread level parallelism together with instruction level parallelism for enhancing the processor performance. This has encouraged the development of chip multicore processors. The current study analyzes the ability of the Intel Ivy Bridge and Intel Broadwell micro-architectures to handle the branch instructions and cache memory. In this study, all the considered processors were subjected to test runs under the observation of Intel VTune amplifier software that was used to study the branch mispredictions, cache misses and to gather the respective data.