Miracle-dz/UniFAR-FTUs huggingface dataset:
CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective

Intention, emotion and action are important psychological factors in human activities, which play an important role in the interaction between individuals. How to model the interaction process between individuals by analyzing the relationship of their intentions, emotions, and actions at the cognitive level is challenging. In this paper, we propose a novel cognitive framework of individual interaction. The core of the framework is that individuals achieve interaction through external action driven by their inner intention. Based on this idea, the interactions between individuals can be constructed by establishing relationships between the intention, emotion and action. Furthermore, we conduct analysis on the interaction between individuals and give a reasonable explanation for the predicting results. To verify the effectiveness of the framework, we reconstruct a dataset and propose three tasks as well as the corresponding baseline models, including action abduction, emotion prediction and action generation. The novel framework shows an interesting perspective on mimicking the mental state of human beings in cognitive science.
Some new results on aggregate claim amounts from two heterogeneous Marshall–Olkin extended exponential portfolios

ABSTRACT In this work, we discuss some stochastic comparisons of two aggregate claim amounts. Applications of our results to the value-at-risk and tail-value-at-risk are also mentioned. It is also shown that the aggregate claim amounts of risks exhibiting a weak form of dependence known as positive cumulative dependence (negatively associated) is larger (smaller) in convex order than the corresponding aggregate claim amounts under the theoretical independence assumption. The obtained results show that the correlations between individual risks increase stop-loss premiums corresponding to aggregate claim amounts. The results established here complete and extend the results of Barmalzan, Payandeh Najafabadi, and Balakrishnan (2017, Theory of Probability and its Applications, to appear).
Algèbres graphiques (sur un concept de dimension dans les langages formels)

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Enzyme engineering: A synthetic biology approach for more effective library generation and automated high-throughput screening

The Golden Gate strategy entails the use of type IIS restriction enzymes, which cut outside of their recognition sequence. It enables unrestricted design of unique DNA fragments that can be readily and seamlessly recombined. Successfully employed in other synthetic biology applications, we demonstrate its advantageous use to engineer a biocatalyst. Hot-spots for mutations were individuated in three distinct regions of Candida antarctica lipase A (Cal-A), the biocatalyst chosen as a target to demonstrate the versatility of this recombination method. The three corresponding gene segments were subjected to the most appropriate method of mutagenesis (targeted or random). Their straightforward reassembly allowed combining products of different mutagenesis methods in a single round for rapid production of a series of diverse libraries, thus facilitating directed evolution. Screening to improve discrimination of short-chain versus long-chain fatty acid substrates was aided by development of a general, automated method for visual discrimination of the hydrolysis of varied substrates by whole cells.
Perception of intersensory synchrony

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Voltage dynamics: study of a generator with voltage control, transmission, and matched MW load

A comprehensive analysis of the dynamic behavior for a rudimentary but representative model of the power system is carried out for the case when control gain and load are varied as parameters. The voltage dynamics model is subject to algebraic constraints in the form of load flow equations and is studied as a differential-algebraic system in state and parameter spaces. Singularities in the state-space (noncausal points) and bifurcations in the parameter space are the principal and interacting structural elements. A rich structure of bifurcations emerges which is analyzed. The mathematical analysis is facilitated by singularly rescaling time, which transforms the differential algebraic system into a smooth dynamic system. In the state space, the characteristics of stability boundaries are observed and a description of the regions of attraction of all equilibria are given. The loosely understood term of voltage collapse is classified into well-defined types on both the dynamic and parameteric sides. >
The Application of the Genetic Algorithm-Back Propagation Neural Network Algorithm in the High-Energy Physics

Multiparticle production mechanism is one of the most phenomena that the high-energy physics concerns. In this work, the evolutionary genetic algorithm (GA) is used to optimize the parameters of the back-propagation neural networks (BPNN). The hybrid evolutionary-neuro model (GA-BPNN) was trained to simulate the rapidity distribution 1/N(dN/dY) of positive and negative pions p-Au, p-Ag and p-Xe for p-Ar, p-Xe interactions at lab momentum Plab =100 GeV/c. Also, for total charged, positive and negative pions for interactions at Plab = 200 GeV/c. Finally, total charged particles for p- Pb collision at center-of-mass energy sqrt(s) = 5.02 TeV are simulated. An efficient ANN network with different connection parameters (weights and biases) have been designed by the GA to calculate and predict the rapidity distribution as a function of the lab momentum Plab, mass number (A) and the number of particles per unit solid angle (Y). Our simulated results have been compared with the experimental data and the matching has been clearly found. It is indicated that the developed GA-BPNN model for rapidity distribution was more successful.
A nation-wide experiment: fuel tax cuts and almost free public transport for three months in Germany -- Report 4 Third wave results

In spring 2022, the German federal government agreed on a set of measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures included among others, a nation-wide public transport ticket for 9EUR per month and a fuel tax cut that reduced fuel prices by more than 15%. In transportation policy and travel behavior research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample 2'263 individuals; for the Munich Study, 919 participants in the survey-and-app group and 425 in the survey-only group have been successfully recruited, while 919 participants have been recruited through a professional panel provider to obtain a representative nation-wide reference group for the three-wave survey. In this fourth report we present the results of the third wave. At the end of the study, all three surveys have been completed by 1'484 participants and 642 participants completed all three surveys and used the travel diary throughout the entire study. Based on our results we conclude that when offering a 49EUR-Ticket as a successor to the 9~EUR-Ticket and a local travel pass for 30EUR/month more than 60% of all 9~EUR-Ticket owners would buy one of the two new travel passes. In other words, a substantial increase in travel pass ownership in Germany can be expected, with our modest estimate being around 20%. With the announcement of the introduction of a successor ticket in 2023 as well as with the prevailing high inflation, this study will continue into the year 2023 to monitor the impact on mobility and daily activities.
P‐10.11: Pixelation of Nano‐LED arrays with high effective light‐emitting area ratio by ion injection: A Simulation study

The etch damage caused by the conventional bench-top etching process makes it difficult to produce Nano-LED devices. Recently, some scholars have studied the use of ion injection to achieve electrical isolation between pixels for the purpose of pixelation. In this paper, we focus on the distribution characteristics of ions in GaN targets to improve the effective light-emitting area ratio of devices by reducing the lateral diffusion effect of ions.
Research Unit for Statistical and Empirical Analysis in Social Sciences (Hi-Stat)

This study investigates the e(cid:11)ect of distance on price di(cid:11)erentials across regions. To identify the distance e(cid:11)ect, we need to incorporate producer heterogeneity and pricing-to-market behavior. Because geographic barriers alter the threshold levels of productivity to set a positive price across markets, the e(cid:11)ect of distance on price di(cid:11)erentials can be underestimated if heterogeneity and pricing to market are not accounted for. By incorporating these factors, empirical analysis using micro-level data reveals that the distance e(cid:11)ect is signi(cid:12)cantly large, suggesting that the price of geographic barriers is still high for regional transportation
From Perception to Conception: Learning Multisensory Representations

The “metamodal” perspective on neural organization states that different neural regions are specialized for performing different functions on sensory data regardless of the modality through which those data were acquired (Pascual-Leone & Hamilton, 2001). If so, this raises an interesting question: Given that we learn about our environments through our senses, how do we acquire abstract concepts that are modality-independent? In other words, how does perception interface with conception? The “grounded cognition” approach emphasizes that abstract representations must be derived from perceptual signals and representations (Barsalou, 2008). However, it leaves open the question of how these representations are acquired. Moreover, it claims that these representations are never fully modality-independent. In this thesis, we cast this problem of perception-to-conception interface in the domain of multisensory perception. We study how perception gives rise to abstract concepts by studying the acquisition of multisensory representations. We describe experimental work demonstrating that people exhibit cross-modal transfer of perceptual knowledge, indicating that they have modality-independent, conceptual representations. We also describe our computational work demonstrating that these representations can be acquired from sensory data.
Diversity of Models Based on Sequence to Sequence for Neural Machine Translation Tasks

One of the most critical areas of Natural Language Processing study is machine translation, which realizes the translation process from source language to target language. After a rapid development, neural machine translation has become mainstream. Most of the tasks of natural language generation are implemented based on sequence to sequence model. The current technology of machine translation is the use of recurrent neural networks and attention mechanisms, which is essentially a set of encoding and decoding techniques. In order to handle the issue of excessive information compression introduced by decoder, attention mechanism is proposed. Transformer is also built with attention, which not only computes faster, but also achieves better results on translation tasks. In this paper, a series of comparison of these models and description of the performance on the dataset were shown.
Initial Dynamical Evolution of Star Clusters with Tidal Field

Abstract Observations have been suggested that star clusters could form from the rapid collapse and violent relaxation of substructured distributions. We investigate the collapse of fractal stellar distributions in no, weak, and very strong tidal fields. We find that the rapid collapse of substructure into spherical clusters happens quickly with no or a weak tidal field, but very strong tidal fields prevent a cluster forming. However, we also find that dense Plummer spheres are also rapidly destroyed in strong tidal fields. We suggest that this is why the low-mass star clusters cannot survive near the galactic centre which has strong tidal field.
Shadow Economies around the World: Model Based Estimates

In this paper, relying on a two-sector dynamic general equilibrium model, we propose and then use a new methodology to construct a novel shadow economy dataset. We calibrate our model to match various reported macroeconomic variables and then back out the size of the shadow economy from the calibrated model. This allows us to construct an unbalanced 161-country panel dataset over the period 1950 and 2009. This aims to be the largest dataset in the literature, particularly with its time-series dimension. We also present certain features of the data along with various descriptive statistics.
Causal Fusion for Recommender System

Recommender system suffers from huge selection bias from users, which makes utilizing causal inference to solve this problem becomes a necessary problem. However, traditional rating model only utilizing biased dataset and unbiased estimator cannot remarkably be improved. Thus, we fused the unbiased dataset into training model and combine it with jointly learning method to better improve the original baselines. Extensive experiments verify that the proposed doubly robust joint leaning method after fusing unbiased, missing rating data can significantly outperform the state-of-the-art methods and can dramatically reduce the error from rating model with the increasing number of unbiased data.
A Sequential Algorithm for Training Text Classifiers

The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
A Sequential Algorithm for Training Text Classi ersDavid

The ability to cheaply train text classiiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classiiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classiied to achieve a given level of eeectiveness.
A multi-level method of support for management of product flow through supply chains

The paper presents a three-level method used to support the management of product ﬂow through supply chain links (e.g. production lines) and between these links: suppliers and recipients of products of various types.The supply chain includes both producers of components (suppliers) and recipients of these components, which are used for the production of complex goods. The method is dedicated to the development of schedules of product ﬂows through particular production plants (links in supply chains) and between individual plants. Each module of the developed system refers to a separate production plant. The organization of product ﬂow through production lines covers diﬀerent types of production routes and diﬀerent conﬁgurations of production systems. At the ﬁrst level of the method, preliminary production schedules are developed for each plant within the supply chain. The second level of the method is dedicated to the development of delivery schedules of components and semi-ﬁnished products to these plants. The determined delivery times of components to the individual production plants constitute data for the third level of the method. At this lowest level, detailed schedules of product ﬂow through production lines with the producers of complex goods are developed. Linear mathematical models have been built for each level of the method. In the developed method, optimization take place in the developed method in the scale of the entire supply chain (cost reduction), as well as in the scale of its links (production lines for which manufacturing schedules are built with various criteria taken into consideration). The computational experiments used for veriﬁcation of the method have been included.
Heavy flavour and quarkonium production at the LHC

— This document presents a review of recent results for quarkonium production at the LHC from ATLAS, CMS, and LHCb. Production cross sections for J/ψ, ψ(2S), and Υ(mS), and production ratios for χc,bJ are found to be in good agreement with predictions from non-relativistic QCD. In contrast, spin-alignment (polarisation) measurements seem to disagree with all theoretical predictions. Some other production channels useful for investigating quarkonium hadroproduction mechanisms are also considered.
The dynamic of mangroves and ponds changes in East Kalimantan, Indonesia

‘More than 50% of Indonesia’s mangrove is lost over the last 30 years, which makes Indonesia has the fastest rate of mangrove destruction in the world. East Kalimantan Province has the third largest mangrove in Indonesia, behind Papua and Riau Province. Mangroves in East Kalimantan experienced great pressure from land conversion into ponds. Hence, this study focuses on mangrove and pond conversion trendlines based on 3 times spatial analyses using an automated method through the Google Earth Engine (GEE) platform using Landsat from 1990, 2000, and Sentinel 2019 imagery data. The total area of mangroves identified in 1990 was 268,147 Ha, in 2000 was 227,202 Ha or a decrease by 15%, and in 2019 was 215,514 Ha or a decrease by 5%. The amount of mangrove area that continues to decrease is aligned with the pond increasing in all East Kalimantan that identified in 1990 was 9,935 Ha, then extremely increase until in 2000 identified an area of 61,083 Ha and increased again in 2019 to reach 86,471 Ha. The analysis shows that it is dominated by the conversion of mangroves to ponds throughout East Kalimantan reaching 65% of the total reduction that occurred both in the period 1990-2000 and 2000-2019.
High performance photodetector based on 2D CH3NH3PbI3 perovskite nanosheets

In this work, a high performance vertical-type photodetector based on two-dimensional (2D) CH3NH3PbI3 perovskite nanosheets was fabricated. The low trap density of the perovskite nanosheets and their short carrier diffusion distance result in a significant performance enhancement of the perovskite-based photodetector. The photoresponsivity of this vertical-type photodetector is as high as 36 mA W−1 at visible wavelength, which is much better than traditional perovskite photodetectors (0.34 mA W−1). Compared with traditional planar-type perovskite-based photodetectors, this vertical-type photodetector also shows the advantages of low-voltage operation and large responsivity. These results may pave the way for exploiting high performance perovskite-based photodetectors with an ingenious device design.
Pulsations and eclipse-time analysis of HW Vir

We analysed recent K2 data of the short-period eclipsing binary system HW Vir, which consists of a hot subdwarf-B type primary with an M-dwarf companion. We determined the mid-times of eclipses, calculated O–C diagrams, and an average shift of the secondary minimum. Our results show that the orbital period is stable within the errors over the course of the 70 days of observations. Interestingly, the offset from mid-orbital phase between the primary and the secondary eclipses is found to be 1.62 s. If the shift is explained solely by light-travel time, the mass of the sdB primary must be 0.26 M⊙, which is too low for the star to be core-helium burning. However, we argue that this result is unlikely to be correct and that a number of effects caused by the relative sizes of the stars conspire to reduce the effective light-travel time measurement. After removing the flux variation caused by the orbit, we calculated the amplitude spectrum to search for pulsations. The spectrum clearly shows periodic signal from close to the orbital frequency up to 4600 µHz, with the majority of peaks found below 2600 µHz. The amplitudes are below 0.1 part-per-thousand, too low to be detected with ground-based photometry. Thus, the high-precision data from the Kepler spacecraft has revealed that the primary of the HW Vir system is a pulsating sdBV star. We argue that the pulsation spectrum of the primary in HW Vir differs from that in other sdB stars due to its relatively fast rotation that is (nearly) phase-locked with the orbit.
Medical Image Segmentation Method Based on Improved Unet

This paper is an attempt to apply 3DUnet system to medical image segmentation. The classifying model is built based on actual medical images from MSD Cardiac dataset. The 3DUnet system, which is developed on Convolutional Neural Networks will be of great significance in medical diagnosis and analysis. The paper analyzes and contains details of the 3DUnet system we built and the algorithms we applied. We applied the method of cutting the input images into patches and K-fold cross validation to obtain better results. The final average value of Dice is over 87%. Codes are available: https://github.com/LRXuanxuan/jubilant-chainsaw.
Systems as an enabler for

With increased dependence on efficient use and inclusion of diverse corporate and Web based data sources for business information analysis, financial information providers will increasingly need agile information integration capabilities. Linked Data is a set of technologies and best practices that provide such a level of agility for information integration, access, and use. Current approaches struggle to cope with multiple data sources inclusion in near real-time, and have looked to Semantic Web technologies for assistance with infrastructure access, and dealing with multiple data formats and their vocabularies. This chapter discusses the challenges of financial data integration, provides the component architecture of Web enabled financial data integration and outlines the emergence of a financial ecosystem, based upon existing Web standards usage. Introductions to Semantic Web technologies are given, and the chapter supports this with insight and discussion gathered from multiple financial services use case implementations. Finally, best practice for integrating Web data based on the Linked Data principles and emergent areas are described.