Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Simulation of Impact and Control in Social Networks with Application to Marketing

The problems of social networks analysis and calculation of the resulting opinions of network agents are considered. Algorithms for identifying strong subgroups and satellites as well as for calculating some quantitative characteristics of a network are implemented by the R programming language and tested on model examples. A new algorithm for calculating the resulting opinions of agents is developed by the R toolkit and tested on model examples. It is important that control actions that exert impact to the opinions should be applied exclusively to the members of strong subgroups (opinion leaders of a target audience), since they fully determine the stable resulting opinions of all network members. This approach allows saving control resources without significantly affecting its efficiency. Much attention is paid to the original models of optimal control (single subject) and conflict control (several competing subjects) under the assumption that the members of strong subgroups (opinion leaders) are already identified at the previous stage of network analysis. Models of optimal opinion control on networks are constructed and investigated by computer simulations using the author’s method of qualitatively representative scenarios. Differential game-based models of opinion control on networks with budget constraints in the form of equalities and inequalities are constructed and analytically investigated. All used notions, approaches and results of this paper are interpreted in terms of marketing problems.
Manually and automatically produced pellet cultures of human primary chondrocytes: A comparative analysis

Cartilage defects are often associated with restriction of the locomotor system. New methods are required to investigate cartilage tissue and for the repair of cartilage tissue. 3D cultures are promising due to better simulation of in vivo conditions. The aim of this study was to provide a model system for studying cartilage tissue. We solved this problem by automated production of pellet cultures of human primary chondrocytes in media with and without antibiotics using the Biomek® Cell Workstation and consequent automated bioscreening with a high‐throughput screening system, and compared with the regular manual processes. The Biomek® Cell Workstation allows the cultivation of different cell types (suspensions cells and adherent cells) and 3D cell cultures (pellet cultures, alginate beads and spheroid cultures). The proliferation was analyzed by DNA quantification and compared with the EZ4U proliferation assay as a new tool for pellet cultures. The toxicity was evaluated by the detection of ubiquitous adenylate kinases. The proliferation increased from day 14 until day 35 and was associated with a decrease in the cytotoxicity. The comparative analysis showed similar results for manual and automated processes. We concluded that the manual methods can be replaced by automated processes (pellet manufacturing and screening), which would allow large‐scale procedures to support studies on cartilage regeneration.
TrimBot2020 Deliverable D3.2 Implementation and evaluation of SLAM, 3D from binocular and motion stereo

Abstract: This report describes and evaluates the Simultaneous Localization and Mapping (SLAM) and 3D data generation pipelines developed in the Trimbot2020 project. In particular, the report provides details on the SLAM system used in the Trimbot2020 project to build 3D maps of the scene and determine the position of the robot within these 3D maps. In addition, the report also describes and evaluates the vehicle localization system developed for the TrimBot2020 project. The description and evaluation was originally planned for Deliverable D3.5 (Arm and vehicle localization). However, it will be presented as part of this deliverable as the vehicle localization system is tightly integrated in the SLAM pipeline.
Solving Multiple Inference by Minimizing Expected Loss

Multiple Inference is the problem of ﬁnding multiple top solutions for an inference problem in a graphical model. It has been shown that it is beneﬁcial for the top solutions to be diverse. However, existing methods, such as diverse M-Best and M-Modes, often rely on a hyper parameter in enforcing diversity. The optimal values of such parameters usually depend on the probability landscape of the graphical model and thus have to be tuned case by case via cross validation. This is not a desirable property. In this paper, we introduce a parameter-free method that directly minimizes the expected loss of each solution in ﬁnding multiple top solutions that have high oracle accuracy, and are automatically diverse. Empirical evaluations show that our method often have better performance than other competing methods.
Potential of future stratospheric ozone loss in the mid-latitudes
under climate change and sulfate geoengineering

Abstract. The potential of heterogeneous chlorine activation in the mid-latitude lowermost stratosphere during summer is a matter of debate. The occurrence of heterogeneous chlorine activation through the presence of aerosol particles could cause ozone destruction. This chemical process requires low temperatures and is accelerated by an enhancement of the stratospheric water vapour and sulfate amount. In particular, the conditions present in the lowermost stratosphere during the North American Summer Monsoon season (NAM) are expected to be cold and moist enough for causing the occurrence of heterogeneous chlorine activation. Furthermore, the temperatures, the water vapour mixing ratio and the sulfate aerosol abundance are affected by future climate change and by the potential application of sulfate geoengineering. Hence, both future scenarios could promote this ozone destruction process. We investigate the likelihood for the occurrence of heterogeneous chlorine activation and its impact on ozone in the lowermost stratospheric mixing layer between tropospheric and stratospheric air above central North America (30.6–49.6° N, 72.25–124.75° W) in summer for conditions today, at the mid and at the end of the 21st century. Therefore, the results of the Geoengineering Large Ensemble Simulations (GLENS) for the lowermost stratospheric mixing layer between tropospheric and stratospheric air are considered together with 10 day box-model simulations performed with the Chemical Lagrangian Model of the Stratosphere (CLaMS). In GLENS two future scenarios are simulated: the RCP8.5 climate change scenario and a geoengineering scenario, where sulfur is additionally injected in the stratosphere to keep the global mean surface temperature from changing. In the GLENS simulations, the mixing layer will warm and moisten in both future scenarios with a larger effect in the geoengineering scenario. The likelihood for chlorine activation to occur in the mixing layer is highest in the years 2040–2050 if geoengineering is applied, accounting for 3.3 %. In comparison, the likelihood for conditions today is 1.0 %. At the end of the 21st century, the likelihood of this ozone destruction process to occur decreases. We found that 0.1 % of the ozone mixing ratios in the mixing layer above central North America is destroyed for conditions today. A maximum ozone destruction of 0.3 % in the mixing layer occurs in the years 2040–2050 if geoengineering is applied. Comparing the southernmost latitude band (30–35° N) and the northernmost latitude band (44–49° N) of the considered region, we found a higher likelihood for the occurrence of heterogeneous chlorine activation in the southernmost latitude band, causing a higher impact on ozone as well. However, the ozone loss process is found to have a minor impact on the mid-latitude ozone column with not more than 0.1 DU today or in the future scenarios.
Decision Scoring Rules

A principal faces a decision and asks an external expert for a recommendation as well as a probabilistic prediction about what outcomes might occur if the recommendation were implemented. The principal then follows the recommendation and observes an outcome. Finally, the principal pays the expert based on the prediction and the outcome, according to some decision scoring rule . In this paper, we ask: What does the class of proper decision scoring rules look like, i.e., what scoring rules incentivize the expert to honestly reveal both the action he believes to be best for the principal and the prediction for that action? We ﬁrst show that in addition to an honest recommendation, proper decision scoring rules can only incentivize the expert to reveal the expected utility of taking the recommended action. The principal cannot strictly incentivize honest reports on other aspects of the conditional distribution over outcomes without setting poor incentives on the recommendation itself. We then characterize proper decision scoring rules as ones which give or sell the expert shares in the principal’s project. Each share pays, e.g., $ 1 per unit of utility obtained by the principal. Owning these shares makes the expert want to maximize the principal’s utility by giving the best-possible recommendation. Furthermore, if shares are oﬀered at a continuum of prices, this makes the expert reveal the value of a share and therefore the expected utility of the principal conditional on following the recommendation. We extend our analysis to the design of mechanisms for eliciting recommendations and predictions from multiple experts. With a few modiﬁcations, the above characterization for the single-expert case carries over. Among other things, this characterization implies that the mechanism should not allow experts to “short-sell” shares in the principal’s project and thereby proﬁt if the project goes poorly.
DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV

The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate traffic data collection and interpretation towards better traffic light control and congestion alleviation. Operating within the coverage of the edge servers, DRLE aggregates data from neighboring edge servers for city-scale traffic light control. DRLE decomposes the highly complex problem of large area control into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning and demonstrate its superiority over several state-of-the-art algorithms via extensive evaluations.
Exploration of Low-power Viterbi Decoders Design for Low-throughput Application

The IEEE 802.11ah is a long range 802.11 Wireless Local Area Network (WLAN) standard. It aims to achieve a cost-effective and large scale wireless network for Internet of Things (IoT) applications. Convolutional encoding is an important process to provide error-correcting codes for the wireless transmission in a 802.11ah transmitter. This thesis work explores the digital integrated circuit (IC) design and implementation of a low-power and small-area convolutional decoder by the Viterbi Algorithm for the receiver to fit the low-throughput and low-power requirement of 802.11ah standard. After investigating different architectures and algorithms to save the power and area, the promising designs are implemented with the Cadence digital IC design tool using the TSMC 40nm technology at 1.1V supply voltage. To fulfill the current project requirements, 5 Mb/s is the target throughput. The results of the Cadence front-end and back-end simulation are analyzed concerning the power and area and also are compared with the state-of-the-art design in the literature. Among all the architectures with the conventional Viterbi Algorithm, the optimal design is the fully-parallel architecture which has 247 uW power and 0.04 mm area at 10 MHz clock. Among all implemented algorithms based on the fully-parallel architecture with the conventional Viterbi Algorithm, the pre-traceback (PTB) algorithm are the optimal with 269 uW power and 0.04 mm area at 5 MHz clock rate. PTB can achieve the maximum throughput (40 Mb/s) among all designs with 40 MHz clock rate. So it is promising to be the optimal low-power design by the voltage and clock rate scaling. The power target based on the scaling results of state-of-the-art designs can be achieved by two selected implementations.
A Data Mining Approach: Application to the Extraction of the Characteristics of IARD Products in the Insurance Sector

In this study, we were interested to multirisk home products (MH) and professional multirisk (MP) of the branch IARD 1 insurance, which covers fire, accidents and various risks of an Algerian insurance company (SAA). We want to know the variables that best characterize each of the two products. To solve this problem, we have combined three methods borrowed from Data Mining and decisional statistics. The use of data mining tools to realize a classification and to bring out the most informative variables is usual tasks of this discipline. The aim of this work is to extract knowledge from an actuarial database by adopting an approach to data mining and to compare the results obtained by querying three techniques, implemented in data mining software: TANAGRA.
Geographical interregional trade frictions: An approach based on municipal data

The border effect literature emphasizes the negative effect of distance on international trade flows between countries in relation to flows within national borders. However, recent works have shown that the border effect arises from a methodological error in measuring the distances within the international trade literature, having practically no effect when it is measured at the highest level of disaggregation (Hillberry and Hummels, 2008; Head and Mayer, 2002). In this context, and using a micro-database on shipments by road within Spain for the period 20032007, we decompose municipal trade flows (Nuts 5 level) into the intensive and the extensive margins using different measures of transport costs defined at the highest possible level of disaggregation: a) real distance covered by the truck; b) travel time; and c) generalized transport cost (Zofío et al., 2011). For all these alternative measures we observe that as transport costs increase, the number of shipments (extensive margin) drops more sharply than their average value per shipment counterpart (intensive margin). Additionally, in a second level of decomposition, the extensive margin is explained by the number of shipment per commodity (product intensive margin) while the average tons explain the intensive margin. At this very detailed geographical level we find that the difference between the intensive and the extensive margins is quite important regardless the measure of transport costs. Furthermore, within the same municipality the total value of trade is explained by the extensive margin. Nevertheless, alternative regional borders have different effects on trade, indeed provinces (Nuts 3 level) have a positive effect on all the trade decomposition variables, while regions (Nuts 2 level) have lower or even negative impact on trade flows. Finally, we observe that the variation in generalized transport costs, along this panel database, explains in a more significant way the variation in trade flows than the rest of transport costs. All these results are robust if we consider trade flows in quantities instead of monetary trade flows. These findings provide further evidence about the so-called illusory border effect problems that arise if transport costs are assumed to be statistical aggregate measures between geographical points rather than considering more detailed geographical flows and measures of transport costs. 1 I thank Thierry Mayer, Johannes Bröcker and, particularly, Horst Raff at the Kiel Institute for World Economy and Kiel University, for their useful comments. Finally, we would like to thank Francisco Requena for his comments at the INTECO meeting (Universitat de Valéncia) and Tamara de la Mata (Universidad Autónoma de Madrid).
Double-Stage Quantile Regression

Two-Stage Least Square estimator and Two-Stage Least Absolute Deviation estimator have been studied in Amemiya (1982) and Powell (1983). Researchers interested in specific parts of the conditional distribution have estimated quantile regressions. Some of them attempt to correct the endogeneity of some explanatory variables by implementing a first stage of least-squares instrumental estimation [Arias et al (2001), Garcia et al (2001)]. In this paper we extend Amemiya (1982) and Powell (1983) by predicting the values of endogenous variables with first stage quantile regressions based on the same quantile than the final quantile regression (Double-Stage Quantile Regression or DSQR). We derive the asymptotic representation and normality of the DSQR estimator and provide a consistent estimator of the covariance matrix of the DSQR estimator. We finally study finite sample properties by using simulations.
Unsupervised Monocular Depth Estimation with Multi-Baseline Stereo

Unsupervised deep learning methods have shown promising performance for single-image depth estimation. Since most of these methods use binocular stereo pairs for self-supervision, the depth range is generally limited. Small-baseline stereo pairs provide small depth range but handle occlusions well. On the other hand, stereo images acquired with a wide-baseline rig cause occlusions-related errors in the near range but estimate depth well in the far range. In this work, we propose to integrate the advantages of the small and wide baselines. By training the network using three horizontally aligned views, we obtain accurate depth predictions for both close and far ranges. Our strategy allows to infer multi-baseline depth from a single image. This is unlike previous multi-baseline systems which employ more than two cameras. The qualitative and quantitative results show the superior performance of multi-baseline approach over previous stereo-based monocular methods. For 0.1 to 80 meters depth range, our approach decreases the absolute relative error of depth by 24% compared to Mon-odepth2. Our approach provides 21 frames per second on a single Nvidia1080 GPU, making it useful for practical applications. The code and dataset are publicly available at https://github.com/saadi297/MultiBaselineDepth
High-order Graph Convolutional Networks for 3D Human Pose Estimation

Graph convolutional networks (GCNs) have been applied to 3D human pose estimation (HPE) from 2D body joint detections and have demonstrated promising performance. However, since the vanilla graph convolution is performed on the one-hop neighbors of each node, it is unable to capture the long-range dependencies between body joints. They can help reduce the uncertainty caused by occlusion or depth ambigu-ity. To resolve this issue, we propose a high-order GCN for 3D HPE. Its core building block, termed a high-order graph convolution, aggregates features of nodes at various distances. As a result, the network can model a wide range of interactions among body joints. Furthermore, we investigate different methods to fuse those multi-order features and compare how they affect the performance. Experimental results demonstrate the effectiveness of the proposed approach.
Quasi-symplectic Langevin Variational Autoencoder

Variational autoencoder (VAE) as one of the well investigated generative model is very popular in nowadays neural learning research works. To leverage VAE in practical tasks which have high dimensions and huge dataset often face the problem of low variance evidence lower bounds construction. Markov chain Monte Carlo (MCMC) is an effective approach to tight the evidence lower bound (ELBO) for approximating the posterior distribution. Hamiltonian Variational Autoencoder (HVAE) is one of the effective MCMC inspired approaches for constructing the unbiased low-variance ELBO which is also amenable for reparameterization trick. The solution significantly improves the performance of the posterior estimation effectiveness, yet, a main drawback of HVAE is the leapfrog method need to access the posterior gradient twice which leads to bad inference efficiency performance and the GPU memory requirement is fair large. This flaw limited the application of Hamiltonian based inference framework for large scale networks inference. To tackle this problem, we propose a Quasi-symplectic Langevin Variational autoencoder (Langevin-VAE), which can be a significant improvement over resource usage efficiency. We qualitatively and quantitatively demonstrate the effectiveness of the Langevin-VAE compared to the state-of-art gradients informed inference framework.
Application of Advanced Back Propagation Algorithm in Electric Load Forecasting

Now a day’s load forecasting leads an immense area of research in power system. It helps to generate the power with minimal cost and ensure the reliability of power systems. It is so much attractive because accurate load forecasting is a challenging task for its difficulties. This paper presents the load forecasting for the Power Grid Company Bangladesh Ltd. (PGCB) by using Advanced Back Propagation Algorithm. It is advanced because here the adaptation mechanism is used to update hidden layer. And it uses newly designed data set where only that kind of inputs are chosen which give the best prediction output. These inputs are chosen by trial and error method. The data are collected from PGCB to train the system. This paper has proposed to train the network in summer for reducing load shedding and in winter, holidays to minimize the power loss as well as the cost of generation. Experimental results show that the system provides the load forecasting with high accuracy.
Two Ensemble Approaches for Forecasting Sulfur Dioxide Concentrations from Kīlauea Volcano

Kīlauea volcano, located on the island of Hawaii, is one of the most active volcanoes in the world. It was in a state of nearly continuous eruption from 1983 to 2018 with copious emissions of sulfur dioxide (SO2) that affected public health, agriculture, and infrastructure over large portions of the island. Since 2010, the University of Hawaiʻi at Mānoa provides publicly available vog forecasts that began in 2010 to aid in the mitigation of volcanic smog (or “vog”) as a hazard. In September 2017, the forecast system began to produce operational ensemble forecasts. The months that preceded Kīlauea’s historic lower east rift zone eruption of 2018 provide an opportunity to evaluate the newly implemented air quality ensemble prediction system and compare it another approach to the generation of ensemble members. One of the two approaches generates perturbations in the wind field while the other perturbs the sulfur dioxide (SO2) emission rate from the volcano. This comparison has implications for the limits of forecast predictability under the particularly dynamic conditions at Kīlauea volcano. We show that for ensemble forecasts of SO2 generated under these conditions, the uncertainty associated with the SO2 emission rate approaches that of the uncertainty in the wind field. However, the inclusion of a fluctuating SO2 emission rate has the potential to improve the prediction of the changes in air quality downwind of the volcano with suitable postprocessing.