Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Material: Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation

In this section we provide qualitative results that supports our claims which were provided in the ablation study (see Geometric-Preserving Translation Network in the ablation study). More specifically, Figure 2 showcases the geometric-preserving quality that the translation network has under our proposed two-flow training scheme (i.e jointly training T ◦R and R ◦ T ). Additionally, in Figure 3 we show that training T and R such that the translation task is performed first (R ◦ T ), yields poorly reconstructed images. Finally, we show in Figure 4 that in the registration first training flow (i.e T ◦R), the translation network supersedes the registration network by performing the translation as well the registration, while the registration network degenerates to performing minor spatial transformation.
Convective uplift of pollution from the Sichuan Basin into the Asian monsoon anticyclone during the StratoClim aircraft campaign

Abstract. The StratoClim airborne campaign took place in Nepal from 27 July to 10 August 2017 to document the physical and chemical properties of the South Asian upper troposphere–lower stratosphere (UTLS) during the Asian summer monsoon (ASM). In the present paper, simulations with the Meso-NH cloud-chemistry model at a horizontal resolution of 15 km are performed over the Asian region to characterize the impact of monsoon deep convection on the composition of Asian monsoon anticyclone (AMA) and on the formation of the Asian tropopause aerosol layer (ATAL) during the StratoClim campaign. StratoClim took place during a break phase of the monsoon with intense convective activity over South China and Sichuan. Comparisons between brightness temperatures (BTs) at 10.8 µm observed by satellite sensors and simulated by Meso-NH highlight the ability of the model to correctly reproduce the life cycle of deep convective clouds. A comparison between CO and O3 concentrations from Meso-NH and airborne observations (StratoClim and IAGOS (In-service Aircraft for a Global Observing System)) demonstrates that the model captures most of the observed variabilities. Nevertheless, for both gases, the model tends to overestimate the concentrations and misses some thin CO plumes related to local convective events probably because the resolution is too coarse, but the convective uplift of pollution is very well captured by the model. We have therefore focused on the impact of Sichuan convection on the AMA composition. A dedicated sensitivity simulation showed that the 7 August convective event brought large amounts of CO deep into the AMA and even across the 380 K isentropic level located at 17.8 km. This Sichuan contribution enhanced the CO concentration by ∼15 % to reach more than 180 ppbv over a large area around 15 km height. It is noteworthy that Meso-NH captures the impact of the diluted Sichuan plume on the CO concentration during a StratoClim flight south of Kathmandu, highlighting its ability to reproduce the transport pathway of Sichuan pollution. According to the model, primary organic aerosol and black carbon particles originating from Sichuan are transported following the same pathway as CO. The large particles are heavily scavenged within the precipitating part of the convective clouds but remain the most important contributor to the particle mass in the AMA. Over the whole AMA region, the 7 August convective event resulted in a 0.5 % increase in CO concentration over the 10–20 km range that lasted about 2 d. The impact of pollution uplift from three regions (India, China, and Sichuan) averaged over the first 10 d of August has also been evaluated with sensitivity simulations. Even during this monsoon break phase, the results confirm the predominant role of India relative to China with respective contributions of 11 % and 7 % to CO concentration in the 10–15 km layer. Moreover, during this period a large part (35 %) of the Chinese contribution comes from the Sichuan Basin alone.
Values Education in Outdoor Environmental Education Programs from the Perspective of Practitioners

Shaping environmental values is considered one of the goals of environmental education. At the same time, this creates questions about the line between indoctrination and education. While values education has been widely discussed from various theoretical perspectives, few studies have analyzed how it is being practiced. This article investigates five outdoor environmental education programs and identifies the values the programs promote as well as the means they use to communicate these values to students. Additionally, the article examines the perspectives of 17 program leaders and center directors regarding the ways in which values should be promoted in environmental education and the approaches they use in their practice. According to the findings, all the observed programs applied a normative, value-laden approach, communicating mainly the values of universalism. The most frequently observed strategy was the inculcation of desirable values by moralizing and modeling. Simultaneously, some of the leaders’ beliefs, while highlighting value-free or pluralistic approaches, contradicted their rather normative practice. This article describes the theory–practice gap identified and discusses the implications of the prevailing use of the normative approach in outdoor environmental education for the field. It calls for opening an in-depth debate on what, why, and how values belong in outdoor environmental education practice.
Engineering genomic deletions and inversions in mouse ES cells using custom designed nucleases (Prot XX)

The recent development of custom designed nucleases, such as Zinc-Finger Nucleases (ZFN), Transcription Activator-Like Effector Nucleases (TALENs) and the Clustered Regularly Interspaced Short Palindromic Repeat Associated system (CRISPR/Cas9) has opened up exciting opportunities to edit genomes in a wide range of organisms (Joung and Sander, 2013 for review). Knocking out proteincoding genes can be easily achieved by using just one pair of such dimeric nucleases, to target the first coding exon, thereby introducing short indels that result in a translational frameshift. Several reports have also demonstrated the possibility to target larger genomic rearrangements by using two pairs of nucleases (Carlson et al., 2012; Gupta et al., 2013; Lee et al., 2011). Although homologous recombination mediated genetic engineering is feasible in some systems, such as mouse embryonic stem cells, this approach requires multiple steps, including the selection of drug-resistant clones, and can be laborious depending on the target and nature of the targeting. This approach is being rapidly superceded by the advent of custom ZFN, TALEN and CRISPR/Cas9 technologies, which enable the extremely rapid and efficient disruption of not only coding, but also non-coding elements, by creating deletions, or by changing local genomic organization by creating inversions.
Phil´eas: Anomaly Detection for IoT Monitoring

A growing number of private companies and public admin-istrations is adopting Internet of Things (IoT) technologies to monitor resources, spaces, activities and events. Ensuring the required levels of quality of service and security is a key aspect in managing a service based on IoT. We introduce Phil´eas, a joint project between Degetel Belgium and the IRIDIA laboratory of the Universit´e Libre de Bruxelles to develop a framework to analyze the activity of IoT systems and to identify possible issues via anomaly detection. In this paper we describe our framework, and we present two real cases that have been tackled using this framework.
Memetic Optimized Expectation Maximization Clustering for Energy Effective Data Congregating in WSN

: Clustering is a problem to be resolved for effectively aggregate and transfer information in WSN. Cluster head performs a significant part in the cluster for data accumulation. The energy in wireless network is quite limited. In order to achieve energy efficient data gathering with minimum delay in WSN, a new model called Memetic Optimized Expectation Maximization Clustering (MOEMC) is proposed. The MOEMC model at first computes residual energy for all sensor nodes in network. After that, MOEMC Model designs Dice Similarity Based Expectation Maximization (DS-EM) Clustering algorithm in order to efficiently construct different number of clusters by means of residual energy level. MOEMC Model then employs Annealed Flip Memetic Optimization (AFMO) algorithm to attain enhanced cluster head selection in WSN. The Memetic Optimization algorithm selects an optimal cluster head according to fitness function evaluation for gathering data from its member. Finally, gathered data are routed to sink node via optimal cluster heads in WSN. Hence, MOEMC Model exploit the network lifespan as well as energy proficiency. The MOEMC Model conducts simulation work using various metrics with reverence to a number of data packets and sensor nodes such as Data delivery rate, Packet loss rate, Delay and Energy Usage. From the results, the proposed MOEMC Model achieves energy efficient data gathering process with minimum Packet loss rate and Energy Usage in the wireless sensor networks.
OCCURRENCE OF SOME MAASTRICHTIAN DINOFLAGELLATE CYSTS FROM THE UPPER CRETACEOUS SEDIMENTS OF THE BIDA BASIN, NIGERIA: IMPLICATIONS FOR AGE AND PALEOENVIRONMENTS

Lithostratigraphic and palynological studies of the shale facies of the upper Cretaceous Patti Formation, southeastern Bida Basin, Nigeria have allowed the determination of the paleoenvironment and age of the sediments. Marine dinocysts assemblage and terrestrial pollen and spores (relatively more abundant) are well preserved in the sections. The marine dinocysts include diverse species of the genera Dinogymnium, Senegalinium, Andalusiella and Spiniferites. This assemblage reflects influence of shallow marine process during sedimentation of the shales and they provide, for the first time, incontrovertible evidence of the presence of marine condition in southeastern part of the Bida Basin. A Maastrichtian age is suggested for the Shale facies of the Patti Formation in the study area based on the occurrence of well known Maastrichtian marine dinoflagellates Dinogymnium acuminatum, Dinogymnium digitus, Senegalinium bicavatum, Senegalinium psilatum and Paleocystodinium australinium. Associated with these forms also are some Maastrichtian spores and pollen; Buttinia andreevi, Cristaeturites cristatus, and Retidiporites magdalenensis. This study therefore, is considered significant as it also confirms marginal marine connection between the Anambra and Bida basins and consequently is a proof that the later formed a part of the links between the Tethys sea and the south Atlantic sea in the Maastrichtian time.
Lightweight Secure Communication Considering Network Path Reliability in IPv6 Wireless Sensor Network

- In recent years, IPv6 wireless sensor networks have been widely spread in various fields including IoT environments, because of the development of low-power sensor devices and wireless communication technologies. However, on these sensor networks, it is difficult to use secure communication technologies that can become large overhead, due to power saving of the wireless nodes is important. As one approach to deal with this problem, a method of focusing on Nonce which is one element of security, and sepa-rating it from secure communication is proposed, though this method can be used only when the reliability of communication ensured. Therefore, it remains a problem that not suit in environments such as wireless sensor networks where the reliability of communication is not ensured, since multi-hop networks and the like is used. In this paper, we propose a Nonce truncation method that can deal with such environments. Our method is implemented on the nodes that establish secure communication, and transfer information of about several bits that can estimate the Nonce associated the ciphertext as the truncated Nonce value. We also evaluated the effectiveness of our method by comparing the lifetime of the devices between our method and the previous method, and could confirm the effectiveness in a simple secure communication model.
China’s Economic Growth - 21 st Century Puzzle

China’s fast economic growth since 1960s was the result of gradual shift in its economic system, open door policy and its accession to the world trade organization. The institutional reforms and access to foreign markets has been followed by investment strategies expanded 45% of Chinese GDP during last 40 years. The consistent vertical economic growth has no precedent in the economic history of the world. China has increased its share in world trade from 0.5% in 1960 to 10% in 2010 and accumulated foreign exchange reserves of US$3.19 trillion by March 2013. It is not less than a miracle. The objective of this study is to investigate into the Chinese labour productivity and output in the short and long-run perspective to detect the real source of Chinese economic growth. Our study is spread over a period starting from 1962 to 2010 because of political and economic stability with minor crisis. The data was taken from China Bureau of National Statistics, IMF, World Bank and relevant research Journals and books. The variables included in this study are: labour productivity, investment, exports, R&D expenses, capital stock, open door policy, real exchange rate and US GDP. The VAR model proposed by Johansen (1988), Johansen and Juselius (1990,1994) and Hendry and Mizon (1993) was used to measure the nature of relations among the above variables. Different tests including unit root test were applied to test the stability of the model. The Econometric results show that international trade and investment in capital stock and R&D expenses by Chinese Government are the major determinants, which are responsible for enhancing labour productivity and output in the long-run, Similarly, real exchange rate appears as an important determinant to explain change in output in the long-run. US GDP has played no role in explaining Chinese output growth.
Evaluation of The Antimicrobial Effect of Aqueous Extracts of Plants or Fruit Dipping Solutions on Raw Meats Inoculated with Salmonella Enteritidis

6Departamento de Medicina y Zootecnia de Aves, FMVZ, Universidad Nacional Autonoma de Mexico (UNAM), Ciudad de Mexico 04510, Mexico. Alexis Rocha-Gonzalez1, Guillermo Tellez-Isaias2, Cesar Garcia-Casillas3, Omar Prado-Rebolledo3, Humberto Vaquera-Huerta4, Judith Alejandre Lopez5, Xochitl Hernandez-Velasco6 and Jesus E. Morales-Barrera5* Food Science & Nutrition Research ISSN 2641-4295 Research Article
Molecular understanding of new-particle formation from α-pinene between −50 and +25 °C

Abstract. Highly oxygenated organic molecules (HOMs) contribute substantially to the formation and growth of atmospheric aerosol particles, which affect air quality, human health and Earth's climate. HOMs are formed by rapid, gas-phase autoxidation of volatile organic compounds (VOCs) such as α -pinene, the most abundant monoterpene in the atmosphere. Due to their abundance and low volatility, HOMs can play an important role in new-particle formation (NPF) and the early growth of atmospheric aerosols, even without any further assistance of other low-volatility compounds such as sulfuric acid. Both the autoxidation reaction forming HOMs and their NPF rates are expected to be strongly dependent on temperature. However, experimental data on both effects are limited. Dedicated experiments were performed at the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber at CERN to address this question. In this study, we show that a decrease in temperature (from +25 to −50 ∘ C) results in a reduced HOM yield and reduced oxidation state of the products, whereas the NPF rates ( J1.7 nm ) increase substantially. Measurements with two different chemical ionization mass spectrometers (using nitrate and protonated water as reagent ion, respectively) provide the molecular composition of the gaseous oxidation products, and a two-dimensional volatility basis set (2D VBS) model provides their volatility distribution. The HOM yield decreases with temperature from 6.2 % at 25 ∘ C to 0.7 % at −50 ∘ C. However, there is a strong reduction of the saturation vapor pressure of each oxidation state as the temperature is reduced. Overall, the reduction in volatility with temperature leads to an increase in the nucleation rates by up to 3 orders of magnitude at −50 ∘ C compared with 25 ∘ C. In addition, the enhancement of the nucleation rates by ions decreases with decreasing temperature, since the neutral molecular clusters have increased stability against evaporation. The resulting data quantify how the interplay between the temperature-dependent oxidation pathways and the associated vapor pressures affect biogenic NPF at the molecular level. Our measurements, therefore, improve our understanding of pure biogenic NPF for a wide range of tropospheric temperatures and precursor concentrations.
Heat Kernel Asymptotics on Homogeneous Bundles

We consider Laplacians acting on sections of homogeneous ve ctor bundles over symmetric spaces. By using an integral representa tio of the heat semi-group we find a formal solution for the heat kernel diago nal that gives a generating function for the whole sequence of heat invaria nts. We argue that the obtained formal solution correctly reproduces the exact heat kernel diagonal after a suitable regularization and analytical co ntinuation.
Ranked Reward: Enabling Self-Play Reinforcement Learning for Bin packing

Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa , producing highly informative training data on the ﬂy. However, the self-play training strategy is not directly applicable to single-player games. Recently, several practically important combinatorial optimization problems, such as the traveling salesman problem and the bin packing problem, have been reformulated as reinforcement learning problems, increasing the importance of enabling the beneﬁts of self-play beyond two-player games. We present the Ranked Reward (R2) algorithm which accomplishes this by ranking the re-wards obtained by a single agent over multiple games to create a relative performance metric. Results from applying the R2 algorithm to instances of a two-dimensional and three-dimensional bin packing problems show that it outperforms generic Monte Carlo tree search, heuristic algorithms and inte-ger programming solvers. We also present an analysis of the ranked reward mechanism, in particular, the effects of problem instances with varying difﬁculty and different ranking thresholds.
Adaptive Force Tracking Control of Redundant Robot Manipulator for Task-space

 Abstract — This study considers the problem of controlling a redundant robot manipulator in the task space. We discuss the relation between the task space and joint space, and also introduce the knowledge of null space to derive the dynamic model of task and null space. Considering the model system of redundant robot manipulator that is a highly nonlinear dynamic model, with the interaction interference and uncertainty, we utilize the position and force controller design to overcome these problems by fuzzy neural network. Meanwhile, the stiffness coefficient of the environment is unknown. Therefore, the gradient descent method is used to estimate the stiffness coefficient of environment to achieve the adaptive force control. The stability analysis of the closed-loop system and the corresponding update laws are given by Lyapunov stability theorem. Finally, we employ our proposed control scheme in the redundant robot manipulator KUKA LWR4 with 7–DOF to illustrate the performance and effectiveness.
Classification of SAT Problem Instances by Machine Learning Methods

Efficient SAT solving is critical in many practical applications. State-of-the-art SAT solvers can solve SAT problems with more than 100.000 variables and millions of clauses. They have usually more than 10 options, and we might combine them. We need those options to get better runtime. Our experience shows that we can get significant speed up if we use the suitable options. Our goal is to create a neural network based system, which can select the best option configuration for the input SAT instance. As a first step, we created a system that can predict the type of a SAT problem. Our system has two main tools: CNFStats and the prediction tool. The CNFStats can compute several properties of the input SAT instance. For example, how many variables, clauses, unit, binary clause are in the input. How many horn clauses, so called black and white clauses are in the input. These properties generated by CNFStats are the input for the prediction tool. The prediction tool is a multi-layer perceptron neural network, which is able to do classification over SAT problem instances found on the SATLIB webpage, see: https://www.cs. ubc.ca/~hoos/SATLIB/benchm.html . The neural network is written in . NET from scratch. It is easy to configure by using a webpage. The user can give the number of layers, number of neurons, and the activation function, train it, test it and save it. In the prediction tool we use those 48 properties which is computed by the CNFStats tool. We used the half of the input problems from SATLIB to train the neural network. Classes were the same as in the webpage. The most successful neural network was using 2 hidden layers, 125 neurons in each hidden layer, the activation function was the sigmoid function (other activation functions did not perform well in these settings), 10.000 training runs with 0.033 learning rate. The accuracy was 95%. The second best neural network was the same but with 1 hidden layer and 250 neurons.
Methodology for Benchmarking Radio-Frequency Channel Sounders Through a System Model

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology – which is both practical and reliable – based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.