Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon dioxide dynamics and fluxes in coastal waters influenced by river plumes

Globally, estuaries are a source of CO2 to the atmosphere, releasing a significant fraction of CO2 that the terrestrial biosphere absorbed from the atmosphere, and thus significantly reduce riverine terrestrial carbon fluxes to the ocean. Although several recent synthesis papers brought the flux lower from 0.5 to 0.25 Pg C yr−1 (Table 7.1), the flux is still quite significant in consideration of global carbon budget and pathways. Most importantly, the likely uncertainty in estimating air-water CO2 flux in coastal waters and wetlands is so large (±0.2 Pg C yr−1) that it becomes a major obstacle for a precise assessment of the CO2 flux at the air earth interface (be it terrestrial or ocean), thus making the effort for further improving the accuracy of estimating air-earth surface CO2 exchange fluxes less meaningful for climate change research – if such uncertainty in coastal systems is not tackled (Cai 2011). Previous works have largely focused on inner estuaries within coastal shorelines, and river plumes extending outside the shorelines largely are ignored for a number of reasons. Most importantly, river plumes are not limited geographically, and their areas are not defined clearly. They also have CO2 values closer to saturation with respect to the atmosphere. These properties are in stark contrast to inner estuaries, which are geographically limited by river banks or shorelines and virtually in all cases have CO2 values distinctly higher than the atmosphere. Hence river plumes are difficult to quantify and generalize in terms of an annual exchange flux of CO2 with the atmosphere. River plumes also reside directly on the continental shelves and thus often are included as part of shelves in global syntheses. Freshwater plumes of some large rivers (e.g., Amazon, Congo, Niger) often even extend beyond the continental shelf into the continental slope and ocean basin (Bianchi and Allison 2009). Because of the lack of reliable air-water CO2 flux estimates and the absence of global estimates of surface areas, river plumes have not been included in previous syntheses of global coastal water CO2 fluxes (Borges 2005; Borges et al. 2005; Cai et al. 2006; Chen and Borges 2009; Laruelle et al. 2010). This chapter first briefly reviews the state of knowledge of global (inner) estuarine CO2 flux and the existing problems. Then, we describe the distribution features of CO2 parameters in and around large river plumes over continental shelves to discuss factors that control CO2 distribution in areas dominated by river plumes. We also attempt to estimate air-sea CO2 flux in river plumes globally and provide a general view on whether inclusion of river plume in the estuarine CO2 flux assessments would change the currently best known global estuarine CO2 flux values.
Different iron storage strategies among bloom-forming diatoms

Significance The availability of the micronutrient iron has profound influences on primary productivity and biogeochemical cycling in the ocean. Here we describe iron storage mechanisms in diatoms, a predominant group of phytoplankton. We substantiate evidence with environmental data that diatoms have evolved to cope with iron limitation by storing iron in different ways. One iron storage mechanism, the protein ferritin, may be well suited for less frequent and pulsed iron inputs. As a result, the diatom community composition may shift to ferritin-using diatoms in potential scenarios where iron availability is reduced. Diatoms are prominent eukaryotic phytoplankton despite being limited by the micronutrient iron in vast expanses of the ocean. As iron inputs are often sporadic, diatoms have evolved mechanisms such as the ability to store iron that enable them to bloom when iron is resupplied and then persist when low iron levels are reinstated. Two iron storage mechanisms have been previously described: the protein ferritin and vacuolar storage. To investigate the ecological role of these mechanisms among diatoms, iron addition and removal incubations were conducted using natural phytoplankton communities from varying iron environments. We show that among the predominant diatoms, Pseudo-nitzschia were favored by iron removal and displayed unique ferritin expression consistent with a long-term storage function. Meanwhile, Chaetoceros and Thalassiosira gene expression aligned with vacuolar storage mechanisms. Pseudo-nitzschia also showed exceptionally high iron storage under steady-state high and low iron conditions, as well as following iron resupply to iron-limited cells. We propose that bloom-forming diatoms use different iron storage mechanisms and that ferritin utilization may provide an advantage in areas of prolonged iron limitation with pulsed iron inputs. As iron distributions and availability change, this speculated ferritin-linked advantage may result in shifts in diatom community composition that can alter marine ecosystems and biogeochemical cycles.
A Comparative Study of Bagging and Boosting of Supervised and Unsupervised Classifiers For Outliers Detection

Dang, Yue. M.S.E.E. , Department of Electrical Engineering, Wright State University, 2017. A Comparative Study of Bagging and Boosting of Supervised and Unsupervised Classifiers For Outliers Detection. The problem of outlier detection has received increasing attention recently because it plays a great role in many fields such as credit fraud detection, cyber security, etc. Machine Learning approach is an excellent choice for outlier detection due to its accuracy and efficiency. Outlier detection problem is unique due to the so-called classes imbalance: the inliers are extreme majority and the outliers are minority. Ensemble methods are popular in classification and regression task in practice to improve the performance of machine learning algorithms. Bagging and boosting are two common methods of them. In this thesis, we want to show the performance of bagging and boosting compared with base algorithms in outlier detection. First of all, some basic algorithms for outlier detection are described for both supervised and unsupervised methods. Next, theoretical analysis and strategies of ensemble are discussed. Furthermore, groups of experiments are conducted and the experiment results confirm the effectiveness of bagging and boosting methods for outlier detection problem.
CHALLENGES AND POSSIBILITIES IN HYDROPONICS: AN INDIAN PERSPECTIVE.

Shailesh Solanki 1 , Nitish Gaurav 1 , Geetha Bhawani 2 and * Abhinav Kumar 3 . 1. Department of Agriculture, Noida International University, Greater Noida. 2. Department of Chemistry, Noida International University, Greater Noida. 3. IILM College of Engineering and Technology, Department of Biotechnology, KP-II, GreaterNoida. Manuscript Info Abstract . Manuscript History
Effects of National Macroeconomic Variables and Political Condition on Dhaka Stock Exchange

National macroeconomic variables which are determined by economic policies taken by the authority and aggregated economic activities of individuals, has definitive infuence on a country’s stock market. Prime share market of Bangladesh, Dhaka Stock Exchange is also shaped by local macroeconomic situation. Additionally, country’s political condition might affect the market as well because as a developing economy our market face significant political instability. It is observed that the mean point value of the DSE general index was slightly lower for observations when the country goes through political instability than for observations when the political situation was stable. However, this difference was not statistically significant. Our investors seem to be quite indifferent to the country’s political condition and do not turn their back to the market just because the country was observing political unrest. Correlation analysis reveals that all the macroeconomic variables are moderately correlated with DGEN while money supply, remittance and IRS are significantly correlated. Money supply, remittance, inflation rate and exchange rate are positively related with the general index when interest rate spread and political unrest shows negative relationship. This relationship is justifiable by the economic fundamentals. Both money supply and remittance inject cash into the economy, which is supposed to be distributed in all sectors, and thus the stock market would naturally grow. Higher IRS means the banks and other financial institutions are better off by just lending money and would be less interested in investing in the capital markets. Two economic models (linear & binomial) are derived to explain this relationship and estimate the DGEN using the macroeconomic variables. Among the two derived models, the binomial one estimate the general index way better than the linear model (in terms of regression R-square) and is able to explain more than 80% of the fluctuations in Dhaka Stock Exchange where the linear model explains only 25% of the fluctuations. Additionally, another correlation analysis, which is carried out after dropping 5 observations when the market observed unusual ups & downs, shows stronger relationships for all variables. Both estimation models explain more of the fluctuations when derived from the engineered dataset. It can be assumed that external force(s) manipulated the market during that time. However, our study cannot draw any definitive conclusion regarding those 5 months of massive fluctuations because there is always a chance that this happened due randomness which is inherited by the stock market like any other system.
EmPoWeb: Empowering Web Applications with Browser Extensions

Browser extensions are third party programs, tightly integrated to browsers, where they execute with elevated privileges in order to provide users with additional functionalities. Unlike web applications, extensions are not subject to the Same Origin Policy (SOP) and therefore can read and write user data on any web application. They also have access to sensitive user information including browsing history, bookmarks, credentials (cookies) and list of installed extensions. They have access to a permanent storage in which they can store data as long as they are installed in the user's browser. They can trigger the download of arbitrary files and save them on the user's device. For security reasons, browser extensions and web applications are executed in separate contexts. Nonetheless, in all major browsers, extensions and web applications can interact by exchanging messages. Through these communication channels, a web application can exploit extension privileged capabilities and thereby access and exfiltrate sensitive user information. In this work, we analyzed the communication interfaces exposed to web applications by Chrome, Firefox and Opera browser extensions. As a result, we identified many extensions that web applications can exploit to access privileged capabilities. Through extensions' APIS, web applications can bypass SOP and access user data on any other web application, access user credentials (cookies), browsing history, bookmarks, list of installed extensions, extensions storage, and download and save arbitrary files in the user's device. Our results demonstrate that the communications between browser extensions and web applications pose serious security and privacy threats to browsers, web applications and more importantly to users. We discuss countermeasures and proposals, and believe that our study and in particular the tool we used to detect and exploit these threats, can be used as part of extensions review process by browser vendors to help them identify and fix the aforementioned problems in extensions.
The importance of information systems in inter-sectoral R&D collaboration: the case of the Australian co-operative research centre (CRC) program

Currently, in the process of creating innovation, some new forms of R & D collaboration are emerging, to fulfil the demands of the knowledge-based systems that must integrate industrial, scientific and commercial elements. These new forms are normally based on an integration of several participants from different institutions becoming involved in an inter-sectoral collaboration. This has been the subject of intensive study in most industrialized countries including Australia since 1980s. The Co-operative Research Centre is the Australian Government’s answer to this new emerging phenomenon. With the growing numbers of inter-sectoral collaborations especially aimed at innovation, the development of new theories in this field remains important and necessary. The current emerging theories of inter-sectoral collaboration are investigating how knowledge can be produced within such a complex environment while others are investigating how participants interact and communicate. So far, little investigation has been spent on the actual content of what is being transacted within such a complex inter sectoral collaborative R & D program, and what this (information) content and its transaction process may imply for the relationship among participants and finally how this may affect the performance of the entire collaboration. This thesis attempts to present an analysis of the above issues, taking Co-operative Research Centres (CRC) in Australia as case studies. From a theoretical perspective, the discussion in the thesis covers collaboration for innovation, information and the process of information transaction (including communication). The theoretical work also reviews the importance of the Internet in the process of information transaction in collaboration for innovation. The theoretical work pays special attention to the concepts of collaboration in the form of Mode-II Knowledge Production and the collaboration where the participants are specifically from University, Industry and Government agencies (the so-called ‘Triple Helix’). The main objective of the theoretical work is to develop a better model to describe the importance of information and information systems in inter sectoral R & D collaboration. This new model is called the ‘Newt’ model.
Take a Look over Privacy preserving Data Publishing Techniques

The process of extraction of useful data from a huge dataset is called as data mining. This process also includes the analysis of the extracted data. Interchange between data is needed nowadays. Recently, the process of interchanging and publishing data is become popular, leading to the need for data security and integrity. In data mining there are some challenges related to the security and privacy of the data. In many aggressive situations, the user’s data needs it’s privacy to be maintained. With the provision of privacy, people can share their social relations with each other. But sometimes, the users’ data is entangled by the attacker by compromising the privacy of the data. Thus, privacy preserving is an important process to be scrutinized. The influences of privacy preserving process are important in data mining. There are many ways for ensuring the privacy of the data. This paper gives an excellent survey on privacy preserving techniques. It also reviews the privacy preserving data publishing techniques.
Predicting microbial growth in a mixed culture from growth curve data

Significance We present a model-based approach for prediction of microbial growth in a mixed culture and relative fitness using data solely from growth curve experiments, which are easier to perform than competition experiments. Our approach combines growth and competition models and utilizes the total densities of mixed cultures. We implemented our approach in an open-source software package, validated it using experiments with bacteria, and demonstrated its application for estimation of relative fitness. Our approach establishes that growth in a mixed culture can be predicted using growth and competition models. It provides a way to infer relative strain or species frequencies even when competition experiments are not feasible, and to determine how differences in growth affect differences in fitness. Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current “gold standard” for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.
A Case study on How Clone Author and User Interact in Software Repository

In the software industry and OSS projects, it is said that code reuse could improve productivity and reliability of software development, and reduce development time. In many software development organizations, reuse of existing libraries and source code is recommended. Developers in such organizations are required to implement reusable source code and reuse existing reliable source code. On the other hand, source code reuse is especially difficult task because the reuse requires developers to understand the source code fully. In order to encourage such reuse, it is significant to analyze developers’ reuse behaviors in the existing projects. However, there exists few analyses about reuse behaviors for each developer. In a software development organization, developers usually belong to multiple projects. An existing research indicated that the situation of the reuse differs by developers, and a developer who recognizes merits of reuse often reuses. Therefore, in this paper, we propose a source code reuse analysis for each developer over two or more projects with using multiple software repositories. As a result, we show that source code reuse analysis in consideration of the difference between each developer is worthy to be conducted.
The Optimal Approximation Factor in Density Estimation

Consider the following problem: given two arbitrary densities $q_1,q_2$ and a sample-access to an unknown target density $p$, find which of the $q_i$'s is closer to $p$ in total variation. A remarkable result due to Yatracos shows that this problem is tractable in the following sense: there exists an algorithm that uses $O(\epsilon^{-2})$ samples from $p$ and outputs~$q_i$ such that with high probability, $TV(q_i,p) \leq 3\cdot\mathsf{opt} + \epsilon$, where $\mathsf{opt}= \min\{TV(q_1,p),TV(q_2,p)\}$. Moreover, this result extends to any finite class of densities $\mathcal{Q}$: there exists an algorithm that outputs the best density in $\mathcal{Q}$ up to a multiplicative approximation factor of 3. We complement and extend this result by showing that: (i) the factor 3 can not be improved if one restricts the algorithm to output a density from $\mathcal{Q}$, and (ii) if one allows the algorithm to output arbitrary densities (e.g.\ a mixture of densities from $\mathcal{Q}$), then the approximation factor can be reduced to 2, which is optimal. In particular this demonstrates an advantage of improper learning over proper in this setup. We develop two approaches to achieve the optimal approximation factor of 2: an adaptive one and a static one. Both approaches are based on a geometric point of view of the problem and rely on estimating surrogate metrics to the total variation. Our sample complexity bounds exploit techniques from {\it Adaptive Data Analysis}.
Akútne koronárne syndrómy u Ïien*

62 MoÈovská Z a spol. Akútne koronárne syndrómy u Ïien Cor Vasa 2006;48(2):62–67 „The ESC has tremendous potential, through its network of 49 countries, to pass on these crucial messages. By encouraging each of our National Cardiac Societies to take up the mission, there is great potential to increase the awareness of women’s cardiovascular disease patterns and directly improve the quality of care of women with heart disease and stroke across Europe”.
Adding Tightly-Integrated Task Scheduling Acceleration to a RISC-V Multi-core Processor

Task Parallelism is a parallel programming model that provides code annotation constructs to outline tasks and describe how their pointer parameters are accessed so that they might be executed in parallel, and asynchronously, by a runtime capable of inferring and honoring their data dependence relationships. It is supported by several parallelization frameworks, as OpenMP and StarSs. Overhead related to automatic dependence inference and to the scheduling of ready-to-run tasks is a major performance limiting factor of Task Parallel systems. To amortize this overhead, programmers usually trade the higher parallelism that could be leveraged from finer-grained work partitions for the higher runtime-efficiency of coarser-grained work partitions. Such problems are even more severe for systems with many cores, as the task spawning frequency required for preserving cores from starvation grows linearly with their number. To mitigate these problems, researchers have designed hardware accelerators to improve runtime performance. Nevertheless, the high CPU-accelerator communication overheads of these solutions hampered their gains. We thus propose a RISC-V based architecture that minimizes communication overhead between the HW Task Scheduler and the CPU by allowing Task Scheduling software to directly interact with the former through custom instructions. Empirical evaluation of the architecture is made possible by an FPGA prototype featuring an eight-core Linux-capable Rocket Chip implementing such instructions. To evaluate the prototype performance, we both (1) adapted Nanos, a mature Task Scheduling runtime, to benefit from the new task-scheduling-accelerating instructions; and (2) developed Phentos, a new HW-accelerated light weight Task Scheduling runtime. Our experiments show that task parallel programs using Nanos-RV --- the Nanos version ported to our system --- are on average 2.13 times faster than those being serviced by baseline Nanos, while programs running on Phentos are 13.19 times faster, considering geometric means. Using eight cores, Nanos-RV is able to deliver speedups with respect to serial execution of up to 5.62 times, while Phentos produces speedups of up to 5.72 times.