Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Restoration from Uniform Motion Blur and Poissonian Noise

Image restoration is to improve the quality of a digital image which has been degraded due to various phenomena like blur, noise. An image with uniform motion blur and Poisson noise is considered. Images acquired at different exposure times are obtained and SNR values for each image are calculated. The blurred and noisy images are restored using the pseudo-inverse filter and SNR values are calculated. The images are then analyzed using the Fourier analysis. The RMSE (Root Mean Square Error) values are obtained. The exposure time at which the restoration performance is better, is considered to be the optimal exposure time which results in the better image quality.
Comparative Analysis of Different Data Dissemination Techniques based Genetic Algorithm and Fuzzy in Vehicular Adhoc Networks (VANETs)

In today‟s world Vehicular adhoc network done promising job towards public safety and provide important element to the transport facility. A Vehicular adhoc network is a new technology which has garnered enormous attention in recent years.VANETs is a special class of MANETs which uses vehicles as a mobile node. It uses the Intelligent transportation system in which vehicles can communicate with each other to avoid large number of problems such as real-time traffic problem, parking availability problem etc. The communication among the vehicles is at greater risk because the messages are broadcasted by wireless channel and vehicles move with high mobility. VANET does not have any fixed infrastructure Data dissemination is the tough job among these vehicles. While driving, a large amount of data and information are accessible to everyone. Many attractive applications over vehicular ad hoc network (VANETs) need data to be transmitted to the remote destinations through multiple paths, but some unique characteristics of VANETs incur unstable data delivery performances. Data dissemination in VANETs is more challenging because vehicles are highly mobile. Efficient data dissemination to a desired number of receivers in a vehicular ad hoc network (VANET) is a new issue and a challenging one considering the dynamic nature of VANETs. To overcome such situation and achieve efficient data dissemination among these vehicles different techniques are used. This paper represents a simple and robust dissemination technique that efficiently deals with data dissemination where the density of roadside base stations and vehicles distribution are both high. This technique divides the users in two categories premium user as well as free users. This paper illustrates three schemes such as fuzzy inference system, genetic algorithm scheme and hybrid of fuzzy inference and genetic algorithm. Two types of users have been taken in this paper.
Current Plant Biology

Duplications are one on the main evolutionary forces in angiosperm, especially in Poaceae . A large number of genes involved in various metabolisms and pathways originate from such duplications (whole genome, segmental or single gene). However, to detect such duplication may be complicated, costly and generally requires heavy human and material investments. Here, we propose an alternative approach for detecting putative recent segmental duplications in haploid or diploid homozygous organisms based on NGS data. We rely on abusive mappings of paralogous sequences that increase apparent heterozygous points at a given locus to identify such duplicated genomic regions. We test our tool on simulated data, then on true rice genomic sequences and were able to identify about 200 candidate duplicated genes in African rice ( Oryza glaberrima ) lineage compared to Asian one ( O. sativa ). © 2017 The Authors. Published by Elsevier B.V. This is
Measuring the Acceptable Word Error Ra Transcrip

The increased availability of broadband connections has recently led to an increase in the use of Internet broadcasting (webcasting). Most webcasts are archived and accessed numerous times retrospectively. One of the hurdles users face when browsing and skimming through archives is the lack of text transcripts of the audio channel of the webcast archive. In this paper, we proposed a procedure for prototyping an Automatic Speech Recognition (ASR) system that generates realistic transcripts of any desired Word Error Rate (WER), thus overcoming the drawbacks of both prototypebased and Wizard of Oz simulations. We used such a system in a study where human subjects perform question-answering tasks using archives of webcast lectures, and showed that their performance and perception of transcript quality is linearly affected by WER, and that transcripts of WER equal or less than 25% would be acceptable for use in webcast archives.
eScholarship@UMassChan eScholarship@UMassChan

Background: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). Objective: This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient’s mortality using their longitudinal EHR data. Methods: We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient’s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians’ input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. Results: Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (<0.86). In addition, physicians’ agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. Conclusions: Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality. (J Med
POLICIES FOR REDUCTION OF GREENHOUSE GASES EMISSION AND THEIR COSTS AND OPPORTUNITIES FOR THE BRAZILIAN INDUSTRY

Brazil has taken an active stance in the Climate Change Negotiations, confirming goals to reduce its greenhouse gas emissions (GHG) and proposing sectoral mitigation plans. Recent Brazilian industrial policies attempt to accelerate the industrial growth and, among other goals, develop a more efficient industry in terms of energy use. However, typical mechanisms of mitigation policies, such as carbon pricing, can act in a counterproductive way against the incentives of the industrial policy. In this paper we fill a gap in Brazilian literature, estimating the impact of policies to reduce emissions in Brazilian industry, imposing caps to the emissions (CAP scenario) or carbon markets (CAP-ANDTRADE scenario). The results show the importance of sectoral considerations and the design of mechanisms in the formulation of mitigation policies. The CAP scenario makes productions costs more expensive for important sectors in the investment composition (cement, steel, non-metallic) and relatively benefits consumer goods sectors (textiles, clothing and footwear). The CAP-AND-TRADE scenario can achieve the same emission reduction goals with less adverse effects.
Meets Processing Power : Large-Scale Computational Resources for BRIMS

The sophistication of computational cognitive architectures has opened the door to model development across a range of human activity. However, a continuing challenge for model developers is validating the model both by exploring the range of a model’s behavior and by optimizing mechanisms, knowledge, and parameters to best account for human cognition and performance. We have been developing an infrastructure to facilitate this process that takes advantage of high performance computing resources and technologies to allow for more ambitious model development and validation efforts. This work involves a combination of increased computational power and increased sophistication in running models using available resources. Examples of the promise of this work are presented, along with current areas of emphasis and future directions. 1. Combinatorics in Cognitive Architecture and Model Development Cognitive architectures (e.g., ACT-R, Soar) are intended to serve an integrating and unifying purpose within the cognitive science community. The goal is to bring together what are typically isolated mathematical and computational theories of the many components of the human perceptual, cognitive, and motor systems into a unified account of the entire complex, integrated system. This is a fantastically ambitious scientific objective because implementing the computational mechanisms, representations, and processes that replicate and explain human performance across the full range of human experience requires simultaneous theorizing at three levels: overarching architectural design and control mechanisms, internal implementation of architectural sub-components/modules, and knowledge. Gray (in press) refers to these levels as Type 1, Type 2, and Type 3 theories of the human cognitive architecture. Each of these three levels of theorizing has its own associated quantitative and qualitative parameters, and each of those internal parameter spaces is very large. The implementation of any particular cognitive model involves committing to a location at the intersection of these three very large (perhaps infinite) theoretical state spaces and subsequently evaluating the extent to which that location in the joint state space is an appropriate account of human performance in that situation. If the account is deemed inadequate, then the search is on for a different combination of qualitative and quantitative parameters that will provide a better account. A thorough search and evaluation of even a modest portion of the total possible theoretical state space will require an unprecedented amount of computing power because of the combinatorics associated with searching
Assessment of line illumination for hyperspectral imaging by a Monte Carlo simulation

In hyperspectral imaging, a push broom method is very common. A sample is illuminated along a single line and reflected light is gathered from the same line. Effects of the illumination geometry on recorded intensity due to the colocation of illumination and data acquisition are investigated by simulating a push broom system geometry using Monte Carlo simulation. Multiple sets of realistic tissue optical parameters are used in the simulations, while illumination line width and its numerical aperture are varied. Values of recorded angular dependent radiance are compared, revealing observable effects of illumination line width. Some effects of illumination line numerical aperture that vanish at experimentally achievable illumination widths are also observed.
Title Dynamic ant : introducing a new benchmark for genetic programming in dynamic environments

In this paper we present a new variant of the ant problem in the dynamic problem domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.
Toward Enumerating the Chains of Maximum Length of Cambrian and m-eralized Cambrian Lattices

The Cambrian lattice corresponding to a Coxeter element c of An, denoted Camb(c), is the subposet of An induced by the c-sortable elements, and the m-eralized Cambrian lattice corresponding to c, denoted Camb(c), is defined as a subposet of the braid group accompanied with the right weak ordering induced by the c-sortable elements under certain conditions. Both of these families generalize the well-studied Tamari lattice Tn first introduced by D. Tamari in 1962. S. Fishel and L. Nelson enumerated the chains of maximum length of Tamari lattices. In this dissertation, I study the chains of maximum length of the Cambrian and m-eralized Cambrian lattices, precisely, I enumerate these chains in terms of other objects, and then find formulas for the number of these chains for all m-eralized Cambrian lattices of A1, A2, A3, and A4. Furthermore, I give an alternative proof for the number of chains of maximum length of the Tamari lattice Tn, and provide conjectures and corollaries for the number of these chains for all m-eralized Cambrian lattices of A5.
A SURVEY OF LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING

Cloud Computing is performing computing using the internet facility. It provides shared pool of configurable resources. The Computing is performed as on demand of the user. The user can scale up or scale down the resources. Cloud Computing provides various services to the end users such as Iaas, PaaS, SaaS. The Cloud Computing Load Balancing algorithms can be applied in Static, Dynamic and Centralized environment. The paper compares and summarizes different static and dynamic load balancing strategies in cloud computing environment. Paper presents a comparative description between the different load balancing algorithms for cloud computing.
Environmental conditions and primary production in a Sahelian shallow lake (Lake Guiers, North Senegal)

This is a study of the environmental conditions and primary phytoplankton production in a Sahelian shallow lake of Senegal, West Africa. Environmental descriptors (nutrient, water transparency, temperature and hydrochemistry) and their effects on primary production were studied. Samples were collected monthly from March 2002 to February 2003 in 3 stations of the central part of water body. Climatic variables show a marked annual pattern following two dominant wind directions: NNE (North-North-East) and NNW (North- North-West). This cyclic evolution affects hydrobiological variables. In this central part of the lake, water body was not stratified with same measures between surface and bottom. Lake water was turbid with lower values during the maritime trade winds period characterized by high water temperatures and conductivity. Water pH was relatively higher than seven (07) associated with high contents of oxygen (approximately 90%) in all stations. The station Typha is marked by a low pH and high fluctuations in the rates of oxygen (20 to 120%). Nutrient concentrations were low, with high variability (from 0 to 30 μg.l -1 for DIN and from 0 to 18 μg.l -1 for PO 4 ). The primary production in Guiers Lake was high; it showed a vertical gradient with production of 2400 mgC.m -3 . days -1 in surface and a low of 200 mgC.m -3 days -1 at the bottom. In this shallow system, environmental conditions regulate the primary production. Keywords : Environmental descriptors, primary production, shallow lake, Senegal.
Approximating Minimum Bounded Degree Spanning Trees to within One of Optimal

In theM INIMUM BOUNDED DEGREESPANNING TREE problem, we are given an undirected graph with a degree upper bound Bv on each vertexv, and the task is to find a spanning tree of minimum cost which satisfies all the degree bounds. Let OPT be the cost of an optimal solution to this problem. In this paper, we present a polynomial time algorithm which returns a spanning tree T of cost at mostOPT anddT (v) ≤ Bv + 1 for all v, wheredT (v) denotes the degree of v in T . This generalizes a result of Furer and Raghavachari [8] to weighted graphs, and settles a 15-year-old conjecture of Goemans [10] affirmatively. The algorithm generalizes when each vertex v has a degree lower bound Av and a degree upper boundBv, and returns a spanning tree with cost at most OPT andAv − 1 ≤ dT (v) ≤ Bv + 1 for all v. This is essentially the best possible. The main technique used is an extension of the iterative rounding method introduced by Jain [12] for the design of approximation algorithms.
Development of a CMOS-compatible PCR chip: comparison of design and system strategies

In the last decade research in chips for DNA amplification through the polymerase chain reaction (PCR) has been relatively abundant, but has taken very diverse approaches, leaving little common ground for a straightforward comparison of results. Here we report the development of a line of PCR chips that is fully compatible with complementary-metal-oxide-semiconductor (CMOS) technology and its revealing use as a general platform to test and compare a wide range of experimental parameters involved in PCR-chip design and operation. Peltier-heated and polysilicon thin-film driven PCR chips have been produced and directly compared in terms of efficiency, speed and power consumption, showing that thin-film systems run faster and more efficiently than Peltier-based ones, but yield inferior PCR products. Serpentine-like chamber designs have also been compared with standard rectangular designs and with the here reported rhomboidal chamber shape, showing that serpentine-like chambers do not have detrimental effects in PCR efficiency when using non-flow-through schemes, and that chamber design has a strong impact on sample insertion/extraction yields. With an accurate temperature control (±0.2 °C) we have optimized reaction kinetics to yield sound PCR amplifications of 25 µl mixtures in 20 min and with 24.4 s cycle times, confirming that a titrated amount of bovine albumin serum (BSA, 2.5 µg µl−1) is essential to counteract polymerase adsorption at chip walls. The reported use of a CMOS-compatible technological process paves the way for an easy adaption to foundry requirements and for a scalable integration of electro-optic detection and control circuitry.
A Statistical Analysis of Rainfall Variability and Paddy Production Trend in Batticaloa District, Sri Lanka

Paddy production, in the district of Batticaloa, in Sri Lanka, experienced severe challenges due to the rainfall variability in the recent past. The main objective of this paper is to assess the rainfall variability and to identify the relationship between paddy production and rainfall, by means of statistical analysis. Rainfall variability was analyzed using the data of 100 years from 1918 to 2017, of Batticaloa district and the relationship of variables was identified using the data of 40 years from 1978 to 2017. This study adopted a trend analysis using nonparametric and parametric econometric techniques to evaluate the relationship between paddy production and rainfall using EViews 9.0 software. The model was evaluated using Ordinary Least Square (OLS) technique. Results revealed that the annual and seasonal rainfall showing a significant increasing trend. After 1957, rainfall had fluctuated with high variability and rainfall intensity had increased in some years, resulting in flood hazard. On the other hand, dry weather had resulted in long drought condition. Based on the regression results, positive relationship between rainfall and paddy production in Maha season was identified and it is also statistically significant at 1% confidence level. A unit increases in the rainfall in Maha season would increase the paddy production by 0.05 times. Moreover, the rainfall in Yala season negatively correlated with paddy production in Batticaloa. However, it is not statistically significant.
Journal of Contemporary Eastern Asia

Joseph S. Nye defined soft power as the power of attraction to affect the behavior of other states through the use of non-coercive instruments including culture, political values and foreign policy. Over the last two decades, environmental issues have grown in importance on the international agenda and become critical components of states’ foreign policy-making. This paper aims to analyze environmental foreign policy as a soft power instrument focusing on two major rising powers: China and India. Traditionally, China and India had been reluctant to make any commitments in the field. However, they have shown greater willingness to act in global environmental governance in the past decade. They started playing more active roles in global climate change negotiations and supported a number of initiatives. Their current rise in global environmental governance has even been praised by the international community as the Paris agreement case demonstrated. This study evaluates China’s and India’s recent efforts in global environmental governance with a focus on climate change negotiations linking their constructive position to their soft power potential. It is argued that environmental issues are used by these two states as foreign policy strategy to gain more influence in international politics. This study finds out that China’s climate-related environmental diplomacy has been more ambitious than that of India and thus has been closer to fulfill its potential as a soft power asset.
This paper is

Sketch algorithms have been extensively studied in the area of network measurement, given their limited resource usage and theoretically bounded errors. However, error bounds provided by existing algorithms remain too coarse-grained: in practice, only a small number of ﬂows (e.g., heavy hitters) actually beneﬁt from the bounds, while the remaining ﬂows still suffer from serious errors. In this paper, we aim to design a nearly-zero-error sketch that achieves negligible per-ﬂow error for almost all ﬂows. We base our study on a technique named compressive sensing. We exploit compressive sensing in two aspects. First, we incorporate the near-perfect recovery of compressive sensing to boost sketch accuracy. Second, we leverage compressive sensing as a novel and uniform methodology to analyze various design choices of sketch algorithms. Guided by the analysis, we propose two sketch algorithms that seamlessly embrace compressive sensing to reach nearly zero errors. We implement our algorithms in OpenVSwitch and P4. Experimental results show that the two algorithms incur less than 0.1% per-ﬂow error for more than 99.72% ﬂows, while preserving the resource efﬁciency of sketch algorithms. The efﬁciency demonstrates the power of our new methodology for sketch analysis and design.
Internal Waves in the East Australian Current

Internal waves, which drive most ocean turbulence and add “noise” to lower‐frequency records, interact with low‐frequency current systems and topography in yet poorly known ways. Taking advantage of a heavily instrumented, 14 month mooring array, internal waves in the East Australian Current (EAC) are examined for the first time. Internal wave horizontal kinetic energy (HKE) is within a factor of 2 of the Garrett‐Munk (1976) spectrum. Continuum internal waves, near‐inertial waves, and internal tides together constitute a significant percentage of the total velocity variance. Mode‐1 internal tide energy fluxes are southward and much smaller than energy times group velocity, consistent with reflection at the continental slope of incident waves generated from near New Caledonia and the Solomon Islands. Internal tide HKE is highly phase variable, consistent with refraction by the variable EAC. Mode‐1 near‐inertial wave energy fluxes are of comparable magnitude and are equatorward and episodic, consistent with generation by storms farther poleward. These processes are considered together in the complex environment of the EAC.