Miracle-dz/UniFAR-FTUs huggingface dataset:
PERFORMANCE INVESTIGATION OF OPTICAL AMPLIFIER FOR 80 ×10 GBPS WDM SYSTEM IN THE PRESENCE AND ABSENCE OF NON- LINEARITIES

In this paper, we have investigated Wavelength Division Multiplexed (WDM) systems at 10 Gb/s for 80 channels without any inline amplification. Further, the proposed system is analyzed for Erbium Doped Fiber Amplifier (EDFA), Semiconductor Optical Amplifier (SOA) and Raman Amplifiers (RA) and the performance has been compared on the basis of transmission distance with and without nonlinearities. We analyzed the results in the terms of maximum output power and Quality factor. It is observed that EDFA is best suitable amplifier due to its high gain performance.
More Accurately Estimating Panel Data Models: A Comparison of Quasi- vs. First-differencing Approaches

Abstract: This study examines whether the consistency and the efficiency can be improved in estimating panel data models when the sample size is realistically finite. In various cases we compare the quasiand the first-differencing approach which are designed to control for unit-specific effects. The equations transformed by the two approaches are estimated by instrumental variables. Empirical results from simulated data support that the quasi-differencing approach dominates the first-differencing one in estimating and testing panel data models, particularly for small-sized samples.
Assessment of How the Use of Biometric Voter Registration (BVR) System Affect Independent Electoral and Boundaries Commission (IEBC) Employees Performance

DOI: 10.21276/sjebm.2018. 5.1. 4 Abstract: The main objective of this study was to assess how the use of Biometric Voter Registration (BVR) system affects Independent Electoral and Boundaries Commission (IEBC) employees‟ performance in Kenya. The study used descriptive survey research design. The target population of the study was the 203 permanent Independent Electoral and Boundary Commission employees working in Nairobi Region and the IEBC headquarter. The study used sampling formula recommended by Nassiuma (2000) to arrive at 98 of employees working in Nairobi Region and headquarters leaving out other regions because the researcher works at the headquarters in Nairobi where he easily co-ordinated data collection. Respondents were randomly picked to form the 98 sample. The researcher used close ended questionnaires based on Likert Scale as the main mode of data collection. The data was analyzed through the use of descriptive statistics and inferential statistics. The relationship between use of Biometric Voter Registration (BVR) system and employees performance was tested using a Pearson‟s Correlation. The study was important since Kenyans expressed complaints from the public about the electoral technology in the 2013 general election, the study therefore will analyze whether use of Biometric Voter Registration (BVR) system was important in enhancing employees performance. The study established that When the IEBC employees continue to upgrade their skills in the use of BVR technology in voters‟ management; there will be increase in their performance in terms of efficiency, work quality, timely delivery and high productivity.
Percieved Workplace Incivility: A Predictive Study of Emotional Regulation and Marital Status among Administrative Staff of Nigerian Universities

This study examined Emotional Regulation and Marital Status as Predictors of Perceived Workplace Incivility among Administrative Staff of Nigerian Universities. Conceptual model hypothesized that emotional regulation and marital status will both significantly predict perceived workplace incivility. The participants comprised two hundred and seventeen (217) workers who were drawn from a population of administrative staff of Nnamdi Azikiwe University, Awka and Chukwuemeka Odumegwu Ojukwu University, Igbariam both in Anambra State, Nigeria. They included 83 males and 134 females whose ages ranged from 27 to 58 years, with a mean age of 34.12yrs and standard deviation of 3.70. The method of sampling was simple sampling technique. Two instruments were used for the study namely; Incivility Scale by O’Reilly (1982) and Emotional Regulation Scale (ERS) by Gross and John. This study is a crosssectional survey research using predictive design and multiple regression analysis as appropriate design and statistics to analyze the data obtained from the field. The result confirmed that only emotional regulation significantly and negatively predicted workplace incivility at β = -2.54*, p < . 05, (n = 217). The finding implies that as emotional regulation of the employees improved, workplace incivility was reduced.
Errata for Wave Curves: Simulating Lagrangian water waves on dynamically deforming surfaces

E1 EFFECTIVE GRAVITY IN “PADDLE” SCENE The simulations of the “paddle” scene in [Skrivan et al. 2020] have a parameter set incorrectly. Instead of using our proposed effective gravity g∗, these examples only use the fixed gravity g. To rectify this mistake, the authors re-ran the simulations with the effective gravity enabled. We provide corrected figures here, and we provide corrected animations in the supplementary files accompanying this document. Figure E1 in this paper should replace Figure 8 in the original paper. It shows the difference between a low-resolution simulation with only 100k wave curve points, compared to a detailed simulation with 800k curve points. These images have fewer visible waves compared to the original paper, because the modified effective gravity causes different wave seeding and decay rates in the simulation. Figure E2 in this paper should replace Figure 9 in the original paper. It shows a simulation with effective gravity enabled and how it looks with only a single wavelength simulated. We do not see a significant qualitative difference between these simulations and those of the original paper. Figure E3 in this paper should replace Figure 12 in the original paper. It shows the comparison between our seeding and random seeding, with effective gravity enabled. The differences between the two simulations are still very clear — our method produces more waves that align with the flow, while random seeding produces random wave orientations before the geometric stretching of the flow naturally destroys poorly-aligned waves. The only difference between the seeding in the original paper and this one is the effective gravity term in the growth equation:
Causal inference on discrete data

Causal inference is one of the fundamental problems in science. To make absolute statements about cause and effect, carefully designed experiments are necessary, in which we consider representative populations, instrument the putative cause, and control for everything else. In practice, setting up such an experiment is often impossible, too expensive, or unethical. The only option then is to consider causal inference from observational studies where data has not been obtained in a controlled manner. A particularly interesting setting is to tell cause from effect between a pair of random variables X and Y given a sample from their joint distribution. For a long period of time, it was thought to be impossible to distinguish between causal structures X → Y and Y → X from observational data as the factorisation of the joint distribution is the same in both directions. In the past decade, researchers have made a long stride in this direction by exploiting sophisticated properties of the joint distribution. Most of the existing methods, however, are for continuous real-valued data. In the first part of the thesis, we consider bivariate causal inference on different discrete data settings—–univariate i.i. d. , univariate non-i.i. d. , and multivariate i.i. d. pairs. To this end, we build upon the principle of algorithmic independence of conditionals (AIC), which states that marginal distribution of the cause is algorithmically independent of conditional distribution of the effect given the cause. However, as Kolmogorov complexity is not computable, we approximate the AIC from above through the statistically sound Minimum Description Length (MDL) principle. On univariate i.i. d. and non-i.i. d. pairs, where causal mechanisms are simple, we use refined MDL codes that are minimax optimal w.r. t. a model class. We resort to crude MDL codes on a pair of multivariate i.i. d. variables. Although useful, saying that there exists a causal relationship from a set of variables towards a certain variable of interest does not always fully satisfy one’s curiosity; for a domain expert it is of particular interest to know those conditions that are most effective, such as the combinations of drugs and their dosages that are most effective towards recovery. Motivated by this problem, in the second part of this thesis, we consider discovering statistically reliable causal rules from observational data. Overall, extensive evaluations show that methods proposed in this thesis are highly accurate, and discover meaningful causations from real-world data.
Two-in-One Refinement for Interactive Segmentation

Deep convolutional neural networks are now mainstream for click-based interactive image segmentation. Most frameworks reﬁne false negatives and false positive regions via a succession of positive and negative clicks placed centrally in these regions. We pro-pose a simple yet intuitive two-in-one reﬁnement strategy placing clicks on the boundary of the object of interest. As boundary clicks are a strong cue for extracting the object of interest and we ﬁnd that they are much more effective in correcting wrong segmentation masks. In addition, we propose a boundary-aware loss that encourages segmentation masks to respect instance boundaries. We place our new reﬁnement scheme and loss formulation within a task-specialized segmentation framework and achieve state-of-the-art performance on the standard datasets - Berkeley, Pascal VOC 2012, DAVIS, and MS COCO. We exceed competing methods by 6 . 5% , 9 . 4% , 10 . 5% and 2 . 5%, respectively.
Partition Reﬁnement: a meaningful technique for Graphs

The partition refinement is a meaningful technique for applications in graphs. Currently, the applications in aim of the graph problems like chordal graphs [7], permutation graphs [6] and modular decomposition [3] focus on increasing the eﬃciency. In those propositions, the partition reﬁnement plays an important role for the resolutions. Therefore, we describe this eﬃcient technique which overcomes the constraints such as the unreasonable complexity and ambiguous understanding of certain applications that are used for graph problems in this paper. Through the illustration with various examples, we will show how eﬃcient the algorithm become according to the partition reﬁnement.
A Novel Switching Mode for High Power Factor Correction and Low THD

A new switching mode has been proposed to obtain high power factor and low THD in single stage AC-DC converter. The conventional voltage mode control in critical conduction mode distorts input current shape with poor THD in flyback topology. Once TRIAC dimmer is connected, visible flicker in the LED lamp is easily detected due to a lack of TRAIC holding current near the input voltage zero cross. The newly proposed method can shape the input current by providing a desired reference voltage so that low THD is obtained by ideal sinusoidal input current in case of no dimmer connection and flat input current performs good TRIAC dimmer compatibility in phase-cut dimming condition. To confirm the validity of the proposed method, theoretical analysis and experimental result from 8W dimmable LED lighting system are presented.
M2KD: Incremental Learning via Multi-model and Multi-level Knowledge Distillation

Incremental learning targets at achieving good performance on new categories without forgetting old ones. Knowledge distillation has been shown critical in preserving the performance on old classes. Conventional methods, however, sequentially distill knowledge only from the penultimate model, leading to performance degradation on the old classes in later incremental learning steps. In this paper, we propose a multi-model and multi-level knowledge distillation strategy. Instead of sequentially distilling knowledge only from the penultimate model, we directly leverage all previous model snapshots. In addition, we incorporate an auxiliary distillation to further preserve knowledge encoded at the intermediate feature levels. To make the model more memory efﬁcient, we adapt mask based pruning to reconstruct all previous models with a small memory footprint. Experiments on standard incremental learning benchmarks show that our method improves the overall performance over standard distillation techniques.
Distribution of major bacterioplankton groups in the Atlantic Ocean

Members of the prokaryotic picoplankton are the main drivers of the biogeochemical cycles over large areas of the world’s oceans. In order to ascertain changes in picoplankton composition in the euphotic and twilight zones at an ocean basin scale we determined the distribution of 11 marine bacterial and archaeal phyla in three different water layers along a transect across the Atlantic Ocean from South Africa (32.9°S) to the United Kingdom (46.4°N) during boreal spring. Depth profiles down to 500 m at 65 stations were analyzed by catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH) and automated epifluorescence microscopy. There was no obvious overall difference in microbial community composition between the surface water layer and the deep chlorophyll maximum (DCM) layer. There were, however, significant differences between the two photic water layers and the mesopelagic zone. SAR11 (35 ± 9%) and Prochlorococcus (12 ± 8%) together dominated the surface waters, whereas SAR11 and Crenarchaeota of the marine group I formed equal proportions of the picoplankton community below the DCM (both ~15%). However, due to their small cell sizes Crenarchaeota contributed distinctly less to total microbial biomass than SAR11 in this mesopelagic water layer. Bacteria from the uncultured Chloroflexi-related clade SAR202 occurred preferentially below the DCM (4-6%). Distinct latitudinal distribution patterns were found both in the photic zone and in the mesopelagic waters: In the photic zone, SAR11 was more abundant in the Northern Atlantic Ocean (up to 45%) than in the Southern Atlantic gyre (~25%), the biomass of Prochlorococcus peaked in the tropical Atlantic Ocean, and Bacteroidetes and Gammaproteobacteria bloomed in the nutrient rich northern temperate waters and in the Benguela upwelling. In mesopelagic waters, higher proportions of SAR202 were present in both central gyre regions, whereas Crenarchaeota were clearly more abundant in the upwelling regions and in higher latitudes. Other phylogenetic groups such as the Planctomycetes, marine group II Euryarchaeota and the uncultured clades SAR406, SAR324, and SAR86 rarely exceeded more than 5% of relative abundance.
Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks

Sum-product networks are expressive efﬁcient probabilistic graphical models that allow for tractable marginal inference. Many tasks however require the computation of maximum-a-posteriori conﬁg-urations, an NP-hard problem for such models. To date there have been very few proposals for computing maximum-a-posteriori conﬁgurations in sum-product networks. This is in sharp difference with other probabilistic frameworks such as Bayesian networks and random Markov ﬁelds, where the problem is also NP-hard. In this work we propose two approaches to reformulate maximum-a-posteriori inference as other combinatorial optimization problems with widely available solvers. The ﬁrst approach casts the problem as a similar inference problem in Bayesian networks, over-coming some limitations of previous similar translations. In addition to making available the toolset of maximum-a-posteriori inference on Bayesian networks to sum-product networks, our reformulation also provides further insight into the connections of these two classes of models. The second approach casts the problem as a mixed-integer linear program, for which there exists very efﬁcient solvers. This allows such inferences to be enriched with integer-linear constraints, increasing the expressivity of the models. We compare our reformulation approaches in a large collection of problems, and against state-of-the-art approaches. The results show that reformulation approaches are competitive.
Not all points are created equal - an anisotropic cost function for facial feature landmark location

In our experiments, we used the PyTorch library. The training and testing of our network were conducted on desktop with OS Mint 18, Intel i7 7700 v4 CPU, 8 GB RAM and NVIDIA GeForce RTX 2080ti 11GB card. When training networks we used weight decay=0.0005, momentum=0.9 and batch size of 8. Each model was trained for 50,000 iterations. We used ResNet50 [13] for all of our experiments and used image resolution 256×256. All the reported results are the average of 5 runs, in some cases we have also reported the standard deviation as well. To perform data augmentation, we followed the same augmentations of [12, 30] where we randomly rotated images [10,10] degrees. In addition, we also used random horizontal image flipping. Finally, we randomly injected Gaussian blur (σ = 1) to 50% of the training images. Codes: htt ps : //github.com/ f arshidrayhan−uom/ananisotropicloss
Turing instability for a attraction-repolsion chemotaxis system with logistic growth

In this paper, we investigate the nonlinear dynamics for an attraction-repulsion chemotaxis Keller-Segel model with logistic source term u1t = d1∆u1 − χ∇(u1∇u2) + ξ∇(u1∇u3) + g(u), x ∈ Td, t > 0, u2t = d2∆u2 + αu1 − βu2, x ∈ Td, t > 0, u3t = d3∆u3 + γu1 − ηu3, x ∈ Td, t > 0, ∂u1 ∂xi = ∂u2 ∂xi = ∂u3 ∂xi = 0, xi = 0, π, 1 ≤ i ≤ d, u1(x, 0) = u10(x), u2(x, 0) = u20(x), u3(x, 0) = u30(x), x ∈ Td(d = 1, 2, 3). Under the assumptions of the unequal diffusion coefficients, the conditions of chemotaxis-driven instability are given in a d-dimensional box Td = (0, π)d(d = 1, 2, 3). It is proved that in the condition of the unique positive constant equilibrium point wc = (u1c, u2c, u3c) of above model is nonlinearly unstable. Moreover, our results provide a quantitative characterization for the early-stage pattern formation in the model.
Music Stimuli Recognition from EEG Signals with EMOTIV EPOC Headset

It is well-known that, music stimuli have powerful emotion trigger effect. When people listening to music, music induces motor system activities in their brain. Therefore, music can be used as a potential stimulus in electroencephalogram (EEG) based emotion research. The goal of emotion recognition is to explore how different kinds of stimuli (e.g. music) from the world around us influence our brain waves. In previous works, the determination of emotional states has based on subjects’ feedback. However, this approach is unreliable almost in all cases because emotional states are changing rather slowly and they are equivocal. In this study we try to recognize music-induced electroencephalogram patterns by shallow artificial neural network from the popular EMOTIV EPOC+ sensor’s signals. This article presents the data acquisition conditions; the efficiency of the neural network with different hyper-parameters; and the effectiveness of EMOTIV EPOC+ over the Neurosky Mindwave device.
Shrub-resident arthropods in an Eremophila and Senna–dominated shrubland

Though shrub-encroached landscapes are viewed by some as ‘ecological deserts’, we know little of the biodiversity that they support. We investigated the shrub-resident arthropods in a turpentine (Eremophila sturtii)-silver cassia (Senna artemisioides) shrubland in eastern Australia, and how fine-scale shrub density affects these communities. We found that turpentine supported six times more arthropods than silver cassia (Hemiptera, Psocoptera and Collembola), as well as a distinct species assemblage of Hemipterans. Fine-scale shrub density also affects the Hemipteran community, particularly on silver cassia. We have shown that shrub-encroached landscapes support healthy arthropod communities, which are structured by shrub species and fine-scale patterns of shrub density.
IMPORT TARIFFS, EXPOSURE TO INTERNATIONAL TRADE, AND FIRM-LEVEL PRODUCTIVITY: EVIDENCE FROM BRAZILIAN INDUSTRIAL FIRMS

Studies integrating trade policy and firms’ exposure to international trade as determinants of firms’ TFP are scarce. We contribute to fill in this gap. We focus on Brazilian manufacturing and mining firms in 2000-2008, when this economy experienced a slow trade liberalization process. However, we still obtain productivity improvements from trade liberalization. We also find that the effects of import tariff reductions spread among all firms. In addition, using Effective Rates of Protection instead of input and output tariffs is misleading and it is also relevant controlling for Exchange Rates. Lastly, results are consistent with learning-by-exporting and learning-by-
Speeding Up SpMV for Power-Law Graph Analytics by Enhancing Locality & Vectorization

Graph analytics applications often target large-scale web and social networks, which are typically power-law graphs. Graph algorithms can often be recast as generalized Sparse Matrix-Vector multiplication (SpMV) operations, making SpMV optimization important for graph analytics. However, executing SpMV on large-scale power-law graphs results in highly irregular memory access patterns with poor cache utilization. Worse, we find that existing SpMV locality and vectorization optimizations are largely ineffective on modern out-of-order (OOO) processors—they are not faster (or only marginally so) than the standard Compressed Sparse Row (CSR) SpMV implementation. To improve performance for power-law graphs on modern OOO processors, we propose Locality-Aware Vectorization (LAV). LAV is a new approach that leverages a graph’s power-law nature to extract locality and enable effective vectorization for SpMV-like memory access patterns. LAV splits the input matrix into a dense and a sparse portion. The dense portion is stored in a new representation, which is vectorization-friendly and exploits data locality. The sparse portion is processed using the standard CSR algorithm. We evaluate LAV with several graphs on an Intel Skylake-SP processor, and find that it is faster than CSR (and prior approaches) by an average of 1.5x. LAV reduces the number of DRAM accesses by 35% on average, with only a 3.3% memory overhead.
Economic Uncertainty and Countercyclical Export Prices

I develop a monopolistic competition model of trade with firm heterogeneity and endogenous mark-ups. The model shows that exporters raise prices thus mark-ups in response to rising economic volatility in export markets. I then examine the hypotheses and investigate the impact of uncertainty on export prices using a panel dataset of Chinese firm exports in 2000-2010 and novel time-variant measures of market specific uncertainty. The empirical findings are broadly consistent with the theoretical predictions.