Miracle-dz/UniFAR-FTUs huggingface dataset:
A new test for the mean vector in high-dimensional data

For the testing of the mean vector where the data are drawn from a multivariate normal population, the renowned Hotelling’s T 2 test is no longer valid when the dimension of the data equals or exceeds the sample size. In this study, we consider the problem of testing the hypothesis : H  μ 0 and propose a new test based on the idea of keeping more information from the sample covariance matrix. The development of the statistic is based on Hotelling’s T 2 distribution and the new test has invariance property under a group of scalar transformation. The asymptotic distribution is derived under the null hypothesis. The simulation results show that the proposed test performs well and is more powerful when the data dimension increases for a given sample size. An analysis of DNA microarray data with the new test is demonstrated.
1 The use of biochemical measurements to identify pre-2 slaughter stress in pasture finished beef cattle 3

The study considers the relationship between pre-slaughter stressors and plasma 32 biomarkers in 488 pasture raised cattle across two experiments. The design aimed to test groups 33 consisting of steer only, heifer only, and mixed sex cattle under direct kill versus rested (14 days in 34 abattoir holding paddocks) protocols. In Experiment One, cattle were sourced from 4 farms, 35 transported by trucks and ships, on the same day. In Experiment Two, cattle were sourced from 4 36 farms, where a comparison was made between marketing via two commercial saleyards or direct farm 37 gate consignment to abattoir. Blood samples were collected at exsanguination for subsequent analyses 38 and relation to meat quality attributes. Muscle damage, as indicated by CK, is the metabolite most 39 correlated to ultimate pH and muscle glycogen concentrations. A two week rest period is effective at 40 lowering this enzyme and improving muscle glycogen concentration. Despite the cattle subjected to a 41 range of stress inducing treatments, we find that plasma biomarkers alone appear insufficient for use 42 as diagnostic stress indicators. 43
UPDATING INTERNATIONAL STRATEGY IN A MUNICIPALITY

The City of Lahti is in the process of renewing its international strategy so that it extends to 2020, the year of Finland’s Presidency of the Council of the European Union, using its 2011-2013 international strategy as the basis. The thesis serves as background research to assist in updating the City of Lahti international strategy. The study focuses on gathering reflections on the former strategy, forms a general understanding of the current state of internationalization in the City of Lahti, and provides insight into the views of the City of Lahti and its stakeholder organizations regarding the future of internationalization in Lahti. The thesis is a qualitative case study. The theoretical background of the study deals with strategy and strategic management focusing on the public sector and municipalities. Sources for the theoretical section include thematically related literature and online publications. For the empirical part, the City of Lahti international strategy 2011-2013 and the merger contract between the City of Lahti and the Municipality of Nastola are reviewed as background. The primary data were obtained by semi-structured interviews with respondents from the City of Lahti and its stakeholder organizations. Based on the interviews and the literature review, the study makes recommendations for the direction of development of the international strategy update. Furthermore, the current state of internationalization is examined using a SWOT analysis. The internal strengths and weaknesses of the City of Lahti and the external opportunities and threats for internationalization are identified. It is concluded that the City of Lahti should focus on developing the implementation and monitoring of its international strategy. Apart from that, the updated international strategy should continue along the same lines as the previous strategy. The existing strengths of the City of Lahti – environment, design, sports events, and culture – should be highlighted as focus areas like in the previous international strategy.
M – 21 MODELLING CASES OF LOW BIRTH-WEIGHT INFANTS WITH GENERALIZED LINEAR MIXED MODEL

Low Birth-Weight (LBW) is defined as a birth weight of a live-born infant of less than 2.500 grams regardless of gestational age. The causes of LBW cases can be grouped into two main causes: premature birth and case of small for gestational age (SGA). There are many risk factors that can induce directly or indirectly so that these causes may occur. Case of LBW is associated with infant mortality, infant morbidity, inhibited growth and slow cognitive development, also chronic diseases in later life. To suppress rate of LBW first we must estimate the rate correctly. Data of LBW comes from Indonesian Health and Demographic Survey (IDHS) 2012 which is divided into 3 groups: written (measured accurately), recall (measured inaccurately) and not weighed (not measured). Published national rate of LBW is 7.3% with provincial rates fall between 4.7-15.7 %. The estimation came from only 2 former groups without consideration of assumed difference accuracy on second group. To estimate the difference and the rate of the third group, Generalized Linear Mixed Model (GLMM) is used with live-born infants as observation units because observations from the same sampling unit tends to correlate due to multistage sampling design. The result of the model at α = 0.05 is highly-significant, with fixed effect variables that are statistically significant to the case of LBW are Estimated Size, Preceding Interval, Pregnancy Complication, Mother’s Age, Province and Education. Higher portion of variance component is on the G-side as a result of multistage sampling, with Household level has highest within variance. On the R-side, recall group data has higher variance than written group. It is an indication of lower accuracy of the birth weight data on this group. Based on the model, estimation of LBW rate including not weighed group result 7.96% slightly higher than direct estimate.
Nitrogen Dynamics in an Established Alfalfa Field under Low Biochar Application Rates

Sustainable nitrogen (N) management in agroecosystems is crucial for supporting crop production and reducing deleterious N losses. Biochar application with N-fixing legumes offers promise for increasing soil N retention and input. Strategic, low application rates (112 kg ha−1) of pine and coconut feedstock biochars were tested in an established alfalfa (Medicago sativa) field. Soil inorganic N and plant growth, N concentrations, and δ15N were monitored over a growing season to follow mineral N availability, and plant N uptake and sourcing. Microbial and gene abundance and enzyme activity were measured to assess the potential for N cycling processes to occur. Biochar application had minimal effects on measured parameters. However, significant temporal dynamics in N cycling and correlations between alfalfa δ15N and soil N availability indicate differing plant N sourcing over time. Our findings indicate that low application rates of biochar in established alfalfa fields do not significantly affect N cycling, and that managing alfalfa to maximize N fixation, for example by intercropping, may be a better solution to increase N stocks and retention in this system. To determine when biochar can be beneficial for alfalfa N cycling, we need additional research to assess various economically-feasible biochar application rates at different alfalfa growth stages.
The Severity of Hypoalbuminemia Predicts the Response to Intradialytic Parenteral Nutrition (IDPN) in Hemodialysis Patients

Background— Intradialytic parenteral nutrition (IDPN) is currently used infrequently to correct hypoalbuminemia in maintenance hemodialysis (MHD) patients. We hypothesized that severity of baseline hypoalbuminemia correlates with the success rate of the IDPN therapy in MHD patients. Methods— In a prospective and contemporary cohort of 196 hypoalbuminemic MHD patients who received IDPN through Pentec Health, predictors of IDPN response were examined using multivariate logistic regression. Results— Out of 196 hypoalbuminemic MHD patients, 134 subjects had severe hypoalbuminemia defined as baseline serum albumin <3.0 g/dL. The average period of IDPN therapy was 5.8±2.4 months. Baseline serum albumin was lower in MHD patients who responded to the IDPN (2.68±0.47 g/dL). The multivariate logistic regression analysis adjusted the associations for age, gender, diabetes, and IDPN time. The presence of severe hypoalbuminemia (serum albumin <3.0 g/dL) at baseline was associated with 2.5 time higher chance of responding to IDPN (95% confidence interval [CI]: 1.3–4.9, p=0.006). The same severe hypoalbuminemia was associated with 3.5 times increased likelihood of serum albumin correction by at least 0.5 g/dL (95% CI: 1.8–6.8, p<0.001). Conclusions— Improvement of hypoalbuminemia occurs in most hypoalbuminemic MHD patients who receive IDPN therapy. The likelihood and magnitude of the response to IDPN is associated with the severity of baseline hypoalbuminemia. These associations need to be verified in controlled trials.
Benchmarking the Performance of Hypervisors on Different Workloads

Many organizations rely on a heterogeneous set of applications in virtual environment to deliver critical services to their customers. Different workloads utilize system resources at different levels. Depending on the resource utilization pattern some workloads may be better suited for hosting on a virtual platform. This paper discusses a framework for benchmarking the performance of Oracle database workloads, such as Online Analytical Processing (OLAP), Online Transaction Processing (OLTP), Web load and Email on two hypervisors, namely Xen and VMware. Further, Design of Experiments (DOE) is used to identify the significance of input parameters, and their overall effect on two hypervisors, which provides a quantitative and qualitative comparative analysis to customers with high degree of accuracy to choose the right hypervisor for their workload in datacenters. Keywords—Virtualization; DOE; Full Factorial Design; Main Effect; Interaction Effect
Approximating Isoneutral Ocean Transport via the Temporal Residual Mean

Ocean volume and tracer transports are commonly computed on density surfaces because doing so approximates the semi-Lagrangian mean advective transport. The resulting density-averaged transport can be related approximately to Eulerian-averaged quantities via the Temporal Residual Mean (TRM), valid in the limit of small isopycnal height fluctuations. This article builds on a formulation of the TRM for volume fluxes within Neutral Density surfaces, (the “NDTRM”), selected because Neutral Density surfaces are constructed to be as neutral as possible while still forming well-defined surfaces. This article derives a TRM, referred to as the “Neutral TRM” (NTRM), that approximates volume fluxes within surfaces whose vertical fluctuations are defined directly by the neutral relation. The purpose of the NTRM is to more closely approximate the semi-Lagrangian mean transport than the NDTRM, because the latter introduces errors associated with differences between the instantaneous state of the modeled/observed ocean and the reference climatology used to assign the Neutral Density variable. It is shown that the NDTRM collapses to the NTRM in the limiting case of a Neutral Density variable defined with reference to the Eulerian-mean salinity, potential temperature and pressure, rather than an external reference climatology, and therefore that the NTRM approximately advects this density variable. This prediction is verified directly using output from an idealized eddy-resolving numerical model. The NTRM therefore offers an efficient and accurate estimate of modeled semi-Lagrangian mean transports without reference to an external reference climatology, but requires that a Neutral Density variable be computed once from the model’s time-mean state in order to estimate isopycnal and diapycnal components of the transport.
Inhibition–Disruption of Candida glabrata Biofilms: Symmetrical Selenoesters as Potential Anti-Biofilm Agents

Candida glabrata is one of the most prevalent pathogenic Candida species in dental plaque on tooth surfaces. Candida biofilms exhibit an enhanced resistance against most antifungal agents. Thus, the development of alternative more potent and effective antimicrobials is required to overcome this resistance. In this study, three novel fluorinated derivatives and nine selenoester compounds were screened as novel antifungal and antibiofilm agents against C. krusei, C. parapsilosis, and C. glabrata (N = 81 dental isolates). C. glabrata strains were susceptible only to fluorinated compounds while C. krusei, C. parapsilosis, and C. glabrata were susceptible to the action of the selenoesters. The evaluated symmetrical selenoester compounds presented very good antifungal activity against all the tested C. glabrata dental isolates (1–4 μg/mL of minimum inhibitory concentration-MIC). The most active compound (Se-5) was able to inhibit and disperse C. glabrata biofilms. These results demonstrated that selenoesters may be novel and promising biocide agents against C. glabrata clinical dental isolates.
THE EFFECTS OF RIVALRY ON CONSPICUOUS CONSUMPTION A Thesis

Two studies examined how interpersonal rivalries affect individuals’ consumption propensities. The current research suggests that comparing poorly to a rival is an aversive state that people seek to compensate for by signaling that they are at par with (or better than) their rivals. One means by which people may compete with their rivals is to engage in conspicuous consumption, that is, the acquisition of goods that signal status or reputation. In Study 1, participants who recalled a rival were significantly more likely to buy a conspicuous good that was not owned by the rival than were participants who recalled a cooperative peer. In Study 2, we found that participants who recalled a rival were significantly more willing to pay for a product than were control participants, but only if the product was associated with status. Furthermore, anticipated happiness upon acquiring the product drove rivals’ willingness to spend on status objects, suggesting that consumption may serve to repair the aversive state of faring poorly to a rival.
SUPEROXIDE DISMUTASE AND PEROXIDASE ACTIVITIES FROM ANTIOXIDATIVE ENZYMES IN Helianthus annuus L. ROOTS DURING Orobanche cumana Wallr. PENETRATION

Orobanche spp. (broomrapes) are holoparasitic angiosperms that subsist on the roots of dicotyledonous plants. Broomrapes are the most destructive weeds since they extract water and photosynthate directly from crop plants, causing crop yield and quality to reduce significantly. The occurrence of total superoxide dismutase and total peroxidase activities during broomrape–sunflower interaction were studied on three different cultivars (cv. Pioneer 4223, cv. Sanay and cv. Isera) of Helianthus annuus. Total superoxide dismutase activities increased on day 7 post-inoculation in cv. Pioneer 4223 and cv. Isera compared with control plants, whereas a decrease during all days in cv. Sanay was observed. In the variety of Orobanche-resistant cv. Pioneer 4223, the increases in the total superoxide dismutase activities were supported by the total peroxidase activities in the first 5 days, which indicate that the detoxification of the reactive oxygen species produced during the Orobanche penetration seems to be related to the Orobanche resistance in the sunflower. An increase has been determined in the total SOD activities from the first to the seventh day, but this increase was not found parallel to that in the total POX activities in cv. Isera. This situation may indicate sensitivity for cv. Isera. Total peroxidase activities increased for the first 7 days in cv. Sanay compared with control plants. Total superoxide dismutase and total peroxidase activities were not a statistically significant change during all days in treatment plants compared with control plants in cv. Sanay. This change in SOD activities may indicate sensitivity of both cv. Isera and cv. Sanay to broomrape unless they are treated with herbicide.
Structure and Oxide Ion Conductivity in (Ba0.5-

The molecular dynamics simulation has been used to investigate the structural and 13 transport properties of (Ba0.5-xSrx)La0.5InO3-δ (x=0, 0.1, 0.2) oxygen-ion conductor. The previous 14 studies reported that the ionic conductivity of Ba-doped LaInO3 decreases because Ba dopant forms 15 narrow oxygen path in the lattice, which could hinder the diffusion of oxygen ion. In this study, we 16 reveal the mechanism to improve the ionic conductivity by Ba and Sr co-doping on La site in LaInO3 17 perovskite oxide. The results show that the ionic conductivity of (Ba0.5-xSrx)La0.5InO3-δ increases with 18 increasing numbers of Sr ions, which oxygen diffusion paths including Sr ion have larger critical 19 radius than Ba ions. The RDF calculations showed the heights of peak in composition including Sr 20 ions is lower and broaden, so oxygen ions moved easily into other oxygen sites. 21
Predicting ground state configuration of energy landscape using graph neural network

We describe a framework for finding ground state configurations of an ensemble of energy landscapes sharing distinct topological features. We construct a model energy landscape ensemble with Ising model Hamiltonian where the interaction matrices are drawn from protein contact map data. Our approach leverages graph neural network to learn a mapping from an interaction matrix J to a ground state configuration, yielding guesses for the set of most probable configurations. Given these guesses, we show that ground state configurations can be searched much faster than with vanilla simulated annealing. We also demonstrate that the search performance is sensitive to the complexity of the Hamiltonian.
Wavelet-Powered Neural Networks for Turbulence

Modeling ﬂuid turbulence and explaining its associated spatio-temporal phenomena are notoriously difﬁcult tasks. Navier-Stokes (NS) equations describe all the details of the ﬂuid motions, but require accounting for unfeasibly many degrees of freedom in the regime of developed turbulence. Model reduction is a general methodology aiming to circumvent the curse of dimensionality. Originally driven by phenomenological considerations, multiple attempts to model-reduce NS equations got a new boost recently with development of Neural Networks (NNs), especially of the Deep Learning (DL) type, trained on the ground truth data, e.g. extracted from high-ﬁdelity Direct Numerical Simulations (DNS). However, early attempts of building NNs to model turbulence has also revealed its lack of interpretability as the most signiﬁcant shortcoming. In this paper we address the key challenge of devising reduced but, at least partially, interpretable model. We take advantage of the balance between strong mathematical foundations and the physical inter-pretability of wavelet theory to build a spatio-temporally reduced dynamical map which fuses wavelet based spatial decomposition with spatio-temporal modeling based on Convolutional Long Short Term Memory (C-LSTM) architecture. It is shown that the wavelet-based NN makes progress in scaling to large ﬂows, by reducing computational costs and GPU memory requirements.
Chapter 2-Balancing

Legged locomotion is a complex hybrid, nonlinear and highly dynamic problem. Animals have solved this complex problem as they are able to generate energy efficient and robust locomotion resulted from million years of evolution. However, different aspects of locomotion in biological legged systems such as mechanical design, actuation and control are still not fully understood. Splitting such a complicated problem to simpler subproblems may facilitate understanding and control of legged locomotion. Inspired from template models explaining biological locomotory systems and legged robots, we define three basic locomotor subfunctions: stance leg function, leg swinging and balancing. Combinations of these three subfunctions can generate different gaits with diverse properties. Basic analysis on human locomotion using conceptual models can result in developing new methods in design and control of legged systems like humanoid robots and assistive devices.
Learning to Control PDEs with Differentiable Physics

Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning. A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom. Existing methods that aim to control the dynamics of such systems are typically limited to relatively short time frames or a small number of interaction parameters. We show that by using a differentiable PDE solver in conjunction with a novel predictor-corrector scheme, we can train neural networks to understand and control complex nonlinear physical systems over long time frames. We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving multiple PDEs, including the incompressible Navier-Stokes equations.
Convolutional neural networks for energy and vertex reconstruction in DUNE

Measuring neutrino CP violation and mass hierarchy is currently one of the biggest challenges in particle physics. The DUNE neutrino experiment is the next-generation flagship neutrino program in the US designed to solve these problems. The DUNE detector uses liquid argon time projection chamber (LArTPC) technology, considerably improving the spatial resolution, neutrino detection efficiency and background rejection. However, reconstructing neutrino events with DUNE presents many challenges due to missing energy caused by argon impurities, nonlinear detector energy responses, invisible energy, hadron identities (mass), and overlaps between lepton and hadron interactions. One way of approaching this problem is using machine learning to reconstruct the neutrino events from pixel map images of interactions in the detectors. Here we present a regression convolutional neural network with a custom architecture to reconstruct neutrino energy and interaction vertices. For neutrino energy, we show considerable performance improvements in Monte Carlo simulations, compared with previous traditional energy reconstruction methods and initial results in interaction vertices.