Miracle-dz/UniFAR-FTUs huggingface dataset:
IRRIGATION MANAGEMENT TO REDUCE NITROUS OXIDE EMISSIONS AND NITRATE LEACHING LOSSES

While the benefits of irrigation for dairy production are well established the environmental consequences are not well quantified. In particular how irrigation management practices affect losses of nitrous oxide (N2O) to the atmosphere and nitrate (NO3) to groundwater. We used the APSIM (Agricultural Production Systems Simulator) model to simulate the effect of six different irrigation management scenarios on N2O emissions from urine patches and non-urine areas on pasture of three different soil types. These were deep poorly drained (Otokia), deep well drained (Templeton) and shallow well drained (Eyre) soils. The effects of different climate and rainfall regimes were simulated using 20 years of data from two climate stations (Lincoln and Hororata, Canterbury). Simulation outputs included irrigation amounts, N2O emissions, NO3 leaching losses and pasture production. Soil type, urine and the timing of urine application had the greatest influence on the variation of N2O emission and NO3 leaching. Greatest N2O emissions were predicted from the Otokia soil, while emissions tended to be similar from the well-drained soils. More frequent irrigation resulted in the largest N2O emissions and NO3 leaching losses. Greatest NO3 leaching losses were predicted from the Eyre soil with the higher rainfall regime. Least were predicted from the Otokia soil with the lower rainfall. Pasture production was largely unaffected by irrigation management, except for the shallow Eyre soil when some loss of production was predicted from the two less frequent irrigation scenarios. Based on the model simulations and supporting field experiments, N2O emissions and NO3 leaching can be reduced without penalising production by irrigating less frequently and maintaining soil water deficits. The contribution of indirect N2O emissions (produced from leached NO3) will be greater from shallow well drained soils compared to deeper (poorly or well drained) soils.
VIBRATION SUPPRESSION WITH POSITIVE POSITION FEEDBACK

This study demonstrates the feasibility of a Modal Positive Position Feedback strategy in controlling the vibration of a multi-degree-of-freedom flexible structure. The control strategy generates its control forces by manipulating only the modal position signals of the structure to provide a damping action to undamped modes. This is in contrast to conventional modal controllers that rely in their operation on negative feedback of velocity. The strategy is very simple to design and implement as it designs the controller at the uncoupled modal level and utilizes simple filters to achieve the positive position feedback effect. The performance of the control system is determined in the time and frequency domains.
Gene replacement and retrieval with recombinant M13mp bacteriophages

We have developed an allele exchange system for shuttling sequences of DNA to and from their original chromosomal loci. Cloned segments of the histidine operon of Salmonella typhimurium and the lactose operon of Escherichia coli served as target sequences and were used to develop the system. Replacement and retrieval of target sequences used the phage M13mp vectors and proceeded through an M13 lysogen intermediate. The intermediates and products of allele exchange were characterized by genetic and hybridization analyses. Several unique properties of M13 lysogens were exploited to devise positive selections to detect integration and excision. These positive selections were used to manipulate phenotypically silent alleles.
Therapeutic Effect of Fimasartan in a Rat Model of Myocardial Infarction Evaluated by Cardiac Positron Emission Tomography with [18F]FPTP

We evaluated the efficacy of fimasartan on perfusion defects and infarction size in an animal model of myocardial infarction (MI), with echocardiography and positron emission tomography (PET) using a 18F-labeled phosphonium cation (5-[18F]-fluoropentyl-triphenylphosphonium salt, [18F]FPTP) as a mitochondrial voltage sensor for myocardial imaging. We induced MI in 33 rats by ligation of the left coronary artery, and checked their cardiac PET image using [18F]FPTP for evaluation of myocardial perfusion. Rats were grouped into 3 groups according to their administered drugs: no drug (n=11), fimasartan 3 mg/kg (n=10), and fimasartan 10 mg/kg (n=12). Each designated drug was administered for 4 weeks, and follow-up PET and histologic examinations were done. In the PET analysis, a perfusion defect size was markedly improved in fimasartan 10 mg/kg group (35.9±7.0% to 28.4±6.9%, p<0.001), whereas treatment with fimasartan 3 mg/kg induced only an insignificant reduction of perfusion defect size (35.9±7.9% to 33.9±7.3%, p=0.095). Using 2, 3, 5-triphenyltetrazolium chloride staining, infarction size was the largest in the control group (36.5±8.3%), and was insignificantly lower in the fimasartan 3 mg/kg group (31.5±6.5%, p for the difference between the control group=0.146) and was significantly lower in the fimasartan 10 mg/kg group (26.3±7.6%, p for the difference between the control group=0.011). PET imaging using a 18F-labeled mitochondrial voltage sensor, [18F]FPTP, is useful in evaluation and monitoring of myocardial perfusion states, and treatment with fimasartan decreases the infarction size in animal MI model.
Comparative evaluation of the efficiency of canine retraction using modified Marcotte and T-loop retraction springs – A split-mouth, randomized clinical trial

OBJECTIVE: The aim of this study was to compare the efficiency of canine retraction using modified Marcotte and T-loop retraction springs. MATERIALS AND METHODS: Twenty-four subjects with a treatment plan involving bilateral extractions of upper first premolars were included in the study. A split-mouth design was used to randomly allocate opposite quadrants to either modified Marcotte spring (MS) or T-loop spring (TLS) for canine retraction. Cephalometric radiographs and models were used to measure the rate of canine retraction (primary outcome) and compare the angular/rotational changes in the canines and anchorage loss in molars (secondary outcomes) following retraction. Subjective assessment of pain and discomfort was compared using visual analog scale (VAS). Paired and independent t-tests were used to evaluate changes. RESULTS: The mean amount and rate of retraction of the canine were found to be significantly higher for MS (3.56 ± 0.696 mm and 1.188 ± 0.232 mm, respectively) when compared with TLS (2.125 ± 0.472 mm and 0.71 ± 0.157 mm, respectively). Distopalatal rotation of the canine was also significantly lesser for MS (2.42°±1.868°) than TLS (5.65° ±2.84°, P < 0.001). However, the amount of anchorage loss and canine tipping were significantly higher for MS. Statistically significant higher values in the VAS score for TLS indicated greater discomfort. CONCLUSION: MS exhibited increased rate of retraction and rotation control when compared with TLS during sectional canine retraction. Patient comfort was better for MS as evidenced by the VAS scores. However, the amount of tipping and anchorage loss obtained with MS were significantly higher than TLS.
Do facial morphology, posture and function change following glossectomy? A systematic review

OBJECTIVES: To systematically investigate and critically appraise the quality of the currently available literature regarding the morphological, postural, and functional changes observed in individuals following glossectomy. MATERIALS AND METHODS: A search without restrictions in eight databases (including grey literature) and hand searching from inception until March 2018 was performed. Data on morphological, postural, and functional changes after glossectomy were reviewed. Methodological quality was evaluated using the risk of bias in nonrandomized studies of intervention tool. RESULTS: Out of 835 initially identified unique records, only three articles following patients for 1 year after glossectomy fulfilled the selection criteria. Overall, no significant morphological, postural, and functional changes were observed. Only the distance between the dorsum and the nasal line increased and the freeway space decreased significantly. CONCLUSIONS: Overall, no significant differences were noted in the medium term, in terms of dentofacial structures adaptation and tongue function following glossectomy. Further research is warranted in order to elucidate the consequences of the altered oral environment.
ALGORITHMICALLY RANDOM GENERALIZED GRAPHS AND ITS TOPOLOGICAL PROPERTIES

This article presents a theoretical investigation of incompressibility and randomness in generalized representations of graphs along with its implications on network topological properties. We extend previous studies on plain algorithmically random classical graphs to plain and preﬁx algorithmi- cally random MultiAspect Graphs (MAGs), which are formal graph-like representations of arbitrary dyadic relations between n -ary tuples. In doing so, we deﬁne recursively labeled MAGs given a companion tuple and recursively labeled families of MAGs. In particular, we show that, unlike recursively labeled classical graphs, the algorithmic information of a MAG may be not equivalent to the algorithmic information of the binary string that determines the pres- ence or absence of edges. Nevertheless, we show that there is a recursively labeled inﬁnite family of nested MAGs (or, as a particular case, of nested classical graphs) that behaves like (and is determined by) an algorithmically random real number. Furthermore, by relating the algorithmic randomness of a MAG and the algorithmic randomness of its isomorphic graph, we study some important topological properties, in particular, vertex degree, connectiv- ity, diameter, and rigidity.
Brillouin Properties of a Novel Strontium Aluminosilicate Glass Optical Fiber

Presented here are the selected optical, physical, and Brillouin-related properties of a novel silica-clad, strontium aluminosilicate (SrAlSi) glass optical fiber produced using the molten core method. The Brillouin gain coefficient was found to be 0.11 × 10-11 m/W, which is about 20 × lower than conventional silica glass optical fibers. The SrAlSi core fiber also has a near-zero temperature coefficient of Brillouin frequency shift (-0.064 MHz/K) and can be considered athermal for all intents and purposes. Additional physical and Brillouin properties of the individual glass components are deduced through the use of an additive model. As a highlight, strontia (SrO) is found to have a large negative Pockels photoelastic coefficient of p12 = - 0.245. Various other bulk properties, such as the acoustic velocity, refractive index, mass density, thermo-optic, and thermo-acoustic, as well as strain-optic and strain-acoustic coefficients are provided.
Impact of training of mothers, drug shop attendants and voluntary health workers on effective diagnosis and treatment of malaria in Lagos, Nigeria

Background: The National Malaria Eradication Program and international agencies are keen on scaling up the use of malaria rapid diagnostic tests (mRDTs) and artemisinin-based combination therapies (ACTs) for effective diagnosis and treatment of the disease. However, poor diagnostic skills and inappropriate treatment are limiting the efforts. In Nigeria, a large proportion of infected patients self-diagnose and treat while many others seek care from informal drug attendants and voluntary health workers. Aims: This study describes the impact of training voluntary health workers, drug shop attendants, and mothers on effective case detection and treatment of malaria in Lagos, Nigeria. METHODS: We trained mothers accessing antenatal care, drug shop attendants, and voluntary health workers selected from the three districts of Lagos, on the use of histidine-rich protein-2-based mRDTs and ACTs. Pre- and post-training assessments, focus group discussions (FGDs), and in-depth interviews (IDIs) were carried out. Results: The knowledge, attitude, and skill of the participants to achieve the goal of “test, treat, and track” using mRDT and ACTs were low (11%–55%). There was a low awareness of other non-malaria fevers among mothers. Self-medication was widely practiced (31.3%). FGDs and IDIs revealed that health-care providers administered antimalarials without diagnosis. Training significantly improved participants' knowledge and expertise on the use of mRDTs and ACTs (P = 0.02). The participants' field performance on mRDT use was significantly correlated with their category (bivariate r = 0.51, P = 0.001). There was no statistically significant association between the participants' level of education or previous field experience and their field performance on mRDT (r = 0.12, P = 0.9; χ2 = 38, df = 2 and P = 0.49). Conclusion: These findings suggest that training of stakeholders in malaria control improves diagnosis and treatment of malaria. However, a broader scope of training in other settings may be required for an effective malaria control in Nigeria.
Novel Germline Mutations of BRCA1 and BRCA2 in Korean Familial Breast Cancer Patients

Breast cancer is the second most common cancer in Korean women. Germline mutations in the BRCA1 and BRCA2 genes cause hereditary breast cancer and are detected in 15–20% of hereditary breast cancer. We investigated the BRCA1 and BRCA2 mutations in 114 familial breast cancer patients using next-generation sequencing. We confirmed 20 different mutations of BRCA1 and BRCA2 in 25 subjects (21.9%). Two such mutations in eight patients were novel (not reported in any variant database or previous study). Six mutations have been reported as disease-causing mutations in public databases. Seven mutations were found only in a single nucleotide polymorphism database and one mutation has been reported in Korea. The BRCA1/2 mutation frequency was similar to that of other studies on familial breast cancer patients in the Korean population. Further studies should examine more cases and mutations of whole exons.
Transient quantum isolation and critical behavior in the magnetization dynamics of half-metallic manganites

We combine time resolved pump-probe Magneto-Optical Kerr Effect and Photoelectron Spectroscopy experiments supported by theoretical analysis to determine the relaxation dynamics of delocalized electrons in half-metallic ferromagnetic manganite $La_{1-x}Sr_{x}MnO_{3}$. We observe that the half-metallic character of $La_{1-x}Sr_{x}MnO_{3}$ determines the timescale of both the electronic phase transition and the quenching of magnetization, revealing a quantum isolation of the spin system in double exchange ferromagnets extending up to hundreds of picoseconds. We demonstrate the use of time-resolved hard X-ray photoelectron spectroscopy (TR-HAXPES) as a unique tool to single out the evolution of strongly correlated electronic states across a second-order phase transition in a complex material.
On the Use of Randomness in Local Distributed Graph Algorithms

We attempt to better understand randomization in local distributed graph algorithms by exploring how randomness is used and what we can gain from it: We first ask the question of how much randomness is needed to obtain efficient randomized algorithms. We show that for all locally checkable problems with poly log n-time randomized algorithms, there are such algorithms even if either (I) there is a only a single (private) independent random bit in each poly log n-neighborhood of the graph, (II) the (private) bits of randomness of different nodes are only poly log n-wise independent, or (III) there are only poly log n bits of global shared randomness (and no private randomness). Second, we study how much we can improve the error probability of randomized algorithms. For all locally checkable problems with poly log n-time randomized algorithms, we show that there are such algorithms that succeed with probability 1-n-2 ε(log log n) 2 and more generally T-round algorithms, for T ≥ poly log n, with success probability 1-n-2 εlog 2T. We also show that poly log n-time randomized algorithms with success probability 1-2-2 log ε n for some ε > 0 can be derandomized to poly log n-time deterministic algorithms. Both of the directions mentioned above, reducing the amount of randomness and improving the success probability, can be seen as partial derandomization of existing randomized algorithms. In all the above cases, we also show that any significant improvement of our results would lead to a major breakthrough, as it would imply significantly more efficient deterministic distributed algorithms for a wide class of problems.
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning

We study the policy evaluation problem in multi-agent reinforcement learning. In this problem, a group of agents works cooperatively to evaluate the value function for the global discounted accumulative reward problem, which is composed of local rewards observed by the agents. Over a series of time steps, the agents act, get rewarded, update their local estimate of the value function, then communicate with their neighbors. The local update at each agent can be interpreted as a distributed consensus-based variant of the popular temporal difference learning algorithm TD(0). While distributed reinforcement learning algorithms have been presented in the literature, almost nothing is known about their convergence rate. Our main contribution is providing a finite-time analysis for the convergence of the distributed TD(0) algorithm. We do this when the communication network between the agents is time-varying in general. We obtain an explicit upper bound on the rate of convergence of this algorithm as a function of the network topology and the discount factor. Our results mirror what we would expect from using distributed stochastic gradient descent for solving convex optimization problems.
BreGMN: scaled-Bregman Generative Modeling Networks

The family of f-divergences is ubiquitously applied to generative modeling in order to adapt the distribution of the model to that of the data. Well-definedness of f-divergences, however, requires the distributions of the data and model to overlap completely in every time step of training. As a result, as soon as the support of distributions of data and model contain non-overlapping portions, gradient based training of the corresponding model becomes hopeless. Recent advances in generative modeling are full of remedies for handling this support mismatch problem: key ideas include either modifying the objective function to integral probability measures (IPMs) that are well-behaved even on disjoint probabilities, or optimizing a well-behaved variational lower bound instead of the true objective. We, on the other hand, establish that a complete change of the objective function is unnecessary, and instead an augmentation of the base measure of the problematic divergence can resolve the issue. Based on this observation, we propose a generative model which leverages the class of Scaled Bregman Divergences and generalizes both f-divergences and Bregman divergences. We analyze this class of divergences and show that with the appropriate choice of base measure it can resolve the support mismatch problem and incorporate geometric information. Finally, we study the performance of the proposed method and demonstrate promising results on MNIST, CelebA and CIFAR-10 datasets.
Learning Domain Randomization Distributions for Transfer of Locomotion Policies

Domain randomization (DR) is a successful technique for learning robust policies for robot systems, when the dynamics of the target robot system are unknown. The success of policies trained with domain randomization however, is highly dependent on the correct selection of the randomization distribution. The majority of success stories typically use real world data in order to carefully select the DR distribution, or incorporate real world trajectories to better estimate appropriate randomization distributions. In this paper, we consider the problem of finding good domain randomization parameters for simulation, without prior access to data from the target system. We explore the use of gradient-based search methods to learn a domain randomization with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution 2) The domain randomization distribution should be wide enough so that the experience similar to the target robot system is observed during training, while addressing the practicality of training finite capacity models. These two properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the domain randomization distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment.
Entanglement Hamiltonian of quantum critical chains and conformal field theories

We consider a lattice version of the Bisognano-Wichmann (BW) modular Hamiltonian as an ansatz for the bipartite entanglement Hamiltonian of the quantum critical chains. Using numerically unbiased methods, we check the accuracy of the BW-ansatz by both comparing the BW Renyi entropy to the exact results, and by investigating the size scaling of the norm distance between the exact reduced density matrix and the BW one. Our study encompasses a variety of models, scanning different universality classes, including transverse field Ising, Potts and XXZ chains. We show that the Renyi entropies obtained via the BW ansatz properly describe the scaling properties predicted by conformal field theory. Remarkably, the BW Renyi entropies faithfully capture also the corrections to the conformal field theory scaling associated to the energy density operator. In addition, we show that the norm distance between the discretized BW density matrix and the exact one asymptotically goes to zero with the system size: this indicates that the BW-ansatz can be also employed to predict properties of the eigenvectors of the reduced density matrices, and is thus potentially applicable to other entanglement-related quantities such as negativity.
Variational Langevin Hamiltonian Monte Carlo for Distant Multi-modal Sampling

The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient information of the target distribution, it can explore the state space much more efficiently than the random-walk proposals. However, probabilistic inference involving multi-modal distributions is very difficult for standard HMC method, especially when the modes are far away from each other. Sampling algorithms are then often incapable of traveling across the places of low probability. In this paper, we propose a novel MCMC algorithm which aims to sample from multi-modal distributions effectively. The method improves Hamiltonian dynamics to reduce the autocorrelation of the samples and uses a variational distribution to explore the phase space and find new modes. A formal proof is provided which shows that the proposed method can converge to target distributions. Both synthetic and real datasets are used to evaluate its properties and performance. The experimental results verify the theory and show superior performance in multi-modal sampling.