Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Generalized Measurement for Unambiguous State Discrimination of Quaternary Phase-Shift-Keying Coherent States

Generalized quantum measurements identifying non-orthogonal states without ambiguity often play an indispensable role in various quantum applications. For such unambiguous state discrimination scenario, we have a finite probability of obtaining inconclusive results and minimizing the probability of inconclusive results is of particular importance. In this paper, we experimentally demonstrate an adaptive generalized measurement that can unambiguously discriminate the quaternary phase-shift-keying coherent states with a near-optimal performance. Our scheme is composed of displacement operations, single photon detections and adaptive control of the displacements dependent on a history of photon detection outcomes. Our experimental results show a clear improvement of both a probability of conclusive results and a ratio of erroneous decision caused by unavoidable experimental imperfections over conventional static generalized measurements.
A three-dimensional compact high-order gas-kinetic scheme on structured mesh

In this paper, a third-order compact gas-kinetic scheme is firstly proposed for three-dimensional computation for the compressible Euler and Navier-Stokes solutions. The scheme achieves its compactness due to the time-dependent gas distribution function in GKS, which provides not only the fluxes but also the time accurate flow variables in the next time level at a cell interface. As a result, the cell averaged first-order spatial derivatives of flow variables can be obtained naturally through the Gauss's theorem. Then, a third-order compact reconstruction involving the cell averaged values and their first-order spatial derivatives can be achieved. The trilinear interpolation is used to treat possible non-coplanar elements on general hexahedral mesh. The constrained least-square technique is applied to improve the accuracy in the smooth case. To deal with both smooth and discontinuous flows, a new HWENO reconstruction is designed in the current scheme by following the ideas in Zhu, 2018. No identification of troubled cells is needed in the current scheme. In contrast to the Riemann solver-based method, the compact scheme can achieve a third-order temporal accuracy with the two-stage two-derivative temporal discretization, instead of the three-stage Runge-Kutta method. Overall, the proposed scheme inherits the high accuracy and efficiency of the previous ones in two-dimensional case. The desired third-order accuracy can be obtained with curved boundary. The robustness of the scheme has been validated through many cases, including strong shocks in both inviscid and viscous flow computations. Quantitative comparisons for both smooth and discontinuous cases show that the current third-order scheme can give competitive results against the fifth-order non-compact GKS under the same mesh. A large CFL number around 0.5 can be used in the present scheme.
Season and Genotype Influence Golf Ball Roll Distance on Creeping Bentgrass

Golfers are demanding increased ball roll distances on a daily basis, but cultural practices to achieve this often are detrimental to the green. One option for increasing ball roll distance without altering cultural practices may be to select creeping bentgrass genotypes that provide less resistance to ball roll. Studies were conducted at the John Seaton Anderson Turfgrass and Ornamental Research Facility near Ithaca, Neb. , and at the Rocky Ford Turfgrass Research Facility in Manhattan, Kans. , to determine genotype and seasonal influences on golf ball roll distance. Eighteen creeping bentgrass (Agrostis palustris Huds.) genotypes were evaluated. Genotype was not a significant source of variability, but the location X season interaction was. Significant seasonal differences in ball roll occurred at both locations. Ball roll distances for spring, summer, and fall were 98, 15, and 31 cm greater at the Nebraska test location than at the Kansas site. Correlations between turfgrass visual quality and ball roll distance were not significant. Therefore, the use of genotypes exhibiting high turfgrass visual quality will not necessarily result in longer ball rolls. Since there were no season x genotype or genotype x location interactions, ball roll distance on genotypes at each location changed similarly with season. Genotype selection appears to have little influence on ball roll distance under the conditions tested at these two locations.
UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network

Recently, COVID-19 has affected a variety of real-life aspects of the world and led to dreadful consequences. More and more tweets about COVID-19 has been shared publicly on Twitter. However, the plurality of those Tweets are uninformative, which is challenging to build automatic systems to detect the informative ones for useful AI applications. In this paper, we present our results at the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. In particular, we propose our simple but effective approach using the transformer-based models based on COVID-Twitter-BERT (CT-BERT) with different fine-tuning techniques. As a result, we achieve the F1-Score of 90.94% with the third place on the leaderboard of this task which attracted 56 submitted teams in total.
Heat transfer in three-dimensional intersecting shock-wave/turbulent boundary-layer interactions with wall-modeled large-eddy simulations

The accurate prediction of aerothermal surface loading is of paramount importance for the design of high speed flight vehicles. In this work, we consider the numerical solution of hypersonic flow over a double-finned geometry, representative of the inlet of an air-breathing flight vehicle, characterized by three-dimensional intersecting shock-wave/turbulent boundary-layer interaction at Mach 8.3. High Reynolds numbers ($Re_L \approx 11.6 \times 10^6$ based on free-stream conditions) and the presence of cold walls ($T_w/T_o \approx 0.27$) leading to large near-wall temperature gradients necessitate the use of wall-modeled large-eddy simulation (WMLES) in order to make calculations computationally tractable. The comparison of the WMLES results with experimental measurements shows good agreement in the time-averaged surface heat flux and wall pressure distributions, and the WMLES predictions show reduced errors with respect to the experimental measurements than prior RANS calculations. The favorable comparisons are obtained using an LES wall model based on equilibrium boundary layer approximations despite the presence of numerous non-equilibrium conditions including three dimensionality, shock-boundary layer interactions, and flow separation. Lastly, it is also demonstrated that the use of semi-local eddy viscosity scaling (in lieu of the commonly used van Driest scaling) in the LES wall model is necessary to accurately predict the surface pressure loading and heat fluxes.
New Results on Delay Robustness of Consensus Algorithms

Consensus of autonomous agents is a benchmark problem in cooperative control. In this paper, we consider standard continuous-time averaging consensus policies (or Laplacian flows) over time-varying graphs and focus on robustness of consensus against communication delays. Such a robustness has been proved under the assumption of uniform quasi-strong connectivity of the graph. It is known, however, that the uniform connectivity is not necessary for consensus. For instance, in the case of undirected graph and undelayed communication consensus requires a much weaker condition of integral connectivity. In this paper, we show that the latter results remain valid in presence of unknown but bounded communication delays, furthermore, the condition of undirected graph can be substantially relaxed and replaced by the conditions of non-instantaneous type-symmetry. Furthermore, consensus can be proved for any feasible solution of the delay differential inequalities associated to the consensus algorithm. Such inequalities naturally arise in problems of containment control, distributed optimization and models of social dynamics.
TRANSPR: Transparency Ray-Accumulating Neural 3D Scene Point Renderer

We propose and evaluate a neural point-based graphics method that can model semi-transparent scene parts. Similarly to its predecessor pipeline, ours uses point clouds to model proxy geometry, and augments each point with a neural descriptor. Additionally, a learnable transparency value is introduced in our approach for each point.Our neural rendering procedure consists of two steps. Firstly, the point cloud is rasterized using ray marching into a multi-channel image. This is followed by the neural rendering step that “translates” the rasterized image into an RGB output using a learnable convolutional network. New scenes can be modeled using gradient-based optimization of neural descriptors and of the rendering network.We show that novel views of semi-transparent point cloud scenes can be generated after training with our approach. Our experiments demonstrate the benefit of introducing semi-transparency into the neural point-based modeling for a range of scenes with semi-transparent parts. The project materials and the code are available at http://saic-violet.github. io/transpr.
Three-Parameter Logarithm and Entropy

A three-parameter logarithmic function is derived using the notion of q -analogue and ansatz technique. The derived three-parameter logarithm is shown to be a generalization of the two-parameter logarithmic function of Schwämmle and Tsallis as the latter is the limiting function of the former as the added parameter goes to 1. The inverse of the three-parameter logarithm and other important properties are also proved. A three-parameter entropic function is then defined and is shown to be analytic and hence Lesche-stable, concave, and convex in some ranges of the parameters.
Alternating Beamforming With Intelligent Reflecting Surface Element Allocation

Intelligent reflecting surface (IRS) has become a promising technology to aid next generation wireless communication systems. In this letter, we develop an alternating beamforming technique with a novel concept of IRS element allocation for multiple-input multiple-output systems when a base station supports multiple single antenna users aided with a single IRS. Specifically, we allocate each IRS element separately to each user, thus, in the beamforming stage allowing the IRS element only consider a single user at a time. In result to this separation, the complexity is vastly decreased. The proposed beamforming technique tries to maximize the minimum rate of all users with minimal complexity. In the numerical results, we show that the proposed technique is comparable to the convex optimization-based benchmark with sufficiently less complexity.
Opening reionization: quantitative morphology of the epoch of reionization and its connection to the cosmic density field

We introduce a versatile and spatially resolved morphological characterisation of binary fields, rooted in the opening transform of mathematical morphology. We subsequently apply it to the thresholded ionization field in simulations of cosmic reionization and study the morphology of ionized regions. We find that an ionized volume element typically resides in an ionized region with radius $\sim8\,h^{-1}\mathrm{cMpc}$ at the midpoint of reionization ($z\approx7.5$) and follow the bubble size distribution even beyond the overlap phase. We find that percolation of the fully ionized component sets in when 25% of the universe is ionized and that the resulting infinite cluster incorporates all ionized regions above $\sim8\,h^{-1}\mathrm{cMpc}$. We also quantify the clustering of ionized regions of varying radius with respect to matter and on small scales detect the formation of superbubbles in the overlap phase. On large scales we quantify the bias values of the centres of ionized and neutral regions of different sizes and not only show that the largest ones at the high-point of reionization can reach $b\approx 30$, but also that early small ionized regions are positively correlated with matter and large neutral regions and late small ionized regions are heavily anti-biased with respect to matter, down to $b\lesssim-20$.
Dengue Seasonality and Non-Monotonic Response to Moisture: A Model-Data Analysis of Sri Lanka Incidence from 2011 to 2016

Dengue fever impacts populations across the tropics. Dengue is caused by a mosquito transmitted flavivirus and its burden is projected to increase under future climate and development scenarios. The transmission process of dengue virus is strongly moderated by hydro-climatic conditions that impact the vector's life cycle and behavior. Here, we study the impact of rainfall seasonality and moisture availability on the monthly distribution of reported dengue cases in Sri Lanka. Through cluster analysis, we find an association between seasonal peaks of rainfall and dengue incidence with a two-month lag. We show that a hydrologically driven epidemiological model (HYSIR), which takes into account hydrologic memory in addition to the nonlinear dynamics of the transmission process, captures the two-month lag between rainfall and dengue cases seasonal peaks. Our analysis reveals a non-monotonic dependence of dengue cases on moisture, whereby an increase of cases with increasing moisture is followed by a reduction for very high levels of water availability. Improvement in prediction of the seasonal peaks in dengue incidence results from a seasonally varying dependence of transmission rate on water availability.
A Causal Framework for Distribution Generalization

We consider the problem of predicting a response <inline-formula><tex-math notation="LaTeX">$Y$</tex-math><alternatives><mml:math><mml:mi>Y</mml:mi></mml:math><inline-graphic xlink:href="christiansen-ieq1-3094760.gif"/></alternatives></inline-formula> from a set of covariates <inline-formula><tex-math notation="LaTeX">$X$</tex-math><alternatives><mml:math><mml:mi>X</mml:mi></mml:math><inline-graphic xlink:href="christiansen-ieq2-3094760.gif"/></alternatives></inline-formula> when test- and training distributions differ. Since such differences may have causal explanations, we consider test distributions that emerge from interventions in a structural causal model, and focus on minimizing the worst-case risk. Causal regression models, which regress the response on its direct causes, remain unchanged under arbitrary interventions on the covariates, but they are not always optimal in the above sense. For example, for linear models and bounded interventions, alternative solutions have been shown to be minimax prediction optimal. We introduce the formal framework of distribution generalization that allows us to analyze the above problem in partially observed nonlinear models for both direct interventions on <inline-formula><tex-math notation="LaTeX">$X$</tex-math><alternatives><mml:math><mml:mi>X</mml:mi></mml:math><inline-graphic xlink:href="christiansen-ieq3-3094760.gif"/></alternatives></inline-formula> and interventions that occur indirectly via exogenous variables <inline-formula><tex-math notation="LaTeX">$A$</tex-math><alternatives><mml:math><mml:mi>A</mml:mi></mml:math><inline-graphic xlink:href="christiansen-ieq4-3094760.gif"/></alternatives></inline-formula>. It takes into account that, in practice, minimax solutions need to be identified from data. Our framework allows us to characterize under which class of interventions the causal function is minimax optimal. We prove sufficient conditions for distribution generalization and present corresponding impossibility results. We propose a practical method, NILE, that achieves distribution generalization in a nonlinear IV setting with linear extrapolation. We prove consistency and present empirical results.
A common explanation of the Hubble tension and anomalous cold spots in the CMB

The standard cosmological paradigm narrates a reassuring story of a universe currently dominated by an enigmatic dark energy component. Disquietingly, its universal explaining power has recently been challenged by, above all, the $\sim4\sigma$ tension in the values of the Hubble constant. Another, less studied anomaly is the repeated observation of integrated Sachs-Wolfe imprints $\sim5\times$ stronger than expected in the $\Lambda$CDM model from R>100 $Mpc/h$ super-structures. Here we show that the inhomogeneous AvERA model of emerging curvature is capable of telling a plausible albeit radically different story that explains both observational anomalies without dark energy. We demonstrate that while stacked imprints of R>100 $Mpc/h$ supervoids in cosmic microwave background temperature maps can discriminate between the AvERA and $\Lambda$CDM models, their characteristic differences may remain hidden using alternative void definitions and stacking methodologies. Testing the extremes, we then also show that the CMB Cold Spot can plausibly be explained in the AvERA model as an ISW imprint. The coldest spot in the AvERA map is aligned with multiple low-$z$ supervoids with R>100 $Mpc/h$ and central underdensity $\delta_{0}\approx-0.3$, resembling the observed large-scale galaxy density field in the Cold Spot area. We hence conclude that the anomalous imprint of supervoids may well be the canary in the coal mine, and existing observational evidence for dark energy should be re-interpreted to further test alternative models.
Chemical races of Lobaria pulmonaria differ in palatability to gastropods

Lichens produce a large variety of carbon based secondary compounds (CBSCs; Huneck & Yoshimura 1996) and a number of biological roles for these have been proposed (e.g. Huneck 1999). For instance, strong experimental evidence confirms that several of these compounds, predominantly the ones situated in the medulla, deter lichenivores (e.g. Slansky 1979; Lawrey 1980; Gauslaa 2005; Pöykkö et al. 2005). In a field transplantation experiment, Asplund & Gauslaa (2008) showed that thalli of Lobaria pulmonaria (L.) Hoffm. with artificially reduced levels of CBSCs were much more grazed than control thalli. Recently a number of quantitative studies on carbonbased secondary compounds in L. pulmonaria have been performed. According to these there are large variations in the concentration of CBSCs in Norwegian L. pulmonaria, ranging from 7 to 130 mg g. These variations seem to depend partly on soil pH and altitude (Vatne et al. 2011), as well as thallus age (Asplund & Gauslaa 2007). However, exposure to solar radiation does not seem to affect these medullary compounds (McEvoy et al. 2007; Nybakken et al. 2007), as opposed to many cortical compounds (reviewed by Gauslaa 2009). Two chemical races of L. pulmonaria var. pulmonaria are described in North America (Culberson 1969). Chemical race I has norstictic, stictic and constictic acids and two unknown substances; chemical race II has norstictic acid and one unknown substance. However, no quantitative data have been published on CBSCs in North American L. pulmonaria. In this study I aim to investigate whether the palatability to lichenivorous gastropods varies between the two chemical races. In addition, I want to confirm that the CBSCs in L. pulmonaria are unaffected by increased irradiance, even after 5 years at a forest edge.
Use of GIS and RS to estimate the light utilization efficiency of the vegetation in Guangdong,China

By the aid of the ARC/INFO 7 2 1 and ERMapper 5 5,using the ground meteorological data and NOAA AVHRR NDVI data,we estimated the light utilization efficiency( e )of the vegetation in Guangdong Province,which lies in subtropical and tropical monsoon climate zone The results showed that the maximum achievable light utilization efficiency(0 389gC MJ -1 ),which was calculated by Potter et al and Field et al and etc ,was underestimated in Guangdong The measured annual mean in Guangdong varied from 0 69 to 1 05gC MJ -1 and the predicted e was consistent with observed values before Moreover,seasonal variations of the vegetation in Guangdong were remarkable,and the mean value in winter was half as high as the mean in summer,because the seasonal swings in temperature and precipitation in Guangdong had a large impact Even in the evergreen broad leaved forests,different subtypes did not have the same mean e ,and all of them have large seasonal changes
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Extensive epidemiological evidence indicates that prolonged treatment with nonsteroidal antiinflammatory drugs (NSAIDs) significantly reduces risk, delays onset, and reduces symptomatic severity of Alzheimer’s disease (AD). Reports that the inflammatory enzyme nitric oxide synthase (iNOS) is expressed in neurons in AD suggest that neuronal iNOS can contribute to AD pathology. These findings led to the recognition that the etiology of AD involves a significant inflammatory component. However, the mechanisms underlying NSAID’s beneficial actions are unclear. The well-characterized action of NSAIDs is inhibition of cyclooxygenases (COX) and thereby prostanoid synthesis. However, COX inhibition alone is unlikely to account for the beneficial action of NSAIDs, insofar as their therapeutic effects occur at concentrations higher than those needed for COX inhibition, and, in a recent AD clinical trial, the selective COX2 inhibitor celecoxib was not protective. Similarly, we find that inhibition of neuronal and microglia activation by NSAIDs, but not by the selective COX2 inhibitor NS-398, prevents neuronal cell death but only at concentrations higher than those that inhibit COX. Together, these data support the existence of alternate targets for NSAID actions. It was recently recognized that NSAIDs directly activate members of the nuclear hormone receptor peroxisome proliferator-activated receptor (PPAR) family. PPARs play a central role in dietary fat and glucose metabolism, which led to the development of antidiabetic thiazolinediones (TZDs) that are in clinical use for type II diabetes (Rezulin, Actos, Avandia). Recently it was shown that activation of the PPARg isoform (PPARg) attenuates proinflammatory gene expression in macrophages and glial cells and blocks neuronal iNOS expression. These results were obtained using structurally distinct ligands, including TZDs, the natural agonist prostaglandin J2, and NSAIDs, including indomethacin and ibuprofen. In our models of neurotoxicity, protective effects were obtained with TZDs, demonstrating the antiinflammatory effectiveness of PPARg activation in these systems. We therefore suggest, based on epidemiological and experimental evidence, that the effects of NSAIDs in AD are mediated principally through activation of PPARg, which is expressed in normal brain and increased in AD. PPARg agonists are an attractive therapeutic option in aged, at-risk populations, as demonstrated prospectively by the ability of NSAIDs to reduce risk and delay the onset of AD in aged populations. Because highly specific PPARg agonists are in clinical use, these drugs can be evaluated in clinical trials for AD.