Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of stand thinning on snowshoe hare population dynamics and feeding damage in lodgepole pine forest

(1) This study assessed the impact of stand thinning on the population dynamics of snowshoe hares (Lepus americanus Erxleben) during the late increase, peak and decline phases of the 10-year cycle. (2) Intensive population monitoring and assessment of feeding damage were conducted in a thinned stand and unthinned (control) stand of young lodgepole pine (Pinus contorta Dougl.) forest near Prince George, British Columbia, Canada from 1979 to 1983. (3) Population density and recruitment increased significantly in the thinned stand during the first winter after thinning, but declined thereafter as the habitat became less attractive to hares. (4) Stand thinning had little or no effect on reproduction or survival (residence) of hares, but did significantly reduce average body weight. (5) The intensity of feeding damage to crop trees coincided with the phases of the cycle with little damage to trees during the first post-thinning winter as hares fed on the fallen pine stems and foliage.
Pulsed Ion Microscope to Probe Quantum Gases

The advent of the quantum gas microscope allowed for the in situ probing of ultracold gaseous matter on an unprecedented level of spatial resolution. The study of phenomena on ever smaller length scales as well as the probing of three-dimensional systems is, however, fundamentally limited by the wavelength of the imaging light, for all techniques based on linear optics. Here we report on a high-resolution ion microscope as a versatile and powerful experimental tool to investigate quantum gases. The instrument clearly resolves atoms in an optical lattice with a spacing of $532\,\text{nm}$ over a field of view of 50 sites and offers an extremely large depth of field on the order of at least $70\,\mu\text{m}$. With a simple model, we extract an upper limit for the achievable resolution of approximately $200\,\text{nm}$ from our data. We demonstrate a pulsed operation mode which in the future will enable 3D imaging and allow for the study of ionic impurities and Rydberg physics.
Evaluation of Antioxidant Activity of Vegetable Extracts and Determination of Some Active Compounds

リノール酸の自動酸化物がβ-カロチンを退色させることを利用し,その退色の防止活性を指標とした抗酸化性の測定法を用いて, 43種の野菜の80%メタノール抽出液の抗酸化活性を測定し,以下の結果を得た. (1) BHAの抗酸化活性と比較し検討したところ,シュンギクやショウガ,アスパラガス等13種は, BHAが生鮮重100g当たり25mg以上含有されていることに相当する程の活性を示すことが分かった. BHA 5mg/100g以下に相当するものは,カボチャやキウリ,カブ,キャベツ等13種であった. (2) 野菜抽出液のポリフェノール含量と抗酸化性の相関性を検討したところ, 43種の野菜でr=0. 7694となり,相関性があることを明らかにした. (3) 比較的抗酸化性の強い3種の野菜の抗酸化性成分をHPLCで分取し,質量分析計等で解析したところ,アスパラガスではルチン,ショウガではヘキサヒドロクルクミンとジンジェロールが同定された. 一方シュンギクでは, 3, 5-ジカフェオイルキナ酸および新規のジカフェオイルキナ酸誘導体と推定される成分が得られた.
Noncoherent OOK Symbol Detection with Supervised-Learning Approach for BCC

There has been a continuing demand for improving the accuracy and ease of use of medical devices used on or around the human body. Communication is critical to medical applications, and wireless body area networks (WBANs) have the potential to revolutionize diagnosis. Despite its importance, WBAN technology is still in its infancy and requires much research. We consider body channel communication (BCC), which uses the whole body as well as the skin as a medium for communication. BCC is sensitive to the body’s natural circulation and movement, which requires a noncoherent model for wireless communication. To accurately handle practical applications for electronic devices working on or inside a human body, we configure a realistic system model for BCC with on-off keying (OOK) modulation. We propose novel detection techniques for OOK symbols and improve the performance by exploiting distributed reception and supervised-learning approaches. Numerical results show that the proposed techniques are valid for noncoherent OOK transmissions for BCC.
Predicting Coordinated Actuated Traffic Signal Change Times using LSTM Neural Networks

Vehicle acceleration and deceleration maneuvers at traffic signals results in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle fuel efficiency. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions. This study details a four-step Long Short-Term Memory deep learning-based methodology that can be used to provide reasonable switching time estimates from green to red and vice versa while being robust to missing data. The four steps are data gathering, data preparation, machine learning model tuning, and model testing and evaluation. The input to the models included controller logic, signal timing parameters, time of day, traffic state from detectors, vehicle actuation data, and pedestrian actuation data. The methodology is applied and evaluated on data from an intersection in Northern Virginia. A comparative analysis is conducted between different loss functions including the mean squared error, mean absolute error, and mean relative error used in LSTM and a new loss function is proposed. The results show that while the proposed loss function outperforms conventional loss functions in terms of overall absolute error values, the choice of the loss function is dependent on the prediction horizon. In particular, the proposed loss function is outperformed by the mean relative error for very short prediction horizons and mean squared error for very long prediction horizons.
Reconciling hints on axion-like-particles from high-energy gamma rays with stellar bounds

It has been recently claimed by two different groups that the spectral modulation observed in gamma rays from Galactic pulsars and supernova remnants can be due to conversion of photons into ultra-light axion-like-particles (ALPs) in large-scale Galactic magnetic fields. While we show the required best-fit photon-ALP coupling, gaγ ∼ 2 × 10-10 GeV-1, to be consistent with constraints from observations of photon-ALPs mixing in vacuum, this is in conflict with other bounds, specifically from the CAST solar axion limit, from the helium-burning lifetime in globular clusters, and from the non-observations of gamma rays in coincidence with SN 1987A. In order to reconcile these different results, we propose that environmental effects in matter would suppress the ALP production in dense astrophysical plasma, allowing to relax previous bounds and make them compatible with photon-ALP conversions in the low-density Galactic medium. If this explanation is correct, the claimed ALP signal would be on the reach of next-generations laboratory experiments such as ALPS II.
An Intensity Mapping Detection of Aggregate CO Line Emission at 3 mm

We present a detection of molecular gas emission at z ∼ 1–5 using the technique of line intensity mapping. We make use of a pair of 3 mm interferometric data sets, the first from the Atacama Large Millimeter/submillimeter Array (ALMA) Spectroscopic Survey in the Hubble Ultra Deep Field, and the second from a series of Atacama Compact Array (ACA) observations conducted between 2016 and 2018, targeting the COSMOS field. At 100 GHz, we measure nonzero power at 97.8% and 99.9% confidence in the ACA and ALMA data sets, respectively. In the joint result, we reject the zero-power hypothesis at 99.99% confidence, finding . After accounting for sample variance effects, the estimated spectral shot power is . We derive a model for the various line species our measurement is expected to be sensitive to, and estimate the shot power to be , , and for CO(2–1) at z = 1.3, CO(3–2) at z = 2.5, and CO(4–3) at z = 3.6, respectively. Using line ratios appropriate for high-redshift galaxies, we find these results to be in good agreement with those from the CO Power Spectrum Survey. Adopting αCO = 3.6 M⊙ (K km s−1 pc2)−1, we estimate a cosmic molecular gas density of ∼ 108 M⊙ Mpc−3 between z = 1–3.
Augmenting Neural Differential Equations to Model Unknown Dynamical Systems with Incomplete State Information

Neural Ordinary Differential Equations replace the right-hand side of a conventional ODE with a neural net, which by virtue of the universal approximation theorem, can be trained to the representation of any function. When we do not know the function itself, but have state trajectories (time evolution) of the ODE system we can still train the neural net to learn the representation of the underlying but unknown ODE. However if the state of the system is incompletely known then the right-hand side of the ODE cannot be calculated. The derivatives to propagate the system are unavailable. We show that a specially augmented Neural ODE can learn the system when given incomplete state information. As a worked example we apply neural ODEs to the Lotka-Voltera problem of 3 species, rabbits, wolves, and bears. We show that even when the data for the bear time series is removed the remaining time series of the rabbits and wolves is sufficient to learn the dynamical system despite the missing the incomplete state information. This is surprising since a conventional ODE system cannot output the correct derivatives without the full state as the input. We implement augmented neural ODEs and differential equation solvers in the julia programming language.
COVID-19 Risk Estimation using a Time-varying SIR-model

Policy-makers require data-driven tools to assess the spread of COVID-19 and inform the public of their risk of infection on an ongoing basis. We propose a rigorous hybrid model-and-data-driven approach to risk scoring based on a time-varying SIR epidemic model that ultimately yields a simplified color-coded risk level for each community. The risk score Γt that we propose is proportional to the probability of someone currently healthy getting infected in the next 24 hours based on their locality. We show how this risk score can be estimated using another useful metric of infection spread, Rt, the time-varying average reproduction number which indicates the average number of individuals an infected person would infect in turn. The proposed approach also allows for quantification of uncertainty in the estimates of Rt and Γt in the form of confidence intervals. Code and data from our effort have been open-sourced and are being applied to assess and communicate the risk of infection in the City and County of Los Angeles.
Popularity Bias in Recommendation: A Multi-stakeholder Perspective

Traditionally, especially in academic research in recommender systems, the focus has been solely on the satisfaction of the end-user. While user satisfaction has, indeed, been associated with the success of the business, it is not the only factor. In many recommendation domains, there are other stakeholders whose needs should be taken into account in the recommendation generation and evaluation. In this dissertation, I describe the notion of multi-stakeholder recommendation. In particular, I study one of the most important challenges in recommendation research, popularity bias, from a multi-stakeholder perspective since, as I show later in this dissertation, it impacts different stakeholders in a recommender system. Popularity bias is a well-known phenomenon in recommender systems where popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendation domains. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail items overall. The effectiveness of these approaches, however, has not been assessed in multi-stakeholder environments. In this dissertation, I study the impact of popularity bias in recommender systems from a multi-stakeholder perspective. In addition, I propose several algorithms each approaching the popularity bias mitigation from a different angle and compare their performances using several metrics with some other state-of-the-art approaches in the literature. I show that, often, the standard evaluation measures of popularity bias mitigation in the literature do not reflect the real picture of an algorithm's performance when it is evaluated from a multi-stakeholder point of view.
Microvariability in AGNs: study of different statistical methods – II. Light curves from simulated images

In a previous paper, we studied two statistical methods used to analyse the variability of active galactic nuclei (AGNs): the C and F statistics. Applying them to observed differential light-curves of 39 AGNs, we found that, even though the C criterion cannot be considered as an actual statistical test, it could still be a useful parameter to detect variability, whereas F is a good detector of non-variability. In order to test these results under controlled input conditions, so that the different error sources could be individually evaluated, we generated a series of synthetic differential light curves simulating astronomical images with different atmospheric conditions, such as cloud cover, seeing or sky brightness, as well as several types of intrinsic variability of the AGN, all with a specific instrumental configuration. Having obtained light curves for each case, we applied both statistics to them in order to test their reliability. We found that a weight factor should always be used with these indices. The F test has a tendency to classify noisy non-variable curves as variable (i.e. false positives), although it is reliable and robust to correctly classify non-variable curves. On the contrary, although the C index tends to give false negatives, we found that whenever the C index indicates a source to be variable, it effectively is. Finally, light curves with low amplitude variabilities are more likely to be affected by changes in atmospheric conditions.
Instance-Aware Graph Convolutional Network for Multi-Label Classification

Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task by introducing label dependencies based on statistical label co-occurrence of data. However, in previous methods, label correlation is computed based on statistical information of data and therefore the same for all samples, and this makes graph inference on labels insufficient to handle huge variations among numerous image instances. In this paper, we propose an instance-aware graph convolutional neural network (IA-GCN) framework for multi-label classification. As a whole, two fused branches of sub-networks are involved in the framework: a global branch modeling the whole image and a region-based branch exploring dependencies among regions of interests (ROIs). For label diffusion of instance-awareness in graph convolution, rather than using the statistical label correlation alone, an image-dependent label correlation matrix (LCM), fusing both the statistical LCM and an individual one of each image instance, is constructed for graph inference on labels to inject adaptive information of label-awareness into the learned features of the model. Specifically, the individual LCM of each image is obtained by mining the label dependencies based on the scores of labels about detected ROIs. In this process, considering the contribution differences of ROIs to multi-label classification, variational inference is introduced to learn adaptive scaling factors for those ROIs by considering their complex distribution. Finally, extensive experiments on MS-COCO and VOC datasets show that our proposed approach outperforms existing state-of-the-art methods.
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics

Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning, and control. The problem has recently been studied from a generative perspective through latent dynamics: high-dimensional observations are embedded into a lower-dimensional space in which the dynamics can be learned. Despite some successes, latent dynamics models have not yet been applied to real-world robotic systems where learned representations must be robust to a variety of perceptual confounds, and noise sources not seen during training. In this letter, we present a method to jointly learn a latent state representation, and the associated dynamics that is amenable for long-term planning, and closed-loop control under perceptually difficult conditions. As our main contribution, we describe how our representation is able to capture a notion of heteroscedastic or input-specific uncertainty at test time by detecting novel or out-of-distribution (OOD) inputs. We present results from prediction, and control experiments on two image-based tasks: a simulated pendulum balancing task, and a real-world robotic manipulator reaching task. We demonstrate that our model produces significantly more accurate predictions, and exhibits improved control performance, compared to a model that assumes homoscedastic uncertainty only, in the presence of varying degrees of input degradation.
TNT: Target-driveN Trajectory Prediction

Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and intrinsically multimodal. Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states. This leads to our target-driven trajectory prediction (TNT) framework. TNT has three stages which are trained end-to-end. It first predicts an agent's potential target states $T$ steps into the future, by encoding its interactions with the environment and the other agents. TNT then generates trajectory state sequences conditioned on targets. A final stage estimates trajectory likelihoods and a final compact set of trajectory predictions is selected. This is in contrast to previous work which models agent intents as latent variables, and relies on test-time sampling to generate diverse trajectories. We benchmark TNT on trajectory prediction of vehicles and pedestrians, where we outperform state-of-the-art on Argoverse Forecasting, INTERACTION, Stanford Drone and an in-house Pedestrian-at-Intersection dataset.
Learning Tuple Compatibility for Conditional Outfit Recommendation

Outfit recommendation requires the answers of some challenging outfit compatibility questions such as 'Which pair of boots and school bag go well with my jeans and sweater? '. It is more complicated than conventional similarity search, and needs to consider not only visual aesthetics but also the intrinsic fine-grained and multi-category nature of fashion items. Some existing approaches solve the problem through sequential models or learning pair-wise distances between items. However, most of them only consider coarse category information in defining fashion compatibility while neglecting the fine-grained category information often desired in practical applications. To better define the fashion compatibility and more flexibly meet different needs, we propose a novel problem of learning compatibility among multiple tuples (each consisting of an item and category pair), and recommending fashion items following the category choices from customers. Our contributions include: 1) Designing a Mixed Category Attention Net (MCAN) which integrates both fine-grained and coarse category information into recommendation and learns the compatibility among fashion tuples. MCAN can explicitly and effectively generate diverse and controllable recommendations based on need. 2) Contributing a new dataset IQON, which follows eastern culture and can be used to test the generalization of recommendation systems. Our extensive experiments on a reference dataset Polyvore and our dataset IQON demonstrate that our method significantly outperforms state-of-the-art recommendation methods.
Deep Neural Networks for automatic extraction of features in time series satellite images

Many earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. In this work, we exploit both temporal and spatial information provided by these images to generate land cover maps. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are provided. Experimental results show that the temporal information provided by time series images allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth.
Mg ii Absorbers in High-resolution Quasar Spectra. I. Voigt Profile Models

We present the Voigt profile (VP) models, column densities, Doppler b parameters, kinematics, and distribution of components for 422 Mg ii absorbers found in a survey of 249 HIRES and UVES quasar spectra. The equivalent width range of the sample is 0.006 ≤ W r (2796) ≤ 6.23 Å and the redshift range is 0.19 ≤ z ≤ 2.55, with a mean of . Based on historical precedent, we classified 180 absorbers as weak systems (W r (2796) < 0.3 Å) and 242 as strong systems (W r (2796) ≥ 0.3 Å). Assuming a minimum number of significant components per system, the VP fitting, yielded a total of 2989 components, with an average of 2.7 and 10.3 components found for the weak and strong Mg ii subsamples, respectively. The VP component line density for the full sample is 8.62 ± 0.23 clouds Å−1. The distribution of VP component column density over the range 12.4 ≤ log N(Mg ii) ≤ 17.0 cm−2 is well modeled with a power-law slope of −1.45 ± 0.01. The median Doppler b parameters are 4.5 ± 3.5 km s−1, 6.0 ± 4.5 km s−1, and 5.7 ± 4.4 km s−1 for the weak, strong, and full samples. We modeled the probability of component velocity splitting (the two-point velocity correlation function) of our full sample using a three-component composite Gaussian function. Our resulting velocity dispersions are σ 1 = 25.4 km s−1, σ 2 = 68.7 km s−1, and σ 3 = 207.1 km s−1. These data provide an excellent database for studying the cosmic evolution of Mg ii absorber kinematic evolution.
Globalized distributionally robust optimization problems under the moment-based framework

This paper is devoted to reduce the conservatism of distributionally robust optimization with moments information. Since the optimal solution of distributionally robust optimization is required to be feasible for all uncertain distributions in a given ambiguity distribution set and so the conservatism of the optimal solution is inevitable. To address this issue, we introduce the globalized distributionally robust counterpart (GDRC) which allows constraint violations controlled by functional distance of the true distribution to the inner uncertainty distribution set. We obtain the deterministic equivalent forms for several GDRCs under the moment-based framework. To be specific, we show the deterministic equivalent system of inequalities for the GDRCs under second order moment information with a separable distance function and a jointly convex distance function, respectively. Moreover, the feasible set of the system is convex. We also develop the deterministic equivalent inequality for the GDRC under first order moment and support information. The computationally tractable examples are presented for these GDRCs. A numerical tests of a portfolio optimization problem is given to show the efficiency of our methods and the results demonstrate that the globalized distributionally robust solutions is non-conservative and flexible compared to the distributionally robust solutions.