Miracle-dz/UniFAR-FTUs huggingface dataset:
Maritime Coverage Enhancement Using UAVs Coordinated With Hybrid Satellite-Terrestrial Networks

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.
Quantum dark solitons in the one-dimensional Bose gas

Dark and grey soliton-like states are shown to emerge from numerically constructed superpositions of translationally-invariant eigenstates of the interacting Bose gas in a toroidal trap. The exact quantum many-body dynamics reveals a density depression with superdiffusive spreading that is absent in the mean-field treatment of solitons. A simple theory based on finite-size bound states of holes with quantum-mechanical center-of-mass motion quantitatively explains the time-evolution of the superposition states and predicts quantum effects that could be observed in ultra-cold gas experiments. The soliton phase step is shown to be a key ingredient of an accurate finite size approximation, which enables us to compare the theory with numerical simulations. The fundamental soliton width, an invariant property of the quantum dark soliton, is shown to deviate from the Gross-Pitaevskii predictions in the interacting regime and vanishes in the Tonks-Girardeau limit.
Inferring Dynamic Representations of Facial Actions from a Still Image

Facial actions are spatio-temporal signals by nature, and therefore their modeling is crucially dependent on the availability of temporal information. In this paper, we focus on inferring such temporal dynamics of facial actions when no explicit temporal information is available, i.e. from still images. We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation. In particular, 1) we propose a framework that infers a dynamic representation (DR) from a still image, which captures the bi-directional flow of time within a short time-window centered at the input image; 2) we show that we can train our method without the need of explicitly generating target representations, allowing the network to represent dynamics more broadly; and 3) we propose to apply a multiple temporal scale approach that infers DRs for different window lengths (MDR) from a still image. We empirically validate the value of our approach on the task of frame ranking, and show how our proposed MDR attains state of the art results on BP4D for AU intensity estimation and on SEMAINE for dimensional affect estimation, using only still images at test time.
Strongly intensive fluctuations and correlations in ultrarelativistic nuclear collisions in the model with string fusion

The several types of strongly intensive correlation variables are studied in nuclear collisions at LHC energy. These quantities are expected not to depend on centrality class width. They have been calculated in the dipole-based parton-string Monte Carlo model with string fusion. The centrality dependence of the mean transverse momentum correlation coefficient and strongly intensive quantity Σ between multiplicity and PT have been obtained. Dynamical charge fluctuation vdyn has been also calculated and compared with experimental data. It is shown that string fusion improves agreement with the experiment.
Quantifying the Relative Contribution of Climate Change and Anthropogenic Activities on Runoff Variations in the Central Part of Tajikistan in Central Asia

Quantifying the relative contribution of climate change and anthropogenic activities to runoff alterations are essential for the sustainable management of water resources in Central Asian countries. In the Kofarnihon River Basin (KRB) in Central Asia, both changing climate conditions and anthropogenic activities are known to have caused changes to the hydrological cycle. Therefore, quantifying the net influence of anthropogenic contribution to the runoff changes is a challenge. This study applied the original and modified Mann–Kendall trend test, including the Sen’s slope test, Pettitt’s test, double cumulative curve, and elasticity methods. These methods were applied to determine the historical trends, magnitude changes and change points of the temperature, precipitation, potential evapotranspiration, and runoff from 1950 to 2016. In addition, the contributions of climate change and anthropogenic activities to runoff changes in the KRB were evaluated. The trend analysis showed a significant increasing trend in annual temperature and potential evapotranspiration, while the annual precipitation trend showed an insignificant decreasing trend during the 1950–2016 time period. The change point in runoff occurred in 1986 in the upstream region and 1991 in the downstream region. Further, the time series (1950–2016) is separated into the prior impacted period (1950–1986 and 1950–1991) and post impacted period (1987–2016 and 1992–2016) for the upstream and downstream regions, respectively. During the post impacted period, climate change and anthropogenic activities contributed to 87.96% and 12.04% in the upstream region and 7.53% and 92.47% in the downstream region of the KRB. The results showed that in runoff changes, the anthropogenic activities played a dominant role in the downstream (97.78%) and the climate change impacts played a dominant factor in the upstream region (87.96%). In the land-use type changes, the dominant role was played by construction land, which showed that the area from 248.63 km2 in 1990 increased to 685.45 km2 (175.69%) in 2015. These findings suggest that it is essential to adopt effective steps for the sustainable development of the ecological, hydrological, and social order in the KRB in Central Asia.
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning

Various NLP problems – such as the prediction of sentence similarity, entailment, and discourse relations – are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g. concatenation or sum) of those vectors as features for the prediction. At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction.
Answer-based Adversarial Training for Generating Clarification Questions

We present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete. We propose that modeling hypothetical answers (to clarification questions) as latent variables can guide our approach into generating more useful clarification questions. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.
Bose polarons near quantum criticality

Immersing the impurities In solids, moving electrons can polarize their environment, the ionic crystal lattice, creating quasi particles known as polarons. An analogous phenomenon can be studied in quantum gases by placing impurity atoms into a much denser gas of atoms of a different species. Yan et al. studied the behavior of Bose polarons, quasiparticles consisting of impurity atoms of potassium-40 immersed in a Bose gas of sodium-23 atoms. In the vicinity of a quantum critical point, the lifetime of the polarons followed the so-called Planckian scale. Science, this issue p. 190 Quasiparticles consisting of impurity atoms of potassium-40 immersed in a gas of sodium-23 atoms are studied. The emergence of quasiparticles in interacting matter represents one of the cornerstones of modern physics. However, in the vicinity of a quantum critical point, the existence of quasiparticles comes under question. Here, we created Bose polarons near quantum criticality by immersing atomic impurities in a Bose-Einstein condensate (BEC) with near-resonant interactions. Using radiofrequency spectroscopy, we probed the energy, spectral width, and short-range correlations of the impurities as a function of temperature. Far below the superfluid critical temperature, the impurities formed well-defined quasiparticles. Their inverse lifetime, given by their spectral width, increased linearly with temperature at the so-called Planckian scale, consistent with quantum critical behavior. Close to the BEC critical temperature, the spectral width exceeded the impurity’s binding energy, signaling a breakdown of the quasiparticle picture.
Heterogeneous Government Spending Multipliers in the Era Surrounding the Great Recession

Abstract We use novel quarterly data of U.S. states to examine the dynamics of relative spending multipliers in the decade surrounding the Great Recession. While multipliers were around 1 in expansions, they reached values above 4 when a state was in a recession. Also a high (low) degree of household indebtedness augmented (lowered) a state's multiplier by 0.5 in expansions and 2 in recessions. We further document modest positive spillover effects across states and show that a mere redistribution of spending across states also had a significant influence on the aggregate U.S. economy due to cross-state heterogeneity of the effects.
Analysis of offshore wind energy potential for power generation in three selected locations in Nigeria

The rise in the global carbon footprint arising from fossil fuel sources has necessitated the need to explore sustainable and eco-friendly sources of energy. Wind as a source of renewable energy has been underutilized in Nigeria and this study is focused on investigating the wind energy potential in three offshore regions of Nigeria. Three statistical models namely, Weibull, Rayleigh and Exponential distribution were used to analyze the daily time series wind data for the offshore regions in Lagos (VI), Rivers (Abonemma) and Warri (Koko). This was done for a 10-year period between the year 2002–2011. The annual mean wind speeds for Lagos (VI), Rivers (Abonemma) and Warri (Koko) were determined to be 6.25, 7.32 and 7.29 m/s, respectively. The annual mean wind power densities were determined to be 171.47, 240.43 and 237.60 W/m2, respectively. The values of the wind speed carrying the maximum energy were determined to be 7.43, 8.37 and 8.96 m/s, respectively. It was found that the wind classes of the three locations are suitable for power generation.
Power Law Size Distributions in Geoscience Revisited

The size or energy of diverse structures or phenomena in geoscience appears to follow power law distributions. A rigorous statistical analysis of such observations is tricky, though. Observables can span several orders of magnitude, but the range for which the power law may be valid is typically truncated, usually because the smallest events are too tiny to be detected and the largest ones are limited by the system size. We revisit several examples of proposed power law distributions dealing with potentially damaging natural phenomena. Adequate fits of the distributions of sizes are especially important in these cases, given that they may be used to assess long‐term hazard. After reviewing the theoretical background for power law distributions, we improve an objective statistical fitting method and apply it to diverse data sets. The method is described in full detail, and it is easy to implement. Our analysis elucidates the range of validity of the power law fit and the corresponding exponent and whether a power law tail is improved by a truncated lognormal. We confirm that impact fireballs and Californian earthquakes show untruncated power law behavior, whereas global earthquakes follow a double power law. Rain precipitation over space and time and tropical cyclones show a truncated power law regime. Karst sinkholes and wildfires, in contrast, are better described by truncated lognormals, although wildfires also may show power law regimes. Our conclusions only apply to the analyzed data sets but show the potential of applying this robust statistical technique in the future.
Template-Based Automatic Search of Compact Semantic Segmentation Architectures

Automatic search of neural architectures for various vision and natural language tasks is becoming a prominent tool as it allows to discover high-performing structures on any dataset of interest. Nevertheless, on more difficult domains, such as dense per-pixel classification, current automatic approaches are limited in their scope – due to their strong reliance on existing image classifiers they tend to search only for a handful of additional layers with discovered architectures still containing a large number of parameters. In contrast, in this work we propose a novel solution able to find light-weight and accurate segmentation architectures starting from only few blocks of a pre-trained classification network. To this end, we progressively build up a methodology that relies on templates of sets of operations, predicts which template and how many times should be applied at each step, while also generating the connectivity structure and downsampling factors. All these decisions are being made by a recurrent neural network that is rewarded based on the score of the emitted architecture on the holdout set and trained using reinforcement learning. One discovered architecture achieves 63.2% mean IoU on CamVid and 67.8% on CityScapes having only 270K parameters.
Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction.

The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections. To alleviate this problem, in this paper, we propose 3DRotNet: a fully self-supervised approach to learn spatiotemporal features from unlabeled videos. A set of rotations are applied to all videos, and a pretext task is defined as prediction of these rotations. When accomplishing this task, 3DRotNet is actually trained to understand the semantic concepts and motions in videos. In other words, it learns a spatiotemporal video representation, which can be transferred to improve video understanding tasks in small datasets. Our extensive experiments successfully demonstrate the effectiveness of the proposed framework on action recognition, leading to significant improvements over the state-of-the-art self-supervised methods. With the self-supervised pre-trained 3DRotNet from large datasets, the recognition accuracy is boosted up by 20.4% on UCF101 and 16.7% on HMDB51 respectively, compared to the models trained from scratch.
Robust Multi-agent Counterfactual Prediction

We consider the problem of using logged data to make predictions about what would happen if we changed the `rules of the game' in a multi-agent system. This task is difficult because in many cases we observe actions individuals take but not their private information or their full reward functions. In addition, agents are strategic, so when the rules change, they will also change their actions. Existing methods (e.g. structural estimation, inverse reinforcement learning) make counterfactual predictions by constructing a model of the game, adding the assumption that agents' behavior comes from optimizing given some goals, and then inverting observed actions to learn agent's underlying utility function (a.k.a. type). Once the agent types are known, making counterfactual predictions amounts to solving for the equilibrium of the counterfactual environment. This approach imposes heavy assumptions such as rationality of the agents being observed, correctness of the analyst's model of the environment/parametric form of the agents' utility functions, and various other conditions to make point identification possible. We propose a method for analyzing the sensitivity of counterfactual conclusions to violations of these assumptions. We refer to this method as robust multi-agent counterfactual prediction (RMAC). We apply our technique to investigating the robustness of counterfactual claims for classic environments in market design: auctions, school choice, and social choice. Importantly, we show RMAC can be used in regimes where point identification is impossible (e.g. those which have multiple equilibria or non-injective maps from type distributions to outcomes).
Dynamics of branes in Double Field Theory

In this contribution the construction of the Wess-Zumino term for Dp-branes and NS 5-branes invariant under T-duality is described. The cases of the massless Type IIA/B and the massive Type IIA theories are covered. Mass parameter is introduced by generalized Scherk-Schwarz ansatz of O(10,10) spinor and tensor fields. The results lead to the picture where a given brane is understood as an invariant object rotated into the full doubled space.
A multi‐criteria decision‐making approach for assembling optimal powertrain technology portfolios in low GHG emission environments

Environmental regulations force car manufacturers to renew the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. In turn, automotive companies face the task of identifying the “right” powertrain technology portfolio consisting of, for example, internal combustion engines and electric vehicles, because the selection of a particular powertrain technology portfolio affects different company targets simultaneously. What makes this decision even more challenging is that future market shares of the different technologies are uncertain. Our research presents a new decision‐support approach for assembling optimal powertrain technology portfolios while making decision‐makers aware of the trade‐offs between the achievable profit, the achievable market share, the market share risk, and the GHG emissions generated by the selected vehicle fleet. The proposed approach combines “a posteriori” decision‐making with multi‐objective optimization. In an application case, we feed the outlooks of selected market studies into the proposed decision‐support system. The result is a visualization and analysis of the current real‐world decision‐making problem faced by many automotive companies. Our findings indicate that for the proposed GHG restriction at work in 2030 in the European Union, no optimal powertrain technology portfolio with less than 35% of vehicles equipped with an electric motor exists.
Constraining Mass-transfer Histories of Blue Straggler Stars with COS Spectroscopy of White Dwarf Companions

Recent studies show that the majority of blue straggler stars (BSSs) in old open clusters are formed through mass transfer from an evolved star onto a main-sequence companion, resulting in a BSS and white dwarf (WD) in a binary system. We present constraints on the mass transfer histories of two BSS–WD binaries in the open cluster NGC 188, using WD temperatures and surface gravities measured with Hubble Space Telescope COS far-ultraviolet spectroscopy. Adopting a Gaia-based cluster distance of 1847 ± 107 pc, we determine that one system, WOCS 4540, formed through Case C mass transfer resulting in a CO-core WD with Teff = K and a log g = , corresponding to a mass of M⊙ and a cooling age of Myr. The other system, WOCS 5379, formed through Case B mass transfer resulting in a He-core WD with Teff = K and a log g = , corresponding to a mass of M⊙ and an age of Myr. The WD parameters are consistent across four different cluster distance assumptions. We determine possible progenitor binary systems with a grid of accretion models using MESA, and investigate whether these systems would lead to stable or unstable mass transfer. WOCS 4540 likely resulted from stable mass transfer during periastron passage in an eccentric binary system, while WOCS 5379 challenges our current understanding of the expected outcomes for mass transfer from red giant branch stars. Both systems are examples of the value in using detailed analyses to fine-tune our physical understanding of binary evolutionary processes.