Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method for Image Denoising with Nonlocal Means

Denoising is an important problem in image processing, as it can influence the following processing step and decide the final visual effect. To denoise the image and preserve the details, this paper enhances the Nonlocal Means. When estimating the value of a disturbed pixel, we use the information of edges and textures as the weights of pixels involved in calculation, instead of simple Gaussian distance weights. The weights of edge and texture are obtained by direction grads and the coefficients of variation (CV). Finally, based on the results from different images, the enhanced algorithm is shown to have better performance than traditional NLM in SSIM, and has a better visual effect in subjective evaluations. Keywords-denoise; nonlocal means; direction grads; coefficient of variation.
The crustal thickness of West Antarctica

P‐to‐S receiver functions (PRFs) from the Polar Earth Observing Network (POLENET) GPS and seismic leg of POLENET spanning West Antarctica and the Transantarctic Mountains deployment of seismographic stations provide new estimates of crustal thickness across West Antarctica, including the West Antarctic Rift System (WARS), Marie Byrd Land (MBL) dome, and the Transantarctic Mountains (TAM) margin. We show that complications arising from ice sheet multiples can be effectively managed and further information concerning low‐velocity subglacial sediment thickness may be determined, via top‐down utilization of synthetic receiver function models. We combine shallow structure constraints with the response of deeper layers using a regularized Markov chain Monte Carlo methodology to constrain bulk crustal properties. Crustal thickness estimates range from 17.0±4 km at Fishtail Point in the western WARS to 45±5 km at Lonewolf Nunataks in the TAM. Symmetric regions of crustal thinning observed in a transect deployment across the West Antarctic Ice Sheet correlate with deep subice basins, consistent with pure shear crustal necking under past localized extension. Subglacial sediment deposit thicknesses generally correlate with trough/dome expectations, with the thickest inferred subice low‐velocity sediment estimated as ∼0.4 km within the Bentley Subglacial Trench. Inverted PRFs from this study and other published crustal estimates are combined with ambient noise surface wave constraints to generate a crustal thickness map for West Antarctica south of 75°S. Observations are consistent with isostatic crustal compensation across the central WARS but indicate significant mantle compensation across the TAM, Ellsworth Block, MBL dome, and eastern and western sectors of thinnest WARS crust, consistent with low density and likely dynamic, low‐viscosity high‐temperature mantle.
Fast N-Dimensional Hilbert Mapping Algorithm

To address the problem of complexity of the high dimensional Hilbert curve, we present a novel way for analyzing Hilbert curve, which is based on a static evolvement rule table, called Hilbert gene. Based on the Hilbert gene, we present algorithms for implementing N-dimensional Hilbert mappings. According to the commands of the Hilbert gene, through performing the coordinate transformations, it can implement highly efficient Hilbert mappings. The experimental results show that our method can perform Hilbert mappings as efficiently as Z-order mappings in up to about 10 dimensions. It indicates that Hilbert mapping can take the place of Z-order mapping in practice.
Database Research: Are We at a Crossroad? Reflection on NoSQL

The demand to process large sets of data has increased in the last few years from both the scientific and business community. To serve this demand, a number of new databases have been introduced that are not based on relational models. This group of databases is popularly known as NoSQL. The underlying data and transaction models in the NoSQL are different from relational databases. Much interest has been placed by organizations in adopting this new technology and has created a buzz in database research. The fact that the underlying principles are different to relational models has placed a dilemma in the database research community. Would this new technology change the shape of database research and industry?
Longitudinal rms emittance preservation during adiabatic capture for Gaussian beams

This paper presents an analysis of the emittance preservation during a heavy ion bunching process. This analysis aims at finding the correlation between the rms emittance of a coasting beam and that of the resulting bunches. The emittance of a Gaussian coasting beam bunched adiabatically in synchrotrons is investigated using theoretical considerations and particle tracking simulations. New results are presented on the relation between the emittance before and after the bunching process. It is found that a constant factor can be determined to describe this relation. This factor can be used to avoid time-consuming simulations. If the rms emittance is known before the bunching process, it can be calculated for the bunched beam afterwards. This also makes it possible to determine the required bucket area for a certain filling ratio.
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs

Medical crowdfunding is a popular channel for people needing financial help paying medical bills to collect donations from large numbers of people. However, large heterogeneity exists in donations across cases, and fundraisers face significant uncertainty in whether their crowdfunding campaigns can meet fundraising goals. Therefore, it is important to provide early warnings for fundraisers if such a channel will eventually fail. In this study, we aim to develop novel algorithms to provide accurate and timely predictions of fundraising performance, to better inform fundraisers. In particular, we propose a new approach to combine time-series features and time-invariant features in the deep learning model, to process diverse sources of input data. Compared with baseline models, our model achieves better accuracy and requires a shorter observation window of the time-varying features from the campaign launch to provide robust predictions with high confidence. To extract interpretable insights, we further conduct a multivariate time-series clustering analysis and identify four typical temporal donation patterns. This demonstrates the heterogeneity in the features and how they relate to the fundraising outcome. The prediction model and the interpretable insights can be applied to assist fundraisers with better promoting their fundraising campaigns and can potentially help crowdfunding platforms to provide more timely feedback to all fundraisers. Our proposed framework is also generalizable to other fields where diverse structured and unstructured data are valuable for predictions.
Recombination: A family of Markov chains for redistricting

Redistricting is the problem of partitioning a set of geographical units into a fixed number of districts, subject to a list of often-vague rules and priorities. In recent years, the use of randomized methods to sample from the vast space of districting plans has been gaining traction in courts of law for identifying partisan gerrymanders, and it is now emerging as a possible analytical tool for legislatures and independent commissions. In this paper, we set up redistricting as a graph partition problem and introduce a new family of Markov chains called Recombination (or ReCom) on the space of graph partitions. The main point of comparison will be the commonly used Flip walk, which randomly changes the assignment label of a single node at a time. We present evidence that ReCom mixes efficiently, especially in contrast to the slow-mixing Flip, and provide experiments that demonstrate its qualitative behavior. We demonstrate the advantages of ReCom on real-world data and explain both the challenges of the Markov chain approach and the analytical tools that it enables. We close with a short case study involving the Virginia House of Delegates.
Control of chaos in Frenkel–Kontorova model using reinforcement learning

It is shown that we can control spatiotemporal chaos in the Frenkel–Kontorova (FK) model by a model-free control method based on reinforcement learning. The method uses Q-learning to find optimal control strategies based on the reward feedback from the environment that maximizes its performance. The optimal control strategies are recorded in a Q-table and then employed to implement controllers. The advantage of the method is that it does not require an explicit knowledge of the system, target states, and unstable periodic orbits. All that we need is the parameters that we are trying to control and an unknown simulation model that represents the interactive environment. To control the FK model, we employ the perturbation policy on two different kinds of parameters, i.e., the pendulum lengths and the phase angles. We show that both of the two perturbation techniques, i.e., changing the lengths and changing their phase angles, can suppress chaos in the system and make it create the periodic patterns. The form of patterns depends on the initial values of the angular displacements and velocities. In particular, we show that the pinning control strategy, which only changes a small number of lengths or phase angles, can be put into effect.
The Strength of Connectivity of Random Graphs induced by Pairwise Key Predistribution Schemes: Implications on Security and Reliability of Heterogeneous Sensor Networks

Random key predistribution schemes have emerged as a widely adopted solution for facilitating secure communication in Wireless Sensor Networks (WSNs). Emerging real world networks increasingly rely on integrating information from sensor nodes with different resources and requirements. We analyze the strength of connectivity of a heterogeneous WSN under the random pairwise key predistribution scheme of Chan et al. According to this scheme, each of the $n$ sensor nodes is classified as type-1 (respectively, type-2) with probability $\mu$ (respectively, $1-\mu)$ where $0<\mu<1$. Each type-1 (respectively, type-2) node is paired with 1 (respectively, $K_n$) other node selected uniformly at random; each pair is then assigned a unique pairwise key so that they can securely communicate with each other. A main question in the design of secure networks is how should the parameters $n$, $\mu$, and $K_n$ be selected such that the resulting network exhibits certain desirable properties with high probability. It was recently established that achieving even 1-connectivity a.a. s. requires $K_n=\omega(1)$ for the inhomogeneous random K-out graph model naturally induced under the heterogeneous pairwise scheme. In this paper, we analyse $k$-connectivity, the property that the network remains connected despite the removal of any $k-1$ nodes or links. First, we establish a sharp zero-one law for $k$-connectivity, showing that for the network to be $k$-connected a.a. s. , we need to set $K_n = \frac{1}{1-\mu}(\log n +(k-2)\log\log n + \omega(1))$ for all $k=2, 3, \ldots$. Despite such large scaling of $K_n$ being required for $k$-connectivity, we also show that merely having $K_n =3$ ensures that all but finitely many nodes will form a connected sub-network a.a. s. We provide comprehensive simulations to demonstrate the usefulness of our results in choosing network parameters in the finite node regime.
Laser cooling below the Doppler limit by polarization gradients: simple theoretical models

We present two cooling mechanisms that lead to temperatures well below the Doppler limit. These mechanisms are based on laser polarization gradients and work at low laser power when the optical-pumping time between different ground-state sublevels becomes long. There is then a large time lag between the internal atomic response and the atomic motion, which leads to a large cooling force. In the simple case of one-dimensional molasses, we identify two types of polarization gradient that occur when the two counterpropagating waves have either orthogonal linear polarizations or orthogonal circular polarizations. In the first case, the light shifts of the ground-state Zeeman sublevels are spatially modulated, and optical pumping among them leads to dipole forces and to a Sisyphus effect analogous to the one that occurs in stimulated molasses. In the second case (σ+−σ− configuration), the cooling mechanism is radically different. Even at very low velocity, atomic motion produces a population difference among ground-state sublevels, which gives rise to unbalanced radiation pressures. From semiclassical optical Bloch equations, we derive for the two cases quantitative expressions for friction coefficients and velocity capture ranges. The friction coefficients are shown in both cases to be independent of the laser power, which produces an equilibrium temperature proportional to the laser power. The lowest achievable temperatures then approach the one-photon recoil energy. We briefly outline a full quantum treatment of such a limit.
A Convergent Off-Policy Temporal Difference Algorithm

Learning the value function of a given policy (target policy) from the data samples obtained from a different policy (behavior policy) is an important problem in Reinforcement Learning (RL). This problem is studied under the setting of off-policy prediction. Temporal Difference (TD) learning algorithms are a popular class of algorithms for solving the prediction problem. TD algorithms with linear function approximation are shown to be convergent when the samples are generated from the target policy (known as on-policy prediction). However, it has been well established in the literature that off-policy TD algorithms under linear function approximation diverge. In this work, we propose a convergent on-line off-policy TD algorithm under linear function approximation. The main idea is to penalize the updates of the algorithm in a way as to ensure convergence of the iterates. We provide a convergence analysis of our algorithm. Through numerical evaluations, we further demonstrate the effectiveness of our algorithm.
State Estimation for Legged Robots Using Contact-Centric Leg Odometry

Our goal is to send legged robots into challenging, unstructured terrains that wheeled systems cannot traverse. Moreover, precise estimation of the robot's position and orientation in rough terrain is especially difficult. To address this problem, we introduce a new state estimation algorithm which we term Contact-Centric Leg Odometry (COCLO). This new estimator uses a Square Root Unscented Kalman Filter (SR-UKF) to fuse multiple proprioceptive sensors available on a legged robot. In contrast to IMU-centric filtering approaches, COCLO formulates prediction and measurement models according to the contact status of legs. Additionally, COCLO has an indirect measurement model using joint velocities to estimate the robot body velocity. In rough terrain, when IMUs suffer from large amounts of noise, COCLO's contact-centric approach outperforms previous IMU-centric methods. To demonstrate improved state estimation accuracy, we compare COCLO with Visual Inertial Navigation System (VINS), a state-of-the-art visual inertial odometry, in three different environments: flat ground, ramps, and stairs. COCLO achieves better estimation precision than VINS in all three environments and is robust to unstable motion. Finally, we also show that COCLO and a modified VINS can work in tandem to improve each other's performance.
The effect of geographic sampling on extreme precipitation: from models to observations and back again

In light of the significant uncertainties present in global climate models' characterization of precipitation extremes, it is important to properly use observational data sets to determine whether a particular climate model is suitable for simulating extremes. In this paper, we identify two problems with traditional approaches for comparing global climate models and observational data products with respect to extremes: first, daily gridded products are a suboptimal data source to use for this comparison, and second, failing to account for the geographic locations of weather station data can paint a misleading picture with respect to model performance. To demonstrate these problems, we utilize in situ measurements of daily precipitation along with a spatial statistical extreme value analysis to evaluate and compare model performance with respect to extreme climatology. As an illustration, we use model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment (Haarsma et al., 2016), comparing integrated metrics of an "extreme" bias and Taylor diagrams. Our main point is that the choice of methodology for model comparison of extremes (with respect to choice of observational data and accounting for geographic sampling) is relatively unimportant for well-sampled areas but very important for sparsely sampled areas. While we focus on the contiguous United States in this paper, our results have important implications for other global land regions where the sampling problem is more severe.
Capture Aware Sequential Waterfilling for LoRaWAN Adaptive Data Rate

LoRaWAN (Long 1 Range Wide Area Network) is an attractive network infrastructure and protocol suite for ultra low power Internet of Things devices. Even if the technology itself is quite mature and specified, the currently deployed wireless resource allocation strategies are still coarse and based on rough heuristics. This paper proposes an innovative “sequential waterfilling” strategy for assigning spreading factors to End Devices. Our design relies on three complementary approaches: i) equalize the Time-on-Air of packets transmitted by the system’s End Devices in each spreading factor’s group; ii) balance the spreading factors across multiple gateways and iii) keep into account the channel capture, which our experimental results show to be very substantial in LoRa. While retaining an extremely simple and scalable implementation, this strategy yields a significant improvement (up to 38%) in the network capacity over the Adaptive Data Rate used by many network operators on the basis of the design suggested by Semtech, and appears to be extremely robust to different operating/load conditions and network topology configurations.
Existence of Golkar Party in Banggai Regency 1999-2009

Golkar Party had become the rulling party in Banggai District for ten years starting from 1999-2009. The fantastic victory achieved by the Golkar Party was about 58 percent of the vote in 1999, followed by 32.78 percent in 2004, and then 37 percent votes in 2009. Banggai District has become a granary for the Golkar Party. Therefore, it is not surprising that Golkar Party has solidly demonstrated its existence in both legislative and executive institutions for ten years. This article aims to describe the Golkar Party's strategies in maintaining its existence in Banggai District since 1999-2009. Data were obtained through interviews, documentation and literature study. The results of this study include: First, Mass Basis; Second, the selection of cadres; Third, the relation of Golkar Party with socialorganizations that become the foundation for strengthening its existence in Banggai Regency. The sub-districts were used as the basis of the masses other than placing the Golkar Party cadres as sub-district heads. Tribal politics, identity, cadres became networks to gain votes. The construction business network of Golkar Party cadres has become an economic power for Golkar Party. Finally, the organizationKarangtaruna, KNPI, SOKSI, KOSGORO, and FKPPI become an instrument also in the cadres. Therefore, Golkar Party had become a hegemonic party in Banggai District from 199-2009. Keywords—Existence; Golkar Party; Banggai Regency
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review

We consider the issue of multiple agents learning to communicate through reinforcement learning within partially observable environments, with a focus on information asymmetry in the second part of our work. We provide a review of the recent algorithms developed to improve the agents' policy by allowing the sharing of information between agents and the learning of communication strategies, with a focus on Deep Recurrent Q-Network-based models. We also describe recent efforts to interpret the languages generated by these agents and study their properties in an attempt to generate human-language-like sentences. We discuss the metrics used to evaluate the generated communication strategies and propose a novel entropy-based evaluation metric. Finally, we address the issue of the cost of communication and introduce the idea of an experimental setup to expose this cost in cooperative-competitive game.
Chukar Watering Patterns and Water Site Selection

Abstract We evaluated chukar (Alectoris chukar) watering patterns as well as habitat variables influencing water site selection in western Utah. Motion-sensing cameras and chukar dropping counts were primary techniques to evaluate watering patterns. We took vegetative and other habitat measurements at each water source (n = 43) to discriminate use from nonuse sites using logistic regression. Chukars watered during daylight hours with a modal hour from 1200 hours to 1300 hours daylight savings time. Annual patterns suggest limited use of water sources from November to May with first observed visits occurring in June and last observed visits in October. Shrub canopy cover was the only variable to discriminate between site types (P < 0.01). Cross validation showed a predictive success rate of 84%. Significant differences were found between use and nonuse sites in terms of security cover (P < 0.01), but not total cover (P > 0.05). Chukars seem to have a loose shrub canopy threshold near 11% that is likely due to predation risk. Water sources meeting this threshold received use, whereas those not meeting this threshold did not. Increasing shrub canopy cover above 11% did not translate into increased water source use. Managers might want to consider annual patterns when setting hunt season timing and structure as well as judging sites for new water developments based on shrub canopy cover. More generally, these results suggest a behavioral constraint on the use of water sources as a function of predation risk—we should expect other species to demonstrate similar behavioral constraints. These constraints must be considered in any effort to determine benefits or impacts of water developments.
Enhanced Voice Post Processing Using Voice Decoder Guidance Indicators

Voice enhancement and voice coding are imperative and important functions in a voice-communication system. However, both functions are commonly treated independently, even though both utilize similar features of the underlying signals. Our proposal is to leverage information from one function to the benefit of the other. Specifically, our proposed changes are focused on changes to the voice enhancement at the downlink side and utilizing information of the voice decoding. Preliminary results show that such an approach results in improved quality. Additionally, suggestions are provided on future extensions of the proposed concept.
Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment

This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs; and using more anchors with various scales to detect traffic signs with different sizes and shapes. The proposed algorithm was trained and tested in a series of autonomous driving front-view images in a virtual simulation environment. Results show that the algorithm performed extremely well under good illumination and weather conditions. Its drawbacks are that it sometimes failed to detect object under bad weather conditions like snow and failed to distinguish speed limits signs with different limit values.