Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure of the OSR1 kinase, a hypertension drug target

The oxidative stress-responsive kinase-1 (OSR1) and STE20/SPS1-related proline/alanine-rich kinase (SPAK) interact, phosphorylate, and stimulate the activity of the cation-chloride cotransporters (NKCC1, NKCC2, and the Na1:Cl2 cotransporter (NCC)). 1–4 These cotransporters play key roles in regulating salt intake and secretion from cells (reviewed in Refs. 5,6). Some of the most commonly prescribed blood pressure-lowering thiazide drugs exert their physiological effects by inhibiting NCC. The SPAK and OSR1 enzymes are phosphorylated and activated by the WNK1 and WNK4 protein kinases in response to hyperosmotic and hypotonic stress.7–9 This may increase blood pressure by stimulating phosphorylation and activation of NCC.3 Consistent with this, WNK1 and WNK4 genes are mutated in patients suffering from an inherited hypertension and hyperkalemia (elevated plasma K) disorder, termed pseudohypoaldosteronism type II.10,11 SPAK and OSR1 are 68% identical in sequence and possess a highly similar kinase catalytic domain as well as a conserved C-terminal (CCT) domain.4 The CCT domain operates as a docking site enabling OSR1 and SPAK to interact with RFXV (Arg-Phe-Xaa-Val) motifs found in both their upstream activators as well as their substrates.2,12,13 Structural analysis revealed that the CCT domain forms a novel protein fold.14 The WNK1 and WNK4 protein kinases phosphorylate OSR1 and SPAK at their T-loop residue within the kinase catalytic domain (Thr185 in OSR1, Thr233 in SPAK). 9 It is possible that inhibition of the WNK/OSR1/NCC phosphorylation cascade with kinase inhibitors may provide an alternative chemotherapeutic approach to direct inhibition of NCC. As a first step towards this, we describe the first crystal structure of an OSR1 fragment encompassing the catalytic domain of the enzyme.
Cost-Optimized Microblog Distribution over Geo-Distributed Data Centers

The unprecedent growth of microblog services poses significant challenges on network traffic and service latency to the underlay infrastructure (i.e., geo-distributed data centers). Furthermore, the dynamic evolution in microblog status generates a huge workload on data consistence maintenance. In this article, motivated by insights of cross-media analysis-based propagation patterns, we propose a novel cache strategy for microblog service systems to reduce the inter-data center traffic and consistence maintenance cost, while achieving low service latency. Specifically, we first present a microblog classification method, which utilizes the external knowledge from correlated domains, to categorize microblogs. Then we conduct a large-scale measurement on a representative online social network system to study the category-based propagation diversity on region and time scales. These insights illustrate social common habits on creating and consuming microblogs and further motivate our architecture design. Finally, we formulate the content cache problem as a constrained optimization problem. By jointly using the Lyapunov optimization framework and simplex gradient method, we find the optimal online control strategy. Extensive trace-driven experiments further demonstrate that our algorithm reduces the system cost by 24.5% against traditional approaches with the same service latency.
The Influence of Induction Motors on Voltage Sag Propagation—Part I: Accounting for the Change in Sag Characteristics

This paper presents an analytical approach to the analysis of the interaction between induction motors (IMs) and voltage sags. The presented methodology enables quick assessment of the influence of IM on sag characteristics and avoids time-consuming transient simulations. The accuracy of the derived analytical formulae is verified through classical transient simulations using the PSCAD/EMTDC package. This paper further proposes a simple, yet efficient transformation of resulting nonrectangular voltage sags, based on the loss of voltage index, into equivalent rectangular sags suitable for conventional voltage sag performance benchmarking.
Cutoff Rate of Sparse Code Multiple Access in Downlink Broadcast Channels

For the sake of supporting massive connectivity in the future 5G networks, non-orthogonal multiple access (NOMA) techniques are advocated. As a promising NOMA technique, in recent years sparse code multiple access (SCMA) has attracted substantial attention. However, there is a paucity of studies on the theoretical analysis of its error-freely achievable data rate, especially, in the downlink context. Hence, we derive the cutoff rate of SCMA in downlink broadcast channels, which indicates the lower-bound of a system’s error-freely achievable rate. However, we will demonstrate that when considering the conventional categorization of pairwise error events, the accuracy of the cutoff rate rapidly degrades in the low-SNR region owing to the fact that multi-user SCMA systems typically encounter an extremely large constellation size. Alternatively, by invoking Bergmans’ concept from 1973 in the categorization of pairwise error events, we obtain a more accurate cutoff rate both in the low- SNR regions and the high-SNR regions. Moreover, we provide insights into the cutoff rate derivation process, which reveals some general guidelines for designing a beneficial codebook, capable of improving SCMA with respect to its original low-density signature-based counterpart.
Learning to cluster web search results

Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents (typically a list of titles and snippets) returned by a certain Web search engine, our method first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. The documents are assigned to relevant salient phrases to form candidate clusters, and the final clusters are generated by merging these candidate clusters. Experimental results verify our method's feasibility and effectiveness.
Network utility problem and easy reliability polynomials

We model a communication system by a network, were the terminals are perfect but links may fail randomly, with identical probability q = 1 - p. This defines a partial random network. The all-terminal reliability R(p) is the probability that this random graph is connected, and it is a polynomial in p. Finding the reliability polynomial can be reduced to a hard counting problem. The contributions of this paper are two-fold. First, we fully determine all subgraphs that accept an easy counting technique, and as a consequence, the reliability polynomial is directly retrieved. More specifically, we define the “level of difficulty” of a graph, and find the reliability polynomials of all graphs with non-positive level of difficulty. The second contribution is to propose a fundamental problem from survivable network design, called the Network Utility Problem. The goal is to maximize the network utility, under a minimum edge-connectivity requirement. The network utility is defined as the opposite of the level of difficulty minus one, and it is never greater than unity. The upper-bound is achieved only in trees and cycles. We prove that Harary graphs achieve the optimal value for the Network Utility Problem. Finally, we present open problems that provide hints for future work.
Repairing data with conflict-free conditional dependencies

Conditional functional dependencies (CFDs) have proved effective in improving the quality of data. Not only are CFDs useful in detecting inconsistencies in data, they are also used to resolve inconsistencies in a database (or to repair the data). Most existing CFD-based repairing algorithms perform a variation of the so-called chase procedure, a standard algorithm in databases. However, chasing a database with CFDs introduces new challenges. Indeed, the chase does not necessarily terminate (and hence no repair can be found in a finite amount of time) or even when the chase terminates, it does not guarantee a unique repair. Existing CFD-based repairing methods remedy these problems by modifying the chase procedure in one way or another. In this work, we assume that if the CFDs specified by human experts are correct then the chase procedure should always terminate and result in a unique repair. In other words, we see non-termination and non-uniqueness as deficiencies of the specified CFDs rather than of the chase procedure. For this reason, there is need to investigate under which conditions a set of CFDs is conflict-free, i.e. when does a set of CFDs provide a unique repair on any database. This is precisely the aim of this thesis. Furthermore, in case that the given CFDs are not conflict-free, we want to provide the user with informative debugging information such that he or she can modify the set of CFDs appropriately, i.e. make them conflict-free. To accommodate for this, we present an algorithm to debug a given set of CFDs that generates debugging information to assist the expert user in resolving the inconsistencies in the CFDs rather than the data. Furthermore, in case the user can or does not want to make the CFDs conflictfree we provide an inspection database that will not be repaired but instead provides additional debugging information. On the other hand, the conflict-free CFDs are used to repair the remaining tuples in the database and for those tuples a unique repair is obtained. Finally, we present an experimental evaluation of our algorithms.
Planning in view of future needs : a Bayesian model of anticipated motivation

Traditional neuroeconomic theories of decision-making assume that utilities are based on intrinsic values of outcomes and that those values depend on how salient are outcomes in relation to the current motivational state. The fact that humans, and possibly also other animals, are able to plan in view of future motivations is not accounted by this view. So far, it is not clear which are the structures and the computational mechanisms employed by the brain during these processes. In this article, we present a Bayesian computational model that describes how the brain considers future motivations and assigns value to outcomes in relation to this information. We compare our model of anticipated motivation with a model that implements the standard perspective in decision-making and assigns value only based on the animal’s current motivations. The results of our simulations indicate an advantage of the model of anticipated motivation in volatile environments. Finally we connect our computational proposal to animal and human studies on prospection and foresight abilities and to neurophysiological investigations on their neural underpinnings.
Rotorcraft collision avoidance using spherical image-based visual servoing and single point features

This paper presents a reactive collision avoidance method for small unmanned rotorcraft using spherical image-based visual servoing. Only a single point feature is used to guide the aircraft in a safe spiral like trajectory around the target, whilst a spherical camera model ensures the target always remains visible. A decision strategy to stop the avoidance control is derived based on the properties of spiral like motion, and the effect of accurate range measurements on the control scheme is discussed. We show that using a poor range estimate does not significantly degrade the collision avoidance performance, thus relaxing the need for accurate range measurements. We present simulated and experimental results using a small quad rotor to validate the approach.
Realistic Animation of Liquids

Abstract We present a comprehensive methodology for realistically animating liquid phenomena. Our approach unifies existing computer graphics techniques for simulating fluids and extends them by incorporating more complex behavior. It is based on the Navier–Stokes equations which couple momentum and mass conservation to completely describe fluid motion. Our starting point is an environment containing an arbitrary distribution of fluid, and submerged or semisubmerged obstacles. Velocity and pressure are defined everywhere within this environment and updated using a set of finite difference expressions. The resulting vector and scalar fields are used to drive a height field equation representing the liquid surface. The nature of the coupling between obstacles in the environment and free variables allows for the simulation of a wide range of effects that were not possible with previous computer graphics fluid models. Wave effects such as reflection, refraction, and diffraction, as well as rotational effects such as eddies, vorticity, and splashing are a natural consequence of solving the system. In addition, the Lagrange equations of motion are used to place buoyant dynamic objects into a scene and track the position of spray and foam during the animation process. Typical disadvantages to dynamic simulations such as poor scalability and lack of control are addressed by assuming that stationary obstacles align with grid cells during the finite difference discretization, and by appending terms to the Navier–Stokes equations to include forcing functions. Free surfaces in our system are represented as either a collection of massless particles in 2D, or a height field which is suitable for many of the water rendering algorithms presented by researchers in recent years.
Hint based Power Efficient Routing Protocol for MANETs

Energy efficient hint based routing protocol (EEHP) in wireless networks is the problem of finding energy efficient shortest active path which also senses the node breakage before occurrence of disconnection and performs a handoff. In the existing Hint Based Routing Protocol (HBP), the nodes discover an active path to the destination exploiting a set of routing meta-information (called hints). The active paths based on hint computation is alone not sufficient for nodes in the ad hoc network for which there may be energy loss during packet transfer leading to disconnection. to avoid path loss a new energy efficient hint based routing approach outperforms the existing HBP protocol by inheriting the node handoff strategy into the HBP protocol which finds the active shortest path based on hint computation followed by route maintenance based on power of the nodes in the active route. The simulation results show that the proposed protocol performs well when a link failure occurrence is identified preventing the route from being broken, leading to energy efficient and better performance in the network.
Keyframe control of smoke simulations

We describe a method for controlling smoke simulations through user-specified keyframes. To achieve the desired behavior, a continuous quasi-Newton optimization solves for appropriate "wind" forces to be applied to the underlying velocity field throughout the simulation. The cornerstone of our approach is a method to efficiently compute exact derivatives through the steps of a fluid simulation. We formulate an objective function corresponding to how well a simulation matches the user's keyframes, and use the derivatives to solve for force parameters that minimize this function. For animations with several keyframes, we present a novel multiple-shooting approach. By splitting large problems into smaller overlapping subproblems, we greatly speed up the optimization process while avoiding certain local minima.
Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification. (1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents’ regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy.
Robust and accurate pose estimation for vision-based localisation

Camera pose estimation (perspective-n-points, or PnP) is a well-studied problem in computer vision with many applications in robotics. However state-of-art approaches often consider the task of pose estimation with respect to an object where the ratio between the furthest and closest point is small. Localisation in the context of simultaneous localisation and mapping (SLAM) violates this constraint and the naive application of PnP algorithms can lead to biased and imprecise estimates. In this article, we explore weighted object-space errors to provide an efficient, accurate and robust pose estimation solution. State-of-the-art results are shown in realistic scenarios and a practical vision framework is proposed that can be used for visual SLAM. An implementation of the proposed algorithm has been made available as open-source software in Matlab and C++.
A Simple Method to Improve the Dynamic Response of Single-Phase PWM Rectifiers

A single-phase boost rectifier system with conventional low-bandwidth voltage loop exhibits poor dynamic response. A simple method is presented to improve the dynamic response of the rectifier without affecting its steady-state performance. A fast voltage controller is used to improve the dynamic response of the rectifier. The increased low-frequency ripple at the output of the voltage controller is filtered out using a new filter. Design methodology for the voltage loop is presented. The filter is simple enough for analog and digital implementations. Low input current distortion, fast voltage-loop response, and improved dynamic response against line and load disturbances are demonstrated experimentally on a 300-W digitally controlled boost rectifier operating at a switching frequency of 100 kHz.
Evolution of age and length at maturation of Alaskan salmon under size-selective harvest

Spatial and temporal trends and variation in life‐history traits, including age and length at maturation, can be influenced by environmental and anthropogenic processes, including size‐selective exploitation. Spawning adults in many wild Alaskan sockeye salmon populations have become shorter at a given age over the past half‐century, but their age composition has not changed. These fish have been exploited by a gillnet fishery since the late 1800s that has tended to remove the larger fish. Using a rare, long‐term dataset, we estimated probabilistic maturation reaction norms (PMRNs) for males and females in nine populations in two basins and correlated these changes with fishery size selection and intensity to determine whether such selection contributed to microevolutionary changes in maturation length. PMRN midpoints decreased in six of nine populations for both sexes, consistent with the harvest. These results support the hypothesis that environmental changes in the ocean (likely from competition) combined with adaptive microevolution (decreased PMRNs) have produced the observed life‐history patterns. PMRNs did not decrease in all populations, and we documented differences in magnitude and consistency of size selection and exploitation rates among populations. Incorporating evolutionary considerations and tracking further changes in life‐history traits can support continued sustainable exploitation and productivity in these and other exploited natural resources.
N-Terminal Region of the Catalytic Domain of Human N-Myristoyltransferase 1 Acts as an Inhibitory Module

N-myristoyltransferase (NMT) plays critical roles in the modulation of various signaling molecules, however, the regulation of this enzyme in diverse cellular states remains poorly understood. We provide experimental evidence to show for the first time that for the isoform 1 of human NMT (hNMT1), the regulatory roles extend into the catalytic core. In our present study, we expressed, purified, and characterized a truncation mutant devoid of 28 N-terminal amino acids from the catalytic module (Δ28-hNMT1s) and compared its properties to the full-length catalytic domain of hNMT1. The deletion of the N-terminal peptide had no effect on the enzyme stability. Our findings suggest that the N-terminal region in the catalytic module of hNMT1 functions serves as a regulatory control element. The observations of an ~3 fold increase in enzymatic efficiency following removal of the N-terminal peptide of hNMT1s indicates that N-terminal amino acids acts as an inhibitory segment and negatively regulate the enzyme activity. Our findings that the N-terminal region confers control over activity, taken together with the earlier observations that the N-terminal of hNMT1 is differentially processed in diverse cellular states, suggests that the proteolytic processing of the peptide segment containing the inhibitory region provides a molecular mechanism for physiological up-regulation of myristoyltransferase activity.
Outer bounds on the capacity of Gaussian interference channels

Two outer bounds on the capacity region of the two-user Gaussian interference channel (IFC) are derived. The idea of the first bound is to let a genie give each receiver just enough information to decode both messages. This bound unifies and improves the best known outer bounds of Sato and Carleial. Furthermore, the bound extends to discrete memoryless IFCs and is shown to be equivalent to another bound of Carleial. The second bound follows directly from existing results of Costa and Sato and possesses certain optimality properties for weak interference.
Estimating fractional snow cover from MODIS using the normalized difference snow index

Snow-cover information is important for a wide variety of scientific studies, water supply and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) provides improved capabilities to observe snow cover from space and has been successfully using a normalized difference snow index (NDSI), along with threshold tests, to provide global, automated binary maps of snow cover. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible MODIS spectral bands to identify snow versus other features in a scene. This study has evaluated whether there is a ‘‘signal’’ in the NDSI that could be used to estimate the fraction of snow within a 500 mMODIS pixel and thereby enhance the use of the NDSI approach in monitoring snow cover. Using Landsat 30-m observations as ‘‘ground truth,’’ the percentage of snow cover was calculated for 500m cells. Then a regression relationship between 500-m NDSI observations and fractional snow cover was developed over three different snowcovered regions and tested over other areas. The overall results indicate that the relationship between fractional snow cover and NDSI is reasonably robust when applied locally and over large areas like North America. The relationship offers advantages relative to other published fractional snow cover algorithms developed for global-scale use with MODIS. This study indicates that the fraction of snow cover within a MODIS pixel using this approach can be provided with a mean absolute error less than 0.1 over the range from 0.0 to 1.0 in fractional snow cover. D 2003 Elsevier Inc. All rights reserved.