Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions

A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust at high speed mobility.
Recognition of human body motion using phase space constraints

A new method for representing and recognizing human body movements is presented. The basic idea is to identify sets of constraints that are diagnostic of a movement: expressed using body-centered coordinates such as joint angles and in force only during a particular movement. Assuming the availability of Cartesian tracking data, we develop techniques for a representation of movements defined by space curves in subspaces of a "phase space. " The phase space has axes of joint angles and torso location and attitude, and the axes of the subspaces are subsets of the axes of the phase space. Using this representation we develop a system for learning new movements from ground truth data by searching for constraints. We then use the learned representation for recognizing movements in unsegmented data. We train and test the system on nine fundamental steps from classical ballet performed by two dancers; the system accurately recognizes the movements in the unsegmented stream of motion. <<ETX>>
Concentric RadViz: Visual Exploration of Multi-task Classification

The discovery of patterns in large data collections is a difficult task. Visualization and machine learning techniques have emerged as a way to facilitate data analysis, providing tools to uncover relevant patterns from the data. This paper presents Concentric Rad Viz, a general purpose class visualization system that takes into account multi-class, multi-label and multi-task classifiers. Concentric Rad Viz uses a force attenuation scheme, which minimizes cluttering and ambiguity in the visual layout. In~addition, the user can add concentric circles to the layout in order to represent classification tasks. Our validation results and the application of Concentric Rad Viz for two real collections suggest that this tool can reveal important data patterns and relations. In our application, the user can interact with the visualization by selecting regions of interest according to specific criteria and changing projection parameters.
In Planta Processing and Glycosylation of a Nematode CLAVATA3/ENDOSPERM SURROUNDING REGION-Like Effector and Its Interaction with a Host CLAVATA2-Like Receptor to Promote Parasitism1[OPEN]

A nematode effector peptide is modified and processed in planta and the resulting glycopeptide interacts with a plant membrane-associated receptor to facilitate parasite infection. Like other biotrophic plant pathogens, plant-parasitic nematodes secrete effector proteins into host cells to facilitate infection. Effector proteins that mimic plant CLAVATA3/ENDOSPERM SURROUNDING REGION-related (CLE) proteins have been identified in several cyst nematodes, including the potato cyst nematode (PCN); however, the mechanistic details of this cross-kingdom mimicry are poorly understood. Plant CLEs are posttranslationally modified and proteolytically processed to function as bioactive ligands critical to various aspects of plant development. Using ectopic expression coupled with nanoliquid chromatography-tandem mass spectrometry analysis, we show that the in planta mature form of proGrCLE1, a multidomain CLE effector secreted by PCN during infection, is a 12-amino acid arabinosylated glycopeptide (named GrCLE1-1Hyp4,7g) with striking structural similarity to mature plant CLE peptides. This glycopeptide is more resistant to hydrolytic degradation and binds with higher affinity to a CLAVATA2-like receptor (StCLV2) from potato (Solanum tuberosum) than its nonglycosylated forms. We further show that StCLV2 is highly up-regulated at nematode infection sites and that transgenic potatoes with reduced StCLV2 expression are less susceptible to PCN infection, indicating that interference of the CLV2-mediated signaling pathway confers nematode resistance in crop plants. These results strongly suggest that phytonematodes have evolved to utilize host cellular posttranslational modification and processing machinery for the activation of CLE effectors following secretion into plant cells and highlight the significance of arabinosylation in regulating nematode CLE effector activity. Our finding also provides evidence that multidomain CLEs are modified and processed similarly to single-domain CLEs, adding new insight into CLE maturation in plants.
now happens-before later: static schedule analysis of fine-grained parallelism with explicit happens-before relationships

Current compilers are still largely ignorant of the scheduling of parallel tasks at runtime. Without this information, however, they have difficulties optimizing and verifying concurrent programs. In this paper, we present a programming model where the program contains explicit scheduling constraints in the form of happens-before relationships between scheduled tasks. This model allows for flexible and fine-grained ad hoc parallelism while still enabling us to statically extract an abstraction of the runtime schedule. The result of this schedule analysis can answer the question as to whether two tasks execute in sequence, exclusively, or in parallel with each other.
Simulations of Anisotropic Grain Growth Subject to Thermal Gradients Using Q-State Monte Carlo

The Q-state Monte Carlo, Potts model is used to investigate 2D, anisotropic, grain growth of single-phase materials subject to temperature gradients. Anisotropy is simulated via the use of nonuniform grain boundary surface energies, and thermal gradients are simulated through the use of variable grain boundary mobilities. Hexagonal grain elements are employed, and elliptical Wulff plots are used to assign surface energies to grain lattices. The mobility is set to vary in accordance with solutions to a generalized heat equation and is solved for two separate values of the mobility coefficient. Among other findings, the results reveal that like isotropic grain growth, under the influence of a thermal gradient, anisotropic grain growth also demonstrates locally normal growth kinetics.
Interval Analysis for Certified Numerical Solution of Problems in Robotics

Interval Analysis for Certified Numerical Solution of Problems in Robotics Interval analysis is a relatively new mathematical tool that allows one to deal with problems that may have to be solved numerically with a computer. Examples of such problems are system solving and global optimization, but numerous other problems may be addressed as well. This approach has the following general advantages: (a) it allows to find solutions of a problem only within some finite domain which make sense as soon as the unknowns in the problem are physical parameters; (b) numerical computer round-off errors are taken into account so that the solutions are guaranteed; (c) it allows one to take into account the uncertainties that are inherent to a physical system. Properties (a) and (c) are of special interest in robotics problems, in which many of the variables are parameters that are measured (i.e., known only up to some bounded errors) while the modeling of the robot is based on parameters that are submitted to uncertainties (e.g., because of manufacturing tolerances). Taking into account these uncertainties is essential for many robotics applications such as medical or space robotics for which safety is a crucial issue. A further inherent property of interval analysis that is of interest for robotics problems is that this approach allows one to deal with the uncertainties that are unavoidable in robotics. Although the basic principles of interval analysis are easy to understand and to implement, this approach will be efficient only if the right heuristics are used and if the problem at hand is formulated appropriately. In this paper we will emphasize various robotics problems that have been solved with interval analysis, many of which are currently beyond the reach of other mathematical approaches.
Group Feature Selection with Streaming Features

Group feature selection makes use of structural information among features to discover a meaningful subset of features. Existing group feature selection algorithms only deal with pre-given candidate feature sets and they are incapable of handling streaming features. On the other hand, feature selection algorithms targeted for streaming features can only perform at the individual feature level without considering intrinsic group structures of the features. In this paper, we perform group feature selection with streaming features. We propose to perform feature selection at the group and individual feature levels simultaneously in a manner of a feature stream rather than a pre-given candidate feature set. In our approach, the group structures are fully utilized to reduce the cost of evaluating streaming features. We have extensively evaluated the proposed method. Experimental results have demonstrated that our proposed algorithms statistically outperform state-of-the-art methods of feature selection in terms of classification accuracy.
Fluorescent Sensing of Fluoride in Cellular System

Fluoride ions have the important roles in a lot of physiological activities related with biological and medical system, such as water fluoridation, caries treatment, and bone disease treatment. Great efforts have been made to develop new methods and strategies for F- detection in the past decades. Traditional methods for the detection of F- including ion chromatography, ion-selective electrodes, and spectroscopic techniques have the limitations in the biomedicine research. The fluorescent probes for F- are very promising that overcome some drawbacks of traditional fluoride detection methods. These probes exhibit high selectivity, high sensitivity as well as quick response to the detection of fluoride anions. The review commences with a brief description of photophysical mechanisms for fluorescent probes for fluoride, including photo induced electron transfer (PET), intramolecular charge transfer (ICT), fluorescence resonance energy transfer (FRET), and excited-state intramolecular proton transfer (ESIPT). Followed by a discussion about common dyes for fluorescent fluoride probes, such as anthracene, naphalimide, pyrene, BODIPY, fluorescein, rhodamine, resorufin, coumarin, cyanine, and near-infrared (NIR) dyes. We divide the fluorescent probes for fluoride in cellular application systems into nine groups, for example, type of hydrogen bonds, type of cleavage of Si-O bonds, type of Si-O bond cleavage and cylization reactions, etc. We also review the recent reported carriers in the delivery of fluorescent fluoride probes. Seventy-four typical fluorescent fluoride probes are listed and compared in detail, including quantum yield, reaction medium, excitation and emission wavelengths, linear detection range, selectivity for F-, mechanism, and analytical applications. Finally, we discuss the future challenges of the application of fluorescent fluoride probes in cellular system and in vivo. We wish that more and more excellent fluorescent fluoride probes will be developed and applied in the biomedicine field in the future.
Keystroke Dynamics

15.1 EXISTING BENCHMARK DATASETS FOR KEYSTROKE DYNAMICS Unlike other biometric modalities, there are only a few public datasets available for keystroke dynamics, some of which are composed of several subdatasets. Giot et al. [1] compiled a list of current public databases for keystroke dynamics using a keyboard in collecting the data. The data provided by these, though not mobile, can be extended into mobile in the future.
Densification of powders by particle deformation

AbstractBased on a previous experimental study of particle deformation during powder compaction, a model is developed for describing the densification behaviour of an irregular packing of spherical particles. Using the radial density function of a ‘random dense packing’, the increase in both the average size and the number of contact faces are calculated. A simple criterion for local yielding allows the compaction pressure to be determined for relative densities up to 90%. In the final stage of compaction, particle deformation, now constrained by neighbouring contacts, is modelled by extrusion into the remaining pore space. A compaction equation encompassing both stages is presented; its application to non-spherical powders elucidates the role of particle shape during powder densification. PM/0150
Linear processing for dual-hop AF relay systems with interference: Outage probability analysis

This paper investigates the impact of different linear processing techniques on the outage performance of dual-hop amplify-and-forward (AF) relaying systems with co-channel interference (CCI) at the multiple antenna relay. Specifically, three heuristic linear precoding schemes are proposed to combat the detrimental effect of CCI, namely, 1) Maximum ratio combining/maximal ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT), 3) Minimum mean-square error/MRT (MMSE/MRT). New exact outage expressions as well as simple high signal-to-noise ratio (SNR) outage approximations are derived for all three schemes. Our results demonstrate that while the MRC/MRT and the MMSE/MRT schemes achieve a full diversity order of N, the ZF/MRT scheme can only achieve a diversity order of N-M, where N is the number of relay antennas and M is the number of interferers. In addition, it is observed that the MMSE/MRT scheme always achieves the best outage performance. The ZF/MRT scheme outperforms the MRC/MRT scheme in the low SNR regime, and exhibits an inferior performance compared to the MRC/MRT scheme in the high SNR regime.
SCF (Fbxl17) ubiquitylation of Sufu regulates Hedgehog signaling and medulloblastoma development

Skp1‐Cul1‐F‐box protein (SCF) ubiquitin ligases direct cell survival decisions by controlling protein ubiquitylation and degradation. Sufu (Suppressor of fused) is a central regulator of Hh (Hedgehog) signaling and acts as a tumor suppressor by maintaining the Gli (Glioma‐associated oncogene homolog) transcription factors inactive. Although Sufu has a pivotal role in Hh signaling, the players involved in controlling Sufu levels and their role in tumor growth are unknown. Here, we show that Fbxl17 (F‐box and leucine‐rich repeat protein 17) targets Sufu for proteolysis in the nucleus. The ubiquitylation of Sufu, mediated by Fbxl17, allows the release of Gli1 from Sufu for proper Hh signal transduction. Depletion of Fbxl17 leads to defective Hh signaling associated with an impaired cancer cell proliferation and medulloblastoma tumor growth. Furthermore, we identify a mutation in Sufu, occurring in medulloblastoma of patients with Gorlin syndrome, which increases Sufu turnover through Fbxl17‐mediated polyubiquitylation and leads to a sustained Hh signaling activation. In summary, our findings reveal Fbxl17 as a novel regulator of Hh pathway and highlight the perturbation of the Fbxl17–Sufu axis in the pathogenesis of medulloblastoma.
Dense photometric stereo using a mirror sphere and graph cut

We present a surprisingly simple system that performs robust normal reconstruction by dense photometric stereo, in the presence of large shadows, highlight, transparencies, complex geometry, variable attenuation in light intensity and inaccurate light directions. Our system consists of a mirror sphere, a spotlight and a DV camera only. Using this, we infer a dense set of unbiased but noisy photometric data uniformly distributed on the light direction sphere. We use this dense set to derive a very robust matching cost for our MRF photometric stereo model, where the maximum a posteriori (MAP) solution is estimated. To aggregate support for candidate normals in the normal refinement process, we introduce a compatibility function that is translated into a discontinuity-preserving metric, thus speeding up the MAP estimation by energy minimization using graph cut. No reference object of similar material is used. We perform detailed comparison on our approach with conventional convex minimization. We show very good normals estimated from very noisy data on a wide range of difficult objects to show the robustness and usefulness of our method.
Surface Nanocrystallization of Nickel Produced by High-Current Pulsed Electron Beam Irradiation

The nanocrystalline surface was obtained on bulk pure nickel by using high-current pulsed electron beam (HCPEB) technique. The temperature field induced by HCPEB was numerically simulated. The structures of the nanocrystallized surface were characterized by scanning electron beam (SEM), which showed that after HCPEB irradiation, the initial coarse-grained structure on the surface was refined into fine grains with sizes of about 70nm. It was revealed that melting surface caused by HCPEB irradiation and subsequently rapid cooling was the dominant mechanism of the surface nanocrystallization of bulk nickel. The HCPEB technique provides a new method for rapid fabricating surface-nanocrystallized materials.
Association of Genetic Polymorphisms in STAT1 Gene with Increased Risk of Hepatocellular Carcinoma

Objective: Although signal transducer and activator of transcription 1 (STAT1), a transcription factor, plays a critical role in carcinogenesis and has been implicated as a tumor suppressor, few studies have investigated the associations between polymorphisms of this gene and the risk of cancer development. The aim of this study was to examine whether STAT1 gene polymorphisms are associated with the risk of hepatocellular carcinoma (HCC). Methods: Ten single nucleotide polymorphisms in the STAT1 gene were genotyped by TaqMan assays in 469 HCC cases and 558 age-, sex- and HBsAg-matched controls in a Chinese population. Results: Minor allele homozygous genotypes at rs867637 (9,046 bp 3′ of STP A>G), rs3771300 (IVS24–153T>G), and rs2280235 (IVS20–103G>A), compared with their homozygote genotypes of common alleles, were associated with 1.6- (95% CI 1.1–2.3), 1.6- (95% CI 1.1–2.4), and 1.4-fold (95% CI 0.95–1.9) increased risk of HCC, respectively. The GGA haplotype, comprised of risk alleles at rs867637, rs3771300 and rs2280235, conferred a 1.2-fold (95% CI 1.0–1.5) increased risk of HCC, as compared to the most common haplotype of ATG. Diplotype GGA/GGA conferred a 1.6-fold (95% CI 1.0–2.5) increased risk of HCC compared with diplotype ATG/ATG. Conclusion: Our results demonstrate for the first time that polymorphisms in the STAT1 gene are associated with HCC susceptibility.
The Anisotropic Distribution of M31 Satellite Galaxies: A Polar Great Plane of Early-type Companions

The highly anisotropic distribution and apparent alignment of the Galactic satellites in polar great planes begs the question of how common such distributions are. The satellite system of M31 is the only nearby system for which we currently have sufficiently accurate distances to study the three-dimensional satellite distribution. We present the spatial distribution of the 15 currently known M31 companions in a coordinate system centered on M31 and aligned with its disk. Through a detailed statistical analysis we show that the full satellite sample describes a plane that is inclined by -56° with respect to the poles of M31 and has an rms height of 100 kpc. At 88% the statistical significance of this plane is low, and it is unlikely to have a physical meaning. We note that the great stellar stream found near Andromeda is inclined to this plane by 7°. Most of the M31 satellites are found within <±40° of M31's disk; i.e., there is little evidence for a Holmberg effect. If we confine our analysis to early-type dwarfs, we find a best-fit polar plane within 5°-7° from the pole of M31. This polar great plane has a statistical significance of 99.7% and includes all dSphs (except for And II), M32, NGC 147, and PegDIG. The rms distance of these galaxies from the polar plane is 16 kpc. The nearby spiral M33 has a distance of only ∼3 kpc from this plane, which points toward the M81 group. We discuss the anisotropic distribution of M31's early-type companions in the framework of three scenarios, namely, as remnants of the breakup of a larger progenitor, as a tracer of a prolate dark matter halo, and as a tracer of collapse along large-scale filaments. The first scenario requires that the breakup must have occurred at very early times and that the dwarfs continued to form stars thereafter to account for their stellar population content and luminosity-metallicity relation. The third scenario seems to be plausible, especially when considering the apparent alignment of our potential satellite filament with several nearby groups. The current data do not permit us to rule out any of the scenarios. Orbit information is needed to test the physical reality of the polar plane and of the different scenarios in more detail.
Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures

Prior knowledge constraints are imposed upon a learning problem in the form of distance measures. Prototypical 2D point sets and graphs are learned by clustering with point-matching and graph-matching distance measures. The point-matching distance measure is approximately invariant under affine transformationstranslation, rotation, scale, and shearand permutations. It operates between noisy images with missing and spurious points. The graph-matching distance measure operates on weighted graphs and is invariant under permutations. Learning is formulated as an optimization problem. Large objectives so formulated ( million variables) are efficiently minimized using a combination of optimization techniquessoftassign, algebraic transformations, clocked objectives, and deterministic annealing.
"We Thinking" and Its Consequences

Increasingly, economists are drawing on concepts from outside economics--such as "norms," "esteem," and "identity"--to model agents' social natures. A key reason for studying such social motivation is to shed light on the conditions that facilitate--or deter--collective action. It has been widely observed, for instance, that groups are more able to engage in collective action when they have a common, group identity. This paper gives one explanation for such a link. The paper develops a new concept, "we thinking"; and it also provides a deeper understanding of the concepts of norms, identity, and esteem.