Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Transparency and Local Electronic Structure of Yb-Doped Y2O3 Ceramics with Tetravalent Additives

The results of optical transmission and X-ray core-level spectra measurements of Yb:Y2O3 ceramics with different tetravalent sintering additives (ZrO2, CeO2 and HfO2) fabricated from nanopowders (produced by the laser ablation method) and then annealed at 1400 ℃ in air for 2 h are presented. It is found that the transmission values for ZrO2- and HfO2-doped ceramics at the lasing wavelengths are higher than those of CeO2-doped samples. The X-ray photoelectron spectra (XPS) O 1s spectra show that the relative intensity of oxygen defect peak detected for 3Yb:Y2O3 + 5CeO2 ceramics decreases substantially and consistently compared to that of 5Yb:Y2O3 + 5HfO2 and 3Yb:Y2O3 + 5ZrO2 samples. This can be attributed to a more complete filling of oxygen vacancies due to annealing-induced oxygen diffusion into the highly defective sintered ceramics. The measurements of XPS Ce 3d spectra showed that the insufficiently complete filling of the oxygen vacancies in the 3Yb:Y2O3 + 5CeO2 compound is due to the appreciable presence of trivalent cerium ions.
Online Turnover-free Control for a Mobile Agent with a Low-cost Terrain Prediction Sensor

—This paper proposes a turnover-free control method for a teleoperated mobile agent (or vehicle) moving through uneven terrain. The teleoperated agent is primarily driven by an operator at a remote site and is able to react autonomously when a possible turnover is predicted. In order to predict the turnover, a low-cost terrain prediction sensor has been developed using a camera vision with a structured laser light. Since it is difficult for an operator to predict the reactive motion of the agent, a force reflection technique with a force feedback joystick is employed to intuitively recognize the inconsistency between the intended motion and the reactive motion of the agent. Finally, to verify the feasibility and effectiveness of the proposed method, experiments with the ROBHAZ-DT (actual mobile agent) have been carried out.
On the connection between radiative outbursts and timing irregularities in magnetars

Magnetars are strongly magnetized pulsars and they occasionally show violent radiative outbursts. They also often exhibit glitches which are sudden changes in the spin frequency. It was found that some glitches were associated with outbursts but their connection remains unclear. We present a systematic study to identify possible correlations between them. We find that the glitch size of magnetars likely shows a bimodal distribution, different from the distribution of the Vela-like recurrent glitches but consistent with the high end of that of normal pulsars. A glitch is likely a necessary condition for an outburst but not a sufficient condition because only 30\% of glitches were associated with outbursts. In the outburst cases, the glitches tend to induce larger frequency changes compared to those unassociated ones. We argue that a larger glitch is more likely to trigger the outburst mechanism, either by reconfiguration of the magnetosphere or deformation of the crust. A more frequent and deeper monitoring of magnetars is necessary for further investigation of their connection.
A High-Gain Sparse Phased Array With Wide-Angle Scanning Performance and Low Sidelobe Levels

This paper introduces the concept of the sparse array into the design of a wide-angle scanning phased array to obtain the low sidelobe level (SLL). The adopted array element is a half-mode substrate integrated waveguide slot array, which can radiate a high-gain and wide-beam pattern in the E-plane. A 16-element phased array with an optimized non-periodic array element arrangement, which is obtained by a semi-full-wave simulation-based synthesis method, is designed and studied. Compared with two periodic arrays with the same array element and element number, the sparse one exhibits the advantage of low SLL. Besides, the designed phased array has an ultra-low profile of only about <inline-formula> <tex-math notation="LaTeX">$0.017\lambda _{0}$ </tex-math></inline-formula>. A prototype is fabricated and measured, and measured results show that the proposed array can scan its main beam from −70° to +70° with a realized gain higher than 18.2 dBi and peak SLL lower than −15 dB.
Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency

Fruit counting is a fundamental component for yield estimation applications. Most of the existing approaches address this problem by relying on fruit models (i.e., by using object detectors) or by explicitly learning to count. Despite the impressive results achieved by these approaches, all of them need strong supervision information during the training phase. In agricultural applications, manual labeling may require a huge effort or, in some cases, it could be impossible to acquire fine-grained ground truth labels. In this letter, we tackle this problem by proposing a weakly supervised framework that learns to count fruits without the need for task-specific supervision labels. In particular, we devise a novel convolutional neural network architecture that requires only a simple image level binary classifier to detect whether the image contains instances of the fruits or not and combines this information with image spatial consistency constraints. The result is an architecture that learns to count without task-specific labels (e.g., object bounding boxes or the multiplicity of fruit instances in the image). The experiments on three different varieties of fruits (i.e., olives, almonds, and apples) show that our approach reaches performances that are comparable with SotA approaches based on the supervised paradigm.
Spectral Efficiency Enhancement Based on Sparsely Indexed Modulation for Green Radio Communication

Jointly enhancing both energy efficiency (EE) and spectrum efficiency (SE) of modulation schemes becomes one of the main issues for 5G mobile communications. Recently, an indexed modulation (IM) technique provides an interesting tradeoff between EE and SE. Data can be conveyed through the combination of subcarriers pattern that can be divided between activated/non-activated subcarriers in the frequency domain. Maximum SE can be attained at half subcarrier activation, hence producing symbols with half energy of the conventional orthogonal frequency-division multiplexing (OFDM) system. In this paper, alternatively, the new concept of sparsely indexing modulation (SIM) on overall subcarrier space is clarified. Sparse (few) subcarrier activations provide much higher EE, while the combinatorial indexing of the sparse subcarriers on the overall subcarriers as a single group spans huge combinatorial space that provides approximately the same SE of the plain OFDM system. The fallacy of indexing difficulty on overall subcarrier space without grouping is resolved. Moreover, a further SE improvement is suggested by introducing permutation-based indexing and combinatorial indexing on over-complete dictionaries. Sparsely indexing represents the cornerstone, which enables compressive sensing tools to enforce IM gains. Based on the conducted simulations, the proposed SIM scheme outperforms the conventional OFDM system in terms of the error performance, the peak-to-average power ratio, and the EE with the same spectral efficiency without channel coding complexity. The proposed SIM scheme is considered one of the energy saving-oriented modulations.
A Survey of the Methods on Fingerprint Orientation Field Estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods.
Deep learning based decomposition of brain networks

A brain network is the essence of the intelligence where it consists of nodes that are anatomically defined brain regions, and edges that connect a pair of brain regions. The diffusion-weighted magnetic resonance (MR) images and the advances in computer-aided tractography algorithms let us know strong association between human brain networks and cognitive functions. Brain regions dedicated to a certain specific cognitive function were spatially clustered and efficiently connected each other; it is called local functional segregation. However, it is not well known that such a local segregation is associated with a certain sub-network which may act as a building block of the brain network. In this work, using a graph auto-encoder, we extracted building blocks of brain networks and investigate whether they are affected by a neurological disease, Alzheimer’s disease. We found that the brain network of each person is linear summation of the learned building blocks. Also, the activation levels of these building blocks vary in the normal controls and patients with Alzheimer’s disease, showing that network deterioration in the disease group.
Cymbalo: An Efficient Graph Processing Framework for Machine Learning

Due to the growth of data scale, distributed machine learning has become more important than ever. Some recent work, like TuX^2, show promising prospect in dealing with distributed machine learning by leveraging the power of graph computation, but still leave some key problems unsolved. In this paper, we propose Cymbalo, a new distributed graph processing framework for large-scale machine learning algorithms. To satisfy the specific characteristics of machine learning, Cymbalo employs a heterogeneity-aware data model, a hybrid computing model and a vector-aware programming model, to ensure small memory footprint, good computation efficiency and expressiveness. The experiment results show that Cymbalo outperforms Spark by 2.4×-3.2×, and PowerGraph by up to 5.8×. Moreover, Cymbalo can also outperform Angel, a recent parameter server system, by 1.6×-2.1×.
Psychometric Properties of the God Locus of Health Control Scale in Churchgoing Latinas

This study evaluated the psychometric properties of the Spanish version of the God Locus of Health Control scale, a measure of the extent to which an individual believes God has control over one’s health, among a sample of churchgoing Latinas (N = 398). Confirmatory factor analysis showed support for a one-factor structure and internal consistency reliability, as measured by Cronbach’s coefficient alpha, was good. Evidence for convergent validity was demonstrated by significant correlations in the expected magnitudes and directions with two measures of perceived religious involvement in health. These results suggest that the God Locus of Health Control scale can be used to examine the extent to which God is perceived to control an individual’s health among Latinas.
Neighboring and Non-Neighboring Features for Pedestrian Detection

Pedestrian detection is used in many real-time applications. However, most existing pedestrian detectors cannot achieve a tradeoff between speed and accuracy. Based on the hand-crafted feature method, this paper proposes novel non-neighboring features. The features can be used to identify pedestrians with different postures through the comparison of pedestrians' both sides. In addition, we propose corresponding neighboring features and an novel algorithm that can perform occlusion analysis. Using HOG+LUV as low-level features input and sliding window approach, this paper uses neighboring and non-neighboring features to achieve best performance on the Caltech pedestrian dataset after adding the occlusion analysis algorithm. Experimental results show that proposed model outperforms the state-of-art where it reduces the miss rate by 0.92%. In addition, the processing speed is 18 FPS in CPU environment.
Assessing the Profitability of Timely Opening Range Breakout on Index Futures Markets

This paper presents a timely open range breakout (TORB) strategies for index futures market trading via using one-minute intraday data. We observe that the trading volumes and fluctuations in returns on each one-minute interval of trading hours in the futures markets reach their peaks at the opening and closing of the underlying stock markets. With these observations, we align the active hours of an index futures market with its underlying stock market and test the proposed TORB strategies on the DJIA, S&P 500, NASDAQ, HSI, and TAIEX index futures from 2003 to 2013. In the experiments, the proposed strategy achieves over 8% annual returns with <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-values less than 3% in all of the five markets; the best performance, 20.28% annual returns at a <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-value of <inline-formula> <tex-math notation="LaTeX">$3.1\times 10^{-5}$ </tex-math></inline-formula>%, is reached in the TAIEX. For each market, we also find the best probing time, which is relatively short in the U.S. market and relatively long in Asian markets. Furthermore, we conduct experiments on a TAIEX futures transaction dataset to analyze the relationship between the TORB signals and trader behavior, and find that the TORB signals are in the same direction as institutional traders, especially foreign investment institutions.
Evaluating Subsurface Parameterization to Simulate Hyporheic Exchange: The Steinlach River Test Site

Hyporheic exchange is the interaction of river water and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic exchange has been attributed to the representation of heterogeneous subsurface properties. Our study evaluates the trade‐offs between intrinsic (irreducible) and epistemic (reducible) model errors when choosing between homogeneous and highly complex subsurface parameter structures. We modeled the Steinlach River Test Site in Southwest Germany using a fully coupled surface water‐groundwater model to simulate hyporheic exchange and to assess the predictive errors and uncertainties of transit time distributions. A highly parameterized model was built, treated as a “virtual reality” and used as a reference. We found that if the parameter structure is too simple, it will be limited by intrinsic model errors. By increasing subsurface complexity through the addition of zones or heterogeneity, we can begin to exchange intrinsic for epistemic errors. Thus, the appropriate level of detail to represent the subsurface depends on the acceptable range of intrinsic structural errors for the given modeling objectives and the available site data. We found that a zonated model is capable of reproducing the transit time distributions of a more detailed model, but only if the geological structures are known. An interpolated heterogeneous parameter field (cf. pilot points) showed the best trade‐offs between the two errors, indicating fitness for practical applications. Parameter fields generated by multiple‐point geostatistics (MPS) produce transit time distributions with the largest uncertainties, however, these are reducible by additional hydrogeological data, particularly flux measurements.
Is it possible to reconcile extragalactic IMF variations with a universal Milky Way IMF?

One of the most robust observations of the stellar initial mass function (IMF) is its near-universality in the Milky Way and neighbouring galaxies. But recent observations of early-type galaxies can be interpreted to imply a ‘bottom-heavy’ IMF, while others of ultrafaint dwarfs could imply a ‘top-heavy’ IMF. This would impose powerful constraints on star formation models. We explore what sort of ‘cloud-scale’ IMF models could possibly satisfy these constraints. We utilize simulated galaxies that reproduce (broadly) the observed galaxy properties, while they also provide the detailed star formation history and properties of each progenitor star-forming cloud. We then consider generic models where the characteristic mass of the IMF is some arbitrary power-law function of progenitor cloud properties, along with well-known literature IMF models which scale with Jeans mass, ‘turbulent Bonnor–Ebert mass’, temperature, the opacity limit, metallicity, or the ‘protostellar heating mass’. We show that no IMF models currently in the literature – nor any model where the turnover mass is an arbitrary power-law function of a combination of cloud temperature/density/size/metallicity/velocity dispersion/magnetic field – can reproduce the claimed IMF variation in ellipticals or dwarfs without severely violating observational constraints in the Milky Way. Specifically, they predict too much variation in the ‘extreme’ environments of the Galaxy compared to that observed. Either the IMF varies in a more complicated manner, or alternative interpretations of the extragalactic observations must be explored.
Two Predictions of Supernova: GRB 130427A/SN 2013cq and GRB 180728A/SN 2018fip

On 2018 July 28, GRB 180728A triggered Swift satellites and, soon after the determination of the redshift, we identified this source as a type II binary-driven hypernova (BdHN II) in our model. Consequently, we predicted the appearance time of its associated supernova (SN), which was later confirmed as SN 2018fip. A BdHN II originates in a binary composed of a carbon–oxygen core (COcore) undergoing SN, and the SN ejecta hypercritically accrete onto a companion neutron star (NS). From the time of the SN shock breakout to the time when the hypercritical accretion starts, we infer the binary separation ≃3 × 1010 cm. The accretion explains the prompt emission of isotropic energy ≃3 × 1051 erg, lasting ∼10 s, and the accompanying observed blackbody emission from a thermal convective instability bubble. The new neutron star (νNS) originating from the SN powers the late afterglow from which a νNS initial spin of 2.5 ms is inferred. We compare GRB 180728A with GRB 130427A, a type I binary-driven hypernova (BdHN I) with isotropic energy >1054 erg. For GRB 130427A we have inferred an initially closer binary separation of ≃1010 cm, implying a higher accretion rate leading to the collapse of the NS companion with consequent black hole formation, and a faster, 1 ms spinning νNS. In both cases, the optical spectra of the SNe are similar, and not correlated to the energy of the gamma-ray burst. We present three-dimensional smoothed-particle-hydrodynamic simulations and visualizations of the BdHNe I and II.
Dispersed Silver Nanowires Enhance Radiofrequency Heating

Radiofrequency ablation has been intensively investigated for non-invasive cancer treatment. To enhance the specificity and efficacy of the traditional invasive radiofrequency ablation technique, we used silver nanowires as radiofrequency receptors to enhance local heating effect. The silver nanowires were prepared by a conventional polyol method, and then dispersed in the specially designed solvent formula of ISOPAR-H with assistant surfactants. Silver nanoparticles and non-dispersed silver nanowires were also prepared to serve as references. It was found that silver nanoparticles could hardly increase the radiofrequency heating, and that non-dispersed silver nanowires exhibited an interesting non-uniform enhancement of the radiofrequency heating. In comparison, significant enhancement of radiofrequency heating was observed when dispersed silver nanowires were radiated by a 13.56 MHz electromagnetic field. In addition, size dependent effects have also been investigated. An in vitro study using Hela cells was also conducted to find that radiofrequency illumination was lethal to silver nanowires incubated cancer cells. Such effects promise a bright future for applications of nanoparticle-mediated radiofrequency thermotherapy to cancer treatment.
A Process Model Collection and Gold Standard Correspondences for Process Model Matching

Business process models are the conceptual models to depict the workflow of an organization. Process model matching (PMM) refers to the automatic identification of corresponding activities between a pair of process models that show similar or the same behavior. During the last few years, PMM has received much of the researchers’ attention due to its wide range of applications, such as clone detection and harmonization of process models. Consequently, a plethora of PMM techniques has been developed. In order to evaluate the effectiveness of these techniques, experts have developed three benchmark datasets, formally called PMMC’15 datasets. Furthermore, the process models in the datasets have been converted into OAEI’17 ontologies. These resources are a valuable asset for the PMM community to evaluate process model matching techniques. However, these resources (PMMC’15 and OAEI’17) are limited to fewer models and a handful collection of corresponding activities among these models that may not be sufficient to rigorously evaluate the PMM techniques. To fill this gap, this paper provides a large, diverse, and a carefully handcrafted collection of process models, along with their benchmark correspondences. The process model collection and benchmark correspondences between these models are freely available for the community [1]. Our newly developed dataset, together with the existing resources, can be used for a thorough evaluation of PMM techniques, especially in the context of the vocabulary mismatch problem. At last, we have evaluated the characteristics of our dataset by a series of experiments while involving widely used similarity measures in PMM research. The results reveal that our dataset is larger, diverse, and challenging as compared to existing datasets in the PMM domain.
Targeting senescent cells alleviates obesity‐induced metabolic dysfunction

Adipose tissue inflammation and dysfunction are associated with obesity‐related insulin resistance and diabetes, but mechanisms underlying this relationship are unclear. Although senescent cells accumulate in adipose tissue of obese humans and rodents, a direct pathogenic role for these cells in the development of diabetes remains to be demonstrated. Here, we show that reducing senescent cell burden in obese mice, either by activating drug‐inducible “suicide” genes driven by the p16Ink4a promoter or by treatment with senolytic agents, alleviates metabolic and adipose tissue dysfunction. These senolytic interventions improved glucose tolerance, enhanced insulin sensitivity, lowered circulating inflammatory mediators, and promoted adipogenesis in obese mice. Elimination of senescent cells also prevented the migration of transplanted monocytes into intra‐abdominal adipose tissue and reduced the number of macrophages in this tissue. In addition, microalbuminuria, renal podocyte function, and cardiac diastolic function improved with senolytic therapy. Our results implicate cellular senescence as a causal factor in obesity‐related inflammation and metabolic derangements and show that emerging senolytic agents hold promise for treating obesity‐related metabolic dysfunction and its complications.