Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascaded Architecture for Memristor Crossbar Array Based Larger-Scale Neuromorphic Computing

Multiply-accumulate calculations using a memristor crossbar array is an important method to realize neuromorphic computing. However, the memristor array fabrication technology is still immature, and it is difficult to fabricate large-scale arrays with high-yield, which restricts the development of memristor-based neuromorphic computing technology. Therefore, cascading small-scale arrays to achieve the neuromorphic computational ability that can be achieved by large-scale arrays, which is of great significance for promoting the application of memristor-based neuromorphic computing. To address this issue, we present a memristor-based cascaded framework with some basic computation units, several neural network processing units can be cascaded by this means to improve the processing capability of the dataset. Besides, we introduce a split method to reduce the pressure of the input terminal. Compared with VGGNet and GoogLeNet, the proposed cascaded framework can achieve 93.54% Fashion-MNIST accuracy and 86.64% CIFAR-10 accuracy under the 4.15M parameters. The extensive experiments with Ti/AlOx/TaOx/Pt we fabricated are conducted to show that the circuit simulation results can still provide a high recognition accuracy, and the recognition accuracy loss after circuit simulation can be controlled at around 0.39%.
HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications

Body area networks, including smart sensors, are widely reshaping health applications in the new era of smart cities. To meet increasing security and privacy requirements, physiological signal-based biometric human identification is gaining tremendous attention. This paper focuses on two major impediments: the signal processing technique is usually both complicated and data-dependent and the feature engineering is time-consuming and can fit only specific datasets. To enable a data-independent and highly generalizable signal processing and feature learning process, a novel wavelet domain multiresolution convolutional neural network is proposed. Specifically, it allows for blindly selecting a physiological signal segment for identification purpose, avoiding the complicated signal fiducial characteristics extraction process. To enrich the data representation, the random chosen signal segment is then transformed to the wavelet domain, where multiresolution time-frequency representation is achieved. An auto-correlation operation is applied to the transformed data to remove the phase difference as the result of the blind segmentation operation. Afterward, a multiresolution 1-D-convolutional neural network (1-D-CNN) is introduced to automatically learn the intrinsic hierarchical features from the wavelet domain raw data without data-dependent and heavy feature engineering, and perform the user identification task. The effectiveness of the proposed algorithm is thoroughly evaluated on eight electrocardiogram datasets with diverse behaviors, such as with or without severe heart diseases, and with different sensor placement methods. Our evaluation is much more extensive than the state-of-the-art works, and an average identification rate of 93.5% is achieved. The proposed multiresolution 1-D-CNN algorithm can effectively identify human subjects, even from randomly selected signal segments and without heavy feature engineering. This paper is expected to demonstrate the feasibility and effectiveness of applying the blind signal processing and deep learning techniques to biometric human identification, to enable a low algorithm engineering effort and also a high generalization ability.
Quantitative Monitoring of Pluripotency Gene Activation after Somatic Cloning in Cattle1

Abstract The development of somatic cell nuclear transfer (SCNT) embryos critically depends on appropriate reprogramming and expression of pluripotency genes, such as Pou5f1/POU5F1 (previously known as Oct4/OCT4). To study POU5F1 transcription activation in living bovine SCNT embryos without interference by maternal POU5F1 mRNA, we generated chromosomally normal fetal fibroblast donor cells stably carrying a mouse Pou5f1 promoter-driven enhanced green fluorescent protein (EGFP) reporter gene at a single integration site without detectable EGFP expression. Morphologic and quantitative analyses of whole-mount SCNT embryos by confocal microscopy revealed robust initial activation of the Pou5f1 reporter gene during the fourth cell cycle. In Day 6 SCNT embryos EGFP expression levels were markedly higher than in Day 4 embryos but varied substantially between individual embryos, even at comparable cell numbers. Embryos with low EGFP levels had far more morphologically abnormal cell nuclei than those with high EGFP levels. Our data strongly suggest that bovine SCNT embryos consistently start activation of the POU5F1 promoter during the fourth cell cycle, whereas later in development the expression level substantially differs between individual embryos, which may be associated with developmental potential. In fibroblasts from phenotypically normal SCNT fetuses recovered on Day 34, the Pou5f1 reporter promoter was silent but was activated by a second round of SCNT. The restoration of pluripotency can be directly observed in living cells or SCNT embryos from such Pou5f1-EGFP transgenic fetuses, providing an attractive model for systematic investigation of epigenetic reprogramming in large mammals.
Longitudinal 1H MRS of rat forebrain from infancy to adulthood reveals adolescence as a distinctive phase of neurometabolite development

This study represents the first longitudinal, within‐subject 1H MRS investigation of the developing rat brain spanning infancy, adolescence and early adulthood. We obtained neurometabolite profiles from a voxel located in a central location of the forebrain, centered on the striatum, with smaller contributions for the cortex, thalamus and hypothalamus, on postnatal days 7, 35 and 60. Water‐scaled metabolite signals were corrected for T1 effects and quantified using the automated processing software LCModel, yielding molal concentrations. Our findings indicate age‐related concentration changes in N‐acetylaspartate + N‐acetylaspartylglutamate, myo‐inositol, glutamate + glutamine, taurine, creatine + phosphocreatine and glycerophosphocholine + phosphocholine. Using a repeated measures design and analysis, we identified significant neurodevelopment changes across all three developmental ages and identified adolescence as a distinctive phase in normative neurometabolic brain development. Between postnatal days 35 and 60, changes were observed in the concentrations of N‐acetylaspartate + N‐acetylaspartylglutamate, glutamate + glutamine and glycerophosphocholine + phosphocholine. Our data replicate past studies of early neurometabolite development and, for the first time, link maturational profiles in the same subjects across infancy, adolescence and adulthood. Copyright © 2013 John Wiley & Sons, Ltd.
Perspectives on the Terrestrial Organic Matter Transport and Burial along the Land-Deep Sea Continuum: Caveats in Our Understanding of Biogeochemical Processes and Future Needs

The natural carbon cycle is immensely intricate to fully understand its sources, fluxes and the processes that are responsible for their cycling in different reservoirs and their balances on a global scale. Anthropogenic perturbations add another dimension to such a complex cycle. Therefore, it is necessary to update the global carbon cycle by combining both natural and anthropogenic sources, fluxes and sinks along the land-sea continuum to assess whether these terms are currently in balance or not. Here, we review the export and it burial rates of terrestrial organic carbon in the oceans to understand the issue of “missing terrigenous carbon” by comparing data- and model-based estimates of terrestrial carbon fluxes. Our review reveals large disparities between field-based data and model output in terms of dissolved and particulate organic carbon/matter (OC/OM) fluxes and their ratios, especially for Oceania and Arctic rivers, suggesting the need of additional investigations in these regions to refine terrestrial OC export budget. Based on our budgeting of global sources and sinks of OC with updated estimates of marine productivity and terrestrial OM burial rate, we find that the marginal sediments are key burial sites of terrestrial OM, which is consistent with earlier views of Berner (1982) and Hedges and Keil (1995). While about 60‒80% of TOM is remineralized in the margins, the estimated budget further reveals the ocean derived OM is efficiently remineralized than that of terrestrial OM, emphasizing the need of further improvements of carbon burial estimation in the marine realm. When we look back in the past, higher terrestrial OC burial (by ~50%) in the deep ocean during the glacials than during the interglacials suggests the subdued role of continental margins and an efficient transfer of OM from the shelf to deep sea in glacials. Based on the review of terrestrial and marine OM burial, we suggest some critical regions/ways that need to be investigated/addressed further, identification of new biogeochemical proxies and their grouping to better constrain the global carbon cycle along the land-deep sea continuum in future.
Clinical Significance of Serum Ornithine Carbamoyltransferase in Liver Diseases – Is the Ratio of OCT/ALT a New Tumor Marker?

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and the third most common cause of cancer-related mortality (World Health Organization. Mortality database, 2010). According to the most recent nationwide Japanese registration data, primary liver cancer ranks fourth for men and sixth for women as cause of death from malignant neoplasm (Journal of Health and Welfare Statistics. Health and Welfare Statistics Association, 2009).
The Clarke Tax as a Consensus Mechanism Among Automated Agents

When autonomous agents attempt to coordinate action, it is often necessary that they reach some kind of consensus. Reaching such a consensus has traditionally been dealt with in the Distributed Artificial Intelligence literature via the mechanism of negotiation. Another alternative is to have agents bypass negotiation by using a voting mechanism; each agent expresses its preferences, and a group choice mechanism is used to select the result. Some choice mechanisms are better than others, and ideally we would like one that. cannot be manipulated by an untruthful agent. One such non-manipulable choice mechanism is the Clarke tax [Clarke, 1971]. Though theoretically attractive, the Clarke tax presents a number of difficulties when one attempts to use it in a practical implementation. This paper examines how the Clarke tax could be used as an effective "preference revealer" in the domain of automated agents, reducing the need for explicit negotiation.
College Student Work Habits are Related to Physical Activity and Fitness

Despite the known benefits of regular physical activity, research shows a significant decline in physical activity participation and an increase in sedentary behavior during young adulthood during the college years. Studies examining the relationship between academic outcomes and fitness/physical activity have not extensively examined this among college students. Therefore, the purpose of this study was to examine the relationship between fitness measures (cardiovascular endurance, muscular endurance, flexibility and body composition), physical activity, and academic outcomes in college students. This cross-sectional study had college students complete a one-time fitness assessment and survey examining their physical activity and academic factors (GPA, study habits, course load). Correlations examined relationships between fitness, physical activity and academic outcomes, t-tests compared differences for fitness and behavioral outcomes between groups by academic factors. The final sample (n=512) was 50.4% male, 78% Non-Hispanic White, and 67% upperclassmen. The majority (76%) of participants reported meeting current PA guidelines. Hours of studying and social media use were both positively associated with body fat. Course load was negatively associated with vigorous activity. Study time was negatively associated with cardiovascular endurance, positively associated with hip flexibility and sedentary behavior. Higher GPA was associated with a higher BMI and a higher credit load was associated with less vigorous physical activity. These findings indicated that academic outcomes and physical activity may have a different relationship among college students compared with younger age groups. This study provides insight for the development of future campus-based health initiatives to have a shared focus of academic outcomes and physical activity.
Recognizing Occluded 3 d Face Using Block Matching Algorithm and PCA

With improvement in sensor technology 3d face recognition has become an imminent technology due to its non co-operative nature. Many researches in 3d face recognition have been dealing with the problem of pose variation, expression, aging and occlusion. We show that under utmost occlusion due to hair, hands, scarves, hates, glass typical 3d face recognition system exhibit poor performance. This paper presents restoration strategy for effective 3d face recognition. In this paper we propose two algorithms for occlusion detection and face recognition. First the occluded face region is detected by matching blocks with the original face. The non occluded regions are then used to restore the missing information. BMA used to restore occluded face. Then apply PCA for recognition. The experimental results show the higher computational efficiency and robustness to occlusion.
Data Driven Adaptive Predictive Control for Holonomic Constrained Under-Actuated Biped Robots

Fundamentally, control system designs are concerned with the flow of signals in the closed loop. In this paper, we are to present the control technique at the next level of abstraction in control system design. We construct a control using implicit function with support vector regression-based data-driven model for the biped, in the presence of parametric and functional dynamics uncertainties. Based on Lyapunov synthesis, we develop decoupled adaptive control based on the model predictive and the data-driven techniques and construct the control directly from online or offline data. The adaptive predictive control mechanisms use the advantage of data-driven technique combined with online parameters estimation strategy in order to achieve an efficient approximation. Simulation results are presented to verify the effectiveness of the proposed control.
Stigma Scale for Receiving Psychological Help (SSRPH)

Stigma is one of the greatest obstacles to care and impedes recovery. The Stigma Scale for Receiving Professional Psychological Help (SSRPH), a measure of public stigma, has been used in young adults and has limited psychometric data in adolescents. This article reports the reliability and validity of the SSRPH in adolescent girls (N = 156, age = 13-17). Discriminant validity was supported, but concurrent validity was not. The confirmatory factor analysis revealed excellent model fit and serves as beginning evidence for construct validity. Cronbach’s alpha for the SSRPH was . 65. The SSRPH was stable over 8 weeks. Findings suggest that the SSRPH can serve as a foundation for further instrument development. Future studies may explore enhancing the reliability and validity of the SSRPH and use advanced analytic techniques to examine the overall global construct of stigma, the latent constructs of public and private stigma, and associations of individual items to these constructs.
Training oscillatory neural networks using natural gradient particle swarm optimization

SUMMARY In this paper, a new design of neural networks is introduced, which is able to generate oscillatory patterns in its output. The oscillatory neural network is used in a biped robot to enable it to learn to walk. The fundamental building block of the neural network proposed in this paper is O-neurons, which can generate oscillations in its transfer functions. O-neurons are connected and coupled with each other in order to shape a network, and their unknown parameters are found by a particle swarm optimization method. The main contribution of this paper is the learning algorithm that can combine natural policy gradient with particle swarm optimization methods. The oscillatory neural network has six outputs that determine set points for proportional-integral-derivative controllers in 6-DOF humanoid robots. Our experiment on the simulated humanoid robot presents smooth and flexible walking.
Lithium Iron Phosphate Battery Electric Vehicle State-of-Charge Estimation Based on Evolutionary Gaussian Mixture Regression

Lithium batteries have the characteristics of high energy density and charge-discharge rate, but exhibit high chemical activity. State-of-charge (SOC) estimation is critical to the lithium battery electric vehicle (EV) operation safety. In this paper, a novel SOC estimation method is proposed based on Gaussian process regression. A mixture Gaussian process is used in this model to strengthen the reliability of data description and to increase the estimation accuracy. Optimal number of Gaussian processes is obtained by a revolutionary expectation maximum method. A nonlinear correlation feature selection method is introduced to improve the model efficiency. The effectiveness of the proposed method is verified by an EV field test. Compared with other data-based approaches, this method exhibits higher estimation accuracy and computational efficiency.
Mice Deficient in Biglycan and Fibromodulin as a Model for Temporomandibular Joint Osteoarthritis

The temporomandibular joint (TMJ) within the craniofacial complex is unique. In humans, the TMJ can become diseased resulting in severe and disabling pain. There are no cures for TMJ disease at this time. Animal models of TMJ disease are scarce, but some exist, and they are described in this paper. We present in greater detail one animal model that is deficient in two extracellular matrix (ECM) proteoglycans, biglycan (BGN) and fibromodulin (FMOD). Doubly deficient BGN/FMOD mice develop premature TMJ osteoarthritis (OA). In order to explore the mechanistic basis of TMJ-OA, tissues from the condyle of mutant mice were examined for their relative capacity to differentiate and undergo apoptosis. Our data show that there is a redistribution of the critical ECM protein, type II collagen, in mutant mice compared with controls. Mutant mice also have increased apoptosis of the chondrocytes embedded in the articular cartilage. We speculate that the overall imbalance in apoptosis may be the cellular basis for the abnormal production of structural ECM proteins. The abnormal production of the ECM could, in turn, lead to premature erosion and degradation of the articular surface resulting in TMJ-OA. These data underscore the importance of the ECM in controlling the structural integrity of the TMJ.
Manipulation of the angiopoietic/hemangiopoietic commitment in the avian embryo.

The hypothesis that the endothelial and hemopoietic lineages have a common ontogenic origin is currently being revived. We have shown previously by means of quail/chick transplantations that two subsets of the mesoderm give rise to endothelial precursors: a dorsal one, the somite, produces pure angioblasts (angiopoietic potential), while a ventral one, the splanchnopleural mesoderm, gives rise to progenitors with a dual endothelial and hemopoietic potential (hemangiopoietic potential). To investigate the cellular and molecular controls of the angiopoietic/hemangiopoietic potential, we devised an in vivo assay based on the polarized homing of hemopoietic cell precursors to the floor of the aorta detectable in the quail/chick model. In the present work, quail mesoderm was grafted, after various pretreatments, onto the splanchnopleure of a chick host; the homing pattern and nature of graft-derived QH1(+) cells were analyzed thereafter. We report that transient contact with endoderm or ectoderm could change the behavior of cells derived from treated mesoderm, and that the effect of these germ layers could be mimicked by treatment with several growth factors VEGF, bFGF, TGFbeta1, EGF and TGF(&agr;), known to be involved in endothelial commitment and proliferation, and/or hemopoietic processes. The endoderm induced a hemangiopoietic potential in the associated mesoderm. Indeed, the association of somatopleural mesoderm with endoderm promoted the 'ventral homing' and the production of hemopoietic cells from mesoderm not normally endowed with this potential. The hemangiopoietic induction by endoderm could be mimicked by VEGF, bFGF and TGFbeta1. In contrast, contact with ectoderm or EGF/TGF(&agr;) treatments totally abrogated the hemangiopoietic capacity of the splanchnopleural mesoderm, which produced pure angioblasts with no 'ventral homing' behaviour. We postulate that two gradients, one positive and one negative, modulate the angiopoietic/hemangiopoietic potential of the mesoderm.
Multilevel Plane Wave Based Near-Field Far-Field Transformation for Electrically Large Antennas in Free-Space or Above Material Halfspace

A recently presented fully probe-corrected near-field far-field transformation employing plane wave expansion and diagonal translation operators enables near-field far-field transformation for arbitrary measurement contours and arbitrary antennas. A multilevel extension, inspired by the multilevel fast multipole method, is presented that is suitable for the efficient transformation of electrically large antennas with a size of tens or even hundreds of wavelengths. The measurement points are grouped in a multilevel fashion and translations are carried out to the box centers on the highest level only. The plane waves are processed through the different levels to the measurement points using a disaggregation and anterpolation procedure resulting in a reduced overall complexity. In the second part of this paper, the influence of perfectly conducting ground planes and dielectric halfspaces, as an approximation for ground effects in a real measurement setup, is investigated. As such ground reflected waves are assumed, which propagate from the investigated antenna to the field probe and add to the direct wave contributions. The far-field conditions required for these assumptions are achieved by a source box grouping scheme. By this extension ground effects are directly considered within the near-field far-field transformation. Transformation results using simulated and measured near-field data are shown.