Negative Magnetoresistance in Dirac Semimetal Cd3As2

A large negative magnetoresistance is anticipated in topological semimetals in the parallel magnetic and electric field configuration as a consequence of the nontrivial topological properties. The negative magnetoresistance is believed to demonstrate the chiral anomaly, a long-sought high-energy physics effect, in solid-state systems. Recent experiments reveal that Cd3As2, a Dirac topological semimetal, has the record-high mobility and exhibits positive linear magnetoresistance in the orthogonal magnetic and electric field configuration. However, the negative magnetoresistance in the parallel magnetic and electric field configuration remains unveiled. Here, we report the observation of the negative magnetoresistance in Cd3As2 microribbons in the parallel magnetic and electric field configuration as large as 66% at 50 K and even visible at room temperatures. The observed negative magnetoresistance is sensitive to the angle between magnetic and electrical field, robust against temperature, and dependent on the carrier density. We have found that carrier densities of our Cd3As2 samples obey an Arrhenius's law, decreasing from 3.0x10^17 cm^-3 at 300 K to 2.2x10^16 cm^-3 below 50 K. The low carrier densities result in the large values of the negative magnetoresistance. We therefore attribute the observed negative magnetoresistance to the chiral anomaly. Furthermore, in the perpendicular magnetic and electric field configuration a positive non-saturating linear magnetoresistance up to 1670% at 14 T and 2 K is also observed. This work demonstrates potential applications of topological semimetals in magnetic devices

Towards Multi-perspective Conformance Checking with Fuzzy Sets

Nowadays organizations often need to employ data-driven techniques to audit their business processes and ensure they comply with laws and internal/external regulations. Failing in complying with the expected process behavior can indeed pave the way to inefficiencies or, worse, to frauds or abuses. An increasingly popular approach to automatically assess the compliance of the executions of organization processes is represented by alignment-based conformance checking. These techniques are able to compare real process executions with models representing the expected behaviors, providing diagnostics able to pinpoint possible discrepancies. However, the diagnostics generated by state of the art techniques still suffer from some limitations. They perform a crisp evaluation of process compliance, marking process behavior either as compliant or deviant, without taking into account the severity of the identified deviation. This hampers the accuracy of the obtained diagnostics and can lead to misleading results, especially in contexts where there is some tolerance with respect to violations of the process guidelines. In the present work, we discuss the impact and the drawbacks of a crisp deviation assessment approach. Then, we propose a novel conformance checking approach aimed at representing actors’ tolerance with respect to process deviations, taking it into account when assessing the severity of the deviations. As a proof of concept, we performed a set of synthetic experiments to assess the approach. The obtained results point out the potential of the usage of a more flexible evaluation of process deviations, and its impact on the quality and the interpretation of the obtained diagnostics

Species specific exome probes reveal new insights in positively selected genes in nonhuman primates

Nonhuman primates (NHP) are important biomedical animal models for the study of human disease. Of these, the most widely used models in biomedical research currently are from the genus Macaca. However, evolutionary genetic divergence between human and NHP species makes human-based probes inefficient for the capture of genomic regions of NHP for sequencing and study. Here we introduce a new method to resequence the exome of NHP species by a designed capture approach specifically targeted to the NHP, and demonstrate its superior performance on four NHP species or subspecies. Detailed investigation on biomedically relevant genes demonstrated superior capture by the new approach. We identified 28 genes that appeared to be pseudogenized and inactivated in macaque. Finally, we identified 187 genes showing strong evidence for positive selection across all branches of the primate phylogeny including many novel findings

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware

Phylophenetic properties of metabolic pathway topologies as revealed by global analysis

BACKGROUND: As phenotypic features derived from heritable characters, the topologies of metabolic pathways contain both phylogenetic and phenetic components. In the post-genomic era, it is possible to measure the "phylophenetic" contents of different pathways topologies from a global perspective. RESULTS: We reconstructed phylophenetic trees for all available metabolic pathways based on topological similarities, and compared them to the corresponding 16S rRNA-based trees. Similarity values for each pair of trees ranged from 0.044 to 0.297. Using the quartet method, single pathways trees were merged into a comprehensive tree containing information from a large part of the entire metabolic networks. This tree showed considerably higher similarity (0.386) to the corresponding 16S rRNA-based tree than any tree based on a single pathway, but was, on the other hand, sufficiently distinct to preserve unique phylogenetic information not reflected by the 16S rRNA tree. CONCLUSION: We observed that the topology of different metabolic pathways provided different phylogenetic and phenetic information, depicting the compromise between phylogenetic information and varying evolutionary pressures forming metabolic pathway topologies in different organisms. The phylogenetic information content of the comprehensive tree is substantially higher than that of any tree based on a single pathway, which also gave clues to constraints working on the topology of the global metabolic networks, information that is only partly reflected by the topologies of individual metabolic pathways

The effect of waste engine oil and waste polyethylene on UV aging resistance of asphalt.

Waste engine oil (WEO) and waste polyethylene (WPE) are two common wastes, which are easy to pollute the environment. As the primary material in road construction, natural asphalt is a non-renewable energy source and asphalt is vulnerable to ultraviolet (UV) radiation during the service life. It results in degradation of asphalt pavement performance. In this paper, 22 wt % to 82 wt % of WEO and WPE were used to modify asphalts and the UV aging simulation experiment was carried out. The physical parameters of asphalts before the UV aging experiment show that the asphalt containing 42 wt % WPE and 62 wt % WEO mixture (42 wt % WPE + 62 wt % WEO) has similar physical properties with that of the matrix asphalt. Besides, gel permeation chromatography (GPC) verifies that the molecular weight distribution of the asphalt containing 42 wt % WPE + 62 wt % WEO is close to that of the matrix asphalt. The storage stability test shows that 42 wt % WPE + 62 wt % WEO has good compatibility with the matrix asphalt. The functional groups and micro-morphology of asphalts before and after the UV aging experiment were investigated by Fourier transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM). FTIR results display that 42 wt % WPE + 62 wt % WEO can effectively reduce the formation of carbonyl and sulfoxide functional groups. AFM shows that 42 wt % WPE + 62 wt % WEO can also retard the formation of a bee-like structure in asphalt after the UV aging experiment. Based on the above results, it can be concluded that WEO and WPE mixture can replace part of asphalt and improve the UV aging resistance of asphalt

UWB sensor based indoor LOS/NLOS localization with support vector machine learning

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms