Sub-graph based joint sparse graph for sparse code multiple access systems

Sparse code multiple access (SCMA) is a promising air interface candidate technique for next generation mobile networks, especially for massive machine type communications (mMTC). In this paper, we design a LDPC coded SCMA detector by combining the sparse graphs of LDPC and SCMA into one joint sparse graph (JSG). In our proposed scheme, SCMA sparse graph (SSG) defined by small size indicator matrix is utilized to construct the JSG, which is termed as sub-graph based joint sparse graph of SCMA (SG-JSG-SCMA). In this paper, we first study the binary-LDPC (B-LDPC) coded SGJSG- SCMA system. To combine the SCMA variable node (SVN) and LDPC variable node (LVN) into one joint variable node (JVN), a non-binary LDPC (NB-LDPC) coded SG-JSG-SCMA is also proposed. Furthermore, to reduce the complexity of NBLDPC coded SG-JSG-SCMA, a joint trellis representation (JTR) is introduced to represent the search space of NB-LDPC coded SG-JSG-SCMA. Based on JTR, a low complexity joint trellis based detection and decoding (JTDD) algorithm is proposed to reduce the computational complexity of NB-LDPC coded SGJSG- SCMA system. According to the simulation results, SG-JSGSCMA brings significant performance improvement compare to the conventional receiver using the disjoint approach, and it can also outperform a Turbo-structured receiver with comparable complexity. Moreover, the joint approach also has advantages in terms of processing latency compare to the Turbo approaches

Left hemisphere predominance of pilocarpine-induced rat epileptiform discharges

<p>Abstract</p> <p>Background</p> <p>The left cerebral hemisphere predominance in human focal epilepsy has been observed in a few studies, however, there is no related systematic study in epileptic animal on hemisphere predominance. The main goal of this paper is to observe if the epileptiform discharges (EDs) of Pilocarpine-induced epileptic rats could present difference between left hemisphere and right hemisphere or not.</p> <p>Methods</p> <p>The electrocorticogram (ECoG) and electrohippocampogram (EHG) from Pilocarpine-induced epileptic rats were recorded and analyzed using Synchronization likelihood (SL) in order to determine the synchronization relation between different brain regions, then visual check and cross-correlation analysis were adopted to evaluate if the EDs were originated more frequently from the left hemisphere than the right hemisphere.</p> <p>Results</p> <p>The data show that the synchronization between left-EHG and right-EHG, left-ECoG and left-EHG, right-ECoG and right-EHG, left-ECoG and right-ECoG, are significantly strengthened after the brain functional state transforms from non-epileptiform discharges to continuous-epileptiform discharges(p < 0.05). When the state transforms from continuous EDs to periodic EDs, the synchronization is significantly weakened between left-ECoG and left-EHG, left-EHG and right-EHG (p < 0.05). Visual check and the time delay (τ) based cross-correlation analysis finds that 10 out of 13 EDs have a left predominance (77%) and 3 out of 13 EDs are right predominance (23%).</p> <p>Conclusion</p> <p>The results suggest that the left hemisphere may be more prone to EDs in the Pilocarpine-induced rat epilepsy model and implicate that the left hemisphere might play an important role in epilepsy states transition.</p

The Study of Returns to Private Investment in Higher Education from the Point of Employment

The quantitative methods are used to compare the difference of vocation and employment between the university graduates and high school graduates, which including the secondary school graduates. And the following four respects are involved to describe the impact of higher education to employment: the relative concentration degree, the difference of vocation, the weekly working time and the working industry. So we come to the conclution from the aspect of employment that private investment gets not only great market returns but also nonmarket returns. Key words: higher education; private investment; non-market returns; employment Résumé: Les méthodes quantitatives sont utilisées pour comparer les différences dans la vacation et dans le type de travail entre les diplômés des universités et les diplômés des collèges, y compris les écoles secondaires. Les quatre aspects suivants sont impliqués pour décrire l’impact d’une éducation supérieure sur l’emploi : le degré de concentration relatif, les différences de vocation, les heures de travail hebdomadaires, et le type de l’industrie dans lequelle ils travaillent. Du point de vue de l’emploi, nous arrivons à la conculsion que l’investissment privé peut obtenir non seulement des rendements du marché mais aussi des rendements qui ne proviennent pas du marché. Mots-Clés: enseignement supérieur; investissement supérieur; rendements qui ne proviennent pas du marché; emplo

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Quasi-Equivalence of Width and Depth of Neural Networks

While classic studies proved that wide networks allow universal approximation, recent research and successes of deep learning demonstrate the power of the network depth. Based on a symmetric consideration, we investigate if the design of artificial neural networks should have a directional preference, and what the mechanism of interaction is between the width and depth of a network. We address this fundamental question by establishing a quasi-equivalence between the width and depth of ReLU networks. Specifically, we formulate a transformation from an arbitrary ReLU network to a wide network and a deep network for either regression or classification so that an essentially same capability of the original network can be implemented. That is, a deep regression/classification ReLU network has a wide equivalent, and vice versa, subject to an arbitrarily small error. Interestingly, the quasi-equivalence between wide and deep classification ReLU networks is a data-driven version of the De Morgan law