Elementary Network Research Articles

Comparison Between Elementary Network Structure and Prophet Model for Time-Series Forecasting: Merchandise Sales

Comparison Between Elementary Network Structure and Prophet Model for Time-Series Forecasting: Merchandise Sales

Science for our children: Othermothering leadership within an elementary science network

AbstractTransformational equity‐centered science education requires the fields of science education and school leadership to critically consider the limited preparation elementary principals are offered to lead for science education. Thus far, little effort has been made to foster a transdisciplinary curricula beyond traditional organizational theories related to school culture and climate; learning sciences; and supervision. School leadership programs are currently inadequately preparing elementary leaders in rigorous pedagogies involving science education. Although the role of principals is often not discussed concerning science implementation in elementary education, principals play a critical role in science decision‐making. In this study, the authors present a case study of one elementary principal who also served as an othermother. Othermothers have been described as those who share mothering responsibilities in Black communities. Through interview transcripts, field notes, and social network data, the authors examine how this othermother cared for her students and community by advocating for science instruction. By strategically navigating the socio‐political and policy climate and drawing on her authentic relationships, this othermother was critical in implementing a science agenda for elementary science. Three themes that emerged from the data analysis are, (1) othermothers view science as a potential means to transform lives and fulfill the needs of the local community, (2) science policies (i.e., federal, state, and local) can limit the potential of the vision of science an othermother has for the community, (3) othermothers draw on their community to guide equitable science instruction. Overall, othermothers have visions for what science can do. However, they cannot counter the status quo individually. Collective action among educators in various roles is one means of moving an equity agenda concerning science education forward.

Binary‐Stochasticity‐Enabled Highly Efficient Neuromorphic Deep Learning Achieves Better‐than‐Software Accuracy

In this work, the requirement of using high‐precision (HP) signals is lifted and the circuits for implementing deep learning algorithms in memristor‐based hardware are simplified. The use of HP signals is required by the backpropagation learning algorithm since the gradient descent learning rule relies on the chain product of partial derivatives. However, it is both challenging and biologically implausible to implement such an HP algorithm in noisy and analog memristor‐based hardware systems. Herein, it is demonstrated that the requirement for HP signals handling is not necessary and more efficient deep learning can be achieved when using a binary stochastic learning algorithm. The new algorithm proposed in this work modifies elementary neural network operations, which improves energy efficiency by two orders of magnitude compared to traditional memristor‐based hardware and three orders of magnitude compared to complementary metal–oxide–semiconductor‐based hardware. It also provides better accuracy in pattern recognition tasks than the HP learning algorithm benchmarks.

Entanglement of Trapped-Ion Qubits Separated by 230Meters.

We report on an elementary quantum network of two atomic ions separated by 230m. The ions are trapped in different buildings and connected with 520(2)m of optical fiber. At each network node, the electronic state of an ion is entangled with the polarization state of a single cavity photon; subsequent to interference of the photons at a beam splitter, photon detection heralds entanglement between the two ions. Fidelities of up to (88.0+2.2-4.7)% are achieved with respect to a maximally entangled Bell state, with a success probability of 4×10^{-5}. We analyze the routes to improve these metrics, paving the way for long-distance networks of entangled quantum processors.

From modular decomposition trees to level-1 networks: Pseudo-cographs, polar-cats and prime polar-cats

The modular decomposition of a graph G is a natural construction to capture key features of G in terms of a labeled tree ( T , t ) whose vertices are labeled as “series” (1), “parallel” (0) or “prime”. However, full information of G is provided by its modular decomposition tree ( T , t ) only, if G does not contain prime modules. In this case, ( T , t ) explains G , i.e., { x , y } ∈ E ( G ) if and only if the lowest common ancestor lca T ( x , y ) of x and y has label “1”. This information, however, gets lost whenever ( T , t ) contains vertices with label “prime”. In this contribution, we aim at replacing “prime” vertices in ( T , t ) by simple 0/1-labeled cycles, which leads to the concept of rooted labeled level-1 networks ( N , t ) . We characterize graphs that can be explained by such level-1 networks ( N , t ) , which generalizes the concept of graphs that can be explained by labeled trees, that is, cographs. We provide three novel graph classes: polar-cats are a proper subclass of pseudo-cographs which forms a proper subclass of prime polar-cats . In particular, every cograph is a pseudo-cograph and prime polar-cats are precisely those graphs that can be explained by a labeled level-1 network. The class of prime polar-cats is defined in terms of the modular decomposition of graphs and the property that all prime modules “induce” polar-cats. We provide a plethora of structural results and characterizations for graphs of these new classes. In particular, Polar-cats are precisely those graphs that can be explained by an elementary level-1 network ( N , t ) , i.e., ( N , t ) contains exactly one cycle C that is rooted at the root ρ N of N and where ρ N has exactly two children while every vertex distinct from ρ N has a unique child that is not located in C . Pseudo-cographs are less restrictive and those graphs that can be explained by particular level-1 networks ( N , t ) that contain at most one cycle. These findings, eventually, help us to characterize the class of all graphs that can be explained by labeled level-1 networks, namely prime polar-cats. Moreover, we show under which conditions there is a unique least-resolved labeled level-1 network that explains a given graph. In addition, we provide linear-time algorithms to recognize all these types of graphs and to construct level-1 networks to explain them.

CO hydrogenation on stepped Cu and CuZn alloy surfaces: Competition between methanol synthesis and methanation pathways

Comprehensive fundamental understanding of CO hydrogenation reactions over Cu and ZnCu alloy surfaces is of great importance. Herein, we report a comparative DFT calculation study of elementary surface reaction network of CO hydrogenation reactions on stepped Cu(211), Cu(611), ZnCu(211) and ZnCu(611) surfaces. On ZnCu(211) and ZnCu(611) surfaces, the energetic favorable reaction path of CO hydrogenation reaction follows CO* → HCO* → H2CO* → H3CO* → CH3OH* → CH3OH with H3CO* hydrogenation as the rate-limiting step and proceeds more facilely on ZnCu(611) surface than on ZnCu(211) surface. On Cu(211) and Cu(611) surfaces, the energetic favorable reaction path of CO hydrogenation reaction follows CO* → HCO* → HCOH* → H2COH* → H3COH* → CH3* → CH4* → CH4 with H2COH* hydrogenation as the rate-limiting step and proceeds more facilely on Cu(611) than on Cu(211). The key difference of CO hydrogenation reaction on ZnCu alloy surface and Cu is that the resulting CH3OH* species desorbs to produce CH3OH on ZnCu alloy but undergoes H*-assisted decomposition to CH3* and eventually to CH4 on Cu surface. These results successfully unveil elementary surface reaction networks and structure sensitivity of Cu and ZnCu alloy-catalyzed CO hydrogenation reactions.

An elementary quantum network of entangled optical atomic clocks.

Optical atomic clocks are our most precise tools to measure time and frequency1-3. Precision frequency comparisons between clocks in separate locations enable one to probe the space-time variation of fundamental constants4,5 and the properties of dark matter6,7, to perform geodesy8-10 and to evaluate systematic clock shifts. Measurements on independent systems are limited by the standard quantum limit; measurements on entangled systems can surpass the standard quantum limit to reach the ultimate precision allowed by quantum theory-the Heisenberg limit. Although local entangling operations have demonstrated this enhancement at microscopic distances11-16, comparisons between remote atomic clocks require the rapid generation of high-fidelity entanglement between systems that have no intrinsic interactions. Here we report the use of a photonic link17,18 to entangle two 88Sr+ ions separated by a macroscopic distance19 (approximately 2 m) to demonstrate an elementary quantum network of entangled optical clocks. For frequency comparisons between the ions, we find that entanglement reduces the measurement uncertainty by nearly [Formula: see text], the value predicted for the Heisenberg limit. Today's optical clocks are typically limited by dephasing of the probe laser20; in this regime, we find that entanglement yields a factor of 2 reduction in the measurement uncertainty compared with conventional correlation spectroscopy techniques20-22. We demonstrate this enhancement for the measurement of a frequency shift applied to one of the clocks. This two-node network could be extended to additional nodes23, to other species of trapped particles or-through local operations-to larger entangled systems.

Garden at scholl: agroecological paths and multidisciplinary dialogues in municipal schools in Canaã do Carajás-PA

This work presents the university extension actions, carried out by the Federal University of the South and Southeast of Pará, in schools of the municipal elementary and high school network included in the "Horta nas Escolas" Project, located in the municipality of Canaã dos Carajás - PA. We deal, especially in this article, with the implementation of agroecological demonstration units for vegetables in a sustainable and organic way with the objective of providing students with multidisciplinary living laboratories in order to complement the teaching and learning of the students. The implementation of the first agricultural agroecological units in schools in the municipal network had promising results in relation to the implementation of multidisciplinary and agroecological teaching.

Completely quantum neural networks

Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a state-of-the-art quantum annealer, we develop three crucial ingredients: binary encoding the free parameters of the network, polynomial approximation of the activation function, and reduction of binary higher-order polynomials into quadratic ones. Together, these ideas allow encoding the loss function as an Ising model Hamiltonian. The quantum annealer then trains the network by finding the ground state. We implement this for an elementary network and illustrate the advantages of quantum training: its consistency in finding the global minimum of the loss function and the fact that the network training converges in a single annealing step, which leads to short training times while maintaining a high classification performance. Our approach opens a novel avenue for the quantum training of general machine learning models.

Solving Visual Analogies Using Neural Algorithmic Reasoning (Student Abstract)

We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs. This program synthesis task can be easily solved via symbolic search. Using a variation of the ‘neural analogical reasoning’ approach, we instead search for a sequence of elementary neural network transformations that manipulate distributed representations derived from a symbolic space, to which input images are directly encoded. We evaluate the extent to which our ‘neural reasoning’ approach generalises for images with unseen shapes and positions.

Environmental contact modeling for the earthworm-like robot via the novel elementary mechanical network

Environmental contact modeling for the earthworm-like robot via the novel elementary mechanical network

Easy-plane spin Hall nano-oscillators as spiking neurons for neuromorphic computing

We show analytically using a macrospin approximation that easy-plane spin Hall nano-oscillators excited by a spin-current polarized perpendicularly to the easy-plane have phase dynamics analogous to that of Josephson junctions. Similarly to Josephson junctions, they can reproduce the spiking behavior of biological neurons that is appropriate for neuromorphic computing. We perform micromagnetic simulations of such oscillators realized in the nano-constriction geometry and show that the easy-plane spiking dynamics is preserved in an experimentally feasible architecture. Finally we simulate two elementary neural network blocks that implement operations essential for neuromorphic computing. First, we show that output spikes energies from two neurons can be summed and injected into a following layer neuron and second, we demonstrate that outputs can be multiplied by synaptic weights implemented by locally modifying the anisotropy.

Tracking of Winding and Magnet Hotspots in SPMSMs Based on Synergized Lumped-parameter and Sub-domain Thermal Models

Lumped-parameter thermal models (LPTMs) have been widely used to estimate critical temperatures in permanent magnet synchronous machines (PMSMs). However, in some cases, they may be incapable of predicting the hotspots and even cause temperature misestimation. In this paper, the synergized lumped-parameter and sub-domain thermal models are proposed for surface-mounted PMSMs to track the hotspots in the most vulnerable components, i.e. active/end-windings and rotor magnet. Firstly, the T-type elementary network based LPTM is developed to predict the average temperatures in main components and the boundary conditions. Secondly, the boundary conditions are applied to three sub-domain thermal models for the active/end-windings and the rotor magnet to predict the respective hotspots and to further correct the misestimated average temperatures. Finally, the proposed synergized thermal model is verified by the experiments on a prototype surface-mounted PMSM under various load conditions.

Using high-throughput multi-omics data to investigate structural balance in elementary gene regulatory network motifs.

MotivationThe simultaneous availability of ATAC-seq and RNA-seq experiments allows to obtain a more in-depth knowledge on the regulatory mechanisms occurring in gene regulatory networks. In this article, we highlight and analyze two novel aspects that leverage on the possibility of pairing RNA-seq and ATAC-seq data. Namely we investigate the causality of the relationships between transcription factors, chromatin and target genes and the internal consistency between the two omics, here measured in terms of structural balance in the sample correlations along elementary length-3 cycles.ResultsWe propose a framework that uses the a priori knowledge on the data to infer elementary causal regulatory motifs (namely chains and forks) in the network. It is based on the notions of conditional independence and partial correlation, and can be applied to both longitudinal and non-longitudinal data. Our analysis highlights a strong connection between the causal regulatory motifs that are selected by the data and the structural balance of the underlying sample correlation graphs: strikingly, of the selected regulatory motifs belong to a balanced subgraph. This result shows that internal consistency, as measured by structural balance, is close to a necessary condition for 3-node regulatory motifs to satisfy causality rules.Availability and implementationThe analysis was carried out in MATLAB and the code can be found at https://github.com/albertozenere/Multi-omics-elementary-regulatory-motifs.Supplementary information Supplementary data are available at Bioinformatics online.

Elementary tripartite quantum communication photonic network at the telecom wavelength

An elementary tripartite quantum communication network for discrete variables is proposed. In this network three nodes are quantum wired with quantum channel created by a pair of Greenberger–Horne–Zeilinger (GHZ) triplet. We show that in this network by local operations and classical communication an arbitrary two-qubit state can be deterministically teleported from any node to any other node. In this network tripartite teleportation of two-qubit state is achieved by first splitting the qubit into fragments and then at the destination, qubit is re-constructed from the fragments. A high flux source for generation of fully inseparable GHZ photonic quantum channel required for developing an ideal tripartite quantum network is presented. This GHZ source employ a group velocity matched periodically poled potassium titanyl phosphate crystal in pulsed polarization Sagnac interferometer configuration and is based on selecting a three-photon entangled state from the product state of an entangled photon pair and a weak coherent state. This GHZ source generates tripartite entangled photons at telecom wavelength (1584 nm). Genuine entanglement in the generated GHZ state is detected with a witness (W) operator. Complete detail of the experimental apparatus required for developing an ideal tripartite quantum communication photonic network, which employ fully inseparable quantum channel, is presented. This quantum network fully operates at wavelength 1584 nm, which occurs in the L-band of the technologically important third telecom window. Success probability for teleportation of two-qubit photonic state within the tripartite quantum network is determined. We show that such quantum network is experimentally feasible. Experimental procedure to unambiguously verify the teleportation of an arbitrary two-qubit photonic state within the network is discussed. It is shown that our scheme can be implemented in a programmed teleportation system and it paves the way towards a large-scale quantum network.

A Novel Approach for Dynamic Traffic Assignment Based on Multi-Agent Node Reservation: Comparative Study on Two Competing Roads

This paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behavior of the vehicle agent that benefits from a more accurate estimation of its travel time. The main novelty is that, rather than considering the average travel times in the network links according to the present vehicles, the vehicle must first know at what time it is able to cross the nodes located at both extremities of the road. This is achieved through a negotiation between the vehicle agent and the node agents to book the crossing time. In order to assess this new paradigm, this paper compares it with well-known approaches in an elementary network. The result invites to extend the approach to more general cases. A discussion of the opportunities and limitations of the approach extension is provided in this paper. One of the notable opportunities, is that the proposed approach has a great potential to improve the energy efficiency by exploring mobile navigation applications and connected autonomous vehicles.

Microwave Quantum Link between Superconducting Circuits Housed in Spatially Separated Cryogenic Systems.

Superconducting circuits are a strong contender for realizing quantum computing systems and are also successfully used to study quantum optics and hybrid quantum systems. However, their cryogenic operation temperatures and the current lack of coherence-preserving microwave-to-optical conversion solutions have hindered the realization of superconducting quantum networks spanning different cryogenic systems or larger distances. Here, we report the successful operation of a cryogenic waveguide coherently linking transmon qubits located in two dilution refrigerators separated by a physical distance of five meters. We transfer qubit states and generate entanglement on demand with average transfer and target state fidelities of 85.8% and 79.5%, respectively, between the two nodes of this elementary network. Cryogenic microwave links provide an opportunity to scale up systems for quantum computing and create local area superconducting quantum communication networks over length scales of at least tens of meters.

Understanding the role of VSC control strategies in the limits of power electronics integration in AC grids using modal analysis

In this article, the interactions between AC grid and existing VSC control modes are analyzed. First, three VSC control modes, namely grid-feeding, grid-supporting and grid-forming, are compared with each other in order to assess VSC/grid coupling level. Then, in order to underscore the growing role of VSCs in transmission systems, an elementary benchmark network is considered. It includes an equivalent SM (Synchronous Machine), a VSC, a load and a transmission line with a varying electrical distance. Thereafter, a modal analysis is conducted on this model to study the interactions between the different VSC controls and the SM dynamics using a Matlab/Simulink environment. The converter penetration rate is represented by a variable power ratio between the converter and the machine, while the electrical distance by an RL dynamic impedance. This approach makes it possible to assess the stability limits on the VSC share imposed by the system strength. Negative interactions are identified and recommendations for novel converter control strategies proposed. Besides, the influence of the AVR with the VSC in grid-forming as well as PLL-related stability issues with the VSC in grid-feeding or grid-supporting are shown to be the key limiting factors for increasing the penetration of PE-based sources.

Role of Coadsorbates in Shaping the Reaction Pathways of Alkyl Fragments on Co Surfaces

Understanding the elementary surface reaction network of the Co-catalyzed F–T synthesis (FTS) reaction is of great interest. In this Article, we have studied the surface reactions of CHx(a) prepare...

Optimizing target nodes selection for the control energy of directed complex networks

The energy needed in controlling a complex network is a problem of practical importance. Recent works have focused on the reduction of control energy either via strategic placement of driver nodes, or by decreasing the cardinality of nodes to be controlled. However, optimizing control energy with respect to target nodes selection has yet been considered. In this work, we propose an iterative method based on Stiefel manifold optimization of selectable target node matrix to reduce control energy. We derive the matrix derivative gradient needed for the search algorithm in a general way, and search for target nodes which result in reduced control energy, assuming that driver nodes placement is fixed. Our findings reveal that the control energy is optimal when the path distances from driver nodes to target nodes are minimized. We corroborate our algorithm with extensive simulations on elementary network topologies, random and scale-free networks, as well as various real networks. The simulation results show that the control energy found using our algorithm outperforms heuristic selection strategies for choosing target nodes by a few orders of magnitude. Our work may be applicable to opinion networks, where one is interested in identifying the optimal group of individuals that the driver nodes can influence.