Specific Topology Research Articles

DEEP LEARNING-DRIVEN DIFFERENTIATED TRAFFIC SCHEDULING IN CLOUD-IOT DATA CENTER NETWORKS

The development of 5G technology has enabled the cloud-internet of things (IoT) to impact all areas of our lives. Sensors in cloud-IoT generate large-scale data, and the demand for massive data processing is also increasing. The performance of a single machine can no longer meet the needs of existing users. In contrast, a data center (DC) integrates computing power and storage resources through a specific network topology and satisfies the need to process massive data. Regarding large-scale heterogeneous traffic in DCs, differentiated traffic scheduling on demand reduces transmission latency and improves throughput. Therefore, this paper presents a traffic scheduling method based on deep Q-networks (DQN). This method collects network parameters, delivers them to the environment module, and completes the environment construction of network information and reinforcement learning elements through the environment module. Thus, the final transmission path of the elephant flow is converted based on the action given by DQN. The experimental results show that the method proposed in this paper effectively reduces the transmission latency and improves the link utilization and throughput to a certain extent.

A Spiking Neural Network with Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces.

Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. However, most existing methods do not effectively mine the specific spatial topology of EEG channels and temporal dependencies of the encoded EEG spikes. Moreover, most are designed for specific BCI tasks and lack some generality. Hence, this study presents a novel SNN model with the customized spike-based adaptive graph convolution and long short-term memory (LSTM), termed SGLNet, for EEG-based BCIs. Specifically, we first adopt a learnable spike encoder to convert the raw EEG signals into spike trains. Then, we tailor the concepts of the multi-head adaptive graph convolution to SNN so that it can make good use of the intrinsic spatial topology information among distinct EEG channels. Finally, we design the spike-based LSTM units to further capture the temporal dependencies of the spikes. We evaluate our proposed model on two publicly available datasets from two representative fields of BCI, notably emotion recognition, and motor imagery decoding. The empirical evaluations demonstrate that SGLNet consistently outperforms existing state-of-the-art EEG classification algorithms. This work provides a new perspective for exploring high-performance SNNs for future BCIs with rich spatiotemporal dynamics.

Pixel detector hybridisation with anisotropic conductive films

Hybrid pixel detectors require a reliable and cost-effective interconnect technology adapted to the pitch and die sizes of the respective applications. During the ASIC and sensor R&D phase, and in general for small-scale applications, such interconnect technologies need to be suitable for the assembly of single-dies, typically available from Multi-Project-Wafer submissions. Within the CERN EP R&D programme and the AIDAinnova collaboration, innovative hybridisation concepts targeting vertex-detector applications at future colliders are under development. This contribution presents recent results of a newly developed in-house single-die interconnection process based on Anisotropic Conductive Film (ACF). The ACF interconnect technology replaces the solder bumps with conductive particles embedded in an adhesive film. The electro-mechanical connection between the sensor and the read-out chip is achieved via thermo-compression of the ACF using a flip-chip device bonder. A specific pad topology is required to enable the connection via conductive particles and create cavities into which excess epoxy can flow. This pixel-pad topology is achieved with an in-house Electroless Nickel Immersion Gold (ENIG) plating process that is also under development within the project. The ENIG and ACF processes are qualified with the Timepix3 ASIC and sensors, with 55 µm pixel pitch and 14 µm pad diameter. The ACF technology can also be used for ASIC-PCB/FPC integration, replacing wire bonding or large-pitch solder bumping techniques. This contribution introduces the ENIG plating and ACF processes and presents recent results on Timepix3 hybrid assemblies.

Measurement-Based Identification of Lumped Parameter Thermal Networks for sub-Kw Outer Rotor PM Machines

This work is on deriving precise lumped parameter thermal networks for modeling the transient thermal characteristics of electric machines under variable load conditions. The goal is to facilitate an accurate estimation of the temperatures of critical machines' components and to allow for running the derived model in real time to adapt the motor control based on the load history and maximum permissible temperatures. Consequently, the machine's capabilities can be exhausted at best considering a highly-utilized drive. The model shall be as simple as possible without sacrificing the exactness of the predicted temperatures. Accordingly, a specific lumped parameter thermal network topology was selected and its characteristics are explained in detail. The measurement data based optimization of its critical parameters through an evolutionary optimization strategy, and the therefore utilized experimental setup will be described in detail here. Measurement cycles were recorded for modeling and verification purposes including both static and dynamic test cycles with changing load torque and speed requirements. Applying the proposed hybrid approach for determining the model's parameters through involving physics-based equations as well as numerical optimization followed a significant improvement of the preciseness of the predicted motor temperatures compared to solely determining the networks's coefficients based on expert knowledge. Thereby, the validation included both the original measurement data as well as extra measurement runs. The proposed and applied strategy provides an excellent basis for future thermal modeling of electric machines.

Topology driven and soft phonon mode enabled Na-ion diffusion in quaternary chalcogenides, Na3ZnGaX4 (X = S, and Se)

A shallow potential energy surface facilitated by a specific structural topology of 32g sites key to three dimensional Na-diffusion.

Echo-state-network-based iterative learning control of distributed systems

The paper proposes an effective modeling and control procedure for the distributed-parameter systems using the echo-state network. The main idea is to reconstruct the spatiotemporal dynamics defined in a given multi-dimensional domain. In the investigated problem positions of both sensors and actuators are fixed allowing to delegate the complex system dynamics to echo-state network. Imposing a proper partitioning of the spatial domain, a specific topology of a neural network is used to form a reservoir capable to follow not only temporal but also spatial dynamics of the system. Based on available historical data, neural network model is initially trained and then used to derive the control law in the framework of iterative learning control. The echo-state network can be retrained after a particular control iterate in order to reduce model uncertainty and to fit it to the current operating conditions as much as possible. The performance of the proposed approach is tested and evaluated on the example of the squared clamped plate control.

Полемика о структуре психоаналитического поля: ответ Габриэлю Тупинамба

This article develops the discussion initiated by the Gabriel Tupinambá’s Desire of Psychoanalysis, which needs to be supplemented by a clarification and revision of the attitude established in Lacanist discourse towards other disciplines and domains — first of all, science and politics. The position of psychoanalysis in relation to them appears to the author to be problematic and inconsistent with the theoretical level of psychoanalysis itself. In other words, in the interaction with other areas, the analysis feels much less confident than in the clinic and in working with its own concepts. The author hypothesizes why, among other disciplines, psychoanalysis invariably finds itself in this position. Following Tupinamba, it is argued that the main causal factor is the specific topology of the unconscious, which was established by Sigmund Freud and further developed by Jacques Lacan. In the clinic, in conversation with the general public, in dialogue with other disciplines, in each of these dimensions of psychoanalysis’s existence this topology is triggered and generates its effects, for which psychoanalysis eventually has to pay the price in its own territory. The inherent form of the axiomatic topology gives psychoanalysis certain advantages, but today it is time to state that psychoanalysis can no longer exploit its resources and must radically reconsider it and the long-term strategic problems related to it. The positions from which this revision is tentatively proposed are intended to mark and interpret the current crisis of the psychoanalytic community.

Topology optimisation of architected cellular materials from additive manufacturing: Analysis, design, and experiments

This work deals with an experimental/numerical validation of the optimised topologies found through a special density-based topology optimisation (TO) method wherein the topological descriptor, i.e., the pseudo-density field, is represented through a non-uniform rational basis spline (NURBS) hyper-surface. The framework is that of multi-scale TO methods to design architected cellular materials (ACMs). Specifically, in the most general case, the topological variables are defined at the scale of the representative volume element (RVE) of the ACM and at the macroscopic scale of the structure. The transition among scales is performed via a numerical homogenisation scheme based on the strain energy of elements. The proposed formulation exploits the properties of NURBS entities to determine the relationships occurring among the topological variables defined at different scales to correctly state the optimisation problem and to satisfy the hypotheses at the basis of the homogenisation method. Three design cases are considered: in the first one, TO is performed only at the macroscopic scale; in the second one, TO is performed only at the RVE scale; in the last one, TO is performed simultaneously at both scales. Multiple design requirements related to lightness, scale separation condition (to ensure the validity of the results of the homogenisation method) and minimum printable size are included in the problem formulation. Particularly, the last two requirements are implicitly satisfied by controlling the integer parameters of the NURBS entity (describing the pseudo-density field at each scale) without introducing explicit optimisation constraints. The multi-scale TO strategy is applied to a structure made of ACM subject to three-point bending test-like boundary conditions: for each design case, the optimised topology is manufactured through stereo-lithography and a comparison between experimental and numerical results (obtained through non-linear analysis conducted a posteriori on the optimised topology) is performed to assess the effectiveness of the approach.

Artificial and Semi‐artificial Photosynthesis (AP and SAP) Systems Based on Metal‐Organic Frameworks

Comprehensive SummaryRecently, artificial and semi‐artificial photosynthesis have attracted extensive attentions in addressing the crisis of energy from fossil fuels and reducing excessive CO2 emission. Metal‐organic frameworks (MOFs) have been considered as ideal platforms for constructing artificial photosynthesis systems due to their unique properties like large specific surface area, high porosity and diverse framework topology, and tunable functionalities. This review discussed the characteristics, superiorities and challenges of MOF‐based photocatalysts, and detailed summarization of several common design strategies for MOF‐based artificial systems, including i) enhancement of light absorption, ii) acceleration of the charge separation and transfer, and iii) introduction of additional active units. Particularly, we give examples showing the applications of MOF‐based photocatalysts, where the mechanisms of superior photocatalytic activity and selectivity are also analyzed, thereby providing theoretical guidance for rational design of MOF‐based photocatalysts. Finally, the challenges and future research directions of MOF‐based photocatalysts are prospected.

A hands-on gaze on HTTP/3 security through the lens of HTTP/2 and a public dataset

Following QUIC protocol ratification on May 2021, the third major version of the Hypertext Transfer Protocol, namely HTTP/3, was published around one year later in RFC 9114. In light of these consequential advancements, the current work aspires to provide a full-blown coverage of the following issues, which to our knowledge have received feeble or no attention in the literature so far. First, we provide a complete review of attacks against HTTP/2, and elaborate on if and in which way they can be migrated to HTTP/3. Second, through the creation of a testbed comprising the at present six most popular HTTP/3-enabled servers, we examine the effectiveness of a quartet of attacks, either stemming directly from the HTTP/2 relevant literature or being entirely new. This scrutiny led to the assignment of at least one CVE ID with a critical base score by MITRE. No less important, by capitalizing on a realistic, abundant in devices testbed, we compiled a voluminous, labeled corpus containing traces of ten diverse attacks against HTTP and QUIC services. An initial evaluation of the dataset mainly by means of machine learning techniques is included as well. Given that the 30 GB dataset is made available in both pcap and CSV formats, forthcoming research can easily take advantage of any subset of features, contingent upon the specific network topology and configuration.

Multiplexing-based control of stochastic resonance.

We show that multiplexing (Here, the term "multiplexing" means a special network topology where a one-layer network is connected to another one-layer networks through coupling between replica nodes. In the present paper, this term does not refer to the signal processing issues and telecommunications.) allows us to control noise-induced dynamics of multilayer networks in the regime of stochastic resonance. We illustrate this effect on an example of two- and multi-layer networks of bistable overdamped oscillators. In particular, we demonstrate that multiplexing suppresses the effect of stochastic resonance if the periodic forcing is present in only one layer. In contrast, multiplexing allows us to enhance the stochastic resonance if the periodic forcing and noise are present in all the interacting layers. In such a case, the impact of multiplexing has a resonant character: the most pronounced effect of stochastic resonance is achieved for an appropriate intermediate value of coupling strength between the layers. Moreover, multiplexing-induced enhancement of the stochastic resonance can become more pronounced for the increasing number of coupled layers. To visualize the revealed phenomena, we use the evolution of the dependence of the signal-to-noise ratio on the noise intensity for varying strength of coupling between the layers.

Bulk-like ductility of cold spray additively manufactured copper in the as-sprayed state

• As-sprayed CSAM Cu can exhibit a simultaneous high ductility and strength. • This is triggered by a specific topology of bimodal grain size. • The deposits have a high thermal conductivity comparable to bulk. • They are perfectly gas-tight with He leakage rate of less than 1 × 10 −7 mbar•l/s. • All these were achieved with 99% efficiency using inexpensive nitrogen. In recent years, cold spray process is increasingly used for additive manufacturing of metallic components, referred to as cold spray additive manufacturing (CSAM). Unlike the fusion-based AM processes, CSAM is achieved in a solid-state, bringing several advantages such as the absence of severe oxidation or phase composition changes. At the moment, the main limitation of CSAM is the generally low ductility of the as-sprayed deposits. In this paper, using Cu as a model material, we demonstrate a way to overcome this limitation. Importantly, a high ductility of the deposits in their as-sprayed state is achieved without a trade-off in mechanical strength. Furthermore, we show that without any post-heat treatment, the properties of CSAM Cu are comparable to bulk, non-AM Cu.

False data injection attacks detection with modified temporal multi-graph convolutional network in smart grids

False Data Injection Attack (FDIA) detection can prevent the tampering of important data in the smart grid. This is of great significance to the operation and control of modern power systems. Since the existing FDIA detection methods are limited by the sequential input of data in Euclidean space, they cannot accurately describe the compelling correlation between data components. Therefore, this paper proposes a novel FDIA localization detection method based on graph data modeling and graph deep learning. The proposed approach tries to disaggregate the primary data into graph structured data with graph topological relationships based on graph theory, then designs specialized networks for data with different graph topologies. Moreover, the designed multi-graph mechanism and temporal correlation layer can better fully mine the correlation features between data components, with its attribute characteristics, to construct deep learning on the specific graph topology for FDIA detection. Extensive simulation experiments and visualization show that the proposed scheme is more effective than the conventional detection model, and its overall accuracy in 14-bus, 118-bus and 300-bus systems is 98.3%, 96.4% and 95.8%. It also proves that this scheme has high robustness and generalization ability in different scenarios.

A two-level network-on-chip architecture with multicast support

It is essential for implementing processing systems of edge computing, internet of things (IoT) and wireless multimedia sensor networks (WMSN) to use low-power parallel and distributed architectures with high speed and low power consumption. Most of the artificial intelligence and machine learning applications need to be executed on efficient distributed architectures with multicast support. In this paper, TLAM, a high-performance and low-cost NoC architecture is proposed. TLAM stands for Two-Level network-on-chip Architecture with Multicast support and includes a hybrid path/tree based multicast routing algorithm and a specific two-level mesh topology. The routing algorithm of TLAM is basically working according to the Hamiltonian paths. In the proposed architecture, the topology is partitioned and the two-level links provide an efficient infrastructure for low-cost and low-latency transmission of multicast and broadcast messages between partitions. In TLAM, in addition to multicasting routing algorithm, hardware components for handling multicasting packets are designed in order to achieve performance improvements. The goal is to improve the efficiency and performance, especially latency, while handling both unicast and multicast packets. Experimental evaluations including network-level and router-level analysis with different configurations under various traffic patterns were performed. The evaluations in terms of latency, throughput, area and power consumption, indicate that TLAM provides higher performance, especially for dense multicasting and broadcasting, in comparison with the existing architectures.

Network motifs shape distinct functioning of Earth’s moisture recycling hubs

Earth’s hydrological cycle critically depends on the atmospheric moisture flows connecting evaporation to precipitation. Here we convert a decade of reanalysis-based moisture simulations into a high-resolution global directed network of spatial moisture provisions. We reveal global and local network structures that offer a new view of the global hydrological cycle. We identify four terrestrial moisture recycling hubs: the Amazon Basin, the Congo Rainforest, South Asia and the Indonesian Archipelago. Network motifs reveal contrasting functioning of these regions, where the Amazon strongly relies on directed connections (feed-forward loops) for moisture redistribution and the other hubs on reciprocal moisture connections (zero loops and neighboring loops). We conclude that Earth’s moisture recycling hubs are characterized by specific topologies shaping heterogeneous effects of land-use changes and climatic warming on precipitation patterns.

Semantic Graph Attention With Explicit Anatomical Association Modeling for Tooth Segmentation From CBCT Images.

Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here we propose a semantic graph-based method to explicitly model the spatial associations between different anatomical targets (i.e., teeth) for their precise delineation in a coarse-to-fine fashion. First, to efficiently control the bilaterally symmetric confusion in segmentation, we employ a lightweight network to roughly separate teeth as four quadrants. Then, designing a semantic graph attention mechanism to explicitly model the anatomical topology of the teeth in each quadrant, based on which voxel-wise discriminative feature embeddings are learned for the accurate delineation of teeth boundaries. Extensive experiments on a clinical dental CBCT dataset demonstrate the superior performance of the proposed method compared with other state-of-the-art approaches.

One‐Step Generation of Porous GelMA Microgels by Droplet‐Based Chaotic Advection Effect

AbstractPorous GelMA microgels are promising scaffolds for biomedical research and regenerative medicine due to their special topology, cell‐loading capacity, and high biocompatibility. However, the traditional preparation of porous microgels is often limited by some complex postprocessing processes such as freeze‐drying and swelling equilibrium. Herein, we present a simple strategy for in situ generation of porous GelMA microgels as cell carriers by integrating the placeholder of polyethylene oxide (PEO) and chaotic advection effect of droplets in microfluidic devices. The device is mainly composed of four functional units, including droplet generation, rapid mixing, photopolymerization, and collection. The strategy allows precise control of the size and porosity of the microgels by changing the flow rates of the continuous and dispersed phases. Furthermore, both human mesenchymal stem cells (hMSCs) and umbilical vein endothelial cells (HUVECs) cultured on the surface of obtained microgels show high cell viability, spreading, and proliferation, which indicate their good biocompatibility and potential as microcarriers for various cell types. The proposed strategy provides a novel approach for the preparation of porous microgels holding promise in tissue engineering and scalable cell expansion.

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

BackgroundRepresenting the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators.ResultsSUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein–protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels.ConclusionsWe developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC.

Equivalent SU(3)f approaches for two-body anti-triplet charmed baryon decays

For the two-body Bc → BM decays, where Bc denotes the anti-triplet charm baryon and B(M) the octet baryon (meson), there exist two theoretical studies based on the SU(3) flavor [SU(3)f] symmetry. One is the irreducible SU(3)f approach (IRA). In the irreducible SU(3)f representation, the effective Hamiltonian related to the initial and final states forms the amplitudes for Bc → BM. The other is the topological-diagram approach (TDA), where the W-boson emission and W-boson exchange topologies are drawn and parameterized for the decays. As required by the group theoretical consideration, we present the same number of the IRA and TDA amplitudes. We can hence relate the two kinds of the amplitudes, and demonstrate the equivalence of the two SU(3)f approaches. We find a specific W-boson exchange topology only contributing to {Xi}_c^0 →BM. Denoted by EM, it plays a key role in explaining mathcal{B} ( {Xi}_c^0 → Λ0 {K}_S^0 , Σ0 {K}_S^0 , Σ+K−). We consider that {Lambda}_c^{+} → nπ+ and {Lambda}_c^{+} → pπ0 proceed through the constructive and destructive interfering effects, respectively, which leads to mathcal{B} ( {Lambda}_c^{+} → nπ+) » mathcal{B} ( {Lambda}_c^{+} → pπ0) in agreement with the data. With the exact and broken SU(3)f symmetries, we predict the branching fractions of Bc→BM to be tested by future measurements.

Parameter Sensitivity Analysis of the Democratic Behavior of Swarm Robots

Aims: The purpose of this studyis to determine under what conditions, such as noise and malfunction, successful consensus achievement in swarmrobotics is possible Background: Swarm robots can be used to solve exploration problems, such as the best-of-n problem. Consensus achievement plays a crucial role as the swarm must collectively agree on a solution. This task can be even more challenging considering noise and malfunctioning or rogue agents. Objective: This study aims to determine how robust the consensus achievement algorithm is against noise and rogue agents, considering the effect of adding memory to the agents and further parameter tuning. Methods: We implement a baseline based on the democratic honeybees algorithm and investigate the performance and robustness of the consensus achievement during a number of computational experiments. In particular, the number of agents in the swarm, the number of iterations, the number of positions an agent can visit per iteration, the number of neighbors an agent shares its best option with, and the majority threshold defining the majority based on a fraction of agents in the swarm, and the minimum number of iterations to achieve consensus are investigated regarding their impact. Results: For better performance, memory has been implemented so that each agent remembers and retains their previous highest quality score if no one better has been found in the current exploration phase. We show that the algorithm is viable and offers robustness in the considered scenarios when memory is added. In particular, we establish a baseline for the democratic honeybees algorithm and ascertain adequate parameter values to ensure the algorithm's best performance. The algorithm is sufficiently robust against noise, and to an extent, against rogue agents. Furthermore, parameter tuning also proved to help the swarm explore very large search spaces Conclusion: The consensus algorithm appears sufficiently effective under adverse conditions such as noise and rogue agents, especially when countermeasures are considered Other: Further scenarios such as specific communication topologies could be investigated in future research.