Performance Gain Research Articles

Enhancing System Capacity Through Joint Space‐Ground Multi‐Beam Coordination for LEO Satellite Systems

ABSTRACTDue to the long transmission distance of the constellation network represented by the low‐orbit satellite system, the satellite‐to‐ground link is subject to large path loss and limited communication performance. This paper uses multi‐satellite and multi‐beam joint transmission technology to form a virtual multiplex network with ground multi‐antenna terminals (Multiple‐Input Multiple‐Output [MIMO] system). However, due to the high‐speed movement of satellites, the topology of the MIMO system is unstable, which in turn affects the stability of the system throughput. This article proposes two inter‐satellite cooperation modes to improve the stability of MIMO system capacity. The first method is to optimize the channel capacity by rationally allocating the beam power of the gateway station so that the gateway station can achieve transparent forwarding through satellites. The second method rotates the angle of the user's receiving antenna to combat channel changes caused by changes in satellite position. Simulation results show that both methods can effectively improve the minimum channel capacity. Transparent forwarding can increase the capacity by about 22.7%, while the rotating antenna can still improve the performance gain by at least 36.3% even with a limited rotation angle. This research contributes to the development of stable and efficient communication systems in satellite‐ground cooperative networks.

AsterX: a new open-source GPU-accelerated GRMHD code for dynamical spacetimes

Abstract We present AsterX, a novel open-source, modular, GPU-accelerated, fully general relativistic magnetohydrodynamic (GRMHD) code designed for dynamic spacetimes in 3D Cartesian coordinates, and tailored for exascale computing. We utilize block-structured adaptive mesh refinement (AMR) through CarpetX, the new driver for the Einstein Toolkit, which is built on AMReX, a software framework for massively parallel applications. AsterX employs the Valencia formulation for GRMHD, coupled with the Z4c formalism for spacetime evolution, while incorporating high resolution shock capturing schemes to accurately handle the hydrodynamics. AsterX has undergone rigorous testing in both static and dynamic spacetime, demonstrating remarkable accuracy and agreement with other codes in literature. Using subcycling in time, we find an overall performance gain of factor 2.5 to 4.5. Benchmarking the code through scaling tests on OLCF's Frontier supercomputer, we demonstrate a weak scaling efficiency of about 67\%-77\% on 4096 nodes compared to an 8-node performance.

Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation.

Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes with limited support examples. Existing methods based on Region Proposal Networks (RPNs) face two issues: 1) overfitting suppresses novel class objects and 2) dual-branch models require complex spatial correlation strategies to prevent spatial information loss when generating class prototypes. We introduce a unified framework, Reference Twice (RefT), to exploit the relationship between support and query features for FSIS and related tasks. Our three main contributions are: 1) a novel transformer-based baseline that avoids overfitting, offering a new direction for FSIS; 2) demonstrating that support object queries encode key factors after base training, allowing query features to be enhanced twice at both feature and query levels using simple cross-attention, thus avoiding complex spatial correlation interaction; and 3) introducing a class-enhanced base knowledge distillation loss to address the issue of DETR-like models struggling with incremental settings due to the input projection layer, enabling easy extension to incremental FSIS. Extensive experimental evaluations on the COCO dataset under three FSIS settings demonstrate that our method performs favorably against existing approaches across different shots, e.g., +8.2/+9.4 performance gain over state-of-the-art methods with 10/30-shots.

Meta surface Assisted Open radio access networks

The paradigm of the open radio access networks (O-RAN) seeks to carry intelligence and openness (multi-vendors) to the conventional proprietary and closed radio access networks (RAN) schemes and deliver performance enhancement, cost-efficiency, and flexibility, in both the network's operation and deployment. On the other hand, Reconfigurable Intelligent Surfaces (RIS) are suggested for future networks due to their effectiveness in terms of cost and energy consumption. However, due to the fast time-varying feature of the dense wireless systems, it becomes difficult to allow optimal user association and beamforming in terms of signalling overheads and processing in RIS assisted RANs with the limited capacity of the fronthaul. Therefore, the objective of this study is to attain a trade-off between the costs (signalling overhead/complexity) and the throughput performance. In other words, the study sets the challenges of the RIS aided O-RAN technology regarding the joint selection of user’s equipment (UEs)/open radio unit (O-RU)-RIS pairs and the designing of beamforming at the O-RU/RIS and open distributed unit (O-DU). From this point, to addressing the designing challenges, this work suggests a simple and potential beamforming strategy in RIS aided O-RAN architecture taking into account the specification of the interfaces between different O-RAN units to split opportunities between the radio and distributing units. In specific, a channel-gain-based selection of UEs/O-RU-RIS pairs joint with duality theory (DT) and transform of quadratic function (TQF) algorithm (namely DT_TQF) is proposed. Firstly, the non-convex optimization problem is relaxed via duality and transform of quadratic functions, and then an iterative approach is carried out for the active and passive beamformers via a simple alternating optimization approach. This approach can achieve flexibility in the environment of a high-traffic transmission while lowering the interference between radio units and the signalling burden required for beamforming tasks. Numerical simulation results justify the effectiveness of the algorithm for different systems' parameter settings and validate the important of installing RIS. For example, for a certain environment, the performance gain is about 52.9 % in comparison to the classic null-steering/random phase shifter scheme.

Transformer Module Networks for Systematic Generalization in Visual Question Answering.

Transformers achieve great performance on Visual Question Answering (VQA). However, their systematic generalization capabilities, i.e., handling novel combinations of known concepts, is unclear. We reveal that Neural Module Networks (NMNs), i.e., question-specific compositions of modules that tackle a sub-task, achieve better or similar systematic generalization performance than the conventional Transformers, even though NMNs' modules are CNN-based. In order to address this shortcoming of Transformers with respect to NMNs, in this paper we investigate whether and how modularity can bring benefits to Transformers. Namely, we introduce Transformer Module Network (TMN), a novel NMN based on compositions of Transformer modules. TMNs achieve state-of-the-art systematic generalization performance in three VQA datasets, improving more than 30% over standard Transformers for novel compositions of sub-tasks. We show that not only the module composition but also the module specialization for each sub-task are the key of such performance gain.

Optimization of SM2 Algorithm Based on Polynomial Segmentation and Parallel Computing

The SM2 public key cryptographic algorithm is widely utilized for secure communication and data protection due to its strong security and compact key size. However, the intensive large integer operations it requires pose significant computational challenges, which can limit the performance of Internet of Things (IoT) terminal devices. This paper introduces an optimized implementation of the SM2 algorithm specifically designed for IoT contexts. By segmenting large integers as polynomials within a modified Montgomery modular multiplication algorithm, the proposed method enables parallel modular multiplication and reduction, thus addressing storage constraints and reducing computational redundancy. For scalar multiplication, a Co-Z Montgomery ladder algorithm is employed alongside Single Instruction Multiple Data (SIMD) instructions to enhance parallelism, significantly improving efficiency. Experimental results demonstrate that the proposed scheme reduces the computation time for the SM2 algorithm’s digital signature by approximately 20% and enhances data encryption and decryption efficiency by about 15% over existing methods, marking a substantial performance gain for IoT applications.

Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective

Graph neural networks (GNNs) based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy labeling approaches focus on the visual domain or graph node classification tasks and analyze the impact of noisy labels only from a utility perspective. Unlike existing work, in this paper, we measure the effects of noise labels on graph classification from data privacy and model utility perspectives. We find that noise labels degrade the model’s generalization performance and enhance the ability of membership inference attacks on graph data privacy. To this end, we propose the robust graph neural network (RGLC) approach with noisy labeled graph classification. Specifically, we first accurately filter the noisy samples by high-confidence samples and the first feature principal component vector of each class. Then, the robust principal component vectors and the model output under data augmentation are utilized to achieve noise label correction guided by dual spatial information. Finally, supervised graph contrastive learning is introduced to enhance the embedding quality of the model and protect the privacy of the training graph data. The utility and privacy of the proposed method are validated by comparing twelve different methods on eight real graph classification datasets. Compared with the state-of-the-art methods, the RGLC method achieves at most and at least 7.8% and 0.8% performance gain at 30% noisy labeling rate, respectively, and reduces the accuracy of privacy attacks to below 60%.

Improving cache-enabled D2D communications using actor–critic networks over licensed and unlicensed spectrum

Cache-enabled Device-to-Device (D2D) communications is an effective way to improve data sharing. User Equipment (UE)-level caching holds the potential to reduce the data traffic burden on the core network. Licensed spectrum is utilized for D2D communications, but due to spectrum scarcity, exploring unlicensed spectrum is essential to enhance network capacity. In this paper, we propose caching at the UE-level and exploit both licensed and unlicensed spectrum for optimizing throughput. First, we propose a reinforcement learning-based data caching scheme leveraging an actor–critic network to improve cache-enabled D2D communications. Besides, licensed and unlicensed spectrum are devised for D2D communications considering interference from existing cellular and Wi-Fi users. A duty cycle-based unlicensed spectrum access algorithm is employed, guaranteeing the Signal-to-Interference and Noise Ratio (SINR) required by the users. The unlicensed spectrum is prone to data packets collisions. Therefore, Request-to-Send/Clear-to-Send (RTS/CTS) mechanism is utilized in conjunction with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to alleviate both the interference and packets collision problems of the unlicensed spectrum. Extensive simulations are performed to analyze the performance gain of our proposed scheme compared to the benchmarks under different network scenarios. The obtained results demonstrate that our proposed scheme possesses the potential to optimize network performance.

Double-side integration of the fluorinated self-assembling monolayers for enhanced stability of inverted perovskite solar cells

Traps and structural defects at the hole and electron transport interfaces of the microcrystalline absorber limits the efficiency and long-term stability of perovskite solar cells (PSCs) due to accumulation of the ionic clusters, non-radiative recombination and electrochemical corrosion. Surface engineering using self-assembled monolayers (SAM) was considered as an effective strategy for modification of charge-collection junctions. In this work, we demonstrate the first report about complex integration of a SAM for double-side passivation in p-i-n PSCs. Integrating the novel 5-(4-[bis(4-fluorophenyl)amino]phenyl)thiophene-2-carboxylic acid (FTPATC) as a fluorinated SAM at the hole-transport interface reduced potential barriers and lattice stresses in the absorber. At the electron-transport side, FTPATC interacted with the A-site cations of the perovskite molecule (Cs, formamidinium), inducing a dipole for defect compensation. Using the passivation approach with fluorinated SAM demonstrated benefits in the gain of the output performance up to 22.2 %. The key-advantage of double-side passivation was confirmed by the enhanced stability under continuous light-soaking (1-sun equivalent, 65 °C, ISOS-L-2), maintaining 88 % of the initial performance over 1680 h and thermal stabilization under harsh heating at 90 °C.

Optimizing thermoelectric properties of CoTiP half-Heusler via doping with Br-, Se- and Ge atoms using first principle study

The optimization of thermoelectric materials is crucial for advancing energy conversion technologies. This study explores the electrical and thermoelectric properties of Br-, Ge-, and Se-doped CoTiP half-Heusler compounds using the plane-augmented-wave (PAW) method based on Density Functional Theory (DFT) alongside the semiclassical Boltzmann transport equation (BTE) and Debye-Callaway approximation. While previous research has focused on various doping strategies to enhance thermoelectric performance, specific impacts of Br, Ge, and Se doping on the electronic structure of CoTiP remain unexplored. Our analysis reveals that Ge-doped CoTiP exhibits the largest band gap energy of 1.2597 eV, followed by Se- and Br-doped structures with band gaps of 0.8064 eV and 0.678 eV, respectively. The Fermi level shifts towards the conduction band for both Br- and Se-doped alloys while shifting towards the valence band for Ge-doped alloys. Upon doping, we observe significant enhancements in the Seebeck coefficient and electrical conductivity. Power factor (S2σ) enhancements range from 0.01611 W/m K2 for CoTiP0.875Br0.125, 0.03445 W/m K2 for CoTiP0.875Se0.125 and finally, 0.04191 W/m K2 for CoTiP0.875Ge0.125, surpassing undoped material values by up to 93 %. Finally, the optimal value of figure of merit (ZT) increases to 0.65, 0.57, and 0.2 at 900 K, achieved by doping Ge, Se and Br, respectively, at the P site, with performance gain about 92 %. Hence, doping has optimized the thermoelectric performance of the CoTiP half-Heusler.

Design and performance evaluation of a 3D printed aerostatic bearing concept with high number of micro-orifices

This study suggests a 3D printed aerostatic bearing concept. The design has a high number of orifices distributed around the bearing to obtain smoother pressure distribution for performance. Inner channels are formed to connect and feed air to each orifice using the advantage of additive manufacturing. The concept is investigated numerically to determine the performance gain of the high number of orifices by calculating bearing stiffness and load carrying capacity. In order to validate the simulation and performance gain an experimental study is conducted by testing Stereolithography (SLA) type 3D printed bearings with various orifice numbers. The bearing stiffnesses and mass flow rates of the specimens are measured, and results are discussed in terms of performance by comparing numerical results. The 3D printed bearings concept has shown promising results in terms of performance while keeping air consumption limited compared to conventional air bearings.

Selection of Superior Clones of Cedrela odorata L. at Early Age in an Asexual Seed Orchard for Genetic Improvement

The selection of plant age in forest species is crucial in a genetic improvement program, as time is a key factor in decision-making. This study aimed to evaluate the variation in clonal growth of Cedrela odorata L. to identify clones with superior performance at an early age. The orchard was established in September 2014, utilizing a completely randomized block design with a planting distance of 4 x 4 m. The experiment comprised 70 clones (ortets), each with 35 replicates (ramets), totaling 2,450 ramets. Selection was based on four key traits: total height (th), clear stem height (csh), normal diameter (nd), and crown diameter (cd). These variables were used to calculate the percentage gain or loss in performance for each clone relative to the population mean. A principal component analysis, analysis of variance (ANOVA), Tukey test (p≤0.05), and cluster analysis were performed to assess the data. The results identified 16 out of the 70 clones as having superior traits. The superior clones included 53-CoSFcoChan07, 2-CoGua344, 15-CoB191, 19-CoKal126, 22-CoKal142, 41-CoTux558, 35-CoTez642, 56-CoTez539, 45-CoTez549, 49-CoTez540, 50-CoTez540, 59-CoTux555, 62-CoCar5, 30-CoBac02, 48-CoTez544, and 10-CoKal16. The average traits of these superior clones were 8.0±0.4 m for total height, 2.6±0.1 m for clear stem height, 0.13±0.0 m for normal diameter, and 4.1±0.2 m for crown diameter, representing a 15% increase over the population mean.

JamBIT: RL-based framework for disrupting adversarial information in battlefields

During battlefield operations, military radios (hereafter nodes) exchange information among various units using a mobile ad-hoc network (MANET) due to its infrastructure-less and self-healing capabilities. Adversarial cyberwarfare plays a crucial role in modern combat by disrupting communication between critical nodes (i.e., nodes mainly responsible for propagating important information) to gain dominance over the opposing side. However, determining critical nodes within a complex network is an NP-hard problem. This paper formulates a mathematical model to identify important links and their connected nodes, and presents JamBIT, a reinforcement learning-based framework with an encoder–decoder architecture, for efficiently detecting and jamming critical nodes. The encoder transforms network structures into embedding vectors, while the decoder assigns a score to the embedding vector with the highest reward. Our framework is trained and tested on custom-built MANET topologies using the Named Data Networking (NDN) protocol. JamBIT has been evaluated across various scales and weighting methods for both connected node and network dismantling problems. Our proposed method outperformed existing RL-based baselines, with a 24% performance gain for smaller topologies (50–100 nodes) and 8% for larger ones (400–500 nodes) in connected node problems, and a 7% gain for smaller topologies and 15% for larger ones in network dismantling problems.

A Comparison of Uncertainty Estimation Methods for Building Footprint Change Detection from Sentinel-2 Imagery

Abstract. This manuscript investigates the effects of uncertainty methods applied to the problem of deep learning-based semantic segmentation of building footprints on moderate-resolution satellite imagery. While the recent efforts of big corporations to add information about building locations and sizes on a global or continental scale are generally valuable, still the overall challenge persists in identifying the spatial-temporal patterns of growing urbanization. In this work, we extend UNet-type architectures to perform binary building footprint classification based on Sentinel-2 imagery resulting in five different models. While previous studies focused on urban areas in the Western world we conduct all training and evaluation in India. All models are trained on Microsoft building footprint products while for evaluation purposes high-quality reference data is manually selected from regions with especially good open-street-map coverage. Quantitative and qualitative experiments are conducted where a significant performance gain is found for a model trained with a mixture of aleatoric and epistemic uncertainty measures. The performance gain is even more pronounced for subsequent quantitative multi-temporal change detection experiments.

Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain.

Learning and decision-making undergo substantial developmental changes, with adolescence being a particular vulnerable window of opportunity. In adolescents, developmental changes in specific choice behaviors have been observed (e.g., goal-directed behavior, motivational influences over choice). Elevated levels of decision noise, i.e., choosing suboptimal options, were reported consistently in adolescents. However, it remains unknown whether these observations, the development of specific and more sophisticated choice processes and higher decision noise, are independent or related. It is conceivable, but has not yet been investigated, that the development of specific choice processes might be impacted by age-dependent changes in decision noise. To answer this, we examined 93 participants (12 to 42 years) who completed 3 reinforcement learning (RL) tasks: a motivational Go/NoGo task assessing motivational influences over choices, a reversal learning task capturing adaptive decision-making in response to environmental changes, and a sequential choice task measuring goal-directed behavior. This allowed testing of (1) cross-task generalization of computational parameters focusing on decision noise; and (2) assessment of mediation effects of noise on specific choice behaviors. Firstly, we found only noise levels to be strongly correlated across RL tasks. Second, and critically, noise levels mediated age-dependent increases in more sophisticated choice behaviors and performance gain. Our findings provide novel insights into the computational processes underlying developmental changes in decision-making: namely a vital role of seemingly unspecific changes in noise in the specific development of more complex choice components. Studying the neurocomputational mechanisms of how varying levels of noise impact distinct aspects of learning and decision processes may also be key to better understand the developmental onset of psychiatric diseases.

An autoencoder-based confederated clustering leveraging a robust model fusion strategy for federated unsupervised learning

Concerns related to data privacy, security, and ethical considerations become more prominent as data volumes continue to grow. In contrast to centralized setups, where all data is accessible at a single location, model-based clustering approaches can be successfully employed in federated settings. However, this approach to clustering in federated settings is still relatively unexplored and requires further attention. As federated clustering deals with remote data and requires privacy and security to be maintained, it poses particular challenges as well as possibilities. While model-based clustering offers promise in federated environments, a robust model aggregation method is essential for clustering rather than the generic model aggregation method like Federated Averaging (FedAvg). In this research, we proposed an autoencoder-based clustering method by introducing a novel model aggregation method FednadamN, which is a fusion of Adam and Nadam optimization approaches in a federated learning setting. Therefore, the proposed FednadamN adopted the adaptive learning rates based on the first and second moments of gradients from Adam which offered fast convergence and robustness to noisy data. Furthermore, FednadamN also incorporated the Nesterov-accelerated gradients from Nadam to further enhance the convergence speed and stability. We have studied the performance of the proposed Autoencoder-based clustering methods on benchmark datasets and using the novel FednadamN model aggregation strategy. It shows remarkable performance gain in federated clustering in comparison to the state-of-the-art.

Two-stage Rule-induction visual reasoning on RPMs with an application to video prediction

Raven’s Progressive Matrices (RPMs) are frequently used in evaluating human’s visual reasoning ability. Researchers have made considerable efforts in developing systems to automatically solve the RPM problem, often through a black-box end-to-end convolutional neural network for both visual recognition and logical reasoning tasks. Based on the intrinsic natures of RPM problem, we propose a Two-stage Rule-Induction Visual Reasoner (TRIVR), which consists of a perception module and a reasoning module, to tackle the challenges of real-world visual recognition and subsequent logical reasoning tasks, respectively. For the reasoning module, we further propose a “2+1” formulation that models human’s thinking in solving RPMs and significantly reduces the model complexity. It derives a reasoning rule from each RPM sample, which is not feasible for existing methods. As a result, the proposed reasoning module is capable of yielding a set of reasoning rules modeling human in solving the RPM problems. To validate the proposed method on real-world applications, an RPM-like Video Prediction (RVP) dataset is constructed, where visual reasoning is conducted on RPMs constructed using real-world video frames. Experimental results on various RPM-like datasets demonstrate that the proposed TRIVR achieves a significant and consistent performance gain compared with state-of-the-art models.

Optimal Capacitance Design for IRS Aided Wireless Power Transfer for Sustainable IoT Communication

ABSTRACTIntelligent reflecting surface (IRS) is a cutting‐edge technique that can significantly improve wireless propagation. It can efficiently utilize wireless power transfer to enable sustainable Internet‐of‐Things (IoT) transmission by reconfiguring the incident signal from the active transmitter. However, the flexibility of capacitance tuning in the IRS system, which controls underlying reflections, is often overlooked. The effective capacitance design in the IRS system can provide a new degree of freedom in the IoT communication system, which can enable additional performance gain in the received power. To achieve this, a novel IRS circuital optimization model is proposed in this work. It incorporates various electrical parameters of the meta‐surface unit cell for improved IoT‐enabled communication. The proposed optimization model provides an optimal capacitance as a function of phase shift (PS), which is controlled by IRS, incident frequency, and other IRS electrical parameters. This optimal capacitance is then used to define the received power. The convexity of the optimization problem is proved, and the global optimal capacitance is obtained for received power maximization. Our simulations show that the proposed optimization model outperforms the existing constructive interference‐based optimal PS method, for which the capacitance is first calculated. Finally, the analytical results are numerically validated with several optimal design insights.

Bridging chemical structure and conceptual knowledge enables accurate prediction of compound-protein interaction

BackgroundAccurate prediction of compound-protein interaction (CPI) plays a crucial role in drug discovery. Existing data-driven methods aim to learn from the chemical structures of compounds and proteins yet ignore the conceptual knowledge that is the interrelationships among the fundamental elements in the biomedical knowledge graph (KG). Knowledge graphs provide a comprehensive view of entities and relationships beyond individual compounds and proteins. They encompass a wealth of information like pathways, diseases, and biological processes, offering a richer context for CPI prediction. This contextual information can be used to identify indirect interactions, infer potential relationships, and improve prediction accuracy. In real-world applications, the prevalence of knowledge-missing compounds and proteins is a critical barrier for injecting knowledge into data-driven models.ResultsHere, we propose BEACON, a data and knowledge dual-driven framework that bridges chemical structure and conceptual knowledge for CPI prediction. The proposed BEACON learns the consistent representations by maximizing the mutual information between chemical structure and conceptual knowledge and predicts the missing representations by minimizing their conditional entropy. BEACON achieves state-of-the-art performance on multiple datasets compared to competing methods, notably with 5.1% and 6.6% performance gain on the BIOSNAP and DrugBank datasets, respectively. Moreover, BEACON is the only approach capable of effectively predicting knowledge representations for knowledge-lacking compounds and proteins.ConclusionsOverall, our work provides a general approach for directly injecting conceptual knowledge to enhance the performance of CPI prediction.

Multidisciplinary Optimization of Gyroid Topologies for a Cold Plate Heat Exchanger Design

Abstract The strive to reduce the environmental impact of aviation has led to electrification and increasing demand for powerful on-board power electronic systems. These high-performance electrical components are bound to produce significant amounts of low-quality heat waste that, if not dissipated properly, will lead to malfunctioning and even permanent damage. For this reason, high-performance heat exchangers (HX) represent a key enabler for future advances in aircraft systems electrification and are vital to meet net zero goals and reduce the aviation's carbon footprint. For a given volume of the exchanger, the heat flow rate can be increased by adopting more sophisticated fluid domains. However, excessive geometrical complexity will lead to an increase in pressure losses, often resulting in inhomogeneous temperature distributions. In this paper, a novel optimization procedure is employed to maximize the efficiency of a high-performance heat exchanger, while minimizing overall pressure loss and temperature gradients. The optimization is performed with full three-dimensional high-fidelity computational flow simulations. The geometry of the fluid domain is constituted by triply periodic minimal surfaces (TPMS), with a parametrization based on thickness and aspect ratios, done by using the ntopology suite. To assess the performance gain, the topology-optimized design is compared against the datum case and a conventional serpentine design.