bluuebunny/crossref_metadata_2025 huggingface dataset:
Evolution of an interacting fermion–antifermion pair in the near-horizon of the BTZ black hole
Authors: Guvendi Abdullah
Published: February 2024

In this study, we investigate the evolution of a composite system comprising a fermion–antifermion pair engaged in Cornell-type non-minimal interaction in the near-horizon region of a BTZ black hole. Our exploration involves the derivation of an exact solution for the covariant two-body Dirac equation, derived from quantum electrodynamics through the action principle. To commence our analysis, we formulate the relevant equation, resulting in a $$4\times 4$$

4
×
4

dimensional matrix equation that governs the relative motion of the fermion–antifermion pair. Notably, we demonstrate that this matrix equation results in an exactly solvable wave equation, enabling us to determine the relativistic frequency modes for this spinless static composite system. Our findings unveil a temporal decay in these modes, with the decay time explicitly dependent on both the inter-particle interaction and the spacetime parameters. Our comprehensive examination extends to a detailed analysis of the system’s evolution, shedding light on the influence of inter-particle interaction on the evolution of a fermion–antifermion pair in the near-horizon region of the BTZ black hole.
Designs of the divider and special multiplier optimizing T and CNOT gates
Authors: Fan Ping , Li Hai-Sheng
Published: February 2024

Quantum circuits for multiplication and division are necessary for scientific computing on quantum computers. Clifford + T circuits are widely used in fault-tolerant realizations. T gates are more expensive than other gates in Clifford + T circuits. But neglecting the cost of CNOT gates may lead to a significant underestimation. Moreover, the small number of qubits available in existing quantum devices is another constraint on quantum circuits. As a result, reducing T-count, T-depth, CNOT-count, CNOT-depth, and circuit width has become the important optimization goal. We use 3-bit Hermitian gates to design basic arithmetic operations. Then, we present a special multiplier and a divider using basic arithmetic operations, where ‘special’ means that one of the two operands of multiplication is non-zero. Next, we use new rules to optimize the Clifford + T circuits of the special multiplier and divider in terms of T-count, T-depth, CNOT-count, CNOT-depth, and circuit width. Comparative analysis shows that the proposed multiplier and divider have lower T-count, T-depth, CNOT-count, and CNOT-depth than the current works. For instance, the proposed 32-bit divider achieves improvement ratios of 40.41 percent, 31.64 percent, 45.27 percent, and 65.93 percent in terms of T-count, T-depth, CNOT-count, and CNOT-depth compared to the best current work. Further, the circuit widths of the proposed n-bit multiplier and divider are 3n. I.e., our multiplier and divider reach the minimum width of multipliers and dividers, keeping an operand unchanged.
Van der Waals black holes in rainbow gravity
Authors: Oubagha R. , Hamil B. , Merad M. , Lütfüoğlu B. C.
Published: February 2024

Recently, Rajagopal et al. presented an asymptotically AdS black hole metric whose thermodynamics qualitatively mimics the behavior of the Van der Waals fluid by treating the cosmological constant as a thermodynamic pressure. In some studies in the literature, authors have discussed the effects of deformed algebras such as generalized and extended uncertainty principles on the thermal quantities of these black holes. In this paper, we considered another deformation, the rainbow gravity formalism, and we investigated its impact on the Van der Waal black hole thermodynamics. To this end, we first generated the modified lapse and mass functions, and then we derived the modified thermal quantities such as thermodynamic volume, Hawking temperature, entropy, and specific heat functions. Finally, we explored the thermodynamics of a black hole, which mimics the thermodynamics of an ideal gas, under the influence of the rainbow gravity formalism.
Sparse Approximate Pseudoinverse Preconditioning for Sparse Supervised Learning Problems with More Features than Samples
Authors: Lipitakis Anastasia-Dimitra E. , Gravvanis George A. , Filelis-Papadopoulos Christos K. , Kotsiantis Sotiris , Anagnostopoulos Dimosthenis
Published: February 2024

In this paper, we propose a new technique for handling a class of sparse supervised learning problems, specifically focusing on regression, binary, and multi-class classification problems. Sparse regression problems are associated with datasets including both arithmetic and categorical features and by encoding the dataset leads to an underdetermined sparse linear system. Furthermore, logistic regression can be used for both sparse binary and multi-class classification problems, solving an underdetermined sparse linear system. A new technique called Sparse Approximate Pseudoinverse Preconditioning (SAPP), namely the Explicit Preconditioned Conjugate Gradient for Normal Equations (EPCGNE) method based on Generic Approximate Sparse Pseudoinverse matrices is introduced for solving underdetermined sparse least square problems. Numerical experiments were carried out demonstrating a significant improvement of the performance metrics for the proposed SAPP scheme compared to other learners.
Energy Absorption Characteristic of Biomimetic Gradient Hierarchical Multicellular Tubes Under Axial Impact
Authors: Qian Jun , Deng Xiaolin , Huang Cuiping , Cai Zhenzhen
Published: February 2024

This paper introduces a biomimetic gradient hierarchical multicellular structure consisting of two tree-like fractal variants: one based on vertex connections (HCV) and the other based on wall connections (HCW). We investigate their mechanical behavior and deformation through numerical simulations. Our findings reveal that, irrespective of whether they have the same wall thickness or mass, second-order and third-order structures exhibit superior energy absorption capacity (EA) and crushing force efficiency (CFE) compared to first-order structures, resulting in significantly enhanced crashworthiness performance. In the case of HCV structures with identical wall thickness, the third-order structure outperforms the first-order structure by 79.73% in specific energy absorption (SEA) and by 38.51% in CFE. Similarly, for HCW structures, the third-order variant surpasses the first-order one by 45.57% in SEA and 28.39% in CFE. We also conduct a parametric study, exploring the influence of inner circle diameter, fractal coefficient, and fractal angle on the crashworthiness of biomimetic gradient hierarchical multicellular structures. We identify the optimal fractal coefficient and inner diameter distribution range for HCV when the fractal angle is 60[Formula: see text]. Lastly, we compare these structures with traditional multicellular tubes, demonstrating that biomimetic gradient hierarchical multicellular tubes achieve up to 50.34% higher SEA and 55.13% higher CFE. The results of this study offer valuable design insights for developing lightweight and efficient energy-absorbing structures.
Assessment of Axisymmetric Dynamic Snap-Through and Thermally Induced Vibrations in FGM Cylindrical Shells Under Instantaneous Heating
Authors: Keibolahi A. , Eslami M. R. , Kiani Y.
Published: February 2024

This paper addresses the investigation of axisymmetric thermally induced vibrations in Functionally Graded Material (FGM) cylindrical shells. It considers temperature-dependent (TD) properties and geometric non-linearity (the von Karman effect). The study systematically solves a transient heat conduction equation using finite differences method and the Crank–Nicolson method. During the heating stages, the evaluation of thermal forces and moments takes place. Equations of motion are derived through the application of Hamilton’s principle. Spatial dependencies are discretized using the generalized Ritz method, while temporal dependencies are approximated using the [Formula: see text]-Newmark method with Newton–Raphson linearization. A comparative analysis validates the procedure’s efficiency and precision. Parametric studies explore the influence of parameters, including the temperature-dependency material properties, geometric nonlinearity, and shell’s power-law index, providing valuable insights into FGM shell behavior under thermal shock.
Discriminant Decision Making of Cardiovascular Diseases Based on Cloud-Based Convolutional Attention Network
Authors: Liu Wei , Rao Congjun
Published: February 2024

Cardiovascular diseases (CVDs) have become the number one killer affecting human health. In order to reduce the burden of medical workers, facilitate government screening of the population and enable patients to conduct their own health status checks, there is an urgent need for a complementary diagnostic system to predict the occurrence of CVD. In this study, a new cloud-based convolutional attention network (C-CAN) model is proposed for the discriminant decision making of CVD. In this model, the indicator data for discriminant decision making of CVD are trained using an improved one-dimensional convolutional neural network (1D CNN) model structure based on the correlation of factors influencing CVD given by decision-making trial and evaluation laboratory (DEMATEL) and cloud models. This 1D CNN model consists of a convolutional pooling module, an attention module and a fully connected module. The cloud model is used to process the original data based on the discriminating opinion of experts, so as to select the important factors that affect CVD. The attention mechanism is effective in augmenting attention to the essential elements of the data and reducing attention to the less important features. Both have similarities in that they are effective in augmenting the important features in the data and combine with each other to achieve better results. Moreover, the C-CAN is compared with decision tree (DT), [Formula: see text]-nearest neighbors (KNN), random forests (RF) and normal CNN according to the CVD dataset from the Kaggle platform. The results show that the classification accuracy, precision, recall and F1 value of C-CAN are all higher than that of all compared models. Further, the proposed model is further externally validated using other imbalanced datasets, and the results indicate that C-CAN has good resilience for imbalanced data. Our findings suggest that C-CAN represents a promising new approach that may somehow address the challenges associated with deep learning (DL) in the medical field.
Reliability Evaluation of Binary Group Decision-Making Mechanism
Authors: Liu Qiang , Peng Xinyu , Liu Qingmiao , Li Qiao
Published: February 2024

Decision-making is an important management activity. This study evaluates the reliability of group decision-making (GDM) and multi-attribute GDM (MAGDM) mechanisms for a class of 0–1 binary decision-making problem. We define the reliability of GDM and MAGDM, use the weighted voting system to model the GDM and MAGDM mechanisms, and propose two algorithms to evaluate the reliability of GDM and MAGDM considering the participation of general or professional decision makers. Additionally, the influence of some system parameters, such as the number of decision makers or attributes, cognitive accuracy of decision makers, and threshold of weighted majority voting rule, on the reliability of GDM and MAGDM was analyzed using random simulation experiments. The results of the random experiment show that: increasing the number of decision makers or attributes could improve the decision accuracy; the reduction in the individual subjective accuracy reduces the overall decision accuracy, which was difficult to compensate for by increasing the number of DMs; guiding DMs to reach consensus through group discussion decreased the decision accuracy of GDM and MAGDM.
Alternative Low-Order Triangular Elements for Thick and Thin Shell Analysis
Authors: Petrolito J. , Ionescu D.
Published: February 2024

In this paper, the authors discuss the development of six low-order triangular elements for the linear static analysis of thick and thin shell structures. The elements combine two triangular plane stress elements with three triangular thick plate bending elements to produce three-node flat shell elements with 18 degrees of freedom. The two plane stress components are based on the free formulation and the template formulation, while the three plate bending components are based on the free formulation, the deviatoric strain formulation and the hybrid-Trefftz formulation. The theoretical backgrounds and formulations of all elements are briefly discussed to provide a concise summary, while further details are given in the quoted references. The elements are used to analyze five standard shell benchmark examples, and the results obtained are compared to previous work in this area. The elements provide accurate and robust results for the benchmarks, with only small differences for the results as the meshes are refined. While all the proposed elements can be recommended for general shell analysis, the combination of the template plane stress element and the hybrid-Trefftz plate bending element generally provides the best results.
The Orchestration Approach in Innovation: A Systematic Literature Review
Authors: Lobo Ezequiel Alves , de Melo Filho José Iran Batista , Tahim Elda Fontenele , Câmara Samuel Façanha
Published: February 2024

The orchestration approach has been gaining prominence and relevance in recent years, due to high competitiveness in the business context and the search for more efficient strategies to promote innovation. In light of this reality, this paper conducts a systematic literature review (SLR) aiming to contribute to the emerging theme of innovation orchestration. We examined the abstracts of 761 articles and found 75 that were relevant to the topic, which was analyzed using the biblioshiny tool. These documents were published between 2006 and January 2023 and were collected from two databases, Web of Science Core Collection and Scopus. We discovered that the highest number of articles was published in 2022. Furthermore, we identified the conceptual evolution of the theme, dividing it into three periods. Through the overall thematic map, we were able to uncover niche and emerging topics such as innovation orchestration in social entrepreneurship networks, innovation orchestration in ecosystems and orchestration mechanisms. Additionally, we contribute by pointing out pathways for future studies, which is a key aspect of advancing knowledge in the field.
Degree-based indices of hypergraphs: Definitions and first results
Authors: Vetrík Tomáš
Published: February 2024

For a [Formula: see text]-uniform hypergraph [Formula: see text], we introduce degree-based indices such as the general sum-connectivity index [Formula: see text] and the general Randić index [Formula: see text], where [Formula: see text], [Formula: see text] is the set of hyperedges of [Formula: see text] and [Formula: see text] is the degree of a vertex [Formula: see text] in [Formula: see text]; [Formula: see text] and [Formula: see text]. Other indices such as the first and second Zagreb index, first and second hyper-Zagreb index, classical sum-connectivity index, classical Randić index and harmonic index of a hypergraph [Formula: see text] are special cases of the general indices. For [Formula: see text], we obtain upper bounds on [Formula: see text] and [Formula: see text] for a uniform hypergraph [Formula: see text] with given order, order and number of isolated vertices, order and maximum degree, order and diameter at least [Formula: see text], and lower bounds for uniform hypergraphs with given order and no isolated vertices, order and minimum degree, and order and maximum possible degree. We also present extremal graphs for all the bounds. Bounds on Zagreb indices follow from our results on general indices.
Estimating the reliability bounds of communication system by using sum of disjoint product method
Authors: Mutar Emad Kareem
Published: February 2024

In this paper, an algorithm is present that uses the sum of disjoint products (SDP) to calculate the reliability bounds of a complex system. The algorithm requires a set of all minimal paths and cuts to compute the bounds of the reliability polynomial. Furthermore, using Boolean algebra to construct a sum-of-products expression. This research aims to create the lower and upper bounds of reliability for a complex communication system (Double-bridge). By minimizing the number of disjointed (mutually exclusive) variables, the method can reduce the computer time required to estimate the probability of the principal events. The communication system is also used to establish the usefulness of the provided algorithm, which is better than the currently used SDP method for big data sets and more specific to understand and implement. Furthermore, simulations and numerical examples have shown that the new technique typically improves Spross bounds, particularly for highly reliable systems.
Physical analysis of eccentricity-based topological indices for Hex-derived networks
Authors: Babar Usman , Naseem Asim , Shaker Hani , Siddiqui Muhammad Kamran , Nadeem Muhammad Faisal
Published: February 2024

The molecular topological descriptors, which are the numerical invariants of a molecule’s structure, are very good in foretelling a molecule’s bioactivity. Because of its usefulness in many areas of mathematical chemistry, graph theory has achieved tremendous advancements in this field and has become quite popular among scientists. Many of these indices are used and researched in the domains of chemistry, pharmacology, drug manufacture, and other related subjects. This paper calculates the eccentricity indices of a HDN network of the third kind, as well as the total and average eccentricities, [Formula: see text], [Formula: see text], and eccentric version of Zagreb indices. For these descriptors, we also provide analytically closed equations that could be applied in order to explore the network’s underlying topologies.
Neutrosophic linear fractional optimization problem using fuzzy goal programming approach
Authors: Verma Vaishaly , Singh Pitam
Published: February 2024

This research paper develops an algorithm to solve a neutrosophic linear fractional optimization problem, where the cost of the objective functions, the technology coefficients, and the resources are neutrosophic numbers (triangular and trapezoidal numbers). Two defuzzification methods are used here to convert the neutrosophic linear fractional optimization problem into a multi-objective linear fractional optimization problem (MOLFOP). The fractional objective function is defuzzified using the component-wise optimization method, and the linear constraints are defuzzified using the aggregation ranking function strategy. Hence, the MOLFOP is then solved using the fuzzy goal programming method. In order to further explain our suggested algorithm, two numerical examples are solved at the end, and the outcomes obtained by our method are compared with the outcomes obtained using the other algorithm.
INCOME, EXTREME TEMPERATURE, AND RESIDENTIAL ELECTRICITY CONSUMPTION IN THE SPANISH PROVINCES
Authors: PABLO-ROMERO MARIA DEL P. , SÁNCHEZ-BRAZA ANTONIO , IZQUIERDO CLAUDIA PRISCILA
Published: February 2024

The effect of income and extreme temperatures on the residential electricity consumption in Spain is analyzed. An electricity demand function is estimated by means of ordinary least squares-Driscoll and Kraay, feasible generalized least squares, and quantiles panel data techniques, for the total of the sample, and by coastal climatic zones. The results support the energy-environmental Kuznets curve hypothesis for Spain, the continental, and Mediterranean zones. An N form is found for the Atlantic zone. The results also reflect that increases in temperature above 22∘C and decreases below 15∘C increase electricity consumption, this increase being progressive as temperatures vary. Thus, extreme temperature generates electricity consumption growth. The Mediterranean zone is most sensitive to these temperature changes. The results also show that electricity consumption is more sensitive to cold than to heat. The results suggest an inefficient use of heating and cooling appliances in the areas with the highest electricity consumption.
Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos
Authors: Deng Guanghong , Lin Liangpei , Lin Baihao , Jing Wenlong , Zhao Xiaodan , Li Yong , Yang Rui , Xie Shidun , Wei Pengfeng
Published: February 2024

The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3[Formula: see text] image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplabv3[Formula: see text] algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1[Formula: see text]ms and 13.2[Formula: see text]ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7[Formula: see text]ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7[Formula: see text]ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly improved the accuracy and efficiency of intelligent inspection of substation meter readings.
Earth's earliest forest: fossilized trees and vegetation-induced sedimentary structures from the Middle Devonian (Eifelian) Hangman Sandstone Formation, Somerset and Devon, SW England
Authors: Davies Neil S. , McMahon William J. , Berry Christopher M.
Published: February 2024

The evolution of trees and forests through the Devonian Period fundamentally changed the Earth's land biosphere, as well as impacting physical environments and geomorphology by stabilizing sediments and interacting with flowing air and water. From the mid-Givetian Age onwards, lignophyte flora are known to have been key parts of the machinery in the so-called Devonian landscape factory, but the impact of earlier forests, dominated by less woody cladoxylopsids, are not as well understood. We report here evidence for a previously unrecognized cladoxylopsid forest landscape, archived within the Eifelian Hangman Sandstone Formation of Somerset and Devon, SW England. This unit has previously been considered palaeobotanically depauperate, but is here shown to contain the earliest fossil evidence for such trees in the British record, as well as the oldest known evidence globally for the relative position of standing trees: in common parlance, a fossil forest. In addition to abundant fossil material attributable to the cladoxylopsid tree
Calamophyton
, and other early Mid-Devonian flora, the sedimentary context of the plant remains sheds light on the biogeomorphic impacts of these earliest forests. The trees colonized a sizeable distributive fluvial system that was prone to seasonal disturbance events. The nature of the sedimentary system has created a bias to those facies where biogeomorphic signatures are most frequently recorded (from the distal parts of the system), but across the whole system there is evidence of plant–sediment interactions in the form of vegetation-induced sedimentary structures, rooting features and accumulations of plant debris. Plant remains are also found in nearshore facies adjacent to the distributive fluvial system, attesting to the development of a novel non-marine/marine teleconnection from the production and export of new biological sedimentary particles. The Hangman Sandstone Formation is illustrative of the revolutionary power of cladoxylopsid trees as biogeomorphic agents, forming densely spaced forests and shedding exceptionally abundant plant debris, while also impacting local landforms and sediment accumulations and profoundly changing landform resilience against flood disturbance events. These findings provide evidence that the Eifelian Stage (393.3–387.7 Ma) marks the onset of tree-driven changes to physical environments that would forever change Earth's non-marine landscapes and biosphere.

Supplementary material:
Details of sedimentary facies and additional images of plant fossils are available at
https://doi.org/10.6084/m9.figshare.c.7084873
A Family of Centrality Measures for Graph Data Based on Subgraphs
Authors: Bugedo Sebastián , Riveros Cristian , Salas Jorge
Published: February 2024

We present the theoretical foundations and first experimental study of a new approach in centrality measures for graph data. The main principle is straightforward: the more relevant subgraphs around a vertex, the more central it is in the network. We formalize the notion of “relevant subgraphs” by choosing a family of subgraphs that, given a graph
G
and a vertex
v
, assigns a subset of connected subgraphs of
G
that contains
v
. Any of such families defines a measure of centrality by counting the number of subgraphs assigned to the vertex, i.e., a vertex will be more important for the network if it belongs to more subgraphs in the family. We show several examples of this approach. In particular, we propose the All-Subgraphs (All-Trees) centrality, a centrality measure that considers every subgraph (tree). We study fundamental properties over families of subgraphs that guarantee desirable properties over the centrality measure. Interestingly, All-Subgraphs and All-Trees satisfy all these properties, showing their robustness as centrality notions. To conclude the theoretical analysis, we study the computational complexity of counting certain families of subgraphs and show a linear time algorithm to compute the All-Subgraphs and All-Trees centrality for graphs with bounded treewidth. Finally, we implemented these algorithms and computed these measures over more than one hundred real-world networks. With this data, we present an empirical comparison between well-known centrality measures and those proposed in this work.
Tensorial Evolutionary Optimization for Natural Image Matting
Authors: Lei Si-Chao , Gong Yue-Jiao , Xiao Xiao-Lin , Zhou Yi-Cong , Zhang Jun
Published: February 2024

Natural image matting has garnered increasing attention in various computer vision applications. The matting problem aims to find the optimal foreground/background (F/B) color pair for each unknown pixel and thus obtain an alpha matte indicating the opacity of the foreground object. This problem is typically modeled as a large-scale pixel pair combinatorial optimization (PPCO) problem. Heuristic optimization is widely employed to tackle the PPCO problem owing to its gradient-free property and promising search ability. However, traditional heuristic methods often encode F/B solutions to a one-dimensional (1D) representation and then evolve the solutions in a 1D manner. This 1D representation destroys the intrinsic two-dimensional (2D) structure of images, where the significant spatial correlations among pixels are ignored. Moreover, the 1D representation also brings operation inefficiency. To address the above issues, this article develops a spatial-aware tensorial evolutionary image matting (TEIM) method. Specifically, the matting problem is modeled as a 2D Spatial-PPCO (S-PPCO) problem, and a global tensorial evolutionary optimizer is proposed to tackle the S-PPCO problem. The entire population is represented as a whole by a third-order tensor, in which individuals are classified into two types: F and B individuals for denoting the 2D F/B solutions, respectively. The evolution process, consisting of three tensorial evolutionary operators, is implemented based on pure tensor computation for efficiently seeking F/B solutions. The local spatial smoothness of images is also integrated into the evaluation process for obtaining a high-quality alpha matte. Experimental results compared with state-of-the-art methods validate the effectiveness of TEIM.
Homeostasis: Design and Implementation of a Self-Stabilizing Compiler
Authors: Nougrahiya Aman , Nandivada V. Krishna
Published: February 2024

Mainstream compilers perform a multitude of analyses and optimizations on the given input program. Each analysis (such as points-to analysis) may generate a
program-abstraction
(such as points-to graph). Each optimization is typically composed of multiple alternating phases of
inspection
of such program-abstractions and
transformations
of the program. Upon transformation of a program, the program-abstractions generated by various analyses may become inconsistent with the modified program. Consequently, the correctness of the downstream inspection (and consequent transformation) phases cannot be ensured until the relevant program-abstractions are
stabilized
; that is, the program-abstractions are either invalidated or made consistent with the modified program. In general, the existing compiler frameworks do not perform automated stabilization of the program-abstractions and instead leave it to the compiler pass writers to deal with the complex task of identifying the relevant program-abstractions to be stabilized, the points where the stabilization is to be performed, and the exact procedure of stabilization. In this article, we address these challenges by providing the design and implementation of a novel compiler-design framework called
Homeostasis
.

Homeostasis
automatically captures all the program changes performed by each transformation phase, and later, triggers the required stabilization using the captured information, if needed. We also provide a formal description of
Homeostasis
and a correctness proof thereof. To assess the feasibility of using
Homeostasis
in compilers of parallel programs, we have implemented our proposed idea in IMOP, a compiler framework for OpenMP C programs. Furthermore, to illustrate the benefits of using
Homeostasis
, we have implemented a set of standard data-flow passes, and a set of involved optimizations that are used to remove redundant barriers in OpenMP C programs. Implementations of none of these optimizations in IMOP required any additional lines of code for stabilization of the program-abstractions. We present an evaluation in the context of these optimizations and analyses, which demonstrates that
Homeostasis
is efficient and easy to use.
The Challenge of Data Analytics with Climate-neutral Urban Mobility (Vision Paper)
Authors: Winter Stephan , Sester Monika , Tomko Martin , Millonig Alexandra
Published: February 2024

Urban mobility is a major contributor to human-induced climate change, a challenge that urban and transport planning and spatial computing academic communities have been actively addressing. In this article we argue, however, that the common data analytics research into incremental efficiency improvements of originally non-sustainable urban mobility systems will never be able to help reach climate
neutrality
—the goal we must achieve by 2050 as per the Paris Agreement. This imperative is exacerbated by the observation that improvements, by data analytics, in one segment of urban mobility typically have unintended and often adverse consequences in other segments. In this vision paper we argue for a data analytics agenda to advance climate action at the core of urban mobility research. This agenda must disrupt the way we think and operate, as much as it is disrupting the accessibility issues of society in cities.
Agent-Based Model of Initial Token Allocations: Simulating Distributions post Fair Launch
Authors: Fernandez Joaquin Delgado , Barbereau Tom , Papageorgiou Orestis
Published: February 2024

With advancements in distributed ledger technologies and smart contracts, tokenized voting rights gained prominence within decentralized finance (DeFi). Voting rights tokens (a.k.a. governance tokens) are fungible tokens that grant individual holders the right to vote upon the fate of a project. The motivation behind these tokens is to achieve decentral control within a decentralized autonomous organization (DAO). Because the initial allocations of these tokens is often undemocratic, the DeFi project and DAO of Yearn Finance experimented with a fair launch allocation where no tokens are pre-mined and all participants have an equal opportunity to receive them. Regardless, research on voting rights tokens highlights the formation of timocracies over time. The consideration is that the tokens’ tradability is the cause of concentration. To examine this proposition, this article uses an agent-based model to simulate and analyze the concentration of voting rights tokens post three fair launch allocation scenarios under different trading modalities. The results show that regardless of the allocation, concentration persistently occurs. It confirms the consideration that the ‘disease’ is endogenous: the cause of concentration is the tokens’ tradability. The findings inform theoretical understandings and practical implications for on-chain governance mediated by tokens.
Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems
Authors: Kang SeongKu , Kweon Wonbin , Lee Dongha , Lian Jianxun , Xie Xing , Yu Hwanjo
Published: February 2024

In recent years, recommender systems have achieved remarkable performance by using ensembles of heterogeneous models. However, this approach is costly due to the resources and inference latency proportional to the number of models, creating a bottleneck for production. Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), reducing inference costs while maintaining high accuracy. We find that the efficacy of distillation decreases when transferring knowledge from heterogeneous teachers. To address this, we propose a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from teachers’ trajectories. HetComp uses dynamic knowledge construction to provide progressively difficult ranking knowledge and adaptive knowledge transfer to gradually transfer finer-grained ranking information. Although HetComp improves accuracy, it exacerbates popularity bias, resulting in a high popularity lift. To mitigate this issue, we introduce two strategies that leverage models’ disagreement knowledge (i.e., dissensus) for heterogeneous comparison. Our experiments demonstrate that HetComp significantly enhances distillation quality and the student model’s generalization capabilities. Furthermore, we provide extensive experimental results supporting the effectiveness of our dissensus-based debiasing techniques in mitigating the popularity lift caused by HetComp.
Foreword from the general chairs
Authors: Braverman Anton , Gupta Varun
Published: February 2024

It is our pleasure to welcome you to the 41st IFIP PERFORMANCE International Conference, organized by the Kellogg School of Management at Northwestern University, USA. This is the first post-COVID PERFORMANCE conference held fully in person, after the online format of the 2020 and 2021 editions and the hybrid format of the joint SIGMETRICS/PERFORMANCE conference in 2022. The conference will be held at the downtown Chicago campus of Northwestern University.
Message from the Technical Program Committee Chairs
Authors: Barford Paul , Vlasiou Maria , Ying Lei
Published: February 2024

It is with great pleasure that we present to you this publication, the proceedings of IFIP Performance 2023. This issue includes extended abstracts of all regular papers accepted at the conference and the full short papers. This year's program boasts a remarkable collection of contributions spanning various topics, including queuing theory, network calculus, fake post detection, data centres, resource allocation, and more.
Quality Competition Among Internet Service Providers
Authors: Scherrer Simon , Tabaeiaghdaei Seyedali , Perrig Adrian
Published: February 2024

Internet service providers (ISPs) have a variety of quality attributes that determine their attractiveness for data transmission, ranging from quality-of-service metrics such as jitter to security properties such as the presence of DDoS defense systems. ISPs should optimize these attributes in line with their profit objective, i.e., maximize revenue from attracted traffic while minimizing attribute-related cost, all in the context of alternative offers by competing ISPs. However, this attribute optimization is difficult not least because many aspects of ISP competition are barely understood on a systematic level, e.g., the multi-dimensional and cost-driving nature of path quality, and the distributed decision making of ISPs on the same path.

In this paper, we improve this understanding by analyzing how ISP competition affects path quality and ISP profits. To that end, we develop a game-theoretic model in which ISPs (i) affect path quality via multiple attributes that entail costs, (ii) are on paths together with other selfish ISPs, and (iii) are in competition with alternative paths when attracting traffic. The model enables an extensive theoretical analysis, surprisingly showing that competition can have both positive and negative effects on path quality and ISP profits, depending on the network topology and the cost structure of ISPs. However, a large-scale simulation, which draws on real-world data to instantiate the model, shows that the positive effects will likely prevail in practice: If the number of selectable paths towards any destination increases from 1 to 5, the prevalence of quality attributes increases by at least 50%, while 75% of ISPs improve their profit.
Minimizing Age of Information under Arbitrary Arrival Model with Arbitrary Packet Size
Authors: Saurav Kumar , Vaze Rahul
Published: February 2024

In networked systems such as internet-of-things, cyber-physical systems, etc., information timeliness is a critical requirement. Timely updates ensure that the information available at the subsystems (nodes) are accurate, and actions are well-coordinated. One popular metric to capture information timeliness is the age of information (AoI) [1]. At any time, for a source-destination pair, AoI is equal to the time elapsed since the generation time of the latest update of the source that has been completely transmitted to the destination. Thus, at time t, the AoI Δ(t) = t - λ(t), where λ (t) denotes the generation time of the latest update of the source that has been completely transmitted until time t.
ASIP tandem queues with consumption
Authors: Yeger Yaron , Boxma Onno , Resing Jacques , Vlasiou Maria
Published: February 2024

The Asymmetric Inclusion Process (ASIP) tandem queue is a model of stations in series with a gate after each station. At a gate opening, all customers in that station instantaneously move to the next station unidirectionally. We enhance the ASIP model by introducing the capability for individual customers to independently move from one station to the next, and by allowing both individual customers and batches from any station to exit the system.
WattScope: Non-intrusive Application-level Power Disaggregation in Datacenters
Authors: Guan Xiaoding , Bashir Noman , Irwin David , Shenoy Prashant
Published: February 2024

WattScope is a system for non-intrusively estimating the power consumption of individual applications using external measurements of a server's aggregate power usage and without requiring direct access to the server's operating system or applications. Our key insight is that, based on an analysis of production traces, the power characteristics of datacenter workloads, e.g., low variability, low magnitude, and high periodicity, are highly amenable to disaggregation of a server's total power consumption into application-specific values. WattScope adapts and extends a machine learning-based technique for disaggregating building power and applies it to server- and rack-level power measurements that are already available in datacenters. We evaluate WattScope's accuracy on a production workload and show that it yields high accuracy, e.g., often <∼10% normalized mean absolute error, and is thus a potentially useful tool for datacenters in externally monitoring application-level power usage.
Breaking Cyclic Dependencies for Network Calculus using Service Partitioning
Authors: Zhou Boyang , Howenstine Isaac , Cheng Liang , Bondorf Steffen
Published: February 2024

Network Calculus (NC) is a method for providing certification evidence in networked systems, ensuring proper functioning of time-critical traffic. Traditional NC analyses focus on feedforward networks that are networks without cyclic dependencies. However, some methods, like the fix-point method and turn prohibition, apply NC to non-feedforward networks but exhibit limitations in stability, adaptability, and flexibility. We propose an alternative method, service partitioning, supported by theorems and lemmas, to address these limitations. Service partitioning breaks cyclic dependencies in non-feedforward networks using a breaking method that leverages Quality of Service (QoS) mechanisms (Weighted Round-Robin, Static Priority, Time-Aware Shaper), by assigning flows that form cycles to distinct buffers with dedicated service allocations. This method offers enhanced flexibility by allocating different network resources to buffers based on multi-class scheduling during the breaking process. In contrast to existing methods, service partitioning does not require rerouting or additional hardware and avoids deriving invalid solutions. The paper investigates the performance of service partitioning in terms of flexibility, result tightness, adaptability, and stability to show that service partitioning is superior to existing methods. One limitation of service partitioning is that it cannot fully break cyclic dependencies in some scenarios, requiring the assist from solving methods, which can be used to apply network calculus to networks with cyclic dependencies. However, when combined with solving methods, service partitioning can still improve solution quality, reducing potentially invalid results in simulated ring networks by over 30% compared with results derived by solving methods alone.
A Foundation for Real-time Applications onFunction-as-a-Service
Authors: Duc Nguyen Hai , Chien Andrew A.
Published: February 2024

Serverless (or Function-as-a-Service) compute model enables new applications with dynamic scaling. However, all current Serverless systems are best-effort, and as we prove this means they cannot guarantee hard real-time deadlines, rendering them unsuitable for such real-time applications.

We analyze a proposed extension of the Serverless model that adds a guaranteed invocation rate to the serverless model called Real-time Serverless. This approach aims to meet real-time deadlines with dynamically allocated function invocations. We first prove that the Serverless model does not support real-time guarantees. Next, we analyze Real-time Serverless, showing it can guarantee application real-time deadlines for rate-monotonic real-time workloads. Further, we derive bounds on the required invocation rate to meet any set of workload runtimes and periods. Subsequently, we explore an application technique, pre-invocation, and show that it can reduce the required guaranteed invocation rate. We derive bounds for the feasible rate guarantee reduction, and corresponding overhead in wasted compute resources. Finally, we apply the theoretical results to improve the experience quality of a distributed virtual reality/ augmented reality application as well as simplify the application design and resource management.
Experimental Investigation of Naphthalene Induced Degradation of Reversible Solid Oxide Cells Operated on Bio-Syngas
Authors: Steinrücken B. , Herrmann S. , Kerscher F. , Feigl K. , Schwiers A. , Lenser C. , Menzler N. H. , Guillon O. , Spliethoff H.
Published: February 2024

The influence of the operational conditions and the electrode material on the degradation induced by the model tar naphthalene on 10 × 10 cm2 fuel-electrode supported solid oxide cells operated on artificial bio-syngas under internal reforming conditions is experimentally investigated. Two different fuel electrodes are investigated based on yttrium-stabilized zirconia (YSZ) and gadolinium-doped ceria (GDC). A parameter study of the operational temperature and the operation mode (fuel cell or electrolysis mode) is conducted with the YSZ-based fuel electrode to quantify the effects on the cell degradation. The fuel off-gas composition and electrochemical impedance spectra are used to characterize the cell prior to the experiment and monitor its performance during the poisoning process. The influence of the tar on the Ni-YSZ electrode cell is investigated for an operation at 700 °C and 800 °C in fuel cell mode, and an operation at 700 °C in electrolysis mode. The experiment at 700 °C is repeated with the Ni-GDC electrode cell. At the increased operational temperature, the Ni-YSZ cell is more robust but carbon depositions occur. The voltage degradation during electrolysis operation increases significantly compared to fuel cell operation. The Ni-GDC electrode does not show an increased robustness compared to the Ni-YSZ electrode.
Bio-Interface Analysis and Detection of Aβ using GaN HEMT-based Biosensor
Authors: Thakur Rajiv Ranjan , KC Sarathlal , Mishra Shivanshu , Taliyan Rajeev , Chaturvedi Nidhi
Published: February 2024

Early detection, prognosis, and diagnosis of devastating neurological disorders such as TBI and AD are crucial for developing treatment strategies, efficient patient outcomes, and management in biomedical fields. The work reports the design, development, suitability analysis, and validation of a label-free GaN HEMT-based biosensing platform for the non-invasive detection of FDA-approved biomarker Aβ in saliva sample concentration ranges. The biofunctionalization assay has been validated with both electrical and FTIR spectroscopy-based measurements. A comparative analysis with ELISA assay shows good agreement with ∼2.0% measurement errors demonstrating platform stability and accuracy for Aβ detection. The platform offers a peak sensitivity of 27.20 μA/pg ml−1, 19.10 μA pg−1 ml−1, and 1.48 μA/pg ml−1 for detection in saliva, serum, and CSF concentration ranges with high specificity, excellent repeatability, and reproducibility of the results. The platform offers a sensitivity of 2.40 μA/pg ml−1, 15.40 μA pg−1 ml−1, and 27.20 μA/pg ml−1 for Sbias of 1.0 V, 3.3 V, and 5.0 V respectively. The key biosensor features include fast detection with a response time of 5–10 s and a low sample volume requirement of ∼1–2 μl. The platform comparison with ELISA shows a similar and acceptable linearity trend. A novel equation has been established for ELISA and developed platform-based detection for possible detection accuracy and validation useful for correlating the sensor response with ELISA test results and vice-versa for any target Aβ concentrations. To the best of our knowledge, this is the first time reporting of Aβ detection using a GaN HEMT-based biosensing platform.