Fundamental Operation Research Articles

Scalable and Effective Conductance-Based Graph Clustering

Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph analysis applications. Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities. To overcome these limitations, we devise a powerful peeling-based graph clustering framework PCon. We show that many existing solutions can be reduced to our framework. Namely, they first define a score function for each vertex, then iteratively remove the vertex with the smallest score. Finally, they output the result with the smallest conductance during the peeling process. Based on our framework, we propose two novel algorithms PCon_core and PCon_de with linear time and space complexity, which can efficiently and effectively identify clusters from massive graphs with more than a few billion edges. Surprisingly, we prove that PCon_de can identify clusters with near-constant approximation ratio, resulting in an important theoretical improvement over the well-known quadratic Cheeger bound. Empirical results on real-life and synthetic datasets show that our algorithms can achieve 5~42 times speedup with a high clustering accuracy, while using 1.4~7.8 times less memory than the baseline algorithms.

A simple sensor device for power cycle degradation sensing

A new sensor device is proposed for detection of power cycle degradation. Proposed sensor device is consisting of a Schottky-barrier MOSFET and the sensing principle is based on change of I-V characteristics by mechanical stress. Samples were fabricated by metal lift-off process and SiO2 gate insulator film deposition and the fundamental operation was evaluated by four-point bending test for mechanical stress applying. The fabricated device showed monotonically drain current change with number of stress cycles and about 5 times current change compared with initial current after 10,000 cycles of 4 N stress. Large current change by cyclic mechanical stress is attractive for a simple detection of power cycle degradation without expensive analog-circuits.

Active Token Mixer

The three existing dominant network families, i.e., CNNs, Transformers and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving designing more effective token-mixing mechanisms at the core of backbone architecture development. In this work, we propose an innovative token-mixer, dubbed Active Token Mixer (ATM), to actively incorporate contextual information from other tokens in the global scope into the given query token. This fundamental operator actively predicts where to capture useful contexts and learns how to fuse the captured contexts with the query token at channel level. In this way, the spatial range of token-mixing can be expanded to a global scope with limited computational complexity, where the way of token-mixing is reformed. We take ATMs as the primary operators and assemble them into a cascade architecture, dubbed ATMNet. Extensive experiments demonstrate that ATMNet is generally applicable and comprehensively surpasses different families of SOTA vision backbones by a clear margin on a broad range of vision tasks, including visual recognition and dense prediction tasks. Code is available at https://github.com/microsoft/ActiveMLP.

C and CP violation in effective field theories

The quest for new sources of the simultaneous violation of C and CP symmetry was popular in the 1960s and has since been mostly neglected for more than half a century. In this work we revisit fundamental quark-level operators that break C and CP up to and including mass dimension 8 for flavor-conserving transitions, relying on the complete operator sets of the so-called Standard Model effective field theory and the low-energy effective field theory. With the formalism of chiral perturbation theory, we match these quark operators to light-meson physics, derive C- and CP -odd Lagrangians for several processes in the η, η', and pion sectors, and furthermore, as a proof of principle, give estimates for the respective observables in explicit dependence of the underlying high-energy scale for new physics.

Multi-pulse dynamic patterns in a mode-locked erbium-doped fiber laser based on an Sb2S3-PVA saturable absorber

Multi-pulse dynamic patterns have been experimentally documented in a passively mode-locked (PML) erbium-doped fiber (EDF) laser using an Sb2S3-PVA saturable absorber (SA). The fundamental mode-locking operation, with a repetition rate of ∼3.22 MHz, a pulse width of ∼2.5 ps, a signal-to-noise ratio (SNR) of ∼50 dB and a peak power over 200 W, was achieved under a pump power from 280 to 360 mW with appropriate polarization states introduced by the polarization controllers (PCs). By rotating the orientation of the intra-cavity PCs carefully and slowly at a pump power of 350 mW, it was found that a multi-pulse bunch was transformed gradually from a single-pulse to a twelve-pulse bunch, with several intermediate transition states of multi-pulse bunches being observed. In addition, other characteristic modes including disordered multi-pulses and soliton rains have been experimentally observed by meticulously adjusting the polarization states of PCs at a pump power of 350 mW. Our systematic study clearly demonstrates that Sb2S3 has potential as an effective SA for generating different operation states of multi-pulses in PML anomalous-dispersion EDF lasers.

In search of a queer pathos: Connections between Aby Warburg and Queer Studies

The paper investigates connections between queer studies and the contributions of Aby Warburg to image theories, specifically the notion of time as a fundamental conceptual operator in the work of Warburg and his main exegete, Georges Didi-Huberman, from whom we sought help in the endeavor of envisioning a queer pathos that would survive in visual culture. Time is also at the core of a certain criticism by some authors of queer studies to the notion of chrononormativity as biopolitical regulation. This paper attempts to understand how Warburgian notions of Nachleben and Pathosformel help to investigate how abject and anachronistic gestures which keep reappearing as a queer symptom in culture, disorganizing a “natural model” of history of art and breaking with the history of progress that organizes relations between gender, body and sexuality according to disciplinary temporal schema.

Modulation and performance of synchronous demodulation for speech signal detection and dialect intelligibility

Abstract Speech processing is one of the fundamental operations in computer science and it is particularly difficult to process and distinguish speech in different Arabic dialects when background noise is present. In any nation, communication skills are crucial. Pushing a button is all it takes for the typical person to make phone calls and leave voicemails but for telecommunications experts, the process is very different. We understand how communication actually works. The terms detection and demodulation are commonly used when addressing the full demodulation process. The procedures and circuits are substantially the same under both designations. As the name implies, demodulation is the opposite of modulation, which is applying a signal, such as an audio signal, to a carrier. The demodulation process isolates the output signal from the audio or other signal that was transmitted using amplitude shifts on the carrier. In this study, a system for distinguishing speech signals was developed using modulation and demodulation to transmit speech by extracting it from a variety of factors, the most significant of which is background noise in addition to a wide variety of dialects, which poses a significant challenge in speech processing. The proposed system was applied to a dataset that was created for a group of voices in different dialects, and by using important techniques, the noise accompanying the voices was deleted and then the voices were processed with other techniques such as modulation and demodulation to distinguish the dialect. The system has proven effective by distinguishing dialects.

Deep Learning Inferencing with High-performance Hardware Accelerators

As computer architectures continue to integrate application-specific hardware, it is critical to understand the relative performance of devices for maximum app acceleration. The goal of benchmarking suites, such as MLPerf for analyzing machine learning (ML) hardware performance, is to standardize a fair comparison of different hardware architectures. However, there are many apps that are not well represented by these standards that require different workloads, such as ML models and datasets, to achieve similar goals. Additionally, many apps, like real-time video processing, are focused on latency of computations rather than strictly on throughput. This research analyzes multiple compute architectures that feature ML-specific hardware on a case study of handwritten Chinese character recognition. Specifically, AlexNet and a custom version of GoogLeNet are benchmarked in terms of their streaming latency and maximum throughput for optical character recognition. Considering that these models are composed of fundamental neural network operations yet architecturally different from each other, these models can stress devices in different yet insightful ways that generalizations of the performance of other models can be drawn from. Many devices featuring ML-specific hardware and optimizations are analyzed including Intel and AMD CPUs, Xilinx and Intel FPGAs, NVIDIA GPUs, and Google TPUs. Overall, ML-oriented hardware added to the Intel Xeon CPUs helps to boost throughput by 3.7× and to reduce latency by up to 34.7×, which makes the latency of Intel Xeon CPUs competitive on more parallel models. The TPU devices were limited in terms of throughput due to large data transfer times and not competitive in terms of latency. The FPGA frameworks showcase the lowest latency on the Xilinx Alveo U200 FPGA achieving 0.48 ms on AlexNet using Mipsology Zebra and 0.39 ms on GoogLeNet using Vitis-AI. Through their custom acceleration datapaths coupled with high-performance SRAM, the FPGAs are able to keep critical model data closer to processing elements for lower latency. The massively parallel and high-memory GPU devices with Tensor Core accelerators achieve the best throughput. The NVIDIA Tesla A100 GPU showcases the highest throughput at 42,513 and 52,484 images/second for AlexNet and GoogLeNet, respectively. 1

Hereditary Cohesive Subgraphs Enumeration on Bipartite Graphs: The Power of Pivot-based Approaches

Finding cohesive subgraphs from a bipartite graph is a fundamental operator in bipartite graph analysis. In this paper, we focus on the problem of mining cohesive subgraphs from a bipartite graph that satisfy a hereditary property. Here a cohesive subgraph meets the hereditary property if all of its subgraphs satisfy the same property as itself. We show that several important cohesive subgraph models, such as maximal biclique and maximal k-biplex, satisfy the hereditary property. The problem of enumerating all maximal hereditary subgraphs was known to be NP-hard. To solve this problem, we first propose a novel and general pivot-based enumeration framework to efficiently enumerate all maximal hereditary subgraphs in a bipartite graph. Then, based on our general framework, we develop a new pivot-based algorithm with several pruning techniques to enumerate all maximal bicliques. We prove that the worst-case time complexity of our pivot-based maximal biclique enumeration algorithm is O(m x 2n/2 ) (or O(m\times 1.414^n)) which is near optimal since there exist up to O(2n/2 ) maximal bicliques in a bipartite graph with n vertices and m edges. Moreover, we also show that our algorithm can achieve polynomial-delay time complexity with a slight modification. Third, on the basis of our general framework, we also devise a novel pivot-based algorithm with several non-trivial pruning techniques to enumerate maximal k-biplexes in a bipartite graph. Finally, we conduct extensive experiments using 11 real-world bipartite graphs to evaluate the proposed algorithms. The results show that our pivot-based solutions can achieve one order of magnitude (three orders of magnitude) faster than the state-of-the-art maximal biclique enumeration algorithms (maximal k-biplex enumeration algorithms).

Using algorithmic thinking to design algorithms: The case of critical path analysis

Algorithmic thinking is emerging as an important competence in mathematics education, yet research appears to be lagging this shift in curricular focus. The aim of this generative study is to examine how students use the cognitive skills of algorithmic thinking to design algorithms. Task-based interviews were conducted with four pairs of Year 12 students (n = 8) to analyze how they used decomposition and abstraction to specify the projects, designed algorithms to solve scheduling problems by first devising fundamental operations and then using algorithmic concepts to account for complex and special cases of the problems, and tested and debugged their algorithms. A deductive-inductive analytical process was used to classify students’ responses according to the four cognitive skills to develop sets of subskills to describe how the students engaged these cognitive skills.

Experimental Implementation of the Optical Fractional Fourier Transform in the Time-Frequency Domain.

The fractional Fourier transform (FrFT), a fundamental operation in physics that corresponds to a rotation of phase space by any angle, is also an indispensable tool employed in digital signal processing for noise reduction. Processing of optical signals in their time-frequency degree of freedom bypasses the digitization step and presents an opportunity to enhance many protocols in quantum and classical communication, sensing, and computing. In this Letter, we present the experimental realization of the fractional Fourier transform in the time-frequency domain using an atomic quantum-optical memory system with processing capabilities. Our scheme performs the operation by imposing programmable interleaved spectral and temporal phases. We have verified the FrFT by analyses of chroncyclic Wigner functions measured via a shot-noise limited homodyne detector. Our results hold prospects for achieving temporal-mode sorting, processing, and superresolved parameter estimation.

Photo-induced electrostatic actuators towards micro gap regulator and micro gripper based on lead lanthanum zirconate titanate ceramics

Photo-induced actuators enable potential applications involving soft robots, biomedical engineering, and microelectromechanical system due to the contactless energy supply and high spatial and temporal controllability of light. However, obtaining a fast and reliable photo-induced actuation remains a great challenge. Herein, we report a strategy to realize rapid photo-induced actuation by utilizing a composite actuation mechanism, which combines photoelectric effect and electrostatic actuation mechanism. Novel photo-induced PLZT-based electrostatic actuators (PPEAs) are then proposed towards micro gap regulator (MGR) and micro gripper. Within the electrostatic pull-in point, we demonstrate that the gap of the PPEA regarded as an MGR can be tunable upon light irradiation through simulations. Besides, the pull-in instability enables the PPEA with rapid clamping action to be a micro gripper. Micro grippers with sandwich-like multilayer arms are fabricated using a simplified photolithography process and the fundamental operation and performance of our gripper are demonstrated through a series of experiments. More importantly, the two developed components are driven rapidly and reliably by light, providing great potential to facilitate the current research in photo-induced actuation.

Low Loss, Broadband, and Non-Volatile ‘Directed Logic Operations’ Using Phase Change Materials in Silicon Photonics

In this paper, we present novel architectures for non-volatile directed logic circuits, that demonstrate low insertion losses, high extinction ratios, as well as multi-bit and broadband operations. The non-volatile switching operations are performed using a non-volatile phase change material Ge <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Sb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Se <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (GSST). The phase of the GSST is controlled by electro-thermal switching (from amorphous to crystalline and vice versa) using graphene micro-heaters. We demonstrate all the fundamental logic operations (such as OR, AND, XOR, NOR, NAND, and XNOR), adder, comparator, and parity checker with multi-bit operation capability. Moreover, we demonstrate directed logic architectures that can perform multiple logic operations (which includes providing inverted outputs) using the same logic circuit. The designs of the logic circuits show low insertion losses (< 1 dB) and high extinction ratios (> 10 dB) with broadband operations (~ 100 nm) operating in the telecommunication C-band. To the best of our knowledge, this is the first demonstration of the broadband operation for photonic directed logic circuits. For cascading multiple logic operations, the proposed architectures are ideal in terms of logic delay, extinction ratios and the ability to drive the number of other circuits. This study provides a practical technique to design future high-speed nanoscale non-volatile photonic integrated circuits for logic computing applications.

Symmetry in the Asymmetric Universe: Remarks on Kayne (2022)

Abstract Kayne (2022) has proposed that the asymmetry of syntax be built into the fundamental operation of Merge itself. This squib reviews some of his proposals and supporting evidence. Departing from Kayne, this squib hypothesized that the asymmetric patterns mainly lie in the functional domain of syntax, and the lexical domain may remain symmetric either within a language or cross-linguistically. The Functional Asymmetry Hypothesis (FAH) is supported by the global symmetry of the VO/OV word order, the commutative conjunction structures in the lexical domain, and the free ordering of event-internal adverbs. If the observation is on the right track, it suggests that the asymmetry of syntax, while empirically robust, cannot be entirely reduced to the operation Merge.

Maximal Defective Clique Enumeration

Maximal clique enumeration is a fundamental operator in graph analysis. The model of clique, however, is typically too restrictive for real-world applications as it requires an edge for every pair of vertices. To remedy this restriction, practical graph analysis applications often resort to find relaxed cliques as alternatives. In this work, we investigate a notable relaxed clique model, called s-defective clique, which allows at most s edges to be missing. Similar to the complexity of maximal clique enumeration, the problem of enumerating all maximal s-defective cliques is also NP-hard. To solve this problem, we first develop a new polynomial-delay algorithm based on a carefully-designed reverse search technique, which can output two consecutive results within polynomial time. To achieve better practical efficiency, we propose a branch-and-bound algorithm with a novel pivoting technique. We prove that the time complexity of this algorithm depends only on O(α_sn) or O(αsδ) when using a degeneracy ordering optimization, where αs is a positive real number strictly less than 2, and δ (δ &lt;n) is the degeneracy of the graph. To our knowledge, this is the first algorithm that can break the O(2n) time complexity to enumerate all maximal s-defective cliques (s&gt;0). We also develop several new pruning techniques to further improve the efficiency of our branch-and-bound algorithm to enumerate all relatively-large maximal s-defective cliques. In addition, we further generalize our pivot-based branch-and-bound algorithm to enumerate all maximal subgraphs satisfying a hereditary property. Here we call a graph meeting the hereditary property if all its subgraphs have the same property as itself. Finally, extensive experiments on 11 datasets demonstrate the efficiency, effectiveness, and scalability of the proposed solutions.

FPDT: a multi-scale feature pyramidal object detection transformer

Object detection is a fundamental part of autonomous driving algorithms, and with the promotions of transformers in a couple of years, numerous computer vision tasks are integrating transformers into object detectors to acquire a better generalization ability. Building a pure transformer-based detector seems to be a wonderful choice; however, transformers are not omnipotent, and they come with painful drawbacks. Its fundamental operator, multi-head self-attention (MHSA), suffers from the need for computational resources due to its quadratic complexity, which demands an unreasonably high memory usage and critically low throughput. To address this issue, we use a convolution operation to simulate MHSA from transformers by referencing the philosophy and principle of MHSA and making an application migration on convolutional neural networks (CNNs). This gives a detector with power and speed simultaneously. Furthermore, a multi-scale pyramidal feature extractor gives the detector a better view over various scales. In general, our proposed object detector mainly follows the philosophy of attention mechanism, which is implemented by a multi-scale feature pyramidal CNN encoder that simulates the transformer, and a real transformer query neck to extract all of the objects once and, eventually, feed them to the output heads. After training on the COCO2017 dataset, by combining the construction philosophy of the object detector and the philosophy and characteristics of the transformer, our FPDT-Tiny gives an average precision (AP) of up to 34.1 in 150 lower epochs, which is 16.0 and 10.8 higher than CNN-based YOLOv3-Base and SSD-300, respectively. Also, the AP given by our FPDT-Small is up to 37.7 under the same epoch, which is 10.4 and 7.9 higher than the transformer-based detector YOLOS-Small and DETR-ResNet-152, respectively, also demonstrating a comparable performance.

Notch-δ-doped InP Gunn diodes for low-THz band applications

The viability of the indium phosphide (InP) Gunn diode as a source for low-THz band applications is analyzed based on a notch-δ-doped structure using the Monte Carlo modeling. The presence of the δ-doped layer could enhance the current harmonic amplitude (A0) and the fundamental operating frequency (f0) of the InP Gunn diode beyond 300 ​GHz as compared with the conventional notch-doped structure for a 600-nm length device. With its superior electron transport properties, the notch-δ-doped InP Gunn diodes outperform the corresponding gallium arsenide (GaAs) diodes with up to 1.35 times higher in f0 and 2.4 times larger in A0 under DC biases. An optimized InP notch-δ-doped structure is estimated to be capable of generating 0.32-W radio-frequency (RF) power at 361 ​GHz. The Monte Carlo simulations predict a reduction of 44% in RF power, when the device temperature is increased from 300 ​K to 500 ​K; however, its operating frequency lies at 280 ​GHz which is within the low-THz band. This shows that the notch-δ-doped InP Gunn diode is a highly promising signal source for low-THz sensors, which are in a high demand in the autonomous vehicle industry.

Early infectious diseases identification based on complex probabilistic hesitant fuzzy N-soft information.

This paper aims to assess and deal with the challenges experienced by medical professionals caring for infectious diseases. In Pakistan, public health is still a serious concern and the main contributor to morbidity and mortality is infectious diseases. The major issue is a resemblance in the clinical symptoms of infectious diseases such as tuberculosis, hepatitis, COVID-19, dengue, and malaria. Early detection of infectious disease is crucial in order to start treatment with counseling and medication. This can only be done if several infections with similar clinical traits can be diagnosed depending on several criteria, including the availability of various kits, the ability to carry out diagnostic procedures, money, and technical staff. But woefully Pakistan's economy is badly battered due to several circumstances. Therefore, we are unable to provide patients with enough diagnostic testing kits and broadly accessible therapy choices, which makes diagnosis more difficult and create hesitancy with fuzziness and randomness. For this purpose, we introduced the new concept of the complex probabilistic hesitant fuzzy N-soft set. We defined its fundamental operations (like restricted and extended union, restricted and extended intersection, weak, top and bottom weak complements, as well as soft max-AND or soft min-OR) with examples. We also discussed their many properties with their proofs and theorems. Furthermore, we developed the algorithms for decision-making where doctors use the complex probabilistic hesitant fuzzy N-soft information to identify a particular disease. Furthermore, we explained numerical illustration of two case studies. Moreover, a sensitive and comparative analysis is discussed. In the last, we conclude the whole study.

Applying Enterprises’ Frugal Innovation in Ecologically Vulnerable Areas: A Fuzzy-Set Qualitative Comparative Analysis of the Influence Paths

Frugal innovation has become popular in ecologically vulnerable areas in the past decade, as it was initiated under a resource-constrained innovation paradigm. However, there has been limited research on its influence factors and fundamental operating mechanisms in such areas. This paper aims to address this gap by using qualitative comparative analysis (QCA) to analyze unique datasets from three ecologically vulnerable areas in China. Based on the theory of planned behavior, the study identifies three key influence factors in frugal innovation: behavioral attitude, subjective norms, and perceived behavior control. The relationships among these factors and pathways are also described. The study’s findings suggest: (1) frugal innovation is an integrated product of multiple influence factors and pathways, where the same goal can be achieved through different pathways; (2) Three pathways for conducting high-level frugal innovation are identified: attitude-oriented, norm-oriented, and attitude-norm-oriented pathways. Behavioral attitudes and subjective norms are the two dominant factors that influence enterprises to carry out frugal innovation. (3) Perceived behavior control does not play a leading role in frugal innovation as the resource-constrained environment in ecologically vulnerable areas and the characteristics of enterprises heavily influence frugal innovation. (4) The lack of behavioral attitudes and subjective norms are the main causes leading to the low-level frugal innovation of enterprises. In conclusion, this study systematically investigates the different influence factors and pathways of frugal innovation in ecologically vulnerable areas. The findings enrich the frugal theory and promote the effective development strategy of innovation for enterprises in such areas.

Assessment of Solar Panel Using Multiattribute Decision-Making Approach Based on Intuitionistic Fuzzy Aczel Alsina Heronian Mean Operator

Multiattribute decision-making (MADM) approach is an effective method for handling ambiguous information in a practical situation. The process of the MADM technique has drawn a lot of interest from various academic and selection processes of extensive analysis. The aggregation operators (AOs) are the best mathematical tools and received a lot of attention from researchers. This article explored the theory of intuitionistic fuzzy IF sets (IFSs) and their certain fundamental operations. The theory of triangular norms also explores Aczel Alsina operations (AAOs) in advanced mathematical tools. The concepts of Heronian mean (HM) and geometric HM (GHM) operators are presented to define interrelationships among different opinions. We developed a list of certain AOs by utilizing AAOs under the system IF information, namely, IF Aczel Alsina HM (IFAAHM), IF Aczel Alsina weighted HM (IFAAWHM), IF Aczel Alsina GHM (IFAAGHM), and IF Aczel Alsina weighted GHM (IFAAWGHM) operators. Some particular characteristics of our invented methodologies are also presented. Solar energy is an effective, efficient resource to enhance electricity production and the country’s economic growth. Therefore, we studied an application of solar panel systems to solve real-life problems under a robust technique of the MADM approach by utilizing our invented approaches of IFAAWHM and IFAAWGHM operators. A numerical example was also given to select more suitable solar panels under our proposed methodologies. To find the competitiveness and feasibility of discussed methodologies, we make an inclusive comparative study in which we contrast the results of existing AOs with the consequences of current approaches.