Complexity Trade-off Research Articles

Memory Checking Requires Logarithmic Overhead

We study the complexity of memory checkers with computational security and prove the first general tight lower bound. Memory checkers, first introduced over 30 years ago by Blum, Evans, Gemmel, Kannan, and Naor (FOCS ’91, Algorithmica ’94), allow a user to store and maintain a large memory on a remote and unreliable server by using small trusted local storage. The user can issue instructions to the server and after every instruction, obtain either the correct value or a failure (but not an incorrect answer) with high probability. The main complexity measure of interest is the size of the local storage and the number of queries the memory checker makes upon every logical instruction. The most efficient known construction has query complexity O (log n /log log n ) and local space proportional to a computational security parameter, assuming one-way functions, where n is the logical memory size. Dwork, Naor, Rothblum, and Vaikuntanathan (TCC ’09) showed that for a restricted class of “deterministic and non-adaptive” memory checkers, this construction is optimal, up to constant factors. However, going beyond the small class of deterministic and non-adaptive constructions has remained a major open problem. In this work, we fully resolve the complexity of memory checkers by showing that any construction with local space p and query complexity q must satisfy \[ p \ge \frac{n}{(\log n)^{O(q)}} \;. \] This implies, as a special case, that q ≥ Ω (log n /log log n ) in any scheme, assuming that p ≤ n 1 − ε for ε > 0. The bound applies to any scheme with computational security, completeness 2/3, and inverse polynomial in n soundness (all of which make our lower bound only stronger). We further extend the lower bound to schemes where the read complexity q r and write complexity q w differ. For instance, we show the tight bound that if q r = O (1) and p ≤ n 1 − ε for ε > 0, then q w ≥ n Ω (1) . This is the first lower bound, for any non-trivial class of constructions, showing a read-write query complexity trade-off. Our proof is via a delicate compression argument showing that a “too good to be true” memory checker can be used to compress random bits of information. We draw inspiration from tools recently developed for lower bounds for relaxed locally decodable codes. However, our proof itself significantly departs from these works, necessitated by the differences between settings.

Low power RTL implementation of Tiny-YOLO-v2 DCNN hardware accelerator on Virtex-7 FPGA

Deep Convolution Neural Networks (DCNNs) are widely used in real-time applications, including image classification, speech recognition, and object detection. However, there are challenges for real-time applications on portable devices like mobile phones or embedded systems. Most object detection models are optimized for desktop configurations, requiring fast GPUs. Shallower networks with fewer computational complexities have been proposed for real-time detection, but ultimately compromise detection accuracy. Performance and complexity trade-offs are significant for computationally complex deep networks. This work aims to implement the CNN-based object detection model Tiny-Yolo-v2 on a Field Programmable Gate Array (FPGA) using Register Transfer Logic (RTL) as a native language. Hardware implementation is synthesized on The AMD Virtex 7 FPGA VC709 Connectivity Kit using VHDL code on Vivado 2020.1. This is the first, up to the authors' knowledge, RTL implementation of the Tiny-Yolo-v2 object identification algorithm on FPGA. The power consumed by the CNN layers equals 7.09W at a frequency of 100MHz.

Optimizing $c$-sum BKW and Faster Quantum Variant for LWE

The Learning with Errors (LWE) problem has become one of the most prominent candidates of post-quantum cryptography, offering promising potential to meet the challenge of quantum computing. From a theoretical perspective, optimizing algorithms to solve LWE is a vital task for the analysis of this cryptographic primitive. In this paper, we propose a fine-grained time/memory trade-off method to analyze c-sum BKW variants for LWE in both classical and quantum models, then offer new complexity bounds for multiple BKW variants determined by modulus q, dimension k, error rate alpha, and stripe size b. Through our analysis, optimal parameters can be efficiently found for different settings, and the minimized complexities are lower than existing results. Furthermore, we enhance the performance of c-sum BKW in the quantum computing model by adopting the quantum Meet-in-the-Middle technique as c-sum solver instead of the naive c-sum technique. Our complexity trade-off formula also applies to the quantum version of BKW, and optimizes the theoretical quantum time and memory costs, which are exponentially lower than existing quantum c-sum BKW variants.

Multicarrier dual‐index permuted chaos shift keying system for efficiency improvement in chaotic communications

AbstractWe propose a multicarrier chaotic system that incorporates dual‐index and M‐ary modulation techniques. The proposed system improves the energy and spectrum efficiencies with a tradeoff in computational complexity. In addition, it applies index modulation to select the chaotic signal and permuted chaotic signals along with ‐ary modulated symbols. As a result, the spectral and energy efficiencies are increased in chaotic systems. Theoretical expressions are derived to calculate the bit error rate of the proposed system under additive white Gaussian noise and Rayleigh fading channels. The equations are validated through Monte Carlo simulations, whose results demonstrate a reduction in bit error rate for the proposed system. The data rate, spectral efficiency, energy efficiency, and computational complexity of the proposed system are analyzed and compared with those of existing multicarrier chaotic systems.

Evaluating ARM and RISC-V Architectures for High-Performance Computing with Docker and Kubernetes

This paper thoroughly assesses the ARM and RISC-V architectures in the context of high-performance computing (HPC). It includes an analysis of Docker and Kubernetes integration. Our study aims to evaluate and compare these systems’ performance, scalability, and practicality in a general context and then assess the impact they might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in working with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM’s preparedness and the potential of RISC-V while acknowledging the increased complexity and significant trade-offs involved at this point.

Relaxed TS Fuzzy Model Transformation to Improve the Approximation Accuracy/Complexity Tradeoff and Relax the Computation Complexity

Relaxed TS Fuzzy Model Transformation to Improve the Approximation Accuracy/Complexity Tradeoff and Relax the Computation Complexity

Long-range attention classification for substation point cloud

Point cloud classification for substation is challenging due to the occluding layout as well as the complexity of methods. Most previous methods trade inference speed for precision by building complex extractors. To alleviate the paradox of performance and complexity trade-off, this paper proposes the lightweight long-range attention classification for substation point cloud, including shuffle channel attention(SCA) and dilated channel attention(DCA). First, SCA captures local cross-channel interaction via 1D convolution and global interaction via channel shuffling, shown promising performance. Second, to further reduce the amount of computation involved in shuffling, we propose a more elegant method DCA by resizing the 1D vector after pooling into 2D feature map. Note that proposed DCA are implemented by just 2D convolution, determining the coverage of cross-channel interaction, with a significantly higher inference speed. Furthermore, we develop a heuristic algorithm to adaptively determine parameters like kernel size, shuffle group and size of 2D feature map. Besides ModelNet40 and ScanObjectNN(PB_T50_RS), this paper also selects ten of main objects in substation for training and testing. Finally, experimental results suggest that proposed methods bring notable performance gain, nearly 1%. DCA reaches 93.801% and just increases 0.0003M parameters but 15.5% faster than SCA. We also make an attempt to simplify proposed methods via reducing the coding dimensions and coding blocks. And simplified DCA achieves 93.4% performance with almost quarter parameters and 112% faster, guaranteeing both efficiency and effectiveness. A simplified version is used to achieve the highest accuracy of 85.115% and 83.304% on ScanObjectNN, with the highest improvements of 1.735% and 2.391%. This paper also conducts robustness test where different proportions of points are missing. Results show that when 93.75% missing, the accuracy only decreases by 4.093%, and can still reach 89%, which is significantly ahead of other methods.

Advanced Neural Network-Based Equalization in Intensity-Modulated Direct-Detection Optical Systems: Current Status and Future Trends

Intensity-modulated direct-detection (IM/DD) optical systems are most widely employed in short-reach optical interconnects due to their simple structure and cost-effectiveness. However, IM/DD systems face mixed linear and nonlinear channel impairments, mainly induced by the combination of square-law detection and chromatic dispersion, as well as the utilization of low-cost non-ideal transceivers. To solve this issue, recent years have witnessed a growing trend of introducing machine learning technologies such as neural networks (NNs) into IM/DD systems for channel equalization. NNs usually present better system performance than traditional approaches, and various types of NNs have been investigated. Despite the excellent system performance, the associated high computational complexity is a major drawback that hinders the practical application of NN-based equalizers. This paper focuses on the performance and complexity trade-off of NNs employed in IM/DD systems, presenting a systematic review of the current status of NN-based equalizers as well as a number of effective complexity reduction approaches. The future trends of leveraging advanced NN in IM/DD links are also discussed.

A Symmetric Efficient Spatial and Channel Attention (ESCA) Module Based on Convolutional Neural Networks

In recent years, attention mechanisms have shown great potential in various computer vision tasks. However, most existing methods focus on developing more complex attention modules for better performance, which inevitably increases the complexity of the model. To overcome performance and complexity tradeoffs, this paper proposes efficient spatial and channel attention (ESCA), a symmetric, comprehensive, and efficient attention module. By analyzing squeeze-and-excitation (SE), convolutional block attention module (CBAM), coordinate attention (CA), and efficient channel attention (ECA) modules, we abandon the dimension-reduction operation of SE module, verify the negative impact of global max pooling (GMP) on the model, and apply a local cross-channel interaction strategy without dimension reduction to learn attention. We not only care about the channel features of the image, we also care about the spatial location of the target on the image, and we take into account the effectiveness of channel attention, so we designed the symmetric ESCA module. The ESCA module is effective, as demonstrated by its application in the ResNet-50 classification benchmark. With 26.26 M parameters and 8.545 G FLOPs, it introduces a mere 0.14% increment in FLOPs while achieving over 6.33% improvement in Top-1 accuracy and exceeding 3.25% gain in Top-5 accuracy. We perform image classification and object detection tasks on ResNet, MobileNet, YOLO, and other architectures on popular datasets such as Mini ImageNet, CIFAR-10, and VOC 2007. Experiments show that ESCA can achieve great improvement in model accuracy at a very small cost, and it performs well among similar models.

Linear Matching of JavaScript Regular Expressions

Modern regex languages have strayed far from well-understood traditional regular expressions: they include features that fundamentally transform the matching problem. In exchange for these features, modern regex engines at times suffer from exponential complexity blowups, a frequent source of denial-of-service vulnerabilities in JavaScript applications. Worse, regex semantics differ across languages, and the impact of these divergences on algorithmic design and worst-case matching complexity has seldom been investigated. This paper provides a novel perspective on JavaScript's regex semantics by identifying a larger-than-previously-understood subset of the language that can be matched with linear time guarantees. In the process, we discover several cases where state-of-the-art algorithms were either wrong (semantically incorrect), inefficient (suffering from superlinear complexity) or excessively restrictive (assuming certain features could not be matched linearly). We introduce novel algorithms to restore correctness and linear complexity. We further advance the state-of-the-art in linear regex matching by presenting the first nonbacktracking algorithms for matching lookarounds in linear time: one supporting captureless lookbehinds in any regex language, and another leveraging a JavaScript property to support unrestricted lookaheads and lookbehinds. Finally, we describe new time and space complexity tradeoffs for regex engines. All of our algorithms are practical: we validated them in a prototype implementation, and some have also been merged in the V8 JavaScript implementation used in Chrome and Node.js.

Load-frequency and voltage control for power quality enhancement in a SPV/Wind utility-tied system using GA & PSO optimization

Load Frequency Control (LFC) and Voltage Control (VC) are critical aspects of hybrid generation systems. In this work, the performance comparison of three different control approaches for LFC and VC: Genetics Algorithm (GA)-tuned Proportional Integral Differentiator (PID), Particle Swarm Optimization (PSO)-PID, and a conventional PID controller is presented. Especially, the performance is assessed and analyzed for convergence speed and computational complexity for each approach. Mathematical framework for each approach is discussed, including the required equations for hybrid generation system. It is reported that the traditional PID controller exhibits fast convergence due to its direct adjustment of control parameters. Simulation results reveal that it requires manual tuning and has low computational complexity. In contrast, the GA-PID utilizes a GA optimization process which automatically tunes the PID gains. Although, it may require multiple generations to converge to the optimal solution, however, it offers better control performance. Moreso, it comes at the cost of higher computational complexity compared to the traditional PID controller. In contrast, the PSO-PID employs an algorithm for parameter optimization. It converges faster than the GA-PID but still requires more iterations than the traditional PID controller. Similar to the GA-PID, it has higher computational complexity due to fitness function evaluation and particle updates. The optimization results provide insights into the convergence speed and computational complexity trade-offs between the three control approaches. Practitioners in the field of hybrid energy systems can utilize the outcomes to make informed decisions based on their specific requirements and available computational resources.

On Predictive Planning and Counterfactual Learning in Active Inference.

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. This paper examines two decision-making schemes in active inference based on "planning" and "learning from experience". Furthermore, we also introduce a mixed model that navigates the data complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyse the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making.

Complexity trade-off in morphosyntactic module: suggestions from Japanese dialects

Abstract Using the GiNZA v4 natural language processing library, this study examines the case marking richness and word order flexibility in Japanese dialects across 47 regions to investigate the dynamic self-adaptation within the language subsystem, specifically morphology and syntax. Case marking richness is evaluated using the moving-average mean size of paradigm, while word order flexibility is assessed through entropy. The findings reveal a positive correlation between case marking richness and word order flexibility, providing empirical support for the “complexity trade-off hypothesis”. Leveraging the distinct morphosyntactic features inherent in dialects, this study proposes a hypothesis for categorizing Japanese dialects.

Aligning security and energy-efficiency using change detection and partial encryption for wireless camera networks

Wireless camera networks (WCNs) have seen widespread deployment in recent years, leading to an upsurge in monitoring and surveillance systems aimed at detecting foreign objects and changes within remote environments. However, wireless communication remains susceptible to cybersecurity threats, enabling eavesdroppers to access and manipulate confidential multimedia data effortlessly. Moreover, wireless cameras are low-power devices, encounter challenges as many state-of-the-art schemes adopt intricate security mechanisms, resulting in elevated costs, latency, and power consumption. This significantly compromises the stability of the node power and network lifetime and limits the applicability of security schemes in real-time implementations. This study addresses these issues by presenting DyimEnc, a dynamic image encryption scheme, to establish secure image transmission within wireless camera networks. The proposed scheme dynamically implements a security, complexity, and power consumption trade-off based on the currently available resources in each camera node. In the DyimEnc scheme, an effective in-node change-detection method is used to detect changes in the captured images and reduce the false transmission of images across the network. Additionally, a dynamic lightweight partial-encryption method is proposed to encrypt the edge maps of sensitive images that include changes. Moreover, a flexible key-management method is proposed for image encryption and transmission. Simulation results underscore DyimEnc's substantial impact, revealing significant reductions in power consumption, encryption cost, and transmission latency. It effectively achieves an average throughput of 254.9 KB/s and a remarkable 48 % decrease in network power consumption, showcasing a substantial 59 % improvement compared to a related study. Security analysis proved the robustness of our scheme in resisting attacks and satisfying the security requirements of WCNs.

Building Variable-Sized Models via Learngene Pool

Recently, Stitchable Neural Networks (SN-Net) is proposed to stitch some pre-trained networks for quickly building numerous networks with different complexity and performance trade-offs. In this way, the burdens of designing or training the variable-sized networks, which can be used in application scenarios with diverse resource constraints, are alleviated. However, SN-Net still faces a few challenges. 1) Stitching from multiple independently pre-trained anchors introduces high storage resource consumption. 2) SN-Net faces challenges to build smaller models for low resource constraints. 3). SN-Net uses an unlearned initialization method for stitch layers, limiting the final performance. To overcome these challenges, motivated by the recently proposed Learngene framework, we propose a novel method called Learngene Pool. Briefly, Learngene distills the critical knowledge from a large pre-trained model into a small part (termed as learngene) and then expands this small part into a few variable-sized models. In our proposed method, we distill one pre-trained large model into multiple small models whose network blocks are used as learngene instances to construct the learngene pool. Since only one large model is used, we do not need to store more large models as SN-Net and after distilling, smaller learngene instances can be created to build small models to satisfy low resource constraints. We also insert learnable transformation matrices between the instances to stitch them into variable-sized models to improve the performance of these models. Exhaustive experiments have been implemented and the results validate the effectiveness of the proposed Learngene Pool compared with SN-Net.

Classical shadows based on locally-entangled measurements

We study classical shadows protocols based on randomized measurements in n-qubit entangled bases, generalizing the random Pauli measurement protocol (n=1). We show that entangled measurements (n≥2) enable nontrivial and potentially advantageous trade-offs in the sample complexity of learning Pauli expectation values. This is sharply illustrated by shadows based on two-qubit Bell measurements: the scaling of sample complexity with Pauli weight k improves quadratically (from ∼3k down to ∼3k/2) for many operators, while others become impossible to learn. Tuning the amount of entanglement in the measurement bases defines a family of protocols that interpolate between Pauli and Bell shadows, retaining some of the benefits of both. For large n, we show that randomized measurements in n-qubit GHZ bases further improve the best scaling to ∼(3/2)k, albeit on an increasingly restricted set of operators. Despite their simplicity and lower hardware requirements, these protocols can match or outperform recently-introduced "shallow shadows" in some practically-relevant Pauli estimation tasks.

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches.

An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.

Performance and Complexity Trade-Off between Short-Length Regular and Irregular LDPC

Performance and Complexity Trade-Off between Short-Length Regular and Irregular LDPC

Machine learning classification based on k-Nearest Neighbors for PolSAR data.

In this work, we focus on obtaining insights of the performances of some well-known machine learning image classification techniques (k-NN, Support Vector Machine, randomized decision tree and one based on stochastic distances) for PolSAR (Polarimetric Synthetic Aperture Radar) imagery. We test the classifiers methods on a set of actual PolSAR data and provide some conclusions. The aim of this work is to show that suitable adapted standard machine learning methods offer excellent performances vs.computational complexity trade-off for PolSAR image classification. In this work, we evaluate well-known machine learning techniques for PolSAR (Polarimetric Synthetic Aperture Radar) image classification, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), randomized decision tree, and a method based on the Kullback-Leibler stochastic distance. Our experiments with real PolSAR data show that standard machine learning methods, when adapted appropriately, offer a favourable trade-off between performance and computational complexity. The KNN and SVM perform poorly on these data, likely due to their failure to account for the inherent speckle presence and properties of the studied reliefs. Overall, our findings highlight the potential of the Kullback-Leibler stochastic distance method for PolSAR image classification.

Regret and Belief Complexity Trade-off in Gaussian Process Bandits via Information Thresholding

Regret and Belief Complexity Trade-off in Gaussian Process Bandits via Information Thresholding