arXiv subjects

Muhammad Usman

Publications and source records attributed to Muhammad Usman.

At least 127 records · Page 7Linked to original sources

arXiv2023

The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents

Previous studies regarding the perception of emotions for embodied virtual agents have shown the effectiveness of using virtual characters in conveying emotions through interactions with humans. However, creating an autonomous embodied conversational agent with expressive behaviors presents two major challenges. The first challenge is the difficulty of synthesizing the conversational behaviors for each modality that are as expressive as real human behaviors. The second challenge is that the affects are modeled independently, which makes it difficult to generate multimodal responses with consistent emotions across all modalities. In this work, we propose a conceptual framework, ACTOR (Affect-Consistent mulTimodal behaviOR generation), that aims to increase the perception of affects by generating multimodal behaviors conditioned on a consistent driving affect. We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect. The result shows that among all model conditions, our affect-consistent framework receives the highest Likert scores for the perception of driving affects. Our statistical analysis suggests that making a modality affect-inconsistent significantly decreases the perception of driving affects. We also observe that multimodal behaviors conditioned on consistent affects are more expressive compared to behaviors with inconsistent affects. Therefore, we conclude that multimodal emotion conditioning and affect consistency are vital to enhancing the perception of affects for embodied conversational agents.

cs.HC↗

arXiv2023

Artificial Neural Network Syndrome Decoding on IBM Quantum Processors

Syndrome decoding is an integral but computationally demanding step in the implementation of quantum error correction for fault-tolerant quantum computing. Here, we report the development and benchmarking of Artificial Neural Network (ANN) decoding on IBM Quantum Processors. We demonstrate that ANNs can efficiently decode syndrome measurement data from heavy-hexagonal code architecture and apply appropriate corrections to facilitate error protection. The current physical error rates of IBM devices are above the code's threshold and restrict the scope of our ANN decoder for logical error rate suppression. However, our work confirms the applicability of ANN decoding methods of syndrome data retrieved from experimental devices and establishes machine learning as a promising pathway for quantum error correction when quantum devices with below threshold error rates become available in the near future.

quant-ph↗

arXiv2023

Large scale reuse of microservices using DevOps and InnerSource practices -- A longitudinal case study

Contemporary practices such as InnerSource and DevOps promote software reuse. This study investigates the implications of using contemporary practices on software reuse. In particular, we investigate the costs, benefits, challenges, and potential improvements in contemporary reuse at Ericsson. We performed the study in two phases: a) the initial data collection based on a combination of data collection methods (e.g., interviews, discussions, company portals), and b) a follow-up group discussion after a year to understand the status of the challenges and improvements identified in the first phase. Our results indicate that developing reusable assets resulted in upfront costs, such as additional effort in ensuring compliance. Furthermore, development with reuse also resulted in additional effort, for example, in integrating and understanding reusable assets. Ericsson perceived the additional effort as an investment resulting in long-term benefits such as improved quality, productivity, customer experience, and way of working. Ericsson's main challenge was increased pressure on the producers of reusable assets, which was mitigated by scaling the InnerSource adoption. InnerSource success is evident from the increase in the contributions to reusable assets. In addition, Ericsson implemented measures such as automating the compliance check, which enhanced the maturity of reusable assets and resulted in increased reuse.

cs.SE↗

arXiv2023

DSLOT-NN: Digit-Serial Left-to-Right Neural Network Accelerator

We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing technique called DSLOT-NN with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs). The proposed work has the ability to assess and terminate the ineffective convolutions which results in massive power and energy savings. The processing engine is comprised of low-latency most-significant-digit-first (MSDF) (also called online) multipliers and adders that processes data from left-to-right, allowing the execution of subsequent operations in digit-pipelined manner. Use of online operators eliminates the need for the development of complex mechanism of identifying the negative activation, as the output with highest weight value is generated first, and the sign of the result can be identified as soon as first non-zero digit is generated. The precision of the online operators can be tuned at run-time, making them extremely useful in situations where accuracy can be compromised for power and energy savings. The proposed design has been implemented on Xilinx Virtex-7 FPGA and is compared with state-of-the-art Stripes on various performance metrics. The results show the proposed design presents power savings, has shorter cycle time, and approximately 50% higher OPS per watt.

cs.AR↗

arXiv2023

Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification

Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of work which suggests that generic, data agnostic quantum machine learning (QML) architectures may suffer from severe trainability issues, with the gradient of typical variational parameters vanishing exponentially in the number of qubits. Additionally, the high expressibility of QML models can lead to overfitting on training data and poor generalisation performance. A promising strategy to combat both of these difficulties is to construct models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML). In this work, we utilise the techniques of GQML for the task of image classification, building new QML models which are equivariant with respect to reflections of the images. We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets, bringing high-resolution image classification via quantum computers closer to reality. Our work highlights a potential pathway for the future development and implementation of powerful QML models which directly exploit the symmetries of data.

quant-ph↗

arXiv2023

Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning

Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realisation for practical applications remains impeded by challenges. Amongst those, a key barrier is the computationally expensive task of encoding classical data into a quantum state, which could erase any prospective speed-ups over classical algorithms. In this work, we implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Our results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation, obtaining drastic savings in circuit depth and gate count without unduly sacrificing classification accuracy. Additionally, the QML models trained and evaluated on approximately encoded data display an increased robustness to adversarially generated input data perturbations. This partial alleviation of adversarial vulnerability, possible due to the "drowning out" of adversarial perturbations while retaining the meaningful large-scale features of the data, constitutes a considerable benefit for approximate state preparation in addition to lessening the requirements of the quantum hardware. Our results, based on simulations and experiments on IBM quantum devices, highlight a promising pathway for the future implementation of accurate and robust QML models on complex datasets relevant for practical applications, bringing the possibility of NISQ-era QML advantage closer to reality.

quant-ph↗

arXiv2023

LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based computational neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multi-residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

eess.IV↗

arXiv2023

Perturbation determinant and Levinson's formula for Schrödinger operators with generalized point interaction

We consider the one dimensional Schrödinger operator with properly connecting generalized point interaction at the origin. We derive a trace formula for trace of difference of resolvents of perturbed and unperturbed Schrödinger operators in terms of a Wronskian which results into an explicit expression for perturbation determinant. Using the estimate for large time real argument on the trace norm of the resolvent difference of the perturbed and unperturbed Schrödinger operators we express the spectral shift function in terms of perturbation determinant. Under certain integrability condition on the potential function, we calculate low energy asymptotics for the perturbation determinant and prove an analog of Levinson's formula.

math-ph↗

arXiv2023

Pragmatic Comparison Analysis of Alternative Option Pricing Models

In this paper, we price European Call three different option pricing models, where the volatility is dynamically changing i.e. non constant. In stochastic volatility (SV) models for option pricing a closed form approximation technique is used, indicating that these models are computationally efficient and have the same level of performance as existing ones. We show that the calibration of SV models, such as Heston model and the High Order Moment based Stochastic Volatility (MSV) is often faster and easier. On 15 different datasets of index options, we show that models which incorporates stochastic volatility achieves accuracy comparable with the existing models. Further, we compare the In Sample and Out Sample pricing errors of each model on each date. Lastly, the pricing of models is compared among three different market to check model performance in different markets. Keywords: Option Pricing Model, Simulations, Index Options, Stochastic Volatility Models, Loss Function http://www.sci-int.com/pdf/638279543859822650.pdf

q-fin.PR↗

arXiv2023

Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging

The recent physical realisation of quantum computers with dozens to hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. Such algorithms have begun to be applied to data intensive problems in particle physics, driven by the expected increased capacity for pattern recognition of quantum computers. In this work we develop and apply QML methods to B meson flavour tagging, an important component of experiments in particle physics which probe heavy quark mixing and CP violation in order to obtain a better understanding of the matter-antimatter asymmetry observed in the universe. We simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit-based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2% respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of our classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which we find to be highly prone to overfitting. These results are obtained despite the strong constraint of working with QSVM architectures that are classically simulable, and we find evidence that continuous variable QSVMs beyond the classically simulable regime may be able to realise even higher performance, surpassing the reported classical results, when sufficiently powerful quantum hardware is developed to execute them.

quant-ph↗

arXiv2023

Efficacy of mannan-oligosaccharide and live yeast feed additives on performance, rumen morphology, serum biochemical parameters and muscle morphometric characteristics in buffalo calves

The objective of the current study was to assess the effect of dietary supplementations of mannan-oligosaccharide, live yeast, and a combination of these two additives on growth performance, histo-morphology of the rumen, and muscle morphometric attributes in buffalo calves. A total of twenty buffalo calves (average weight of 25 kg) having 3 months of age were distributed according to a complete randomized design. All animals were individually stalled in the shed and were fed ad-libitum. Experimental animals were divided into four groups for 67 days: Control group(without the inclusion of dietary supplementation); MOS group (Mannan oligosaccharide 5 g/clave/day; Yeast group (Live yeast 2g/calve/day) and Mixed group (MOS + Live Yeast 2.5g + 1g )/calve/day. Experimental results revealed that combined supplementation of MOS and Yeast and MOS alone resulted in an increased number of short-chain fatty acids in the rumen as well as ruminal pH (P<0.05). Results showed a significant improvement in average daily gain and FCR of MOS and Mixed supplemented groups (P<0.05). Histomorphological evaluation of rumen mucosal epithelium showed a significant improvement in the mixed-supplemented group (P<0.05) as compared to the yeast-supplemented and control groups. Muscle quality parameters such as meat texture showed significant improvement in MOS and mix-supplemented groups. Histological examination of longissimus dorsi muscle cross-section showed a significantly higher(P<0.05) muscle fiber and muscle fascicle diameter in both MOS and mix-supplemented calves groups. In conclusion, the results of this experiment revealed that the dietary addition of MOS, Live yeast, and their combination have positive effects on growth performance, rumen histology indices, and muscle morphometric features in buffalo calves.

q-bio.TO↗

arXiv2023

A scalable and fast artificial neural network syndrome decoder for surface codes

Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabilizer codes, surface code computations consist of a syndrome decoding step where measured stabilizer operators are used to determine appropriate corrections for errors in physical qubits. Decoding algorithms have undergone substantial development, with recent work incorporating machine learning (ML) techniques. Despite promising initial results, the ML-based syndrome decoders are still limited to small scale demonstrations with low latency and are incapable of handling surface codes with boundary conditions and various shapes needed for lattice surgery and braiding. Here, we report the development of an artificial neural network (ANN) based scalable and fast syndrome decoder capable of decoding surface codes of arbitrary shape and size with data qubits suffering from the depolarizing error model. Based on rigorous training over 50 million random quantum error instances, our ANN decoder is shown to work with code distances exceeding 1000 (more than 4 million physical qubits), which is the largest ML-based decoder demonstration to-date. The established ANN decoder demonstrates an execution time in principle independent of code distance, implying that its implementation on dedicated hardware could potentially offer surface code decoding times of O($μ$sec), commensurate with the experimentally realisable qubit coherence times. With the anticipated scale-up of quantum processors within the next decade, their augmentation with a fast and scalable syndrome decoder such as developed in our work is expected to play a decisive role towards experimental implementation of fault-tolerant quantum information processing.

quant-ph↗

arXiv2023

Towards quantum enhanced adversarial robustness in machine learning

Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge. The integration of machine learning with quantum computing has the potential to yield tools offering not only better accuracy and computational efficiency, but also superior robustness against adversarial attacks. Indeed, recent work has employed quantum mechanical phenomena to defend against adversarial attacks, spurring the rapid development of the field of quantum adversarial machine learning (QAML) and potentially yielding a new source of quantum advantage. Despite promising early results, there remain challenges towards building robust real-world QAML tools. In this review we discuss recent progress in QAML and identify key challenges. We also suggest future research directions which could determine the route to practicality for QAML approaches as quantum computing hardware scales up and noise levels are reduced.

quant-ph↗

arXiv2023

Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs

Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms.

cs.SE↗

arXiv2023

Real-Time Decoding for Fault-Tolerant Quantum Computing: Progress, Challenges and Outlook

Quantum computing is poised to solve practically useful problems which are computationally intractable for classical supercomputers. However, the current generation of quantum computers are limited by errors that may only partially be mitigated by developing higher-quality qubits. Quantum error correction (QEC) will thus be necessary to ensure fault tolerance. QEC protects the logical information by cyclically measuring syndrome information about the errors. An essential part of QEC is the decoder, which uses the syndrome to compute the likely effect of the errors on the logical degrees of freedom and provide a tentative correction. The decoder must be accurate, fast enough to keep pace with the QEC cycle (e.g., on a microsecond timescale for superconducting qubits) and with hard real-time system integration to support logical operations. As such, real-time decoding is essential to realize fault-tolerant quantum computing and to achieve quantum advantage. In this work, we highlight some of the key challenges facing the implementation of real-time decoders while providing a succinct summary of the progress to-date. Furthermore, we lay out our perspective for the future development and provide a possible roadmap for the field of real-time decoding in the next few years. As the quantum hardware is anticipated to scale up, this perspective article will provide a guidance for researchers, focusing on the most pressing issues in real-time decoding and facilitating the development of solutions across quantum and computer science.

quant-ph↗

arXiv2023

Low-Latency Online Multiplier with Reduced Activities and Minimized Interconnect for Inner Product Arrays

Multiplication is indispensable and is one of the core operations in many modern applications including signal processing and neural networks. Conventional right-to-left (RL) multiplier extensively contributes to the power consumption, area utilization and critical path delay in such applications. This paper proposes a low latency multiplier based on online or left-to-right (LR) arithmetic which can increase throughput and reduce latency by digit-level pipelining. Online arithmetic enables overlapping successive operations regardless of data dependency because of the most significant digit first mode of operation. To produce most significant digit first, it uses redundant number system and we can have a carry-free addition, therefore, the delay of the arithmetic operation is independent of operand bit width. The operations are performed digit by digit serially from left to right which allows gradual increase in the slice activities making it suitable for implementation on reconfigurable devices. Serial nature of the online algorithm and gradual increment/decrement of active slices minimize the interconnects and signal activities resulting in overall reduction of area and power consumption. We present online multipliers with; both inputs in serial, and one in serial and one in parallel. Pipelined and non-pipelined designs of the proposed multipliers have been synthesized with GSCL 45nm technology on Synopsys Design Compiler. Thorough comparative analysis has been performed using widely used performance metrics. The results show that the proposed online multipliers outperform the RL multipliers.

cs.AR↗

arXiv2023

Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques

The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this paper, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. These techniques require a small number of experimental data points and does not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network-based prediction task for the Ohmic resistance value in Gallium Nitride devices. A 70% reduction in mean absolute error when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.

cs.LG↗

arXiv2023

iQuantum: A Case for Modeling and Simulation of Quantum Computing Environments

Today's quantum computers are primarily accessible through the cloud and potentially shifting to the edge network in the future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources. This demand has highlighted the need for designing efficient and adaptable resource management strategies and service models for quantum computing. However, the limited quantity, quality, and accessibility of quantum resources pose significant challenges to practical research in quantum software and systems. To address these challenges, we propose iQuantum, a first-of-its-kind simulation toolkit that can model hybrid quantum-classical computing environments for prototyping and evaluating system design and scheduling algorithms. This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum. Our proposed iQuantum simulator is anticipated to boost research in quantum software and systems, particularly in the creation and evaluation of policies and algorithms for resource management, job scheduling, and hybrid quantum-classical task orchestration in quantum computing environments integrating edge and cloud resources.

quant-ph↗