Quantum-inspired algorithms have attracted considerable attention for their effective approach to addressing various optimization problems, drawing inspiration from quantum properties. This article introduces a portfo...
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Quantum-inspired algorithms have attracted considerable attention for their effective approach to addressing various optimization problems, drawing inspiration from quantum properties. This article introduces a portfolio recommendation system based on trend ratio and quantum-inspired optimization specifically designed for global cross-stock markets. The proposed intelligent portfolio optimization model excels at identifying strong, stable uptrends within individual markets and extends its effectiveness to cross-market analysis. Furthermore, this financial application prioritizes explainability and transparency, empowering investors to comprehend AI-generated results and, in turn, fostering trust in the proposed recommendation system. Results demonstrate its ability to accurately assess complex market relationships, reflect stock connections across countries, and highlight its robustness and reliability in explaining market performance.
Reports on the concept of shoehorning system requirements into (possibly) poor solutions. Some examples are provided that demonstrate that shoehorning may result in inadequate or even catastrophic system solutions.
Reports on the concept of shoehorning system requirements into (possibly) poor solutions. Some examples are provided that demonstrate that shoehorning may result in inadequate or even catastrophic system solutions.
We present a novel approach to whole slide imaging (WSI) that consists of a uniquely designed, low-cost, single-unit-portable, optically isolated dust-free, onboard mini-computer integrated, fully enclosed WSI scanner...
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We present a novel approach to whole slide imaging (WSI) that consists of a uniquely designed, low-cost, single-unit-portable, optically isolated dust-free, onboard mini-computer integrated, fully enclosed WSI scanner device that can be wirelessly controlled through an easy-to-use, intuitive user interface application on an iPad. A Laboratory Virtual Instrument Engineering Workbench (LabVIEW)-based control software interfaces with multiple hardware modules within the device using the queued message handler (QMH) architecture optimized to handle synchronous and parallel real-time processes. The system scans an area of 15 x 15 mm(2) in similar to 5 min at a resolution of similar to 0.25 mu m with a 40x objective lens. With a low weight of 12 kg, the enclosure dimensions being 340 x 265 x 300 mm, and its low cost of building the device makes it more portable and affordable to the intended rural and low-tier medical centers. The instrument incorporates features such as automatic slide loading, slide registration, brightness control, slide scanning, and an autofocus algorithm implementing a unique "convolution-histogram mean value" method to determine the Z-focus map. The work showcases a shift in preprocessing computations to the cloud server and integrating AI/ML tools for advanced database and image processing techniques on a portable device. A nonmedical application of studying plasma science using the instrument has also been presented, demonstrating its wider application in medical and nonmedical fields.
Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recent...
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Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took similar to 71.1s to extract 494,872 biclusters. In the human PPI database of size 4085 x 4085, our method generates 1840 biclusters in similar to 48.6 s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes 101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEG-Genriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at (https://***/CMATERJU-BIOINFO/RUBic) for academic use only.
Cloud data centers have become a popular infrastructure to host diversified application services for tenants. To provide agility and elasticity in resource usage for cloud services, the virtual data center (VDC) is pr...
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Cloud data centers have become a popular infrastructure to host diversified application services for tenants. To provide agility and elasticity in resource usage for cloud services, the virtual data center (VDC) is proposed to allocate both virtual machines (VM) and network bandwidth. However, at cloud scale, hardware (e.g., link, server, and switch) failures are inevitable, which may lead to degradation in service performance. To address this challenge, we study the survivable virtual data center allocation problem (SVAP), which aims at allocating survivable virtual data center (SVDC) to each tenant to guarantee resource demands will always be satisfied even after failures. Our objective is to minimize the total bandwidth consumption in order to accommodate more SVDCs. We prove that SVAP is NP-hard and design the Collocation-Aware survivable VM placement and Link Mapping algorithm (CALM). CALM solves the problem in two stages, i.e., VM placement (VMP) and virtual link mapping (VLM). We further find that without an appropriate VMP strategy, VLM cannot lead to the minimum network resource usage. Therefore, we propose a polynomial-time algorithm called collocation-aware survivable placement (CASP) for VMP. For the VLM stage, we formulate a linear programming model to map flows onto the data center network in order to ensure survivability under switch failures. We evaluate the performance via simulations and show that CALM could save up to 42 percent network resource compared to the baseline algorithm. We further show that CALM uses only additional 13 percent network resource to guarantee survivability as compared to a typical VDC strategy.
The vast increase in the available computational capability has allowed the application of Particle-Filter (PF)-based approaches for monocular 3D-model-based tracking. These filters depend on the computation of a like...
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The vast increase in the available computational capability has allowed the application of Particle-Filter (PF)-based approaches for monocular 3D-model-based tracking. These filters depend on the computation of a likelihood function that is usually unavailable and can be approximated using a similarity metric. We can use temporal filtering techniques between filter iterations to achieve better results when dealing with this suboptimal approximation, which is particularly important when dealing with the Unmanned Aerial Vehicle (UAV) model symmetry. The similarity metric evaluation time is another critical concern since we usually want a real-time implementation. We explored, tested, and compared with the same dataset two different types of PFs, (i) an Unscented Bingham Filter (UBiF) and (ii) an Unscented Bingham-Gauss Filter (UBiGaF), using pose optimization in both implementations. Using optimization steps between iterations increases the convergence capability of the filter and decreases the obtained error. A new tree-based similarity metric approach is also explored based on the Distance Transform (DT), allowing a faster evaluation of the possibilities without losing accuracy. The results showed that the obtained pose estimation error is compatible with the automatic landing requirements.
The energy efficiency in wireless sensor networks (WSNs) is a significant challenge. A clustering-based approach is an energy-saving approach in WSN. The practical clustering approach divides the network into various ...
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The energy efficiency in wireless sensor networks (WSNs) is a significant challenge. A clustering-based approach is an energy-saving approach in WSN. The practical clustering approach divides the network into various clusters that directly affect the total energy consumption and reduce the network lifetime of the WSN. As a result, this paper introduces novel energy-efficient clustering approaches for cluster head (CH) selection and cluster formulation. The cluster head (CH) selection is designed based on the threshold-based advanced LEACH (ADV-LEACH2) approach. The cluster formulation (sensor node distribution) among the cluster heads is done with modified fuzzy c-mean (MFCM) approaches. The clusters are formed using the MFCM approach, and cluster heads (CHs) are selected using ADV-LEACH2. The cluster formulation and head selection are selected at the beginning of each round. The MFCM approach forms balanced clusters and balances sensor node distribution among the cluster heads. The proposed approach maintains the cluster head energy. The experimental simulation outcome justifies that the proposed approach, TEEECH surpasses the existing clustering approach by an average of 13%. The comparison with DEC, SEP-E, EBCS, MEDDEEC, and EDDEEC presents the TEEECH approach to maintain the CH energy and prolong the network lifetime. The proposed approach, TEEECH, also significantly improves the network stability and lifetime based on Half node dead (HND).
This special section of the IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS (OJCAS) is dedicated to highlight the state-of-the-art research progress on circuits, systems, and algorithms associated with the design of beyond ...
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This special section of the IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS (OJCAS) is dedicated to highlight the state-of-the-art research progress on circuits, systems, and algorithms associated with the design of beyond 5G (B5G) and 6G wireless systems.
There is, today, a lot of hype around the issue of attacks on artificial intelligence (AI). There are huge numbers of research papers, white papers and reports about those attacks and potential defenses—which is reas...
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There is, today, a lot of hype around the issue of attacks on artificial intelligence (AI). There are huge numbers of research papers, white papers and reports about those attacks and potential defenses—which is reasonable given that AI techniques are today pervasive in all applications we may think of. As we increasingly rely on AI techniques for critical decisions, forecasts and analyses, concerns about whether these AI techniques can be attacked are certainly legitimate.
Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition ...
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Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition that leveraging device-level noise as a source of controlled stochasticity represents an exciting prospect of achieving brain-like capabilities in probabilistic neural algorithms, but the reality of integrating stochastic devices with deterministic devices in an already-challenging neuromorphic circuit design process is formidable. Here, we explore how the brain combines different signaling modalities into its neural circuits as well as consider the implications of more tightly integrated stochastic, analog, and digital circuits. By acknowledging that a fully CMOS implementation is the appropriate baseline, we conclude that if mixing modalities is going to be successful for neuromorphic computing, it will be critical that device choices consider strengths and limitations at the overall circuit level.
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