Abstract

In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with the Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for dictionary enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.

Abstract

Quantum computing hardware has progressed rapidly. Simultaneously, there has been a proliferation of programming languages and program optimization tools for quantum computing. Existing quantum compilers use intermediate representations (IRs) where quantum programs are described as circuits. Such IRs fail to leverage existing work on compiler optimizations. In such IRs, it is non-trivial to statically check for physical constraints such as the no-cloning theorem, which states that qubits cannot be copied. We introduce QSSA, a novel quantum IR based on static single assignment (SSA) that enables decades of research in compiler optimizations to be applied to quantum compilation. QSSA models quantum operations as being side-effect-free. The inputs and outputs of the operation are in one-to-one correspondence; qubits cannot be created or destroyed. As a result, our IR supports a static analysis pass that verifies no-cloning at compile-time. The quantum circuit is fully encoded within the def-use chain of the IR, allowing us to leverage existing optimization passes on SSA representations such as redundancy elimination and dead-code elimination. Running our QSSA-based compiler on the QASMBench and IBM Quantum Challenge datasets, we show that our optimizations perform comparably to IBM’s Qiskit quantum

Abstract

Matrix Algebra and Deep Neural Networks represent foundational classes of computational algorithms across multiple emerging applications like Augmented Reality or Virtual Reality, autonomous navigation (cars, drones, robots), data science, and various artificial intelligence-driven solutions. An accelerator-based architecture can provide performance and energy efficiency supporting fixed functions through customized data paths. However, constrained Edge systems requiring multiple applications and diverse matrix operations to be efficiently supported, cannot afford numerous custom accelerators. In this article, we present MxCore, a unified architecture that comprises tightly coupled vector and programmable cores sharing data through highly optimized interconnects along with a configurable hardware scheduler managing the co-execution. We submit MxCore as the generalized approach to facilitate the flexible acceleration of multiple Matrix Algebra and Deep-learning applications across a range of sparsity levels. Unified compute resources improve overall resource utilization and performance per unit area. Aggressive and novel microarchitecture techniques along with block-level sparsity support optimize compute and

Abstract

Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells. Keywords: blood cell; new BCNet framework; deep transfer learning; multi-class identification; verification and validation

Abstract

According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model’s ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature. Keywords: lung disease; COVID-19; pneumonia; chest X-rays images; feature extraction; automatic detection; artificial intelligence; epidemic

Abstract

: Herbal medicine and nutritional supplements are suggested to treat premenstrual somatic and psycho‐behavioural symptoms in clinical guidelines; nonetheless, this is at present based on poor‐quality trial evidence. Hence, we aimed to design a systematic review and meta‐analysis for their effectiveness in alleviating premenstrual symptoms. The published randomized controlled tri‐ als (RCTs) were extracted from Google scholar, PubMed, Scopus and PROSPERO databases. The risk of bias in randomized trials was assessed by Cochrane risk‐of‐bias tool. The main outcome pa‐ rameters were analysed separately based on the Premenstrual Symptom Screening Tool and PMTS and DRSP scores. Secondary parameters of somatic, psychological, and behavioural subscale symp‐ toms of PSST were also analysed. Data synthesis was performed assuming a random‐effects model, and standardized mean difference (SMDs) was analysed using SPSS version 28.0.0 (IBM, Armonk, NY, USA). A total of 754 articles were screened, and 15 RCTs were included (n = 1211 patients). Primary results for participants randomized to an intervention reported reduced PSST (n = 9), PMTS (n = 2), and DSR (n = 4) scores with (SMD = −1.44; 95% CI: −1.72 to −1.17), (SMD = −1.69; 95% CI: −3.80 to 0.42) and (SMD = 2.86; 95% CI: 1.02 to 4.69) verses comparator with substantial heterogeneity. Physical (SMD = −1.61; 95% CI = −2.56 to −0.66), behavioural (SMD = −0.60; 95% CI = −1.55 to0.35) and mood (SMD = 0.57; 95% CI = −0.96 to 2.11) subscale symptom groupings of PSST displayed similar findings. Fifty‐three studies (n = 8) were considered at low risk of bias with high quality. Mild adverse events were reported by four RCTs. Based on the existing evidence, herbal medicine and nutritional supplements may be effective and safe for PMS

Abstract

Efforts have been devoted for the discovery and development of positive allosteric modulators (PAMs) of 5-HT2CR because of their potential advantages over the orthosteric agonist like Lorcaserin that was withdrawn from the market. On the other hand, pursuing a positive ago-allosteric modulator (PAAM) is considered as beneficial particularly when an agonist is not capable of affecting the potency of the endogenous agonist sufficiently. In search of a suitable PAAM of 5-HT2CR we adopted an in silico based approach that indicated the potential of the 3-(1-hydroxycycloalkyl) substituted isoquinolin-1-one derivatives against the 5-HT2CR as majority of these molecules interacted with the site other than that of Lorcaserin with superior docking scores. These compounds along with the regioisomeric 3-methyleneisoindolin-1-one derivatives were prepared via the Cu(OAc)2 catalyzed coupling of 2-iodobenzamide with 1-ethynylcycloalkanol under ultrasound irradiation. According to the in vitro studies, most of these compounds were not only found to be potent and selective agonists but also emerged as PAAM of 5-HT2CR whereas Lorcaserin did not show PAAM activities. According to the SAR study the isoquinolin-1(2H)-ones appeared as better PAAM than isoindolin-1-ones whereas the presence of hydroxyl group appeared to be crucial for the activity. With the potent PAAM activity for 5-HT2CR (EC50 = 1 nM) and 107 and 86-fold selectivity towards 5-HT2C over 5-HT2A and 5-HT2B the compound 4i was identified as a hit molecule. The compound showed good stability in male BALB/c mice brain homogenate (∼85 % remaining after 2 h), moderate stability in the presence of rat liver microsomes (42 % remaining after 1 h) and acceptable PK properties with fast reaching in the brain maintaining ∼ 1:1 brain/plasma concentration ratio. The compound at a dose of 50 mg/kg exhibited decreased trend in the food intake starting from day 3 in S.D. rats, which reached significant by 5th day, and the effect was comparable to Lorcaserin (10 mg/kg) on day 5. Thus, being the first example of PAAM of 5-HT2CR the compound 4i is of further medicinal interest.

Abstract

Abstract Background: Automatic functional annotation of proteins is an open research problem in bioinformatics. The growing number of protein entries in public databases, for example in UniProtKB, poses challenges in manual functional annotation. Manual annotation requires expert human curators to search and read related research articles, interpret the results, and assign the annotations to the proteins. Thus, it is a time-con‑ suming and expensive process. Therefore, designing computational tools to perform automatic annotation leveraging the high quality manual annotations that already exist in UniProtKB/SwissProt is an important research problem Results: In this paper, we extend and adapt the GrAPFI (graph-based automatic protein function inference) (Sarker et al. in BMC Bioinform 21, 2020; Sarker et al., in: Pro‑ ceedings of 7th international conference on complex networks and their applications, Cambridge, 2018) method for automatic annotation of proteins with gene ontology (GO) terms renaming it as GrAPFI-GO. The original GrAPFI method uses label propaga‑ tion in a similarity graph where proteins are linked through the domains, families, and superfamilies that they share. Here, we also explore various types of similarity measures based on common neighbors in the graph. Moreover, GO terms are arranged in a hier‑ archical manner according to semantic parent–child relations. Therefore, we propose an efcient pruning and post-processing technique that integrates both semantic similarity and hierarchical relations between the GO terms. We produce experimental results comparing the GrAPFI-GO method with and without considering common neighbors similarity. We also test the performance of GrAPFI-GO and other annotation tools for GO annotation on a benchmark of proteins with and without the proposed pruning and post-processing procedure. Conclusion: Our results show that the proposed semantic hierarchical post-pro‑ cessing potentially improves the performance of GrAPFI-GO and of other annotation tools as well. Thus, GrAPFI-GO exposes an original efcient and reusable procedure, to exploit the semantic relations among the GO terms in order to improve the automatic annotation of protein functions

Abstract

Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach is imitation learning (IL) from human driving demonstrations. However, most previous studies on IL for autonomous driving face several critical challenges: (1) poor generalization ability toward the unseen environment due to distribution shift problems such as changes in driving views and weather conditions; (2) lack of interpretability; and (3) mostly trained to learn the single driving task. To address these challenges, we propose a view-invariant spatiotemporal attentive planning and control network for autonomous vehicles. The proposed method first extracts spatiotemporal representations from images of a front and top driving view sequence through attentive Siamese 3DResNet. Then, the maximum mean discrepancy loss (MMD) is employed to minimize spatiotemporal discrepancies between these driving views and produce an invariant spatiotemporal representation, which reduces domain shift due to view change. Finally, the multitasking learning (MTL) method is employed to jointly train trajectory planning and high-level control tasks based on learned representations and previous motions. Results of extensive experimental evaluations on a large autonomous driving dataset with various weather/lighting conditions verified that the proposed method is effective for feasible motion planning and control in autonomous vehicles.

Abstract

Image Fusion maximizes the visual information at each pixel location by merging content from multiple images in order to produce an enhanced image. Exposure Fusion, specifically, fuses a bracketed exposure stack of poorly lit images to generate a properly illuminated image. Given a single input image, exposure fusion can still be employed on a ‘simulated’ exposure stack, leading to direct single image contrast and low-light enhancement. In this work, we present a novel ‘Quaternion Factorized Simulated Exposure Fusion’ (QFSEF) method by factorizing an input image into multiple illumination consistent layers. To this end, we use an iterative sparse matrix factorization scheme by representing the image as a two-dimensional pure quaternion matrix. Theoretically, our representation is based on the dichromatic reflection model and accounts for the two scene illumination characteristics by factorizing each progressively generated image into separate specular and diffuse components. We empirically prove the advantages of our factorization scheme over other exposure simulation methods by using it for the low-light image enhancement task. Furthermore, we provide three exposure fusion strategies which can be used with our simulated stack and provide a comprehensive performance analysis. Finally, in order to validate our claims, we show extensive qualitative and quantitative comparisons against relevant state-of-the-art solutions on multiple standard datasets along with relevant ablation analysis to support our proposition. Our code and data are publicly available for easy reproducibility