Reviewerly/exHarmony huggingface dataset:
Sieve: Scalable In-situ DRAM-based Accelerator Designs for Massively Parallel k-mer Matching

The rapid influx of biosequence data, coupled with the stagnation of the processing power of modern computing systems, highlights the critical need for exploring high-performance accelerators that can meet the ever-increasing throughput demands of modern bioinformatics applications. This work argues that processing in memory (PIM) is an effective solution to enhance the performance of k-mer matching, a critical bottleneck stage in standard bioinformatics pipelines, that is characterized by random access patterns and low computational intensity.This work proposes three DRAM-based in-situ k-mer matching accelerator designs (one optimized for area, one optimized for throughput, and one that strikes a balance between hardware cost and performance), dubbed Sieve, that leverage a novel data mapping scheme to allow for simultaneous comparisons of millions of DNA base pairs, lightweight matching circuitry for fast pattern matching, and an early termination mechanism that prunes unnecessary DRAM row activation to reduce latency and save energy. Evaluation of Sieve using state-of-the-art workloads with real-world datasets shows that the most aggressive design provides an average of 326x/32x speedup and 74X/48x energy savings over multi-core-CPU/GPU baselines for k-mer matching.
A rough margin-based multi-task <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e5416" altimg="si42.svg"><mml:mi>ν</mml:mi></mml:math>-twin support vector machine for pattern classification

Twin support vector machine (TSVM) has attracted significant attention in recent years, but it is suitable for solving the single-task learning (STL) problems. It trains each task independently and neglects the relationships among all tasks. Conversely, multi-task learning (MTL) explores the shared information between multiple correlated tasks, which obtains a better classifier than STL. Nevertheless, the existing multi-task twin support vector machines give the same penalties to the misclassified samples. In fact, the misclassified samples play a different role in generating separating hyperplane. Motivated by above studies, we put forward a rough margin-based multi-task ν-twin support vector machine (rough MT-ν-TSVM) in this paper. The proposed rough MT-ν-TSVM gives different penalties to the misclassified samples depending on their positions. It not only takes full advantage of rough ν-TSVM, but also discovers the commonality among tasks and maintains the individuality of each task. Therefore, compared with the state-of-the-art algorithms, our method yields better classification performance. In addition, we apply it to Chinese wine dataset to verify the effectiveness. Finally, the related extensions are further discussed, especially a fast SMO-type decomposition method (SDM) is introduced to handle relatively large-scale problems for acceleration. Comprehensive experiments are conducted on eleven benchmark datasets and an image dataset. The results demonstrate that our proposed algorithm can avoid over-fitting and achieve better classification accuracy, meanwhile it does not increase computational time compared with DMTSVM and MT-ν-TSVM.
Effective fake news detection using graph and summarization techniques

Nowadays, fake news is widely spreading in various media, and this fake information is causing serious damage in many areas. Therefore, there is an increasing need to accurately detect fake news to prevent such damage. In this paper, we propose a novel method that uses graph and summarization techniques for fake news detection. Our proposed method represents the relationship of all sentences in a graph structure to accurately understand the context information of the document. Accordingly, the relationship between sentences in the graph is calculated as a score through the attention mechanism. Then, the summarization technique is used to reflect the sentence subject information in the graph update process. Our proposed method shows better performance than Karimi’s and BERT based models by approximately 10.34%p and 3.72%p, respectively.
Factors Affecting On-Time Delivery in Large-Scale Agile Software Development

Late delivery of software projects and cost overruns have been common problems in the software industry for decades.Both problems are manifestations of deficiencies in effort estimation during project planning.With software projects being complex socio-technical systems, a large pool of factors can affect effort estimation and on-time delivery.To identify the most relevant factors and their interactions affecting schedule deviations in large-scale agile software development, we conducted a mixed-methods case study at ING: two rounds of surveys revealed a multitude of organizational, people, process, project and technical factors which were then quantified and statistically modeled using software repository data from 185 teams.We find that factors such as requirements refinement, task dependencies, organizational alignment and organizational politics are perceived to have the greatest impact on ontime delivery, whereas proxy measures such as project size, number of dependencies, historical delivery performance and team familiarity can help explain a large degree of schedule deviations.We also discover hierarchical interactions among factors: organizational factors are perceived to interact with people factors, which in turn impact technical factors.We compose our findings in the form of a conceptual framework representing influential factors and their relationships to on-time delivery.Our results can help practitioners identify and manage delay risks in agile settings, can inform the design of automated tools to predict schedule overruns and can contribute towards the development of a relational theory of software project management.
Artificial intelligence image recognition based on 5G deep learning edge algorithm of Digestive endoscopy on medical construction

In this paper, we use artificial intelligence image recognition to obtain Digestive endoscopy image, and process the image based on 5G Deep learning edge algorithm to judge the disease type of the patient, and then consider the treatment plan. The combination of body area network and edge computing technology can meet the demand of low delay in body area network. In this case, the resource constrained body area network gateway node can process the physiological data collected by it into an offloadable task, and then unload the task and data to the edge computing node according to a certain strategy. The edge computing node completes the corresponding task processing and data storage, and finally provides the results to the relevant medical institutions and body area network users for reading auxiliary diagnosis and treatment of diseases. Studies have shown that 25% of patients with colon polyps have CD4 cells in peripheral blood based on 5G deep learning edge algorithm under artificial intelligence image recognition of Digestive endoscopy. The number of lymphocyte in group of differentiation was less than 200/μL, and the blood RNA in 92.3% patients was lower than 100 IU/ml, while fam CTP (A-cyclic peptide) was lower than 100 IU/ml. Opportunistic infections of the intestine and viruses can directly cause enteropathy because the fluorescence intensity of the probe is essentially unchanged and cannot form a triple helix structure. In terms of feature recognition accuracy, the 5G deep learning edge algorithm in this paper improves accuracy by 68% compared to the simple Yolo algorithm, and is similar in speed. Compared with RCNN algorithm, the accuracy and speed are improved by 21% and 85% respectively. Therefore, the 5G deep learning edge algorithm based on artificial intelligence image recognition has the advantages of accuracy and speed in digestive endoscopy of intelligent medical.
Classification System for Cervical Cell Images based on Hu Moment Invariants Methods and Support Vector Machine

Cervical cancer is one of the reproductive health diseases of women, which is still a major problem in the world due to the number of new cases and the high number of deaths, especially women in developing countries. Cervical cancer can be prevented by early detection. In developed countries, early detection of cervical cancer is carried out by the pap smear screening method. However, it is less effective if applied in developing countries due to limited human resources, expensive cost, inadequate infrastructure, and it is time-consuming. This study offers a cervical cell image classification system using Hu moment invariants for feature extraction technique and Support Vector Machine (SVM) classification with three types of cervical cell images, Normal, LSIL (Low-Grade Squamous Intraepithelial Lesion), and HSIL (High-Grade Squamous Intraepithelial Lesion). The classification system utilized three SVM models of Cubic SVM, Quadratic SVM and Fine Gaussian SVM, with HSIL class as positive data and LSIL and Normal as negative data. The accuracy value of the SVM classification results with the Hue moment for feature extraction was 71.9% for 0.98705s.
MISS GAN: A Multi-IlluStrator style generative adversarial network for image to illustration translation

Unsupervised style transfer that supports diverse input styles using only one trained generator is a challenging and interesting task in computer vision. This paper proposes a Multi-IlluStrator Style Generative Adversarial Network (MISS GAN) that is a multi-style framework for unsupervised image-to-illustration translation, which can generate styled yet content preserving images. The illustrations dataset is a challenging one since it is comprised of illustrations of seven different illustrators, hence contains diverse styles. Existing methods require to train several generators (as the number of illustrators) to handle the different illustrators' styles, which limits their practical usage, or require to train an image specific network, which ignores the style information provided in other images of the illustrator. MISS GAN is both input image specific and uses the information of other images using only one trained model.
Determinants of financial development in Ethiopia: ARDL approach

Sustainable economic growth requires financial sector development. However, financial development is low in Ethiopia. Hence, the main objective of the study was to examine determinants of financial development in Ethiopia. Annual data from 1980 to 2019 was used and examined by ARDL estimation technique. Private sector credit and money supply as percentage of GDP were used as dependent variables. Independent variables include external debt, reserve requirement, real exchange rate, lending interest rate, inflation, political freedom index, trade openness and economic growth. Broad money supply model is positively affected by political freedom index, economic growth and trade openness both in the short run and long run. While it is negatively affected by interest rate and reserve requirement. However, real exchange rate has negative effect in the long run and insignificant effect in the short run. On the other hand, credit to private sector model is positively affected by inflation, and political freedom, economic growth and trade openness. While it is negatively affected by external debt, reserve requirement and lending interest rate. Error correction estimation result of credit to private sector model shows the adjustment coefficient is −0.263 and statistically significant at 5% level of significance, which means short run deviation from long run equilibrium is adjusted at a rate of 26.3%. The adjustment coefficient for broad money supply model is −0.254. The study recommends that the government needs to improve trade and the quality of factors of production, reduce reserve requirement, reducing interest rate on lending, political.
Frames-of-Reference-Based Learning: Overcoming Perceptual Aliasing in Multistep Decision-Making Tasks

Perceptual aliasing challenges reinforcement learning agents.They struggle to learn stable policies by failing to identify and disambiguate perceptually identical states in the environment that require different actions to reach a goal.As the agent often has only a local frame of reference, it cannot represent the global environment.Frame-of-reference-based learning is a feature of vertebrate intelligence that allows multiple simultaneous representations of an environment at different levels of abstraction.This enables the resolution of patterns that are made up of patterns that are made up of features.The evolutionary computation technique of learning classifier systems has shown promise in learning nested patterns in single-step domains.This work uses the frame-of-reference concept within a learning classifier system to learn stable policies in non-Markov multistep domains.Considering aliased states at a constituent level enables the system to place them appropriately in holistic-level policies.Instead of enumerating a huge search space, evolution computation empowers the novel system to evolve fitter rules and policies.The experimental results show that the novel system effectively solves complex aliasing patterns in non-Markov environments that have been challenging to artificial agents.For example, the novel system utilizes only 6.5, 3.71, and 3.22 steps to resolve Maze10, Littman57, and Woods102, respectively.
An abnormal event detection method based on the Riemannian manifold and LSTM network

In this paper, we propose an abnormal event detection method based on the combination of Riemannian manifold and LSTM network. We divide the video into a series of non overlapping regions, and extract HOG and HOF as original appearance and motion features. Because the existing abnormal event detection methods pay little attention to the changes rate of appearance and motion, we propose a new feature called Riemannian manifold distance feature that can represent the topological relationship of features at different moment in the same position and capture the change rate of the current feature. Once the change rate of the current features changes, the topological relationship of features will change. We use ISOMAP algorithm to embed manifold into low dimensional Euclidean space. In the training phase, the dimension-reduced features of the same position at different historical moment are used as the input of each time step of the LSTM network, and the dimension-reduced feature of the current moment is used as expected output. And in this stage, we only use the normal video, so the LSTM will learn the changing rules of the features contained in normal events. In the testing phase, we use LSTM network to predict current dimension-reduced HOG and HOF features. After that, we can obtain the Riemannian manifold distance feature of actual dimension-reduced feature and predicted dimension-reduced feature. In order to detect abnormal events, we propose an abnormal score, which is used to measure whether the appearance/ movement and the change rate of them are close to the expectation. The abnormal score can detect the abnormal event according to dimension-reduced appearance feature, dimension-reduced motion feature, appearance Riemannian manifold distance feature and motion Riemannian manifold distance feature. Experiments on four benchmark datasets show the competitive performance of our method with the state-of-the-art methods.
Photon-number-dependent effective Lamb shift

The Lamb shift, an energy shift arising from the presence of the electromagnetic vacuum, has been observed in various quantum systems and established as the part of the energy shift independent of the environmental photon number. However, typical studies are based on simplistic bosonic models which may be challenged in practical quantum devices. We demonstrate a hybrid bosonic-fermionic environment for a linear resonator mode and observe that the photon number in the environment can dramatically increase both the dissipation and the effective Lamb shift of the mode. Our observations are quantitatively described by a first-principles model which we develop here also to guide device design for future quantum-technological applications. The device demonstrated here can be utilized as a fully rf-operated quantum-circuit refrigerator to quickly reset superconducting qubits.
Semi-Supervised Video Object Segmentation via Learning Object-Aware Global-Local Correspondence

In semi-supervised video object segmentation (VOS) task, temporal coherent object-level cues play a key role yet are hard to accurately model. To this end, this paper presents an object-aware global-local correspondence architecture, which enables to extract the inter-frame temporal coherent object-level features for accurate VOS. Specifically, we first generate a set of object masks by the ground-truth segmentation, and then we squeeze the current frame representation inside the object masks into a set of global object embeddings. Second, we compute the similarity between each embedding and the feature map, producing an object-aware weight for each pixel. The object-aware feature at each pixel is then constructed by summing the object embeddings weighted by their corresponding object-aware weights, which is able to capture rich object category information. Third, to establish the accurate correspondences between the inter-frame temporal coherent cues, we further design a novel global-local correspondence module to refine the temporal feature representations. Finally, we augment the object-aware features with the global-local aligned information to produce a strong spatio-temporal representation, which is essential to a more reliable pixel-wise segmentation prediction. Extensive evaluations are conducted on three popular VOS benchmarks containing Youtube-VOS, Davis2017 and Davis2016, demonstrating that the proposed method achieves favourable performance compared to the state-of-the-arts.
The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders

The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years.PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation.We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.
Spatial search on Johnson graphs by continuous-time quantum walk

Spatial search on graphs is one of the most important algorithmic applications of quantum walks. To show that a quantum-walk-based search is more efficient than a random-walk-based search is a difficult problem, which has been addressed in several ways. Usually, graph symmetries aid in the calculation of the algorithm's computational complexity, and Johnson graphs are an interesting class regarding symmetries because they are regular, Hamilton-connected, vertex- and distance-transitive. In this work, we show that spatial search on Johnson graphs by continuous-time quantum walk achieves the Grover lower bound $\pi\sqrt{N}/2$ with success probability $1$ asymptotically for every fixed diameter, where $N$ is the number of vertices. The proof is mathematically rigorous and can be used for other graph classes.
Histopathology Image Segmentation Using MobileNetV2 based U-net Model

Histopathology image segmentation is a significant step in the early detection of diseases. Compared to traditional segmentation methods, deep learning models provide better segmentation even though the images are complex in nature. U-net is an effective deep model for the segmentation of medical images. In this work, we propose a MobileNetV2 based U-net model for the segmentation of nuclei regions from Triple Negative Breast Cancer (TNBC) histopathology images. Accuracy, AUC and Jaccard coefficient values are used as the evaluation metrics. The proposed method achieves a Jaccard value of 59% on the TNBC nuclei segmentation dataset.
Labels are Not Perfect: Inferring Spatial Uncertainty in Object Detection

The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity or even failures in object labels due to error-prone annotation process or sensor observation noise. Current public object detection datasets only provide deterministic object labels without considering their inherent uncertainty, as does the common training process or evaluation metrics for object detectors. As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection. In this work, we infer the uncertainty in bounding box labels from LiDAR point clouds based on a generative model, and define a new representation of the probabilistic bounding box through a spatial uncertainty distribution. Comprehensive experiments show that the proposed model reflects complex environmental noises in LiDAR perception and the label quality. Furthermore, we propose Jaccard IoU (JIoU) as a new evaluation metric that extends IoU by incorporating label uncertainty. We conduct an in-depth comparison among several LiDAR-based object detectors using the JIoU metric. Finally, we incorporate the proposed label uncertainty in a loss function to train a probabilistic object detector and to improve its detection accuracy. We verify our proposed methods on two public datasets (KITTI, Waymo), as well as on simulation data. Code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/</uri> ZiningWang/Inferring-Spatial-Uncertainty-in-Object-Detection.