Miracle-dz/UniFAR-FTUs huggingface dataset:
Crane: Mitigating Accelerator Under-utilization Caused by Sparsity Irregularities in CNNs

Convolutional neural networks (CNNs) have achieved great success in numerous AI applications. To improve inference efficiency of CNNs, researchers have proposed various pruning techniques to reduce both computation intensity and storage overhead. These pruning techniques result in multi-level sparsity irregularities in CNNs. Together with that in activation matrices, which is induced by employment of ReLU activation function, all these sparsity irregularities cause a serious problem of computation resource under-utilization in sparse CNN accelerators. To mitigate this problem, we propose a method of load-balancing based on a workload stealing technique. We demonstrate that this method can be applied to two major inference data-flows, which cover all state-of-the-art sparse CNN accelerators. Based on this method, we present an accelerator, called Crane, which addresses all kinds of sparsity irregularities in CNNs. We perform a fair comparison between Crane and state-of-the-art prior approaches. Experimental results show that Crane improves performance by <inline-formula><tex-math notation="LaTeX">$27\%\sim 88\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>27</mml:mn><mml:mo>%</mml:mo><mml:mo>∼</mml:mo><mml:mn>88</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="guan-ieq1-2981080.gif"/></alternatives></inline-formula> and reduces energy consumption by <inline-formula><tex-math notation="LaTeX">$16\%\sim 48\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>16</mml:mn><mml:mo>%</mml:mo><mml:mo>∼</mml:mo><mml:mn>48</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="guan-ieq2-2981080.gif"/></alternatives></inline-formula>, respectively, compared to the counterparts.
A study of pollen viability and longevity in Oryza rufipogon, O. sativa, and their hybrids

The length of pollen viability after anther dehiscence is crucial to successful pollination (Stone et al1995), particularly for outbreeders. It determines the possibility of outcrossing between species, provided that limited specific reproductive barriers exist. With the rapid development of biotechnology, more transgenic crops, including transgenic rice, are becoming available, resulting in biosafety concerns about possible ecological risks. Evaluation of transgenes introgressing to wild rice species becomes an important issue for rice breeders, environmentalists, and policymakers. Studying pollen viability and longevity of wild and cultivated rice species as well as their hybrids is important in evaluating outcrossing frequencies.
Meniscal allograft transplantation for post-meniscectomy syndrome

Post-meniscectomy syndrome is broadly characterised by intractable pain following the partial or total removal of a meniscus. An new treatment approach is meniscal allograft transplantation (MAT), which involves the transplant of a cadaveric meniscal allograft into the knee of a patient who has had a prior (sub)total meniscectomy. The aim of our report was to evaluate the efficacy and safety of MAT for the treatment of post-meniscectomy syndrome, compared to conservative treatment. Overall, the level of evidence was very low. We could only identify one controlled trial, which showed superiority of MAT in some (but not all) outcomes compared to conservative treatment. In addition, we identified five case series to evaluate the safety. The complications rate in these case series was 0-38.5%. The inclusion of MAT in the hospital benefit catalogue is therefore not recommended.
Interoperability of web computational plugins for large microscopy image analyses

The Information Technology Laboratory (ITL) at the National Institute of Standards and 78 Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation’s measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology. ITL’s responsibilities include the development of management, administrative, technical, and physical standards and guidelines for the cost-effective security and privacy of other than national security-related information in federal information systems. Abstract This document summarizes conclusions from a workshop focused on Interoperability of Web Computational Plugins for Large Microscopy Image Analyses. The workshop conclusions are classified as practical recommendations and agreements on development and future research related to (1) containerization of execution code, (2) data storage, (3) interoperability requirements of workflow engines for running containerized plugins, (4) standard packaging of web user interface modules, and (5) security of container-based distribution.
Generating high resolution digital mammogram from digitized film mammogram with conditional generative adversarial network

Deep-learning based application for digital mammography screening is limited due to lack of labeled data. Generating digital mammogram (DM) from existing labeled digitized screen-film mammogram (DFM) dataset is one approach that may alleviate the problem. Generating high resolution DMs from DFMs is a challenge due to the limitations of network capacity and lack of GPU memory. In this study, we developed a deep learning framework, Cycle-HDDM, with which high resolution DMs were generated from DFMs. Our Cycle-HDDM model first used a sliding window to crop DFMs and DMs into patches of 256 by 256 in size. Then, we divided the patches into three categories (breast, background and boundary) using breast masks. We paired patches from the DFM and DMdatasets for training with the constraint that these paired patches should be sampled from the same category of the two different image sets. We used U-Net as the generators and modified the discriminators so that the outputs of the discriminators were a two-channel image, one channel for distinguishing real and synthesized DMs, and the other for representing a probability map for breast mask. We designed a study to evaluate the usefulness of Cycle-HDDM in a segmentation task, the objective of which was to estimate the percentage of breast density (PD) on DMs using deep neural network (DNN). With IRB approval, 1651 DFMs and 813 DMs were collected. Both DFMs and DMs were normalized to a pixel size of 100μm × 100μm for the experiments. The results show that the synthesized DMs by Cycle-HDDM could significantly improve (p < 0.001) the DNN-based mammographic density segmentation.
Evaluation of the Short, Medium and Long-term Prognosis of Pulmonary Embolism

Introduction: Pulmonary Embolism (PE) is the most severe form of venous thromboembolic disease. This is a serious condition not only because of its mortality but also its sequelae. Methodology: The aim of this study is to evaluate the prognosis of patients who had an episode of PE, hospitalized in the cardiology department of the Aristide Le Dantec University Hospital in Dakar. We performed a cohort study that included all patients admitted for pulmonary embolism during the period of May 1, 2011 to February 29, 2016. Subjects were contacted for a re-evaluation during the period of August 1st to September 1st 2016. We evaluated the status of patients (death or alive), to look for signs of chronic pulmonary heart based on the electrocardiogram (right atrial and ventricular hypertrophies, aspect S1S2S3, right bundle branch) and especially on the echocardiogram (right cavitary and parietal dimensions, measurement of the PASP). Results: A total of 77 pulmonary embolism cases were counted during the study period; 70 files were retained. It was a predominantly female population, with an average age of 51.2. Low risk forms (PESI I-II) accounted for 85.7% of the sample, versus 14.3% of PESI III-IV-V forms. At the re-evaluation, we counted 18 deaths (25.7%); twenty-two patients were lost to follow up. No recurrence was noted since hospitalization. Two patients had bilateral, noninflammatory edema. Dyspnea was found in 3 patients. No patient had any sign of right heart failure. Doppler echocardiography showed dilatation of the right ventricle along parasternal long axis in 6 cases and along apical section in 5 cases respectively. Right ventricular wall hypertrophy was noted in 6 patients. The systolic function of the right ventricle was normal in all cases. The right atrium was dilated in 5 cases. Pulmonary hypertension and increased pulmonary vascular resistance were noted in 7 cases, respectively. The overall mortality factors were acute circular insufficiency, right ventricular systolic dysfunction, PESI score of at least 1, tachycardia, PASP value greater than 50 mmHg, right heart failure and dilatation of the right atrium. Conclusion: Wall dilatation and hypertrophy of the right ventricle as well as pulmonary hypertension are frequent sequelae. The parameters of the hemodynamic impact of pulmonary embolism constitute the main prognostic factors.
When Recommender Systems Meet Fleet Management: Practical Study in Online Driver Repositioning System

E-hailing platforms have become an important component of public transportation in recent years. The supply (online drivers) and demand (passenger requests) are intrinsically imbalanced because of the pattern of human behavior, especially in time and locations such as peak hours and train stations. Hence, how to balance supply and demand is one of the key problems to satisfy passengers and drivers and increase social welfare. As an intuitive and effective approach to address this problem, driver repositioning has been employed by some real-world e-hailing platforms. In this paper, we describe a novel framework of driver repositioning system, which meets various requirements in practical situations, including robust driver experience satisfaction and multi-driver collaboration. We introduce an effective and user-friendly driver interaction design called “driver repositioning task”. A novel modularized algorithm is developed to generate the repositioning tasks in real time. To our knowledge, this is the first industry-level application of driver repositioning. We evaluate the proposed method in real-world experiments, achieving a 2% improvement of driver income. Our framework has been fully deployed in the online system of DiDi Chuxing and serves millions of drivers on a daily basis.
Multi-Area Throughput and Energy Optimization of UAV-Aided Cellular Networks Powered by Solar Panels and Grid

Small cells (SCs) mounted on top of Unmanned Aerial Vehicles (UAVs) can be used to boost the radio capacity in hotspot zones. However, UAV-SCs are subject to tight battery constraints, resulting in frequent recharges operated at the ground sites. To meet the UAV-SCs energy demanded to the ground sites, the operator leverages a set of Solar Panels (SPs) and grid connection. In this work, we demonstrate that both i) the level of throughput provided to a set of areas and ii) the amount of energy that is exchanged with the grid by the ground sites play a critical role in such UAV-aided cellular network. We then formulate the J-MATE model to jointly optimize the energy and throughput through revenue and cost components. In addition, we design the BBSR algorithm, which is able to retrieve a solution even for large problem instances. We evaluate J-MATE and BBSR over a realistic scenario composed of dozens of areas and multiple ground sites, showing that: i) both J-MATE and BBSR outperform previous approaches targeting either the throughput maximization or the energy minimization, and ii) the computation time and the memory occupation of BBSR are reduced up to five orders of magnitude compared to J-MATE.
An efficient scheme for secure feature location using data fusion and data mining in internet of things environment

Feature location (FL) is performed to find the relationships between domain concepts and other software artifacts. One major problem in maintaining a software system is to understand how many functional features exist in a system and how these features are implemented. Also, poor security is the prime problem in the FL system. However, the existing recent FL techniques use a textual and dynamic approach, which is not found to be secure, keeping in view the changes in the description of security attacks. To overcome this drawback, this work proposed a novel secure approach for FL utilizing data fusion as well as data mining for the internet of things environment. Firstly, the repeated test cases (TC) are eradicated as of the labeled TC. Next, important attributes are selected using the artificial flora optimization algorithm from the removed labeled TC. Then, association rule mining is performed to ascertain closed attributes. Subsequently, encrypt the closed attributes utilizing Caesar Cipher‐Rivest, Shamirs, as well as Adelman algorithm. After that, the score value of the closed attributes counts was found utilizing entropy calculation. Finally, the score value is given as input to the normalized‐K‐Means (N‐[K‐Means]) algorithm, where the score value is normalized utilizing min‐max normalization and then grouped utilizing K‐Means algorithm (KMA). It proffers better results for FL in the source code. The proposed N‐(K‐Means) performance is found better in comparison to the KMA and latent semantic indexing methods. The proposed system proffered better FL results in comparison to the other prevailing methods.
Deep Learning in Kernel Machines

The attempt to build algorithms to solve cognitive tasks such as visual object or pattern recognition, speech perception, language understanding etc. have attracted the attention of many machine learning researchers in the recent past. The theoretical and biological arguments in this context strongly suggest that building such systems requires deep learning architectures that involve many layers of nonlinear information processing. Deep learning approach has originally emerged and been widely used in the area of neural networks. The techniques developed from deep learning research have already been impacting a wide range of signal and information processing applications. In the recent past, excited by the startling performance that deep learning approaches have to offer, there are many attempts to embrace deep learning techniques in other machine learning paradigms, particularly in kernel machines. Convex optimization, structural risk minimization, margin maximization, etc. are the some of the elegant features that makes kernel machines popular among the researchers. With the advent of recently developed multi layered kernel called arc-cosine kernel, the multilayer computations is made possible in kernel machines. The multi-layered feature learning perceptiveness of deep learning architecture have been re-created in kernel machines through the model called Multilayer Kernel Machines(MKMs). Support vector machines were often used as the classifier in these models. These deep models have been widely used in many applications that involves small-size datasets. However the scalability, multilayer multiple kernel learning, unsupervised feature learning etc. were untouched in the context of kernel machines. This research explored above problems and developed three deep kernel learning models viz; (i) Deep kernel learning in core vector machine that analyze the behavior of arc-cosine kernel and modeled a scalable deep kernel machine by incorporating arc-cosine kernel in core vector machines. (ii) Deep multiple multilayer kernel learning in core vector machines modeled a scalable deep learning architecture with unsupervised feature extraction. Each feature extraction layer in this model exploit multiple kernel learning framework that involves both single layer and multilayer kernel computations. (iii) Deep kernel based extreme learning machine combines the multilayer kernel computation of arc-cosine kernel and fast, non-iterative learning mechanism of Extreme Learning Machines. The theoretical and empirical analysis of the proposed methods show promising results. viii
Stasis in the Shark Tank: Persuading an Audience of Funders to Act on Behalf of Entrepreneurs

This study investigates the role of stasis, an ancient rhetorical tool with both heuristic and analytic capabilities, in entrepreneurial rhetoric, specifically in pitching and question-and-answer sessions. Drawing from a multiyear sample of Shark Tank pitches, the author found that funders expect entrepreneurs to account for stases of being, quality, quantity, and place. The findings suggest a series of associated questions within each stasis. When these questions are answered unsuccessfully, standstills occur within the funding argument; when they are successfully addressed, the stasis passes, and ventures are more likely to receive funding. The author discusses the implications of this study for entrepreneurship and professional communication.
Mesh adaptation for pseudospectral ultrasound simulations

High-intensity focussed ultrasound (HIFU) is an emerging cancer therapy that holds great promise, as it is minimially invasive, requires no ionising radiation, and can treat small volumes precisely. However, currently therapies are hindered by an inadequate capacity for treatment planning, as the interactions between the sound waves and tissue are complex and difficult to simulate. The Fourier pseudospectral method is one way of efficiently performing these simulations, as it can provide high accuracies with low computational costs. However, it is typically used with uniform computational meshes, wasting resolution in regions of the simulation where only low frequencies are present, and typically under-resolving the acoustic field in the focal region. This thesis addresses this problem in two ways: First, a bandwidth-based measure of the spatial resolution requirements for a model solution is developed and integrated into a moving mesh method. This allows spatially and temporallyvarying resolution requirements to be met. Bandwidth-based meshes are shown to perform very well when compared with current mesh adaptation approaches. Second, a technique is presented for discretising arbitrary acoustic source distributions that does not rely on the source’s region of support coinciding with the mesh. This not only allows sources to be represented with adaptive meshes, but greatly improves the accuracy of source discretisations for uniform meshes as well. These two contributions are of vital importance in the context of HIFU simulation, and can easily be applied to the many other problems for which the Fourier pseudospectral method is used.
Multiview spectral clustering via complementary information

In this article, multiview spectral clustering via complementary information (MSCC) is proposed, in which both the consensus information and the complementary information are explored for multiview clustering. In contrast to most multiview spectral clustering methods, the proposed MSCC considers the differences among multiple views and constructs a similarity matrix for clustering. Furthermore, a convex relaxation is employed and an algorithm that is based on the augmented Lagrange multiplier is proposed for optimizing the objective function of MSCC. In extensive experiments on five real‐world benchmark datasets, our proposed method outperforms two baselines and has significantly improved to several state‐of‐the‐art multiview clustering methods.
A Sentiment Analysis of Peer to Peer Energy Trading Topics from Twitter

The emergence of the Peer-to-Peer (P2P) energy trading platforms provides a new method for the general public to use and trade green energy. How to design the peer to peer energy trading platform thus becomes important in facilitating user trading experience. This study will use the data mining method to evaluate factors impacting P2P energy trading experience. Python was used to analyze data extracted from Twitter and Natural Language Processing (NLP) method was implemented with hierarchical Latent Dirichlet Process (hLDA) model. The study’s findings will be examined in detail.
Generative part design for additive manufacturing

iii List of Tables . xv Glossary . xvii Chapter 1 Introduction . 1 1.1. Research Background . 1 1.2. Thesis Outline . 6 Chapter 2 – Literature Review . 8 2.
Semantic-Based Process Mining Technique for Annotation and Modelling of Domain Processes

Semantic technologies aim to represent information or models in formatsthat are not just machine-readable but also machine-understandable. To this effect, thispaper shows how the semantic concepts can be layered on top of the derived models toprovide a more contextual analysis of the models through the conceptualization method.Technically, the method involves augmentation of informative value of the resulting mod-els by semantically annotating the process elements with concepts that they represent inreal-time settings, and then linking them to an ontology in order to allow for a moreabstract analysis of the extracted logs or models. The work illustrates the method usingthe case study of a learning process domain. Consequently, the results show that a systemwhich is formally encoded with semantic labelling (annotation), semantic representation(ontology) and semantic reasoning (reasoner) has the capacity to lift the process miningand analysis from the syntactic to a more conceptual level.
A Novel 5x5 Edge Detection Operator for Blood Vessel Images

Background: Blood vessel appearance is an important indicator for many diagnoses, including diabetes, hypertension, and arteriosclerosis. Aims: Blood Vessel edge detection in retinal images is very important in medical image processing. A lot of algorithms have been suggested for extracting medical image edges. Methodology: In this paper a new 5x5 edge detection masks are proposed based on the finite difference method. Results: The proposed method is applied on a set of blood vessel images and we obtained good results in comparison with 5x5 Sobel mask.
A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers

In the study of vehicle dynamics and controls, modeling ultra-high performance maneuvers (i.e., minimum-time vehicle maneuvering) is a fascinating problem that explores the boundaries of capabilities for a human controlling a machine. Professional human drivers are still considered the benchmark for controlling a vehicle during these limit handling maneuvers. Different drivers possess unique driving styles, i.e. preferences and tendencies in their local decisions and corresponding inputs to the vehicle. These differences in the driving style among professional drivers or sets of drivers are duly considered in the vehicle development process for component selection and system tuning to push the limits of achievable lap times. This work aims to provide a mathematical framework for modeling driving styles of professional drivers that can then be embedded in the vehicle design and development process. This research is conducted in three separate phases. The first part of this work introduces a cascaded optimization structure that is capable of modeling driving style. Model Predictive Control (MPC) provides a natural framework for modeling the human decision process. In this work, the inner loop of the cascaded structure uses an MPC receding horizon control strategy which is tasked with finding the optimal control inputs (steering, brake, throttle, etc.) over each horizon while minimizing a local cost function. Therein, we extend the typical fixed-cost function to be a blended cost capable of optimizing different objectives. Then, an outer loop finds the objective weights used in each MPC control horizon. It is shown that by varying the driver’s objective between key horizons, some of the sub-optimality inherent to the MPC process can be alleviated.