Miracle-dz/UniFAR-FTUs huggingface dataset:
Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

With the ubiquity of accelerators, such as FPGAs and GPUs, the complexity of high-performance programming is increasing beyond the skill-set of the average scientist in domains outside of computer science. It is thus imperative to decouple programming paradigms and architecture-specific implementation from the underlying scientific computations. We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that facilitates high performance application development and optimization. By combining fine-grained data dependencies with high-level control flow, SDFGs are both expressive and amenable to high-level program transformations, such as tiling, vectorization, and double buffering. These transformations are then applied to the SDFG in an interactive process, using extensible pattern matching and graph rewriting. To facilitate this process, we provide a graphical user interface that enables applying transformations, as well as creating custom optimizations, reusable across applications. We demonstrate SDFGs on CPUs, GPUs, and FPGAs, using a wide variety of applications and motifs — from fundamental computational kernels, through polyhedral applications, to graph analytics. We show that the representation is both expressive and performant, allowing domain scientists to develop applications that can be tuned to approach peak hardware performance without modifying the original scientific code. 1 Motivation In scientific and high-performance computing, there are two types of people who interact with code: the Domain Scientist, who knows what to compute; and the Performance Engineer, who knows how to compute it efficiently. The importance of efficiency is amplified in large-scale computations, which involve scheduling tasks to thousands of computers, taking days or weeks to complete. Because computing time and energy can be expensive, supercomputing centers employ teams of such performance engineers who improve many different applications. The two main ingredients of a scientific application are computations and memory accesses. With the emergence of new processing technologies, arithmetic operations and energy efficiency improve, whereas data movement does
A Wireless Sensor Network based approach to monitor and control air Pollution in large urban areas

Air pollution is a major concern in large urban areas. Various studies have been made to monitor and control the pollution level emitted by the vehicles but some main factors like ease of implementation or feasibility of the proposed approaches are not focused in the told studies. In this paper we propose a wireless sensor network solution to monitor vehicles exhaust systems pollution emission. Furthermore, we evaluate the feasibility of our proposed approach with respect to energy consumption and network lifetime by means of simulation. Our results indicate that our proposed scheme is practical for implementation using well known MAC layer protocols.
Channel Max Pooling Layer for Fine-Grained Vehicle Classification

Deep convolutional networks have recently shown excellent performance on Fine-Grained Vehicle Classification. Based on these existing works, we consider that the back-probation algorithm does not focus on extracting less discriminative feature as much as possible, but focus on that the loss function equals zero. Intuitively, if we can learn less discriminative features, and these features still could fit the training data well, the generalization ability of neural network could be improved. Therefore, we propose a new layer which is placed between fully connected layers and convolutional layers, called as Chanel Max Pooling. The proposed layer groups the features map first and then compress each group into a new feature map by computing maximum of pixels with same positions in the group of feature maps. Meanwhile, the proposed layer has an advantage that it could help neural network reduce massive parameters. Experimental results on two fine-grained vehicle datasets, the Stanford Cars-196 dataset and the Comp Cars dataset, demonstrate that the proposed layer could improve classification accuracies of deep neural networks on fine-grained vehicle classification in the situation that a massive of parameters are reduced. Moreover, it has a competitive performance with the-state-of-art performance on the two datasets.
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN

Deep neural networks have been widely deployed in various machine learning tasks. However, recent works have demonstrated that they are vulnerable to adversarial examples: carefully crafted small perturbations to cause misclassification by the network. In this work, we propose a novel defense mechanism called Boundary Conditional GAN to enhance the robustness of deep neural networks against adversarial examples. Boundary Conditional GAN, a modified version of Conditional GAN, can generate boundary samples with true labels near the decision boundary of a pre-trained classifier. These boundary samples are fed to the pre-trained classifier as data augmentation to make the decision boundary more robust. We empirically show that the model improved by our approach consistently defenses against various types of adversarial attacks successfully. Further quantitative investigations about the improvement of robustness and visualization of decision boundaries are also provided to justify the effectiveness of our strategy. This new defense mechanism that uses boundary samples to enhance the robustness of networks opens up a new way to defense adversarial attacks consistently.
GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

In this letter, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORB-SLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone. Source code is available at: https://github.com/jiexiong2016/GCNv2_SLAM.
Bayesian Anomaly Detection and Classification

Statistical uncertainties are rarely incorporated into machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating an algorithm’s ability to detect anomalies.
Developing and Using Special-Purpose Lexicons for Cohort Selection from Clinical Notes

Background and Significance: Selecting cohorts for a clinical trial typically requires costly and time-consuming manual chart reviews resulting in poor participation. To help automate the process, National NLP Clinical Challenges (N2C2) conducted a shared challenge by defining 13 criteria for clinical trial cohort selection and by providing training and test datasets. This research was motivated by the N2C2 challenge. Methods: We broke down the task into 13 independent subtasks corresponding to each criterion and implemented subtasks using rules or a supervised machine learning model. Each task critically depended on knowledge resources in the form of task-specific lexicons, for which we developed a novel model-driven approach. The approach allowed us to first expand the lexicon from a seed set and then remove noise from the list, thus improving the accuracy. Results: Our system achieved an overall F measure of 0.9003 at the challenge, and was statistically tied for the first place out of 45 participants. The model-driven lexicon development and further debugging the rules/code on the training set improved overall F measure to 0.9140, overtaking the best numerical result at the challenge. Discussion: Cohort selection, like phenotype extraction and classification, is amenable to rule-based or simple machine learning methods, however, the lexicons involved, such as medication names or medical terms referring to a medical problem, critically determine the overall accuracy. Automated lexicon development has the potential for scalability and accuracy.
DRAMNet: Authentication based on Physical Unique Features of DRAM Using Deep Convolutional Neural Networks

Nowadays, there is an increasing interest in the development of Autonomous Vehicles (AV). However, there are two types of attack challenges that can affect AVs and are yet to be resolved, i.e., sensor attacks and vehicle access attacks. This paper, to the best of our knowledge, is the first work that proposes a novel authentication scheme involving DRAM power-up unique features using deep Convolutional Neural Network (CNN), which can be used to implement secure access control of autonomous vehicles. Our approach consists of two parts. First, we convert raw power-up sequence data from DRAM cells into a two-dimensional (2D) format to generate a DRAM image structure. Second, we apply deep CNN to DRAM images, in order to extract unique features from each memory to classify them for authentication. To evaluate our proposed approach, we utilize data from three Commercial-Off-The-Shelf (COTS) DRAMs taken under various environmental and other conditions (high/low temperature, high/low supply voltage and aging effects). Based on our results, our proposed authentication method ``DRAMNet'' achieves 98.63% accuracy and 98.49% precision. In comparison to other state-of-the-art CNN architectures, such as the AlexNet and VGGNet models, our DRAMNet approach fares equally well or better than them.
Enumerating Minimal Dominating Sets in Kt-free Graphs and Variants

It is a long-standing open problem whether the minimal dominating sets of a graph can be enumerated in output-polynomial time. In this article we investigate this problem in graph classes defined by forbidding an induced subgraph. In particular, we provide output-polynomial time algorithms for Kt-free graphs and for several related graph classes. This answers a question of Kanté et al. about enumeration in bipartite graphs.
Overcoming Multi-Model Forgetting

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search. Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.
Learning a Deep ConvNet for Multi-Label Classification With Partial Labels

Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently multi-label. Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex. Furthermore, collecting clean multi-label annotations is more difficult to scale-up than single-label annotations. To reduce the annotation cost, we propose to train a model with partial labels i.e. only some labels are known per image. We first empirically compare different labeling strategies to show the potential for using partial labels on multi-label datasets. Then to learn with partial labels, we introduce a new classification loss that exploits the proportion of known labels per example. Our approach allows the use of the same training settings as when learning with all the annotations. We further explore several curriculum learning based strategies to predict missing labels. Experiments are performed on three large-scale multi-label datasets: MS COCO, NUS-WIDE and Open Images.
Neural Reverse Engineering of Stripped Binaries

We address the problem of predicting procedure names in stripped executables which contain no debug information. Predicting procedure names can dramatically ease the task of reverse engineering, saving precious time and human effort. We present a novel approach that leverages static analysis of binaries with encoder-decoder-based neural networks. The main idea is to use static analysis to obtain enriched representations of API call sites; encode a set of sequences of these call sites; and finally, attend to the encoded sequences while decoding the target name token-by-token. We evaluate our model by predicting procedure names over $60,000$ procedures in $10,000$ stripped executables. Our model achieves $81.70$ precision and $80.12$ recall in predicting procedure names within GNU packages, and $55.48$ precision and $51.31$ recall in a diverse, cross-package, dataset. Comparing to previous approaches, the predictions made by our model are much more accurate and informative.
Learning to Update for Object Tracking With Recurrent Meta-Learner

Model update lies at the heart of object tracking. Generally, model update is formulated as an online learning problem, where a target model is learned over the online training set. Our key innovation is to formulate the model update problem in the meta-learning framework and learn the online learning algorithm itself using large numbers of offline videos, i.e., learning to update. The learned updater takes as input the online training set and outputs an updated target model. As a first attempt, we design the learned updater based on recurrent neural networks and demonstrate its application in a template-based tracker and a correlation filter-based tracker. Our learned updater consistently improves the base trackers and runs faster than real time on GPU, while requiring small memory footprint during testing. The experiments on standard benchmarks demonstrate that our learned updater outperforms commonly used update baselines including the efficient exponential moving average-based update and the well-designed stochastic gradient descent-based update. Equipped with our learned updater, the template-based tracker achieves state-of-the-art performance among real-time trackers on GPU.
A retrospective study

To evaluate retrospectively any association between the degree of deformity correction by medial open-wedge high tibial osteotomy (HTO) and patellofemoral joint degeneration. We hypothesized that development of patellofemoral joint degeneration depended on the degree of intraoperative deformity correction. Fifty-seven patients who underwent medial open-wedge HTO for treatment of osteoarthritis in one knee were included in this study. Knees were classified into degeneration (D) and non-degeneration (ND) groups according to worsening of the patellar and/or femoral trochlear cartilage at the time of hardware removal (D group, 27 knees) and no degeneration or improvement (ND group, 30 knees). We compared preto post-surgery change in hip-knee-ankle angle (HKA) and medial-proximal-tibial angle (MPTA), openwedge HTO correction angle, and arthroscopic findings between groups. Mean age, height, weight, and body mass index were 54.1±9.9 years, 160.4±8.7cm, 66.4±12.1kg, and 25.7±3.3kg/m, respectively. Change in both HKA andMPTA differed significantly between groups. TheMPTA cut-off values to predict patellofemoral degeneration were determined to be 10°, associated with an AUC of 0.75 (95% confidence interval [CI] 0.62–0.87). This study evaluated retrospectively the effect of the correction angle during medial open-wedge HTO on patellofemoral joint degeneration. If deformity correction exceeds anMPTA of 10° during open-wedge HTO, degeneration of patellofemoral joint needs to be considered. Level of evidence: Level IV. Abbreviations: AUC = area under curve, HKA = hip-knee-ankle, HTO = high tibial osteotomy, ICRS = International Cartilage Repair Society, MPTA = medial proximal tibial angle, ROC = receiver operation characteristic.
Connectivity Analysis of Cognitive Radio Ad-Hoc Networks with Multi-Pair Primary Networks

In this paper, we study the connectivity of cognitive radio ad-hoc networks (CRAHNs) where primary users (PUs) and secondary users (SUs) are randomly distributed in a given area following a homogeneous Poisson process. Moreover, for the sake of more realistic CRAHNs, contrary to previous works in the literature, we consider the case that primary network is comprised of multiple communication pairs which are spatial-temporal distributed in the network area. We also take into consideration the differences in transmission range and interference range of both PUs and SUs. The connectivity of such CRAHN is studied from three viewpoints. First, we mathematically analyze the probability of isolated secondary transmitter and secondary receiver. Second, we derive the approximation expression of the link probability between two adjacent SUs. Third, we investigate the path connectivity between two arbitrary SUs by using the simulation analysis approach. The correctness of our mathematical expressions is confirmed by comparing analytical results with simulation results. The results in this paper provide insights into how multiple communication pairs in primary network affect the connectivity of secondary network, which can be useful guidelines for the design of CRAHNs.
Electron Transport in Carbon Nanotubes with Adsorbed Chromium Impurities

We employ Green’s function method for describing multiband models with magnetic impurities and apply the formalism to the problem of chromium impurities adsorbed onto a carbon nanotube. Density functional theory is used to determine the bandstructure, which is then fit to a tight-binding model to allow for the subsequent Green’s function description. Electron–electron interactions, electron–phonon coupling, and disorder scattering are all taken into account (perturbatively) with a theory that involves a cluster extension of the coherent potential approximation. We show how increasing the cluster size produces more accurate results and how the final calculations converge as a function of the cluster size. We examine the spin-polarized electrical current on the nanotube generated by the magnetic impurities adsorbed onto the nanotube surface. The spin polarization increases with both increasing concentration of chromium impurities and with increasing magnetic field. Its origin arises from the strong electron correlations generated by the Cr impurities.
Degradation of Mitochondria and Oxidative Stress as the Main Mechanism of Toxicity of Pristine Graphene on U87 Glioblastoma Cells and Tumors and HS-5 Cells

Due to the development of nanotechnologies, graphene and graphene-based nanomaterials have attracted immense scientific interest owing to their extraordinary properties. Graphene can be used in many fields, including biomedicine. To date, little is known about the impact graphene may have on human health in the case of intentional exposure. The present study was carried out on U87 glioma cells and non-cancer HS-5 cell lines as in vitro model and U87 tumors cultured on chicken embryo chorioallantoic membrane as in vivo model, on which the effects of pristine graphene platelets (GPs) were evaluated. The investigation consisted of structural analysis of GPs using transmission electron microscopy, Fourier transmission infrared measurements, zeta potential measurements, evaluation of cell morphology, assessment of cell viability, investigation of reactive oxygen species production, and investigation of mitochondrial membrane potential. The toxicity of U87 glioma tumors was evaluated by calculating the weight and volume of tumors and performing analyses of the ultrastructure, histology, and protein expression. The in vitro results indicate that GPs have dose-dependent cytotoxicity via ROS overproduction and depletion of the mitochondrial membrane potential. The mass and volume of tumors were reduced in vivo after injection of GPs. Additionally, the level of apoptotic and necrotic markers increased in GPs-treated tumors.
Realization of Artificial Neuron Using MXene Bi-Directional Threshold Switching Memristors

Artificial neurons and synapses are critical units for processing intricate information in brain-inspired neuromorphic systems. Memristors are frequently engineered as artificial synapses due to their simple structures, nonlinear dynamics, and high-density integration. However, the development of artificial neurons on memristors has less progress. In this letter, we propose a rich dynamics-driven artificial neuron based on two-dimensional materials MXene. Partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, were successfully emulated in a unified manner. The space-charge-limited current (SCLC) model accompanied by electrochemical metallization effect was used to explain electrical characteristics. This work will provide a useful guideline for designing and manipulating memristor as artificial neurons for brain-inspired systems.