Miracle-dz/UniFAR-FTUs huggingface dataset:
Lane Detection based on Object Detection and Image-to-image Translation

In this paper, we propose a method to detect various types of lane markings and road boundaries simultaneously from a monocular camera image. This method detects lane markings and road boundaries in IPM images obtained by the Inverse Perspective Mapping of input images. First, bounding boxes surrounding a lane marking or the road boundary are extracted by the object detection network. At the same time, these areas are labelled with a solid line, a dashed line, a zebra line, a curb, grass, a sidewall and so on. Next, in each bounding box, lane marking boundaries or road boundaries are drawn by the image-to-image translation network. We use YOLOv3 for the object detection and pix2pix for the image translation. We create our own datasets including various types of lane markings and road boundaries and evaluate our approach using these datasets qualitatively and quantitatively.
VSB2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing

Zero-shot hashing aims at learning hashing model from seen classes and the obtained model is capable of generalizing to unseen classes for image retrieval. Inspired by zero-shot learning, existing zero-shot hashing methods usually transfer the supervised knowledge from seen to unseen classes, by embedding the hamming space to a shared semantic space. However, this makes instances difficult to distinguish due to limited hashing bit numbers, especially for semantically similar unseen classes. We propose a novel inductive zero-shot hashing framework, i.e., VSB2-Net, where both semantic space and visual feature space are embedded to the same hamming space instead. The reconstructive semantic relationships are established in the hamming space, preserving local similarity relationships and explicitly enlarging the discrepancy between semantic hamming vectors. A two-task architecture, comprising of classification module and visual feature reconstruction module, is employed to enhance the generalization and transfer abilities. Extensive evaluation results on several benchmark datasets demonstrate the superiority of our proposed method compared to several state-of-the-art baselines.
Towards life-long mapping of dynamic environments using temporal persistence modeling

The contemporary SLAM mapping systems assume a static environment and build a map that is then used for mobile robot navigation disregarding the dynamic changes in this environment. The paper at hand presents a novel solution for the problem of life-long mapping that continually updates a metric map represented as a 2D occupancy grid in large scale indoor environments with movable objects such as people, robots, objects etc. suitable for industrial applications. We formalize each cell's occupancy as a failure analysis problem and contribute temporal persistence modeling (TPM), an algorithm for probabilistic prediction of the time that a cell in an observed location is expected to be “occupied” or “empty” given sparse prior observations from a task specific mobile robot. Our work is evaluated in Gazebo simulation environment against the nominal occupancy of cells and the estimated obstacles persistence. We also show that robot navigation with life-long mapping demands less replans and leads to more efficient navigation in highly dynamic environments.
PrivAttNet: Predicting Privacy Risks in Images Using Visual Attention

Visual privacy concerns associated with image sharing is a critical issue that need to be addressed to enable safe and lawful use of online social platforms. Users of social media platforms often suffer from no guidance in sharing sensitive images in public, and often face with social and legal consequences. Given the recent success of visual attention based deep learning methods in measuring abstract phenomena like image memorability, we are motivated to investigate whether visual attention based methods could be useful in measuring psychophysical phenomena like “privacy sensitivity”. In this paper we propose PrivAttNet - a visual attention based approach, that can be trained end-to-end to estimate the privacy sensitivity of images without explicitly detecting sensitive objects and attributes present in the image. We show that our PrivAttNet model outperforms various SOTA and baseline strategies – a 1.6 fold reduction in $L1$ – error over SOTA and 7%–10% improvement in Spearman-rank correlation between the predicted and ground truth sensitivity scores. Additionally, the attention maps from PrivAttNet are found to be useful in directing the users to the regions that are responsible for generating the privacy risk score.
Low-Rank Joint Embedding and Its Application for Robust Process Monitoring

Industrial data are in general corrupted by noises and outliers. In this context, robustness to the contaminated data is a challenging issue in process monitoring. In this article, a novel method named low-rank joint embedding is proposed for robust process monitoring. By learning a low-rank coefficient matrix, low-rank joint embedding can capture the global structure of the original data and alleviate the negative effect of outliers, making the monitoring results more reliable. Moreover, a manifold regularization is introduced to preserve the local geometric structure of data, which enables the extracted low-dimensional representation of data to be more faithful and informative to enhance the monitoring capability. Based on projection learning, the low-rank joint embedding can learn an explicit projection that transforms the data not involved in the training data into the low-dimensional space, avoiding the out-of-sample problem. Furthermore, a reconstruction-based contribution plots based on the low-rank joint embedding is developed to identify the potential faulty variables. Case studies on the Tennessee Eastman process and a real industrial application demonstrate the effectiveness of the proposed monitoring approach.
Virtualization Overhead of Multithreading in X86 State-of-the-Art & Remaining Challenges

Despite great advancements in hardware-assisted virtualization of the x86 architecture, certain workloads still suffer significant overhead. This article dissects said overhead in the context of multi-threading. We describe the state-of-the-art, pinpoint challenges, and suggest improvements, aiming to provide a valuable reference to developers and users of virtualization systems alike. We study the virtualization overhead of the PARSEC and SPLASH2X multithreaded benchmarks in a variety of scenarios using a state-of-the-art system. Through controlled experiments, source code analysis and literature review, we quantify the virtualization overhead multithreading still induces and link it to its root causes, after which we suggest possible mitigation strategies. Multithreading still induces high virtualization overhead, mainly caused by synchronization, spinning at user level and NUMA management. The overhead is diverse in nature and embodiment as it is a function of many system and workload properties. System-level solutions are feasible, but often imply difficult trade-offs. Systematic workload optimization is a promising alternative.
RISEdb: a Novel Indoor Localization Dataset

In this paper we introduce a novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. Our acquisition pipeline is based on a portable LiDAR SLAM backpack to map the buildings and to accurately track the pose of the user as it moves freely inside them. We introduce the calibration procedures that enable us to acquire and geo-reference live data coming from different independent sensors rigidly attached to the backpack. This has allowed us to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.
SoftmaxOut Transformation-Permutation Network for Facial Template Protection

In this paper, we propose a data-driven cancellable biometrics scheme, referred to as SoftmaxOut Transformation-Permutation Network (SOTPN). The SOTPN is a neural version of Random Permutation Maxout (RPM) transform, which was introduced for facial template protection. We present a specialized SoftmaxOut layer integrated with the permutable MaxOut units and the parameterized softmax function to approximate the nondifferentiable permutation and the winner-takes-all operations in the RPM transform. On top of that, a novel pairwise ArcFace loss and a code balancing loss are also formulated to ensure that the SOTPN-transformed facial template is cancellable, discriminative, high entropy and free from quantization errors when coupled with the SoftmaxOut layer. The proposed SOTPN is evaluated on three face datasets, namely LFW, YouTube Face and Facescrub, and our experimental results disclosed that the SOTPN outperforms the RPM transform significantly.
Arrhythmia Classification Using Biased Dropout and Morphology-Rhythm Feature With Incremental Broad Learning

In recent times, people have become increasingly health-conscious. To obtain timely and accurate information on the status of the heart, one of the most important organs of the human body, there is a growing demand among individuals and doctors for accurate and real-time automatic classification of arrhythmias. Consequently, this paper proposes a fast and accurate classification method for arrhythmias. In the proposed method, we build an incremental broad learning (IBL) classification model based on the biased dropout technique for arrhythmia-type recognition. In particular, we extract the morphological-rhythm features of the denoised signal as the input data of the IBL model in the electrocardiogram signal preprocessing. The IBL model enhances the classification effect of the node optimization model by using improved features. To the best of our knowledge, this study is the first application of the IBL model to the study of arrhythmia classification. The results of experiments conducted on the MIT-BIH database indicate that the proposed method is effective and achieves superior classification results. The average classification accuracy for six types of arrhythmias was 99%, and the training time required was only 2.7 s. In addition, based on the evaluation index recommended by the ANSI/AAMI EC57:2012 standard, our method is superior to existing methods on all indexes, except for the positive predictive rate of ventricular ectopic beats. Therefore, the proposed classification method outperforms state-of-the-art methods in terms of real-time performance and accuracy and provides a new approach for further improvements in arrhythmia classification.
A D-Band High-Gain and Low-Power LNA in 65-nm CMOS by Adopting Simultaneous Noise- and Input-Matched Gmax-Core

This article proposes a high-gain and low-power low-noise amplifier (LNA) by adopting a simultaneous noise- and input-matched (SNIM) maximum achievable gain (<inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>) core. The <inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>-core is implemented by adjusting the infinite combinations of embedding networks with three passive elements. Based on the proposed two-port noise analysis for implementing the <inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>-core, the input stage <inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>-core can achieve a simultaneous power gain and noise matching. The adoption of the <inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>-core in the input stage can maximize the amount of gain per stage, leading to higher total power gain and lower noise figure (NF). The two-stage 150-GHz LNA adopting the SNIM <inline-formula> <tex-math notation="LaTeX">$G_{max}$ </tex-math></inline-formula>-core is implemented in a 65-nm CMOS process. The measurement results show a peak gain of 17.9 dB at 152.2 GHz, 3-dB bandwidth of 11 GHz, NF of 4.7 and 6.2 dB at 148 and 150 GHz, respectively, and a peak power added efficiency (PAE) of 7.7% while dissipating only 13.73 mW. This work shows the highest gain per stage and the lowest NF with the lowest dc power consumption among other reported CMOS <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>-band amplifiers.
TextureVAE: Learning Interpretable Representations of Material Microstructures Using Variational Autoencoders

We propose a variational autoencoder model based on style loss for learning representations of material microstructure images. We show using latent space traversals that the model captures important attributes of microstructures that are responsible for mechanical properties of materials and is capable of generating microstructures with particular attributes. We discuss how the latent vectors can be used to establish a linkage between structure and properties and enable inverse inference which is crucial for designing materials and products with target properties.
Fine-tuning Convolutional Neural Networks: a comprehensive guide and benchmark analysis for Glaucoma Screening

This work aimed at giving a comprehensive, in-detailed and benchmark guide on the route to fine-tuning Convolutional Neural Networks (CNNs) for glaucoma screening. Transfer learning consists in a promising alternative to train CNNs from scratch, to avoid the huge data and resources requirements. After a thorough study of five state-of-the-art CNNs architectures, a complete and well-explained strategy for fine-tuning these networks is proposed, using hyperparameter grid-searching and two-phase training approach. Excellent performance is reached on model evaluation, with a 0.9772 AUROC validation rate, giving arise to reliable glaucoma diagnosis-help systems. Also, a baseline benchmark analysis is conducted, studying the models according to performance indices such as model complexity and size, AUROC density and inference time. This in-depth analysis allows a rigorous comparison between model characteristics, and is useful for giving practioners important trademarks for prospective applications and deployments.
An Ultralow-Power Current-Reused Direct-Conversion Bluetooth-Low-Energy Receiver Front-End in 40-nm CMOS

An ultralow-power direct-conversion Bluetooth-low-energy (BLE) receiver (RX) front-end that employs current-reuse and subthreshold techniques is presented. A stacked and self-biased low-noise amplifier (LNA) with active balun consumes <inline-formula> <tex-math notation="LaTeX">$400~\mu \text{W}$ </tex-math></inline-formula> and achieves a noise figure (NF) of 3.2-dB and a high gain (programmable from 18 to 31 dB), meeting the system specifications over the process, supply voltage, and temperature (PVT) variations. The RX front-end has a measured integrated NF of 5.2 dB from 50 KHz to 1 MHz that corresponds to a sensitivity of −95.1 dBm. At an RX gain of 47 dB, IIP3 was measured at −19.7 dBm. The LC VCO operates at twice the carrier frequency with a tuning range of 4.55 to 5.15 GHz in an integer-N phase-locked loop (PLL). An ultralow-power CML divider is used to generate the LO <inline-formula> <tex-math notation="LaTeX">$I/Q$ </tex-math></inline-formula>. The integrated phase noise (IPN) of the LO at 2.4 GHz is 0.83° with spot phase noise of −119.9 dBc/Hz at 3 MHz frequency offset. The PLL is entirely placed inside the VCO inductor resulting in an overall die area reduction of 8%. A 6-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> automatic dc offset-calibrator avoids saturation of consecutive baseband blocks. Multiple <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>-level feedback control loops are used in the design to make it robust over PVT variations. The RX front-end prototype is implemented in a 40-nm LP CMOS process and occupies a silicon area of 0.7 mm <sup>2</sup>.
Multi-Scale and Attention based ResNet for Heartbeat Classification

This paper presents a novel deep learning framework for the electrocardiogram (ECG) heartbeat classification. Although there have been some studies with excellent overall accuracy, these studies have not been very accurate in the diagnosis of arrhythmia classes especially such as supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB). In our work, we propose a Multi-Scale and Attention based Res Net for heartbeat classification in intra-patient and inter-patient paradigms respectively. Firstly, we extract shallow features from a convolutional layer. Secondly, the shallow features are sent into three branches with different convolution kernels in order to combine receptive fields of different sizes. Finally, fully connected layers are used to classify the heartbeat. Besides, we design a new attention mechanism based on the characteristics of heartbeat data. At last, extensive experiments on benchmark dataset demonstrate the effectiveness of our proposed model.
AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network

Graph Convolutional Network (GCN) is adopted to tackle the problem of convolution operation in non-Euclidean space. Previous works on GCN have made some progress, however, one of their limitations is that the design of Adjacency Matrix (AM) as GCN input requires domain knowledge and such process is cumbersome, tedious and error-prone. In addition, entries of a fixed Adjacency Matrix are generally designed as binary values (i.e., ones and zeros) which can not reflect the real relationship between nodes. Meanwhile, many applications require a weighted and dynamic Adjacency Matrix instead of an unweighted and fixed AM, and there are few works focusing on designing a more flexible Adjacency Matrix. To that end, we propose an end-to-end algorithm to improve the GCN performance by focusing on the Adjacency Matrix. We first provide a calculation method called node information entropy to update the matrix. Then, we perform the search strategy in a continuous space and introduce the Deep Deterministic Policy Gradient (DDPG) method to overcome the drawback of the discrete space search. Finally, we integrate the GCN and reinforcement learning into an end-to-end framework. Our method can automatically define the Adjacency Matrix without prior knowledge. At the same time, the proposed approach can deal with any size of the matrix and provide a better AM for network. Four popular datasets are selected to evaluate the capability of our algorithm. The method in this paper achieves the state-of-the-art performance on Cora and Pubmed datasets, with the accuracy of 84.6% and 81.6% respectively.
Multi-Scale Deep Pixel Distribution Learning for Concrete Crack Detection

A number of methods including image processing technologies (IPTs) and deep learning methods, have been used to detect defects in civilian infrastructure. These methods have been introduced to extract features representing cracks in concrete surfaces. Inspired by recent advances of a pixel distribution learning method in background subtraction, we propose a novel multi-scale deep learning method (MS-DPDL) for concrete crack detection. The designed CNN network is trained on the dataset CRACK500 [1], [2] and tested on it for concrete segmentation. To show good transferability of our proposed model, it is later tested on the dataset Concrete Crack Images for Classification [3]. Several existing deep learning methods are used to compare the performance of the proposed MS-DPDL method. Results show that our method has good performance and can effectively find concrete cracks in practical situations.
Deep Gait Relative Attribute using a Signed Quadratic Contrastive Loss

This paper presents a deep learning-based method to estimate gait attributes (e.g., stately, cool, relax, etc.). Similarly to the existing studies on relative attribute, human perception-based annotations on the gait attributes are given to pairs of gait videos (i.e., the first one is better, tie, and the second one is better), and the relative annotations are utilized to train a ranking model of the gait attribute. More specifically, we design a Siamese (i.e., two-stream) network which takes a pair of gait inputs and output gait attribute score for each. We then introduce a suitable loss function called a signed contrastive loss to train the network parameters with the relative annotation. Unlike the existing loss functions for learning to rank does not inherit a nice property of a quadratic contrastive loss, the proposed signed quadratic contrastive loss function inherits the nice property. The quantitative evaluation results reveal that the proposed method shows better or comparable accuracies of relative attribute prediction against the baseline methods.
On Identification and Retrieval of Near-Duplicate Biological Images: a New Dataset and Protocol

Manipulation and re-use of images in scientific publications is a growing issue, not only for biomedical publishers, but also for the research community in general. In this work we introduce BINDER - Bio-Image Near-Duplicate Examples Repository, a novel dataset to help researchers develop, train, and test models to detect same-source biomedical images. BINDER contains 7,490 unique image patches for model training, 1,821 same-size patch duplicates for validation and testing, and 868 different-size image/patch pairs for image retrieval validation and testing. Except for the training set, patches already contain manipulations including rotation, translation, scale, perspective transform, contrast adjustment and/or compression artifacts. We further use the dataset to demonstrate how novel adaptations of existing image retrieval and metric learning models can be applied to achieve high-accuracy inference results, creating a baseline for future work. In aggregate, we thus present a supervised protocol for near-duplicate image identification and retrieval without any “real-world” training example. Our dataset and source code are available at hms-idac.github. io/BINDER.
Symmetry, Equilibria, and Robustness in Common-Payoff Games

Although it has been known since the 1970s that a globally optimal strategy profile in a common-payoff game is a Nash equilibrium, global optimality is a strict requirement that limits the result’s applicability. In this work, we show that any locally optimal symmetric strategy profile is also a (global) Nash equilibrium. Applied to machine learning, our result provides a global guarantee for any gradient method that finds a local optimum in symmetric strategy space. Furthermore, we show that this result is robust to perturbations to the common payoff and to the local optimum. While these results indicate stability to unilateral deviation, we neverthe-less identify broad classes of games where mixed local optima are unstable under joint , asymmetric deviations. We analyze the prevalence of instability by running learning algorithms in a suite of symmetric games, and we conclude with results on the complexity of computing game symmetries.