Miracle-dz/UniFAR-FTUs huggingface dataset:
AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction

In this paper, we consider the Click-Through-Rate (CTR) prediction problem. Factorization Machines and their variants consider pair-wise feature interactions, but normally we won't do high-order feature interactions using FM due to high time complexity. Given the success of deep neural networks (DNNs) in many fields, researchers have proposed several DNN-based models to learn high-order feature interactions. Multi-layer perceptrons (MLP) have been widely employed to learn reliable mappings from feature embeddings to final logits. In this paper, we aim to explore more about these high-order features interactions. However, high-order feature interaction deserves more attention and further development. Inspired by the great achievements of Densely Connected Convolutional Networks (DenseNet) in computer vision, we propose a novel model called Attentive DenseNet based Factorization Machines (AdnFM). AdnFM can extract more comprehensive deep features by using all the hidden layers from a feed-forward neural network as implicit high-order features, then selects dominant features via an attention mechanism. Also, high-order interactions in the implicit way using DNNs are more cost-efficient than in the explicit way, for example in FM. Extensive experiments on two real-world datasets show that the proposed model can effectively improve the performance of CTR prediction.
Dynamics of a degenerate Cs-Yb mixture with attractive interspecies interactions

We probe the collective dynamics of a quantum degenerate Bose-Bose mixture of Cs and $^{174}$Yb with attractive interspecies interactions. Specifically, we excite vertical center of mass oscillations of the Cs condensate, and observe significant damping for the Cs dipole mode, due to the rapid transfer of energy to the larger Yb component, and the ensuing acoustic dissipation. Numerical simulations based on coupled Gross-Pitaevskii equations provide excellent agreement, and additionally reveal the possibility of late-time revivals (beating) which are found to be highly sensitive to the Cs and Yb atom number combinations. By further tuning the interaction strength of Cs using a broad Feshbach resonance, we explore the stability of the degenerate mixture, and observe collapse of the Cs condensate mediated by the attractive Cs-Yb interaction when $a_{\mathrm{Cs}}<50 \, a_0$, well above the single-species collapse threshold, in good agreement with simulations.
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks

Convolutional neural networks (CNN) have been more and more applied in mobile robotics such as intelligent vehicles. Security of CNNs in robotics applications is an important issue, for which potential adversarial attacks on CNNs are worth research. Pooling is a typical step of dimension reduction and information discarding in CNNs. Such information discarding may result in mis-deletion and mis-preservation of data features which largely influence the output of the network. This may aggravate the vulnerability of CNNs to adversarial attacks. In this paper, we conduct adversarial attacks on CNNs from the perspective of network structure by investigating and exploiting the vulnerability of pooling. First, a novel adversarial attack methodology named Strict Layer-Output Manipulation (SLOM) is proposed. Then an attack method based on Strict Pooling Manipulation (SPM) which is an instantiation of the SLOM spirit is designed to effectively realize both type I and type II adversarial attacks on a target CNN. Performances of attacks based on SPM at different depths are also investigated and compared. Moreover, performances of attack methods designed by instantiating the SLOM spirit with different operation layers of CNNs are compared. Experiment results reflect that pooling tends to be more vulnerable to adversarial attacks than other operations in CNNs.
Communication and Localization With Extremely Large Lens Antenna Array

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increases the computational complexity of the system. Motivated by the inherent advantages of lens antenna arrays in reducing system complexity, we consider communication and localization problems with an extremely large lens antenna array, which we call “ExLens”. Since radiative near-field property emerges in the setting, we derive the closed-form array response of the lens antenna array with spherical wave, which includes the array response obtained on the basis of uniform plane wave as a special case. Our derivation result reveals a window effect for energy focusing property of ExLens, which indicates that ExLens has great potential in position sensing and multi-user communication. We also propose an effective method for location and channel parameters estimation, which is able to achieve the localization performance close to the Cramér-Rao lower bound. Finally, we examine the multi-user communication performance of ExLens that serves coexisting near-field and far-field users. Numerical results demonstrate the effectiveness of the proposed channel estimation method and show that ExLens with a minimum mean square error receiver achieves significant spectral efficiency gains and complexity-and-cost reductions compared with a uniform linear array.
Towards Complex and Continuous Manipulation: A Gesture Based Anthropomorphic Robotic Hand Design

Most current anthropomorphicrobotic hands can realize part of the human hand functions, particularly for object grasping. However, due to the complexity of the human hand, few current designs target at daily object manipulations, even for simple actions like rotating a pen. To tackle this problem, we introduce a gesture based framework, which adopts the widely-used 33 grasping gestures of Feix as the bases for hand design and implementation of manipulation. In the proposed framework, we first measure the motion ranges of human fingers for each gesture, and based on the results, we propose a simple yet dexterous robotic hand design with 13 degrees of actuation. Furthermore, we adopt a frame interpolation based method, in which we consider the base gestures as the key frames to represent a manipulation task, and use the simple linear interpolation strategy to accomplish the manipulation. To demonstrate the effectiveness of our framework, we define a three-level benchmark, which includes not only 62 test gestures from previous research, but also multiple complex and continuous actions. Experimental results on this benchmark validate the dexterity of the proposed design and our video is available in https://drive.google. com/file/d/1wPtkd2P0zolYSBW7_3tVMUHrZEeXLXgD/view? usp=sharing.
Camera-aware Proxies for Unsupervised Person Re-Identification

This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re-ID models progressively. These methods are relatively simple but effective. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the large intra-ID variance caused mainly by the change of camera views. To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning. Based on the camera-aware proxies, we design both intra and inter-camera contrastive learning components for our Re-ID model to effectively learn the ID discrimination ability within and across cameras. Meanwhile, a proxy-balanced sampling strategy is also designed, which facilitates our learning further. Extensive experiments on three large-scale Re-ID datasets show that our proposed approach outperforms most unsupervised methods by a significant margin. Especially, on the challenging MSMT17 dataset, we gain 14.3 percent Rank-1 and 10.2 percent mAP improvements when compared to the second place. Code is available at: https://github.com/Terminator8758/CAP-master.
Blurring Fools the Network - Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring

Existing pixel-level adversarial attacks on neural networks may be deficient in real scenarios, since pixel-level changes on the data cannot be fully delivered to the neural network after camera capture and multiple image preprocessing steps. In contrast, in this paper, we argue from another perspective that gaussian blurring, a common technique of image preprocessing, can be aggressive itself in specific occasions, thus exposing the network to real-world adversarial attacks. We first propose an adversarial attack demo named peak suppression (PS) by suppressing the values of peak elements in the features of the data. Based on the blurring spirit of PS, we further apply gaussian blurring to the data, to investigate the potential influence and threats of gaussian blurring to performance of the network. Experiment results show that PS and well-designed gaussian blurring can form adversarial attacks that completely change classification results of a well-trained target network. With the strong physical significance and wide applications of gaussian blurring, the proposed approach will also be capable of conducting real world attacks.
Detecting Group Anomalies in Tera-Scale Multi-Aspect Data via Dense-Subtensor Mining

How can we detect fraudulent lockstep behavior in large-scale multi-aspect data (i.e., tensors)? Can we detect it when data are too large to fit in memory or even on a disk? Past studies have shown that dense subtensors in real-world tensors (e.g., social media, Wikipedia, TCP dumps, etc.) signal anomalous or fraudulent behavior such as retweet boosting, bot activities, and network attacks. Thus, various approaches, including tensor decomposition and search, have been proposed for detecting dense subtensors rapidly and accurately. However, existing methods suffer from low accuracy, or they assume that tensors are small enough to fit in main memory, which is unrealistic in many real-world applications such as social media and web. To overcome these limitations, we propose D-Cube, a disk-based dense-subtensor detection method, which also can run in a distributed manner across multiple machines. Compared to state-of-the-art methods, D-Cube is (1) Memory Efficient: requires up to 1,561× less memory and handles 1,000× larger data (2.6TB), (2) Fast: up to 7× faster due to its near-linear scalability, (3) Provably Accurate: gives a guarantee on the densities of the detected subtensors, and (4) Effective: spotted network attacks from TCP dumps and synchronized behavior in rating data most accurately.
Esophageal Tumor Segmentation in CT Images Using a Dilated Dense Attention Unet (DDAUnet)

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with air pockets, feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79 ± 0.20, a mean surface distance of 5.4 ± 20.2mm and 95% Hausdorff distance of 14.7 ± 25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via https://github.com/yousefis/DenseUnet_Esophagus_Segmentation.
Taking Control of Intra-class Variation in Conditional GANs Under Weak Supervision

Generative Adversarial Networks (GANs) are able to learn mappings between simple, relatively low-dimensional, random distributions and points on the manifold of realistic images in image-space. The semantics of this mapping, however, are typically entangled such that meaningful image properties cannot be controlled independently of one another. Conditional GANs (cGANs) provide a potential solution to this problem, allowing specific semantics to be enforced during training. This solution, however, depends on the availability of precise labels, which are sometimes difficult or near impossible to obtain, e.g. labels representing lighting conditions or describing the background. In this paper we introduce a new formulation of the cGAN that is able to learn disentangled, multivariate models of semantically meaningful variation and which has the advantage of requiring only the weak supervision of binary attribute labels. For example, given only labels of ambient / non-ambient lighting, our method is able to learn multivariate lighting models disentangled from other factors such as the identity and pose. We coin the method intra-class variation isolation (IVI) and the resulting network the IVI-GAN. We evaluate IVI-GAN on the CelebA dataset and on synthetic 3D morphable model data, learning to disentangle attributes such as lighting, pose, expression, and even the background.
Siamese Anchor Proposal Network for High-Speed Aerial Tracking

In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency. Therefore, their real-world deployment on mobile platforms like the unmanned aerial vehicle (UAV) is impeded. In this work, a novel two-stage Siamese network-based method is proposed for aerial tracking, i.e., stage-1 for high-quality anchor proposal generation, stage-2 for refining the anchor proposal. Different from anchor-based methods with numerous pre-defined fixed-sized anchors, our no-prior method can 1) increase the robustness and generalization to different objects with various sizes, especially to small, occluded, and fast-moving objects, under complex scenarios in light of the adaptive anchor generation, 2) make calculation feasible due to the substantial decrease of anchor numbers. In addition, compared to anchor-free methods, our framework has better performance owing to refinement at stage-2. Comprehensive experiments on three benchmarks have proven the superior performance of our approach, with a speed of ∼200 frames/s.
Hedge Connectivity without Hedge Overlaps

Connectivity is a central notion of graph theory and plays an important role in graph algorithm design and applications. With emerging new applications in networks, a new type of graph connectivity problem has been getting more attention--hedge connectivity. In this paper, we consider the model of hedge graphs without hedge overlaps, where edges are partitioned into subsets called hedges that fail together. The hedge connectivity of a graph is the minimum number of hedges whose removal disconnects the graph. This model is more general than the hypergraph, which brings new computational challenges. It has been a long open problem whether this problem is solvable in polynomial time. In this paper, we study the combinatorial properties of hedge graph connectivity without hedge overlaps, based on its extremal conditions as well as hedge contraction operations, which provide new insights into its algorithmic progress.
Finding Sparse Structure for Domain Specific Neural Machine Translation

Fine-tuning is a major approach for domain adaptation in Neural Machine Translation (NMT). However, unconstrained fine-tuning requires very careful hyper-parameter tuning otherwise it is easy to fall into over-fitting on the target domain and degradation on the general domain. To mitigate it, we propose PRUNE-TUNE, a novel domain adaptation method via gradual pruning. It learns tiny domain-specific subnetworks for tuning. During adaptation to a new domain, we only tune its corresponding subnetwork. PRUNE-TUNE alleviates the over-fitting and the degradation problem without model modification. Additionally, with no overlapping between domain-specific subnetworks, PRUNE-TUNE is also capable of sequential multi-domain learning. Empirical experiment results show that PRUNE-TUNE outperforms several strong competitors in the target domain test set without the quality degradation of the general domain in both single and multiple domain settings.
Quantum K-medians Algorithm Using Parallel Euclidean Distance Estimator

Quantum machine learning, though in its initial stage, has demonstrated its potential to speed up some of the costly machine learning calculations when compared to the existing classical approaches. Among the challenging subroutines, computing distance between with the large and high-dimensional data sets by the classical k-medians clustering algorithm is one of them. To tackle this challenge, this paper proposes an efficient quantum k-medians clustering algorithm using the powerful quantum Euclidean estimator algorithm. The proposed quantum k-medians algorithm has provided an exponential speed up as compared to the classical version of it. If and only if we allow the input and the output vectors to be quantum states. The proposed algorithm implementation handled in python with the help of third-party module known as QISKit. The implemented quantum algorithm was executed on the IBM Quantum simulators through cloud. The results from the experiment and simulation suggest that quantum distance estimator algorithms could give benefits for other distance-based machine learning algorithms like k-nearest neighbor classification, support vector machine, hierarchical clustering and k-means clustering. This work sheds light on the bright future of the age of big data making use of exponential speed up provided by quantum theory.
Predicting the Objective and Priority of Issue Reports in a Cross project Context

Proper documentation plays an important role in successful software management and maintenance. Software repositories such as GitHub host a large number of software entities. Developers collaboratively discuss, implement, use, and share these entities. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and ﬁnally, to document the highlight of their team’s effort. An issue report can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage them manually. GitHub provides labels for tagging issues, as a means of issue management. However, about half of the issues in GitHub’s top 1000 repositories do not have any labels. In this work, we aim at automating the process of managing issue reports for software teams. We propose a two-stage approach to predict both the objective behind opening an issue and its priority level using feature engineering methods and state-of-the-art text classiﬁers. We train and evaluate our models in both project-based and cross-project settings. The latter approach provides a generic prediction model applicable for any unseen software project or projects with little historical data. Our proposed approach can successfully predict the objective and priority level of issue reports with 83% and 75% accuracy, respectively.
Kinetic study of the hydrogel + hydrogen reaction from 800 to 1550 K

Direct measurements of the rate constant for the bimolecular reaction OH + H{sub 2} {r_arrow} H + H{sub 2}O have been completed in the temperature range 800-1550K. In these experiments, small amounts of water vapor were photolyzed with an excimer laser to {open_quotes}instantaneously{close_quotes} create OH radicals and H atoms. The subsequent time history of the OH radicals was monitored by using laser-induced fluorescence. Analysis of the OH removal rate as a function of added hydrogen yielded the rate constant for the above reaction. The temperature range of these measurements bridges the gap between the recent shock tube data (1246-2581 K) of Michael and Sutherland; Frank and Just; Davidson, Chang, and Hanson and the quartz cell flash photolysis data (250-1050 K) of Tully, Ravishankara, and co-workers. These data are in very good agreement with previous data sets in the overlap region. By combining the author`s data with these four data sets, the authors derive a new rate constant expression. 1 = (3.56 x 10{sup {minus}16})T{sup 1.52} exp[-1736/T] cm{sup 3} molecule{sup {minus}1} s{sup {minus}1}, that is applicable in the temperature range 250-2581 K. 18 refs., 3 figs., 1 tab.
Sequential Detection of Replay Attacks

One of the most studied forms of attacks on the cyber-physical systems is the replay attack. The statistical similarities of the replayed signal and the true observations make the replay attack difficult to detect. In this article, we address the problem of replay attack detection by adding watermarking to the control inputs and then perform resilient detection using cumulative sum (CUSUM) test on the joint statistics of the innovation signal and the watermarking signal, whereas existing work considers only the marginal distribution of the innovation signal. We derive the expression of the Kullback–Liebler divergence (KLD) between the two joint distributions before and after the replay attack, which is, asymptotically, inversely proportional to the detection delay. We perform a structural analysis of the derived KLD expression and suggest a technique to improve the KLD for the systems with relative degree greater than one. A scheme to find the optimal watermarking signal variance for a fixed increase in the control cost to maximize the KLD under the CUSUM test is presented. We provide various numerical simulation results to support our theory. The proposed method is also compared with a state-of-the-art method based on the Neyman–Pearson detector, illustrating the smaller detection delay of the proposed sequential detector.