Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Unsupervised Anomaly Detection with Score-Guided Network

Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems. Owing to the limited number of anomaly labels in these complex systems, unsupervised anomaly detection methods have attracted great attention in recent years. Two major challenges faced by the existing unsupervised methods are as follows: 1) distinguishing between normal and abnormal data when they are highly mixed together and 2) defining an effective metric to maximize the gap between normal and abnormal data in a hypothesis space, which is built by a representation learner. To that end, this work proposes a novel scoring network with a score-guided regularization to learn and enlarge the anomaly score disparities between normal and abnormal data, enhancing the capability of anomaly detection. With such score-guided strategy, the representation learner can gradually learn more informative representation during the model training stage, especially for the samples in the transition field. Moreover, the scoring network can be incorporated into most of the deep unsupervised representation learning (URL)-based anomaly detection models and enhances them as a plug-in component. We next integrate the scoring network into an autoencoder (AE) and four state-of-the-art models to demonstrate the effectiveness and transferability of the design. These score-guided models are collectively called SG-Models. Extensive experiments on both synthetic and real-world datasets confirm the state-of-the-art performance of SG-Models.
Multistability of Carbon Nanotube Packings on Flat Substrate

It is shown by the method of molecular dynamics using a chain model that a multilayer packaging of identical single‐walled carbon nanotubes with a diameter of D > 2.5 nm located on a flat substrate is a multistable system. The system has many stationary states, which are characterized by the portion of collapsed nanotubes. The thickness of the package monotonically decreases with an increase in the portion of such nanotubes. For nanotubes with a chirality index (60,0), depending on the portion of collapsed nanotubes, the thickness of the 11‐layer package can vary from 12 to 36 nm. All stationary states of the package are stable to thermal fluctuations at T = 300 K. The transverse compression of the package is not elastic; due to the collapse of a part of the nanotubes, it only transfers the package from one stationary state to another with a smaller thickness.
An Execution Fingerprint Dictionary for HPC Application Recognition

Applications running on HPC systems waste time and energy if they: (a) use resources inefficiently, (b) deviate from allocation purpose (e.g. cryptocurrency mining), or (c) encounter errors and failures. It is important to know which applications are running on the system, how they use the system, and whether they have been executed before. To recognize known applications during execution on a noisy system, we draw inspiration from the way Shazam recognizes known songs playing in a crowded bar. Our contribution is an Execution Fingerprint Dictionary (EFD) that stores execution fingerprints of system metrics (keys) linked to application and input size information (values) as key-value pairs for application recognition. Related work often relies on extensive system monitoring (many system metrics collected over large time windows) and employs machine learning methods to identify applications. Our solution only uses the first 2 minutes and a single system metric to achieve F-scores above 95 percent, providing comparable results to related work but with a fraction of the necessary data and a straightforward mechanism of recognition.
Where Should I Look? Optimised Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments

As robots operate in increasingly complex and dynamic environments, fast motion re-planning has become a widely explored area of research. In a real-world deployment, we often lack the ability to fully observe the environment at all times, giving rise to the challenge of determining how to best perceive the environment given a continuously updated motion plan. We provide the first investigation into a ‘smart’ controller for gaze control with the objective of providing effective perception of the environment for obstacle avoidance and motion planning in dynamic and unknown environments. We detail the novel problem of determining the best head camera behaviour for mobile robots when constrained by a trajectory. Furthermore, we propose a greedy optimization-based solution that uses a combination of voxelised rewards and motion primitives. We demonstrate that our method outperforms the benchmark methods in 2D and 3D environments, in respect of both the ability to explore the local surroundings, as well as in a superior success rate of finding collision-free trajectories – our method is shown to provide 7.4x better map exploration while consistently achieving a higher success rate for generating collision-free trajectories. We verify our findings on a physical Toyota Human Support Robot (HSR) using a GPU-accelerated perception framework.
FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features

In recent years, with the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of any control and verification mechanism has led to the spread of fake news, as one of the most important threats to democracy, economy, journalism and freedom of expression. Designing and using automatic methods to detect fake news on social media has become a significant challenge. In this paper, we examine the publishers' role in detecting fake news on social media. We also suggest a high accurate multi-modal framework, namely FR-Detect, using user-related and content-related features with early detection capability. For this purpose, two new user-related features, namely Activity Credibility and Influence, have been introduced for publishers. Furthermore, a sentence-level convolutional neural network is provided to combine these features with latent textual content features properly. Experimental results have shown that the publishers' features can improve the performance of content-based models by up to 13% and 29% in accuracy and F1-score, respectively.
A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy

The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data. It can be computationally expensive in the worst case due to the conditional independence tests are performed in an exhaustive-searching manner. This makes the algorithm computationally intractable when the task contains several hundred or thousand nodes, particularly when the true underlying causal graph is dense. We propose a critical observation that the conditional set rendering two nodes independent is non-unique, and including certain redundant nodes do not sacrifice result accuracy. Based on this finding, the innovations of our work are two-folds. First, we innovate on a reserve order linkage pruning PC algorithm which significantly increases the algorithm's efficiency. Second, we propose a parallel computing strategy for statistical independence tests by leveraging tensor computation, which brings further speedup. We also prove the proposed algorithm does not induce statistical power loss under mild graph and data dimensionality assumptions. Experimental results show that the single-threaded version of the proposed algorithm can achieve a 6-fold speedup compared to the PC algorithm on a dense 95-node graph, and the parallel version can make a 825-fold speed-up. We also provide proof that the proposed algorithm is consistent under the same set of conditions with conventional PC algorithm.
HELIX: A data-driven characterization of Brazilian land snails

Decision-support systems benefit from hidden patterns extracted from digital information. In the specific domain of gastropod characterization, morphometrical measurements support biologists in the identification of land snail specimens. Although snails can be easily identified by their excretory and reproductive systems, the after-death mollusk body is commonly inaccessible because of either soft material deterioration or fossilization. This study aims at characterizing Brazilian land snails by morphometrical data features manually taken from the shells. In particular, we examined a dataset of shells by using different learning models that labeled snail specimens with a precision up to 97.5% (F1-Score = .975, CKC = .967 and ROC Area = .998). The extracted patterns describe similarities and trends among land snail species and indicates possible outliers physiologies due to climate traits and breeding. Finally, we show some morphometrical characteristics dominate others according to different feature selection biases. Those data-based patterns can be applied to fast land snail identification whenever their bodies are unavailable, as in the recurrent cases of lost shells in nature or private and museum collections.
On Inverse Inertia Matrix and Contact-Force Model for Robotic Manipulators at Normal Impacts

State-of-the-art impact dynamics models either apply for free-flying objects or do not account that a robotic manipulator is commonly high-stiffness controlled. Thus, we lack tailor-made models for manipulators mounted on a fixed base. Focusing on orthogonal point-to-surface impacts (no tangential velocities), we revisit two essential elements of an impact dynamics model: the contact-force model and the inverse inertia matrix. We collect contact-force measurements by impacting a 7 DOF Panda robot against a sensorized rigid environment with various joint configurations and velocities. Evaluating the measurements from 150 trials, we suggest a viscoelastic contact-force model and computing the inverse inertia matrix assuming the robot is a composite-rigid body.
View Blind-spot as Inpainting: Self-Supervised Denoising with Mask Guided Residual Convolution

In recent years, self-supervised denoising methods have shown impressive performance, which circumvent painstaking collection procedure of noisy-clean image pairs in supervised denoising methods and boost denoising applicability in real world. One of well-known self-supervised denoising strategies is the blind-spot training scheme. However, a few works attempt to improve blind-spot based self-denoiser in the aspect of network architecture. In this paper, we take an intuitive view of blind-spot strategy and consider its process of using neighbor pixels to predict manipulated pixels as an inpainting process. Therefore, we propose a novel Mask Guided Residual Convolution (MGRConv) into common convolutional neural networks, e.g. U-Net, to promote blind-spot based denoising. Our MGRConv can be regarded as soft partial convolution and find a trade-off among partial convolution, learnable attention maps, and gated convolution. It enables dynamic mask learning with appropriate mask constrain. Different from partial convolution and gated convolution, it provides moderate freedom for network learning. It also avoids leveraging external learnable parameters for mask activation, unlike learnable attention maps. The experiments show that our proposed plug-and-play MGRConv can assist blind-spot based denoising network to reach promising results on both existing single-image based and dataset-based methods.
A Holistic Approach to Reactive Mobile Manipulation

We present the design and implementation of a taskable reactive mobile manipulation system. In contrary to related work, we treat the arm and base degrees of freedom as a holistic structure which greatly improves the speed and fluidity of the resulting motion. At the core of this approach is a robust and reactive motion controller which can achieve a desired end-effector pose, while avoiding joint position and velocity limits, and ensuring the mobile manipulator is manoeuvrable throughout the trajectory. This can support sensor-based behaviours such as closed-loop visual grasping. As no planning is involved in our approach, the robot is never stationary thinking about what to do next. We show the versatility of our holistic motion controller by implementing a pick and place system using behaviour trees and demonstrate this task on a 9-degree-of-freedom mobile manipulator. Additionally, we provide an open-source implementation of our motion controller for both non-holonomic and omnidirectional mobile manipulators available at jhavl.github. io/holistic.
Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.
An exponential bound on the number of non-isotopic commutative semifields

We show that the number of non-isotopic commutative semiﬁelds of odd order p n is exponential in n when n = 4 t and t is not a power of 2. We introduce a new family of commutative semiﬁelds and a method for proving isotopy results on commutative semiﬁelds that we use to deduce the aforementioned bound. The previous best bound on the number of non-isotopic commutative semiﬁelds of odd order was quadratic in n and given by Zhou and Pott [Adv. Math. 234 (2013)]. Similar bounds in the case of even order were given in Kantor [J. Algebra 270 (2003)] and Kantor and Williams [Trans. Amer. Math. Soc. 356 (2004)].
Exploring the 3D Ising gauge-Higgs model in exact Coulomb gauge and with a gauge-invariant substitute for Landau gauge

The Z2 gauge-Higgs model in three dimensions has two different types of phase transition, confinement-deconfinement and Higgs magnetization. Here they are explored through two order parameters, the Coulomb magnetization which is a local order parameter for confinement, and a replica-based gauge-invariant order parameter which tracks the Higgs transition in a way similar to Landau-gauge magnetization. Minimal Coulomb gauge is set exactly, using the polynomial-time minimum-weight matching algorithm of Edmonds. This is a tremendous speed improvement over relaxation/annealing methods and completely eliminates the systematic error. The replicaoverlap is also an improvement over relaxation methods for setting Landau gauge, in that it has an easily controllable and measurable systematic error. These simulations show the phase transitions not ending at the first-order endpoint but bifurcating near there and continuing even through the expected analyticity region. The specific heat critical exponents, α, are highly negative, explaining the lack of strong signals in the energy. Nevertheless, energy moments are seen to follow curves consistent with the predictions from the order parameters, showing these to be true thermal transitions, albeit weak ones. These results cast further doubt on Fradkin-Shenker analyticity. They also suggest one or more new yet to be explored phases in gauge-Higgs models in the bifurcated region. PACS:11.15. Ha, 64.60. De. keywords: lattice gauge theory, gauge-Higgs theory, spontaneous symmetry breaking, Coulomb gauge, Landau gauge, phase transitions ar X iv :2 10 9. 04 56 0v 1 [ he pla t] 9 S ep 2 02 1
Knowledge-Aware Meta-learning for Low-Resource Text Classification

Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.
Emerging AI Security Threats for Autonomous Cars - Case Studies

Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning. However, AI models require a large amount of sensitive training data and are usually computationally intensive to build. The commercial value of such models motivates attackers to mount various attacks. Adversaries can launch model extraction attacks for monetization purposes or step-ping-stone towards other attacks like model evasion. In specific cases, it even results in destroying brand reputation, differentiation, and value proposition. In addition, IP laws and AI-related legalities are still evolving and are not uniform across countries. We discuss model extraction attacks in detail with two use-cases and a generic kill-chain that can compromise autonomous cars. It is essential to investigate strategies to manage and mitigate the risk of model theft.
Closing the Sim2Real Gap in Dynamic Cloth Manipulation

—Cloth manipulation is a challenging task due to the many degrees of freedom and properties of the material affecting the dynamics of the cloth. The nonlinear dynamics of the cloth have particularly strong signiﬁcance in dynamic cloth manipulation, where some parts of the cloth are not directly controllable. In this paper, we present a novel approach for solving dynamic cloth manipulation by training policies using reinforcement learning (RL) in simulation and transferring the learned policies to the real world in a zero-shot manner. The proposed method uses visual feedback and material property randomization in a physics simulator to achieve generalization in the real world. Experimental results show that using only visual feedback is enough for the policies to learn the dynamic manipulation task in a way that transfers from simulation to the real world. In addition, the randomization of the dynamics in simulation enables capturing the behavior of a variety of cloths in the real world.
Fault-Tolerant Multi-Qubit Geometric Entangling Gates Using Photonic Cat Qubits

Ye-Hong Chen, Roberto Stassi, 2 Wei Qin, Adam Miranowicz, 3 and Franco Nori 4, 5 Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Saitama 351-0198, Japan Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università di Messina, 98166, Messina, Italy Institute of Spintronics and Quantum Information, Faculty of Physics, Adam Mickiewicz University, 61-614 Poznań, Poland Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama 351-0198, Japan (Dated: September 13, 2021)
Discretizing Dynamics for Maximum Likelihood Constraint Inference

Maximum likelihood constraint inference is a powerful technique for identifying unmodeled constraints that affect the behavior of a demonstrator acting under a known objective function. However, it was originally formulated only for discrete state-action spaces. Continuous dynamics are more useful for modeling many real-world systems of interest, including the movements of humans and robots. We present a method to generate a tabular state-action space that approximates continuous dynamics and can be used for constraint inference on demonstrations that obey the true system dynamics. We then demonstrate accurate constraint inference on nonlinear pendulum systems with 2- and 4-dimensional state spaces, and show that performance is robust to a range of hyperparameters. The demonstrations are not required to be fully optimal with respect to the objective, and the most likely constraints can be identified even when demonstrations cover only a small portion of the state space. For these reasons, the proposed approach may be especially useful for inferring constraints on human demonstrators, which has important applications in human-robot interaction and biomechanical medicine.
C-MinHash: Practically Reducing Two Permutations to Just One

Traditional minwise hashing (MinHash) requires applying $K$ independent permutations to estimate the Jaccard similarity in massive binary (0/1) data, where $K$ can be (e.g.,) 1024 or even larger, depending on applications. The recent work on C-MinHash (Li and Li, 2021) has shown, with rigorous proofs, that only two permutations are needed. An initial permutation is applied to break whatever structures which might exist in the data, and a second permutation is re-used $K$ times to produce $K$ hashes, via a circulant shifting fashion. (Li and Li, 2021) has proved that, perhaps surprisingly, even though the $K$ hashes are correlated, the estimation variance is strictly smaller than the variance of the traditional MinHash. It has been demonstrated in (Li and Li, 2021) that the initial permutation in C-MinHash is indeed necessary. For the ease of theoretical analysis, they have used two independent permutations. In this paper, we show that one can actually simply use one permutation. That is, one single permutation is used for both the initial pre-processing step to break the structures in the data and the circulant hashing step to generate $K$ hashes. Although the theoretical analysis becomes very complicated, we are able to explicitly write down the expression for the expectation of the estimator. The new estimator is no longer unbiased but the bias is extremely small and has essentially no impact on the estimation accuracy (mean square errors). An extensive set of experiments are provided to verify our claim for using just one permutation.