Miracle-dz/UniFAR-FTUs huggingface dataset:
One-sample Guided Object Representation Disassembling

The ability to disassemble the features of objects and background is crucial for many machine learning tasks, including image classiﬁcation, image editing, visual concepts learning, and so on. However, existing (semi-)supervised methods all need a large amount of annotated samples, while unsupervised methods can’t handle real-world images with complicated backgrounds. In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images. For the annotated one-sample, we ﬁrst adopt some data augmentation strategies to generate some synthetic samples, which can guide the disassembling of the object features and background features. For the unannotated images, two self-supervised mechanisms: dual-swapping and fuzzy classiﬁcation are introduced to disassemble object features from the background with the guidance of annotated one-sample. What’s more, we devise two metrics to evaluate the disassembling performance from the perspective of representation and image, respectively. Experiments demonstrate that the One-GORD achieves competitive dissembling performance and can handle natural scenes with complicated backgrounds.
Risk of serious spinal adverse events associated with epidural corticosteroid injections in the Medicare population

Background Epidural corticosteroid injections (ESIs) are widely performed and have an unquantified risk of serious spinal adverse events (SSAEs). We sought to determine the rate of SSAEs following ESI and to compare the rates by spinal level, injection approach and corticosteroid formulation. Methods We included patients enrolled in Medicare parts A and B who had an ESI between 1 January 2009 and 30 September 2015. We identified potential cases as patients with spine-related diagnoses within 3 days after the first eligible ESI. Event categorization as probable, possible or non-case was based on review of medical records. The rates of probable and possible cases were expressed per 1 000 000 patients overall, and by spinal level, injection approach and corticosteroid formulation. A score test was used to compare these rates. Results We identified 1 355 957 eligible ESIs during the study period. Of the 110 potential cases, 43 were selected for medical record review and 11 were categorized as probable, yielding a rate of 8.1 per 1 000 000 patients (95% CI 4.5 to 14.5). Risk of SSAEs was statistically higher with cervical/thoracic injections (29.4, 95% CI 12.5 to 68.8) compared with lumbar/sacral injections (5.1, 95% CI 2.3 to 11.0) (p value 0.001). Event rates for lumbar/sacral non-transforaminal injections was 8.8 (95% CI 4.0 to 19.1). Event rates for particulate (7.5, 95% CI 3.9 to 14.2) and non-particulate formulations (13.1, 95% CI 3.6 to 47.9) appeared similar (p value 0.47). Conclusion Between 2009 and 2015, rates of SSAEs following ESI in the Medicare population were low. Patients receiving cervical/thoracic ESIs were at higher risk of SSAE than those receiving lumbar/sacral ESIs. Event rates were similar for each corticosteroid formulation.
Numerical Simulation for the Starting Characteristics of a Wind Turbine

There are many types of wind turbine. Large propeller-type wind turbines are used mainly for large wind farms and offshore wind power generation. Small vertical-axis wind turbines (VAWTs) are often used in distributed energy systems. In previous studies on wind turbines, the basic characteristics such as torque coefficient have often been obtained during rotation, with the turbine rotating at a constant speed. Such studies are necessary for the proper design of wind turbines. However, it is also necessary to conduct research under conditions in which the wind direction and wind speed change over time. Numerical simulation of the starting characteristics is carried out in this study. Based on the flow field around the wind turbine, the force required to rotate the turbine is calculated. The force used to stop the turbine is modeled based on friction in relation to the bearing. Equations for the motion of the turbine are solved by their use as external force. Wind turbine operation from the stationary state to the start of rotation is simulated. Five parameters, namely, blade length, wind turbine radius, overlap, gap, and blade thickness, are changed and the optimum shape is obtained. The simulation results tend to qualitatively agree with the experimental results for steadily rotating wind turbines in terms of two aspects: (1) the optimal shape has an 20% overlap of the turbine radius, and (2) the larger the gap, the lower the efficiency.
Novel Electrical Signaling: First Fast Voltage-Gated Sodium Channel Identified Outside of the Animal Kingdom

Electrical signals or action potentials are present in animals, plants, and other organisms such as single-celled diatoms ([Moran et al., 2015][1]; [Hedrich et al., 2016][2]; [Helliwell et al., 2019][3]). Electrical signals are short-lived, locally restricted changes in membrane potential (measured
How Do Students and Researchers Behave and Feel While Playing the "Online Attention Game"?: An Exploratory Study on Digital Identity Education

Ill-considered postings by young social media users hungry for attention have recently garnered headlines. Imprudent online postings may distort posters' digital identity, which is someone's online image as perceived by others. Online attention-seeking behaviour may result in digital identities that are divorced from a person's true nature, which can lead to social and mental harm. To mitigate these harms, effective educational material is needed to help non-technical young users understand the risks and consequences of thoughtless, attention-seeking online behaviour. Because such materials do not exist, we took the first step towards fulfilling this educational need by developing the Online Attention Game (OAG). The OAG is an online game played in a laboratory or classroom setting that replicates the online competition involved in seeking other social media users' attention. Through four OAG games (two with students and two with researchers), we confirmed the differences in posting and evaluation behaviour between young university students and adult researchers.
The Construction and Validation of the Dimensions and Scale of Service Quality of The Police Service

There is a scarcity of literature on the service quality of the public sector. This is particularly true for the police agencies. A rating scale has been introduced by some texts on other professions, but hardly for the sector of public service. This results in the lack of an appropriate rating scale for the police agencies. This paper is an attempt to explore the dimensions and rating scale of the SERVQUAL service quality model in combination with the quality service rating scales of other professions and the sector of public service. The findings from this research are expected to serve as a reference for the police agencies for upgrading the service quality of the police. involved in seeking other social media users' attention. Through four OAG games (two with students and two with researchers), we confirmed the differences in posting and evaluation behaviour between young university students and adult researchers.
Research on Mobile Tongue Diagnosis in Traditional Chinese Medicine

With the development of the Internet and the popularity of the intelligent devices, the research on mobile tongue diagnosis in traditional Chinese medicine is deepening. This paper mainly introduces four relative critical technologies, namely acquisition, color correction, segmentation and feature processing of tongue images. At the same time, the research progress of relevant technologies in recent years is summarized to provide reference for further study on mobile tongue diagnosis platform of TCM(Traditional Chinese Medicine).
The Evolution and Emerging Trends of Cloud Computing Adoption Research: Visual Analysis of CiteSpace Based on WOS Papers

With the continuous promotion and application of cloud computing, the issue of its adoption has increasingly attracted widespread attention from the theoretical and practical circles at home and abroad. Therefore, it is of great significance to summarize and analyze the research status and hotspots of cloud computing adoption and promote the subsequent research on cloud computing adoption. This paper uses CiteSpace, a visualized literature analysis software, to draw core authors, core journals, key words cluster and other knowledge maps in the field of cloud computing adoption based on 668 documents related to cloud computing adoption from 2011 to 2019 included in the Web of Science database. The research status and trend of cloud computing adoption are visualized and analyzed. The results of the study indicate that the attention paid to cloud computing adoption is gradually increasing. It shows that the research on the security, risk and other technical characteristics of cloud computing, the research on the influencing factors of cloud computing adoption and user behavior, and the research on the cost and performance of cloud computing are the three main research contents in the field of cloud computing adoption. The research hotspots in the field of cloud computing adoption have experienced the evolution process from specific research issues such as cloud computing business and technology, to the application and implementation of cloud computing in enterprises, to the integration of cloud computing and other emerging technologies.
Transport Layer Scanning for Attack Surface Detection in Vehicular Networks

In the beginning of every security analysis or penetration test of a system, information about the target has to be gathered. On IT-Systems a port scan is usually performed as a first step of an investigation. Since the communication protocols differ in automotive systems, generic port scanning tools can’t be used for a security analysis of CANs. More complex protocols have a higher likelihood of implementation errors and bugs. On CAN networks, such payloads are transferred through International Standard Transport Protocol (ISO-TP) communication. We designed a new methodology to identify ISO-TP endpoints in automotive networks. Every of these endpoints can provide exploitable application layer protocols and therefor has to be considered during penetration testing and security analysis. We contribute a new scan approach for the automated evaluation of possible attack surfaces in automotive CAN networks which has a higher coverage and multiple advantages than state of the art approaches.
Adversarial Attacks on Deep Graph Matching

Despite achieving remarkable performance, deep graph learning models, such as node classiﬁcation and network embedding, suffer from harassment caused by small adversarial perturbations. However, the vulnerability analysis of graph matching under adversarial attacks has not been fully investigated yet. This paper proposes an adversarial attack model with two novel attack techniques to perturb the graph structure and degrade the quality of deep graph matching: (1) a kernel density estimation approach is utilized to estimate and maximize node densities to derive imperceptible perturbations, by pushing attacked nodes to dense regions in two graphs, such that they are indistinguishable from many neighbors; and (2) a meta learning-based projected gradient descent method is developed to well choose attack starting points and to improve the search performance for producing effective perturbations. We evaluate the effectiveness of the attack model on real datasets and validate that the attacks can be transferable to other graph learning models.
Dark/Light Mode Adaptation for Graphical User Interfaces on Near-Eye Displays

In the ﬁelds of augmented reality (AR) and virtual reality (VR), many applications involve user interfaces (UIs) to display various types of information to users. Such UIs are an important component that inﬂuences user experience and human factors in AR/VR because the users are directly facing and interacting with them to absorb the visualized information and manipulate the content. While consumer’s interests in different forms of near-eye displays, such as AR/VR head-mounted displays (HMDs), are increasing, research on the design standard for AR/VR UIs and human factors becomes more and more interesting and timely important. Although UI conﬁgurations, such as dark mode and light mode, have increased in popularity on other display types over the last several years, they have yet to make their way into AR/VR devices as built in features. This demo showcases several use cases of dark mode and light mode UIs on AR/VR HMDs, and provides general guidelines for when they should be used to provide perceptual beneﬁts to the user.
Bayesian Multi-type Mean Field Multi-agent Imitation Learning

Multi-agent Imitation learning (MAIL) refers to the problem that agents learn to perform a task interactively in a multi-agent system through observing and mimicking expert demonstrations, without any knowledge of a reward function from the environment. MAIL has received a lot of attention due to promising results achieved on synthesized tasks, with the potential to be applied to complex real-world multi-agent tasks. Key challenges for MAIL include sample efﬁciency and scalability. In this paper, we proposed Bayesian multi-type mean ﬁeld multi-agent imitation learning (BM3IL). Our method improves sample efﬁciency through establishing a Bayesian formulation for MAIL, and enhances scalability through introducing a new multi-type mean ﬁeld approximation. We demonstrate the performance of our algorithm through benchmarking with three state-of-the-art multi-agent imitation learning algorithms on several tasks, including solving a multi-agent trafﬁc optimization problem in a real-world transportation network. Experimental results indicate that our algorithm signiﬁcantly outperforms all other algorithms in all scenarios.
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration

This work proposes a novel smoothing method, called Bend, Mix and Release (BMR) , that extends two well-known smooth approximations of the convex optimization literature: randomized smoothing and the Moreau envelope. The BMR smoothing method allows to trade-off between the computational simplicity of randomized smoothing (RS) and the approximation efﬁciency of the Moreau envelope (ME). More speciﬁcally, we show that BMR achieves up to a √ d multiplicative improvement compared to the approximation error of RS, where d is the dimension of the search space, while being less computation intensive than the ME. For non-convex objectives, BMR also has the desirable property to widen local minima, allowing optimization methods to reach small cracks and crevices of extremely irregular and non-convex functions, while being well-suited to a distributed setting. This novel smoothing method is then used to improve ﬁrst-order non-smooth optimization (both convex and non-convex) by allowing for a local exploration of the search space. More speciﬁcally, our analysis sheds light on the similarities be-tween evolution strategies and BMR, creating a link between exploration strategies of zeroth-order methods and the regularity of ﬁrst-order optimization problems. Finally, we evidence the impact of BMR through synthetic experiments.
Influence of Methods Approximating Fractional-Order Differentiation on the Output Signal Illustrated by Three Variants of Oustaloup Filter

Fractional-order (FO) differential equations are more and more frequently applied to describe real-world applications or models of phenomena. Despite such models exhibiting high flexibility and good fits to experimental data, they introduce their inherent inaccuracy related to the order of approximation. This article shows that the chosen model influences the dynamic properties of signals. First, we calculated symbolically the steady-state values of an FO inertia using three variants of the Oustaloup filter approximation. Then, we showed how the models influence the Nyquist plots in the frequency domain. The unit step responses calculated using different models also have different plots. An example of FO control system evidenced different trajectories dependent on applied models. We concluded that publicized parameters of FO models should also consist of the name of the model used in calculations in order to correctly reproduce described phenomena. For this reason, the inappropriate use of FO models may lead to drawing incorrect conclusions about the described system.
Semi-automated Annotation of Repetitive Ornaments on 3D Painted Pottery Surfaces

The creation of drawings from the surface of painted pottery artifacts is an important practice in archaeological research and documentation. Traditional approaches include manual drawings using pen and paper, either directly on the physical surface, or from photographs, while more recent approaches are supported by photography or ﬂattening of 3D digitized objects. Elaborate vase paintings, mostly showing ﬁgural scenes, often comprise ornamental decorations in secondary position or in the background, exhibiting repetitive patterns. We propose a tool supporting the creation of archaeological drawings with a semi-automatic extraction of ornamental surface sections, based on a combination of user-deﬁned queries and self-similarity detection. Appropriate heuristics allow to detect the presence and positions of ornamental bands, a frequently occurring scheme, where ornamental primitives are evenly spaced along the tangential direction of a vessel’s solid of revolution. Our interactive tool allows domain experts to efﬁciently select ornamental queries, and assess the quality of resulting similarity detections. First experiments with real world artifacts from ancient Greek and Peruvian cultures conﬁrm the feasibility of the approach.
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains

We propose to jointly analyze experts’ eye movements and verbal narrations to discover important and interpretable knowledge patterns to better understand their decision-making processes. The discovered patterns can further enhance data-driven statistical models by fusing experts’ domain knowledge to support complex human-machine collaborative decision-making. Our key contribution is a novel dynamic Bayesian nonparametric model that assigns latent knowledge patterns into key phases involved in complex decision-making. Each phase is characterized by a unique distribution of word topics discovered from verbal narrations and their dynamic interactions with eye movement patterns, indicating experts’ special perceptual behavior within a given decision-making stage. A new split-merge-switch sampler is developed to efﬁciently explore the posterior state space with an improved mixing rate. Case studies on diagnostic error prediction and disease morphology categorization help demonstrate the effectiveness of the proposed model and discovered knowledge patterns.
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective

Recently signiﬁcant progress has been made in pedestrian detection, but it remains challenging to achieve high performance in occluded and crowded scenes. It could be attributed mostly to the widely used representation of pedestrians, i.e . , 2D axis-aligned bounding box, which just describes the approximate location and size of the object. Bounding box models the object as a uniform distribution within the boundary, making pedestrians indistinguishable in occluded and crowded scenes due to much noise. To eliminate the problem, we propose a novel representation based on 2D beta distribution, named Beta Representation. It pictures a pedestrian by explicitly constructing the relationship between full-body and visible boxes, and emphasizes the center of visual mass by assigning different probability values to pixels. As a result, Beta Representation is much better for distinguishing highly-overlapped instances in crowded scenes with a new NMS strategy named BetaNMS. What’s more, to fully exploit Beta Representation, a novel pipeline Beta R-CNN equipped with BetaHead and BetaMask is proposed, leading to high detection performance in occluded and crowded scenes.
Efficient Parasitic-aware gm/ID-based Hybrid Sizing Methodology for Analog and RF Integrated Circuits

As the primary second-order effect, parasitic issues have to be seriously addressed when synthesizing high-performance analog and RF integrated circuits (ICs). In this article, a two-phase hybrid sizing methodology for analog and RF ICs is proposed to take into account parasitic effect in the early design stage. It involves symbolic modeling and mixed-integer nonlinear programming (MINLP) in the first phase, and a many-objective evolutionary algorithm (many-OEA)-based sizing refiner in the second phase. With the aid of our proposed current density factor and piecewise curve fitting technique, the gm/ID concept, which is typically utilized to solve the analog circuit design problem, can provide theoretical support to our accurate symbolic modeling. Thus, the intrinsic and interconnect parasitics can be accurately considered in our work with moderate modeling effort. A variety of electrical, geometric, and parasitic (including parasitic mismatch) constraints can be conveniently integrated into our MINLP problem formulation. Moreover, numerical simulations are embedded into the many-OEA-based sizing phase, which is able to tackle floorplan co-optimization. With such dynamic floorplan variation, the parasitics accuracy can be sustained along the evolution. The experimental results demonstrate high efficacy of our proposed parasitic-aware hybrid sizing methodology.
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut

Spectral clustering and k -means, both as two major traditional clustering methods, are still attracting a lot of attention, although a variety of novel clustering algorithms have been proposed in recent years. Firstly, a uniﬁed framework of k -means and ratio-cut is revisited, and a novel and efﬁcient clustering algorithm is then proposed based on this framework. The time and space complexity of our method are both linear with respect to the number of samples, and are independent of the number of clusters to construct, more importantly. These properties mean that it is easily scalable and applicable to large practical problems. Extensive experiments on 12 real-world benchmark and 8 facial datasets validate the advantages of the proposed algorithm compared to the state-of-the-art clustering algorithms. In particular, over 15x and 7x speed-up can be obtained with respect to k -means on the synthetic dataset of 1 million samples and the benchmark dataset (CelebA) of 200k samples, respectively [GitHub].