Miracle-dz/UniFAR-FTUs huggingface dataset:
Stop the [Image] Steal

Images are powerful. Visual information can attract attention, improve persuasion, trigger stronger emotions, and is easy to share and spread. We examine the characteristics of the popular images shared on Twitter as part of "Stop the Steal'', the widespread misinformation campaign during the 2020 U.S. election. We analyze the spread of the forty most popular images shared on Twitter as part of this campaign. Using a coding process, we categorize and label the images according to their type, content, origin, and role, and perform a mixed-method analysis of these images' spread on Twitter. Our results show that popular images include both photographs and text rendered as image. Only very few of these popular images included alleged photographic evidence of fraud; and none of the popular photographs had been manipulated. Most images reached a significant portion of their total spread within several hours from their first appearance, and both popular- and less-popular accounts were involved in various stages of their spread.
Exploiting Pre-trained Feature Networks for Generative Adversarial Networks in Audio-domain Loop Generation

While generative adversarial networks (GANs) have been widely used in research on audio generation, the training of a GAN model is known to be unstable, time consuming, and data inefficient. Among the attempts to ameliorate the training process of GANs, the idea of Projected GAN emerges as an effective solution for GAN-based image generation, establishing the state-of-the-art in different image applications. The core idea is to use a pre-trained classifier to constrain the feature space of the discriminator to stabilize and improve GAN training. This paper investigates whether Projected GAN can similarly improve audio generation, by evaluating the performance of a StyleGAN2-based audio-domain loop generation model with and without using a pre-trained feature space in the discriminator. Moreover, we compare the performance of using a general versus domain-specific classifier as the pre-trained audio classifier. With experiments on both drum loop and synth loop generation, we show that a general audio classifier works better, and that with Projected GAN our loop generation models can converge around 5 times faster without performance degradation.
Avgust: automating usage-based test generation from videos of app executions

Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced auto- mated approaches for UI test generation, these approaches typically focus on testing for crashes or maximizing code coverage. By contrast, recent research has shown that developers prefer usage-based tests, which center around specific uses of app features, to help support activities such as regression testing. Very few existing techniques support the generation of such tests, as doing so requires automating the difficult task of understanding the semantics of UI screens and user inputs. In this paper, we introduce Avgust, which automates key steps of generating usage-based tests. Avgust uses neural models for image understanding to process video recordings of app uses to synthesize an app-agnostic state-machine encoding of those uses. Then, Avgust uses this encoding to synthesize test cases for a new target app. We evaluate Avgust on 374 videos of common uses of 18 popular apps and show that 69% of the tests Avgust generates successfully execute the desired usage, and that Avgust’s classifiers outperform the state of the art.
The static energy of a quark-antiquark pair from Laplacian eigenmodes

We test a method for computing the static quark-antiquark potential in lattice QCD, which is not based on Wilson loops, but where the trial states are formed by eigenvector components of the covariant lattice Laplace operator. The runtime of this method is significantly smaller than the standard Wilson loop calculation, when computing the static potential not only for on-axis, but also for many off-axis quark-antiquark separations, i.e., when a fine spatial resolution is required. We further improve the signal by using multiple eigenvector pairs, weighted with Gaussian profile functions of the eigenvalues, providing a basis for a generalized eigenvalue problem (GEVP), as it was recently introduced to improve distillation in meson spectroscopy. We show results with the new method for the static potential with dynamical fermions and demonstrate its efficiency compared to traditional Wilson loop calculations. The method presented here can also be applied to compute hybrid or tetra-quark potentials and to static-light systems.
Extension of a Linear Controller Scheme to Non-Linear Systems and its Application on Inverted Pendulum

This paper presents the control and stabilization of the rotary inverted pendulum based on a general controller scheme. The proposed scheme has its foundation in classical control theory, and the importance of an integrator in disturbance rejection is emphasized. The system’s dynamics are obtained by the Euler Lagrange method and are approximated for small-angle as balancing the pendulum is the objective. Experimental results demonstrate that the proposed control scheme can achieve the stabilization of a non-linear system. Also, the boundedness and convergence of the non-linear system with the controller subjected to the initial condition are validated.
Cross-Camera Deep Colorization

In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efﬁcient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the previous colorization methods, ours can adapt to color and monochrome cameras with distinctive spatial-temporal resolutions, rendering the ﬂexibility and robustness in practical applications. The key ingredient of our method is a cross-camera alignment module that generates multi-scale correspondences for cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we val-idate the ﬂexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, i.e. , around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: github.com/IndigoPurple/CCDC.
"iCub, We Forgive You!" Investigating Trust in a Game Scenario with Kids

This study presents novel strategies to investigate the mutual influence of trust and group dynamics in children-robot interaction. We implemented a game-like experimental activity with the humanoid robot iCub and designed a questionnaire to assess how the children perceived the interaction. We also aim to verify if the sensors, setups, and tasks are suitable for studying such aspects. The questionnaires' results demonstrate that youths perceive iCub as a friend and, typically, in a positive way. Other preliminary results suggest that, generally, children trusted iCub during the activity and, after its mistakes, they tried to reassure it with sentences such as:"Don't worry iCub, we forgive you". Furthermore, trust towards the robot in group cognitive activity appears to change according to gender: after two consecutive mistakes by the robot, girls tended to trust iCub more than boys. Finally, no significant difference has been evidenced between different age groups across points computed from the game and the self-reported scales. The tool we proposed is suitable for studying trust in human-robot interaction (HRI) across different ages and seems appropriate to understand the mechanism of trust in group interactions.
Recent progress with observations and models to characterize the magnetic fields from star-forming cores to protostellar disks

In this review article, we aim at providing a global outlook on the progresses made in the recent years to characterize the role of magnetic fields during the embedded phases of the star formation process. Thanks to the development of observational capabilities and the parallel progress in numerical models, capturing most of the important physics at work during star formation; it has recently become possible to confront detailed predictions of magnetized models to observational properties of the youngest protostars. We provide an overview of the most important consequences when adding magnetic fields to state-of-the-art models of protostellar formation, emphasizing their role to shape the resulting star(s) and their disk(s). We discuss the importance of magnetic field coupling to set the efficiency of magnetic processes and provide a review of observational works putting constraints on the two main agents responsible for the coupling in star-forming cores: dust grains and ionized gas. We recall the physical processes and observational methods, which allow to trace the magnetic field topology and its intensity in embedded protostars and review the main steps, success, and limitations in comparing real observations to synthetic observations from the non-ideal MHD models. Finally, we discuss the main threads of observational evidence that suggest a key role of magnetic fields for star and disk formation, and propose a scenario solving the angular momentum for star formation, also highlighting the remaining tensions that exist between models and observations.
Robust Centerline Prediction for Accurate Vessel Wall Visualization of Intracranial Vessels in Multi-Contrast 3D MRI Data

An accurate planar visualization (curved planar reformation – CPR) of intracranial arteries is mandatory for an improved luminal and mural assessment especially in low resolution Magnetic Resonance Imaging (MRI) data sets acquired in standard clinical settings. CPR visualization methods based on the centerline of the desired structure are fast and easy to implement but the accuracy strongly depends on the spatial resolution of the 3D data set and the size of the desired vessel. In the manuscript, a novel algorithm for fast and robust centerline calculation in multi-contrast 3D MRI data is presented. It considers the extracted surface of the vessel structure for a more accurate centerline prediction resulting in an enhanced CPR visualization of small vessels.
Prototype-Aware Heterogeneous Task for Point Cloud Completion

Point cloud completion, which aims at recovering original shape information from partial point clouds, has attracted attention on 3D vision community. Existing methods usually succeed in completion for standard shape, while failing to generate local details of point clouds for some non-standard shapes. To achieve desirable local details, guidance from global shape information is of critical importance. In this work, we design an effective way to distinguish standard/non-standard shapes with the help of intra-class shape prototypical representation, which can be calculated by the proposed supervised shape clustering pretext task, resulting in a heterogeneous component w.r. t completion network. The representative prototype, defined as feature centroid of shape categories, can provide global shape guidance, which is referred to as soft-perceptual prior, to inject into downstream completion network by the desired selective perceptual feature fusion module in a multi-scale manner. Moreover, for effective training, we consider difficulty-based sampling strategy to encourage the network to pay more attention to some partial point clouds with fewer geometric information. Experimental results show that our method outperforms other state-of-the-art methods and has strong ability on completing complex geometric shapes.
Hexagonalization in $AdS_3 \times S^3 \times T^4$: Mirror Corrections

A big open problem in $AdS_3 \times S^3 \times T^4$ holographic duality is to compute the CFT data of the dual theory. In this direction recently it was introduced the hexagonalization framework in the $AdS_3$ context. It allows the computation of the structure constants of the CFT$_2$ dual in the planar limit non-perturbatively, however in this proposal it was introduced only the asymptotic part of the hexagon valid for correlators with asymptotically large bridge lengths. In this work we complete this picture by computing the so called mirror corrections that allow to describe structure constants for finite bridge lengths and as a byproduct we also prove that the half-BPS operators in the theory do not receive these corrections. We end up by giving the first steps on using hexagonalization to compute $n$-point functions in the $AdS_3 \times S^3 \times T^4$ holographic duality.
Illegal But Not Malware: An Underground Economy App Detection System Based on Usage Scenario

This paper focuses on mobile apps serving the underground economy by providing illegal services in the mobile system (e.g., gambling, porn, scam). These apps are named as underground economy apps, or UEware for short. As most UEware do not have malicious payloads, traditional malware detection approaches are ineffective to perform the detection. To address this problem, we propose a novel approach to effectively and efficiently detect UEware by considering the transition orders of the user interfaces (UIs), which determine the usage scenarios of these apps. Based on the proposed approach, we design a system named DeUEDroid to detect the UEware via scene graph. To evaluate DeUEDroid, we collect 26, 591 apps to evaluate DeUEDroid and build up the first large-scale ground-truth UEware dataset (1, 720 underground economy apps and 831 legitimate apps). The evaluation result shows that DeUEDroid can construct scene graph accurately, and achieve the accuracy scores of 77.70% on the five-classification task (i.e., gambling game, porn, financial scam, miscellaneous, and legitimate apps), reaching obvious improvements over the SOTA approaches. Running further on 24, 017 apps, DeUEDroid performs well in the real-world scenario to mitigate the threat. Specifically, by using DeUEDroid, we found that UEware are prevalent, i.e., 61% apps in the wild and 21% apps in the app stores are UEware (with over 72% accuracy after the manual investigation). We will release our dataset and system to engage the community after been accepted.
Investigating magnetically induced distortions of neutron stars through gamma-ray burst X-ray plateaus

Magnetic field may distort neutron stars (NSs), but its effect has not been robustly tested through gravitational-wave observation yet due to the absence of a fast rotating Galactic magnetar. Part of gamma-ray bursts (GRBs) are potential to investigate the magnetically-induced distortion since their central objects may be millisecond magnetars. In this paper, we propose a method to estimate the distortions of these possible magnetars under GRB magnetar scenario. According to the case study of GRB 070521, we find a relation between the effective magnetically-induced ellipticity, $\epsilon_{\rm B,eff}$, and the effective dipole magnetic field strength on NS surfaces, $B_{\rm eff}$, namely $\epsilon_{\rm B,eff}\sim 10^{-3}(B_{\rm eff}/10^{15}\rm G)^{2}$. Furthermore, we constrain the internal magnetic-field structure of the magnetar to be $B_{\rm eff}\sim 0.02$ and $B_{\rm eff}\sim 0. 1B_{\rm t}$, where $$ is the volume-averaged internal toroidal field. The constraint may be used as the initial condition in modeling the structure of NS magnetospheres. At last, the possibility of testing the method shown in this paper through gravitational-wave observations is discussed.
Cooperative Predictive Cruise Control: A Bargaining Game Approach

This paper considers a cooperative cruise control problem from a predictive control perspective. Online decision-making is used to be executed during the driving process based on the information obtained from the network. We formalize a synchronization problem approach from a predictive control theory using bargaining games to ﬁnd an operating agreement between the vehicles. Finally, we test these results in an emulation environment in a hardware-in-the-loop system.
GRB 080928 afterglow imaging and spectro-polarimetry

Among the large variety of astrophysical sources that we can observe, gamma-ray bursts (GRBs) are the most energetic of the whole Universe. The definition of a general picture describing the physics behind GRBs has always been a compelling task, but the results obtained so far from observations have revealed a puzzling landscape. The lack of a clear, unique paradigm calls for further observations and additional, independent techniques for this purpose. Polarimetry constitutes a very useful example as it allows us to investigate some features of the source such as the geometry of the emitting region and the magnetic field configuration. To date, only a handful of bursts detected by space telescopes have been accompanied by ground-based spectro-polarimetric follow-up, and therefore such an analysis of more GRBs is of crucial importance in order to increase the sample of bursts with multi-epoch polarisation analysis. In this work, we present the analysis of the GRB 080928 optical afterglow, with observations performed with the ESO-VLT FORS1 instrument. We find that the GRB optical afterglow was not significantly polarised on the first observing night. The polarisation degree ($P$) grew on the following night to a level of $P \sim$ 4.5%, giving evidence of polarised radiation at a 4 $\sigma$ confidence level. The GRB 080928 light curve is not fully consistent with standard afterglow models, making any comparison with polarimetric models partly inconclusive. The most conservative interpretation is that the GRB emission was characterised by a homogeneous jet and was observed at an angle of 0.6 $<\theta_{obs}/\theta_{jet}<$ 0.8. Moreover, the non-zero polarisation degree on the second night suggests the presence of a dominant locally ordered magnetic field in the emitting region.
Single-Source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification

Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention. It is necessary to train a model only on source domain (SD) and directly transferring the model to target domain (TD), when TD needs to be processed in real time and cannot be reused for training. Based on the idea of domain generalization, a Single-source Domain Expansion Network (SDEnet) is developed to ensure the reliability and effectiveness of domain extension. The method uses generative adversarial learning to train in SD and test in TD. A generator including semantic encoder and morph encoder is designed to generate the extended domain (ED) based on encoder-randomization-decoder architecture, where spatial randomization and spectral randomization are specifically used to generate variable spatial and spectral information, and the morphological knowledge is implicitly applied as domain invariant information during domain expansion. Furthermore, the supervised contrastive learning is employed in the discriminator to learn class-wise domain invariant representation, which drives intra-class samples of SD and ED. Meanwhile, adversarial training is designed to optimize the generator to drive intra-class samples of SD and ED to be separated. Extensive experiments on two public HSI datasets and one additional multispectral image (MSI) dataset demonstrate the superiority of the proposed method when compared with state-of-the-art techniques. The codes will be available from the website:https://github.com/YuxiangZhang-BIT/IEEE_TIP_SDEnet.
Detecting unanticipated mutual recursion using Elegant Objects representation of object-oriented programs

Elegant Objects (EO) is a variation of the object-oriented programming paradigm that favors pure objects and decoration. EO programming language is based on these ideas and has been suggested by Bugayenko as an intermediate representation for object-oriented programs. This paper provides plausible representations in EO of some class-based constructions from Java, C++, and Python. We then reformulate the classical fragile base class problem in the context of these representations. Finally, we discuss an algorithm for detecting a subset of fragile base class patterns in EO programs. We show that using EO as an intermediate language is plausible and discuss possible improvements to the language to assist in richer static analysis.
Unifying Generative Models with GFlowNets

There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. Here, we demonstrate the connections between existing deep generative models and the recently introduced GFlowNet framework, a probabilistic inference machine which treats sampling as a decision-making process. This analysis sheds light on their overlapping traits and provides a unifying viewpoint through the lens of learning with Markovian trajectories. Our framework provides a means for unifying training and inference algorithms, and provides a route to shine a unifying light over many generative models. Beyond this, we provide a practical and experimentally verified recipe for improving generative modeling with insights from the GFlowNet perspective.
Compressing integer lists with Contextual Arithmetic Trits

Inverted indexes allow to query large databases without needing to search in the database at each query. An important line of research is to construct the most efficient inverted indexes, both in terms of compression ratio and time efficiency. In this article, we show how to use trit encoding, combined with contextual methods for computing inverted indexes. We perform an extensive study of different variants of these methods and show that our method consistently outperforms the Binary Interpolative Method -- which is one of the golden standards in this topic -- with respect to compression size. We apply our methods to a variety of datasets and make available the source code that produced the results, together with all our datasets.
Conjecture C Still Stands

More than ten years ago the author described a parameter $K(\rho )$ for the complexity of $n$-qubit quantum state $\rho$ and raised the conjecture (referred to as"Conjecture C") that when this parameter is superpolynomial in $n$, the state $\rho$ is not experimentally feasible (and will not be experimentally achieved without quantum fault-tolerance). Shortly afterward [6] (arXiv:1204.3404), Steve Flammia and Aram Harrow claimed that the simple easy-to-construct $W$ states are counterexamples to"Conjecture C. "We point out that Flammia and Harrow's argument regarding $W$-states is incomplete. Moreover, the emergent picture from experimental progress of the past decade on noisy intermediate scale quantum (NISQ) computers suggests that $W$-states, as simple as they appear, cannot be achieved experimentally by NISQ computers, and can not be constructed without quantum fault-tolerance.
Big Bang Nucleosynthesis Constraints on f(T,TG) Gravity

We confront f(T,TG) gravity, with big bang nucleosynthesis (BBN) requirements. The former is obtained using both the torsion scalar, as well as the teleparallel equivalent of the Gauss–Bonnet term, in the Lagrangian, resulting to modified Friedmann equations in which the extra torsional terms constitute an effective dark energy sector. We calculate the deviations of the freeze-out temperature Tf, caused by the extra torsion terms in comparison to ΛCDM paradigm. Then, we impose five specific f(T,TG) models and extract the constraints on the model parameters in order for the ratio |ΔTf/Tf| to satisfy the observational BBN bound. As we find, in most of the models the involved parameters are bounded in a narrow window around their general relativity values as expected, asin the power-law model, where the exponent n needs to be n≲0.5. Nevertheless, the logarithmic model can easily satisfy the BBN constraints for large regions of the model parameters. This feature should be taken into account in future model building.