Miracle-dz/UniFAR-FTUs huggingface dataset:
A Convex Optimal Control Framework for Autonomous Vehicle Intersection Crossing

Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency. This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. The problem is approached by a hierarchical centralized coordination scheme that successively optimizes the crossing order and velocity trajectories of a group of vehicles so as to minimize their total energy consumption and travel time required to pass the intersection. For an accurate estimate of the energy consumption of each CAV, the vehicle modeling framework in this paper captures 1) friction losses that affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line with a battery-electric architecture. It is shown that the underlying optimization problem subject to safety constraints for powertrain operation, cornering and collision avoidance, after convexification and relaxation in some aspects can be formulated as two second-order cone programs, which ensures a rapid solution search and a unique global optimum. Simulation case studies are provided showing the tightness of the convex relaxation bounds, the overall effectiveness of the proposed approach, and its advantages over a benchmark solution invoking the widely used first-in-first-out policy. The investigation of Pareto optimal solutions for the two objectives (travel time and energy consumption) highlights the importance of optimizing their trade-off, as small compromises in travel time could produce significant energy savings.
Analyzing Wrap-Up Effects through an Information-Theoretic Lens

Numerous analyses of reading time (RT) data have been undertaken in the effort to learn more about the internal processes that occur during reading comprehension. However, data measured on words at the end of a sentence–or even clause–is often omitted due to the confounding factors introduced by so-called “wrap-up effects,” which manifests as a skewed distribution of RTs for these words. Consequently, the understanding of the cognitive processes that might be involved in these effects is limited. In this work, we attempt to learn more about these processes by looking for the existence–or absence–of a link between wrap-up effects and information theoretic quantities, such as word and context information content. We find that the information distribution of prior context is often predictive of sentence- and clause-final RTs (while not of sentence-medial RTs), which lends support to several prior hypotheses about the processes involved in wrap-up effects.
Supplementary File: Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination

We study the effect of input resolution and compare our method with the previous best AxialGAN [2]. Input resolution is set to 8 × 8, 16 × 16, 32 × 32 and 64 × 64. For simplicity, we report Deg. which reflects identity preserving ability and is highly correlated to the verification accuracy. Results are shown in Figure 1. There is no surprise that the performance of both methods is improved as resolution increases. Our approach significantly outperforms AxialGAN at all resolutions, validating the benefits of the VPGAN.
Optical Characterization of Two Cataclysmic Variables: RBS 0490 and SDSS J075939.79+191417.3

We present optical photometric and spectroscopic observations of two cataclysmic variables (CVs), namely RBS 0490 and SDSS J075939.79+191417.3. The optical variations of RBS 0490 have been found to occur with a period of 1.689 ± 0.001 hr, which appears to be the probable orbital period of the system. Present photometric observations of SDSS J075939.79+191417.3 confirm and refine the previously determined orbital period as 3.14240928 ± 0.00000096 hr. The presence of long-duration eclipse features in the light curves of SDSS J075939.79+191417.3 indicates that eclipses might be due to an accretion disk and bright spot. The orbital inclination of SDSS J075939.79+191417.3 is estimated to be ∼78° using the eclipse morphology. The phased light-curve variations during the orbital cycle of RBS 0490 provide evidence of emission from an independent second accretion region or a second fainter pole. Optical spectra of RBS 0490 and SDSS J075939.79+191417.3 show the presence of strong Balmer and weak He ii (λ4686) emission lines, along with the detection of strong Hβ emission lines with a large equivalent width. The characteristic features of RBS 0490 seem to favor low-field polars, while SDSS J075939.79+191417.3 appears to be similar to nonmagnetic systems.
CatIss: An Intelligent Tool for Categorizing Issues Reports using Transformers

Users use Issue Tracking Systems to keep track and manage issue reports in their repositories. An issue is a rich source of software information that contains different reports including a 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. Thus, automatic approaches are proposed to help facilitate the management of issue reports. This paper describes CatIss, an automatic Categorizer of Issue reports which is built upon the Transformer-based pre-trained RoBERTa model. CatIss classifies issue reports into three main categories of Bug report, Enhancement/feature request, and Question. First, the datasets provided for the NLBSE tool competition are cleaned and preprocessed. Then, the pre-trained RoBERTa model is fine-tuned on the preprocessed dataset. Evaluating CatIss on about 80 thousand issue reports from GitHub, indicates that it performs very well surpassing the competition baseline, TicketTagger, and achieving 87.2% F1-score (micro average). Additionally, as CatIss is trained on a wide set of repositories, it is a generic prediction model, hence applicable for any unseen software project or projects with little historical data. Scripts for cleaning the datasets, training CatIss and evaluating the model are publicly available. 1
Theory of magnetic spin and orbital Hall and Nernst effects in bulk ferromagnets

The magnetic spin Hall effect (MSHE) is an anomalous charge-to-spin conversion phenomenon which occurs in ferromagnetic materials. In contrast to the conventional spin Hall effect (SHE), being a time-reversal even effect, the magnetic counterpart is time-reversal odd. In this work, we use ab initio calculations to investigate the MSHE for the bulk ferromagnets Fe, Co, and Ni. The magnitudes of the MSHE of Fe and Co are comparable to those of the SHE, but the MSHE is strongly dependent on the electron lifetime and the MSHE and SHE can moreover have opposite signs. For Ni the MSHE is smaller than the SHE, but in general, the MSHE cannot be ignored for spin-orbit torques. Considering a charge current we analyze how both the MSHE and SHE contribute to a total Hall angle. We extend our analysis of the MSHE to its orbital counterpart, that is, the magnetic orbital Hall effect (MOHE), for which we show that the MOHE is in general smaller than the orbital Hall effect (OHE). We compute furthermore the thermal analogs, i.e., the spin and orbital Nernst effects, and their magnetic counterparts. Here our calculations show that the magnetic spin and orbital Nernst effects of Ni are substantially larger than those of Fe and Co.
Disjoint hypercyclicity, Sidon sets and weakly mixing operators

<jats:p>We prove that a finite set of natural numbers <jats:italic>J</jats:italic> satisfies that <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0143385723000548_inline1. png" /> <jats:tex-math> $J\cup \{0\}$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> is not Sidon if and only if for any operator <jats:italic>T</jats:italic>, the disjoint hypercyclicity of <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0143385723000548_inline2. png" /> <jats:tex-math> $\{T^j:j\in J\}$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> implies that <jats:italic>T</jats:italic> is weakly mixing. As an application we show the existence of a non-weakly mixing operator <jats:italic>T</jats:italic> such that <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0143385723000548_inline3. png" /> <jats:tex-math> $T\oplus T^2\oplus\cdots \oplus T^n$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> is hypercyclic for every <jats:italic>n</jats:italic>. </jats:p>
Controllable augmentations for video representation learning

This paper focuses on self-supervised video representation learning. Most existing approaches follow the contrastive learning pipeline to construct positive and negative pairs by sampling different clips. However, this formulation tends to bias the static background and has difficulty establishing global temporal structures. The major reason is that the positive pairs, i.e., different clips sampled from the same video, have limited temporal receptive fields, and usually share similar backgrounds but differ in motions. To address these problems, we propose a framework to jointly utilize local clips and global videos to learn from detailed region-level correspondence as well as general long-term temporal relations. Based on a set of designed controllable augmentations, we implement accurate appearance and motion pattern alignment through soft spatio-temporal region contrast. Our formulation avoids the low-level redundancy shortcut with an adversarial mutual information minimization objective to improve the generalization ability. Moreover, we introduce local-global temporal order dependency to further bridge the gap between clip-level and video-level representations for robust temporal modeling. Extensive experiments demonstrate that our framework is superior on three video benchmarks in action recognition and video retrieval, and captures more accurate temporal dynamics.
Recent improvements of ASR models in the face of adversarial attacks

Like many other tasks involving neural networks, Speech Recognition models are vulnerable to adversarial attacks. However recent research has pointed out differences between attacks and defenses on ASR models compared to image models. Improving the robustness of ASR models requires a paradigm shift from evaluating attacks on one or a few models to a systemic approach in evaluation. We lay the ground for such research by evaluating on various architectures a representative set of adversarial attacks: targeted and untargeted, optimization and speech processing-based, white-box, black-box and targeted attacks. Our results show that the relative strengths of different attack algorithms vary considerably when changing the model architecture, and that the results of some attacks are not to be blindly trusted. They also indicate that training choices such as self-supervised pretraining can significantly impact robustness by enabling transferable perturbations. We release our source code as a package that should help future research in evaluating their attacks and defenses.
Decayless Oscillations in Solar Coronal Bright Points

Decayless kink oscillations of solar coronal loops (or decayless oscillations for short) have attracted great attention since their discovery. Coronal bright points (CBPs) are mini-active regions and consist of loops with a small size. However, decayless oscillations in CBPs have not been widely reported. In this study, we identified this kind of oscillations in some CBPs using 171 Å images taken by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. After using the motion magnification algorithm to increase oscillation amplitudes, we made time–distance maps to identify the oscillatory signals. We also estimated the loop lengths and velocity amplitudes. We analyzed 23 CBPs and found 31 oscillation events in 16 of them. The oscillation periods range from 1 to 8 minutes (on average about 5 minutes), and the displacement amplitudes have an average value of 0.07 Mm. The average loop length and velocity amplitude are 23 Mm and 1.57 km s−1, respectively. Relationships between different oscillation parameters are also examined. Additionally, we performed a simple model to illustrate how these subpixel oscillation amplitudes (less than 0.4 Mm) could be detected. Results of the model confirm the reliability of our data processing methods. Our study shows for the first time that decayless oscillations are common in small-scale loops of CBPs. These oscillations allow for seismological diagnostics of the Alfvén speed and magnetic field strength in the corona.
Logit Normalization for Long-tail Object Detection

Real-world data exhibiting skewed distributions pose a serious challenge to existing object detectors. Moreover, the samplers in detectors lead to shifted training label distributions, while the tremendous proportion of background to foreground samples severely harms foreground classification. To mitigate these issues, in this paper, we propose Logit Normalization (LogN), a simple technique to self-calibrate the classified logits of detectors in a similar way to batch normalization. In general, our LogN is training- and tuning-free (i.e. require no extra training and tuning process), model- and label distribution-agnostic (i.e. generalization to different kinds of detectors and datasets), and also plug-and-play (i.e. direct application without any bells and whistles). Extensive experiments on the LVIS dataset demonstrate superior performance of LogN to state-of-the-art methods with various detectors and backbones. We also provide in-depth studies on different aspects of our LogN. Further experiments on ImageNet-LT reveal its competitiveness and generalizability. Our LogN can serve as a strong baseline for long-tail object detection and is expected to inspire future research in this field. Code and trained models will be publicly available at https://github.com/MCG-NJU/LogN.
Can X-Ray Observations Improve Optical-UV-based Accretion-rate Estimates for Quasars?

Current estimates of the normalized accretion rates of quasars (L/L Edd) rely on measuring the velocity widths of broad optical-UV emission lines (e.g., Hβ and Mg ii λ2800). However, such lines tend to be weak or inaccessible in the most distant quasars, leading to increasing uncertainty in L/L Edd estimates at z > 6. Utilizing a carefully selected sample of 53 radio-quiet quasars that have Hβ and C iv λ1549 spectroscopy as well as Chandra coverage, we searched for a robust accretion-rate indicator for quasars, particularly at the highest-accessible redshifts (z ∼ 6–7). Our analysis explored relationships between the Hβ-based L/L Edd, the equivalent width (EW) of C iv, and the optical-to-X-ray spectral slope (α ox). Our results show that EW(C iv) is the strongest indicator of the Hβ-based L/L Edd parameter, consistent with previous studies, although significant scatter persists particularly for sources with weak C iv lines. We do not find evidence for the α ox parameter improving this relation, and we do not find a significant correlation between α ox and Hβ-based L/L Edd. This absence of an improved relationship may reveal a limitation of our sample. X-ray observations of additional luminous sources, found at z ≳ 1, may allow us to mitigate the biases inherent in our archival sample and test whether X-ray data could improve L/L Edd estimates. Furthermore, deeper X-ray observations of our sources may provide accurate measurements of the hard-X-ray power-law photon index (Γ), which is considered an unbiased L/L Edd indicator. Correlations between EW(C iv) and α ox with a Γ-based L/L Edd may yield a more robust prediction of a quasar normalized accretion rate.
Decentralized convex optimization under affine constraints for power systems control

Modern power systems are now in continuous process of massive changes. Increased penetration of distributed generation, usage of energy storage and controllable demand require introduction of a new control paradigm that does not rely on massive information exchange required by centralized approaches. Distributed algorithms can rely only on limited information from neighbours to obtain an optimal solution for various optimization problems, such as optimal power ﬂow, unit commit-ment etc. As a generalization of these problems we consider the problem of decentralized minimization of the smooth and convex partially separable function f = P f k ( x k ˜ x ) under the coupled and the shared ˜ A ˜ x − ˜ b ≤ 0 aﬃne constraints, where the information about A k and b k is only available for the k -th node of the computational network.Oneway to handle the coupled constraints in a distributed manner is to rewrite them in a distributed-friendly form using the Laplace matrix of the communication graph and auxiliary variables (Khamisov, CDC, 2017). Instead of using this method we reformulate the constrained optimization problem as a saddle point problem (SPP) and utilize the consensus constraint technique to make it distributed-friendly. Then we provide a complexity analysis for state-of-the-art SPP solving algorithms applied to this SPP.
Quantum transport in chaotic cavities with tunnel barriers

We bring together the semiclassical approximation, matrix integrals and the theory of symmetric polynomials in order to solve a long standing problem in the ﬁeld of quantum chaos: to compute transport moments when tunnel barriers are present and the number of open channels, M , is small. In contrast to previous approaches, ours is non-perturbative in M ; instead, we arrive at an explicit expression in the form of a power series in the barrier’s reﬂectivity, whose coeﬃcients are rational functions of M . For general moments we must require that the barriers are equal and time reversal symmetry is broken, but for conductance we treat the general situation. Our method accounts for exponentially small non-perturbative terms that were not accessible to previous semiclassical approaches. We also show how to include more than two leads in the system.
Influence of the recoil-order and radiative correction on the 
β
 decay correlation coefficients in mirror decays

Measurements of the beta decay correlation coeﬃcients in nuclear decay aim for a precision below 1% and theoretical predictions should follow this trend. In this work, the inﬂuence of the two dominant Standard Model correction terms, i.e. the recoil-order and the radiative correction, are studied for the most commonly measured beta correlations, i.e. the β -asymmetry parameter ( A β ) and the β − ν angular correlation ( a βν ). The recoil-order correction is calculated with the well-known Holstein formalism using the impulse approximation to evaluate experimentally inaccessible form factors. For the β − ν angular correlation previously unpublished, semi-analytical radiative correction values are tabulated. Results are presented for the mirror beta decays up to A = 45 . We examine the eﬀect of both corrections and provide a comparison between diﬀerent isotopes. This comparison will help planning, analysing, and comparing future experimental eﬀorts.
Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similar-ity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores the fact that two crops may truly contain different image information, e.g., background and small objects, and thus tends to restrain the diversity of the learned representations. In this work, we address this issue by introducing a new self-supervised learning strat-egy, LoGo, that explicitly reasons about Local and Global crops. To achieve view invariance, LoGo encourages similarity between global crops from the same image, as well as between a global and a local crop. However, to correctly encode the fact that the content of smaller crops may differ entirely, LoGo promotes two local crops to have dissimi-lar representations, while being close to global crops. Our LoGo strategy can easily be applied to existing SSL meth-ods. Our extensive experiments on a variety of datasets and using different self-supervised learning frameworks vali-date its superiority over existing approaches. Noticeably, we achieve better results than supervised models on trans-fer learning when using only 1/10 of the data. 11Our code and pretrained models can be found at https://github.com/ztt1024/LoGo-SSL.
Push-Subgradient Is of the Optimal Convergence Rate

— The push-sum based subgradient is an important method for distributed convex optimization over unbalanced directed graphs, which is known to converge at a rate of O (ln t/ √ t ) . This paper shows that the push-subgradient al-gorithm actually converges at a rate of O (1 / √ t ) , which is the same as that of the single-agent subgradient and thus optimal. The proposed tool for analyzing push-sum based algorithms is of independent interest.
A Self-learning Neural Network Approach for Radio Frequency Interference Detection and Removal in Radio Astronomy

We present a novel neural network (NN) method for the detection and removal of radio frequency interference (RFI) from the raw digitized signal in the signal processing chain of a typical radio astronomy experiment. The main advantage of our method is that it does not require a training set. Instead, our method relies on the fact that the true signal of interest coming from astronomical sources is thermal and therefore described as a Gaussian random process, which cannot be compressed. We employ a variational encoder/decoder network to find the compressible information in the data stream that can explain the most variance with the fewest degrees of freedom. We demonstrate it on a set of toy problems and stored ring buffers from the Baryon Mapping eXperiment prototype. We find that the RFI subtraction is effective at cleaning simulated time streams: While we find that the power spectra of the RFI-cleaned time streams output by the NN suffer from extra signal consistent with additive noise, we find that it is generally around percent level across the band and sub 10% in contaminated spectral channels even when RFI power is an order of magnitude larger than the signal. We discuss the advantages and limitations of this method and possible implementation in the front end of future radio experiments.
Joyce structures on spaces of quadratic differentials

Consider the space parameterising curves of genus g>1 equipped with a quadratic differential with simple zeroes. We use the geometry of isomonodromic deformations to construct a complex hyperkahler structure on the total space of its tangent bundle. This provides non-trivial examples of the Joyce structures recently introduced in relation to Donaldson-Thomas theory.
Viscosity Solutions for Obstacle Problems on Wasserstein Space

This paper is a continuation of our accompanying paper [Talbi, Touzi and Zhang (2021)], where we characterized the mean field optimal stopping problem by an obstacle equation on the Wasserstein space of probability measures, provided that the value function is smooth. Our purpose here is to establish this characterization under weaker regularity requirements. We shall define a notion of viscosity solutions for such equation, and prove existence, stability, and comparison principle.