Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Adaptive Droop Design via a Modified Relaxation of the OPF

The ever-increasing penetration of renewable energy resources (RESs) in power distribution networks has brought, among others, the challenge of maintaining the grid voltages within the secure region. Employing droop voltage regulators on the RES’s inverters is an efficient and low-cost solution to reach this objective. However, fixing droop parameters or optimizing them only for overvoltage conditions does not provide the required robustness and optimality under changing operating conditions. In this article, a convex optimization approach is proposed for reconfiguring <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> droop regulators during online operation. The objective is to minimize power curtailment, power losses, and voltage deviation subject to electrical security constraints. This enables to optimally operate the grid with high RES penetration under variable conditions while preserving electrical security constraints (e.g., current and voltage limits). As a first contribution, a mixed-integer linear model of the droop characteristics is developed. According to this model, a droop-regulated generation unit is represented as a constant-power generator in parallel with a constant-impedance load. As a second contribution, a modified augmented relaxed optimal power flow (MAR-OPF) formulation is proposed to guarantee that the electrical security constraints are respected in the presence of constant-impedance loads in the network. Sufficient conditions for the feasibility of the MAR-OPF solution are provided. Those conditions can be checked a priori and are valid for several real distribution networks. Furthermore, an iterative approach is proposed to derive an approximate solution to the MAR-OPF that is close to the global optimal one. The performance of the MAR-OPF approach and the accuracy of the proposed model are evaluated on standard 34- and 85-bus test networks.
Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace

Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior during the optimization process. To obtain high-performing algorithm instances, automated algorithm configuration techniques have been developed. One of the most popular tools is irace, which evaluates configurations in sequential races, at the end of which a statistical test is used to determine the set of survivor configurations. It then selects up to five elite configurations from this set, via greedy truncation selection. Wedemonstrate that an alternative selection of the elites can improve the performance of irace. Our strategy keeps the best survivor and selects the remaining configurations uniformly at random from the set of survivors. We apply this alternative selection method to tune ant colony optimization algorithms for traveling salesperson problems and to configure an exact tree search solver for satisfiability problems. We also experiment with two non-elitist selection criteria, based on entropy and Gower’s distance, respectively. Both methods provide more diverse configurations than irace, making them an interesting approach for exploring a wide range of solutions and understanding algorithms’ performance. Moreover, the entropy-based selection performs better on our benchmarks than the default selection of irace.
HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions

Monocular 3D motion capture (mocap) is beneficial to many applications. The use of a single camera, however, often fails to handle occlusions of different body parts and hence it is limited to capture relatively simple movements. We present a light-weight, hybrid mocap technique called HybridCap that augments the camera with only 4 Inertial Measurement Units (IMUs) in a novel learning-and-optimization framework. We first employ a weakly-supervised and hierarchical motion inference module based on cooperative pure residual recurrent blocks that serve as limb, body and root trackers as well as an inverse kinematics solver. Our network effectively narrows the search space of plausible motions via coarse-to-fine pose estimation and manages to tackle challenging movements with high efficiency. We further develop a hybrid optimization scheme that combines inertial feedback and visual cues to improve tracking accuracy. Extensive experiments on various datasets demonstrate HybridCap can robustly handle challenging movements ranging from fitness actions to Latin dance. It also achieves real-time performance up to 60 fps with state-of-the-art accuracy.
Mass ejection and time variability in protostellar outflows: Cep E. SOLIS XVI

Context. Protostellar jets are an important agent of star formation feedback, tightly connected with the mass-accretion process. The history of jet formation and mass ejection provides constraints on the mass accretion history and on the nature of the driving source. Aims. characterize the time-variability of the mass-ejection phenomena at work in the class 0 protostellar phase in order to better understand the dynamics of the outﬂowing gas and bring more constraints on the origin of the jet chemical composition and the mass-accretion history. Methods. Using the NOrthern Extended Millimeter Array (NOEMA) interferometer, we have observed the emission of the CO 2–1 and SO N J = 4 –4 3 rotational transitions at an angular resolution of 1 . 0 (cid:48)(cid:48) (820 au) and 0 . 4 (cid:48)(cid:48) (330 au), respectively, toward the intermediate-mass class 0 protostellar system CepE.
A Systematic Study of Android Non-SDK (Hidden) Service API Security

Android allows apps to communicate with its system services via system service helpers so that these apps can use various functions provided by the system services. Meanwhile, the system services rely on their service helpers to enforce security checks for protection. Unfortunately, the security checks in the service helpers may be bypassed via directly exploiting the non-SDK (hidden) APIs, degrading the stability and posing severe security threats such as privilege escalation, automatic function execution without users’ interactions, crashes, and DoS attacks. Google has proposed various approaches to address this problem, e.g., case-by-case fixing the bugs or even proposing a blacklist to block all the non-SDK APIs. However, the developers can still figure out new ways of exploiting these hidden APIs to evade the non-SDKs restrictions. In this article, we systematically study the vulnerabilities due to the hidden API exploitation and analyze the effectiveness of Google’s countermeasures. We aim to answer if there are still vulnerable hidden APIs that can be exploited in newest Android 12. We develop a static analysis tool called <inline-formula><tex-math notation="LaTeX">${{\sf ServiceAudit}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">ServiceAudit</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3160872.gif"/></alternatives></inline-formula> to automatically mine the inconsistent security enforcement between service helper classes and the hidden service APIs. We apply <inline-formula><tex-math notation="LaTeX">${{\sf ServiceAudit}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">ServiceAudit</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3160872.gif"/></alternatives></inline-formula> to Android 6<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="li-ieq3-3160872.gif"/></alternatives></inline-formula>12. Our tool discovers 112 vulnerabilities in Android 6 with a higher precision than existing approaches. Moreover, in Android 11 and 12, we identify more than 25 hidden APIs with inconsistent protections; however, only one of the vulnerable APIs can lead to severe security problem in Android 11, and none of them work on Android 12.
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension

Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reasoning, while symbolic reasoners cannot directly apply to text. To meet the challenge, we present a neural-symbolic approach which, to predict an answer, passes messages over a graph representing logical relations between text units. It incorporates an adaptive logic graph network (AdaLoGN) which adaptively infers logical relations to extend the graph and, essentially, realizes mutual and iterative reinforcement between neural and symbolic reasoning. We also implement a novel subgraph-to-node message passing mechanism to enhance context-option interaction for answering multiple-choice questions. Our approach shows promising results on ReClor and LogiQA.
cRoK: A Composable Robotics Benchmark

—Selecting an optimal robot and conﬁguring it for a given task is currently mostly done by human expertise or trial and error. To evaluate automatic selection and adaptation of robots to speciﬁc tasks, we introduce a benchmark suite encompassing a common format for robots, environments, and task descriptions. Our benchmark suite is especially useful for modular robots, where the creation of the robots themselves creates a host of additional parameters to optimize. The benchmark deﬁnes this optimization and facilitates the comparison of solution algorithms. All benchmarks are accessible through a website to conveniently share, reference, and compare solutions.
Quasinormal modes of Schwarzschild black holes in metric affine Chern-Simons theory

We reformulate the Chern-Simons modiﬁed gravity in the metric-aﬃne formalism, by en-larging the Pontryagin density with homothetic curvature terms which restore projective invariance without spoiling topologicity. The latter is then violated by promoting the coupling of the Chern-Simons term to a (pseudo)-scalar ﬁeld. We derive the perturbative solutions for torsion and nonmetricity from the background ﬁelds, and we describe the dynamics for the resulting linearized metric and the scalar ﬁelds in a Schwarzschild black hole background. Then, by adopting numerical techniques we compute the quasinormal mode spectrum and the late-time tails for scalar and metric perturbations.
Community-Driven Comprehensive Scientific Paper Summarization: Insight from cvpaper.challenge

The present paper introduces a group activity involving writing summaries of conference proceedings by volunteer participants. The rapid increase in scientific papers is a heavy burden for researchers, especially non-native speakers, who need to survey scientific literature. To alleviate this problem, we organized a group of non-native English speakers to write summaries of papers presented at a computer vision conference to share the knowledge of the papers read by the group. We summarized a total of 2,000 papers presented at the Conference on Computer Vision and Pattern Recognition, a top-tier conference on computer vision, in 2019 and 2020. We quantitatively analyzed participants’ selection regarding which papers they read among the many available papers. The experimental results suggest that we can summarize a wide range of papers without asking participants to read papers unrelated
Isolating an Outflow Component in Single-Epoch Spectra of Quasars

Gaseous outflows appear to be a universal property of type-1 and type-2 active galactic nuclei (AGN). The main diagnostic is provided by emission features shifted to higher frequencies via the Doppler effect, implying that the emitting gas is moving toward the observer. However, beyond the presence of blueshift, the observational signatures of the outflows are often unclear, and no established criteria exist to isolate the outflow contribution in the integrated, single-epoch spectra of type-1 AGN. The emission spectrum collected the typical apertures of long-slit spectroscopy or of fiber optics sample contributions over a broad range of spatial scales, making it difficult to analyze the line profiles in terms of different kinematical components. Nevertheless, hundred of thousands of quasars spectra collected at moderate resolution demand a proper analysis of the line profiles for proper dynamical modeling of the emitting regions. In this small contribution, we analyze several profiles of the Hi Balmer line Hβ from composite and individual spectra of sources radiating at moderate Eddington ratio (Population B). Features and profile shapes that might be traced to outflow due to narrow-line region gas are detected over a wide range of luminosity.
Selection and Parallel Trends

We study the role of selection into treatment in difference-in-differences (DiD) designs. We derive necessary and sufficient conditions for parallel trends assumptions under general classes of selection mechanisms. These conditions characterize the empirical content of parallel trends. For settings where the necessary conditions are questionable, we propose tools for selection-based sensitivity analysis. We also provide templates for justifying DiD in applications with and without covariates. A reanalysis of the causal effect of NSW training programs demonstrates the usefulness of our selection-based approach to sensitivity analysis.
Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.
Explainability in Graph Neural Networks: An Experimental Survey

Graph neural networks (GNNs) have been extensively developed for graph representation learning in various application domains. However, similar to all other neural networks models, GNNs suffer from the black-box problem as people cannot understand the mechanism underlying them. To solve this problem, several GNN explainability methods have been proposed to explain the decisions made by GNNs. In this survey, we give an overview of the state-of-the-art GNN explainability methods and how they are evaluated. Furthermore, we propose a new evaluation metric and conduct thorough experiments to compare GNN explainability methods on real world datasets. We also suggest future directions for GNN explainability.
Snowmass White Paper: String Perturbation Theory

The purpose of this White Paper is to review recent progress towards elucidating and evaluating string amplitudes, relating them to quantum field theory amplitudes, applying their predictions to string dualities, exploring their connection with gravitational physics, and deepening our understanding of their mathematical structure. We also present a selection of targets for future research.
One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning

Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene. In conventional models, the edge image generated is ambiguous, and the edge lines are also very thick, which typically necessitates the use of nonmaximum suppression (NMS) and morphological thinning operations to generate clear and thin edge images. In this article, we aim to propose a one-stage neural network model that can generate high-quality edge images without postprocessing. The proposed model adopts a classic encoder–decoder framework in which a pretrained neural model is used as the encoder and a multifeature-fusion mechanism that merges the features of each level with each other functions as a learnable decoder. Further, we propose a new loss function that addresses the pixel-level imbalance in the edge image by suppressing the false positive edge information near the true positive edge and the false negative nonedge. The results of experiments conducted on several benchmark datasets indicate that the proposed method achieves state-of-the-art (SOTA) results without using NMS and morphological thinning operations.
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?

What can pre-trained multilingual sequence-to-sequence models like mBART contribute to translating low-resource languages? We conduct a thorough empirical experiment in 10 languages to ascertain this, considering five factors: (1) the amount of fine-tuning data, (2) the noise in the fine-tuning data, (3) the amount of pre-training data in the model, (4) the impact of domain mismatch, and (5) language typology. In addition to yielding several heuristics, the experiments form a framework for evaluating the data sensitivities of machine translation systems. While mBART is robust to domain differences, its translations for unseen and typologically distant languages remain below 3.0 BLEU. In answer to our title’s question, mBART is not a low-resource panacea; we therefore encourage shifting the emphasis from new models to new data.
Adaptive objective configuration in bi-objective evolutionary optimization for cervical cancer brachytherapy treatment planning

The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been proven effective and eficient in solving real-world problems. A prime example is optimizing treatment plans for prostate cancer brachytherapy, an internal form of radiation treatment, for which equally important clinical aims from a base protocol are grouped into two objectives and bi-objectively optimized. This use of MO-RV-GOMEA was recently successfully introduced into clinical practice. Brachytherapy can also play an important role in treating cervical cancer. However, using the same approach to optimize treatment plans often does not immediately lead to clinically desirable results. Concordantly, medical experts indicate that they use additional aims beyond the cervix base protocol. Moreover, these aims have different priorities and can be patient-specifically adjusted. For this reason, we propose a novel adaptive objective configuration method to use with MO-RV-GOMEA so that we can accommodate additional aims of this nature. Based on results using only the base protocol, in consultation with medical experts, we configured key additional aims. We show how, for 10 patient cases, the new approach achieves the intended result, properly taking into account the additional aims. Consequently, plans resulting from the new approach are preferred by medical specialists in 8/10 cases.
Distinguishing 6D (1, 0) SCFTs: An extension to the geometric construction

We provide a new extension to the geometric construction of 6d $(1,0)$ SCFTs that encapsulates Higgs branch structures with identical global symmetry but different spectra. In particular, we find that there exist distinct 6d $(1,0)$ SCFTs that may appear to share their tensor branch description, flavor symmetry algebras, and central charges. For example, such subtleties arise for the very even nilpotent Higgsing of $(\mathfrak{so}_{4k}, \mathfrak{so}_{4k})$ conformal matter; we propose a method to predict at which conformal dimension the Higgs branch operators of the two theories differ via augmenting the tensor branch description with the Higgs branch chiral ring generators of the building block theories. Torus compactifications of these 6d $(1,0)$ SCFTs give rise to 4d $\mathcal{N}=2$ SCFTs of class $\mathcal{S}$ and the Higgs branch of such 4d theories are captured via the Hall--Littlewood index. We confirm that the resulting 4d theories indeed differ in their spectra in the predicted conformal dimension from their Hall--Littlewood indices. We highlight how this ambiguity in the tensor branch description arises beyond the very even nilpotent Higgsing of $(\mathfrak{so}_{4k}, \mathfrak{so}_{4k})$ conformal matter, and hence should be understood for more general classes of 6d $(1,0)$ SCFTs.
Dual-domain Attention-based Deep Network for Sparse-view CT Artifact Reduction

Due to the wide applications of X-ray computed tomography (CT) in medical imaging activities, radiation exposure has become a major concern for public health. Sparse-view CT is a promising approach to reduce the radiation dose by down-sampling the total number of acquired projections. However, the CT images reconstructed by this sparse-view imaging approach suffer from severe streaking artifacts and structural information loss. In this work, an end-to-end dual-domain attention-based deep network (DDANet) is proposed to solve such an ill-posed CT image reconstruction problem. The image-domain CT image and the projection-domain sinogram are put into the two parallel sub-networks of the DDANet to independently extract the distinct high-level feature maps. In addition, a specified attention module is introduced to fuse the aforementioned dual-domain feature maps to allow complementary optimizations of removing the streaking artifacts and mitigating the loss of structure. Numerical simulations, anthropomorphic thorax phantom and in vivo pre-clinical experiments are conducted to verify the sparse-view CT imaging performance of the DDANet. Results demonstrate that this newly developed approach is able to robustly remove the streaking artifacts while maintaining the fine structures. As a result, the DDANet provides a promising solution in achieving high quality sparse-view CT imaging.