Miracle-dz/UniFAR-FTUs huggingface dataset:
Linguistically Driven Graph Capsule Network for Visual Question Reasoning

Recently, studies of visual question answering have explored various architectures of end-to-end networks and achieved promising results on both natural and synthetic datasets, which require explicitly compositional reasoning. However, it has been argued that these black-box approaches lack interpretability of results, and thus cannot perform well on generalization tasks due to overfitting the dataset bias. In this work, we aim to combine the benefits of both sides and overcome their limitations to achieve an end-to-end interpretable structural reasoning for general images without the requirement of layout annotations. Inspired by the property of a capsule network that can carve a tree structure inside a regular convolutional neural network (CNN), we propose a hierarchical compositional reasoning model called the "Linguistically driven Graph Capsule Network", where the compositional process is guided by the linguistic parse tree. Specifically, we bind each capsule in the lowest layer to bridge the linguistic embedding of a single word in the original question with visual evidence and then route them to the same capsule if they are siblings in the parse tree. This compositional process is achieved by performing inference on a linguistically driven conditional random field (CRF) and is performed across multiple graph capsule layers, which results in a compositional reasoning process inside a CNN. Experiments on the CLEVR dataset, CLEVR compositional generation test, and FigureQA dataset demonstrate the effectiveness and composition generalization ability of our end-to-end model.
Timing stability of three black widow pulsars

We study the timing stability of three black widow pulsars (BWPs), both in terms of their long-term spin evolution and their shorter term orbital stability. The erratic timing behaviour and radio eclipses of the first two BWP systems discovered (PSRs B1957+20 and J2051−0827) were assumed to be representative for this class of pulsars. With several new black widow systems added to this population in the last decade, there are now several systems known that do not show these typical orbital variations or radio eclipses. We present timing solutions using 7–8 yr of observations from four of the European Pulsar Timing Array telescopes for PSRs J0023+0923, J2214+3000, and J2234+0944, and confirm that two of these systems do not show any significant orbital variability over our observing time span, both in terms of secular or orbital parameters. The third pulsar PSR J0023+0923 shows orbital variability and we discuss the implications for the timing solution. Our results from the long-term timing of these pulsars provide several new or improved parameters compared to earlier works. We discuss our results regarding the stability of these pulsars, and the stability of the class of BWPs in general, in the context of the binary parameters, and discuss the potential of the Roche lobe filling factor of the companion star being an indicator for stability of these systems.
Architectural Resilience to Foreground-and-Background Adversarial Noise

Adversarial attacks in the form of imperceptible perturbations of normal images have been extensively studied, and for every new defense methodology created, multiple adversarial attacks are found to counteract it. In particular, a popular style of attack, exemplified in recent years by DeepFool and Carlini-Wagner, relies solely on white-box scenarios in which full access to the predictive model and its weights are required. In this work, we instead propose distinct model-agnostic benchmark perturbations of images in order to investigate the resilience and robustness of different network architectures. Results empirically determine that increasing depth within most types of Convolutional Neural Networks typically improves model resilience towards general attacks, with improvement steadily decreasing as the model becomes deeper. Additionally, we find that a notable difference in adversarial robustness exists between residual architectures with skip connections and non-residual architectures of similar complexity. Our findings provide direction for future understanding of residual connections and depth on network robustness.
PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

Grasping for novel objects is important for robot manipulation in unstructured environments. Most of current works require a grasp sampling process to obtain grasp candidates, combined with local feature extractor using deep learning. This pipeline is time-costly, expecially when grasp points are sparse such as at the edge of a bowl.In this paper, we propose an end-to-end approach to directly predict the poses, categories and scores (qualities) of all the grasps. It takes the whole sparse point clouds as the input and requires no sampling or search process. Moreover, to generate training data of multi-object scene, we propose a fast multi-object grasp detection algorithm based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB object set, 23.7k grasps) and a multi-object dataset (20k point clouds with annotations and masks) are generated. A PointNet++ based network combined with multi-mask loss is introduced to deal with different training points. The whole weight size of our network is only about 11.6M, which takes about 102ms for a whole prediction process using a GeForce 840M GPU. Our experiment shows our work get 71.43% success rate and 91.60% completion rate, which performs better than current state-of-art works.
Dialect Identification of Spoken North Sámi Language Varieties Using Prosodic Features

This work explores the application of various supervised classification approaches using prosodic information for the identification of spoken North Sami language varieties. Dialects are language varieties that enclose characteristics specific for a given region or community. These characteristics reflect segmental and suprasegmental (prosodic) differences but also high-level properties such as lexical and morphosyntactic. One aspect that is of particular interest and that has not been studied extensively is how the differences in prosody may underpin the potential differences among different dialects. To address this, this work focuses on investigating the standard acoustic prosodic features of energy, fundamental frequency, spectral tilt, duration, and their combinations, using sequential and context-independent supervised classification methods, and evaluated separately over two different units in speech: words and syllables. The primary aim of this work is to gain a better understanding on the role of prosody in identifying among the different language varieties. Our results show that prosodic information holds an important role in distinguishing between the five areal varieties of North Sami where the inclusion of contextual information for all acoustic prosodic features is critical for the identification of dialects for words and syllables.
Reconstruction of Radio Relics and X-Ray Tails in an Off-axis Cluster Merger: Hydrodynamical Simulations of A115

Although a merging galaxy cluster is a useful laboratory to study many important astrophysical processes and fundamental physics, only limited interpretations are possible without careful analysis of the merger scenario. However, the study is demanding because a thorough comparison of multiwavelength observations with detailed numerical simulations is required. In this paper, we present such a study for the off-axis binary merger A115. The system possesses a number of remarkable observational features, but no convincing merger scenario explaining the shape and location of the radio relic in harmony with the orientation of the cometary X-ray tails has been presented. Our hydrodynamical simulation with adaptive mesh refinement suggests that the cometary X-ray tail of A115 might be a slingshot tail that can arise ∼0.3 Gyr after the impact, before the two subclusters reach their apocenters. This scenario can predict the location and orientation of the giant radio relic, which is parallel to the northern X-ray tail. In addition, our study indicates that diffusive shock acceleration alone cannot generate the observed radio power unless aided by stronger magnetic fields and/or more significant presence of fossil electrons.
Efficient and Stable Finite Difference Modelling of Acoustic Wave Propagation in Variable-density Media

In this paper, we consider the development and analysis of a new explicit compact high-order finite difference scheme for acoustic wave equation formulated in divergence form, which is widely used to describe seismic wave propagation through a heterogeneous media with variable media density and acoustic velocity. The new scheme is compact and of fourth-order accuracy in space and second-order accuracy in time. The compactness of the scheme is obtained by the so-called combined finite difference method, which utilizes the boundary values of the spatial derivatives and those boundary values are obtained by one-sided finite difference approximation. An empirical stability analysis has been conducted to obtain the Currant-Friedrichs-Lewy (CFL) condition, which confirmed the conditional stability of the new scheme. Four numerical examples have been conducted to validate the convergence and effectiveness of the new scheme. The application of the new scheme to a realistic wave propagation problem with Perfect Matched Layer boundary condition is also validated in this paper as well.
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees.

The article discusses distributed gradient-descent algorithms for computing local and global minima in nonconvex optimization. For local optimization, we focus on distributed stochastic gradient descent (D-SGD)--a simple network-based variant of classical SGD. We discuss local minima convergence guarantees and explore the simple but critical role of the stable-manifold theorem in analyzing saddle-point avoidance. For global optimization, we discuss annealing-based methods in which slowly decaying noise is added to D-SGD. Conditions are discussed under which convergence to global minima is guaranteed. Numerical examples illustrate the key concepts in the paper.
Robustness of prediction for extreme adaptive optics systems under various observing conditions

Context. For high-contrast imaging systems, such as VLT/SPHERE, the performance of the system at small angular separations is contaminated by the wind-driven halo in the science image. This halo is a result of the servo-lag error in the adaptive optics (AO) system due to the finite time between measuring the wavefront phase and applying the phase correction. One approach to mitigating the servo-lag error is predictive control. Aims. We aim to estimate and understand the potential on-sky performance that linear data-driven prediction would provide for VLT/SPHERE under various turbulence conditions. Methods. We used a linear minimum mean square error predictor and applied it to 27 different AO telemetry data sets from VLT/SPHERE taken over many nights under various turbulence conditions. We evaluated the performance of the predictor using residual wavefront phase variance as a performance metric. Results. We show that prediction always results in a reduction in the temporal wavefront phase variance compared to the current VLT/SPHERE AO performance. We find an average improvement factor of 5.1 in phase variance for prediction compared to the VLT/SPHERE residuals. When comparing to an idealised VLT/SPHERE, we find an improvement factor of 2.0. Under our 27 different cases, we find the predictor results in a smaller spread of the residual temporal phase variance. Finally, we show there is no benefit to including spatial information in the predictor in contrast to what might have been expected from the frozen flow hypothesis. A purely temporal predictor is best suited for AO on VLT/SPHERE. Conclusions. Linear prediction leads to a significant reduction in phase variance for VLT/SPHERE under a variety of observing conditions and reduces the servo-lag error. Furthermore, prediction improves the reliability of the AO system performance, making it less sensitive to different conditions.
Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a specific cross-modal retrieval task for searching natural images given free-hand sketches under the zero-shot scenario. Most existing methods solve this problem by simultaneously projecting visual features and semantic supervision into a low-dimensional common space for efficient retrieval. However, such low-dimensional projection destroys the completeness of semantic knowledge in original semantic space, so that it is unable to transfer useful knowledge well when learning semantic features from different modalities. Moreover, the domain information and semantic information are entangled in visual features, which is not conducive for cross-modal matching since it will hinder the reduction of domain gap between sketch and image. In this paper, we propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR. Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR. The progressive projection strategy maintains strong semantic supervision. Besides, to guarantee the retrieval features to capture clean and complete semantic information, the cross-reconstruction loss is introduced to encourage that any combinations of retrieval features and domain features can reconstruct the visual features. Extensive experiments demonstrate the superiority of our PDFD over state-of-the-art competitors.
Being Fast Means Being Chatty: The Local Information Cost of Graph Spanners

We introduce a new measure for quantifying the amount of information that the nodes in a network need to learn to jointly solve a graph problem. We show that the local information cost ($\textsf{LIC}$) presents a natural lower bound on the communication complexity of distributed algorithms. For the synchronous CONGEST-KT1 model, where each node has initial knowledge of its neighbors' IDs, we prove that $\Omega(\textsf{LIC}_\gamma(P)/ \log\tau \log n)$ bits are required for solving a graph problem $P$ with a $\tau$-round algorithm that errs with probability at most $\gamma$. Our result is the first lower bound that yields a general trade-off between communication and time for graph problems in the CONGEST-KT1 model. We demonstrate how to apply the local information cost by deriving a lower bound on the communication complexity of computing a $(2t-1)$-spanner that consists of at most $O(n^{1+1/t + \epsilon})$ edges, where $\epsilon = \Theta(1/t^2)$. Our main result is that any $O(\textsf{poly}(n))$-time algorithm must send at least $\tilde\Omega((1/t^2) n^{1+1/2t})$ bits in the CONGEST model under the KT1 assumption. Previously, only a trivial lower bound of $\tilde \Omega(n)$ bits was known for this problem. A consequence of our lower bound is that achieving both time- and communication-optimality is impossible when designing a distributed spanner algorithm. In light of the work of King, Kutten, and Thorup (PODC 2015), this shows that computing a minimum spanning tree can be done significantly faster than finding a spanner when considering algorithms with $\tilde O(n)$ communication complexity. Our result also implies time complexity lower bounds for constructing a spanner in the node-congested clique of Augustine et al. (2019) and in the push-pull gossip model with limited bandwidth.
A Synopses Data Engine for Interactive Extreme-Scale Analytics

We detail the novel architecture of a Synopses Data Engine (SDE) which combines the virtues of parallel processing and stream summarization towards interactive analytics at scale. Our SDE, built on top of Apache Flink, has a unique design that supports a very wide variety of synopses, allows for dynamically adding new functionality to it at runtime, and introduces a synopsis-as-a-service paradigm to enable various types of scalability.
Excitation and charge transfer in low-energy hydrogen atom collisions with neutral manganese and titanium

Data for inelastic processes due to hydrogen atom collisions with manganese and titanium are needed for accurate modeling of the corresponding spectra in late-type stars. In this work excitation and charge transfer in low-energy Mn+H and Ti+H collisions have been studied theoretically using a method based on an asymptotic two-electron linear combination of an atomic orbitals model of ionic-covalent interactions in the neutral atom-hydrogen-atom system, together with the multichannel Landau-Zener model to treat the dynamics. Extensive calculations of charge transfer (mutual neutralization, ion-pair production), excitation and de-excitation processes in the two collisional systems are carried out for all transitions between covalent states dissociating to energies below the first ionic limit and the dominating ionic states. Rate coefficients are determined for temperatures in the range 1000–20 000 K in steps of 1000 K. Like for earlier studies of other atomic species, charge transfer processes are found to lead to much larger rate coefficients than excitation processes.
Accelerating Deep Reinforcement Learning With the Aid of a Partial Model: Power-Efficient Predictive Video Streaming

Predictive power allocation is conceived for power-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption over a complete video streaming session for a mobile user under the quality of service constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive resource policies that first predict future information with historical data and then optimize the policy based on the predicted information, the proposed policy operates in an on-line and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, the signaling overhead between the edge server and the base stations can be reduced, and the dynamics of the system can be learned effortlessly. To improve the robustness of streaming and accelerate learning, we further exploit the partially known dynamics of the system by integrating the concepts of safer layer, post-decision state, and virtual experience into the basic DDPG algorithm. Our simulation results show that the proposed policies converge to the optimal policy derived based on perfect prediction of the future large-scale channel gains and outperforms the first-predict-then-optimize policy in the presence of prediction errors. By harnessing the partially known model of the system dynamics, the convergence speed can be dramatically improved.
Anisotropic electrical and thermal magnetotransport in the magnetic semimetal GdPtBi

The half-Heusler rare-earth intermetallic GdPtBi has recently gained attention due to peculiar magnetotransport phenomena that have been associated with the possible existence of Weyl fermions, thought to arise from the crossings of spin-split conduction and valence bands. On the other hand, similar magnetotransport phenomena observed in other rare-earth intermetallics have often been attributed to the interaction of itinerant carriers with localized magnetic moments stemming from the $4f$-shell of the rare-earth element. In order to address the origin of the magnetotransport phenomena in GdPtBi, we performed a comprehensive study of the magnetization, electrical and thermal magnetoresistivity on two single-crystalline GdPtBi samples. In addition, we performed an analysis of the Fermi surface via Shubnikov-de Haas oscillations in one of the samples and compared the results to \emph{ab initio} band structure calculations. Our findings indicate that the electrical and thermal magnetotransport in GdPtBi cannot be solely explained by Weyl physics and is strongly influenced by the interaction of both itinerant charge carriers and phonons with localized magnetic Gd-ions and possibly also paramagnetic impurities.
Interdisciplinarity metric based on the co-citation network

Quantifying the interdisciplinarity of a research is a relevant problem in the evaluative bibliometrics. The concept of interdisciplinarity is ambiguous and multidimensional. Thus, different measures of interdisciplinarity have been propose in the literature. However, few studies have proposed interdisciplinary metrics without previously defining classification sets, and no one has used the co-citation network for this purpose. In this study we propose an interdisciplinary metric based on the co-citation network. This is a way to define the publication’s field without resorting to pre-defined classification sets. We present a characterization of a publication’s field and then we use this definition to propose a new metric of the interdisciplinarity degree for publications (papers) and journals as units of analysis. The proposed measure has an aggregative property that makes it scalable from a paper individually to a set of them (journal) without more than adding the numerators and denominators in the proportions that define this new indicator. Moreover, the aggregated value of two or more units is strictly among all the individual values.
A MEMS-based Foveating LIDAR to enable Real-time Adaptive Depth Sensing

Most active depth sensors sample their visual field using a fixed pattern, decided by accuracy, speed and cost trade-offs, rather than scene content. However, a number of recent works have demonstrated that adapting measurement patterns to scene content can offer significantly better trade-offs. We propose a hardware LIDAR design that allows flexible real-time measurements according to dynamically specified measurement patterns. Our flexible depth sensor design consists of a controllable scanning LIDAR that can foveate, or increase resolution in regions of interest, and that can fully leverage the power of adaptive depth sensing. We describe our optical setup and calibration, which enables fast sparse depth measurements using a scanning MEMS (micro-electro mechanical) mirror. We validate the efficacy of our prototype LIDAR design by testing on over 75 static and dynamic scenes spanning a range of environments. We also show CNN-based depth-map completion of sparse measurements obtained by our sensor. Our experiments show that our sensor can realize adaptive depth sensing systems.