Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing Fundamental Physics with Gravitational Waves

The explosive coalescence of two black holes 1.3 billion light years away has for the very first time allowed us to peer into the extreme gravity region of spacetime surrounding these events. With these maximally compact objects reaching speeds up to 60% the speed of light, collision events such as these create harsh spacetime environments where the fields are strong, non-linear, and highly dynamical -- a place yet un-probed in human history. On September 14, 2015, the iconic chirp signal from such an event was registered simultaneously by both of the Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors -- by an unparalleled feat of modern engineering. Dubbed "GW150914", this gravitational wave event paved the way for an entirely new observing window into the universe, providing for the unique opportunity to probe fundamental physics from an entirely new viewpoint. Since this historic event, the LIGO/Virgo collaboration (LVC) has further identified ten additional gravitational wave signals in its first two observing runs, composed of a myriad of different events. Important among these new cataloged detections is GW170817, the first detection of gravitational waves from the merger of two neutron stars, giving way to new insight into the supranuclear physics resident within. This thesis explores this new unique opportunity to harness the information encoded within gravitational waves in regards to their source whence they came, to probe fundamental physics from an entirely new perspective. Part A focuses on probing nuclear physics by way of the tidal information encoded within gravitational waves from binary neutron star mergers. Part B focuses on testing general relativity from such events by way of the remnants of such spacetime encoded within the gravitational wave signal.
Deep Neural Network Test Coverage: How Far Are We?

DNN testing is one of the most effective methods to guarantee the quality of DNN. In DNN testing, many test coverage metrics have been proposed to measure test effectiveness, including structural coverage and non-structural coverage (which are classified according to whether considering which structural elements are covered during testing). Those test coverage metrics are proposed based on the assumption: they are correlated with test effectiveness (i.e., the generation of adversarial test inputs or the error-revealing capability of test inputs in DNN testing studies). However, it is still unknown whether the assumption is tenable. In this work, we conducted the first extensive study to systematically validate the assumption by controlling for the size of test sets. In the study, we studied seven typical test coverage metrics based on 9 pairs of datasets and models with great diversity (including four pairs that have never been used to evaluate these test coverage metrics before). The results demonstrate that the assumption fails for structural coverage in general but holds for non-structural coverage on more than half of subjects, indicating that measuring the difference of DNN behaviors between test inputs and training data is more promising than measuring which structural elements are covered by test inputs for measuring test effectiveness. Even so, the current non-structural coverage metrics still can be improved from several aspects such as unfriendly parameters and unstable performance. That indicates that although a lot of test coverage metrics have been proposed before, there is still a lot of room for improvement of measuring test effectiveness in DNN testing, and our study has pointed out some promising directions.
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion

Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-weight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show the capability in handling various relation patterns including symmetry, antisymmetry, inversion and composition. However, previous translating embedding approaches defined in complex vector space suffer from two main issues: 1) representing and modeling capacities of the model are limited by the translation function with rigorous multiplication of two complex numbers; and 2) embedding ambiguity caused by one-to-many relations is not explicitly alleviated. In this paper, we propose a relation-adaptive translation function built upon a novel weighted product in complex space, where the weights are learnable, relation-specific and independent to embedding size. The translation function only requires eight more scalar parameters each relation, but improves expressive power and alleviates embedding ambiguity problem. Based on the function, we then present our Relation-adaptive translating Embedding (RatE) approach to score each graph triple. Moreover, a novel negative sampling method is proposed to utilize both prior knowledge and self-adversarial learning for effective optimization. Experiments verify RatE achieves state-of-the-art performance on four link prediction benchmarks.
Numerical continuation of spiral waves in heteroclinic networks of cyclic dominance

Heteroclinic-induced spiral waves may arise in systems of partial differential equations that exhibit robust heteroclinic cycles between spatially uniform equilibria. Robust heteroclinic cycles arise naturally in systems with invariant subspaces, and their robustness is considered with respect to perturbations that preserve these invariances. We make use of particular symmetries in the system to formulate a relatively low-dimensional spatial two-point boundary-value problem in Fourier space that can be solved efficiently in conjunction with numerical continuation. The standard numerical set-up is formulated on an annulus with small inner radius, and Neumann boundary conditions are used on both inner and outer radial boundaries. We derive and implement alternative boundary conditions that allow for continuing the inner radius to zero and so compute spiral waves on a full disk. As our primary example, we investigate the formation of heteroclinic-induced spiral waves in a reaction–diffusion model that describes the spatiotemporal evolution of three competing populations in a 2D spatial domain—much like the Rock–Paper–Scissors game. We further illustrate the efficiency of our method with the computation of spiral waves in a larger network of cyclic dominance between five competing species, which describes the so-called Rock–Paper–Scissors–Lizard–Spock game.
Social Commonsense Reasoning with Multi-Head Knowledge Attention

Social Commonsense Reasoning requires understanding of text, knowledge about social events and their pragmatic implications, as well as commonsense reasoning skills. In this work we propose a novel multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell.We assess the model’s performance on two tasks that require different reasoning skills: Abductive Natural Language Inference and Counterfactual Invariance Prediction as a new task. We show that our proposed model improves performance over strong state-of-the-art models (i.e., RoBERTa) across both reasoning tasks. Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task. We validate the robustness of the model’s reasoning capabilities by perturbing the knowledge and provide qualitative analysis on the model’s knowledge incorporation capabilities.
An Energy-Efficient High Definition Map Data Distribution Mechanism for Autonomous Driving

Autonomous Driving is now the promising future of transportation. As one basis for autonomous driving, High Definition Map (HD map) provides high-precision descriptions of the environment, therefore it enables more accurate perception and localization while improving the efficiency of path planning. However, an extremely large amount of map data needs to be transmitted during driving, thus posing great challenge for real-time and safety requirements for autonomous driving. To this end, we first demonstrate how the existing data distribution mechanism can support HD map services. Furthermore, considering the constraints of vehicle power, vehicle speed, base station bandwidth, etc., we propose a HD map data distribution mechanism on top of Vehicle-to-Infrastructure (V2I) data transmission. By this mechanism, the map provision task is allocated to the selected RSU nodes and transmits proportionate HD map data cooperatively. Their works on map data loading aims to provide in-time HD map data service with optimized in-vehicle energy consumption. Finally, we model the selection of RSU nodes into a partial knapsack problem and propose a greedy strategy-based data transmission algorithm. Experimental results confirm that within limited energy consumption, the proposed mechanism can ensure HD map data service by coordinating multiple RSUs with the shortest data transmission time.
FILM: A Fast, Interpretable, and Low-rank Metric Learning Approach for Sentence Matching

Detection of semantic similarity plays a vital role in sentence matching. It requires to learn discriminative representations of natural language. Recently, owing to more and more sophisticated model architecture, impressive progress has been made, along with a time-consuming training process and not-interpretable inference. To alleviate this problem, we explore a metric learning approach, named FILM (Fast, Interpretable, and Low-rank Metric learning) to efficiently find a high discriminative projection of the high-dimensional data. We construct this metric learning problem as a manifold optimization problem and solve it with the Cayley transformation method with the Barzilai-Borwein step size. In experiments, we apply FILM with triplet loss minimization objective to the Quora Challenge and Semantic Textual Similarity (STS) Task. The results demonstrate that the FILM method achieves superior performance as well as the fastest computation speed, which is consistent with our theoretical analysis of time complexity.
Efficient evaluation of Gaussian integrals in periodic systems.

By using Poisson's summation formula, we calculate periodic integrals over Gaussian basis functions by partitioning the lattice summations between the real and reciprocal space, where both sums converge exponentially fast with a large exponent. We demonstrate that the summation can be performed efficiently to calculate 2-center Gaussian integrals over various kernels including overlap, kinetic, and Coulomb. The summation in real space is performed using an efficient flavor of the McMurchie-Davidson Recurrence Relation (MDRR). The expressions for performing summation in the reciprocal space are also derived and implemented. We find that the resulting algorithm is only between a factor of 5 to 15 slower than that for molecular integrals, indicating the very small number of terms needed in both the real and reciprocal space summations. An outline of the algorithm for calculating 3-center Coulomb integrals is also provided.
A Feedback Scheme to Reorder a Multi-Agent Execution Schedule by Persistently Optimizing a Switchable Action Dependency Graph

In this paper we consider multiple Automated Guided Vehicles (AGVs) navigating a common workspace to fulfill various intralogistics tasks, typically formulated as the Multi-Agent Path Finding (MAPF) problem. To keep plan execution deadlock-free, one approach is to construct an Action Dependency Graph (ADG) which encodes the ordering of AGVs as they proceed along their routes. Using this method, delayed AGVs occasionally require others to wait for them at intersections, thereby affecting the plan execution efficiency. If the workspace is shared by dynamic obstacles such as humans or third party robots, AGVs can experience large delays. A common mitigation approach is to re-solve the MAPF using the current, delayed AGV positions. However, solving the MAPF is time-consuming, making this approach inefficient, especially for large AGV teams. In this work, we present an online method to repeatedly modify a given acyclic ADG to minimize route completion times of each AGV. Our approach persistently maintains an acyclic ADG, necessary for deadlock-free plan execution. We evaluate the approach by considering simulations with random disturbances on the execution and show faster route completion times compared to the baseline ADG-based execution management approach.
Three-Dimensional Swarming Using Cyclic Stochastic Optimization

In this article, we simulate an ensemble of cooperating, mobile sensing agents that implement the cyclic stochastic optimization (CSO) algorithm in an attempt to survey, track, and follow multiple targets. In the CSO algorithm proposed, each agent uses its sensed measurements, its shared information, and its predictions of other agents’ future motion to decide on its next action. This decision is selected to minimize a loss function that decreases as the uncertainty in the target state estimates decreases. Only noisy measurements of this loss function are available to each agent, and, in this study, each agent attempts to minimize this function by calculating its gradient. This article examines, via simulation-based experiments, the implications and applicability of CSO convergence in three dimensions.
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms

Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective. We present an alternative perspective and formulate federated learning as a posterior inference problem, where the goal is to infer a global posterior distribution by having client devices each infer the posterior of their local data. While exact inference is often intractable, this perspective provides a principled way to search for global optima in federated settings. Further, starting with the analysis of federated quadratic objectives, we develop a computation- and communication-efficient approximate posterior inference algorithm -- federated posterior averaging (FedPA). Our algorithm uses MCMC for approximate inference of local posteriors on the clients and efficiently communicates their statistics to the server, where the latter uses them to refine a global estimate of the posterior mode. Finally, we show that FedPA generalizes federated averaging (FedAvg), can similarly benefit from adaptive optimizers, and yields state-of-the-art results on four realistic and challenging benchmarks, converging faster, to better optima.
Evidence of a "current-mediated" turbulent regime in space and astrophysical plasmas.

How the turbulent energy cascade develops below the magnetohydrodynamic scales in space and astrophysical plasmas is a major open question. Here, we measure the power spectrum of magnetic fluctuations in Parker Solar Probe's observations close to the Sun and in state-of-the-art numerical simulations of plasma turbulence. Both reveal a power-law behavior with a slope compatible with $-11/3$ at scales smaller than the ion characteristic scales, steeper than what is typically observed in the solar wind and in the Earth's magnetosheath. We explain such behavior by developing a simple two-fluid model which does not require any kinetic processes nor electron-inertia effects. This is characterized by a significant contribution of the ion kinetic energy to the total turbulent energy cascade at sub-ion scales, although the dynamics is driven by the magnetic field through the current density. We expect that this regime may be relevant for a broad class of low-beta plasmas, e.g. the solar corona, non-relativistic magnetized jets and disks, and laboratory plasmas.
An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems

Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset. We argue that this approach can narrow our understanding of the generalization ability for different summarization systems. In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora. A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways (i.e. abstractive and extractive) on model generalization ability. Further, experimental results shed light on the limitations of existing summarizers. Brief introduction and supplementary code can be found in https://github.com/zide05/CDEvalSumm.
Full Automation for Rapid Modulator Characterization and Accurate Analysis Using SciPy.

Modulator testing involved complex biasing conditions, hardware connections and data analysis. Also, any optical signal distortion due to the grating coupler effect could potentially induce additional difficulty in setting the correct bias condition for an accurate measurement of the modulator performance. In this paper, we proposed to use SciPy, an open-source scientific computing library, for automation in the silicon modulator test with bias setting and data analysis.
CD4~+CD25~+ regulatory T cells and asthma

Asthma is a frequent chronic respiratory disease,whose immunological pathogenesis is not clear completely.CD4+CD25+ regulatory T cell is a special kind of regulatory T cells,which participates in autoimmune regulation and maintains immunological tolerance.The review summarized the characters of CD4+CD25+ regulatory T cell and its roles in the mechanism of pathogenesis,therapy and prognosis of asthma.
Relativism versus statistics in the microscopic foundation of thermodynamics

Highlights: \begin{itemize} \item Relativistic effect of crystal dynamics "freezing". \item Non-statistical model of thermodynamic equilibration. \end{itemize} The dynamics of oscillations of a one-dimensional atomic chain is investigated in the harmonic approximation, taking into account the relativistic effect of retardation of interactions. It is shown that all oscillations of the chain are damped. A non-statistical mechanism for the irreversible thermodynamic equilibration in many-body systems is proposed.
Inference in generalized bilinear models.

Latent factor models are widely used to discover and adjust for hidden variation in modern applications. However, most methods do not fully account for uncertainty in the latent factors, which can lead to miscalibrated inferences such as overconfident p-values. In this article, we develop a fast and accurate method of uncertainty quantification in generalized bilinear models, which are a flexible extension of generalized linear models to include latent factors as well as row covariates, column covariates, and interactions. In particular, we introduce delta propagation, a general technique for propagating uncertainty among model components using the delta method. Further, we provide a rapidly converging algorithm for maximum a posteriori GBM estimation that extends earlier methods by estimating row and column dispersions. In simulation studies, we find that our method provides approximately correct frequentist coverage of most parameters of interest. We demonstrate on RNA-seq gene expression analysis and copy ratio estimation in cancer genomics.
Combining Observational and Experimental Data Using First-stage Covariates

Randomized controlled trials generate experimental variation that can credibly identify causal effects, but often suffer from limited scale, while observational datasets are large, but often violate desired identification assumptions. To improve estimation efficiency, I propose a method that combines experimental and observational datasets when 1) units from these two datasets are sampled from the same population and 2) some characteristics of these units are observed. I show that if these characteristics can partially explain treatment assignment in the observational data, they can be used to derive moment restrictions that, in combination with the experimental data, improve estimation efficiency. I outline three estimators (weighting, shrinkage, or GMM) for implementing this strategy, and show that my methods can reduce variance by up to 50% in typical experimental designs; therefore, only half of the experimental sample is required to attain the same statistical precision. If researchers are allowed to design experiments differently, I show that they can further improve the precision by directly leveraging this correlation between characteristics and assignment. I apply the method to a search listing dataset from Expedia that studies the causal effect of search rankings, and show that my method can substantially improve the precision.
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings

The current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints. This poses a challenge to their deployment for voice assistants such as Amazon Alexa and Google Assistant on edge devices with limited memory budgets. We propose to learn compositional code embeddings to greatly reduce the sizes of BERT-base and RoBERTa-base. We also apply the technique to DistilBERT, ALBERT-base, and ALBERT-large, three already compressed BERT variants which attain similar state-of-the-art performances on semantic parsing with much smaller model sizes. We observe 95.15% 98.46% embedding compression rates and 20.47% 34.22% encoder compression rates, while preserving >97.5% semantic parsing performances. We provide the recipe for training and analyze the trade-off between code embedding sizes and downstream performances.
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds

Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of scene flow benefits many applications, such as autonomous driving and service robot. This paper studies the problem of scene flow estimation from two consecutive 3D point clouds. In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer. The proposed network has a new more-for-less hierarchical architecture. The more-for-less means that the number of input points is greater than the number of output points for scene flow estimation, which brings more input information and balances the precision and resource consumption. In this hierarchical architecture, scene flow of different levels is generated and supervised respectively. A novel attentive embedding module is introduced to aggregate the features of adjacent points using a double attention method in a patch-to-patch manner. The proper layers for flow embedding and flow supervision are carefully considered in our network designment. Experiments show that the proposed network outperforms the state-of-the-art performance of 3D scene flow estimation on the FlyingThings3D and KITTI Scene Flow 2015 datasets. We also apply the proposed network to the realistic LiDAR odometry task, which is a key problem in autonomous driving. The experiment results demonstrate that our proposed network can outperform the ICP-based method and shows good practical application ability. The source codes will be released on https://github.com/IRMVLab/HALFlow.