Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncovering migration systems through spatio-temporal tensor co-clustering

A central problem in the study of human mobility is that of migration systems. Typically, migration systems are defined as a set of relatively stable movements of people between two or more locations over time. While these emergent systems are expected to vary over time, they ideally contain a stable underlying structure that could be discovered empirically. There have been some notable attempts to formally or informally define migration systems, however they have been limited by being hard to operationalize, and by defining migration systems in ways that ignore origin/destination aspects and/or fail to account for migration dynamics. In this work we propose a novel method, spatio-temporal (ST) tensor co-clustering, stemming from signal processing and machine learning theory. To demonstrate its effectiveness for describing stable migration systems we focus on domestic migration between counties in the US from 1990-2018. Relevant data for this period has been made available through the US Internal Revenue Service. Specifically, we concentrate on three illustrative case studies: (i) US Metropolitan Areas, (ii) the state of California, and (iii) Louisiana, focusing on detecting exogenous events such as Hurricane Katrina in 2005. Finally, we conclude with discussion and limitations of this approach.
Laser-induced bistability in coupled micromechanical oscillators

In this work, we experimentally investigate the dynamics of pairs of opto-thermally driven, mechanically coupled, doubly clamped, silicon micromechanical oscillators, and numerically investigate the dynamics of the corresponding lumpedparameter model. Coupled limit cycle oscillators exhibit striking nonlinear dynamics and bifurcations in response to variations in system parameters. We show that the input laser power influences the frequency detuning between non-identical oscillators. As the laser power is changed, different regimes of oscillations such as the synchronized state, the drift state, and the quasi-periodic state are mapped at minimal and high coupling strengths. For nonidentical oscillators, coexistence of two states, the synchronized state and the quasi-periodic state, is demonstrated at strong coupling and high laser power. Experimentally, bistability manifests as irregular oscillations as the system rapidly switches between the two states due to the system’s sensitive dependence on initial conditions in the presence of noise. We provide a qualitative comparison of the experimental and numerical results to elucidate the behavior of the system.
Time Varying Regression with Hidden Linear Dynamics

We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system. Counterintuitively, we show that when the underlying dynamics are stable the parameters of this model can be estimated from data by combining just two ordinary least squares estimates. We offer a finite sample guarantee on the estimation error of our method and discuss certain advantages it has over Expectation-Maximization (EM), which is the main approach proposed by prior work.
Settling the Bias and Variance of Meta-Gradient Estimation for Meta-Reinforcement Learning

Meta-Reinforcement Learning (Meta-RL) refers to the methods that conduct meta-learning on reinforcement learning tasks. In the meta-RL framework, an outer-loop learning procedure is maintained to help the inner-loop reinforcement learner learn fast adaptations thus better generalisation performance. When the inner-loop is optimised by policy gradient methods, it is also called gradient based Meta-RL (GMRL). In recent years, GMRL methods have achieved remarkable successes in either discovering effective online hyperparameter for one single task (Xu et al., 2018) or learning good initialisation for multi-task transfer learning (Finn et al., 2017). Despite the empirical successes, it is often neglected that computing meta-gradients via vanilla backpropagation is ill-defined. In this paper, we argue that the stochastic meta-gradient estimation adopted by many existing MGRL methods are in fact biased; the bias comes from two sources: 1) the compositional bias that is inborn in the structure of compositional optimisation problems and 2) the bias of multi-step Hessian estimation caused by direct automatic differentiation. To better understand ∗Equal Contributions. † Corresponding Author. Email to: benjaminliu.eecs@gmail.com, xidong.feng. 20@ucl.ac. uk, yaodong.yang@pku.edu. cn 1 ar X iv :2 11 2. 15 40 0v 1 [ cs . L G ] 3 1 D ec 2 02 1 the meta-gradient biases, we perform the first of its kind study to quantify the amount for each of them. We start by providing a unifying derivation for existing GMRL algorithms, and then theoretically analyse both the bias and the variance of existing gradient estimation methods. On understanding the underlying principles of bias, we propose two mitigation solutions based on off-policy correction and multi-step Hessian estimation techniques. The off-policy inner-loop policy gradient correction can introduce more samples to lower the inner-loop variance and finally the compositional bias. The multi-step Hessian estimation techniques changes the form of inner-loop policy gradient and this new form can enable us with low/zero-bias Hessian estimation with automatic differentiation. Comprehensive ablation studies have been conducted and results reveals: (1) The existence of these two biases and how they influence the meta-gradient estimation when combined with different estimator/sample size/step and learning rate. (2) The effectiveness of these mitigation approaches for meta-gradient estimation and thereby the final return on two practical Meta-RL algorithms: LOLA-DiCE and Meta-gradient Reinforcement Learning.
Exact time-dependent solution of the Schrödinger equation, its generalization to the phase space and relation to the Gibbs distribution

Using the simplest but fundamental example, the problem of the infinite potential well, this paper makes an ideological attempt (supported by rigorous mathematical proofs) to approach the issue of ≪understanding≫ the mechanism of quantum mechanics processes, despite the well-known examples of the EPR paradox type. The new exact solution of the Schrödinger equation is analyzed from the perspective of quantum mechanics in the phase space. It is the phase space, which has been extensively used recently in quantum computing, quantum informatics and communications, that is the bridge towards classical physics, where understanding of physical reality is still possible. In this paper, an interpretation of time-dependent processes of energy redistribution in a quantum system, probability waves, the temperature and entropy of a quantum system, and the transition to a time-independent ≪frozen state≫ is obtained, which is understandable from the point of view of classical physics. The material of the paper clearly illustrates the solution of the problem from the standpoint of continuum mechanics, statistical physics and, of course, quantum mechanics in the phase space.
FPGA Based Accelerator for Neural Networks Computation with Flexible Pipelining

FPGA is appropriate for fix-point neural networks computing due to high power efficiency and configurability. However, its design must be intensively refined to achieve high performance using limited hardware resources. We present an FPGA-based neural networks accelerator and its optimization framework, which can achieve optimal efficiency for various CNN models and FPGA resources. Targeting high throughput, we adopt layer-wise pipeline architecture for higher DSP utilization. To get the optimal performance, a flexible algorithm to allocate balanced hardware resources to each layer is also proposed, supported by activation buffer design. Through our well-balanced implementation of four CNN models on ZC706, the DSP utilization and efficiency are over 90%. For VGG16 on ZC706, the proposed accelerator achieves the performance of 2.58x, 1.53x and 1.35x better than the referenced non-pipeline architecture [1], pipeline architecture [2] and [3], respectively.
Compositional Data Regression in Insurance with Exponential Family PCA

Compositional data are multivariate observations that carry only relative information between components. Applying standard multivariate statistical methodology directly to analyze compositional data can lead to paradoxes and misinterpretations. Compositional data also frequently appear in insurance, especially with telematics information. However, such type of data does not receive deserved special treatment in most existing actuarial literature. In this paper, we explore and investigate the use of exponential family principal component analysis (EPCA) to analyze compositional data in insurance. The method is applied to analyze a dataset obtained from the U.S. Mine Safety and Health Administration. The numerical results show that EPCA is able to produce principal components that are significant predictors and improve the prediction accuracy of the regression model. The EPCA method can be a promising useful tool for actuaries to analyze compositional data.
On the Number of Incidences when Avoiding the Klan

Given a set of points P and a set of regions O , an incidence is a pair ( p, (cid:111) ) ∈ P × O such that p ∈ (cid:111) . We obtain a number of new results on a classical question in combinatorial geometry: What is the number of incidences (under certain restrictive conditions)? We prove a bound of O (cid:0) kn (log n/ log log n ) d − 1 (cid:1) on the number of incidences between n points and n axis-parallel boxes in R d , if no k boxes contain k common points, that is, if the incidence graph between the points and the boxes does not contain K k,k as a subgraph. This new bound improves over previous work, by Basit, Chernikov, Starchenko, Tao, and Tran (2021), by more than a factor of log d n for d > 2. Furthermore, it matches a lower bound implied by the work of Chazelle (1990), for k = 2, thus settling the question for points and boxes. We also study several other variants of the problem. For halfspaces, using shallow cuttings, we get a linear bound in two and three dimensions. We also present linear (or near linear) bounds for shapes with low union complexity, such as pseudodisks and fat triangles.
PPRviz: Effective and Efficient Graph Visualization based on Personalized PageRank

Graph visualization is an important problem that finds applications in various domains, e.g. , social network analysis, traffic planning, and bioinformatics. Existing solutions for graph visualization, however, fail to scale to large graphs with millions of nodes, as they either provide inferior visualization results or incur significant computational cost. To address the deficiencies of prior works, we propose PPRviz , a multi-level visualization method for large graphs. Lying in the core of PPRviz is a new measure of graph node distance, PDist , that is specifically designed for visualization. In particular, PDist is formulated based on personalized PageRank, and it provides non-trivial theoretical guarantees for two well-adopted aesthetic measures. We present efficient algorithms for estimating PDist with provable accuracy and time complexity, while incurring small preprocessing costs. Extensive experiments show that PPRviz significantly outperforms 13 state-of-the-art competitors on 12 real-world graphs in terms of both effectiveness and efficiency, and that PPRviz provides interactive visualizations within one second on billion-edge graphs.
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [321, 326] is an influential HDC/VSA model that is well known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey [222] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain; however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.
Towards Automated Sample Collection and Return in Extreme Underwater Environments

In this report, we present the system design, operational strategy, and results of coordinated multivehicle field demonstrations of autonomous marine robotic technologies in searchfor- life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of remotely operated vehicle (ROV) manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped seven-degrees-of-freedom hydraulic manipulators.We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a three-dimensional understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research.
Instability of de-Sitter black hole with scalar field coupled to Gauss-Bonnet invariant

The black hole scalarization in a special Einstein-scalar-Gauss-Bonnet (EsGB) gravity has been widely investigated in recent years. Especially, the spontaneous scalarization of scalar-free black hole in de-Sitter (dS) spacetime possesses interesting features due to the existence of cosmological horizon. In this work, we focus on the massive scalar field perturbation on Schwarzschild dS (SdS) black hole in a special EsGB theory. We study the (in)stability of SdS in frequency domain and verify the results in time domain. Then we figure out the unstable/stable regions in (Λ, α)-plane as well as in (m,α)-plane for various perturbation modes, where Λ, α and m denote the cosmological constant, the GB coupling strength and the mass of scalar field, respectively. Our study could be a good preparation for one to further understand the black hole scalarization in dS spacetime. Electronic address: yangzhenhao yzu@163.com †Electronic address: FuguoyangEDU@163.com ‡Electronic address: xmeikuang@yzu.edu. cn (corresponding author) §Electronic address: jianpinwu@yzu.edu. cn 1 ar X iv :2 11 2. 15 05 2v 1 [ gr -q c] 3 0 D ec 2 02 1
Eruption of the Envelope of Massive Stars by Energy Injection with Finite Duration

A significant fraction of supernovae show signatures of dense circumstellar material (CSM). While multiple scenarios for creating a dense CSM exist, mass eruption due to injection of energy at the base of the outer envelope is a likely possibility. We carry out radiation hydrodynamical simulations of eruptive mass loss from a typical red supergiant progenitor with an initial mass of 15 M ⊙, for the first time focusing on the timescale of the injection as well as energy. We find that not only sufficient injection energy but also sufficient rate of energy injection per unit time, L min ∼ 8 × 1040 erg s−1 in this particular model, is required for eruption of unbound CSM. This result suggests that the energy injection rate needs to be greater than the binding energy of the envelope divided by the dynamical timescale for the eruption. The density profile of the resulting CSM, whose shape was analytically and numerically predicted in the limit of instantaneous energy injection, similarly holds for a finite injection timescale. We discuss our findings in the framework of proposed mass outburst scenarios, specifically wave-driven outbursts and common-envelope ejection.
Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions

In this paper, we study Stochastic Control Barrier Functions (SCBFs) to enable the design of probabilistic safe real-time controllers in presence of uncertainties and based on noisy measurements. Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value. To that end, we first estimate the system states from the noisy measurements using an Extended Kalman filter, and compute confidence intervals on the filtering errors. Then, we account for filtering errors and derive sufficient conditions on the control input based on the estimated states to bound the probability that the real states of the system enter an unsafe region within a finite time interval. We show that these sufficient conditions are linear constraints on the control input, and, hence, they can be used in tractable optimization problems to achieve safety, in addition to other properties like reachability, and stability. Our approach is evaluated using a simulation of a lane-changing scenario on a highway with dense traffic.
Space-Efficient FPT Algorithms

We prove algorithmic results showing that a number of natural parameterized problems are in the restricted-space parameterized classes Para-L and FPT + XL. The first class comprises problems solvable in f(k)n time using g(k) + O(log n) bits of space (k is the parameter and n is the input size; f and g are computable functions). The second class comprises problems solvable under the same time bound, but using g(k) log n bits of space instead. Earlier work on these classes has focused largely on their structural aspects and their relationships with various other classes. We complement this with Para-L and FPT + XL algorithms for a restriction of Hitting Set, some graph deletion problems where the target class has an infinite forbidden set characterization, a number of problems parameterized by vertex cover number, and Feedback Vertex Set.
Multi-relation Graph Summarization

Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive model of real-world activities, in which a relation can be a topic in social networks, an interaction type in genetic networks, or a snapshot in temporal graphs. The first approach that we consider for multi-relation graph summarization is a two-step method based on summarizing each relation in isolation, and then aggregating the resulting summaries in some clever way to produce a final unique summary. In doing this, as a side contribution, we provide the first polynomial-time approximation algorithm based on the k-Median clustering for the classic problem of lossless single-relation graph summarization. Then, we demonstrate the shortcomings of these two-step methods, and propose holistic approaches, both approximate and heuristic algorithms, to compute a summary directly for multi-relation graphs. In particular, we prove that the approximation bound of k-Median clustering for the single relation solution can be maintained in a multi-relation graph with proper aggregation operation over adjacency matrices corresponding to its multiple relations. Experimental results and case studies (on co-authorship networks and brain networks) validate the effectiveness and efficiency of the proposed algorithms.
Power Under Multiplicity Project (PUMP): Estimating Power, Minimum Detectable Effect Size, and Sample Size When Adjusting for Multiple Outcomes in Multi-level Experiments

For randomized controlled trials (RCTs) with a single intervention being measured on multiple outcomes, researchers often apply a multiple testing procedure (such as Bonferroni or Benjamini-Hochberg) to adjust $p$-values. Such an adjustment reduces the likelihood of spurious findings, but also changes the statistical power, sometimes substantially, which reduces the probability of detecting effects when they do exist. However, this consideration is frequently ignored in typical power analyses, as existing tools do not easily accommodate the use of multiple testing procedures. We introduce the PUMP R package as a tool for analysts to estimate statistical power, minimum detectable effect size, and sample size requirements for multi-level RCTs with multiple outcomes. Multiple outcomes are accounted for in two ways. First, power estimates from PUMP properly account for the adjustment in $p$-values from applying a multiple testing procedure. Second, as researchers change their focus from one outcome to multiple outcomes, different definitions of statistical power emerge. PUMP allows researchers to consider a variety of definitions of power, as some may be more appropriate for the goals of their study. The package estimates power for frequentist multi-level mixed effects models, and supports a variety of commonly-used RCT designs and models and multiple testing procedures. In addition to the main functionality of estimating power, minimum detectable effect size, and sample size requirements, the package allows the user to easily explore sensitivity of these quantities to changes in underlying assumptions.