Miracle-dz/UniFAR-FTUs huggingface dataset:
Architected kirigami metamorphosis

Kirigami, art of paper cutting, enables two-dimensional sheets transforming into unique shapes which are also hard to reshape once with prescribed cutting patterns. Rare kirigami designs manipulate cuts on three-dimensional objects to compose periodic structures with programmability and/or re-programmability. Here, we propose a new class of three-dimensional modular kirigami by introducing cuts on cuboid-shaped objects, based on which constructing two quasi-threedimensional architected kirigamis with even-flat structural form. We demonstrate the proposed architected kirigamis are with rich mobilities triggered by kinematic bifurcations inherited from their composed modular kirigami, and can undergo living-matter-like metamorphosis evolving into miscellaneous transformable three-dimensional architectures and even a pluripotent platform capable of being re-programmed into curvature different surfaces through inverse design. Such metamorphic structures could find broad applications in reconfigurable metamaterials, transformable robots and architectures.
Minimum complexity interpolation in random features models

Despite their many appealing properties, kernel methods are heavily affected by the curse of dimensionality. For instance, in the case of inner product kernels in $\mathbb{R}^d$, the Reproducing Kernel Hilbert Space (RKHS) norm is often very large for functions that depend strongly on a small subset of directions (ridge functions). Correspondingly, such functions are difficult to learn using kernel methods. This observation has motivated the study of generalizations of kernel methods, whereby the RKHS norm -- which is equivalent to a weighted $\ell_2$ norm -- is replaced by a weighted functional $\ell_p$ norm, which we refer to as $\mathcal{F}_p$ norm. Unfortunately, tractability of these approaches is unclear. The kernel trick is not available and minimizing these norms requires to solve an infinite-dimensional convex problem. We study random features approximations to these norms and show that, for $p>1$, the number of random features required to approximate the original learning problem is upper bounded by a polynomial in the sample size. Hence, learning with $\mathcal{F}_p$ norms is tractable in these cases. We introduce a proof technique based on uniform concentration in the dual, which can be of broader interest in the study of overparametrized models. For $p= 1$, our guarantees for the random features approximation break down. We prove instead that learning with the $\mathcal{F}_1$ norm is $\mathsf{NP}$-hard under a randomized reduction based on the problem of learning halfspaces with noise.
Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact

We present a fast and feature-complete differentiable physics engine, Nimble (nimblephysics.org), that supports Lagrangian dynamics and hard contact constraints for articulated rigid body simulation. Our differentiable physics engine offers a complete set of features that are typically only available in non-differentiable physics simulators commonly used by robotics applications. We solve contact constraints precisely using linear complementarity problems (LCPs). We present efficient and novel analytical gradients through the LCP formulation of inelastic contact that exploit the sparsity of the LCP solution. We support complex contact geometry, and gradients approximating continuous-time elastic collision. We also introduce a novel method to compute complementarity-aware gradients that help downstream optimization tasks avoid stalling in saddle points. We show that an implementation of this combination in an existing physics engine (DART) is capable of a 87x single-core speedup over finite-differencing in computing analytical Jacobians for a single timestep, while preserving all the expressiveness of original DART.
PLAN-B: Predicting Likely Alternative Next Best Sequences for Action Prediction

Action prediction focuses on anticipating actions before they happen. Recent works leverage probabilistic approaches to describe future uncertainties and sample future actions. However, these methods cannot easily find all alternative predictions, which are essential given the inherent unpredictability of the future, and current evaluation protocols do not measure a system’s ability to find such alternatives. We re-examine action prediction in terms of its ability to predict not only the top predictions, but also top alternatives with the accuracy@k metric. In addition, we propose Choice F1: a metric inspired by F1 score which evaluates a prediction system’s ability to find all plausible futures while keeping only the most probable ones. To evaluate this problem, we present a novel method, Predicting the Likely Alternative Next Best, or PLAN-B, for action prediction which automatically finds the set of most likely alternative futures. PLAN-B consists of two novel components: (i) a Choice Table which ensures that all possible futures are found, and (ii) a “Collaborative” RNN system which combines both action sequence and feature information. We demonstrate that our system outperforms state-of-the-art results on benchmark datasets.
Complexity of Projected Newton Methods for Bound-constrained Optimization

We analyze the iteration complexity of two methods based on the projected gradient and Newton methods for solving bound-constrained optimization problems. The ﬁrst method is a scaled variant of Bertsekas’s two-metric projection method [2], which can be shown to output an (cid:15) -approximate ﬁrst-order point in O ( (cid:15) − 2 ) iterations. The second is a projected Newton-Conjugate Gradient (CG) method, which locates an (cid:15) -approximate second-order point with high probability in O ( (cid:15) − 3 / 2 ) iterations, at a cost of O ( (cid:15) − 7 / 4 ) gradient evaluations or Hessian-vector products (omitting logarithmic factors). Besides having good complexity properties, both methods are appealing from a practical point of view, as we show using some illustrative numerical results.
Optimal Transmission Topology for Facilitating the Growth of Renewable Power Generation

Transmission topology control is a tool used by system operators in the role of a control action taken into account as a preventive or corrective action relative to a specific outage or set of outages. However, their inclusion in most electricity market frameworks is limited. With the increasing penetration of intermittent energy sources, optimal topology can be used as a lever of flexibility to decrease the total system cost. This paper demonstrates the evolution of optimal topology control on systems with increasing quantities of intermittent renewable energy along two axes. First, the effects of the increased variable sources on the variations of optimal topology are explored. Second, we elaborate on the growing advantages of exploiting transmission-level grid flexibility in terms of total system cost. Case studies are performed on a modified RTS-96 network.
Understanding Mental Models of AI through Player-AI Interaction

Designing human-centered AI-driven applications require deep understandings of how people develop mental models of AI. Currently, we have little knowledge of this process and limited tools to study it. This paper presents the position that AI-based games, particularly the player-AI interaction component, offer an ideal domain to study the process in which mental models evolve. We present a case study to illustrate the benefits of our approach for explainable AI.
Differentiable Drawing and Sketching

We present a bottom-up differentiable relaxation of the process of drawing points, lines and curves into a pixel raster. Our approach arises from the observation that rasterising a pixel in an image given parameters of a primitive can be reformulated in terms of the primitive's distance transform, and then relaxed to allow the primitive's parameters to be learned. This relaxation allows end-to-end differentiable programs and deep networks to be learned and optimised and provides several building blocks that allow control over how a compositional drawing process is modelled. We emphasise the bottom-up nature of our proposed approach, which allows for drawing operations to be composed in ways that can mimic the physical reality of drawing rather than being tied to, for example, approaches in modern computer graphics. With the proposed approach we demonstrate how sketches can be generated by directly optimising against photographs and how auto-encoders can be built to transform rasterised handwritten digits into vectors without supervision. Extensive experimental results highlight the power of this approach under different modelling assumptions for drawing tasks.
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB

RocksDB is a general-purpose embedded key-value store used in multiple different settings. Its versatility comes at the cost of complex tuning configurations. This paper investigates maximizing the throughput of RocksDB 10 operations by auto-tuning ten parameters of varying ranges. Off-the-shelf optimizers struggle with high-dimensional problem spaces and require a large number of training samples. We propose two techniques to tackle this problem: multitask modeling and dimensionality reduction through clustering. By incorporating adjacent optimization in the model, the model converged faster and found complicated settings that other tuners could not find. This approach had an additional computational complexity overhead, which we mitigated by manually assigning parameters to each sub-goal through our knowledge of RocksDB. The model is then incorporated in a standard Bayesian Optimization loop to find parameters that maximize RocksDB's 10 throughput. Our method achieved x1.3 improvement when bench-marked against a simulation of Facebook's social graph traffic, and converged in ten optimization steps compared to other state-of-the-art methods that required fifty steps.
Matroid stratifications of hypergraph varieties, their realization spaces, and discrete conditional independence models

We study varieties associated to hypergraphs from the point of view of projective geometry and matroid theory. We describe their decompositions into matroid varieties, which may be reducible and can have arbitrary singularities by the Mn¨ev–Sturmfels universality theorem. We focus on various families of hypergraph varieties for which we explicitly compute an irredundant irreducible decomposition. Our main ﬁndings in this direction are threefold: (1) we describe minimal matroids of such hypergraphs; (2) we prove that the varieties of these matroids are irreducible and their union is the hypergraph variety; and (3) we show that every such matroid is realizable over real numbers. As corollaries, we give conceptual decompositions of various, previously-studied, varieties associated with graphs, hypergraphs, and adjacent minors of generic matrices. In particular, our decomposition strategy gives immediate matroid interpretations of the irreducible components of multiple families of varieties associated to conditional independence (CI) models in statistical theory and unravels their symmetric structures which hugely simpliﬁes the computations.
Modeling of interfacial flows based on an explicit volume diffusion concept

A novel volume of fluid model (VoF) called explicit volume diffusion (EVD) is developed for the simulation of interfacial flows, including those with turbulence and primary spray atomisation. The EVD model is derived by volume averaging the VoF equations over a physically-defined length scale. This introduces unclosed sub-volume flux, sub-volume stress and volume averaged surface tension force. Sub-volume fluctuations arise due to both turbulent motions and other interface dynamics which can, in general, occur in both laminar and turbulent flows. Both of these types of fluctuations are attenuated by the volume averaging process. The sub-volume flux is closed by a gradient diffusion model and involves an explicit volume diffusion coefficient that is linked to the physical length scale. The sub-volume stress closure introduces an explicit volume viscosity augmented by turbulent viscosity in turbulent flows. The volume averaged surface tension force closure is based on fractal properties of wrinkled sub-volume interfaces. These closures are evaluated through a priori analysis of resolved flow simulations for a series of two-dimensional laminar and three-dimensional turbulent interfacial shear flows. Subsequently, full EVD simulations are validated for these shear flows and for a laboratory airblast spray jet. Numerical convergence is demonstrated by keeping the physical length scale constant while reducing the numerical grid size so that numerical diffusion diminishes and becomes overwhelmed by the explicit volume diffusion. A sensitivity analysis is also conducted for variations in the physical length scale, which is compared to the boundary layer thickness on the light fluid side of the interface.
Ab initio structure determination of the Γ form of d-sorbitol (d-glucitol) by powder synchrotron X-ray diffraction

A high-resolution powder synchrotron X-ray diffraction pattern of the Γ form of d-sorbitol has been recorded at 293 K on the BM1B beamline at the ESRF (Grenoble). The starting model of the structure was found by Monte Carlo simulated annealing. The final structure was obtained through Rietveld refinements performed with soft restraints on interatomic bond lengths and angles. The symmetry is orthorhombic, space group P21212, with 12 molecules within the cell [a = 24.3012 (2), b = 20.5726 (2), c = 4.8672 (1) A, V = 2433.30 (3) A3, Z′ = 3, 36 non-H independent atoms]. Crystalline cohesion between neighbouring molecules is achieved by three networks of O—H⋯O hydrogen bonds. The width of the Bragg peaks is interpreted through a microstructural approach in terms of anisotropic strain effects.
Physics-based Differentiable Depth Sensor Simulation

Gradient-based algorithms are crucial to modern computer-vision and graphics applications, enabling learning-based optimization and inverse problems. For example, photorealistic differentiable rendering pipelines for color images have been proven highly valuable to applications aiming to map 2D and 3D domains. However, to the best of our knowledge, no effort has been made so far towards extending these gradient-based methods to the generation of depth (2.5D) images, as simulating structured-light depth sensors implies solving complex light transport and stereo-matching problems. In this paper, we introduce a novel end-to-end differentiable simulation pipeline for the generation of realistic 2.5D scans, built on physics-based 3D rendering and custom block-matching algorithms. Each module can be differentiated w.r. t. sensor and scene parameters; e.g., to automatically tune the simulation for new devices over some provided scans or to leverage the pipeline as a 3D-to-2.5D transformer within larger computer-vision applications. Applied to the training of deep-learning methods for various depth-based recognition tasks (classification, pose estimation, semantic segmentation), our simulation greatly improves the performance of the resulting models on real scans, thereby demonstrating the fidelity and value of its synthetic depth data compared to previous static simulations and learning-based domain adaptation schemes.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection

The objective of this paper is to learn context- and depth- aware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i) rather than appealing to the complicated pseudo-LiDAR based approach, we propose a depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context; (ii) this is achieved by first adaptively sampling context-aware nodes in the image context and then dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information; (Hi) by augmenting a center-aware depth encoding (CDE) task, our method successfully alleviates the inaccurate depth prior; (iv) we thoroughly demonstrate the effectiveness of our proposed approach and show state-of-the-art results among the monocular-based approaches on the KITTI benchmark dataset. Particularly, we rank 1st in the highly competitive KITTI monocular 3D object detection track on the submission day (November 16th, 2020). Code and models are released at https: //github.com/fudan-zvg/DDMP
Reputation and Pricing Dynamics in Online Markets

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers’ reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers’ reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares; if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longer-term interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers’ reputations and prices at the stable state.
Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool

Monte Carlo path tracer renders noisy image sequences at low sampling counts. Although great progress has been made on denoising such sequences, existing methods still suffer from spatial and temporary artifacts. In this paper, we tackle the problems in Monte Carlo rendering by proposing a two-stage denoiser based on the adaptive sampling strategy. In the first stage, concurrent to adjusting samples per pixel (spp) on-the-fly, we reuse the computations to generate extra denoising kernels applying on the adaptively rendered image. Rather than a direct prediction of pixel-wise kernels, we save the overhead complexity by interpolating such kernels from a public kernel pool, which can be dynamically updated to fit input signals. In the second stage, we design the position-aware pooling and semantic alignment operators to improve spatial-temporal stability. Our method was first benchmarked on 10 synthesized scenes rendered from the Mitsuba renderer and then validated on 3 additional scenes rendered from our self-built RTX-based renderer. Our method outperforms state-of-the-art counterparts in terms of both numerical error and visual quality.
The JCMT BISTRO Survey: The Distribution of Magnetic Field Strengths toward the OMC-1 Region

Measurement of magnetic field strengths in a molecular cloud is essential for determining the criticality of magnetic support against gravitational collapse. In this paper, as part of the JCMT BISTRO survey, we suggest a new application of the Davis–Chandrasekhar–Fermi (DCF) method to estimate the distribution of magnetic field strengths in the OMC-1 region. We use observations of dust polarization emission at 450 and 850 μm, and C18O (3–2) spectral line data obtained with the JCMT. We estimate the volume density, the velocity dispersion, and the polarization angle dispersion in a box, 40″ × 40″ (5×5 pixels), which moves over the OMC-1 region. By substituting three quantities in each box with the DCF method, we get magnetic field strengths over the OMC-1 region. We note that there are very large uncertainties in the inferred field strengths, as discussed in detail in this paper. The field strengths vary from 0.8 to 26.4 mG, and their mean value is about 6 mG. Additionally, we obtain maps of the mass-to-flux ratio in units of a critical value and the Alfvén Mach number. The central parts of the BN–KL and South (S) clumps in the OMC-1 region are magnetically supercritical, so the magnetic field cannot support the clumps against gravitational collapse. However, the outer parts of the region are magnetically subcritical. The mean Alfvén Mach number is about 0.4 over the region, which implies that the magnetic pressure exceeds the turbulent pressure in the OMC-1 region.
Autocorrect in the Process of Translation — Multi-task Learning Improves Dialogue Machine Translation

Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying results. In this paper, we conduct a deep analysis of a dialogue corpus and summarize three major issues on dialogue translation, including pronoun dropping (), punctuation dropping (), and typos (). In response to these challenges, we propose a joint learning method to identify omission and typo, and utilize context to translate dialogue utterances. To properly evaluate the performance, we propose a manually annotated dataset with 1,931 Chinese-English parallel utterances from 300 dialogues as a benchmark testbed for dialogue translation. Our experiments show that the proposed method improves translation quality by 3.2 BLEU over the baselines. It also elevates the recovery rate of omitted pronouns from 26.09% to 47.16%. We will publish the code and dataset publicly at https://xxx.xx.
Systematic Mapping Protocol: Reasoning Algorithms on Feature Model

Context: The importance of the feature modeling for the software product lines considering the modeling and management of the variability. Objective: Define a protocol to conduct a systematic mapping study to summarize and synthesize the evidence on reasoning algorithms for feature modeling. Method: Application the protocol to conduct a systematic mapping study according the guidelines of K. Petersen. Results: A validated protocol to conduct a systematic mapping study. Conclusions: Initial findings show that a more detailed review for the different reasoning algorithms for feature modeling is needed.
Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems

Based on the Fundamental Lemma by Willems et al., the entire behaviour of a Linear Time-Invariant (LTI) system can be characterised by a single data sequence of the system as long the input is persistently exciting. This is an essential result for data-driven analysis and control. In this work, we aim to generalise this LTI result to Linear Parameter-Varying (LPV) systems. Based on the behavioural framework for LPV systems, we prove that one can obtain a result similar to Willems’. Based on an LPV representation, i.e., embedding, of nonlinear systems, this allows the application of the Fundamental Lemma for systems beyond the linear class.