Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Cages for Detail-Preserving 3D Deformations

We propose a novel learnable representation for detail preserving shape deformation. The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source. Our method extends a traditional cage-based deformation technique, where the source shape is enclosed by a coarse control mesh termed cage, and translations prescribed on the cage vertices are interpolated to any point on the source mesh via special weight functions. The use of this sparse cage scaffolding enables preserving surface details regardless of the shape’s intricacy and topology. Our key contribution is a novel neural network architecture for predicting deformations by controlling the cage. We incorporate a differentiable cage-based deformation module in our architecture, and train our network end-to-end. Our method can be trained with common collections of 3D models in an unsupervised fashion, without any cage-specific annotations. We demonstrate the utility of our method for synthesizing shape variations and deformation transfer.
Formation sites of Population III star formation: The effects of different levels of rotation and turbulence on the fragmentation behaviour of primordial gas

We use the moving-mesh code arepo to investigate the effects of different levels of rotation and turbulence on the fragmentation of primordial gas and the formation of Population III stars. We consider nine different combinations of turbulence and rotation and carry out five different realizations of each setup, yielding one of the largest sets of simulations of Population III star formation ever performed. We find that fragmentation in Population III star-forming systems is a highly chaotic process and show that the outcomes of individual realizations of the same initial conditions often vary significantly. However, some general trends are apparent. Increasing the turbulent energy promotes fragmentation, while increasing the rotational energy inhibits fragmentation. Within the ∼1000 yr period that we simulate, runs including turbulence yield flat protostellar mass functions while purely rotational runs show a more top-heavy distribution. The masses of the individual protostars are distributed over a wide range from a few $10^{-3} \, {\rm M_{\odot }}$ to several tens of M⊙. The total mass growth rate of the stellar systems remains high throughout the simulations and depends only weakly on the degree of rotation and turbulence. Mergers between protostars are common, but predictions of the merger fraction are highly sensitive to the criterion used to decide whether two protostars should merge. Previous studies of Population III star formation have often considered only one realization per set of initial conditions. However, our results demonstrate that robust trends can only be reliably identified by considering averages over a larger sample of runs.
Bonn Activity Maps: Dataset Description

The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest. Previous datasets are focused on either 3D scene representations with semantic information, tracking of multiple persons and recognition of their actions, or activity recognition of a single person in captured 3D environments. We present Bonn Activity Maps, a large-scale dataset for human tracking, activity recognition and anticipation of multiple persons. Our dataset comprises four different scenes that have been recorded by time-synchronized cameras each only capturing the scene partially, the reconstructed 3D models with semantic annotations, motion trajectories for individual people including 3D human poses as well as human activity annotations. We utilize the annotations to generate activity likelihoods on the 3D models called activity maps.
YOLACT++ Better Real-Time Instance Segmentation

We present a simple, fully-convolutional model for real-time (<inline-formula><tex-math notation="LaTeX">$>30$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>30</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-3014297.gif"/></alternatives></inline-formula> fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on <italic>only one GPU</italic>. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn’t depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time.
A scaling-invariant algorithm for linear programming whose running time depends only on the constraint matrix

Following the breakthrough work of Tardos (Oper Res 34:250–256, 1986) in the bit-complexity model, Vavasis and Ye (Math Program 74(1):79–120, 1996) gave the first exact algorithm for linear programming in the real model of computation with running time depending only on the constraint matrix. For solving a linear program (LP) $$\max \, c^\top x,\, Ax = b,\, x \ge 0,\, A \in \mathbb {R}^{m \times n}$$ max c ⊤ x , A x = b , x ≥ 0 , A ∈ R m × n , Vavasis and Ye developed a primal-dual interior point method using a ‘layered least squares’ (LLS) step, and showed that $$O(n^{3.5} \log (\bar{\chi }_A+n))$$ O ( n 3.5 log ( χ ¯ A + n ) ) iterations suffice to solve (LP) exactly, where $$\bar{\chi }_A$$ χ ¯ A is a condition measure controlling the size of solutions to linear systems related to A . Monteiro and Tsuchiya (SIAM J Optim 13(4):1054–1079, 2003), noting that the central path is invariant under rescalings of the columns of A and c , asked whether there exists an LP algorithm depending instead on the measure $$\bar{\chi }^*_A$$ χ ¯ A ∗ , defined as the minimum $$\bar{\chi }_{AD}$$ χ ¯ AD value achievable by a column rescaling AD of A , and gave strong evidence that this should be the case. We resolve this open question affirmatively. Our first main contribution is an $$O(m^2 n^2 + n^3)$$ O ( m 2 n 2 + n 3 ) time algorithm which works on the linear matroid of A to compute a nearly optimal diagonal rescaling D satisfying $$\bar{\chi }_{AD} \le n(\bar{\chi }_A^*)^3$$ χ ¯ AD ≤ n ( χ ¯ A ∗ ) 3 . This algorithm also allows us to approximate the value of $$\bar{\chi }_A$$ χ ¯ A up to a factor $$n (\bar{\chi }_A^*)^2$$ n ( χ ¯ A ∗ ) 2 . This result is in surprising contrast to that of Tunçel (Math Program 86(1):219–223, 1999), who showed NP-hardness for approximating $$\bar{\chi }_A$$ χ ¯ A to within $$2^{\textrm{poly}(\textrm{rank}(A))}$$ 2 poly ( rank ( A ) ) . The key insight for our algorithm is to work with ratios $$g_i/g_j$$ g i / g j of circuits of A —i.e., minimal linear dependencies $$Ag=0$$ A g = 0 —which allow us to approximate the value of $$\bar{\chi }_A^*$$ χ ¯ A ∗ by a maximum geometric mean cycle computation in what we call the ‘circuit ratio digraph’ of A . While this resolves Monteiro and Tsuchiya’s question by appropriate preprocessing, it falls short of providing either a truly scaling invariant algorithm or an improvement upon the base LLS analysis. In this vein, as our second main contribution we develop a scaling invariant LLS algorithm, which uses and dynamically maintains improving estimates of the circuit ratio digraph, together with a refined potential function based analysis for LLS algorithms in general. With this analysis, we derive an improved $$O(n^{2.5} \log (n)\log (\bar{\chi }^*_A+n))$$ O ( n 2.5 log ( n ) log ( χ ¯ A ∗ + n ) ) iteration bound for optimally solving (LP) using our algorithm. The same argument also yields a factor $$n/\log n$$ n / log n improvement on the iteration complexity bound of the original Vavasis–Ye algorithm.
Local Theory for Spatio-Temporal Canards and Delayed Bifurcations

We present a rigorous framework for the local analysis of canards and slow passages through bifurcations in a wide class of infinite-dimensional dynamical systems with time-scale separation. The framework is applicable to models where an infinite-dimensional dynamical system for the fast variables is coupled to a finite-dimensional dynamical system for slow variables. We prove the existence of centre-manifolds for generic models of this type, and study the reduced, finite-dimensional dynamics near bifurcations of (possibly) patterned steady states in the layer problem. Theoretical results are complemented with detailed examples and numerical simulations covering systems of local- and nonlocal-reaction diffusion equations, neural field models, and delay-differential equations. We provide analytical foundations for numerical observations recently reported in literature, such as spatio-temporal canards and slow-passages through Hopf bifurcations in spatially-extended systems subject to slow parameter variations. We also provide a theoretical analysis of slow passage through a Turing bifurcation in local and nonlocal models.
Black Hole Growth and Feedback in Isolated ROMULUS25 Dwarf Galaxies

We investigate the effects of massive black hole growth on the structural evolution of dwarf galaxies within the Romulus25 cosmological hydrodynamical simulation. We study a sample of 205 central, isolated dwarf galaxies with stellar masses and a central BH. We find that the local MBH–Mstar relation exhibits a high degree of scatter below Mstar < 1010M⊙, which we use to classify BHs as overmassive or undermassive relative to their host Mstar. Within isolated dwarf galaxies, only 8% of undermassive BHs ever undergo a BH merger, while 95% of overmassive BHs grow through a mixture of BH mergers and accretion. We find that isolated dwarf galaxies that host overmassive BHs also follow different evolutionary tracks relative to their undermassive BH counterparts, building up their stars and dark matter earlier and experiencing star formation suppression starting around z = 2. By z = 0.05, overmassive BH hosts above Mstar > 109M⊙ are more likely to exhibit lower central stellar mass density, lower H i gas content, and lower star formation rates than their undermassive BH counterparts. Our results suggest that overmassive BHs in isolated galaxies above Mstar > 109M⊙ are capable of driving feedback, in many cases suppressing and even quenching star formation by late times.
Action Modifiers: Learning From Adverbs in Instructional Videos

We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations. Key to our method is the fact that the visual representation of the adverb is highly dependent on the action to which it applies, although the same adverb will modify multiple actions in a similar way. For instance, while ‘spread quickly’ and ‘mix quickly’ will look dissimilar, we can learn a common representation that allows us to recognize both, among other actions. We formulate this as an embedding problem, and use scaled dot product attention to learn from weakly-supervised video narrations. We jointly learn adverbs as invertible transformations which operate on the embedding space, so as to add or remove the effect of the adverb. As there is no prior work on weakly supervised learning from adverbs, we gather paired action-adverb annotations from a subset of the HowTo100M dataset, for 6 adverbs: quickly/slowly, finely/coarsely and partially/completely. Our method outperforms all baselines for video-to-adverb retrieval with a performance of 0.719 mAP. We also demonstrate our model’s ability to attend to the relevant video parts in order to determine the adverb for a given action.
Differential privacy with partial knowledge.

Differential privacy offers formal quantitative guarantees for algorithms over datasets, but it assumes attackers that know and can influence all but one record in the database. This assumption often vastly overapproximates the attackers' actual strength, resulting in unnecessarily poor utility. Recent work has made significant steps towards privacy in the presence of partial background knowledge, which can model a realistic attacker's uncertainty. Prior work, however, has definitional problems for correlated data and does not precisely characterize the underlying attacker model. We propose a practical criterion to prevent problems due to correlations, and we show how to characterize attackers with limited influence or only partial background knowledge over the dataset. We use these foundations to analyze practical scenarios: we significantly improve known results about the privacy of counting queries under partial knowledge, and we show that thresholding can provide formal guarantees against such weak attackers, even with little entropy in the data. Finally, we prove composition results on differential privacy with partial knowledge, which quantifies the privacy leakage of complex mechanisms. Together, our results provide a basis for formally quantifying the privacy of many widely-used mechanisms, e.g. publishing the result of surveys, elections or referendums, and releasing usage statistics of online services.
Architectural Stability Reasoning using Self-Awareness Principles: Case of Self-Adaptive Cloud Architectures

With the increased dependence on software, there is a pressing need for engineering long-lived software. As architectures have a profound effect on the life-span of the software and the provisioned quality of service, stable architectures are significant assets. Architectural stability tends to reflect the success of the system in supporting continuous changes without phasing-out. The \textit{behavioural} aspect of stability is essential for seamless operation, to continuously keep the provision of quality requirements stable and prevent architecture's drifting and phasing-out. In this paper, we present a framework for reasoning about stability during runtime, leveraging on self-awareness principles. Specifically, we employ runtime goals for managing stability goals, online learning for reasoning about stability on the long-run, and stochastic games for managing associated trade-offs. We evaluate the proposed work using the case of cloud architectures for its highly dynamics during runtime. The experimental results have shown the efficiency of self-awareness techniques in realising the expected behaviour stable during runtime operation.
Some mathematics for quasi-symmetry

Quasi-symmetry of a steady magnetic field means integrability of first-order guiding-centre motion. Here we derive many restrictions on the possibilities for a quasi-symmetry. We also derive an analogue of the Grad-Shafranov equation for the flux function in a quasi-symmetric magnetohydrostatic field.
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity. These methods are limited when high-resolution outputs are needed since the memory and time costs grow cubically as the volume resolution increases. In this paper, we propose a both memory and time efficient cost volume formulation that is complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes. First, the proposed cost volume is built upon a standard feature pyramid encoding geometry and context at gradually finer scales. Then, we can narrow the depth (or disparity) range of each stage by the depth (or disparity) map from the previous stage. With gradually higher cost volume resolution and adaptive adjustment of depth (or disparity) intervals, the output is recovered in a coarser to fine manner. We apply the cascade cost volume to the representative MVS-Net, and obtain a 35.6% improvement on DTU benchmark (1st place), with 50.6% and 59.3% reduction in GPU memory and run-time. It is also the state-of-the-art learning-based method on Tanks and Temples benchmark. The statistics of accuracy, run-time and GPU memory on other representative stereo CNNs also validate the effectiveness of our proposed method. Our source code is available at https://github.com/alibaba/cascade-stereo.
Unconstrained Facial Expression Transfer using Style-based Generator

Facial expression transfer and reenactment has been an important research problem given its applications in face editing, image manipulation, and fabricated videos generation. We present a novel method for image-based facial expression transfer, leveraging the recent style-based GAN shown to be very effective for creating realistic looking images. Given two face images, our method can create plausible results that combine the appearance of one image and the expression of the other. To achieve this, we first propose an optimization procedure based on StyleGAN to infer hierarchical style vector from an image that disentangle different attributes of the face. We further introduce a linear combination scheme that fuses the style vectors of the two given images and generate a new face that combines the expression and appearance of the inputs. Our method can create high-quality synthesis with accurate facial reenactment. Unlike many existing methods, we do not rely on geometry annotations, and can be applied to unconstrained facial images of any identities without the need for retraining, making it feasible to generate large-scale expression-transferred results.
RSSI-based Secure Localization in the Presence of Malicious Nodes in Sensor Networks

The ability of a sensor node to determine its location in a sensor network is important in many applications. The infrastructure for the location-based services is an easy target for malicious attacks. We address scenarios where malicious node(s) attempt to disrupt, in an uncoordinated or coordinated manner, the localization process of a target node. We propose four techniques for secure localization: weighted least square (WLS), secure weighted least square (SWLS), and $\ell_1$-norm based techniques LN-1 and LN-1E, in a network that includes one or more compromised anchor nodes. WLS and SWLS techniques are shown to offer significant advantage over existing techniques by assigning larger weights to the anchor nodes that are closer to the target node, and by detecting the malicious nodes and eliminating their measurements from the localization process. In a coordinated attack, the localization problem can be posed as a plane fitting problem where the measurements from non-malicious and malicious anchor nodes lie on two different planes. LN-1E technique estimates these two planes and prevents disruption of the localization process. The Cramer-Rao lower bound (CRLB) for the position estimate is also derived. The proposed techniques are shown to provide better localization accuracy than the existing algorithms.
Simple Bilevel Programming and Extensions Part-II: Algorithms

This article continues our study on simple bilevel and simple MPEC problems. In this article we focus on developing algorithms. We show how using the idea of a gap function one can represent a simple MPEC as a simple bilevel problem with non-smooth data. This motivates us first to develop an algorithm for a simple bilevel problem with non-smooth data and modify the scheme for the same to develop an algorithm for the simple MPEC problem. We also discuss how the simple bilevel formulation of a simple MPEC can help us in formulating a stopping criteria for the simple MPEC problem
Differential tomography of micromechanical evolution in elastic materials of unknown micro/macrostructure

Differential evolution indicators are introduced for 3D spatiotemporal imaging of micromechanical processes in complex materials where progressive variations due to manufacturing and/or aging are housed in a highly scattering background of a-priori unknown or uncertain structure. In this vein, a three-tier imaging platform is established where: (1) the domain is periodically (or continuously) subject to illumination and sensing in an arbitrary configuration; (2) sequential sets of measured data are deployed to distill segment-wise scattering signatures of the domain's internal structure through carefully constructed, non-iterative solutions to the scattering equation; and (3) the resulting solution sequence is then used to rigorously construct an imaging functional carrying appropriate invariance with respect to the unknown stationary components of the background e.g., pre-existing interstitial boundaries and bubbles. This gives birth to differential indicators that specifically recover the 3D support of micromechanical evolution within a network of unknown scatterers. The direct scattering problem is formulated in the frequency domain where the background is comprised of a random distribution of monolithic fragments. The constituents are connected via highly heterogeneous interfaces of unknown elasticity and dissipation which are subject to spatiotemporal evolution. The support of internal boundaries are sequentially illuminated by a set of incident waves and thus-induced scattered fields are captured over a generic observation surface. The performance of the proposed imaging indicator is illustrated through a set of numerical experiments for spatiotemporal reconstruction of progressive damage zones featuring randomly distributed cracks and bubbles.
High-dimensional Statistical Arbitrage with Factor Models and Stochastic Control

ABSTRACT The present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies which are both interpretable and computationally implementable in a high-dimensional setting. Our setup is based on a general statistically constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyse the problem of investing optimally in continuous time and finite horizon under exponential and mean-variance utilities. We also extend our model to incorporate constraints on the investor’s portfolio like dollar-neutrality and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.