Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Weighted Consensus: Dense correspondence confidence maps for 3D shape registration

We present a new paradigm for rigid alignment between point clouds based on learnable weighted consensus which is robust to noise as well as the full spectrum of the rotation group. Current models, learnable or axiomatic, work well for constrained orientations and limited noise levels, usually by an end-to-end learner or an iterative scheme. However, real-world tasks require us to deal with large rotations as well as outliers and all known models fail to deliver. Here we present a different direction. We claim that we can align point clouds out of sampled matched points according to confidence level derived from a dense, soft alignment map. The pipeline is differentiable, and converges under large rotations in the full spectrum of SO(3), even with high noise levels. We compared the network to recently presented methods such as DCP, PointNetLK, RPM-Net, PRnet, and axiomatic methods such as ICP and Go-ICP. We report here a fundamental boost in performance.
Lindblad Tomography of a Superconducting Quantum Processor

As progress is made towards the first generation of error-corrected quantum computers, robust characterization and validation protocols are required to assess the noise environments of physical quantum processors. While standard coherence metrics and characterization protocols such as T1 and T2, process tomography, and randomized benchmarking are now ubiquitous, these techniques provide only partial information about the dynamic multi-qubit loss channels responsible for processor errors, which can be described more fully by a Lindblad operator in the master equation formalism. Here, we introduce and experimentally demonstrate Lindblad tomography, a hardware-agnostic characterization protocol for tomographically reconstructing the Hamiltonian and Lindblad operators of a quantum noise environment from an ensemble of time-domain measurements. Performing Lindblad tomography on a small superconducting quantum processor, we show that this technique characterizes and accounts for state-preparation and measurement (SPAM) errors and allows one to place bounds on the fit to a Markovian model. Comparing the results of single- and two-qubit measurements on a superconducting quantum processor, we demonstrate that Lindblad tomography can also be used to identify and quantify sources of crosstalk on quantum processors, such as the presence of always-on qubit-qubit interactions.
Visual Foresight Trees for Object Retrieval From Clutter With Nonprehensile Rearrangement

This letter considers the problem of retrieving an object from many tightly packed objects using a combination of robotic pushing and grasping actions. Object retrieval in dense clutter is an important skill for robots to operate in households and everyday environments effectively. The proposed solution, Visual Foresight Tree (VFT), intelligently rearranges the clutter surrounding a target object so that it can be grasped easily. Rearrangement with nested nonprehensile actions is challenging as it requires predicting complex object interactions in a combinatorially large configuration space of multiple objects. We first show that a deep neural network can be trained to accurately predict the poses of the packed objects when the robot pushes one of them. The predictive network provides visual foresight and is used in a tree search as a state transition function in the space of scene images. The tree search returns a sequence of consecutive push actions yielding the best arrangement of the clutter for grasping the target object. Experiments in simulation and using a real robot and objects show that the proposed approach outperforms model-free techniques as well as model-based myopic methods both in terms of success rates and the number of executed actions, on several challenging tasks. A video introducing VFT, with robot experiments, is accessible at https://youtu.be/7cL-hmgvyec. The full source code is available at https://github.com/arc-l/vft.
Unifying the Anderson transitions in Hermitian and non-Hermitian systems

Non-Hermiticity enriches the 10-fold Altland-Zirnbauer symmetry class into the 38-fold symmetry class, where critical behavior of the Anderson transitions (ATs) has been extensively studied recently. Here, we establish a correspondence of the universality classes of the ATs between Hermitian and non-Hermitian systems. We demonstrate that the critical exponents of the length scale in non-Hermitian systems coincide with the critical exponents in the corresponding Hermitian systems with additional chiral symmetry. A remarkable consequence is superuniversality, i.e., the ATs in some different symmetry classes of non-Hermitian systems are characterized by the same critical exponent. In addition to the comparisons between the known critical exponents for non-Hermitian systems and their Hermitian counterparts, we obtain the critical exponents in symmetry classes AI, AII, AII$^{\dagger}$, CII$^{\dagger}$, and DIII in two and three dimensions. Our precise critical exponents not only confirm the correspondence, but also predict the unknown critical exponents in Hermitian systems, paving a way to study the ATs of Hermitian systems by the corresponding non-Hermitian systems.
Resilient Time-Varying Output Formation Tracking of Linear Multi-Agent Systems Against Unbounded FDI Sensor Attacks and Unreliable Digraphs

One salient feature of cooperative formation tracking is its distributed nature that relies on localized control and information sharing over a sparse communication network. That is, a distributed control manner could be prone to malicious attacks and unreliable communication that deteriorate the formation tracking performance or even destabilize the whole multi-agent system. This paper studies a safe and reliable time-varying output formation tracking problem of linear multi-agent systems, where an attacker adversely injects any unbounded time-varying signals (false data injection (FDI) attacks), while an interruption of communication channels between the agents is caused by an unreliable network. Both characteristics improve the practical relevance of the problem to be addressed, which poses some technical challenges to the distributed algorithm design and stability analysis. To mitigate the adverse effect, a novel resilient distributed control architecture is established to guarantee time-varying output formation tracking exponentially. The key features of the proposed framework are threefold: 1) an observer-based identifier is integrated to compensate for adverse effects; 2) a reliable distributed algorithm is proposed to deal with time-varying topologies caused by unreliable communication; and 3) in contrast to the existing remedies that deal with attacks as bounded disturbances/faults with known knowledge, we propose resilience strategies to handle unknown and unbounded attacks for exponential convergence of dynamic formation tracking errors, whereas most of existing results achieve uniformly ultimately boundedness (UUB) results. Numerical simulations are given to show the effectiveness of the proposed design.
Faster Algorithms for Bounded Tree Edit Distance

Tree edit distance is a well-studied measure of dissimilarity between rooted trees with node labels. It can be computed in $O(n^3)$ time [Demaine, Mozes, Rossman, and Weimann, ICALP 2007], and fine-grained hardness results suggest that the weighted version of this problem cannot be solved in truly subcubic time unless the APSP conjecture is false [Bringmann, Gawrychowski, Mozes, and Weimann, SODA 2018]. We consider the unweighted version of tree edit distance, where every insertion, deletion, or relabeling operation has unit cost. Given a parameter $k$ as an upper bound on the distance, the previous fastest algorithm for this problem runs in $O(nk^3)$ time [Touzet, CPM 2005], which improves upon the cubic-time algorithm for $k\ll n^{2/3}$. In this paper, we give a faster algorithm taking $O(nk^2 \log n)$ time, improving both of the previous results for almost the full range of $\log n \ll k\ll n/\sqrt{\log n}$.
Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification

Neural network architectures in natural language processing often use attention mechanisms to produce probability distributions over input token representations. Attention has empirically been demonstrated to improve performance in various tasks, while its weights have been extensively used as explanations for model predictions. Recent studies (Jain and Wallace, 2019; Serrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot generally be considered as a faithful explanation (Jacovi and Goldberg, 2020) across encoders and tasks. In this paper, we seek to improve the faithfulness of attention-based explanations for text classification. We achieve this by proposing a new family of Task-Scaling (TaSc) mechanisms that learn task-specific non-contextualised information to scale the original attention weights. Evaluation tests for explanation faithfulness, show that the three proposed variants of TaSc improve attention-based explanations across two attention mechanisms, five encoders and five text classification datasets without sacrificing predictive performance. Finally, we demonstrate that TaSc consistently provides more faithful attention-based explanations compared to three widely-used interpretability techniques.
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach

Undirected graphical models are widely used to model the conditional independence structure of vector-valued data. However, in many modern applications, for example those involving EEG and fMRI data, observations are more appropriately modeled as multivariate random functions rather than vectors. Functional graphical models have been proposed to model the conditional independence structure of such functional data. We propose a neighborhood selection approach to estimate the structure of Gaussian functional graphical models, where we first estimate the neighborhood of each node via a function-on-function regression and subsequently recover the entire graph structure by combining the estimated neighborhoods. Our approach only requires assumptions on the conditional distributions of random functions, and we estimate the conditional independence structure directly. We thus circumvent the need for a well-defined precision operator that may not exist when the functions are infinite dimensional. Additionally, the neighborhood selection approach is computationally efficient and can be easily parallelized. The statistical consistency of the proposed method in the high-dimensional setting is supported by both theory and experimental results. In addition, we study the effect of the choice of the function basis used for dimensionality reduction in an intermediate step. We give a heuristic criterion for choosing a function basis and motivate two practically useful choices, which we justify by both theory and experiments.
Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?

Global Covariance Pooling (GCP) aims at exploiting the second-order statistics of the convolutional feature. Its effectiveness has been demonstrated in boosting the classification performance of Convolutional Neural Networks (CNNs). Singular Value Decomposition (SVD) is used in GCP to compute the matrix square root. However, the approximate matrix square root calculated using Newton-Schulz iteration [14] outperforms the accurate one computed via SVD [15]. We empirically analyze the reason behind the performance gap from the perspectives of data precision and gradient smoothness. Various remedies for computing smooth SVD gradients are investigated. Based on our observation and analyses, a hybrid training protocol is proposed for SVD-based GCP meta-layers such that competitive performances can be achieved against Newton-Schulz iteration. Moreover, we propose a new GCP meta-layer that uses SVD in the forward pass, and Padé approximants in the backward propagation to compute the gradients. The proposed meta-layer has been integrated into different CNN models and achieves state-of-the-art performances on both large-scale and fine-grained datasets.
A Systematic Search for Outbursting AM CVn Systems with the Zwicky Transient Facility

AM CVn systems are a rare type of accreting binary that consists of a white dwarf and a helium-rich, degenerate donor star. Using the Zwicky Transient Facility (ZTF), we searched for new AM CVn systems by focusing on blue, outbursting stars. We first selected outbursting stars using the ZTF alerts. We cross matched the candidates with Gaia and Pan-STARRS catalogs. The initial selection of candidates based on the Gaia BP-RP contains 1751 unknown objects. We used the Pan-STARRS g-r and r-i color in combination with the Gaia color to identify 59 strong AM CVn candidates. We obtained identification spectra of 35 sources, of which 18 are high-priority candidates, and discovered nine new AM CVn systems and one magnetic CV that shows only He-ii lines. Using the outburst recurrence time, we estimate the orbital periods of the nine new AM CVn systems that are in the range of 29–50 minutes. We conclude that targeted follow up of blue, outbursting sources is an efficient method to find new AM CVn systems and we plan to follow up all candidates we identified to systematically study the population of outbursting AM CVn systems.
First-order antiferromagnetic transitions of SrMn2P2 and CaMn2P2 single crystals containing corrugated-honeycomb Mn sublattices

Significance With rare exceptions, an antiferromagnetic (AFM) transition in zero magnetic field is thermodynamically of second order where the thermal-average magnetic moments of the magnetic atoms (ordered moments) vary continuously on cooling through the AFM ordering temperature TN with no latent heat at the transition. Such materials include the AFM pnictides CaMn2As2, SrMn2As2, CaMn2Sb2, SrMn2Sb2, and CaMn2Bi2. Here we demonstrate that the closely related SrMn2P2 and CaMn2P2 insulators instead exhibit first-order AFM transitions at TN=53 and 70 K, respectively, where the heat capacity exhibits a latent heat at TN. The mechanism causing these first-order transitions remains to be explained, but its understanding may lead to the development of novel magnetic materials of technological interest. SrMn2P2 and CaMn2P2 are insulators that adopt the trigonal CaAl2Si2-type structure containing corrugated Mn honeycomb layers. Magnetic susceptibility χ and heat capacity versus temperature T data reveal a weak first-order antiferromagnetic (AFM) transition at the Néel temperature TN=53(1) K for SrMn2P2 and a strong first-order AFM transition at TN=69.8(3) K for CaMn2P2. Both compounds exhibit isotropic and nearly T-independent χ(T≤TN), suggesting magnetic structures in which nearest-neighbor moments are aligned at ≈120° to each other. The 31P NMR measurements confirm the strong first-order transition in CaMn2P2 but show critical slowing down above TN for SrMn2P2, thus also evidencing second-order character. The 31P NMR measurements indicate that the AFM structure of CaMn2P2 is commensurate with the lattice whereas that of SrMn2P2 is incommensurate. These first-order AFM transitions are unique among the class of (Ca, Sr, Ba)Mn2 (P, As, Sb, Bi)2 compounds that otherwise exhibit second-order AFM transitions. This result challenges our understanding of the circumstances under which first-order AFM transitions occur.
A Scalable Lower Bound for the Worst-Case Relay Attack Problem on the Transmission Grid

We consider a bilevel attacker–defender problem to find the worst-case attack on the relays that control transmission grid components. The attacker infiltrates some number of relays and renders all of the components connected to them inoperable with the goal of maximizing load shed. The defender responds by minimizing the resulting load shed, redispatching using a DC optimal power flow (DCOPF) problem on the remaining network. Though worst-case interdiction problems on the transmission grid have been studied for years, there remains a need for exact and scalable methods. Methods based on using duality on the inner problem rely on the bounds of the dual variables of the defender problem in order to reformulate the bilevel problem as a mixed integer linear problem. Valid dual bounds tend to be large, resulting in weak linear programming relaxations and, hence, making the problem more difficult to solve at scale. Often smaller heuristic bounds are used, resulting in a lower bound. In this work, we also consider a lower bound, but instead of bounding the dual variables, we drop the constraints corresponding to Ohm’s law, relaxing DCOPF to capacitated network flow. We present theoretical results showing that, for uncongested networks, approximating DCOPF with network flow yields the same set of injections and, thus, the same load shed, which suggests that this restriction likely gives a high-quality lower bound in the uncongested case. Furthermore, we show that, in the network flow relaxation of the defender problem, the duals are bounded by one, so we can solve our restriction exactly. Finally, because the big-M values in the linearization are equal to one and network flow has a well-known structure, we see empirically that this formulation scales well computationally with increased network size. Through empirical experiments on 16 networks with up to 6,468 buses, we find that this bound is almost always as tight as we can get from guessing the dual bounds even for congested networks in which the theoretical results do not hold. In addition, calculating the bound is approximately 150 times faster than achieving the same bound with the reformulation guessing the dual bounds.
Epidemics in modern economies

How are economies in a modern age impacted by epidemics? In what ways is economic life disrupted? How can pandemics be modeled? What can be done to mitigate and manage the danger? Does the threat of pandemics increase or decrease in the modern world? The Covid-19 pandemic has demonstrated the importance of these questions and the potential of complex systems science to provide answers. This article offers a broad overview of the history of pandemics, of established facts, and of models of infection diffusion, mitigation strategies, and economic impact. The example of the Covid-19 pandemic is used to illustrate the theoretical aspects, but the article also includes considerations concerning other historic epidemics and the danger of more infectious and less controllable outbreaks in the future.
Direct Prediction of Steady-State Flow Fields in Meshed Domain with Graph Networks

We propose a model to directly predict the steady-state flow field for a given geometry setup. The setup is an Eulerian representation of the fluid flow as a meshed domain. We introduce a graph network architecture to process the mesh-space simulation as a graph. The benefit of our model is a strong understanding of the global physical system, while being able to explore the local structure. This is essential to perform direct prediction and is thus superior to other existing methods.
Comprimento critico de declive em sistemas de preparos conservacionistas de solo

A rotating-boom rainfall simulator and field plots were used to investigate critical slope lengths for some conservation tillage systems, from 1992 to 1994, in Eldorado do Sul, State of Rio Grande do Sul, Brazil. Water inflow was added at the upper end of the plots to estimate long slopes. The soil used was a sandy loam red-yellow podzolic (Paleudult) with 0.066 m m -1 slope. Tillage treatments included no-till and chisel plow, all them in the presence and absence of fresh cornstalk and a mixture ofcornstalk and wheat straw residues as left after harvest. Two isolated treatments consisted of no-till with fresh wheat straw and no-till with a mixture of cornstalk and wheat straw residues evaluated 140 days after the wheat had been harvested. In the presence ofcornstalk (12 t ha -1 ) the critical slope length was judged to lie between 328 and 483 m for no-till and between from 147 and 209 m for chisel plow, while in the presence of a mixture of cornstalk and wheat residues (6. 6 t ha -1 ) these ranges were, respectively, 157 to 272 m and 143 to 267 m. No-till with fresh wheat straw (2.6 t ha -1 ) showed a critical slope length between 106 and 143 m, while the no-till whith a mixture of a cornstalk and wheat straw evaluated 140 days after wheat harvesting showed a range between 87 and 174 m.
Effects of a Comprehensive Care Program on the Readmission Rate and Adherence to Treatment in Elderly Patients with Chronic Obstructive Pulmonary Disease

Background: The low treatment adherence of patients with chronic obstructive pulmonary disease (COPD) leads to the exacerbation of their symptoms and readmission. Comprehensive care programs are among interventions that can improve the patients’ adherence to treatment and prevent readmission. The present study aimed to evaluate the effects of a comprehensive care program on the adherence to treatment and readmission of COPD patients. Materials and Methods: This randomized clinical trial was performed in a hospital in Rafsanjan, Iran, in 2017. Sixty elderly patients with COPD were randomly enrolled in this study by pair-matching. The intervention group participated in a comprehensive care program, whereas the control group received routine care. The readmission rate and adherence to treatment were measured at one-, three-, and six-month intervals. To evaluate the patients’ adherence to treatment, an adherence-to-treatment questionnaire for chronic diseases was used. Data were analyzed using Chi-square test, independent t-test, and repeated measures ANOVA at a significance level of 0.05. Results: A significant difference was observed between the two groups in terms of readmission at the end of the study (P=0.03). In the intervention group, the mean level of adherence to treatment and its subscales improved as compared to the control group, and there was a significant difference between the two groups. Conclusion: Although most of the patients in this study were old, with a rather low educational level and socioeconomic status, the care program could improve their treatment adherence and reduce the readmission rate.