Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Residual Flow for Molecular Graph Generation

Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics. Among these models, invertible flow-based approaches are not fully explored yet. In this paper, we propose a powerful invertible flow for molecular graphs, called graph residual flow (GRF). The GRF is based on residual flows, which are known for more flexible and complex non-linear mappings than traditional coupling flows. We theoretically derive non-trivial conditions such that GRF is invertible, and present a way of keeping the entire flows invertible throughout the training and sampling. Experimental results show that a generative model based on the proposed GRF achieves comparable generation performance, with much smaller number of trainable parameters compared to the existing flow-based model.
Facial Expression Recognition Using Disentangled Adversarial Learning

The representation used for Facial Expression Recognition (FER) usually contain expression information along with other variations such as identity and illumination. In this paper, we propose a novel Disentangled Expression learning-Generative Adversarial Network (DE-GAN) to explicitly disentangle facial expression representation from identity information. In this learning by reconstruction method, facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN. The process of expression image reconstruction and disentangled expression representation learning is improved by performing expression and identity classification in the discriminator of DE-GAN. The disentangled facial expression representation is then used for facial expression recognition employing simple classifiers like SVM or MLP. The experiments are performed on publicly available and widely used face expression databases (CK+, MMI, Oulu-CASIA). The experimental results show that the proposed technique produces comparable results with state-of-the-art methods.
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces

Dimensionality reduction is a popular approach to tackle high-dimensional data with low-dimensional nature. Subspace Restricted Isometry Property, a newly-proposed concept, has proved to be a useful tool in analyzing the effect of dimensionality reduction algorithms on subspaces. In this paper, we provide a characterization of subspace Restricted Isometry Property, asserting that matrices which act as a near-isometry on low-dimensional subspaces possess subspace Restricted Isometry Property. This points out a unified approach to discuss subspace Restricted Isometry Property. Its power is further demonstrated by the possibility to prove with this result the subspace RIP for a large variety of random matrices encountered in theory and practice, including subgaussian matrices, partial Fourier matrices, partial Hadamard matrices, partial circulant/Toeplitz matrices, matrices with independent strongly regular rows (for instance, matrices with independent entries having uniformly bounded $4+\epsilon$ moments), and log-concave ensembles. Thus our result could extend the applicability of random projections in subspace-based machine learning algorithms including subspace clustering and allow for the application of some useful random matrices which are easier to implement on hardware or are more efficient to compute.
Efficient Bimanual Manipulation Using Learned Task Schemas

We address the problem of effectively composing skills to solve sparse-reward tasks in the real world. Given a set of parameterized skills (such as exerting a force or doing a top grasp at a location), our goal is to learn policies that invoke these skills to efficiently solve such tasks. Our insight is that for many tasks, the learning process can be decomposed into learning a state-independent task schema (a sequence of skills to execute) and a policy to choose the parameterizations of the skills in a state-dependent manner. For such tasks, we show that explicitly modeling the schema’s state-independence can yield significant improvements in sample efficiency for model-free reinforcement learning algorithms. Furthermore, these schemas can be transferred to solve related tasks, by simply re-learning the parameterizations with which the skills are invoked. We find that doing so enables learning to solve sparse-reward tasks on real-world robotic systems very efficiently. We validate our approach experimentally over a suite of robotic bimanual manipulation tasks, both in simulation and on real hardware. See videos at http://tinyurl.com/chitnis-schema.
Equivariant Hamiltonian Flows

This paper introduces equivariant hamiltonian flows, a method for learning expressive densities that are invariant with respect to a known Lie-algebra of local symmetry transformations while providing an equivariant representation of the data. We provide proof of principle demonstrations of how such flows can be learnt, as well as how the addition of symmetry invariance constraints can improve data efficiency and generalisation. Finally, we make connections to disentangled representation learning and show how this work relates to a recently proposed definition.
Performance Analysis of SSHC Rectifiers used in Ultrasonic Wireless Power Transfer

One of the key design considerations for biomedical implants is to minimize the system volume while achieving high performance. An attractive approach to power biomedical implants is to use wireless power transfer (WPT) with ultrasound. To receive the ultrasonic energy, a piezoelectric transducer is implemented, which is designed to have the resonance frequency at the frequency of the ultrasonic wave. To extract the received energy from the transducer, an energy rectification circuit is typically required. The commonly used rectifier is called a synchronized switch harvesting on inductor (SSHI) rectifier, which employs an inductor to increase the energy efficiency. However, the employment of inductor limits the system miniaturization since it usually needs to be around 1's mH. The synchronized switch harvesting on capacitors (SSHC) rectifier was recently proposed to be used on vibration energy harvesting and this architecture achieves inductor-less fully integrated design. In a vibration energy harvesting system, the piezoelectric transducer usually has large inherent capacitance and low resonant frequency. But the piezoelectric transducer used in ultrasonic WPT typically has very small capacitance and much higher resonant frequency. In this paper, the performance of a SSHC rectifier used in ultrasonic WPT is analyzed. The analysis covers system size and performance. Based on the analysis, this paper presents an important design consideration on designing a SSHC rectifier to be used in ultrasonic WPT, which is the allowed maximal ON resistance in the capacitor-to-capacitor charging loop in function of the transducer capacitance, transducer resonant frequency and the number of SSHC stages.
Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis

In this paper, we present an adaptive algorithm to construct response surface approximations of high‐fidelity models using a hierarchy of lower fidelity models. Our algorithm is based on multi‐index stochastic collocation and automatically balances physical discretization error and response surface error to construct an approximation of model outputs. This surrogate can be used for uncertainty quantification (UQ) and sensitivity analysis (SA) at a fraction of the cost of a purely high‐fidelity approach. We demonstrate the effectiveness of our algorithm on a canonical test problem from the UQ literature and a complex multiphysics model that simulates the performance of an integrated nozzle for an unmanned aerospace vehicle. We find that, when the input‐output response is sufficiently smooth, our algorithm produces approximations that can be over two orders of magnitude more accurate than single fidelity approximations for a fixed computational budget.
Multicomponent stress strength reliability estimation for Pareto distribution based on upper record values.

In this article, inferences about the multicomponent stress strength reliability are drawn under the assumption that strength and stress follow independent Pareto distribution with different shapes $(\alpha_1,\alpha_2)$ and common scale parameter $\theta$. The maximum likelihood estimator, Bayes estimator under squared error and Linear exponential loss function, of multicomponent stress-strength reliability are constructed with corresponding highest posterior density interval for unknown $\theta. $ For known $\theta,$ uniformly minimum variance unbiased estimator and asymptotic distribution of multicomponent stress-strength reliability with asymptotic confidence interval is discussed. Also, various Bootstrap confidence intervals are constructed. A simulation study is conducted to numerically compare the performances of various estimators of multicomponent stress-strength reliability. Finally, a real life example is presented to show the applications of derived results in real life scenarios.
$\sigma_{2}$ Yamabe problem on conic spheres II:boundary compactness of the moduli.

In this short note, we prove a convergence theorem on the moduli space of constant $\sigma_{2}$ metrics for conic 4-spheres. In particular, we show that when a numerical condition is convergent to the boudary case, the geometry of conic 4-spheres has to converge to the boundary case. As a key step, we establish a Bonnesen type isoperimetric inequality for high dimensions, which is of independent interest.
Shape Analysis via Functional Map Construction and Bases Pursuit

We propose a method to simultaneously compute scalar basis functions with an associated functional map for a given pair of triangle meshes. Unlike previous techniques that put emphasis on smoothness with respect to the Laplace--Beltrami operator and thus favor low-frequency eigenfunctions, we aim for a spectrum that allows for better feature matching. This change of perspective introduces many degrees of freedom into the problem which we exploit to improve the accuracy of our computed correspondences. To effectively search in this high dimensional space of solutions, we incorporate into our minimization state-of-the-art regularizers. We solve the resulting highly non-linear and non-convex problem using an iterative scheme via the Alternating Direction Method of Multipliers. At each step, our optimization involves simple to solve linear or Sylvester-type equations. In practice, our method performs well in terms of convergence, and we additionally show that it is similar to a provably convergent problem. We show the advantages of our approach by extensively testing it on multiple datasets in a few applications including shape matching, consistent quadrangulation and scalar function transfer.
Truth or Delusion? A Possible Gravitational Lensing Interpretation of the Ultraluminous Quasar SDSS J010013.02+280225.8 at z = 6.30

Gravitational lensing sometimes dominates the observed properties of apparently very bright objects. We present morphological properties in the high-resolution (FWHM ∼ 0.″15) Atacama Large Millimeter/submillimeter Array (ALMA) 1 mm map for an ultraluminous quasar at z = 6.30, SDSS J010013.02+280225.8 (hereafter J0100+2802), whose black hole (BH) mass MBH is the most massive (∼1.2 × 1010M⊙) at z > 6 ever known. We find that the continuum emission of J0100+2802 is resolved into a quadruple system within a radius of 0. ″2, which can be interpreted as either multiple dusty star-forming regions in the host galaxy or multiple images due to strong gravitational lensing. The Mg ii absorption and the potential Lyα line features have been identified at z = 2.33 in the near-infrared spectroscopy toward J0100+2802, and a simple mass model fitting well reproduces the positions and flux densities of the quadruple system, both of which are consistent with the latter interpretation. Although a high-resolution map taken in the Advanced Camera for Survey on board Hubble Space Telescope (HST) shows a morphology with an apparently single component, in our fiducial lens mass model it can simply be explained by a ∼50 pc scale offset between the ALMA and HST emission regions. In this case, the magnification factor for the observed HST emission is obtained to ∼450, reducing the intrinsic MBH estimate to below 109 M⊙. The confirmation or the rejection of the gravitational lensing scenario is important for our understanding of the supermassive BHs in the early universe.
Real-space Berry curvature of itinerant electron systems with spin-orbit interaction

By considering an extended double-exchange model with spin-orbit coupling (SOC), we derive a general form of the Berry phase $\ensuremath{\gamma}$ that electrons pick up when moving around a closed loop. This form generalizes the well-known result valid for SU(2) invariant systems, $\ensuremath{\gamma}=\mathrm{\ensuremath{\Omega}}/2$, where $\mathrm{\ensuremath{\Omega}}$ is the solid angle subtended by the local magnetic moments enclosed by the loop. The general form of $\ensuremath{\gamma}$ demonstrates that collinear and coplanar magnetic textures can also induce a Berry phase different from 0 or $\ensuremath{\pi}$, smoothly connecting the result for SU(2) invariant systems with the well-known result of Karplus and Luttinger for collinear ferromagnets with finite SOC. By taking the continuum limit of the theory, we also derive the corresponding generalized form of the real-space Berry curvature. The new expression is a generalization of the scalar spin chirality, which is presented in an explicitly covariant form. We finally show how these simple concepts can be used to understand the origin of the spontaneous topological Hall effect that has been recently reported in collinear and coplanar antiferromagnetic phases of correlated materials.
A Network Selection Algorithm Based On Combined Weights

Heterogeneous network selection is a typical multiple attribute decision making problem. This paper studies the determination method of parameters weight in the network selection process and proposes a network selection algorithm based on combined weights. The algorithm respectively used the G1 method and entropy method to determine the subjective weights and objective weights of judgment index, through a linear combination of the subjective and objective weights to get the optimal combination weights. At the same time, make use of network evaluation model based on improved TOPSIS to conduct comprehensive ranking for each candidate network and choose the best one. Simulation results show that the algorithm can obtain lower average latency, jitter, packet loss rate and etc., compared to a typical network selection algorithm based on single weights.
Roadmap-Optimal Multi-robot Motion Planning using Conflict-Based Search

Multi-Agent Pathfinding (MAPF) is the problem of finding a set of feasible paths for a set of agents with specific individual start and goal poses. It is considered computationally hard to solve. Conflict-based search (CBS) has shown optimality in developing solutions for multi-agent pathfinding problems in discrete spaces. However, neither CBS nor other discrete MAPF techniques can be directly applied to solve Multi-Agent Motion Planning (MAMP) problems, the continuous version on multi-agent pathfinding. In this work, we present the extension of the CBS discrete approach to solve Sampling-based Motion planning problems, and we show its capabilities to produce roadmap-optimal solutions for multi-robot motion planning problems.
Extended Hadamard expansions for the Airy functions

A new series expansion for the the Airy function is presented here that stems from the method of steepest descents and can be related to the Hadamard expansions as presented in prevous works cited in the manuscript, and which is convergent for all values of the complex variable. Hadamard expansions were introduced as an extension of the method of steepest descents and are defined in terms of a large number of non-systematic integration path subdivisions. Unlike them, the expansions in the present work originate in the splitting of the steepest descent in a number of segments that is not only finite but very small, and which are defined on the basis of the location of the branch points. One of the segments reaches to infinity and this gives rise to the presence of upper incomplete Gamma functions. This is one of the most important differences with the Hadamard series as defined in the aforementioned references, where all the incomplete Gamma functions are of the lower type. The theoretical interest of the new series expansion is twofold. First of all, it shows how to convert an asymptotic series into a convergent one with a finite splitting of the steepest descent path. Secondly, the inverse of the phase function that is part of the Laplace-type equation is Taylor-expanded around branch points to produce Puiseux series when necessary. In addition to this, the proposed analysis shows again how the Stokes phenomenon for the Airy function is related to the transition of the steepest descent paths at $\arg z = \pm 2 \pi/3$ from one to two. In regard to its computational application, these series expansions require a relatively small number of terms for each of them to reach a very high precision.
Design for tunable optofluidic optical coupler with large dynamic range

A novel scheme for tunable optofluidic optical coupler is proposed by combining directional coupling waveguide structures with microfluidic channel. The normalized optical power at two output ports can be dynamically manipulated by controlling the refractive index of liquid mixture in microfluidic channel. Its optical performance is numerically investigated by employing the beam propagation method (BPM). The simulated results show that the dynamic range of over 45 dB, and optical loss of below 0.06 dB can be easily achieved, and furthermore the dependence of polarization states and operation wavelength in the range from 1500 nm to 1600 nm are very low in our designed device. In addition, the tunable optofluidic coupler has advantages including simple structure and large fabrication tolerance. Accordingly, our proposed device offers a new approach for manipulating optical power output, which has wide potential application in optofluidic systems.
Investigation of On-Chip Inductors for Fully Integrated DC-DC Converters

On-silicon inductors using a bulk 0.18 {\mu}m CMOS process have been designed. By shunting different metal layers in parallel, inductor values and quality factors were simulated. Selected inductors were then employed in two open-loop buck converters for comparison: the first used an off-chip discrete diode, and the second used an on-chip active diode. All inductors and converters were sent for fabrication. The fabricated inductors were then measured to have values more than 250 nH over a wide range of frequency, validating the simulation results. The buck converters were switched at 30 MHz with a fixed duty ratio of 0.5, to generate an output voltage of 1.2 V from an input voltage of 2.4 V. The peak efficiency was measured to be 69.1% for a light load current of 12.9 mA.
Expressive Inverse Kinematics Solving in Real-time for Virtual and Robotic Interactive Characters

With new advancements in interaction techniques, character animation also requires new methods, to support fields such as robotics, and VR/AR. Interactive characters in such fields are becoming driven by AI which opens up the possibility of non-linear and open-ended narratives that may even include interaction with the real, physical world. This paper presents and describes ERIK, an expressive inverse kinematics technique aimed at such applications. Our technique allows an arbitrary kinematic chain, such as an arm, snake, or robotic manipulator, to exhibit an expressive posture while aiming its end-point towards a given target orientation. The technique runs in interactive-time and does not require any pre-processing step such as e.g. training in machine learning techniques, in order to support new embodiments or new postures. That allows it to be integrated in an artist-friendly workflow, bringing artists closer to the development of such AI-driven expressive characters, by allowing them to use their typical animation tools of choice, and to properly pre-visualize the animation during design-time, even on a real robot. The full algorithmic specification is presented and described so that it can be implemented and used throughout the communities of the various fields we address. We demonstrate ERIK on different virtual kinematic structures, and also on a low-fidelity robot that was crafted using wood and hobby-grade servos, to show how well the technique performs even on a low-grade robot. Our evaluation shows how well the technique performs, i.e., how well the character is able to point at the target orientation, while minimally disrupting its target expressive posture, and respecting its mechanical rotation limits.
End-to-End Deep Residual Learning with Dilated Convolutions for Myocardial Infarction Detection and Localization

In this report, I investigate the use of end-to-end deep residual learning with dilated convolutions for myocardial infarction (MI) detection and localization from electrocardiogram (ECG) signals. Although deep residual learning has already been applied to MI detection and localization, I propose a more accurate system that distinguishes among a higher number (i.e., six) of MI locations. Inspired by speech waveform processing with neural networks, I found a more robust front-end than directly arranging the multi-lead ECG signal into an input matrix consisting of the use of a single one-dimensional convolutional layer per ECG lead to extract a pseudo-time-frequency representation and create a compact and discriminative input feature volume. As a result, I end up with a system achieving an MI detection and localization accuracy of 99.99% on the well-known Physikalisch-Technische Bundesanstalt (PTB) database.
Beyond Top-Grasps Through Scene Completion

Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows end-to-end top-grasp planning methods to generate full six-degree-of-freedom grasps using a single RGBD view as input. This is achieved by estimating the complete shape of the object to be grasped, then simulating different viewpoints of the object, passing the simulated viewpoints to an end-to-end grasp generation method, and finally executing the overall best grasp. The method was experimentally validated on a Franka Emika Panda by comparing 429 grasps generated by the state-of-the-art Fully Convolutional Grasp Quality CNN, both on simulated and real camera images. The results show statistically significant improvements in terms of grasp success rate when using simulated images over real camera images, especially when the real camera viewpoint is angled. Code and video are available at https://irobotics.aalto. fi/beyond-topgrasps-through-scene-completion/.