Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell R-CNN V3: A Novel Panoptic Paradigm for Instance Segmentation in Biomedical Images

Instance segmentation is an important task for biomedical image analysis. Due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries, this task still remains challenging. Recently, deep learning based methods have been widely employed to solve these problems and can be categorized into proposal-free and proposal-based methods. However, both proposal-free and proposal-based methods suffer from information loss, as they focus on either global-level semantic or local-level instance features. To tackle this issue, we present a panoptic architecture that unifies the semantic and instance features in this work. Specifically, our proposed method contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features, in order to facilitate the semantic contextual information learning in the instance branch. Then, a mask quality branch is designed to align the confidence score of each object with the quality of the mask prediction. Furthermore, a consistency regularization mechanism is designed between the semantic segmentation tasks in the semantic and instance branches, for the robust learning of both tasks. Extensive experiments demonstrate the effectiveness of our proposed method, which outperforms several state-of-the-art methods on various biomedical datasets.
Quantum materials interfaces: Graphene/bismuth (111) heterostructures

Heterostructures involving graphene and bismuth, with their ability to absorb light over a very wide energy range, are of interest for engineering next-generation opto-electronics. Critical to the technological application of such heterostructures is an understanding of the underlying physics governing their properties. Here, using first-principles calculations, we study the interfacial interactions between graphene and bismuth thin-films. Our study reveals non-intuitive phenomena associated with the moire-physics of these superlattices. We show a preservation of graphene-derived Dirac cones in spite of proximity to a substrate with large spin-orbit coupling, a greater influence of graphene on the electronic structure properties of bismuth, and the surprising presence of a magnetic solution, only slightly higher in energy (by several meV) than the non-magnetic structure, possibly validating experiments. Such subtle and unanticipated phenomena associated with the moire-physics are expected to play key roles in the practical applications of heterogeneous assemblies of two-dimensional quantum systems.
Controlled time series generation for automotive software-in-the-loop testing using GANs

Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge. In current practice, there are two major techniques of input stimulation. One approach is to craft input sequences which eases control and feedback of the test process but falls short of exposing the system to realistic scenarios. The other is to replay sequences recorded from field operations which accounts for reality but requires collecting a well-labeled dataset of sufficient capacity for widespread use, which is expensive. This work applies the well-known unsupervised learning framework of Generative Adversarial Networks (GAN) to learn an unlabeled dataset of recorded in-vehicle signals and uses it for generation of synthetic input stimuli. Additionally, a metric-based linear interpolation algorithm is demonstrated, which guarantees that generated stimuli follow a customizable similarity relationship with specified references. This combination of techniques enables controlled generation of a rich range of meaningful and realistic input patterns, improving virtual test coverage and reducing the need for expensive field tests.
FPGA Based SoC Estimator and Constant Current Charging/Discharging Controller for Lead–Acid Battery

The state of charge (SoC) and the rate of charging/discharging current are the vital parameters associated with a battery by which its accurate runtime can be estimated. This paper aims to design a controller which comprises of a field– programmable gate array, back to back connected dc–dc converters, and a resistive touch display based graphical user interface (GUI). The controller estimates SoC and performs constant current charging or discharging of the battery. The implementation of GUI is to input the reference charging/discharging current from the user and to display the SoC. This research delves into battery and obtains its internal parameters by conducting hybrid pulse power characterization test. Furthermore, the obtained internal parameters are processed through an extended Kalman filter which yields the SoC. This controller has large applications in the renewable energy system, battery testing system, smart residential energy management systems, and micro–grids.
Orbital Ingredients for Cooking X-structures in Edge-on Galaxies

X-structures are often observed in galaxies hosting the so-called B/PS (boxy/peanuts) bulges and are visible from the edge-on view. They are the most notable features of B/PS bulges and appear as four rays protruding from the disk of the host galaxy and distinguishable against the B/PS bulge background. In some works, their origin is thought to be connected with the so-called banana-shaped orbits with a vertical resonance 2:1. A star in such an orbit performs two oscillations in the vertical direction per one revolution in the bar frame. Several recent studies that analyzed ensembles of orbits arising in different N-body models do not confirm the dominance of the resonant 2:1 orbits in X-structures. In our work, we analyze two N-body models and show how the X-structure in our models is gradually assembled from the center to the periphery from orbits with less than 2:1 frequency ratio. The most number of such orbits is enclosed in a “farfalle”-shape (Italian pasta) form and turns out to be non-periodic. We conclude that the X-structure is akin to the envelope curve of regions of high density caused by the crossing or folding of different types of orbits at their highest points, and does not have a “backbone” similar to that of the in-plane bar. Comparing the orbital structure of two different numerical models, we show that the dominance of one or another family of orbits with a certain ratio of the vertical oscillations frequency to the in-plane frequency depends on the parameters of the underlying galaxy and ultimately determines the morphology of the X-structure and the opening angle of its rays.
Estimating the Percolation Centrality of Large Networks through Pseudo-dimension Theory

In this work we investigate the problem of estimating the percolation centrality of every vertex in a graph. This centrality measure quantifies the importance of each vertex in a graph going through a contagious process. It is an open problem whether the percolation centrality can be computed in O(n3-c) time, for any constant c>0. In this paper we present a ~O(m) randomized approximation algorithm for the percolation centrality for every vertex of G, generalizing techniques developed by Riondato, Upfal and Kornaropoulos. The estimation obtained by the algorithm is within ε of the exact value with probability 1- δ, for fixed constants 0 < ε,δ < 1. In fact, we show in our experimental analysis that in the case of real-world complex networks, the output produced by our algorithm is significantly closer to the exact values than its guarantee in terms of theoretical worst case analysis.
Memristive Oscillatory Circuits for Resolution of NP-Complete Logic Puzzles: Sudoku Case

Memristor networks are capable of low-power and massive parallel processing and information storage. Moreover, they have presented the ability to apply for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. Beyond the memory and conventional computing architectures, memristors are widely studied in circuits aiming for increased intelligence that are suitable to tackle complex problems in a power and area efficient manner, offering viable solutions oftenly arriving also from the biological principles of living organisms. In this paper, a memristive circuit exploiting the dynamics of oscillating networks is utilized for the resolution of very popular and NP-complete logic puzzles, like the well-known “Sudoku”. More specifically, the proposed circuit design methodology allows for appropriate usage of interconnections' advantages in a oscillation network and of memristor's switching dynamics resulting to logic-solvable puzzle-instances. The reduced complexity of the proposed circuit and its increased scalability constitute its main advantage against previous approaches and the broadly presented SPICE based simulations provide a clear proof of concept of the aforementioned appealing characteristics.
Circumnavigating a Moving Target with Range-only Measurements

This paper designs a novel coordinate-free controller for an underactuated pursuer to circumnavigate a fully actuated target with range-only measurements. If the range rate is further available, the controller has a simple PD-like form with a bias to eliminate steady-state circumnavigating error. In particular, such a controller drives the circumnavigating error to zero with an exponential convergence speed if the target is stationary, and otherwise to a small region whose size is explicitly proportional to the maximum speed and acceleration of the moving target. To access the range rate, we design a second-order sliding mode (SOSM) filter and show that the SOSM filter can exactly recover the range rate in a finite time. Thus, the above convergence results can be easily achieved even without the explicit range rate. Finally, the effectiveness and advantages of the proposed controller are validated via simulations.
Testing physical models for cosmic ray transport coefficients on galactic scales: self-confinement and extrinsic turbulence at ∼GeV energies

The microphysics of ∼ GeV cosmic ray (CR) transport on galactic scales remain deeply uncertain, with almost all studies adopting simple prescriptions (e.g. constant diffusivity). We explore different physically motivated, anisotropic, dynamical CR transport scalings in high-resolution cosmological Feedback In Realistic Environment (FIRE) simulations of dwarf and ∼L* galaxies where scattering rates vary with local plasma properties motivated by extrinsic turbulence (ET) or self-confinement (SC) scenarios, with varying assumptions about e.g. turbulent power spectra on un-resolved scales, Alfvén-wave damping, etc. We self-consistently predict observables including γ-rays (Lγ), grammage, residence times, and CR energy densities to constrain the models. We demonstrate many non-linear dynamical effects (not captured in simpler models) tend to enhance confinement. For example, in multiphase media, even allowing arbitrary fast transport in neutral gas does not substantially reduce CR residence times (or Lγ), as transport is rate-limited by the ionized WIM and ‘inner CGM’ gaseous halo (104–106 K gas within $\lesssim 10\! -\! 30\,$ kpc), and Lγ can be dominated by trapping in small ‘patches’. Most physical ET models contribute negligible scattering of ∼1–10 GeV CRs, but it is crucial to account for anisotropy and damping (especially of fast modes) or else scattering rates would violate observations. We show that the most widely assumed scalings for SC models produce excessive confinement by factors ≳100 in the warm ionized medium (WIM) and inner CGM, where turbulent and Landau damping dominate. This suggests either a breakdown of quasi-linear theory used to derive the CR transport parameters in SC, or that other novel damping mechanisms dominate in intermediate-density ionized gas.
Spinning disk-remote focusing microscopy

Fast confocal imaging was achieved by combining remote focusing with differential spinning disk optical sectioning to rapidly acquire images of live samples at cellular resolution. Axial and lateral full width half maxima less than 5 µm and 490 nm respectively are demonstrated over 130 µm axial range with a 256 × 128 µm field of view. A water-index calibration slide was used to achieve an alignment that minimises image volume distortion. Application to live biological samples was demonstrated by acquiring image volumes over a 24 µm axial range at 1 volume/s, allowing for the detection of calcium-based neuronal activity in Platynereis dumerilii larvae.
Shifted Lanczos method for quadratic forms with Hermitian matrix resolvents

Quadratic forms of Hermitian matrix resolvents involve the solutions of shifted linear systems. Efficient solutions use the shift-invariance property of Krylov subspaces. The Hermitian Lanczos method reduces a given vector and matrix to a Jacobi matrix (a real symmetric tridiagonal matrix with positive super and sub-diagonal entries) and approximates the quadratic form with the Jacobi matrix. This study develops a shifted Lanczos method that deals directly with the Hermitian matrix resolvent to extend the scope of problems that the Lanczos method can solve. We derive a matrix representation of a linear operator that approximates the resolvent by solving a Vorobyev moment problem associated with the shifted Lanczos method. We show that an entry of the Jacobi matrix resolvent can approximate the quadratic form. We show the moment-matching property of the shifted Lanczos method and give a sufficient condition such that the method does not break down. Numerical experiments on matrices drawn from real-world applications compare the proposed method with previous methods and show that the proposed method outperforms a well-established method in solving some problems.
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians

Pose estimation in the wild is a challenging problem, particularly in situations of (i) occlusions of varying degrees and (ii) crowded outdoor scenes. Most of the existing studies of pose estimation did not report the performance in similar situations. Moreover, pose annotations for occluded parts of human figures have not been provided in any of the relevant standard datasets which in turn creates further difficulties to the required studies for pose estimation of the entire figure of occluded humans. Well known pedestrian detection datasets such as CityPersons contains samples of outdoor scenes but it does not include pose annotations. Here, we propose a novel multi-task framework for end-to-end training towards the entire pose estimation of pedestrians including in situations of any kind of occlusion. To tackle this problem for training the network, we make use of a pose estimation dataset, MS-COCO, and employ unsupervised adversarial instance-level domain adaptation for estimating the entire pose of occluded pedestrians. The experimental studies show that the proposed framework outperforms the SOTA results for pose estimation, instance segmentation and pedestrian detection in cases of heavy occlusions (HO) and reasonable + heavy occlusions (R + HO) on the two benchmark datasets.
Coronal dimming as a proxy for stellar coronal mass ejections

Abstract Solar coronal dimmings have been observed extensively in the past two decades and are believed to have close association with coronal mass ejections (CMEs). Recent study found that coronal dimming is the only signature that could differentiate powerful flares that have CMEs from those that do not. Therefore, dimming might be one of the best candidates to observe the stellar CMEs on distant Sun-like stars. In this study, we investigate the possibility of using coronal dimming as a proxy to diagnose stellar CMEs. By simulating a realistic solar CME event and corresponding coronal dimming using a global magnetohydrodynamics model (AWSoM: Alfvén-wave Solar Model), we first demonstrate the capability of the model to reproduce solar observations. We then extend the model for simulating stellar CMEs by modifying the input magnetic flux density as well as the initial magnetic energy of the CME flux rope. Our result suggests that with improved instrument sensitivity, it is possible to detect the coronal dimming signals induced by the stellar CMEs.
Text‐based question answering from information retrieval and deep neural network perspectives: A survey

Text‐based question answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval (IR) techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning (DL) approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between the questions and the answer sentences. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both a traditional IR perspective and a more recent deep neural network environment. We also introduce well‐known datasets for the task and present available results from the literature to have a comparison between different techniques.
REST: Performance Improvement of a Black Box Model via RL-based Spatial Transformation

In recent years, deep neural networks (DNN) have become a highly active area of research, and shown remarkable achievements on a variety of computer vision tasks. DNNs, however, are known to often make overconfident yet incorrect predictions on out-of-distribution samples, which can be a major obstacle to real-world deployments because the training dataset is always limited compared to diverse real-world samples. Thus, it is fundamental to provide guarantees of robustness to the distribution shift between training and test time when we construct DNN models in practice. Moreover, in many cases, the deep learning models are deployed as black boxes and the performance has been already optimized for a training dataset, thus changing the black box itself can lead to performance degradation. We here study the robustness to the geometric transformations in a specific condition where the black-box image classifier is given. We propose an additional learner, REinforcement Spatial Transform learner (REST), that transforms the warped input data into samples regarded as in-distribution by the black-box models. Our work aims to improve the robustness by adding a REST module in front of any black boxes and training only the REST module without retraining the original black box model in an end-to-end manner, i.e. we try to convert the real-world data into training distribution which the performance of the black-box model is best suited for. We use a confidence score that is obtained from the black-box model to determine whether the transformed input is drawn from in-distribution. We empirically show that our method has an advantage in generalization to geometric transformations and sample efficiency.
PeelNet: Textured 3D reconstruction of human body using single view RGB image

Reconstructing human shape and pose from a single image is a challenging problem due to issues like severe self-occlusions, clothing variations, and changes in lighting to name a few. Many applications in the entertainment industry, e-commerce, health-care (physiotherapy), and mobile-based AR/VR platforms can benefit from recovering the 3D human shape, pose, and texture. In this paper, we present PeelNet, an end-to-end generative adversarial framework to tackle the problem of textured 3D reconstruction of the human body from a single RGB image. Motivated by ray tracing for generating realistic images of a 3D scene, we tackle this problem by representing the human body as a set of peeled depth and RGB maps which are obtained by extending rays beyond the first intersection with the 3D object. This formulation allows us to handle self-occlusions efficiently. Current parametric model-based approaches fail to model loose clothing and surface-level details and are proposed for the underlying naked human body. Majority of non-parametric approaches are either computationally expensive or provide unsatisfactory results. We present a simple non-parametric solution where the peeled maps are generated from a single RGB image as input. Our proposed peeled depth maps are back-projected to 3D volume to obtain a complete 3D shape. The corresponding RGB maps provide vertex-level texture details. We compare our method against current state-of-the-art methods in 3D reconstruction and demonstrate the effectiveness of our method on BUFF and MonoPerfCap datasets.
Concurrent Crossmodal Feedback Assists Target-searching: Displaying Distance Information Through Visual, Auditory and Haptic Modalities

Humans sense of distance depends on the integration of multi sensory cues. The incoming visual luminance, auditory pitch and tactile vibration could all contribute to the ability of distance judgement. This ability can be enhanced if the multimodal cues are associated in a congruent manner, a phenomenon has been referred to as Crossmodal correspondences. In the context of multi-sensory interaction, whether and how such correspondences influence information processing with continuous motor engagement, particularly for target searching activities, has rarely been investigated. This paper presents an experimental user study to address this question. We built a target-searching application based on a Table-top, displayed the unimodal and Crossmodal distance cues concurrently responding to peoples searching movement, measured task performance through kinematic evaluation. We find that the Crossmodal display an audio display lead to improved searching efficiency and accuracy. More interestingly, this improvement is confirmed by kinematic analysis, which also unveiled the underlying movement features that could account for this improvement. We discussed how these findings could shed lights on the design of assistive technology and of other multi sensory interaction.
Pseudo-critical behavior of spin-1/2 Ising diamond and tetrahedral chains

A few paradigmatic one-dimensional lattice-statistical spin models have recently attracted a vigorous scientific interest owing to their peculiar thermodynamic behavior, which is highly reminiscent of a temperature-driven phase transition. The pseudotransitions of one-dimensional lattice-statistical spin models differ from actual phase transitions in several important aspects: the first-order derivatives of the Gibbs free energy such as entropy or magnetization exhibit near a pseudo-transition an abrupt continuous change instead of a true discontinuity, whereas the second-order derivatives of the Gibbs free energy such as specific heat or susceptibility display near a pseudo-transition a vigorous finite peak instead of an actual power-law divergence. In the present chapter we will comprehensively examine a pseudo-critical behavior of the spin-1/2 Ising diamond and tetrahedral chains by a detailed examination of basic magnetothermodynamic quantities such as the entropy, specific heat and susceptibility. It will be demonstrated that density plots of these magnetothermodynamic quantities provide a useful tool for establishing a finite-temperature diagram, which clearly delimits boundaries between individual quasi-phases in spite of a lack of true spontaneous long-range order at any nonzero temperature. It is suggested that a substantial difference between the degeneracies of two ground states of the spin-1/2 Ising diamond and tetrahedral chains is an essential prerequisite for observation of a relevant pseudo-critical behavior in a close vicinity of their ground-state phase boundary.