Miracle-dz/UniFAR-FTUs huggingface dataset:
NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition

Face recognition (FR) using deep convolutional neural networks (DCNNs) has seen remarkable success in recent years. One key ingredient of DCNN-based FR is the appropriate design of a loss function that ensures discrimination between various identities. The state-of-the-art (SOTA) solutions utilise normalised Softmax loss with additive and/or multiplicative margins. Despite being popular, these Softmax+margin based losses are not theoretically motivated and the effectiveness of a margin is justified only intuitively. In this work, we utilise an alternative framework that offers a more direct mechanism of achieving discrimination among the features of various identities. We propose a novel loss that is equivalent to a triplet loss with proxies and an implicit mechanism of hard-negative mining. We give theoretical justification that minimising the proposed loss ensures a minimum separability between all identities. The proposed loss is simple to implement and does not require heavy hyper-parameter tuning as in the SOTA solutions. We give empirical evidence that despite its simplicity, the proposed loss consistently achieves SOTA performance in various benchmarks for both high-resolution and low-resolution FR tasks.
LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles With Online Initialization

The advances in agile micro aerial vehicles (MAVs) have shown great potential in replacing humans for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight poses fundamental challenges in such dim or smoky environments. Current dominated computer-vision based solutions only work in well-lighted texture-rich environments. This paper addresses the challenge by proposing Marvel, an RF backscatter-based state estimation system with online initialization and calibration. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion with an automatic initialization module. Marvel is enabled by three new designs, a backscatter-based pose sensing module, an online initialization and calibration module, and a backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and <inline-formula><tex-math notation="LaTeX">$4.99^{\circ }$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>4</mml:mn><mml:mo>. </mml:mo><mml:msup><mml:mn>99</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3063850.gif"/></alternatives></inline-formula> for orientation estimation. We further demonstrate our online initialization and calibration by comparing to the perfect initial parameter measurements from burdensome manual operations.
Conservative Optimistic Policy Optimization via Multiple Importance Sampling

Reinforcement Learning (RL) has been able to solve hard problems such as playing Atari games or solving the game of Go, with a unified approach. Yet modern deep RL approaches are still not widely used in real-world applications. One reason could be the lack of guarantees on the performance of the intermediate executed policies, compared to an existing (already working) baseline policy. In this paper, we propose an online model-free algorithm that solves conservative exploration in the policy optimization problem. We show that the regret of the proposed approach is bounded by $\tilde{\mathcal{O}}(\sqrt{T})$ for both discrete and continuous parameter spaces.
Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels.
Topological Linear System Identification via Moderate Deviations Theory

Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space. The induced equivalence classes capture qualitative properties such as stability or the oscillatory nature of the state trajectories, for example. In this letter we develop a method to learn the topological class of an unknown stable system from a single trajectory of finitely many state observations. Using a moderate deviations principle for the least squares estimator of the unknown system matrix <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, we prove that the probability of misclassification decays exponentially with the number of observations at a rate that is proportional to the square of the smallest singular value of <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>.
Relationship between magnetic field properties and statistical flow using numerical simulation and magnetic feature tracking on solar photosphere

We perform radiative magnetohydrodynamic calculations for the solar quiet region to investigate the dependence of statistical flow on magnetic properties and the three-dimensional (3D) structure of magnetic patches in the presence of large-scale flow that mimics differential rotation. It has been confirmed that strong magnetic field patches move faster in the longitudinal direction at the solar surface. Consequently, strong magnetic patches penetrate deeper into the solar interior. The motion of the deep-rooted magnetic patches is influenced by the faster differential rotation in the deeper layer. In this study, we perform realistic radiative magnetohydrodynamic calculations using R2D2 code to validate that stronger patches have deeper roots. We also add large-scale flow to mimic the differential rotation. The magnetic patches are automatically detected and tracked, and we evaluate the depth of 30,000 magnetic patches. The velocities of 2.9 million magnetic patches are then measured at the photosphere. We obtain the dependence of these values on the magnetic properties, such as field strength and flux. Our results confirm that strong magnetic patches tend to show deeper roots and faster movement, and we compare our results with observations using the point spread function of instruments at the Hinode and Solar Dynamics Observatory (SDO). Our result is quantitatively consistent with previous observational results of the SDO.
CLAIMED, a visual and scalable component library for Trusted AI

Deep Learning models are getting more and more popular but constraints on explainability, adversarial robustness and fairness are often major concerns for production deployment. Although the open source ecosystem is abundant on addressing those concerns, fully integrated, end to end systems are lacking in open source. Therefore we provide an entirely open source, reusable component framework, visual editor and execution engine for production grade machine learning on top of Kubernetes, a joint effort between IBM and the University Hospital Basel. It uses Kubeflow Pipelines, the AI Explainability360 toolkit, the AI Fairness360 toolkit and the Adversarial Robustness Toolkit on top of Elyra, Kubeflow, Kubernetes and JupyterLab. Using the Elyra pipeline editor, AI pipelines can be developed visually with a set of jupyter notebooks. We explain how we've created a COVID-19 deep learning classification pipeline based on CT scans. We use the toolkit to highlight parts of the images which have been crucial for the models decisions. We detect bias against age and gender and finally, show how to deploy the model to KFServing to share it across different hospital data centers of the Swiss Personalized Health Network. © 2021 CEUR-WS. All rights reserved.
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

In recent years, data-driven machine learning (ML) methods have revolutionized the computer vision community by providing novel efficient solutions to many unsolved (medical) image analysis problems. However, due to the increasing privacy concerns and data fragmentation on many different sites, existing medical data are not fully utilized, thus limiting the potential of ML. Federated learning (FL) enables multiple parties to collaboratively train a ML model without exchanging local data. However, data heterogeneity (non-IID) among the distributed clients is yet a challenge. To this end, we propose a novel federated method, denoted Federated Disentanglement (FedDis), to disentangle the parameter space into shape and appearance, and only share the shape parameter with the clients. FedDis is based on the assumption that the anatomical structure in brain MRI images is similar across multiple institutions, and sharing the shape knowledge would be beneficial in anomaly detection. In this paper, we leverage healthy brain scans of 623 subjects from multiple sites with real data (OASIS, ADNI) in a privacy-preserving fashion to learn a model of normal anatomy, that allows to segment abnormal structures. We demonstrate a superior performance of FedDis on real pathological databases containing 109 subjects; two publicly available MS Lesions (MSLUB, MSISBI), and an in-house database with MS and Glioblastoma (MSI and GBI). FedDis achieved an average dice performance of 0.38, outperforming the state-of-the-art (SOTA) auto-encoder by 42% and the SOTA federated method by 11%. Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.
Decreasing Impatience

We characterize decreasing impatience, a common behavioral phenomenon in intertemporal choice. Discount factors that display decreasing impatience are characterized through a convexity axiom for investments at fixed interest rates. Then we show that they are equivalent to a geometric average of generalized quasi-hyperbolic discount rates. Finally, they emerge through parimutuel preference aggregation of exponential discount factors. (JEL D15, D61, D91)
Optimal Path Planning using CAMIS: a Continuous Anisotropic Model for Inclined Surfaces

The optimal traverse of irregular terrains made by ground mobile robots heavily depends on the adequacy of the cost models used to plan the path they follow. The criteria to define optimality may be based on minimizing energy consumption and/or preserving the robot stability. This entails the proper assessment of anisotropy to account for the robot driving on top of slopes with different directions. To fulfill this demand, this paper presents the Continuous Anisotropic Model for Inclined Surfaces, a cost model compatible with anisotropic path planners like the bi-directional Ordered Upwind Method. This model acknowledges how the orientation of the robot with respect to any slope determines its energetic cost, considering the action of gravity and terramechanic effects such as the slippage. Moreover, the proposed model can be tuned to define a trade-off between energy minimization and Roll angle reduction. The results from two simulation tests demonstrate how, to find the optimal path in scenarios containing slopes, in certain situations the use of this model can be more advantageous than relying on isotropic cost functions. Finally, the outcome of a field experiment involving a skid-steering robot that drives on top of a real slope is also discussed.
Sequence-based deep learning antibody design for in silico antibody affinity maturation

Antibody therapeutics has been extensively studied in drug discovery and development within the past decades. One increasingly popular focus in the antibody discovery pipeline is the optimization step for therapeutic leads. Both traditional methods and in silico approaches aim to generate candidates with high binding affinity against specific target antigens. Traditional in vitro approaches use hybridoma or phage display for candidate selection, and surface plasmon resonance (SPR) for evaluation, while in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process. In the present study, we investigated different graph-based designs for depicting antibody-antigen interactions in terms of antibody affinity prediction using deep learning techniques. While other in silico computations require experimentally determined crystal structures, our study took interest in the capability of sequence-based models for in silico antibody maturation. Our preliminary studies achieved satisfying prediction accuracy on binding affinities comparing to conventional approaches and other deep learning approaches. To further study the antibody-antigen binding specificity, and to simulate the optimization process in real-world scenario, we introduced pairwise prediction strategy. We performed analysis based on both baseline and pairwise prediction results. The resulting prediction and efficiency prove the feasibility and computational efficiency of sequence-based method to be adapted as a scalable industry practice.
Electro-optically modulated polarization mode conversion in lithium niobate ridge waveguides

Lithium niobate on insulator (LNOI) waveguides, as an emerging technology, have proven to offer a promising platform for integrated optics, due to their strong optical confinement comparable to silicon on insulator (SOI) waveguides, while possessing the versatile properties of lithium niobate, such as high electro-optic coefficients. In this paper, we show that mode hybridization, a phenomenon widely found in vertically asymmetric waveguides, can be efficiently modulated in an LNOI ridge waveguide by electro-optic effect, leading to a polarization mode converter with 97% efficiency. Moreover, the proposed device does not require tapering or periodic poling, thereby greatly simplifying the fabrication process. It can also be actively switched by external fields. Such a platform facilitates technological progress of photonics circuits and sensors.
Koopman Operator Based Modeling for Quadrotor Control on SE(3)

In this letter, we propose a Koopman operator based approach to describe the nonlinear dynamics of a quadrotor on <inline-formula> <tex-math notation="LaTeX">$SE(3)$ </tex-math></inline-formula> in terms of an infinite-dimensional linear system which evolves in the space of observables (lifted space) and which is more appropriate for control design purposes. The major challenge when using the Koopman operator is the characterization of a set of observables that can span the lifted space. Most of the existing methods either start from a set of dictionary functions and then search for a subset that best fits the underlying nonlinear dynamics or they rely on machine learning algorithms to learn these observables. Instead of guessing or learning the observables, in this work we derive them in a systematic way for the quadrotor dynamics on <inline-formula> <tex-math notation="LaTeX">$SE(3)$ </tex-math></inline-formula>. In addition, we prove that the proposed sequence of observables converges pointwise to the zero function, which allows us to select only a finite set of observables to form (an approximation of) the lifted space. Our theoretical analysis is also confirmed by numerical simulations which demonstrate that by increasing the dimension of the lifted space, the derived linear state space model can approximate the nonlinear quadrotor dynamics more accurately.
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning

Road detection or traversability analysis has been a key technique for a mobile robot to traverse complex off-road scenes. The problem has been mainly formulated in early works as a binary classification one, e.g. associating pixels with road or non-road labels. Whereas understanding scenes with fine-grained labels are needed for off-road robots, as scenes are very diverse, and the various mechanical performance of off-road robots may lead to different definitions of safe regions to traverse. How to define and annotate fine-grained labels to achieve meaningful scene understanding for a robot to traverse off-road is still an open question. This research proposes a contrastive learning based method. With a set of human-annotated anchor patches, a feature representation is learned to discriminate regions with different traversability, a method of fine-grained semantic segmentation and mapping is subsequently developed for off-road scene understanding. Experiments are conducted on a dataset of three driving segments that represent very diverse off-road scenes. An anchor accuracy of 89.8% is achieved by evaluating the matching with human-annotated image patches in cross-scene validation. Examined by associated 3D LiDAR data, the fine-grained segments of visual images are demonstrated to have different levels of toughness and terrain elevation, which represents their semantical meaningfulness. The resultant maps contain both fine-grained labels and confidence values, providing rich information to support a robot traversing complex off-road scenes.
Generalized emergent dark energy model and the Hubble constant tension

Weiqiang Yang, ∗ Eleonora Di Valentino, † Supriya Pan, ‡ Arman Shafieloo, 5, § and Xiaolei Li ¶ Department of Physics, Liaoning Normal University, Dalian, 116029, P. R. China Institute for Particle Physics Phenomenology, Department of Physics, Durham University, Durham DH1 3LE, UK Department of Mathematics, Presidency University, 86/1 College Street, Kolkata 700073, India Korea Astronomy and Space Science Institute, Daejeon 34055, Korea University of Science and Technology, Yuseong-gu 217 Gajeong-ro, Daejeon 34113, Korea College of Physics, Hebei Normal University, Shijiazhuang 050024, China
Update the Root of Integrity Tree in Secure Non-Volatile Memory Systems with Low Overhead

Data integrity is important for non-volatile memory (NVM) systems that maintain data even without power. The data integrity in NVM is possibly compromised by integrity attacks, which can be defended against by integrity verification via integrity trees. After NVM system failures and reboots, the integrity tree root is responsible for providing a trusted execution environment. However, the root often becomes a performance bottleneck, since updating the root requires high latency on the write critical path to propagate the modifications from leaf nodes to the root. The root and leaf nodes have to ensure the crash consistency between each other to avoid any update failures that potentially result in misreporting the attacks after system reboots. In this paper, we propose an efficient and low-latency scheme, called SCUE, to directly update the root on the SGX integrity tree (SIT) by overlooking the updates upon the intermediate tree nodes. The idea behind SCUE explores and exploits the observation that only the persistent leaf nodes and root are useful to ensure the integrity after system failures and reboots, due to the loss of the cached intermediate tree nodes. To achieve the crash consistency between root and leaf nodes, we accurately predict the updates upon the root and pre-update the root before the leaf nodes are modified. Moreover, the SIT root is difficult to be reconstructed from the leaf nodes since updating one tree node needs its parent node as input. We use a counter-summing approach to reconstructing the SIT from leaf nodes. Our evaluation results show that compared with the state-of-the-art integrity tree update schemes, our SCUE scheme delivers high performance while ensuring the system integrity.
sparta: Sparse Tables and their Algebra for use in High Dimensional Bayesian Networks

A Bayesian network is a multivariate (potentially high dimensional) probabilistic model, which is often formed by combining lower dimensional components. Inference (computation of conditional probabilities) is based on message passing algorithms that utilize conditional independence structures. In Bayesian networks for discrete variables with ﬁnite state spaces, there is a fundamental problem in high dimensions: A discrete distribution is represented by a table of values, and in high dimensions such tables can become prohibitively large. In inference, such tables must be multiplied which can lead to even larger tables. The sparta package tries to meet this challenge by introducing new methods that eﬃciently handles multiplication and marginalization of sparse tables. The package was written in the R programming language and is freely available from the Comprehensive R Archive Network (CRAN). The companion package jti , also on CRAN, was developed to showcase the potential of sparta in connection to the Junction Tree Algorithm. We show that sparta outperforms existing methods for high-dimensional sparse probability tables. Furthermore, we demonstrate, that sparta can handle problems which are otherwise infeasible due to lack of computer memory.