Miracle-dz/UniFAR-FTUs huggingface dataset:
Decorrelative Network Architecture for Robust Electrocardiogram Classification

Artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. As it is not possible to train networks that are accurate in all scenarios, models must recognize situations where they cannot operate confidently. Bayesian deep learning methods sample the model parameter space to estimate uncertainty, but these parameters are often subject to the same vulnerabilities, which can be exploited by adversarial attacks. We propose a novel ensemble approach based on feature decorrelation and Fourier partitioning for teaching networks diverse complementary features, reducing the chance of perturbation-based fooling. We test our approach on single and multi-channel electrocardiogram classification, and adapt adversarial training and DVERGE into the Bayesian ensemble framework for comparison. Our results indicate that the combination of decorrelation and Fourier partitioning generally maintains performance on unperturbed data while demonstrating superior robustness and uncertainty estimation on projected gradient descent and smooth adversarial attacks of various magnitudes. Furthermore, our approach does not require expensive optimization with adversarial samples, adding much less compute to the training process than adversarial training or DVERGE. These methods can be applied to other tasks for more robust and trustworthy models.
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation

Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering the smoothness-based methods have achieved promising progress, in this paper, we advocate applying the consistency constraint under various perturbations to effectively regularize unlabeled 3D points. Specifically, we propose a novel DAT (\textbf{D}ual \textbf{A}daptive \textbf{T}ransformations) model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate that our model can effectively leverage the unlabeled 3D points and achieve significant performance gains on both datasets, setting new state-of-the-art performance for weakly supervised point cloud segmentation.
Magnus integrators for linear and quasilinear delay differential equations

A procedure to numerically integrate non-autonomous linear delay differential equations is presented. It is based on the use of an spectral discretization of the delayed part to transform the original problem into a matrix linear ordinary differential equation which is subsequently solved with numerical integrators obtained from the Magnus expansion. The algorithm can be used in the periodic case to get both accurate approximations of the characteristic multipliers and the solution itself. In addition, it can be extended to deal with certain quasilinear delay equations.
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations. However, how to discover and represent causalities remains a huge gap that hinders the development of causal RL. In this paper, we augment Goal-Conditioned RL (GCRL) with Causal Graph (CG), a structure built upon the relation between objects and events. We novelly formulate the GCRL problem into variational likelihood maximization with CG as latent variables. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventional data to estimate the posterior of CG; using CG to learn generalizable models and interpretable policies. Due to the lack of public benchmarks that verify generalization capability under reasoning, we design nine tasks and then empirically show the effectiveness of the proposed method against five baselines on these tasks. Further theoretical analysis shows that our performance improvement is attributed to the virtuous cycle of causal discovery, transition modeling, and policy training, which aligns with the experimental evidence in extensive ablation studies.
A quantum simulator based on locally controlled logical systems

In a digital quantum simulator, basic two-qubit interactions are manipulated by means of fast local control operations to establish a desired target Hamiltonian. Here we consider a quantum simulator based on logical systems, i.e. where several physical qubits are used to represent a single logical two-level system to obtain enhanced and simple control over effective interactions between logical systems. Fixed, distance-dependent pairwise interactions between the physical qubits lead to effective interactions between the logical systems, which can be fully controlled solely by the choice of their internal state. This allows one to directly manipulate the topology and strength of effective interactions between logical systems. We show how to choose and generate the required states of logical systems for any desired interaction pattern and topology, how to perform arbitrary logical measurements, and how to obtain full control over single logical systems using only the intrinsic two-body interactions and control of individual physical qubits. This leads to a universal quantum simulator based on logical systems. We discuss the advantages of such a logical quantum simulator over standard ones, including the possibility to reach target topologies that are only accessible with large overheads otherwise. We provide several examples of how to obtain different target interaction patterns and topologies from initial long-ranged or short-ranged qubit-qubit interactions with a specific distance dependence.
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent — a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is neglected and only the shared procedure is assumed, which we abstract as an oracle. We find that even under this general setting, single-dimension feature and label can still be recovered from the other party under suitable constraints of batch size, thus demonstrating the potential vulnerability of all frameworks following the same philosophy. Then we look into a popular instantiation of the protocol based on Homomorphic Encryption (HE). We propose an active attack that significantly weaken the constraints on batch size in the previous analysis via generating and compressing auxiliary ciphertext. To address the privacy leakage within the HE-based protocol, we develop a simple-yet-effective countermeasure based on Differential Privacy (DP), and provide both utility and privacy guarantees for the updated algorithm. Finally, we empirically verify the effectiveness of our attack and defense on benchmark datasets. Altogether, our findings suggest that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already demonstrated its power in horizontal federated learning, can also play a crucial role in the vertical setting, especially when coupled with HE or secure multi-party computation (MPC) techniques.
Easy Batch Normalization

It was shown that adversarial examples improve object recognition. But what about their opposite side, easy examples? Easy examples are samples that the machine learning model classiﬁes correctly with high conﬁdence. In our paper, we are making the ﬁrst step toward exploring the poten-tial beneﬁts of using easy examples in the training procedure of neural networks. We propose to use an auxiliary batch normalization for easy examples for the standard and robust accuracy improvement.
On the Slow Drift of Solstices: Milankovic Cycles and Mean Global Temperature

The Earth’s revolution is modiﬁed by changes in inclination of its rotation axis. Despite the fact that the gravity ﬁeld is central, the Earth’s trajectory is not closed and the equinoxes drift. Changes in polar motion and revolution are coupled through the Liouville-Euler equations. Milankovic (1920) argued that the shortest precession period of solstices is 20,700 years: the Summer solstice in one hemisphere takes place alternately every 11 kyr at perihelion and at aphelion. Milankovi´c assumed that the planetary distances to the Sun and the solar ephemerids are constant. There are now observations that allow one to drop these assumptions. We have submitted the time series for the Earth’s pole of rotation, global mean surface temperature and ephemeris to iterative Singular Spectrum Analysis. iSSA extracts from each a trend, a 1yr and a 60yr component. Both the apparent drift of solstices of Earth around the Sun and the global mean temperature exhibit a strong 60yr oscillation. We monitor the precession of the Earth’s elliptical orbit using the positions of the solstices as a function of Sun-Earth distance. The “ﬁxed dates” of solstices actually drift. Comparing the time evolution of the Winter and Summer solstices positions of the rotation pole and the ﬁrst iSSA component (trend) of the temperature allows one to recognize some common features. A basic equation from Milankovic links the derivative of heat received at a given location on Earth to solar insolation, known functions of the location coordinates, solar declination and hour angle, with an inverse square dependence on the Sun-Earth distance. We have translated the drift of solstices as a function of distance to the Sun into the geometrical insolation theory of Milankovic. Shifting the inverse square of the 60yr iSSA drift of solstices by 15 years with respect to the ﬁrst derivative of the 60yr iSSA trend of temperature, that is exactly a quadrature in time, puts the two curves in quasi-exact superimposition. The probability of a chance coincidence appears very low. Correlation does not imply causality when there is no accompanying model. Here Milankovic’s equation can be considered as a model that is widely accepted. This paper identiﬁes a case of agreement between observations and a mathematical formulation, a case in which an element of global surface temperature could be caused by changes in the Earth’s rotation axis .
Multi-step domain adaptation by adversarial attack to H ΔH-divergence

Adversarial examples are transferable between different models. In our paper, we propose to use this property for multi-step domain adaptation. In unsupervised domain adaptation settings, we demonstrate that replacing the source domain with adversarial examples to $\mathcal{H} \Delta \mathcal{H}$-divergence can improve source classifier accuracy on the target domain. Our method can be connected to most domain adaptation techniques. We conducted a range of experiments and achieved improvement in accuracy on Digits and Office-Home datasets.
RCLane: Relay Chain Prediction for Lane Detection

Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies ( Y-shape, Fork-shape, nearly horizontal lane ) of the lane markers, which leads to sub-optimal solution. In this paper, we present a new method for lane detection based on relay chain prediction. Specifically, our model predicts a segmentation map to classify the foreground and background region. For each pixel point in the foreground region, we go through the forward branch and backward branch to recover the whole lane. Each branch decodes a transfer map and a distance map to produce the direction moving to the next point, and how many steps to progressively predict a relay station (next point). As such, our model is able to capture the keypoints along the lanes. Despite its simplicity, our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS .
To what extent can we analyze Kotlin programs using existing Java taint analysis tools?

As an alternative to Java, Kotlin has gained rapid popularity since its introduction and has become the default choice for developing Android apps. However, due to its inter-operability with Java, Kotlin programs may contain almost the same security vulnerabilities as their Java counterparts. Hence, we question: to what extent can one use an existing Java static taint analysis on Kotlin code? In this paper, we investigate the challenges in implementing a taint analysis for Kotlin compared to Java. To answer this question, we performed an exploratory study where each Kotlin construct was examined and compared to its Java equivalent. We identified 18 engineering challenges that static-analysis writers need to handle differently due to Kotlin's unique constructs or the differences in the generated bytecode between the Kotlin and Java compilers. For eight of them, we provide a conceptual solution, while six of those we implemented as part of SECUCHECK-KOTLIN, an extension to the existing Java taint analysis Secucheck.
Modeling emotion for human-like behavior in future intelligent robots

Over the past decades, research in cognitive and affective neuroscience has emphasized that emotion is crucial for human intelligence and in fact inseparable from cognition. Concurrently, there has been growing interest in simulating and modeling emotion-related processes in robots and artificial agents. In this opinion paper, our goal is to provide a snapshot of the present landscape in emotion modeling and to show how neuroscience can help advance the current state of the art. We start with an overview of the existing literature on emotion modeling in three areas of research: affective computing, social robotics, and neurorobotics. Briefly summarizing the current state of knowledge on natural emotion, we then highlight how existing proposals in artificial emotion do not make sufficient contact with neuroscientific evidence. We conclude by providing a set of principles to help guide future research in artificial emotion and intelligent machines more generally. Overall, we argue that a stronger integration of emotion-related processes in robot models is critical for the design of human-like behavior in future intelligent machines. Such integration not only will contribute to the development of autonomous social machines capable of tackling real-world problems but would contribute to advancing understanding of human emotion.
A New Perspective on Stabilizing GANs Training: Direct Adversarial Training

Generative Adversarial Networks (GANs) are the most popular image generation models that have achieved remarkable progress on various computer vision tasks. However, training instability is still one of the open problems for all GAN-based algorithms. Quite a number of methods have been proposed to stabilize the training of GANs, the focuses of which were respectively put on the loss functions, regularization and normalization technologies, training algorithms, and model architectures. Different from the above methods, in this paper, a new perspective on stabilizing GANs training is presented. It is found that sometimes the images produced by the generator act like adversarial examples of the discriminator during the training process, which may be part of the reason causing the unstable training of GANs. With this finding, we propose the Direct Adversarial Training (DAT) method to stabilize the training process of GANs. Furthermore, we prove that the DAT method is able to minimize the Lipschitz constant of the discriminator adaptively. The advanced performance of DAT is verified on multiple loss functions, network architectures, hyper-parameters, and datasets. Specifically, DAT achieves significant improvements of 11.5% FID on CIFAR-100 unconditional generation based on SSGAN, 10.5% FID on STL-10 unconditional generation based on SSGAN, and 13.2% FID on LSUN-Bedroom unconditional generation based on SSGAN.
A Security & Privacy Analysis of US-based Contact Tracing Apps

With the onset of COVID-19, governments worldwide planned to develop and deploy contact tracing (CT) apps to help speed up the contact tracing process. However, experts raised concerns about the long-term privacy and security implications of using these apps. Consequently, several proposals were made to design privacy-preserving CT apps. To this end, Google and Apple developed the Google/Apple Exposure Notification (GAEN) framework to help public health authorities develop privacy-preserving CT apps. In the United States, 26 states used the GAEN framework to develop their CT apps. In this paper, we empirically evaluate the US-based GAEN apps to determine 1) the privileges they have, 2) if the apps comply with their defined privacy policies, and 3) if they contain known vulnerabilities that can be exploited to compromise privacy. The results show that all apps violate their stated privacy policy and contain several known vulnerabilities.
Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning

Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multistep and sparse reward tasks, such as stacking several blocks given only a task-completion reward signal. To improve learning efficiency for such tasks, this paper proposes a DRL exploration technique, termed $\mathbf{A^{2}}$, which integrates two components inspired by human experiences: Abstract demonstrations and Adaptive exploration. $\mathbf{A^{2}}$ starts by decomposing a complex task into subtasks, and then provides the correct orders of subtasks to learn. During training, the agent explores the environment adaptively, acting more deterministically for well-mastered subtasks and more stochastically for ill-learnt subtasks. Ablation and comparative experiments are conducted on several grid-world tasks and three robotic manipulation tasks. We demonstrate that $\mathbf{A^{2}}$ can aid popular DRL algorithms (DQN, DDPG, and SAC) to learn more efficiently and stably in these environments.
Squeezing enhanced nonlinearity sensing in dissipatively coupled optical systems

In this manuscript, we propose a proposal to enhance the sensitivity of two cavities to its intrinsic nonlinearities by pumping one of the cavities. The system consists of two dissipatively coupled micro-ring cavities, one of which is driven by a squeezed laser. With a low pump rate, the spectrum of the system acquires a real spectral singularity. We ﬁnd that this singularity is very sensitive to the pumping and nonlinearity of the system, and compared to the case of coherent drive, the sensitivity of the system to intrinsic nonlinearities at the singularity can be further increased by using the squeezed laser. Moreover, the scheme is robust gainst fabrication imperfections. This work would open a new avenue for quantum sensors, which could ﬁnd applications in many ﬁelds, such as the precise measurement and quantum metrology.
Condition evaluation of suspension bridges for maintenance, repair and rehabilitation: a comprehensive framework

Abstract To indicate health status of bridges and help stakeholders make decision on maintenance, a comprehensive framework has been proposed to evaluate structural efficiency of suspension bridges using analytic hierarchy process. First, the analytical hierarchy model (i.e. hierarchical network together with data aggregation algorithms) has been constructed using multi-source data, including visual inspection, non-destructive testing and structural health monitoring information. Age-dependent variable weight theory is developed to account for the service history of elements ensuring the alignment of variation trend of index weights with the objective law in bridge maintenance and management activities. To overcome the limitations of factor-based variable weight model for weight adjustment, the factor- and age-based variable weight model has been adopted for data aggregation. Finally, four cases are used to test the effectiveness of the three models (i.e. constant weight model, factor-based variable weight model and factor- and age-based variable weight model). By comparing the performance of the three models, the recommended maintenance strategy derived from factor- and age-based variable weight model aligns more with the actual strategy than the other two. The factor- and age-based variable weight model outperforms both the factor-based variable weight model and constant weight model in helping bridge owners make maintenance decisions.
Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge on the label set, which requires distinguishing in the target domain the unknown samples from the known ones. Recent methods usually focused on categorizing a target sample into one of the source classes rather than distinguishing known and unknown samples, which ignores the inter-sample affinity between known and unknown samples and may lead to suboptimal performance. Aiming at this issue, we propose a novel UDA framework where such inter-sample affinity is exploited. Specifically, we introduce a knowability-based labeling scheme which can be divided into two steps: 1) Knowability-guided detection of known and unknown samples based on the intrinsic structure of the neighborhoods of samples, where we leverage the first singular vectors of the affinity matrices to obtain the knowability of every target sample. 2) Label refinement based on neighborhood consistency to relabel the target samples, where we refine the labels of each target sample based on its neighborhood consistency of predictions. Then, auxiliary losses based on the two steps are used to reduce the inter-sample affinity between the unknown and the known target samples. Finally, experiments on four public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods.