Consistent and Truthful Interpretation with Fourier Analysis

For many interdisciplinary fields, ML interpretations need to be consistent with what-if scenarios related to the current case, i.e., if one factor changes, how does the model react? Although the attribution methods are supported by the elegant axiomatic systems, they mainly focus on individual inputs, and are generally inconsistent. To support what-if scenarios, we introduce a new notion called truthful interpretation, and apply Fourier analysis of Boolean functions to get rigorous guarantees. Experimental results show that for neighborhoods with various radii, our method achieves 2x - 50x lower interpretation error compared with the other methods

The Implied Views of Bond Traders on the Spot Equity Market

By using the Black-Derman-Toy (BDT) model, we predict the future trend of the riskless rate, and then we build an equation that relates the market price of zero-coupon bonds and the theoretical price of zero-coupon bonds calculated using a binomial option pricing model. Based on this, we can find the implied daily return $\mu$, implied natural upturn probability $p$, and implied daily volatility $\sigma$ with respect to different time-to-maturity values of zero-coupon bonds. With these results, we can give some suggestions to bond traders

A Comprehensive Study and Comparison of the Robustness of 3D Object Detectors Against Adversarial Attacks

Recent years have witnessed significant advancements in deep learning-based 3D object detection, leading to its widespread adoption in numerous applications. As 3D object detectors become increasingly crucial for security-critical tasks, it is imperative to understand their robustness against adversarial attacks. This paper presents the first comprehensive evaluation and analysis of the robustness of LiDAR-based 3D detectors under adversarial attacks. Specifically, we extend three distinct adversarial attacks to the 3D object detection task, benchmarking the robustness of state-of-the-art LiDAR-based 3D object detectors against attacks on the KITTI and Waymo datasets. We further analyze the relationship between robustness and detector properties. Additionally, we explore the transferability of cross-model, cross-task, and cross-data attacks. Thorough experiments on defensive strategies for 3D detectors are conducted, demonstrating that simple transformations like flipping provide little help in improving robustness when the applied transformation strategy is exposed to attackers. Finally, we propose balanced adversarial focal training, based on conventional adversarial training, to strike a balance between accuracy and robustness. Our findings will facilitate investigations into understanding and defending against adversarial attacks on LiDAR-based 3D object detectors, thus advancing the field. The source code is publicly available at \url{https://github.com/Eaphan/Robust3DOD}.Comment: 30 pages, 14 figure

CAPIA: Cloud Assisted Privacy-Preserving Image Annotation

Using public cloud for image storage has become a prevalent trend with the rapidly increasing number of pictures generated by various devices. For example, today\u27s most smartphones and tablets synchronize photo albums with cloud storage platforms. However, as many images contain sensitive information, such as personal identities and financial data, it is concerning to upload images to cloud storage. To eliminate such privacy concerns in cloud storage while keeping decent data management and search features, a spectrum of keywords-based searchable encryption (SE) schemes have been proposed in the past decade. Unfortunately, there is a fundamental gap remains open for their support of images, i.e., appropriate keywords need to be extracted for images before applying SE schemes to them. On one hand, it is obviously impractical for smartphone users to manually annotate their images. On the other hand, although cloud storage services now offer image annotation services, they rely on access to users\u27 unencrypted images. To fulfill this gap and open the first path from SE schemes to images, this paper proposes a cloud assisted privacy-preserving automatic image annotation scheme, namely CAPIA. CAPIA enables cloud storage users to automatically assign keywords to their images by leveraging the power of cloud computing. Meanwhile, CAPIA prevents the cloud from learning the content of images and their keywords. Thorough analysis is carried out to demonstrate the security of CAPIA. A prototype implementation over the well-known IAPR TC-12 dataset further validates the efficiency and accuracy of CAPIA

Associations Among Self-Regulation, Life Stress, and Suicidal Ideation in Adolescents: A Developmental Psychopathology Approach

Background: Suicide is a major public health concern among adolescents. Although research has made progress in identifying risk factors for youth suicidality, there has been less focus on early developmental antecedents of youth suicidal thoughts and behaviors. Taking a developmental psychopathology perspective, we examined longitudinal associations among multiple aspects of self-regulation (i.e., emotion regulation, emotion reactivity, parasympathetic regulation, inhibitory control), life stress, and suicidal ideation. We hypothesized that deficits in self-regulation during middle childhood and early adolescence and greater life stress during early and middle childhood would predict higher lifetime suicidal ideation reported in adolescence. Method: Participants were adolescents (N = 177) enrolled in a longitudinal follow-up of a randomized control trial evaluating the efficacy of a parenting intervention in infancy. Self-regulation was assessed using parent-reported emotion regulation, self-reported emotion reactivity, parasympathetic regulation (i.e., respiratory sinus arrhythmia at rest and in response to challenge), and inhibitory control during two behavioral tasks. Early life stress was scored based on parent report, and adolescents reported suicidal ideation. Bivariate and multivariate analyses were used to assess socio-demographics, risk group, self-regulation, and life stress as predictors of youth suicidality. Results: Greater emotion reactivity significantly predicted greater suicidal ideation intensity in adolescence, and this association persisted after controlling for sex. Other candidate predictors were not significantly associated with youth suicidality, although life stress during early and middle childhood predicted worse emotion regulation and inhibitory control during middle childhood. Conclusion: Results provide evidence for heightened emotion reactivity as a risk factor for suicidal ideation in adolescents and have clinical implications for prevention and intervention targeting youth suicidality. More research is needed on the role of life stress in predicting self-regulation in middle childhood and beyond. Keywords: Adolescents, suicide, suicidal ideation, self-regulation, early life stres

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

Infrastructures, including bridges, tunnels, sewers, and telecommunications, may be exposed to environmental-induced or traffic-induced deformation and vibrations. Some infrastructures, such as bridges and roadside upright structures, may be sensitive to vibration and displacement where several different types of dynamic and static sensors may be used for their measurement of sensitivity to environmental-induced loads, like wind and earthquake, and traffic-induced loads, such as passing trucks. Remote sensing involves either in situ, on-site, or airborne sensing where in situ sensors, such as strain gauges, displacement transducers, velometers, and accelerometers, are considered conventional but more durable and reliable. With data collected by accelerometers, time histories may be obtained, transformed, and then analyzed to determine their modal frequencies and shapes, while with displacement and strain transducers, structural deflections and internal stress distribution may be measured, respectively. Field tests can be used to characterize the dynamic and static properties of the infrastructures and may be further used to show their changes due to damage. Additionally, representative field applications on bridge dynamic testing, seismology, and earthborn/construction vibration are explained. Sensor data can be analyzed to establish the trend and ensure optimal structural health. At the end, five case studies on bridges and industry facilities are demonstrated in this chapter

Kernel-SSL: Kernel KL Divergence for Self-Supervised Learning

Contrastive learning usually compares one positive anchor sample with lots of negative samples to perform Self-Supervised Learning (SSL). Alternatively, non-contrastive learning, as exemplified by methods like BYOL, SimSiam, and Barlow Twins, accomplishes SSL without the explicit use of negative samples. Inspired by the existing analysis for contrastive learning, we provide a reproducing kernel Hilbert space (RKHS) understanding of many existing non-contrastive learning methods. Subsequently, we propose a novel loss function, Kernel-SSL, which directly optimizes the mean embedding and the covariance operator within the RKHS. In experiments, our method Kernel-SSL outperforms state-of-the-art methods by a large margin on ImageNet datasets under the linear evaluation settings. Specifically, when performing 100 epochs pre-training, our method outperforms SimCLR by 4.6%

RelationMatch: Matching In-batch Relationships for Semi-supervised Learning

Semi-supervised learning has achieved notable success by leveraging very few labeled data and exploiting the wealth of information derived from unlabeled data. However, existing algorithms usually focus on aligning predictions on paired data points augmented from an identical source, and overlook the inter-point relationships within each batch. This paper introduces a novel method, RelationMatch, which exploits in-batch relationships with a matrix cross-entropy (MCE) loss function. Through the application of MCE, our proposed method consistently surpasses the performance of established state-of-the-art methods, such as FixMatch and FlexMatch, across a variety of vision datasets. Notably, we observed a substantial enhancement of 15.21% in accuracy over FlexMatch on the STL-10 dataset using only 40 labels. Moreover, we apply MCE to supervised learning scenarios, and observe consistent improvements as well