Exploiting the Signal-Leak Bias in Diffusion Models

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The observations from the Dark Energy Spectroscopic Instrument (DESI) will significantly increase the numbers of known extremely metal-poor stars by a factor of similar to 10, improving the sample statistics to study the early chemical evolution of the Mil ...

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This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

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Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Bogdan Kulynych, Carmela González Troncoso

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

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Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality

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Visual estimates of stimulus features are systematically biased toward the features of previously encountered stimuli. Such serial dependencies have often been linked to how the brain maintains perceptual continuity. However, serial dependence has mostly b ...

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In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

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Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

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A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

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A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

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Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

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Improving time-resolution and sensitivity of in situ X-ray photoelectron spectroscopy of a powder catalyst by modulated excitation

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Ambient pressure X-ray photoelectron spectroscopy (APXPS) is a powerful tool to characterize the surface structure of heterogeneous catalysts in situ. In order to improve the time resolution and the signal-to-noise (S/N) ratio of photoemission spectra, we ...

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Engineering Hydrogel-coated Neural Interfaces for Chronic Deep Brain Recording

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Implanted brain electrodes provide a unique way of electrically interfacing with individual neurons in vivo. However, chronic recording or stimulating capabilities of neural implants are often impaired by a strong immune response resulting in a fibrous sca ...

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Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

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This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

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Educating Engineering Students to Address Bias and Discrimination Within Their Project Teams

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What training should engineering students receive to enable them to contribute to reducing bias, discrimination and the persistent lack of diversity in engineering? Collaboration is central to professional engineering work and, consequently, teamwork and g ...

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