A geometric‐sensitivity‐difference based algorithm improves object depth‐localization for diffuse optical tomography in a circular‐array outward‐imaging geometry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135030/1/mp1957.pd

A rapid preconcentration method using modified GP-MSE for sensitive determination of trace semivolatile organic pollutants in the gas phase of ambient air

A sensitive concentration method utilising modified gas-purge microsyringe extraction (GP-MSE) was developed. Concentration (reduction in volume) to a microlitre volume was achieved. PAHs were utilised as semivolatile analytes to optimise the various parameters that affect the concentration efficiency. The injection rate and temperature were the key factors that affected the concentration efficiency. An efficient concentration (75.0−96.1%) of PAHs was obtained under the optimised conditions. The method exhibited good reproducibility (RSD values that ranged from 1.5 to 9.0%). The GP-MSE concentration method enhances the volume reduction (concentration factor), leading to a low method detection limit (0.5−15 ng L–1). Furthermore, this method offers the advantage of small-volume sampling, enabling even the detection of diurnal hourly changes in the concentration of PAHs in ambient air. Utilising this method in combination with GC−MS, the diurnal hourly flux of PAHs from the gas phase of ambient air was measured. Indeed, the proposed technique is a simple, fast, low-cost and environmentally friendly

Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning

Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences to extract user preferences, leading to challenges such as popular bias and cold-start problems. In this paper, we propose a hybrid multimodal approach for session-based recommendation to address these challenges. Our approach combines different modalities, including textual content and item IDs, leveraging the complementary nature of these modalities using CatBoost. To learn universal item representations, we design a language representation-based item retrieval architecture that extracts features from the textual content utilizing pre-trained language models. Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation. This technique decouples the sequence representation and item representation space, facilitating bidirectional alignment through dual-queue contrastive learning. Simultaneously, the momentum queue provides a large number of negative samples, effectively enhancing the effectiveness of contrastive learning. Our approach yielded competitive results, securing a 5th place ranking in KDD CUP 2023 Task 1. We have released the source code and pre-trained models associated with this work

The utility of direct-current as compared to frequency domain measurements in spectrally-constrained diffuse optical tomography toward cancer imaging

This work investigates, by means of analytical and simulation studies, the performance of spectrally-constrained image reconstruction in Continuous-Wave or Direct-Current (DC) and Frequency Domain (FD) near-infrared optical tomography. A recent analytic approach for estimating the accuracy of target recovery and the level of background artifact for optical tomography at single wavelength, based on the analysis of parametric reconstruction uncertainty level (PRUL), is extended to spectrally-constrained optical tomography. The analytical model is implemented to rank three sets of wavelengths that had been used as spectral prior in an independent experimental study. Subsequent simulation appraises the recovery of oxygenated hemoglobin (HbO), deoxygenated hemoglobin (Hb), water (H2O), scattering amplitude (A), and scattering power (b) using DC-only, DC-excluded FD, and DC-included FD, based on the three sets of wavelengths as the spectral prior. The simulation results support the analytic ranking of the performance of the three sets of spectral priors, and generally agree with the performance outcome of DC-only versus that of DC-excluded FD and DC-included FD. Specifically, this study indicate that: 1) the rank of overall quality of chromophore recovery is Hb, H2O, and HbO from the highest to lowest; and in the scattering part the A is always better recovered than b. This outcome does suggest that the DC-only information gives rise to unique solution to the image reconstruction routine under the given spectral prior. 2) DC-information is not-redundant in FD-reconstruction, as the artifact levels of DC-included FD reconstruction are always lower than those of DC-excluded FD. 3) The artifact level as represented by the noise-to-contrast-ratio is almost always the lowest in DC-only, leading to generally better resolution of multiple targets of identical contrasts over the background than in FD. However, the FD could outperform DC in the recovery of scattering properties including both A and b when the spectral prior is less optimal, implying the benefit of phase-information in scattering recovery in the context of spectrally-constrained optical tomography.Electrical and Computer Engineerin

A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks

Deep Neural Networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks, i.e., their performances drop when imperceptible perturbations are made to the original inputs, which may further degrade the following visual tasks or introduce new problems such as data and privacy security. Hence, metrics for evaluating the robustness of deep models against adversarial attacks are desired. However, previous metrics are mainly proposed for evaluating the adversarial robustness of shallow networks on the small-scale datasets. Although the Cross Lipschitz Extreme Value for nEtwork Robustness (CLEVER) metric has been proposed for large-scale datasets (e.g., the ImageNet dataset), it is computationally expensive and its performance relies on a tractable number of samples. In this paper, we propose the Adversarial Converging Time Score (ACTS), an attack-dependent metric that quantifies the adversarial robustness of a DNN on a specific input. Our key observation is that local neighborhoods on a DNN's output surface would have different shapes given different inputs. Hence, given different inputs, it requires different time for converging to an adversarial sample. Based on this geometry meaning, ACTS measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset using state-of-the-art deep networks. Extensive experiments show that our ACTS metric is an efficient and effective adversarial metric over the previous CLEVER metric.Comment: ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM

Disease-Associated Mutations Prevent GPR56-Collagen III Interaction

GPR56 is a member of the adhesion G protein-coupled receptor (GPCR) family. Mutations in GPR56 cause a devastating human brain malformation called bilateral frontoparietal polymicrogyria (BFPP). Using the N-terminal fragment of GPR56 (GPR56N) as a probe, we have recently demonstrated that collagen III is the ligand of GPR56 in the developing brain. In this report, we discover a new functional domain in GPR56N, the ligand binding domain. This domain contains four disease-associated mutations and two N-glycosylation sites. Our study reveals that although glycosylation is not required for ligand binding, each of the four disease-associated mutations completely abolish the ligand binding ability of GPR56. Our data indicates that these four single missense mutations cause BFPP mostly by abolishing the ability of GPR56 to bind to its ligand, collagen III, in addition to affecting GPR56 protein surface expression as previously shown