Optimum Water Quality Monitoring Network Design for Bidirectional River Systems

Affected by regular tides, bidirectional water flows play a crucial role in surface river systems. Using optimization theory to design a water quality monitoring network can reduce the redundant monitoring nodes as well as save the costs for building and running a monitoring network. A novel algorithm is proposed to design an optimum water quality monitoring network for tidal rivers with bidirectional water flows. Two optimization objectives of minimum pollution detection time and maximum pollution detection probability are used in our optimization algorithm. We modify the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and develop new fitness functions to calculate pollution detection time and pollution detection probability in a discrete manner. In addition, the Storm Water Management Model (SWMM) is used to simulate hydraulic characteristics and pollution events based on a hypothetical river system studied in the literature. Experimental results show that our algorithm can obtain a better Pareto frontier. The influence of bidirectional water flows to the network design is also identified, which has not been studied in the literature. Besides that, we also find that the probability of bidirectional water flows has no effect on the optimum monitoring network design but slightly changes the mean pollution detection time

Prenatal selective serotonin reuptake inhibitor (SSRI) exposure induces working memory and social recognition deficits by disrupting inhibitory synaptic networks in male mice

Selective serotonin reuptake inhibitors (SSRIs) are commonly prescribed antidepressant drugs in pregnant women. Infants born following prenatal exposure to SSRIs have a higher risk for behavioral abnormalities, however, the underlying mechanisms remains unknown. Therefore, we examined the effects of prenatal fluoxetine, the most commonly prescribed SSRI, in mice. Intriguingly, chronic in utero fluoxetine treatment impaired working memory and social novelty recognition in adult males. In the medial prefrontal cortex (mPFC), a key region regulating these behaviors, we found augmented spontaneous inhibitory synaptic transmission onto the layer 5 pyramidal neurons. Fast-spiking interneurons in mPFC exhibited enhanced intrinsic excitability and serotonin-induced excitability due to upregulated serotonin (5-HT) 2A receptor (5-HT2AR) signaling. More importantly, the behavioral deficits in prenatal fluoxetine treated mice were reversed by the application of a 5-HT2AR antagonist. Taken together, our findings suggest that alterations in inhibitory neuronal modulation are responsible for the behavioral alterations following prenatal exposure to SSRIs

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space

Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance. Compared to the accumulating theoretical studies on related score-based diffusion models, analysis of flow-based models, which are deterministic in both forward (data-to-noise) and reverse (noise-to-data) directions, remain sparse. In this paper, we provide a theoretical guarantee of generating data distribution by a progressive flow model, the so-called JKO flow model, which implements the Jordan-Kinderleherer-Otto (JKO) scheme in a normalizing flow network. Leveraging the exponential convergence of the proximal gradient descent (GD) in Wasserstein space, we prove the Kullback-Leibler (KL) guarantee of data generation by a JKO flow model to be $O(\varepsilon^2)$ when using $N \lesssim \log (1/\varepsilon)$ many JKO steps ($N$ Residual Blocks in the flow) where $\varepsilon$ is the error in the per-step first-order condition. The assumption on data density is merely a finite second moment, and the theory extends to data distributions without density and when there are inversion errors in the reverse process where we obtain KL-$W_2$ mixed error guarantees. The non-asymptotic convergence rate of the JKO-type $W_2$-proximal GD is proved for a general class of convex objective functionals that includes the KL divergence as a special case, which can be of independent interest

“Hiding in the Crowd?” In Pursuit of Perceived Anonymity through a Uniform Visual Presentation on Algorithm-driven Social Media Platforms

Collective anonymity is defined as a large group of social media users collectively adopting a uniform identification presentation (e.g., an identical online pseudonym and avatar). This emerging trend is increasingly prevalent on algorithm-driven social media, proactively leveraged by users to increase perceived anonymity. To conceptualize it and understand its drivers and outcomes, this study investigated one exemplary form of collective anonymity on Xiaohongshu. Using an inductive approach, interview data with fourteen participants was collected and analysed through a thematic approach. Our findings (a) explained the underlying mechanisms of collective anonymity; (b) unpacked users’ internal motivations and extrinsic factors that drive it; (c) uncovered its downstream consequences pertinent to human-human interaction and human-algorithm engagement. Our study also provides important implications on algorithmic regulation and governance, the ethical use of algorithmic recommendations, and the mitigation of disinhibited and deviant behaviour resulting from collective anonymity

Offline Policy Evaluation and Optimization under Confounding

Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning. Using conventional methods for offline RL in the presence of confounding can not only lead to poor decisions and poor policies, but can also have disastrous effects in critical applications such as healthcare and education. We map out the landscape of offline policy evaluation for confounded MDPs, distinguishing assumptions on confounding based on their time-evolution and effect on the data-collection policies. We determine when consistent value estimates are not achievable, providing and discussing algorithms to estimate lower bounds with guarantees in those cases. When consistent estimates are achievable, we provide sample complexity guarantees. We also present new algorithms for offline policy improvement and prove local convergence guarantees. Finally, we experimentally evaluate our algorithms on gridworld and a simulated healthcare setting of managing sepsis patients. We note that in gridworld, our model-based method provides tighter lower bounds than existing methods, while in the sepsis simulator, our methods significantly outperform confounder-oblivious benchmarks

Multimetric structural covariance in first-episode major depressive disorder: a graph theoretical analysis

Background: Abnormalities of cortical morphology have been consistently reported in major depressive disorder (MDD), with widespread focal alterations in cortical thickness, surface area and gyrification. However, it is unclear whether these distributed focal changes disrupt the system-level architecture (topology) of brain morphology in MDD. If present, such a topological disruption might explain the mechanisms that underlie altered cortical morphology in MDD. Methods: Seventy-six patients with first-episode MDD (33 male, 43 female) and 66 healthy controls (32 male, 34 female) underwent structural MRI scans. We calculated cortical indices, including cortical thickness, surface area and local gyrification index, using FreeSurfer. We constructed morphological covariance networks using the 3 cortical indices separately, and we analyzed the topological properties of these group-level morphological covariance networks using graph theoretical approaches. Results: Topological differences between patients with first-episode MDD and healthy controls were restricted to the thickness-based network. We found a significant decrease in global efficiency but an increase in local efficiency of the left superior frontal gyrus and the right paracentral lobule in patients with first-episode MDD. When we simulated targeted lesions affecting the most highly connected nodes, the thickness-based networks in patients with first-episode MDD disintegrated more rapidly than those in healthy controls. Limitations: Our sample of patients with first-episode MDD has limited generalizability to patients with chronic and recurrent MDD. Conclusion: A systems-level disruption in cortical thickness (but not surface area or gyrification) occurs in patients with first-episode MDD