Empowering Mental Health Care Technology in Low- and Middle-Income Countries: Establishing Ethical Development Guidelines

The rising use of technologies and internet in Low- and Middle-income Countries (LMICs) hints at the opportunities of utilising digital tools to bridge the significant gap in mental health care delivery. As more mental health care technologies are used in research projects and even emerged to the market of LMICs, it is critical to establish guidelines to address issues on the existing products and ensure the ethical development of future technologies. Previous guideline research was mainly established based on mental health care technologies in High-Income Countries (HICs), and scarce work has been conducted under limited-resource settings or LMICs. This paper proposes a research plan to (1) identify the factors challenging the ethical development and implementation of mental health care technologies in a representative middle-income country with high inequality, China, and (2) translate the findings into development guidelines to empower future mental health care technologies in China and other LMICs

Globally Gated Deep Linear Networks

Recently proposed Gated Linear Networks present a tractable nonlinear network architecture, and exhibit interesting capabilities such as learning with local error signals and reduced forgetting in sequential learning. In this work, we introduce a novel gating architecture, named Globally Gated Deep Linear Networks (GGDLNs) where gating units are shared among all processing units in each layer, thereby decoupling the architectures of the nonlinear but unlearned gatings and the learned linear processing motifs. We derive exact equations for the generalization properties in these networks in the finite-width thermodynamic limit, defined by $P,N\rightarrow\infty, P/N\sim O(1)$, where P and N are the training sample size and the network width respectively. We find that the statistics of the network predictor can be expressed in terms of kernels that undergo shape renormalization through a data-dependent matrix compared to the GP kernels. Our theory accurately captures the behavior of finite width GGDLNs trained with gradient descent dynamics. We show that kernel shape renormalization gives rise to rich generalization properties w.r.t. network width, depth and L2 regularization amplitude. Interestingly, networks with sufficient gating units behave similarly to standard ReLU networks. Although gatings in the model do not participate in supervised learning, we show the utility of unsupervised learning of the gating parameters. Additionally, our theory allows the evaluation of the network's ability for learning multiple tasks by incorporating task-relevant information into the gating units. In summary, our work is the first exact theoretical solution of learning in a family of nonlinear networks with finite width. The rich and diverse behavior of the GGDLNs suggests that they are helpful analytically tractable models of learning single and multiple tasks, in finite-width nonlinear deep networks

Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime

Artificial neural networks have revolutionized machine learning in recent years, but a complete theoretical framework for their learning process is still lacking. Substantial progress has been made for infinitely wide networks. In this regime, two disparate theoretical frameworks have been used, in which the network's output is described using kernels: one framework is based on the Neural Tangent Kernel (NTK) which assumes linearized gradient descent dynamics, while the Neural Network Gaussian Process (NNGP) kernel assumes a Bayesian framework. However, the relation between these two frameworks has remained elusive. This work unifies these two distinct theories using a Markov proximal learning model for learning dynamics in an ensemble of randomly initialized infinitely wide deep networks. We derive an exact analytical expression for the network input-output function during and after learning, and introduce a new time-dependent Neural Dynamical Kernel (NDK) from which both NTK and NNGP kernels can be derived. We identify two learning phases characterized by different time scales: gradient-driven and diffusive learning. In the initial gradient-driven learning phase, the dynamics is dominated by deterministic gradient descent, and is described by the NTK theory. This phase is followed by the diffusive learning stage, during which the network parameters sample the solution space, ultimately approaching the equilibrium distribution corresponding to NNGP. Combined with numerical evaluations on synthetic and benchmark datasets, we provide novel insights into the different roles of initialization, regularization, and network depth, as well as phenomena such as early stopping and representational drift. This work closes the gap between the NTK and NNGP theories, providing a comprehensive framework for understanding the learning process of deep neural networks in the infinite width limit

ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces

In recent years, neural implicit surface reconstruction has emerged as a popular paradigm for multi-view 3D reconstruction. Unlike traditional multi-view stereo approaches, the neural implicit surface-based methods leverage neural networks to represent 3D scenes as signed distance functions (SDFs). However, they tend to disregard the reconstruction of individual objects within the scene, which limits their performance and practical applications. To address this issue, previous work ObjectSDF introduced a nice framework of object-composition neural implicit surfaces, which utilizes 2D instance masks to supervise individual object SDFs. In this paper, we propose a new framework called ObjectSDF++ to overcome the limitations of ObjectSDF. First, in contrast to ObjectSDF whose performance is primarily restricted by its converted semantic field, the core component of our model is an occlusion-aware object opacity rendering formulation that directly volume-renders object opacity to be supervised with instance masks. Second, we design a novel regularization term for object distinction, which can effectively mitigate the issue that ObjectSDF may result in unexpected reconstruction in invisible regions due to the lack of constraint to prevent collisions. Our extensive experiments demonstrate that our novel framework not only produces superior object reconstruction results but also significantly improves the quality of scene reconstruction. Code and more resources can be found in \url{https://qianyiwu.github.io/objectsdf++}Comment: ICCV 2023. Project Page: https://qianyiwu.github.io/objectsdf++ Code: https://github.com/QianyiWu/objectsdf_plu

Gab2 facilitates epithelial-to-mesenchymal transition via the MEK/ERK/MMP signaling in colorectal cancer

Clinicopathologic factors and Gab2 expression in 35 CRC patients. (DOCX 22 kb

Silver-modified polyniobotungstate for the visible light-induced simultaneous cleavage of C–C and C–N bonds

Silver-modified polyniobotungstate based on Nb/W mixed-addendum polyoxometalate with formula Ag9[P2W15Nb3O62]·21H2O (Ag-Nb/W) was synthesized and then characterized by various analytical and spectral techniques. Ag-Nb/W was proven to be an efficient photocatalyst for the oxidative ring opening of 2-phenylimidazo[1,2-a]pyridine via the simultaneous cleavage of C–C and C–N bonds. Under visible light (430–440 nm) and with oxygen as an oxidant at room temperature, Ag-Nb/W can catalyze the rapid transformation of various 2-phenylimidazo[1,2-a]pyridine derivatives to produce the corresponding oxidative ring-opening product N-(pyridin-2-yl) amides in good isolated yields ranging from 65% to 78%. As a heterogeneous photocatalyst, Ag-Nb/W showed excellent sustainability and recyclability in the recycling experiments. Infrared (IR) spectroscopy and X-ray diffraction (XRD) analysis indicated that Ag-Nb/W could retain its integrity after catalysis. A possible mechanism involving the singlet oxygen for the catalytic reaction was proposed