This paper presents a PVT-resilient, subthreshold SRAM-based computing-in-memory (CIM) macro tailored for energy-efficient spiking neural networks (SNNs). The macro integrates in-situ current sensors and distributed voltage regulators to enable robust large-scale (1024 wordlines, 1304 bitlines and 128 shared neuron cells) subthreshold current-mode CIM, mitigating energy overheads and process-voltage-temperature (PVT) sensitivity. The neuron cells adopt a programmable, memory cell-based firing threshold to enhance neuron robustness against PVT variations. The architecture uses a stride-tick batching schedule to significantly reduce buffer overhead with enhanced input data reuse. Exploiting the high sparsity of SNNs, the proposed system demonstrates significant improvements in energy efficiency and variation tolerance. Fabricated in 28-nm CMOS, the prototype attains 93.64\% accuracy on keyword spotting, delivers up to 1181.42 TOPS/W, and achieves 7.24 TOPS/mm^2, demonstrating a viable and efficient solution for high-performance edge SNN processing.

Manifold embedding algorithms map high-dimensional data down to coordinates in a much lower-dimensional space. One of the aims of dimension reduction is to find intrinsic coordinates that describe the data manifold. The coordinates returned by the embedding algorithm are abstract, and finding their physical or domain-related meaning is not formalized and often left to domain experts. This paper studies the problem of recovering the meaning of the new low-dimensional representation in an automatic, principled fashion. We propose a method to explain embedding coordinates of a manifold as non-linear compositions of functions from a user-defined dictionary. We show that this problem can be set up as a sparse linear Group Lasso recovery problem, find sufficient recovery conditions, and demonstrate its effectiveness on data.

The null space of the $k$-th order Laplacian $\mathbf{\mathcal L}_k$, known as the {\em $k$-th homology vector space}, encodes the non-trivial topology of a manifold or a network. Understanding the structure of the homology embedding can thus disclose geometric or topological information from the data. The study of the null space embedding of the graph Laplacian $\mathbf{\mathcal L}_0$ has spurred new research and applications, such as spectral clustering algorithms with theoretical guarantees and estimators of the Stochastic Block Model. In this work, we investigate the geometry of the $k$-th homology embedding and focus on cases reminiscent of spectral clustering. Namely, we analyze the {\em connected sum} of manifolds as a perturbation to the direct sum of their homology embeddings. We propose an algorithm to factorize the homology embedding into subspaces corresponding to a manifold's simplest topological components. The proposed framework is applied to the {\em shortest homologous loop detection} problem, a problem known to be NP-hard in general. Our spectral loop detection algorithm scales better than existing methods and is effective on diverse data such as point clouds and images.

Dynamic extensions of Stochastic block model (SBM) are of importance in several fields that generate temporal interaction data. These models, besides producing compact and interpretable network representations, can be useful in applications such as link prediction or network forecasting. In this paper we present a conditional pseudo-likelihood based extension to dynamic SBM that can be efficiently estimated by optimizing a regularized objective. Our formulation leads to a highly scalable approach that can handle very large networks, even with millions of nodes. We also extend our formalism to causal impact for networks that allows us to quantify the impact of external events on a time dependent sequence of networks. We support our work with extensive results on both synthetic and real networks.

Many manifold embedding algorithms fail apparently when the data manifold has a large aspect ratio (such as a long, thin strip). Here, we formulate success and failure in terms of finding a smooth embedding, showing also that the problem is pervasive and more complex than previously recognized. Mathematically, success is possible under very broad conditions, provided that embedding is done by carefully selected eigenfunctions of the Laplace-Beltrami operator $\Delta_\M$. Hence, we propose a bicriterial Independent Eigencoordinate Selection (IES) algorithm that selects smooth embeddings with few eigenvectors. The algorithm is grounded in theory, has low computational overhead, and is successful on synthetic and large real data.

Abnormal mRNA export causes many human diseases, including genetic diseases and tumorigenesis. However, the underlying mechanism was not well understood. In this study, we explored the roles of nuclear export factor2 (NXF2) in the development of hepatocellular carcinoma (HCC). We analyzed the expression level of NXF2 in HCC tissue and found down-regulation of NXF2 in HCC. Then we overexpressed NXF2 in HCC cell Huh-7 and found overexpression of NXF2 causes the down-regulation of migration and invasion in Huh-7 cells. Our results demonstrated loss-of-function of NXF2 in HCC clinical samples and the role of NXF2 in HCC tumorigenesis. Finally, we used Ingenuity Pathway Analysis (IPA) to evaluate the molecular interactions and pathway which were regulated by NXF2.

Alternative splicing of precursor mRNA is an important mechanism for increasing the complexity of gene expression, and it plays a key role in cell differentiation and organism development. Accurate alternative splicing profiles and regulation will affect the cellular functions and destiny. Our previous studies showed that amiloride have a potential effect on alternative splicing, but the high effective concentration of amiloride limits its clinical application. In this study, the molecular docking calculation was performed to estimate the binding affinity between a series synthesized amiloride derivatives and SRPK1 protein in silico and then detect its activity in alternative splicing in vitro. The results showed that amiloride derivatives DS010, DS150, and ES013 have better binding affinity and could regulate the alternative splicing of BCL-X transcripts to increase the proportion of BCL-XL in Huh-7 cells. In addition, the effective concentration of DS010 and ES013 are lower than others. These findings suggested that the amiloride derivative DS010 and ES013 may provide therapeutic potential for future cancer treatment.