Valid Randomization Tests in Inexactly Matched Observational Studies via Iterative Convex Programming

In causal inference, matching is one of the most widely used methods to mimic a randomized experiment using observational (non-experimental) data. Ideally, treated units are exactly matched with control units for the covariates so that the treatments are as-if randomly assigned within each matched set, and valid randomization tests for treatment effects can then be conducted as in a randomized experiment. However, inexact matching typically exists, especially when there are continuous or many observed covariates or when unobserved covariates exist. Previous matched observational studies routinely conducted downstream randomization tests as if matching was exact, as long as the matched datasets satisfied some prespecified balance criteria or passed some balance tests. Some recent studies showed that this routine practice could render a highly inflated type-I error rate of randomization tests, especially when the sample size is large. To handle this problem, we propose an iterative convex programming framework for randomization tests with inexactly matched datasets. Under some commonly used regularity conditions, we show that our approach can produce valid randomization tests (i.e., robustly controlling the type-I error rate) for any inexactly matched datasets, even when unobserved covariates exist. Our framework allows the incorporation of flexible machine learning models to better extract information from covariate imbalance while robustly controlling the type-I error rate

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios. Recently, contrastive learning and Transformer-based models have achieved good performance in many long-term series forecasting tasks. However, there are still several issues in existing methods. First, the training paradigm of contrastive learning and downstream prediction tasks are inconsistent, leading to inaccurate prediction results. Second, existing Transformer-based models which resort to similar patterns in historical time series data for predicting future values generally induce severe distribution shift problems, and do not fully leverage the sequence information compared to self-supervised methods. To address these issues, we propose a novel framework named Ti-MAE, in which the input time series are assumed to follow an integrate distribution. In detail, Ti-MAE randomly masks out embedded time series data and learns an autoencoder to reconstruct them at the point-level. Ti-MAE adopts mask modeling (rather than contrastive learning) as the auxiliary task and bridges the connection between existing representation learning and generative Transformer-based methods, reducing the difference between upstream and downstream forecasting tasks while maintaining the utilization of original time series data. Experiments on several public real-world datasets demonstrate that our framework of masked autoencoding could learn strong representations directly from the raw data, yielding better performance in time series forecasting and classification tasks.Comment: 20 pages, 7 figure

The Default Mode Network Supports Episodic Memory in Cognitively Unimpaired Elderly Individuals: Different Contributions to Immediate Recall and Delayed Recall

While the neural correlates of age-related decline in episodic memory have been the subject of much interest, the spontaneous functional architecture of the brain for various memory processes in elderly adults, such as immediate recall (IR) and delayed recall (DR), remains unclear. The present study thus examined the neural correlates of age-related decline of various memory processes. A total of 66 cognitively normal older adults (aged 60-80 years) participated in this study. Memory processes were measured using the Auditory Verbal Learning Test as well as resting-state brain images, which were analyzed using both regional homogeneity (ReHo) and correlation-based functional connectivity (FC) approaches. We found that both IR and DR were significantly correlated with the ReHo of these critical regions, all within the default mode network (DMN), including the parahippocampal gyrus, posterior cingulate cortex/precuneus, inferior parietal lobule, and medial prefrontal cortex. In addition, DR was also related to the FC between these DMN regions. These results suggest that the DMN plays different roles in memory retrieval across different retention intervals, and connections between the DMN regions contribute to memory consolidation of past events in healthy older people

CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity

With distributed machine learning being a prominent technique for large-scale machine learning tasks, communication complexity has become a major bottleneck for speeding up training and scaling up machine numbers. In this paper, we propose a new technique named Common randOm REconstruction(CORE), which can be used to compress the information transmitted between machines in order to reduce communication complexity without other strict conditions. Especially, our technique CORE projects the vector-valued information to a low-dimensional one through common random vectors and reconstructs the information with the same random noises after communication. We apply CORE to two distributed tasks, respectively convex optimization on linear models and generic non-convex optimization, and design new distributed algorithms, which achieve provably lower communication complexities. For example, we show for linear models CORE-based algorithm can encode the gradient vector to $\mathcal{O}(1)$-bits (against $\mathcal{O}(d)$), with the convergence rate not worse, preceding the existing results

Design and Structure-Based Study of New Potential FKBP12 Inhibitors

AbstractBased on the structure of FKBP12 complexed with FK506 or rapamycin, with computer-aided design, two neurotrophic ligands, (3R)-4-(p-Toluenesulfonyl)-1,4-thiazane-3-carboxylic acid-L-Leucine ethyl ester and (3R)-4-(p-Toluenesulfonyl)-1,4-thiazane-3-carboxylic acid-L-phenylalanine benzyl ester, were designed and synthesized. Fluorescence experiments were used to detect the binding affinity between FKBP12 and these two ligands. Complex structures of FKBP12 with these two ligands were obtained by x-ray crystallography. In comparing FKBP12-rapamycin complex and FKBP12-FK506 complex as well as FKBP12-GPI-1046 solution structure with these new complexes, significant volume and surface area effects and obvious contact changes were detected which are expected to cause their different binding energies—showing these two novel ligands will become more effective neuron regeneration drugs than GPI-1046, which is currently undergoing phase II clinical trail as a neurotrophic drug. Analysis of volume and surface area effects also gives a new clue for structure-based drug design

Combined use of in-reservoir geological records for oil-reservoir destruction identification: A case study in the Jingbian area (Ordos Basin, China)

Rapid identification of reservoir destruction is critical to avoid exploration failure. More indicators of reservoir destruction are urgently needed to be developed besides the evaluation methods of trap effectiveness based on structural analysis. Here, we provide a case study in the Ordos Basin to show that the combined use of in-reservoir geological records is a robust tool to rapidly identify oil-reservoir destruction. The sandstones within the Yanchang Formation in the oil-depleted Jingbian area were investigated by petrological and geochemical analysis. The results show that 1) the oils with increased density and viscosity occur in the low permeability sandstones, whereas the high permeability sandstones were occupied by water, 2) abundant solid bitumen occur in the intergranular pores, 3) the n-alkanes with carbon numbers less than 19 are significantly lost from the original oils, and 4) the majority of paleo oil layers have evolved into present water layers. All these in-reservoir physicochemical signatures unravel the same geological event (i.e., oil-reservoir destruction) in the Jingbian area. This oil-reservoir destruction was likely caused by the uplift-induced erosion and the fault activities after oil accumulation during the Late Early Cretaceous

Masonry shell structures with discrete equivalence classes

This paper proposes a method to model masonry shell structures where the shell elements fall into a set of discrete equivalence classes. Such shell structure can reduce the fabrication cost and simplify the physical construction due to reuse of a few template shell elements. Given a freeform surface, our goal is to generate a small set of template shell elements that can be reused to produce a seamless and buildable structure that closely resembles the surface. The major technical challenge in this process is balancing the desire for high reusability of template elements with the need for a seamless and buildable final structure. To address the challenge, we define three error metrics to measure the seamlessness and buildability of shell structures made from discrete equivalence classes and develop a hierarchical cluster-and-optimize approach to generate a small set of template elements that produce a structure closely approximating the surface with low error metrics. We demonstrate the feasibility of our approach on various freeform surfaces and geometric patterns, and validate buildability of our results with four physical prototypes. Code and data of this paper are at https://github.com/Linsanity81/TileableShell