Weak solutions for forward--backward SDEs--a martingale problem approach

In this paper, we propose a new notion of Forward--Backward Martingale Problem (FBMP), and study its relationship with the weak solution to the forward--backward stochastic differential equations (FBSDEs). The FBMP extends the idea of the well-known (forward) martingale problem of Stroock and Varadhan, but it is structured specifically to fit the nature of an FBSDE. We first prove a general sufficient condition for the existence of the solution to the FBMP. In the Markovian case with uniformly continuous coefficients, we show that the weak solution to the FBSDE (or equivalently, the solution to the FBMP) does exist. Moreover, we prove that the uniqueness of the FBMP (whence the uniqueness of the weak solution) is determined by the uniqueness of the viscosity solution of the corresponding quasilinear PDE.Comment: Published in at http://dx.doi.org/10.1214/08-AOP0383 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

Application of Technologies for Temporary Structures during the Design and Construction Phases

The development of technologies such as Building Information Modeling (BIM), light detection and ranging (lidar), and sensor-based systems poses new opportunities for researchers to rethink how construction safety and health can be approached during the design, planning, and construction phases of a project. Nevertheless, the majority of the technology applications developed to date have focused on permanent structures rather than temporary structures. Due to the “temporary” nature of temporary structures, stakeholders might easily underestimate their importance and not pay adequate attention to them in comparison with permanent works. But temporary structures are extensively used, associated with high rates of injuries and fatalities, and nearly three-fourths of the construction workers in the US perform construction activities on or near temporary structures. There are considerable needs to improve the performance of temporary structures. The overarching goal of this research is to advance the body of knowledge and make practical contributions to the integration of temporary structures with advanced technologies. Specifically, this research explores the identification of the desires and needs of adopting technologies in temporary structures, and the development of tools to improve the quality of temporary structures in the design and construction phases of a construction project. To attain the research goal, the study firstly investigated the current design and inspection practices for temporary structures, as well as professionals’ viewpoints of applying technologies on temporary structures by surveying professionals who are familiar with temporary structures and construction technologies. Based on the results of this first step, the study proposed tools to remedy the current deficiencies in design and inspection quality of temporary structures discovered. The type of temporary structure targeted in the research is concrete formwork systems, especially formwork for concrete slabs. For the design and planning phases, a BIM plug-in was developed in Revit using C# to achieve automation in designing and modeling temporary structures with safety considerations. For the construction phase, a wireless sensor-based formwork monitoring system was proposed and developed to improve the inspection quality during concrete placement. The present research contributes to the body of knowledge by identifying the needs and desires of using technologies in temporary structures, as well as the technology selection criteria and areas of improvement for temporary structures, and develops practical tools to improve the design and inspection quality of temporary structures

m-Government in China: Observations and Reflections

Mobile and wireless technologies (MWTs), such as wireless laptop computers, personal digital assistants (PDA), mobile phones, smart phones, etc., have deeply penetrated our lives. Government agencies use MWTs to enhance their managerial effectiveness and provide high-level services to citizens taking advantage of its characteristics of mobility, ubiquity, provision of other location-based government services, and on-time information delivery. Mobile government (m- Government) is forming diversely within (as well as between) different countries. China currently has 738.57 million mobile phone users and 29 cities are deploying “Wireless City” projects. Within this context, we chose six different cities in China to examine m-Government maturity and assess the deployment of m-Government services. We further explored mobile and wireless technology (MWT) application and implications in conjunction with a special project in Beijing. Results are discussed and conclusions are drawn

ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression

Recently, deep learning methods have shown promising results in point cloud compression. For octree-based point cloud compression, previous works show that the information of ancestor nodes and sibling nodes are equally important for predicting current node. However, those works either adopt insufficient context or bring intolerable decoding complexity (e.g. >600s). To address this problem, we propose a sufficient yet efficient context model and design an efficient deep learning codec for point clouds. Specifically, we first propose a window-constrained multi-group coding strategy to exploit the autoregressive context while maintaining decoding efficiency. Then, we propose a dual transformer architecture to utilize the dependency of current node on its ancestors and siblings. We also propose a random-masking pre-train method to enhance our model. Experimental results show that our approach achieves state-of-the-art performance for both lossy and lossless point cloud compression. Moreover, our multi-group coding strategy saves 98% decoding time compared with previous octree-based compression method

THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval

The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant challenge, as verifying hypotheses in the NLI4CT task requires the integration of multiple pieces of evidence from one or two CTR(s) and the application of diverse levels of reasoning, including textual and numerical. To address these problems, we present a multi-granularity system for CTR-based textual entailment and evidence retrieval in this paper. Specifically, we construct a Multi-granularity Inference Network (MGNet) that exploits sentence-level and token-level encoding to handle both textual entailment and evidence retrieval tasks. Moreover, we enhance the numerical inference capability of the system by leveraging a T5-based model, SciFive, which is pre-trained on the medical corpus. Model ensembling and a joint inference method are further utilized in the system to increase the stability and consistency of inference. The system achieves f1-scores of 0.856 and 0.853 on textual entailment and evidence retrieval tasks, resulting in the best performance on both subtasks. The experimental results corroborate the effectiveness of our proposed method. Our code is publicly available at https://github.com/THUMLP/NLI4CT.Comment: Accepted by SemEval202

Towards provably efficient quantum algorithms for large-scale machine-learning models

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as $\mathcal{O}(T^2 \times \text{polylog}(n))$, where $n$ is the size of the models and $T$ is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.Comment: 7+30 pages, 3+5 figure

Common genetic variants in ADCY5 and gestational glycemic traits.

Two meta-analysis of genome wide association studies identified two variants at adenylate cyclase 5 (ADCY5) associated with type 2 diabetes mellitus, fasting and 2-hour glucose in non-pregnant individuals of European descent. The objective of our study was to explore the role of common variants in ADCY5 on gestational glycemic traits, including plasma glucose, insulin values, β cell function and insulin resistance in the fasted state as well as plasma glucose 1 hour after a 50-gram glucose challenge test among Chinese Han women. Homoeostasis model assessment (HOMA) was used to quantify β cell function (HOMA1-β and HOMA2-β) and insulin resistance (HOMA1-IR and HOMA2-IR). Thirty-five single nucleotide polymorphisms (SNPs) in ADCY5 were genotyped in 929 unrelated Chinese Han women with singleton pregnancies. Three SNPs (rs6797915, rs9856662 and rs9875803) displayed evidence for association with plasma glucose 1 hour after a 50-gram glucose challenge test (P = 0.042, 0.018 and 0.018, respectively), one (rs6777397) displayed evidence for association with HOMA1-β (P = 0.014), and one (rs6762009) displayed evidence for association with HOMA1-IR (P = 0.033). These results provide additional insight into the effects of genetic variation within ADCY5 in glucose metabolism, especially during pregnancy and in non-European descent populations

Towards provably efficient quantum algorithms for large-scale machine-learning models

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T2 x polylog(n)), where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems

Zigzag magnetic order in a novel tellurate compound Na4−δ_{4-\delta}NiTeO6_{6} with S\mathit{S} = 1 chains

Na$_{4-\delta}$NiTeO$_{6}$ is a rare example in the transition-metal tellurate family of realizing an $S$ = 1 spin-chain structure. By performing neutron powder diffraction measurements, the ground-state magnetic structure of Na$_{4-\delta}$NiTeO$_{6}$ is determined. These measurements reveal that below $T\rm_{N}$ ${\sim}$ 6.8(2) K, the Ni$^{2+}$ moments form a screwed ferromagnetic (FM) spin-chain structure running along the crystallographic $a$ axis but these FM spin chains are coupled antiferromagnetically along the $b$ and $c$ directions, giving rise to a magnetic propagation vector of $k$ = (0, 1/2, 1/2). This zigzag magnetic order is well supported by first-principles calculations. The moment size of Ni$^{2+}$ spins is determined to be 2.1(1) $\mu$$\rm_{B}$ at 3 K, suggesting a significant quenching of the orbital moment due to the crystalline electric field (CEF) effect. The previously reported metamagnetic transition near $H\rm_{C}$ ${\sim}$ 0.1 T can be understood as a field-induced spin-flip transition. The relatively easy tunability of the dimensionality of its magnetism by external parameters makes Na$_{4-\delta}$NiTeO$_{6}$ a promising candidate for further exploring various types of novel spin-chain physics.Comment: 10 pages, 6 figure