Develop Habit-forming Products Based on the Axiomatic Design Theory

AbstractIt is every manufacturer's desire to drive its target customers to form a long-term habit of regularly using its product. Previous studies indicate that the habit of using a certain product can indeed by formed in a systemic manner, once the right sequence is followed. Against such a background, an existing habit-forming product model, namely the Hook Model, is reviewed with respect to its key components of trigger, action, reward, and investment. Essences of the Hook Model, together with its missing pieces, are reformulated, repositioned, and resynthesized based on the Axiomatic Design Theory. It results in an adapted Axiomatic Design process, which is intended to develop the habit-forming products. The step-by-step design process is explained, and an illustrate example is presented

A brief chronological and bibliographic guide to the history of Chinese mathematics

AbstractThe history of Chinese mathematics remains largely unknown in the West. This situation is the result of several factors: geographic, political, and linguistic. Few Western scholars possess the necessary facility in the Classical Chinese language to seek information about Chinese mathematics from primary sources. Yet despite the deficiency, there does exist a rich, albeit dispersed, literature on the history of Chinese mathematics in Western languages. The purpose of this contribution in to call the reader's attention to this literature and to the history of Chinese mathematics in general

Robust 6D Object Pose Estimation by Learning RGB-D Features

Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping. Previous methods follow a local optimization approach which minimizes the distance between closest point pairs to handle the rotation ambiguity of symmetric objects. In this work, we propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Additionally, the object location is detected by aggregating point-wise vectors pointing to the 3D center. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches. Our code is available at https://github.com/mentian/object-posenet.Comment: Accepted at ICRA 202

CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Design of Efficient and Adaptive Lossy Compression

As supercomputers continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also helps us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ's dual-quantization algorithm to our hardware use case. (4) We evaluate CEAZ on five real-world datasets with both a single FPGA board and 128 nodes from Bridges-2 supercomputer. Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2X of throughput and 9.6X of compression ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 25.8X and 24.8X, respectively.Comment: 14 pages, 17 figures, 8 table

FedNAR: Federated Optimization with Normalized Annealing Regularization

Weight decay is a standard technique to improve generalization performance in modern deep neural network optimization, and is also widely adopted in federated learning (FL) to prevent overfitting in local clients. In this paper, we first explore the choices of weight decay and identify that weight decay value appreciably influences the convergence of existing FL algorithms. While preventing overfitting is crucial, weight decay can introduce a different optimization goal towards the global objective, which is further amplified in FL due to multiple local updates and heterogeneous data distribution. To address this challenge, we develop {\it Federated optimization with Normalized Annealing Regularization} (FedNAR), a simple yet effective and versatile algorithmic plug-in that can be seamlessly integrated into any existing FL algorithms. Essentially, we regulate the magnitude of each update by performing co-clipping of the gradient and weight decay. We provide a comprehensive theoretical analysis of FedNAR's convergence rate and conduct extensive experiments on both vision and language datasets with different backbone federated optimization algorithms. Our experimental results consistently demonstrate that incorporating FedNAR into existing FL algorithms leads to accelerated convergence and heightened model accuracy. Moreover, FedNAR exhibits resilience in the face of various hyperparameter configurations. Specifically, FedNAR has the ability to self-adjust the weight decay when the initial specification is not optimal, while the accuracy of traditional FL algorithms would markedly decline. Our codes are released at \href{https://github.com/ljb121002/fednar}{https://github.com/ljb121002/fednar}.Comment: Thirty-seventh Conference on Neural Information Processing System

Characteristics of Lipo-Oligosaccharide Loci of Campylobacter jejuni Isolates Associated with Guillain-Barré Syndrome from Hebei, China

Ganglioside mimicry by C.jejuni lipo-oligosaccharides (LOS) could induce the production of autoantibodies against gangliosides and the development of Guillain-Barré syndrome (GBS). The LOS biosynthesis region exhibits significant variation with different strains. Using PCR amplifications of genes from published LOS loci and sequencing the LOS biosynthesis loci, the eight GBS-associated C. jejuni strains from HeBei could be classified into four classes. The expression of sialylated LOS structures (class A) or non-sialylated LOS structures(class F, H and P) in the C. jejuni LOS is considered to be two different factors for the induction of GBS

Intrinsic Cerebro-Cerebellar Functional Connectivity Reveals the Function of Cerebellum VI in Reading-Related Skills

Funding This work was supported by grants from the National Natural Science Foundation of China (NSFC: 31971036, 31971039, and 31571158).Peer reviewedPublisher PD