CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition

The Transformer-based encoder-decoder architecture has recently made significant advances in recognizing handwritten mathematical expressions. However, the transformer model still suffers from the lack of coverage problem, making its expression recognition rate (ExpRate) inferior to its RNN counterpart. Coverage information, which records the alignment information of the past steps, has proven effective in the RNN models. In this paper, we propose CoMER, a model that adopts the coverage information in the transformer decoder. Specifically, we propose a novel Attention Refinement Module (ARM) to refine the attention weights with past alignment information without hurting its parallelism. Furthermore, we take coverage information to the extreme by proposing self-coverage and cross-coverage, which utilize the past alignment information from the current and previous layers. Experiments show that CoMER improves the ExpRate by 0.61%/2.09%/1.59% compared to the current state-of-the-art model, and reaches 59.33%/59.81%/62.97% on the CROHME 2014/2016/2019 test sets.Comment: Accept by ECCV 202

Effective grain size refinement of an Fe-24Ni-0.3C metastable austenitic steel by a modified two-step cold rolling and annealing process utilizing the deformation-induced martensitic transformation and its reverse transformation

Metastable austenitic steels having ultrafine grained (UFG) microstructures can be fabricated by conventional cold rolling and annealing processes by utilizing the deformation-induced martensitic transformation during cold rolling and its reverse transformation to austenite upon annealing. However, such processes are not applicable when the austenite has high mechanical stability against deformation-induced martensitic transformation, since there is no sufficient amount of martensite formed during cold rolling. In the present study, a two-step cold rolling and annealing process was applied to an Fe-24Ni-0.3C metastable austenitic steel having high mechanical stability. Prior to the cold rolling, a repetitive subzero treatment and reverse annealing treatment were applied. Such a treatment dramatically decreased the mechanical stability of the austenite and greatly accelerated the formation of deformation-induced martensite during the following cold rolling processes. As a result, the grain refinement was significantly promoted, and a fully recrystallized specimen with a mean austenite grain size of 0.5 μm was successfully fabricated, which exhibited both high strength and high ductility

Channel Acquisition for HF Skywave Massive MIMO-OFDM Communications

In this paper, we investigate channel acquisition for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead. Moreover, we propose a channel prediction method for data segments based on the estimated TB domain channel. To reduce the complexity, we are able to formulate the channel estimation as a sparse signal recovery problem due to the channel sparsity in the TB domain and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm, which can be implemented with low complexity by exploiting the structure of the TB matrix together with the chirp z-transform (CZT). Simulation results demonstrate the superior performance of the proposed channel acquisition approach.Comment: 30 pages, 4 figure

Orf virus DNA vaccines expressing ORFV 011 and ORFV 059 chimeric protein enhances immunogenicity

Background: ORFV attenuated live vaccines have been the main prophylactic measure against contagious ecthyma in sheep and goats in the last decades, which play an important role in preventing the outbreak of the disease. However, the available vaccines do not induce lasting immunity in sheep and goats. On the other hand, variation in the terminal genome of Orf virus vaccine strains during cell culture adaptation may affect the efficacy of a vaccine. Currently, there are no more effective antiviral treatments available for contagious ecthyma. Results: We constructed three eukaryotic expression vectors pcDNA3.1-ORFV011, pcDNA3.1-ORFV059 and pcDNA3.1-ORFV011/ORFV059 and tested their immunogenicity in mouse model. High level expression of the recombinant proteins ORFV011, ORFV059 and ORFV011/ORFV059 was confirmed by western blotting analysis and indirect fluorescence antibody (IFA) tests. The ORFV-specific antibody titers and serum IgG1/IgG2a titers, the proliferation of lymphocytes and ORFV-specific cytokines (IL-2, IL-4, IL-6, IFN-gamma, and TNF-alpha) were examined to evaluate the immune responses of the vaccinated mice. We found that mice inoculated with pcDNA3.1-ORFV 011/ORFV059 had significantly stronger immunological responses than those inoculated with pcDNA3.1-ORFV011, pcDNA3.1-ORFV059, or pcDNA3.1-ORFV011 plus pcDNA3.1-ORFV059. Compared to other vaccine plasmids immunized groups, pcDNA3.1-ORFV011/ORFV059 immunized group enhances immunogenicity. Conclusions: We concluded that DNA vaccine pcDNA3.1-ORFV011/ORFV059 expressing ORFV011 and ORFV059 chemeric-proteins can significantly improve the potency of DNA vaccination and could be served as more effective and safe approach for new vaccines against ORFV.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000304650500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701VirologySCI(E)3ARTICLEnull

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, which leads to low performance and fails to deal with extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (OmniQuant) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights through a learnable equivalent transformation. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family with the size of 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4, W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes and models are available at \url{https://github.com/OpenGVLab/OmniQuant}.Comment: Updated result with 2-bit quantization. A differentiable quantization method for LL

The beneficial effects of curcumin supplementation on blood lipid levels among patients with metabolic related diseases in Asia area: a systematic review and meta-analysis of randomized controlled trials

ObjectivePublished studies suggest that the effects of curcumin on blood lipids in adults are controversial, and it is unclear whether there is a dose response to lipid changes following curcumin supplementation. Therefore, we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the effects of curcumin on triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL), and high-density lipoprotein cholesterol (HDL) in the Asian populations with metabolic diseases.MethodsWe systematically searched four electronic databases, including Web of Science, PubMed, Google Scholar, and Cochrane Library, for randomized controlled trials (RCTs) of the effects of curcumin on TG, TC, LDL, and HDL in the Asian populations with metabolic diseases. Mean difference (MD) indicates effect size with combined 95% confidence interval (95% CI). Heterogeneity among studies was assessed by I2. Subgroup analyses were performed to explore potential sources of heterogeneity.ResultsEvidence from 23 RCTs for TG, 21 RCTs for TC and LDL, and 22 RCTs for HDL showed that curcumin supplementation significantly reduced TG (MD: −18.07 mg/dL, 95% CI: −30.30, −5.85, P < 0. 01), TC (MD: −13.29 mg/dL, 95% CI: −20.43, −6.16, P < 0.01), and LDL (MD: −10.44 mg/dL, 95% CI: −16.87, −4.00, P < 0.01), but no effect on HDL (MD: 1.66 mg/dL, 95% CI: −0.13, 3.44, P = 0.07). In the non-linear dose-response analysis, we observed a significant effect of curcumin supplementation dose on TG levels (P-non-linearity = 0.022).ConclusionIn conclusion, curcumin may be beneficial in reducing TG, TC, and LDL levels in the Asian populations with metabolic diseases. The dose of curcumin intervention may be an underlying factor influencing TG levels