8,447 research outputs found
How To Persuade Non-Mobile Shoppers Into Mobile Shoppers: A Trust Enhancing Perspective
Mobile shopping is getting popular and pervasive. However, the number of mobile users is not parallel to the number of mobile shoppers, because consumers frequently concern about security while conducting mobile transactions. The current study aims to elaborate in what trust enhancing message designs can be used to persuade non-mobile shoppers into mobile shoppers. Drawing on social judgment theory and the model of persuasion, our study has the potential revealing that consumers’ negative attitudes toward ubiquitously using credit cards over the air can be improved by persuasive messages if they are added into the checkout page of a shopping website
XRCC1, but not APE1 and hOGG1 gene polymorphisms is a risk factor for pterygium.
PurposeEpidemiological evidence suggests that UV irradiation plays an important role in pterygium pathogenesis. UV irradiation can produce a wide range of DNA damage. The base excision repair (BER) pathway is considered the most important pathway involved in the repair of radiation-induced DNA damage. Based on previous studies, single-nucleotide polymorphisms (SNPs) in 8-oxoguanine glycosylase-1 (OGG1), X-ray repair cross-complementing-1 (XRCC1), and AP-endonuclease-1 (APE1) genes in the BER pathway have been found to affect the individual sensitivity to radiation exposure and induction of DNA damage. Therefore, we hypothesize that the genetic polymorphisms of these repair genes increase the risk of pterygium.MethodsXRCC1, APE1, and hOGG1 polymorphisms were studied using fluorescence-labeled Taq Man probes on 83 pterygial specimens and 206 normal controls.ResultsThere was a significant difference between the case and control groups in the XRCC1 genotype (p=0.038) but not in hOGG1 (p=0.383) and APE1 (p=0.898). The odds ratio of the XRCC1 A/G polymorphism was 2.592 (95% CI=1.225-5.484, p=0.013) and the G/G polymorphism was 1.212 (95% CI=0.914-1.607), compared to the A/A wild-type genotype. Moreover, individuals who carried at least one C-allele (A/G and G/G) had a 1.710 fold increased risk of developing pterygium compared to those who carried the A/A wild type genotype (OR=1.710; 95% CI: 1.015-2.882, p=0.044). The hOGG1 and APE1 polymorphisms did not have an increased odds ratio compared with the wild type.ConclusionsXRCC1 (Arg399 Glu) is correlated with pterygium and might become a potential marker for the prediction of pterygium susceptibility
Economic order quantity under retailer partial trade credit in two-echelon supply chain
In this paper, we want to investigate the retailer’s inventory policy when the retailer maintains a powerful position in two-echelon supply chain. That is, we assumed that the retailer can obtain the full trade credit offered by
the supplier yet the retailer just offers the partial trade credit to their customers under two-level trade credit situation. Then, we investigate the retailer’s inventory system as a cost minimization problem to determine the retailer’s optimal inventory policy in two-echelon supply chain. Finally, numerical examples are given to illustrate the results and to obtain managerial insights
An easy approach to derive EOQ and EPQ models with shortage and defective items
Huang [Journal of Statistics and Management Systems, Vol. 6, No. 2, pp. 171-180, 2003.] studied the EOQ (Economic Order Quantity) and EPQ (Economic Production Quantity) models with backlogging and defective
items using the algebraic approach. He assumed that a 100% inspection policy and the known proportion of defective items was removed after the screening process prior to storage or use. In this paper, we will offer another simple approach to find both the optimal lot size and backorder level under the minimized total relevant cost per unit time
Estimating Probable Maximum Precipitation and Probable Maximum Flood by Considering the Combined Effect of Typhoon and Monsoon Weather System under Climate Change
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive
A New ZrCuSiAs-Type Superconductor: ThFeAsN
We report the first nitrogen-containing iron-pnictide superconductor ThFeAsN,
which is synthesized by a solid-state reaction in an evacuated container. The
compound crystallizes in a ZrCuSiAs-type structure with the space group P4/nmm
and lattice parameters a=4.0367(1) {\AA} and c=8.5262(2) {\AA} at 300 K. The
electrical resistivity and dc magnetic susceptibility measurements indicate
superconductivity at 30 K for the nominally undoped ThFeAsN.Comment: 6 pages, 4 figures, 1 tabl
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
Personalized dialogue systems have gained significant attention in recent
years for their ability to generate responses in alignment with different
personas. However, most existing approaches rely on pre-defined personal
profiles, which are not only time-consuming and labor-intensive to create but
also lack flexibility. We propose In-Dialogue Learning (IDL), a fine-tuning
framework that enhances the ability of pre-trained large language models to
leverage dialogue history to characterize persona for completing personalized
dialogue generation tasks without pre-defined profiles. Our experiments on
three datasets demonstrate that IDL brings substantial improvements, with BLEU
and ROUGE scores increasing by up to 200% and 247%, respectively. Additionally,
the results of human evaluations further validate the efficacy of our proposed
method
Implicit Temporal Modeling with Learnable Alignment for Video Recognition
Contrastive language-image pretraining (CLIP) has demonstrated remarkable
success in various image tasks. However, how to extend CLIP with effective
temporal modeling is still an open and crucial problem. Existing factorized or
joint spatial-temporal modeling trades off between the efficiency and
performance. While modeling temporal information within straight through tube
is widely adopted in literature, we find that simple frame alignment already
provides enough essence without temporal attention. To this end, in this paper,
we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes
the temporal modeling effort while achieving incredibly high performance.
Specifically, for a frame pair, an interactive point is predicted in each
frame, serving as a mutual information rich region. By enhancing the features
around the interactive point, two frames are implicitly aligned. The aligned
features are then pooled into a single token, which is leveraged in the
subsequent spatial self-attention. Our method allows eliminating the costly or
insufficient temporal self-attention in video. Extensive experiments on
benchmarks demonstrate the superiority and generality of our module.
Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on
Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is
released at https://github.com/Francis-Rings/ILA .Comment: ICCV 2023 oral. 14 pages, 7 figures. Code released at
https://github.com/Francis-Rings/IL
ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection
We propose ImGeoNet, a multi-view image-based 3D object detection framework
that models a 3D space by an image-induced geometry-aware voxel representation.
Unlike previous methods which aggregate 2D features into 3D voxels without
considering geometry, ImGeoNet learns to induce geometry from multi-view images
to alleviate the confusion arising from voxels of free space, and during the
inference phase, only images from multiple views are required. Besides, a
powerful pre-trained 2D feature extractor can be leveraged by our
representation, leading to a more robust performance. To evaluate the
effectiveness of ImGeoNet, we conduct quantitative and qualitative experiments
on three indoor datasets, namely ARKitScenes, ScanNetV2, and ScanNet200. The
results demonstrate that ImGeoNet outperforms the current state-of-the-art
multi-view image-based method, ImVoxelNet, on all three datasets in terms of
detection accuracy. In addition, ImGeoNet shows great data efficiency by
achieving results comparable to ImVoxelNet with 100 views while utilizing only
40 views. Furthermore, our studies indicate that our proposed image-induced
geometry-aware representation can enable image-based methods to attain superior
detection accuracy than the seminal point cloud-based method, VoteNet, in two
practical scenarios: (1) scenarios where point clouds are sparse and noisy,
such as in ARKitScenes, and (2) scenarios involve diverse object classes,
particularly classes of small objects, as in the case in ScanNet200.Comment: ICCV'23; project page: https://ttaoretw.github.io/imgeonet
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