Enhanced Deep Residual Networks for Single Image Super-Resolution

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.Comment: To appear in CVPR 2017 workshop. Best paper award of the NTIRE2017 workshop, and the winners of the NTIRE2017 Challenge on Single Image Super-Resolutio

THE VALUE OF STATE-BUSINESS CONNECTION IN THE POLITICS OF CROSS-BORDER CAPITAL

This dissertation demonstrates that in a highly integrated global economy, state-business relationships have significant value for cross-border investors. The first paper shows that the governments of developed countries establish international investment agreements to protect their firms’ existing international investments. The second paper reveals that these home-country governments further support their firms’ international investments by supplying them with financing through state financial institutions. The third paper builds on the assumption that political relationships increase corporate value; therefore, domestic portfolio stock investors, unlike their foreign counterparts, are able to increase their returns by investing in politically-connected firms. The broad lesson from this series of papers is that governments can increase their firms’ profits abroad and that such increases are experienced primarily by the largest firms

CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets

Automated segmentation of ultrasound images can assist medical experts with diagnostic and therapeutic procedures. Although using the common modality of ultrasound, one typically needs separate datasets in order to segment, for example, different anatomical structures or lesions with different levels of malignancy. In this paper, we consider the problem of jointly learning from heterogeneous datasets so that the model can improve generalization abilities by leveraging the inherent variability among datasets. We merge the heterogeneous datasets into one dataset and refer to each component dataset as a subgroup. We propose to train a single segmentation model so that the model can adapt to each sub-group. For robust segmentation, we leverage recently proposed Segment Anything model (SAM) in order to incorporate sub-group information into the model. We propose SAM with Condition Embedding block (CEmb-SAM) which encodes sub-group conditions and combines them with image embeddings from SAM. The conditional embedding block effectively adapts SAM to each image sub-group by incorporating dataset properties through learnable parameters for normalization. Experiments show that CEmb-SAM outperforms the baseline methods on ultrasound image segmentation for peripheral nerves and breast cancer. The experiments highlight the effectiveness of Cemb-SAM in learning from heterogeneous datasets in medical image segmentation tasks

Quantum Rebound Attacks on Reduced-Round ARIA-Based Hash Functions

ARIA is a block cipher proposed by Kwon et al. at ICISC 2003, and it is widely used as the national standard block cipher in the Republic of Korea. In this study, we identify some flaws in the quantum rebound attack on 7-round ARIA-DM proposed by Dou et al., and we reveal that the limit of this attack is up to 5-round. Our revised attack applies not only to ARIA-DM but also to ARIA-MMO and ARIA-MP among the PGV models, and it is valid for all key lengths of ARIA. Moreover, we present dedicated quantum rebound attacks on 7-round ARIA-Hirose and ARIA-MJH for the first time. These attacks are only valid for the 256-bit key length of ARIA because they are constructed using the degrees of freedom in the key schedule. All our attacks are faster than the generic quantum attack in the cost metric of time–space tradeoff

Long-term results comparison after anterior cervical discectomy with BGS-7 spacer (NOVOMAX®-C) and allograft spacer: A prospective observational study

Introduction: In an anterior cervical discectomy and fusion (ACDF), various types of graft materials including autograft, allograft, and synthetic graft have been used to achieve adequate spinal fusion. Allograft spacer is mainly used in cervical fusion, especially in the anterior approach. The synthetic bone graft material BGS-7(CaO-SiO2-P2O5-B2O3, bioactive Glass-Ceramics) can bind with surrounding bone tissue by forming a hydroxyapatite layer bone bridge, leading to faster graft osseointegration. This study was conducted to compare long-term clinical outcome of BGS-7 spacer and allograft spacer for anterior cervical discectomy and fusion surgery.Materials and Methods: From September 2014 to December 2016, Consecutive anterior cervical discectomy and fusion surgeries using a BGS-7 spacer (N = 18) and Allograft spacer (N = 26) were compared for postoperative clinical outcomes. Radiologic assessments were performed, and Instrumental failure, including breakage, cage migration, subsidence were observed and Fusion status were analyzed. Finite element analysis was performed for simulating mechanical stress between the vertebral body and implant. Clinical outcomes were evaluated using neck VAS, NDI, and JOA on the patient’s final follow-up visits.Results: Among the 44 patients who underwent an anterior cervical discectomy and fusion surgery using the BGS-7 spacer and Allograft spacer, there were 30 men and 14 women. The average age at the operation was 47.69 ± 10.49 in allograft spacer and 51.67 ± 11.03 in BGS-7 spacer. The mean follow-up period was 89.18 ± 5.44 months. Twenty three (88.46%) patients in allograft spacer and 20(100%) patients in BGS-7 spacer were demonstrated radiologic evidence of interbody fusion in last OPD, which accounts for fusion grade 4 or 5. Peak stresses were 343.85 MPa in allograft spacer, and 132.55 MPa in BGS-7 spacer. Long-term clinical outcomes including neck VAS, NDI, and JOA didn’t show statistical differences between the two groups. There were no adverse events related to the BGS-7 spacer.10.3389/fbioe.2023.110046.Conclusion: The BGS-7 spacer demonstrated reliability as a spacer in anterior cervical discectomy and fusionF surgery without instrumental failure. Early stabilization with a bony bridge formation was observed at the intermediate follow-up period, and the long-term clinical outcome was favorable at more than 60 months after surgery without any adverse events. Thus, the BGS-7 spacer is a safe and effective alternative to the allograft spacer in anterior cervical discectomy and fusion surgery

NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs

Panoramic radiography (panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, its applicability is limited as compared to 3D Cone-beam computed tomography (CBCT), because PX only provides 2D flattened images of the oral structure. In this paper, we propose a new framework which estimates 3D oral structure from real-world PX images. Since there are not many matching PX and CBCT data, we used simulated PX from CBCT for training, however, we used real-world panoramic radiographs at the inference time. We propose a new ray-sampling method to make simulated panoramic radiographs inspired by the principle of panoramic radiography along with the rendering function derived from the Beer-Lambert law. Our model consists of three parts: translation module, generation module, and refinement module. The translation module changes the real-world panoramic radiograph to the simulated training image style. The generation module makes the 3D structure from the input image without any prior information such as a dental arch. Our ray-based generation approach makes it possible to reverse the process of generating PX from oral structure in order to reconstruct CBCT data. Lastly, the refinement module enhances the quality of the 3D output. Results show that our approach works better for simulated and real-world images compared to other state-of-the-art methods.Comment: 10 pages, 4 figure

Preimage Attacks on Reduced-Round Ascon-Xof

Ascon, a family of algorithms that supports authenticated encryption and hashing, has been selected as the new standard for lightweight cryptography in the NIST Lightweight Cryptography Project. Ascon’s permutation and authenticated encryption have been actively analyzed, but there are relatively few analyses on the hashing. In this paper, we concentrate on preimage attacks on Ascon-Xof. We focus on linearizing the polynomials leaked by the hash value to find its inverse. In an attack on 2-round Ascon-Xof, we carefully construct the set of guess bits using a greedy algorithm in the context of guess-and-determine. This allows us to attack Ascon-Xof more efficiently than the method in Dobraunig et al., and we fully implement our attack to demonstrate its effectiveness. We also provide the number of guess bits required to linearize one output bit after 3- and 4-round Ascon’s permutation, respectively. In particular, for the first time, we connect the result for 3-round Ascon to a preimage attack on Ascon-Xof with a 64-bit output. Our attacks primarily focus on analyzing weakened versions of Ascon-Xof, where the weakening involves setting all the IV values to 0 and omitting the round constants. Although our attacks do not compromise the security of the full Ascon-Xof, they provide new insights into their security