Improved quantum attack on Type-1 Generalized Feistel Schemes and Its application to CAST-256

Generalized Feistel Schemes (GFS) are important components of symmetric ciphers, which have been extensively researched in classical setting. However, the security evaluations of GFS in quantum setting are rather scanty. In this paper, we give more improved polynomial-time quantum distinguishers on Type-1 GFS in quantum chosen-plaintext attack (qCPA) setting and quantum chosen-ciphertext attack (qCCA) setting. In qCPA setting, we give new quantum polynomial-time distinguishers on $(3d-3)$-round Type-1 GFS with branches $d\geq3$, which gain $d-2$ more rounds than the previous distinguishers. Hence, we could get better key-recovery attacks, whose time complexities gain a factor of $2^{\frac{(d-2)n}{2}}$. In qCCA setting, we get $(3d-3)$-round quantum distinguishers on Type-1 GFS, which gain $d-1$ more rounds than the previous distinguishers. In addition, we give some quantum attacks on CAST-256 block cipher. We find 12-round and 13-round polynomial-time quantum distinguishers in qCPA and qCCA settings, respectively, while the best previous one is only 7 rounds. Hence, we could derive quantum key-recovery attack on 19-round CAST-256. While the best previous quantum key-recovery attack is on 16 rounds. When comparing our quantum attacks with classical attacks, our result also reaches 16 rounds on CAST-256 with 128-bit key under a competitive complexity

Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers

Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple nodular patients, which ignores the relations between nodules. In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules. By treating the multiple nodules from a same patient as a whole, critical relational information between solitary-nodule voxels is extracted. To our knowledge, it is the first study to learn the relations between multiple pulmonary nodules. Inspired by recent advances in natural language processing (NLP) domain, we introduce a self-attention transformer equipped with 3D CNN, named {NoduleSAT}, to replace typical pooling-based aggregation in multiple instance learning. Extensive experiments on lung nodule false positive reduction on LUNA16 database, and malignancy classification on LIDC-IDRI database, validate the effectiveness of the proposed method.Comment: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020

Boosting Point Clouds Rendering via Radiance Mapping

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid spatial frequency collapse and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data would be released soon

Drug-drug interation prediction between ketoconazole and anti-liver cancer drug Gomisin G

Background: Gomisin G, isolated from herb Schisandra chinensis, exhibits anti-tumor activities. Therefore, Gomisin G is a drug candidate for anti-liver cancer therapy. Aims: To predict the metabolic behavior and metabolism-based drug-drug interaction of gomisin G. Methods: Molecular docking method was used. The crystal structure of CYP3A4 with the ligand ketoconazole was chosen from protein data bank (http://www.rcsb.org/pdb). Chemdraw software was used to draw the two-dimensional structure of gomisin G with standard bond lengths and angles. Results: Gomisin G can be well docked into the activity site of CYP3A4, and distance between gomisin G the heme active site was 2.75 \uc5. To evaluate whether the inhibitors of CYP3A4 can affect the metabolism of gomisin G, co-docking of gomisin G and ketoconazole was further performed. The distance between ketoconazole and activity center (2.10 \uc5) is closer than the distance between gomisin G and activity center of CYP3A4, indicating the easy influence of CYP3A4\u2019s strong inhibitor towards the metabolism of gomisin G. Conclusion: Gomisin G is a good substrate of CYP3A4, and CYP3A4 inhibitors easily affect the metabolism of Gomisin G

(Quantum) Collision Attacks on Reduced Simpira v2

Simpira v2 is an AES-based permutation proposed by Gueron and Mouha at ASIACRYPT 2016. In this paper, we build an improved MILP model to count the differential and linear active Sboxes for Simpira v2, which achieves tighter bounds of the minimum number of active Sboxes for a few versions of Simpira v2. Then, based on the new model, we find some new truncated differentials for Simpira v2 and give a series (quantum) collision attacks on two versions of reduced Simpira v2

Anticonvulsant activities of α-asaronol ((E)-3'-hydroxyasarone), an active constituent derived from α-asarone.

BACKGROUND: Epilepsy is one of chronic neurological disorders that affects 0.5-1.0% of the world's population during their lifetime. There is a still significant need to develop novel anticonvulsant drugs that possess superior efficacy, broad spectrum of activities and good safety profile. METHODS: α-Asaronol and two current antiseizure drugs (α-asarone and carbamazepine (CBZ)) were assessed by in vivo anticonvulsant screening with the three most employed standard animal seizure models, including maximal electroshock seizure (MES), subcutaneous injection-pentylenetetrazole (PTZ)-induced seizures and 3-mercaptopropionic acid (3-MP)-induced seizures in mice. Considering drug safety evaluation, acute neurotoxicity was assessed with minimal motor impairment screening determined in the rotarod test, and acute toxicity was also detected in mice. RESULTS: In our results, α-asaronol displayed a broad spectrum of anticonvulsant activity (ACA) and showed better protective indexes (PI = 11.11 in MES, PI = 8.68 in PTZ) and lower acute toxicity (LD50 = 2940 mg/kg) than its metabolic parent compound (α-asarone). Additionally, α-asaronol displayed a prominent anticonvulsant profile with ED50 values of 62.02 mg/kg in the MES and 79.45 mg/kg in the sc-PTZ screen as compared with stiripentol of ED50 of 240 mg/kg and 115 mg/kg in the relevant test, respectively. CONCLUSION: The results of the present study revealed α-asaronol can be developed as a novel molecular in the search for safer and efficient anticonvulsants having neuroprotective effects as well as low toxicity. Meanwhile, the results also suggested that α-asaronol has great potential to develop into another new aromatic allylic alcohols type anticonvulsant drug for add-on therapy of Dravet's syndrome

Bioinspired PVDF piezoelectric generator for harvesting multi‐frequency sound energy

With the rapid development of micro‐energy harvesting technology, noise has great potential as a new type of micro‐energy source by developing a high‐performance acoustic energy harvester (AEH). Nevertheless, the challenges of harvesting energy from noise are low acoustic energy density, multi‐frequency mixed sound, and unstable sound pressure. Piezoelectric‐based AEHs are proposed as a solution, but most reported devices need to work at a certain frequency and very high sound pressure, and exhibit the disadvantages of being bulky and heavy when using an extra sound‐pressure amplifier. Here, the eardrum and cochlea bioinspired polyvinylidene fluoride (PVDF) piezoelectric generator which is lightweight, compact, has a simple structure and is low‐cost, harvests multi‐frequency sound energy. Although using the commercial pure PVDF membrane, the prepared AEH still presents a high acoustoelectric conversion performance with an output power of 8.45 µW and an acoustic sensitivity of 1 V Pa−1 at 200 Hz and 100 dB without using any sound‐pressure amplifier through optimizing the structure–vibration–frequency relationship. More importantly, the bioinspired AEHs do not only exhibit the ability of acoustic energy harvesting function but also have the abilities of frequency recognition and acoustic sensing, which show great potential in the application of self‐powered acoustic sensors

Optimization and Hydration Mechanism of Ecological Ternary Cements Containing Phosphogypsum

Ecological ternary cements (ECP) were prepared with powders of phosphogypsum (PG), fly ash (FA) and Portland cement (PC). The evolution mechanism of the hydration product structure was characterized through macro and micro experiments. The thermodynamic characteristics of the solid phase, solid solution phase and aqueous solution in the process of hydration about the phosphogypsum–fly ash–cement ternary cementitious system were studied based on the Gibbs-free-energy C-S-H thermodynamic model and GEM-Selektor software and compared with the experimental results. The results show that, in the hydration reaction, the thermodynamic interaction between the mineral single-phase and hydration products plays an important role in the spatio-temporal distribution of ions in the cementitious system. The values of CaO, SiO2H and H2Ohyd gradually increased with the increase in the Ca/Si ratio, while the values of CaOext and H2OOH showed a positive proportional relationship and the values of SiO2H and SiO2 showed an inverse proportional relationship. GEM-Selektor accurately simulated the total amount of AFt and AFm mineral phases, and quantitatively analyzed the correlation of complex ion groups about C-S-H gels and C3S