Improved Side Channel Cube Attacks on PRESENT

The paper presents several improved side channel cube attacks on PRESENT based on single bit leakage model. Compared with the previous study of Yang et al in CANS 2009 [30], based on the same model of single bit leakage in the 3rd round, we show that: if the PRESENT cipher structure is unknown, for the leakage bit 0, 32-bit key can be recovered within $2^{7.17}$ chosen plaintexts; if the cipher structure is known, for the leakage bit 4,8,12, 48-bit key can be extracted by $2^{11.92}$ chosen plaintexts, which is less than $2^{15}$ in [30]; then, we extend the single bit leakage model to the 4th round, based on the two level “divide and conquer” analysis strategy, we propose a sliding window side channel cube attack on PRESENT, for the leakage bit 0, about $2^{15.14}$ chosen plaintexts can obtain 60-bit key; in order to obtain more key bits, we propose an iterated side channel cube attack on PRESENT, about $2^{8.15}$ chosen plaintexts can obtain extra 12 equivalent key bits, so overall $2^{15.154}$ chosen plaintexts can reduce the PRESENT-80 key searching space to $2^{8}$; finally, we extend the attack to PRESENT-128, about $2^{15.156}$ chosen plaintexts can extract 85 bits key, and reduce the PRESENT-128 key searching space to $2^{43}$. Compared with the previous study of Abdul-Latip et al in ASIACCS 2011 [31] based on the Hamming weight leakage model, which can extract 64-bit key of PRESENT-80/128 by $2^{13}$ chosen plaintexts, our attacks can extract more key bits, and have certain advantages over [31]

Octree-based hierarchical sampling optimization for the volumetric super-resolution of scientific data

When introducing physics-constrained deep learning solutions to the volumetric super-resolution of scientific data, the training is challenging to converge and always time-consuming. We propose a new hierarchical sampling method based on octree to solve these difficulties. In our approach, scientific data is preprocessed before training, and a hierarchical octree-based data structure is built to guide sampling on the latent context grid. Each leaf node in the octree corresponds to an indivisible subblock of the volumetric data. The dimensions of the subblocks are different, making the number of sample points in each randomly cropped training data block to be adaptive. We reconstruct the octree at intervals according to loss distribution to perform the multi-stage training. With the Rayleigh-B\'enard convection problem, we deploy our method to state-of-the-art models. We constructed adequate experiments to evaluate the training performance and model accuracy of our method. Experiments indicate that our sampling optimization improves the convergence performance of physics-constrained deep learning super-resolution solutions. Furthermore, the sample points and training time are significantly reduced with no drop in model accuracy. We also test our method in training tasks of other deep neural networks, and the results show our sampling optimization has extensive effectiveness and applicability. The code is publicly available at https://github.com/xinjiewang/octree-based_sampling

Role of CTSC in Glioblastoma Based on Oncomine and TCGA Database

Background and objective Glioblastoma (GBM) is one of the malignant tumors causing death worldwide. Most patients were found in the middle and late stages and had poor prognosis. The purpose of this study was to investigate the expression and significance of CTSC in GBM. Methods The information about CTSC in Oncomine database was collected and analyzed twice. The role of CTSC in GBM was meta-analyzed. The expression of CTSC in glioma cell lines was retrieved by CCLE database, and the survival of patients was analyzed by TCGA database. Results A total of 1,459 different types of CTSC were collected in Oncomine database, 134 of which had statistical differences in CTSC expression, 89 of which had increased CTSC expression and 45 of which had decreased CTSC expression. A total of 50 studies involving the expression of CTSC in GBM cancer and normal tissues included 1,189 samples. Compared with the control group, CTSC was highly expressed in GBM (P < 0.05). Moreover, CTSC was highly expressed in glioma cell lines. There was a correlation between the expression of CTSC and the overall survival rate of GBM. The overall survival rate of patients with high expression of CTSC was worse, while the prognosis of patients with low expression of SPC24 was better (P < 0.05). Conclusion Through the in-depth mining of oncomine gene chip database, we propose that CTSC is highly expressed in GBM tissues and is related to the prognosis of GBM, which may provide an important theoretical basis for the treatment of glioma

Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits

The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2−x/Pt memristors and CMOS integrated circuit components.National Science Foundation CCF-1028378Air Force Office of Scientific Research FA9550-12-1-0038Ministerio de Economía y Competitividad TEC2012-37868-C04-0

Adaptive Real-Time Estimation on Road Disturbances Properties Considering Load Variation via Vehicle Vertical Dynamics

Vehicle dynamics are directly dependent on tire-road contact forces and torques which are themselves dependent on the wheels’ load and tire-road friction characteristics. An acquisition of the road disturbance property is essential for the enhancement of vehicle suspension control systems. This paper focuses on designing an adaptive real-time road profile estimation observer considering load variation via vehicle vertical dynamics. Firstly, a road profile estimator based on a linear Kalman filter is proposed, which has great advantages on vehicle online control. Secondly, to minimize the estimation errors, an online identification system based on the Recursive Least-Squares Estimation is applied to estimate sprung mass, which is used to refresh the system matrix of the adaptive observer to improve the road estimation efficiency. Last, for mining road category from the estimated various road profile sequencse, a road categorizer considering road frequency and amplitude simultaneously is approached and its efficiency is validated via numerical simulations, in which the road condition is categorized into six special ranges, and this road detection strategy can provide the suspension control system with a better compromise for the vehicle ride comfort, handling, and safety performance

A CsI hodoscope on CSHINE for Bremsstrahlung {\gamma}-rays in Heavy Ion Reactions

Bremsstrahlung $\gamma$ production in heavy ion reactions at Fermi energies carries important physical information including the nuclear symmetry energy at supra-saturation densities. In order to detect the high energy Bremsstrahlung $\gamma$ rays, a hodoscope consisting of 15 CsI(Tl) crystal read out by photo multiplier tubes has been built, tested and operated in experiment. The resolution, efficiency and linear response of the units to $\gamma$ rays have been studied using radioactive source and $({\rm p},\gamma)$ reactions. The inherent energy resolution of $1.6\%+2\%/E_{\gamma}^{1/2}$ is obtained. Reconstruction method has been established through Geant 4 simulations, reproducing the experimental results where comparison can be made. Using the reconstruction method developed, the whole efficiency of the hodoscope is about $2.6\times 10^{-4}$ against the $4\pi$ emissions at the target position, exhibiting insignificant dependence on the energy of incident $\gamma$ rays above 20 MeV. The hodoscope is operated in the experiment of $^{86}$Kr + $^{124}$Sn at 25 MeV/u, and a full $\gamma$ energy spectrum up to 80 MeV has been obtained.Comment: 9 pages, 19 figure

Mutation in Archain 1, a Subunit of COPI Coatomer Complex, Causes Diluted Coat Color and Purkinje Cell Degeneration

Intracellular trafficking is critical for delivering molecules and organelles to their proper destinations to carry out normal cellular functions. Disruption of intracellular trafficking has been implicated in the pathogenesis of various neurodegenerative disorders. In addition, a number of genes involved in vesicle/organelle trafficking are also essential for pigmentation, and loss of those genes is often associated with mouse coat-color dilution and human hypopigmentary disorders. Hence, we postulated that screening for mouse mutants with both neurological defects and coat-color dilution will help identify additional factors associated with intracellular trafficking in neuronal cells. In this study, we characterized a mouse mutant with a unique N-ethyl-N-nitrosourea (ENU)–induced mutation, named nur17. nur17 mutant mice exhibit both coat-color dilution and ataxia due to Purkinje cell degeneration in the cerebellum. By positional cloning, we identified that the nur17 mouse carries a T-to-C missense mutation in archain 1 (Arcn1) gene which encodes the δ subunit of the coat protein I (COPI) complex required for intracellular trafficking. Consistent with this function, we found that intracellular trafficking is disrupted in nur17 melanocytes. Moreover, the nur17 mutation leads to common characteristics of neurodegenerative disorders such as abnormal protein accumulation, ER stress, and neurofibrillary tangles. Our study documents for the first time the physiological consequences of the impairment of the ARCN1 function in the whole animal and demonstrates a direct association between ARCN1 and neurodegeneration