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Deep learning (DL) has proven to be very effective for image recognition tasks, with a large body of research on various model architectures for object classification. Straight-forward application of DL to side-channel analysis (SCA) has already shown promising success, with experimentation on open-source variable key datasets showing that secret keys can be revealed with 100s traces even in the presence of countermeasures. This paper aims to further improve the application of DL for SCA, by enhancing the power of DL when targeting the secret key of cryptographic algorithms when protected with SCA countermeasures. We propose a new model, CNN-based model with Plaintext feature extension (CNNP) together with multiple convolutional filter kernel sizes and structures with deeper and narrower neural networks, which has empirically proven its effectiveness by outperforming reference profiling attack methods such as template attacks (TAs), convolutional neural networks (CNNs) and multilayer perceptron (MLP) models. Our model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 40 attack traces in comparison with order of 100s traces required by straightforward machine learning (ML) application. During the profiling stage an attacker needs no additional knowledge on the implementation, such as the masking scheme or random mask values, only the ability to record the power consumption or electromagnetic field traces, plaintext/ciphertext and the key. Additionally, no heuristic pre-processing is required in order to break the high-order masking countermeasures of the target implementation., IACR Transactions on Cryptographic Hardware and Embedded Systems, Volume 2020, Issue 4

ObjectiveTo explore the association between early essential newborn care (EENC) policy, practice and environmental interventions and breastfeeding outcomes.DesignCross-sectional observational study.Setting150 national, provincial and district hospitals implementing EENC in eight countries in East Asia and the Pacific.Participants1383 maternal interviews, chart reviews and environmental assessments during 2016 and 2017.Main outcome measuresExclusive breastfeeding (EBF), that is, feeding only breastmilk without other food or fluids since birth and before discharge, and, early breastfeeding initiation, that is, during skin-to-skin contact (SSC) with the mother without separation.ResultsFifty-nine per cent of newborns initiated breastfeeding early and 83.5% were EBF. Duration of SSC showed a strong dose–response relationship with early breastfeeding initiation. SSC of at least 90 min was associated with 368.81 (95% CI 88.76 to 1532.38, pConclusionEENC policy, practice and environmental interventions were associated with breastfeeding outcomes. To maximise the likelihood of early and EBF, newborns, regardless of delivery mode, should receive immediate and uninterrupted SSC for at least 90 min.

This article presents experimental data on visual, mechanical, thermal, and structural characterization by hot-pressing four sources of milled plant powders and coconut fibers. It correlates chemical composition obtained by (FTIR), particle size, and reports bending strength, water resistance morphological (SEM) and thermal stability, structural properties (FTIR and XRD). It further supplements findings of the influence of microfibrillation and chemical composition on hot-pressing plant fibers as presented in the research article "Effects of Chemical Composition, Mild Alkaline Pretreatment and Particle Size on Mechanical, Thermal, and Structural Properties of Binderless Lignocellulosic Biopolymers Prepared by Hot-Pressing Raw Microfibrillated Phoenix Dactylifera and Cocos Nucifera Fibers and Leaves" [1]. For more insights into the difference among non-lignin-, lignin-, and semi lignin-based adhesion refer to the research article [1]. This dataset is made publicly available for potential reuse in recycling agricultural waste fibers for value-added materials.

This research reports the processability and mechanical properties of silk resins prepared by hot-pressing followed by hot-rolling and then analyzes their thermal and structural properties. The results show that regenerated silk (RS) resins are better suited for hot-rolling than Eri and Bombyx mori silk resins (untreated silk). When hot-rolling at 160 &deg, C with a 50% of reduction ratio, maximum bending strength and Young&rsquo, s modulus of RS resin reaches 192 MPa and 10.2 GPa, respectively, after pretreatment by immersion in 40 vol% ethanol, and 229 MPa and 12.5 GPa, respectively, after pretreatment by immersion in boiling water. Increased strength of the material is attributed to the increased content of aggregated strands and intramolecular linking of &beta, sheets (attenuated total reflectance Fourier-transform infrared spectroscopy) and higher crystallinity (X-ray diffraction analysis). After hot-pressing and hot-rolling, RS resins have a stable decomposition temperature (297 &deg, C).

We are concerned with multiple test problems with composite null hypotheses and the estimation of the proportion $\pi_{0}$ of true null hypotheses. The Schweder-Spj\o tvoll estimator $\hat{\pi}_0$ utilizes marginal $p$-values and only works properly if the $p$-values that correspond to the true null hypotheses are uniformly distributed on $[0,1]$ ($\mathrm{Uni}[0,1]$-distributed). In the case of composite null hypotheses, marginal $p$-values are usually computed under least favorable parameter configurations (LFCs). Thus, they are stochastically larger than $\mathrm{Uni}[0,1]$ under non-LFCs in the null hypotheses. When using these LFC-based $p$-values, $\hat{\pi}_0$ tends to overestimate $\pi_{0}$. We introduce a new way of randomizing $p$-values that depends on a tuning parameter $c\in[0,1]$, such that $c=0$ and $c=1$ lead to $\mathrm{Uni}[0,1]$-distributed $p$-values, which are independent of the data, and to the original LFC-based $p$-values, respectively. For a certain value $c=c^{\star}$ the bias of $\hat{\pi}_0$ is minimized when using our randomized $p$-values. This often also entails a smaller mean squared error of the estimator as compared to the usage of the LFC-based $p$-values. We analyze these points theoretically, and we demonstrate them numerically in computer simulations under various standard statistical models.