Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation

We present a PUF key generation scheme that uses the provably optimal method of maximum-likelihood (ML) detection on symbols derived from PUF response bits. Each device forms a noisy, device-specific symbol constellation, based on manufacturing variation. Each detected symbol is a letter in a codeword of an error correction code, resulting in non-binary codewords. We present a three-pronged validation strategy: i. mathematical (deriving an optimal symbol decoder), ii. simulation (comparing against prior approaches), and iii. empirical (using implementation data). We present simulation results demonstrating that for a given PUF noise level and block size (an estimate of helper data size), our new symbol-based ML approach can have orders of magnitude better bit error rates compared to prior schemes such as block coding, repetition coding, and threshold-based pattern matching, especially under high levels of noise due to extreme environmental variation. We demonstrate environmental reliability of a ML symbol-based soft-decision error correction approach in 28nm FPGA silicon, covering -65°C to 105°C ambient (and including 125°C junction), and with 128bit key regeneration error probability ≤ 1 ppm.Bavaria California Technology Center (Grant 2014-1/9

A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity

In this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions, which is compared with experimental data. A test rig is set up to provide high gravity up to 11 g with a heat flux up to 15100 W/m 2 and the mass velocity range from 40 to 2000 kg m −2 s −1. In the current work, a total 531 data samples have been used in the ANN model. The proposed model was developed in a Python Keras environment with Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, inlet pressure, direction, acceleration, tube inner surface area, helical coil diameter) as the inputs and two features (wall temperature, heat transfer coefficient) as the outputs. The deep ANN model composed of three hidden layers with a total number of 1098 neurons and 300,266 trainable parameters has been found as optimal according to statistical error analysis. Performance evaluation is conducted based on six verification statistic metrics (R 2, MSE, MAE, MAPE, RMSE and cosine proximity) between the experimental data and predicted values. The results demonstrate that a 8-512-512-64-2 neural network has the best performance in predicting the helical coil characteristics with (R 2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, cosine proximity=1.000) in the testing stage. It is indicated that with the utilisation of deep learning, the proposed model is able to successfully predict the heat transfer performance in helical coils, and especially achieved excellent performance in predicting outputs that have a very large range of value differences

A noise bifurcation architecture for linear additive physical functions

Physical Unclonable Functions (PUFs) allow a silicon device to be authenticated based on its manufacturing variations using challenge/response evaluations. Popular realizations use linear additive functions as building blocks. Security is scaled up using non-linear mixing (e.g., adding XORs). Because the responses are physically derived and thus noisy, the resulting explosion in noise impacts both the adversary (which is desirable) as well as the verifier (which is undesirable). We present the first architecture for linear additive physical functions where the noise seen by the adversary and the noise seen by the verifier are bifurcated by using a randomized decimation technique and a novel response recovery method at an authentication verification server. We allow the adversary's noise η[subscript a] → 0.50 while keeping the verifier's noise η[subscript v] constant, using a parameter-based authentication modality that does not require explicit challenge/response pair storage at the server. We present supporting data using 28nm FPGA PUF noise results as well as machine learning attack results. We demonstrate that our architecture can also withstand recent side-channel attacks that filter the noise (to clean up training challenge/response labels) prior to machine learning

Performance Metrics and Empirical Results of a PUF Cryptographic Key Generation ASIC

We describe a PUF design with integrated error correction that is robust to various layout implementations and achieves excellent and consistent results in each of the following four areas: Randomness, Uniqueness, Bias and Stability. 133 PUF devices in 0.13 μm technology encompassing seven circuit layout implementations were tested. The PUF-based key generation design achieved less than 0.58 ppm failure rates with 50%+ stability safety margin. 1.75M error correction blocks ran error-free under worst-case V/T corners (±10% V, 125°C/-65°C) and under voltage extremes of ±20% V. All PUF devices demonstrated excellent NIST-random behavior (99 cumulative percentile), a criterion used to qualify random sources for use as keying material for cryptographic-grade applications

A Lockdown Technique to Prevent Machine Learning on PUFs for Lightweight Authentication

We present a lightweight PUF-based authentication approach that is practical in settings where a server authenticates a device, and for use cases where the number of authentications is limited over a device's lifetime. Our scheme uses a server-managed challenge/response pair (CRP) lockdown protocol: unlike prior approaches, an adaptive chosen-challenge adversary with machine learning capabilities cannot obtain new CRPs without the server's implicit permission. The adversary is faced with the problem of deriving a PUF model with a limited amount of machine learning training data. Our system-level approach allows a so-called strong PUF to be used for lightweight authentication in a manner that is heuristically secure against today's best machine learning methods through a worst-case CRP exposure algorithmic validation. We also present a degenerate instantiation using a weak PUF that is secure against computationally unrestricted adversaries, which includes any learning adversary, for practical device lifetimes and read-out rates. We validate our approach using silicon PUF data, and demonstrate the feasibility of supporting 10, 1,000, and 1M authentications, including practical configurations that are not learnable with polynomial resources, e.g., the number of CRPs and the attack runtime, using recent results based on the probably-approximately-correct (PAC) complexity-theoretic framework

Amish in Rural Tourism: Representation of Subcultures in Tourism Marketing

Ethnic culture heritage, as a subculture, provides an important resource for US rural tourism. However, the commodification of culture heritage has affected the representation of cultural heritage in tourism marketing. This study examines 13 counties of Indiana where Amish are a significant proportion of the population and explores how the destination presents Amish culture in their promotional materials. This article conducts content analysis and semiotic analysis of Amish icons and website information through data collection and coding of 50 websites and 458 images. The results calculated the geographic location, symbols, and text distribution of Amish elements in Indiana, and also compared the different usage proportions, meanings, and functions of Amish icons and texts in DMO & commercial websites. And it is also analyzed the Amish meaning system of symbols and culture codes through semiotic models in DMO & commercial websites

Atrial cardiomyopathy: from cell to bedside

Atrial cardiomyopathy refers to structural and electrical remodelling of the atria, which can lead to impaired mechanical function. While historical studies have implicated atrial fibrillation as the leading cause of cardioembolic stroke, atrial cardiomyopathy may be an important, underestimated contributor. To date, the relationship between atrial cardiomyopathy, atrial fibrillation, and cardioembolic stroke remains obscure. This review summarizes the pathogenesis of atrial cardiomyopathy, with a special focus on neurohormonal and inflammatory mechanisms, as well as the role of adipose tissue, especially epicardial fat in atrial remodelling. It reviews the current evidence implicating atrial cardiomyopathy as a cause of embolic stroke, with atrial fibrillation as a lagging marker of an increased thrombogenic atrial substrate. Finally, it discusses the potential of antithrombotic therapy in embolic stroke with undetermined source and appraises the available diagnostic techniques for atrial cardiomyopathy, including imaging techniques such as echocardiography, computed tomography, and magnetic resonance imaging as well as electroanatomic mapping, electrocardiogram, biomarkers, and genetic testing. More prospective studies are needed to define the relationship between atrial cardiomyopathy, atrial fibrillation, and embolic stroke and to establish a prompt diagnosis and specific treatment strategies in these patients with atrial cardiomyopathy for the secondary and even primary prevention of embolic stroke

Dimerization of cAMP phosphodiesterase-4 (PDE4) in living cells requires interfaces located in both the UCR1 and catalytic unit domains

PDE4 family cAMP phosphodiesterases play a pivotal role in determining compartmentalised cAMP signalling through targeted cAMP breakdown. Expressing the widely found PDE4D5 isoform, as both bait and prey in a yeast 2-hybrid system, we demonstrated interaction consistent with the notion that long PDE4 isoforms form dimers. Four potential dimerization sites were uncovered using a scanning peptide array approach, where a recombinant purified PDE4D5 fusion protein was used to probe a 25-mer library of overlapping peptides covering the entire PDE4D5 sequence. Key residues involved in PDE4D5 dimerization were defined using a site-directed mutagenesis programme directed by an alanine scanning peptide array approach. Critical residues stabilising PDE4D5 dimerization were defined within the regulatory UCR1 region found in long, but not short, PDE4 isoforms, namely the Arg173, Asn174 and Asn175 (DD1) cluster. Disruption of the DD1 cluster was not sufficient, in itself, to destabilise PDE4D5 homodimers. Instead, disruption of an additional interface, located on the PDE4 catalytic unit, was also required to convert PDE4D5 into a monomeric form. This second dimerization site on the conserved PDE4 catalytic unit is dependent upon a critical ion pair interaction. This involves Asp463 and Arg499 in PDE4D5, which interact in a trans fashion involving the two PDE4D5 molecules participating in the homodimer. PDE4 long isoforms adopt a dimeric state in living cells that is underpinned by two key contributory interactions, one involving the UCR modules and one involving an interface on the core catalytic domain. We propose that short forms do not adopt a dimeric configuration because, in the absence of the UCR1 module, residual engagement of the remaining core catalytic domain interface provides insufficient free energy to drive dimerization. The functioning of PDE4 long and short forms is thus poised to be inherently distinct due to this difference in quaternary structure

Phosphorylation-dependent assembly and coordination of the DNA damage checkpoint apparatus by Rad4TopBP1

The BRCT-domain protein Rad4(TopBP1) facilitates activation of the DNA damage checkpoint in Schizosaccharomyces pombe by physically coupling the Rad9-Rad1-Hus1 clamp, the Rad3(ATR) -Rad26(ATRIP) kinase complex, and the Crb2(53BP1) mediator. We have now determined crystal structures of the BRCT repeats of Rad4(TopBP1), revealing a distinctive domain architecture, and characterized their phosphorylation-dependent interactions with Rad9 and Crb2(53BP1). We identify a cluster of phosphorylation sites in the N-terminal region of Crb2(53BP1) that mediate interaction with Rad4(TopBP1) and reveal a hierarchical phosphorylation mechanism in which phosphorylation of Crb2(53BP1) residues Thr215 and Thr235 promotes phosphorylation of the noncanonical Thr187 site by scaffolding cyclin-dependent kinase (CDK) recruitment. Finally, we show that the simultaneous interaction of a single Rad4(TopBP1) molecule with both Thr187 phosphorylation sites in a Crb2(53BP1) dimer is essential for establishing the DNA damage checkpoint