Eddy-Covariance Flux Measurements in the Complex Terrain of an Alpine Valley in Switzerland

We measured the surface energy budget of an Alpine grassland in highly complex terrain to explore possibilities and limitations for application of the eddy-covariance technique, also for CO2 flux measurements, at such non-ideal locations. This paper focuses on the influence of complex terrain on the turbulent energy measurements of a characteristic high Alpine grassland on Crap Alv (Alp Weissenstein) in the Swiss Alps during the growing season 2006. Measurements were carried out on a topographic terrace with a slope of 25◦ inclination. Flux data quality is assessed via the closure of the energy budget and the quality flag method used within the CarboEurope project. During 93% of the time the wind direction was along the main valley axis (43% upvalley and 50% downvalley directions). During the transition times of the typical twice daily wind direction changes in a mountain valley the fraction of high and good quality flux data reached a minimum of ≈50%, whereas during the early afternoon ≈70% of all records yielded good to highest quality (CarboEurope flags 0 and 1). The overall energy budget closure was 74±2%. An angular correction for the shortwave energy input to the slope improved the energy budget closure slightly to 82±2% for afternoon conditions. In the daily total, the measured turbulent energy fluxes are only underestimated by around 8% of net radiation. In summary, our results suggest that it is possible to yield realistic energy flux measurements under such conditions. We thus argue that the Crap Alv site and similar topographically complex locations with short-statured vegetation should be well suited also for CO2 flux measurement

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

Non-EST based prediction of exon skipping and intron retention events using Pfam information

Most of the known alternative splice events have been detected by the comparison of expressed sequence tags (ESTs) and cDNAs. However, not all splice events are represented in EST databases since ESTs have several biases. Therefore, non-EST based approaches are needed to extend our view of a transcriptome. Here, we describe a novel method for the ab initio prediction of alternative splice events that is solely based on the annotation of Pfam domains. Furthermore, we applied this approach in a genome-wide manner to all human RefSeq transcripts and predicted a total of 321 exon skipping and intron retention events. We show that this method is very reliable as 78% (250 of 321) of our predictions are confirmed by ESTs or cDNAs. Subsequent analyses of splice events within Pfam domains revealed a significant preference of alternative exon junctions to be located at the protein surface and to avoid secondary structure elements. Thus, splice events within Pfams are probable to alter the structure and function of a domain which makes them highly interesting for detailed biological investigation. As Pfam domains are annotated in many other species, our strategy to predict exon skipping and intron retention events might be important for species with a lower number of ESTs

A New Resistive Adaptive Gate-Driving Concept with Automated Identification of Operational Parameters

This paper proposes a new adaptive gate-driving concept based on parallel-connected resistive driving stages, which allows the modification of the effective gate-resistance for every turn-on and turn-off event during operation. By selecting the appropriate gate-resistance, the switching behavior can be optimized individually for each specific operating point (Vsw, Isw, Tj). As a result, higher efficiency under partial load can be achieved. The selection of effective gate-resistance is based on the results of a here introduced automatic optimization method, which takes constraints such as dv/dt- and di/dt-limits into account. Subject of this paper is also the comparison of the new approach with the widely used single-stage resistive driver

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

Improved identification of conserved cassette exons using Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Alternative splicing is a major contributor to the diversity of eukaryotic transcriptomes and proteomes. Currently, large scale detection of alternative splicing using expressed sequence tags (ESTs) or microarrays does not capture all alternative splicing events. Moreover, for many species genomic data is being produced at a far greater rate than corresponding transcript data, hence <it>in silico </it>methods of predicting alternative splicing have to be improved.</p> <p>Results</p> <p>Here, we show that the use of Bayesian networks (BNs) allows accurate prediction of evolutionary conserved exon skipping events. At a stringent false positive rate of 0.5%, our BN achieves an improved true positive rate of 61%, compared to a previously reported 50% on the same dataset using support vector machines (SVMs). Incorporating several novel discriminative features such as intronic splicing regulatory elements leads to the improvement. Features related to mRNA secondary structure increase the prediction performance, corroborating previous findings that secondary structures are important for exon recognition. Random labelling tests rule out overfitting. Cross-validation on another dataset confirms the increased performance. When using the same dataset and the same set of features, the BN matches the performance of an SVM in earlier literature. Remarkably, we could show that about half of the exons which are labelled constitutive but receive a high probability of being alternative by the BN, are in fact alternative exons according to the latest EST data. Finally, we predict exon skipping without using conservation-based features, and achieve a true positive rate of 29% at a false positive rate of 0.5%.</p> <p>Conclusion</p> <p>BNs can be used to achieve accurate identification of alternative exons and provide clues about possible dependencies between relevant features. The near-identical performance of the BN and SVM when using the same features shows that good classification depends more on features than on the choice of classifier. Conservation based features continue to be the most informative, and hence distinguishing alternative exons from constitutive ones without using conservation based features remains a challenging problem.</p