W(h)ither Fossils? Studying Morphological Character Evolution in the Age of Molecular Sequences

A major challenge in the post-genomics era will be to integrate molecular sequence data from extant organisms with morphological data from fossil and extant taxa into a single, coherent picture of phylogenetic relationships; only then will these phylogenetic hypotheses be effectively applied to the study of morphological character evolution. At least two analytical approaches to solving this problem have been utilized: (1) simultaneous analysis of molecular sequence and morphological data with fossil taxa included as terminals in the analysis, and (2) the molecular scaffold approach, in which morphological data are analyzed over a molecular backbone (with constraints that force extant taxa into positions suggested by sequence data). The perceived obstacles to including fossil taxa directly in simultaneous analyses of morphological and molecular sequence data with extant taxa include: (1) that fossil taxa are missing the molecular sequence portion of the character data; (2) that morphological characters might be misleading due to convergence; and (3) character weighting, specifically how and whether to weight characters in the morphological partition relative to characters in the molecular sequence data partition. The molecular scaffold has been put forward as a potential solution to at least some of these problems. Using examples of simultaneous analyses from the literature, as well as new analyses of previously published morphological and molecular sequence data matrices for extant and fossil Chiroptera (bats), we argue that the simultaneous analysis approach is superior to the molecular scaffold approach, specifically addressing the problems to which the molecular scaffold has been suggested as a solution. Finally, the application of phylogenetic hypotheses including fossil taxa (whatever their derivation) to the study of morphological character evolution is discussed, with special emphasis on scenarios in which fossil taxa are likely to be most enlightening: (1) in determining the sequence of character evolution; (2) in determining the timing of character evolution; and (3) in making inferences about the presence or absence of characteristics in fossil taxa that may not be directly observable in the fossil record. Published By: Missouri Botanical Garde

Statistical Analysis of Federal District Court Cases Seeking Longer Patent Term Adjustments in the Wake of Wyeth v. Kappos, 10 J. Marshall Rev. Intell. Prop. L. 1 (2010)

Over 175 Federal District Court cases filed from September 2008 through July 2010 were analyzed to determine common features noted by applicants seeking longer patent term adjustments (“PTAs”) in view of a Federal District Court ruling, later affirmed by the U.S. Court of Appeals for the Federal Circuit in Wyeth v. Kappos, which held that the United States Patent and Trademark Office (“PTO”) misinterpreted a statute relating to the calculation of PTAs involving overlapping periods of delay attributable to the PTO or to the applicant. Applicant and PTO errors in calculating PTAs were common, often relating to counting errors due to the mischaracterization of events that occur at the beginning or end of specific delay periods. Asymmetries were also noted in the treatment of delay periods encountered in the prosecution of national phase applications based on earlier-filed international applications, compared to applications which take priority only to earlier-filed U.S. applications. Common patterns of delay were noted, and practices that minimize Applicant Delay, maximizing effective PTA, are highlighted. Despite the intent of Congress to compensate applicants for delays in prosecution in an industry-independent manner, applicants seeking reconsideration of a patent term adjustment in Federal District Court are highly-biased toward institutions seeking patents on pharmaceutical and related biotechnology inventions. Unlike patent term extensions, which are sought in a six-month period prior to regulatory approval and sale of a pharmaceutical product, and often long after a patent has issued claiming the product, court cases identifying patents needing longer PTAs provide early notice to the public, including investors and competitors, of technologies considered to have particular value to the applicant. Understanding the complex calculations behind PTAs, patent term extensions, and expiration dates, is key to the development of successful scientific, legal, and business strategies involving licensing and ownership of patented technologies

Reservation-based Resource-Brokering for Grid Computing

In this paper we present the design and implementation of the Migol brokering framework. Migol is a Grid middleware, which addresses the fault-tolerance of long-running and compute-intensive applications. The framework supports e. g. the automatic and transparent recovery respectively the migration of applications. Another core feature of Migol is the discovery, selection, and allocation of resources using advance reservation. Grid broker systems can significantly benefit from advance reservation. With advance reservation brokers and users can obtain execution guarantees from local resource management systems (LRM) without requiring detailed knowledge of current and future workloads or of the resource owner’s policies. Migol’s Advance Reservation Service (ARS) provides an adapter layer for reservation capabilities of different LRMs, which is currently not provided by existing Grid middleware platforms. Further, we propose a shortest expected delay (SED) strategy for scheduling of advance reservations within the Job Broker Service. SED needs information about the earliest start time of an application. This is currently not supported by LRMs. We added this feature for PBSPro. Migol depends on Globus and its security infrastructure. Our performance experiments show the substantial overhead of this serviceoriented approach

Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK

Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection. The QUantum computing Application benchmaRK (QUARK) framework simplifies and standardizes benchmarking studies for quantum computing applications. Here, we propose several extensions of QUARK to include the ability to evaluate the training and deployment of quantum generative models. We describe the updated software architecture and illustrate its flexibility through several example applications: (1) We trained different quantum generative models using several circuit ansatzes, data sets, and data transformations. (2) We evaluated our models on GPU and real quantum hardware. (3) We assessed the generalization capabilities of our generative models using a broad set of metrics that capture, e.g., the novelty and validity of the generated data.Comment: 10 pages, 10 figure

Optimization of insect cell based protein production processes - online monitoring, expression systems, scale-up

Due to the increasing use of insect cell based expression systems in research and industrial recombinant protein production, the development of efficient and reproducible production processes remains a challenging task. In this context, the application of online monitoring techniques is intended to ensure high and reproducible product qualities already during the early phases of process development. In the following chapter, the most common transient and stable insect cell based expression systems are briefly introduced. Novel applications of insect cell based expression systems for the production of insect derived antimicrobial peptides/proteins (AMPs) are discussed using the example of G. mellonella derived gloverin. Suitable in situ sensor techniques for insect cell culture monitoring in disposable and common bioreactor systems are outlined with respect to optical and capacitive sensor concepts. Since scale-up of production processes is one of the most critical steps in process development, a conclusive overview is given about scale up aspects for industrial insect cell culture processes

Towards Application-Aware Quantum Circuit Compilation

Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To this end, several steps are necessary: The underlying problem must be encoded in a quantum circuit, a suitable device must be selected to execute it, and it must be compiled accordingly. This compilation step has a significant influence on the quality of the resulting solution. However, current state-of-the-art compilation tools treat the quantum circuit as a sequence of instructions without considering the actual application it realizes -- wasting a yet untapped potential to increase the solution quality. In this work, a different approach is explored that explicitly incorporates the application considered and aims to optimize its solution quality during compilation. Initial results show the benefits of this approach: For an industry-inspired application of a quantum generative model, the proposed approach outperformed Qiskit's most-optimized compilation scheme and led to better solution quality. Therefore, this work presents a first step towards application-aware compilation.Comment: 8 pages, 3 figures, minor changes, to be published at IEEE International Conference on Quantum Software (QSW), 202

Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK

Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to scale quantum machine learning (QML) to industrial-level applications. This paper investigates the scalability and noise resilience of quantum generative learning applications. We consider the training performance in the presence of statistical noise due to finite-shot noise statistics and quantum noise due to decoherence to analyze the scalability of QML methods. We employ rigorous benchmarking techniques to track progress and identify challenges in scaling QML algorithms, and show how characterization of QML systems can be accelerated, simplified, and made reproducible when the QUARK framework is used. We show that QGANs are not as affected by the curse of dimensionality as QCBMs and to which extent QCBMs are resilient to noise

Extrafloral nectaries in Leguminosae: phylogenetic distribution, morphological diversity and evolution

Extrafloral nectaries (EFNs) mediating ecologically important ant-plant protection mutualisms are especially common and unusually diverse in the Leguminosae. We present the first comprehensively curated list of legume genera with EFNs, detailing and illustrating their systematic and phylogenetic distributions, locations on the plant, morphology and anatomy, based on a unified classification of EFN categories and a time-calibrated phylogeny incorporating 710 of the 768 genera. This new synthesis, the first since McKey (1989)?s seminal paper, increases the number of genera with EFNs to 152 (20% of legumes), distributed across subfamilies Cercidoideae (1), Detarioideae (19), Caesalpinioideae (87) and Papilionoideae (45). EFNs occur at nine locations, and are most prevalent on vegetative plant parts, especially leaves (74%) and inflorescence axes (26%). Four main categories (with eight subcategories) are recognized: formless, trichomatic (exposed, hollow), parenchymatic (embedded, pit, flat, elevated) and abscission zone EFNs (non-differentiated, swollen scars). Phylogenetic reconstruction of EFNs suggests independent evolutionary trajectories of different EFN types, with elevated EFNs restricted almost exclusively to Caesalpinioideae (where they underwent spectacular morphological disparification), flat EFNs in Detarioideae, swollen scar EFNs in Papilionoideae, and Cercidoideae is the only subfamily bearing intrastipular EFNs. We discuss the complex evolutionary history of EFNs and highlight future research directions.Fil: Marazzi, Brigitte. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina. Natural History Museum Of Canton Ticino; SuizaFil: González, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; ArgentinaFil: Delgado Salinas, Alfonso. Universidad Nacional Autónoma de México; MéxicoFil: Luckow, Melissa A.. Cornell University; Estados UnidosFil: Ringelberg, Jens J.. Universitat Zurich; SuizaFil: Hughes, Colin E.. Universitat Zurich; Suiz

A performance characterization of quantum generative models

Quantum generative modeling is a growing area of interest for industry-relevant applications. With the field still in its infancy, there are many competing techniques. This work is an attempt to systematically compare a broad range of these techniques to guide quantum computing practitioners when deciding which models and techniques to use in their applications. We compare fundamentally different architectural ansatzes of parametric quantum circuits used for quantum generative modeling: 1. A continuous architecture, which produces continuous-valued data samples, and 2. a discrete architecture, which samples on a discrete grid. We compare the performance of different data transformations: normalization by the min-max transform or by the probability integral transform. We learn the underlying probability distribution of the data sets via two popular training methods: 1. quantum circuit Born machines (QCBM), and 2. quantum generative adversarial networks (QGAN). We study their performance and trade-offs as the number of model parameters increases, with the baseline of similarly trained classical neural networks. The study is performed on six low-dimensional synthetic and two real financial data sets. Our two key findings are that: 1. For all data sets, our quantum models require similar or fewer parameters than their classical counterparts. In the extreme case, the quantum models require two of orders of magnitude less parameters. 2. We empirically find that a variant of the discrete architecture, which learns the copula of the probability distribution, outperforms all other methods

Stratified Abstraction of Access Control Policies

The shift to cloud-based APIs has made application security critically depend on understanding and reasoning about policies that regulate access to cloud resources. We present stratified predicate abstraction, a new approach that summarizes complex security policies into a compact set of positive and declarative statements that precisely state who has access to a resource. We have implemented stratified abstraction and deployed it as the engine powering AWS’s IAM Access Analyzer service, and hence, demonstrate how formal methods and SMT can be used for security policy explanation