Call-By-Name Is Just Call-By-Value with Delimited Control

Delimited control operator shift0 exhibits versatile capabilities: it canexpress layered monadic effects, or equivalently, algebraic effects. Little didwe know it can express lambda calculus too! We present $\Lambda_\$$, acall-by-value lambda calculus extended with shift0 and control delimiter $\$$with carefully crafted reduction theory, such that the lambda calculus withbeta and eta reductions can be isomorphically embedded into $\Lambda_\$$ viaa right inverse of a continuation-passing style translation. While call-by-namereductions of lambda calculus can trivially simulate its call-by-value version,we show that addition of shift0 and $\$$ is the golden mean of expressivepower that suffices to simulate beta and eta reductions while still admitting asimulation back. As a corollary, calculi $\Lambda\mu_v$, $\lambda_\$$, $\Lambda_\$$ and $\lambda$ all correspond equationally

How to measure the topological quality of protein parse trees?

International audienceHuman readability and, consequently, interpretability is often considered a key advantage of grammatical descriptors. Beyond the natural language, this is also true in analyzing biological sequences of RNA, typically modeled by grammars of at least context-free level of expressiveness. However, in protein sequence analysis, the explanatory power of grammatical descriptors beyond regular has never been thoroughly assessed. Since the biological meaning of a protein molecule is directly related to its spatial structure, it is justified to expect that the parse tree of a protein sequence reflects the spatial structure of the protein. In this piece of research, we propose and assess quantitative measures for comparing topology of the parse tree of a context-free grammar with topology of the protein structure succinctly represented by a contact map. Our results are potentially interesting beyond its bioinformatic context wherever a reference matrix of dependencies between sequence constituents is available

Estimating probabilistic context-free grammars for proteins using contact map constraints

International audienceInteractions between amino acids that are close in the spatial structure, but not necessarily in the sequence, play important structural and functional roles in proteins. These non-local interactions ought to be taken into account when modeling collections of proteins. Yet the most popular representations of sets of related protein sequences remain the profile Hidden Markov Models. By modeling independently the distributions of the conserved columns from an underlying multiple sequence alignment of the proteins, these models are unable to capture dependencies between theprotein residues. Non-local interactions can be represented by using more expressive grammatical models. However, learning such grammars is difficult. In this work, we propose to use information on protein contacts to facilitate the training of probabilistic context-free grammars representing families of protein sequences. We develop the theory behind the introduction of contact constraints in maximum-likelihood and contrastive estimation schemes and implement it in a machine learning framework for protein grammars. The proposed framework is tested on samples of protein motifs in comparison with learning without contact constraints. The evaluation shows high fidelity of grammatical descriptors to protein structures and improved precision in recognizing sequences. Finally, we present an example of using our method in a practical setting and demonstrate its potential beyond the current state of the art by creating a grammatical model of a meta-family of protein motifs. We conclude that the current piece of research is a significant step towards more flexible and accurate modeling of collections of protein sequences. The software package is made available to thecommunity

Position of the Insolvency Department of the Allerhand Institute regarding relevant aspects of the draft bill – Insolvency Law Act and a few other Acts (print no. 3480)

Uchwalona nowelizacja Prawa upadłościowego zasadniczo wchodzi w życie 24 marca 2020 roku. Wyjątkiem jest przepis dotyczący rejestru zadłużonych, który wchodzi w życie 1 grudnia 2020 r. Polska była zobowiązana do wprowadzenia centralnego rejestru upadłości do 26 czerwca 2018 r., jednak do jesieni rejestr nie powstał. Ratio legis tej nowelizacji było uproszczenie upadłości konsumenckiej i dostosowanie regulacji pre-packu czyli przygotowanej likwidacji do prawa europejskiego i poprawienie pre-packu. Poniżej przedstawione zostało stanowisko Sekcji Prawa Upadłościowego Instytutu Allerhanda wobec projektu nowelizacji w zakresie prepacku. Zostało ono zgłoszone w toku procesu legislacyjnego i generalnie nie zostało uwzględnione. W przekonaniu autorów stanowiska dostosowanie do prawa europejskiego opiera się na metodycznym błędzie a nowelizacja zamiast zoptymalizować przygotowaną likwidację utrudni ją lub wręcz wyeliminuje.The Insolvency Department of the Allerhand Institute was involved as an expert institution in the work of the Polish Parliament relating to the amendment of insolvency law that will enter into force in March 2020. The Allerhand Institute criticized the implementation of Art. 23(1) of the Labour Code (protection of employees in case of a transfer of a company) to a pre-pack procedure. The Institute expects a strong limitation of the pre-pack after the amendment’s entry into force