Hidden scaling patterns and universality in written communication

The temporal statistics exhibited by written correspondence appear to be media dependent, with features which have so far proven difficult to characterize. We explain the origin of these difficulties by disentangling the role of spontaneous activity from decision-based prioritizing processes in human dynamics, clocking all waiting times through each agent's `proper time' measured by activity. This unveils the same fundamental patterns in written communication across all media (letters, email, sms), with response times displaying truncated power-law behavior and average exponents near -3/2. When standard time is used, the response time probabilities are theoretically predicted to exhibit a bi-modal character, which is empirically borne out by our new years-long data on email. These novel perspectives on the temporal dynamics of human correspondence should aid in the analysis of interaction phenomena in general, including resource management, optimal pricing and routing, information sharing, emergency handling.Comment: 27 pages, 10 figure

Automatic learning of textual entailments with cross-pair similarities

In this paper we define a novel similarity measure between examples of textual entailments and we use it as a kernel function in Support Vector Machines (SVMs). This allows us to automatically learn the rewrite rules that describe a non trivial set of entailment cases. The experiments with the data sets of the RTE 2005 challenge show an improvement of 4.4% over the state-of-the-art methods

Fast and effective kernels for relational learning from texts

In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such models by optimizing the dynamic programming algorithm of the kernel evaluation. Experiments with Support Vector Machines and the above kernels show the effectiveness and efficiency of our approach on two very important natural language tasks, Textual Entailment Recognition and Question Answering

A Machine learning approach to textual entailment recognition

Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so

Data augmentation using background replacement for automated sorting of littered waste

The introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, which remains unsolved, is how to deal with the large amount of waste that is littered in the environment instead of being collected properly. In this paper, we introduce BackRep: a method for building waste recognizers that can be used for identifying and sorting littered waste directly where it is found. BackRep consists of a data-augmentation procedure, which expands existing datasets by cropping solid waste in images taken on a uniform (white) background and superimposing it on more realistic backgrounds. For our purpose, realistic backgrounds are those representing places where solid waste is usually littered. To experiment with our data-augmentation procedure, we produced a new dataset in realistic settings. We observed that waste recognizers trained on augmented data actually outperform those trained on existing datasets. Hence, our data-augmentation procedure seems a viable approach to support the development of waste recognizers for urban and wild environments

Intorno al viaggio musicale di Andrea Zanzotto

Starting from Viaggio musicale, a small book curated by Paolo Cattelan in dialogue with Andrea Zanzotto on the theme of music and poetry, the article follows the path of formation of the poet to music: the nursery rhymes of childhood, Toti Dal Monte and the Opera arias sung by the villagers, the theatre of puppets and music by Margot Galante Garrone, Federico Fellini and Nino Rota, the musicians who met his poetry and set it to music

Driving-induced crossover: from classical criticality to self-organized criticality

We propose a spin model with quenched disorder which exhibits in slow driving two drastically different types of critical nonequilibrium steady states. One of them corresponds to classical criticality requiring fine-tuning of the disorder. The other is a self-organized criticality which is insensitive to disorder. The crossover between the two types of criticality is determined by the mode of driving. As one moves from "soft" to "hard" driving the universality class of the critical point changes from a classical order-disorder to a quenched Edwards-Wilkinson universality class. The model is viewed as prototypical for a broad class of physical phenomena ranging from magnetism to earthquakes.Comment: 4 pages, 4 figure

Reconstructing promoter activity from Lux bioluminescent reporters

The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from Photorhabdus luminescens --- the source of modern Lux reporters --- while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology

The immunopeptidome from a genomic perspective:Establishing the noncanonical landscape of MHC class I–associated peptides

G.B., D.B., K.W., A.P., R.F., T.R.H., S.K., and J.A.A. received support from Fundacja na rzecz Nauki Polskiej (FNP) (grant ID: MAB/3/2017). D.R.G. received support from Genome Canada & Genome BC (grant ID: 264PRO). D.J.H. received support from NuCana plc (grant ID: SMD0-ZIUN05). H.A. received support from Swedish Cancer Foundation (grant ID: 211709). H.G. received support from United Kingdom Research and Innovation (UKRI) (grant ID: EP/S02431X/1). C.P. received support from Fundação para a Ciência e a Tecnologia (FCT) through LASIGE Research Unit (grant ID: UIDB/00408/2020 and UIDP/00408/2020). A.L. F.M.Z., C.P., A.R., A.P., and J.A.A. received support from European Union’s Horizon 2020 research and innovation programme (grant ID: 101017453). C.B. received support from Agence Nationale de la Recherche (ANR) through GRAL LabEX (grant ID: ANR-10-LABX-49-01) and CBH-EUR-GS 32 (grant ID: ANR-17-EURE0003). S.N.S. received support from Cancer Research UK (CRUK) and the Chief Scientist's Office of Scotland (CSO): Experimental Cancer Medicine Centre (ECMC) (grant ID: ECMCQQR-2022/100017). A.L. received support from Chief Scientist's Office of Scotland (CSO) NRS Career Researcher Fellowship. R.O.N. received support from CRUK Cambridge Centre Thoracic Cancer Programme (grant ID: CTRQQR-2021\100012).Tumor antigens can emerge through multiple mechanisms, including translation of non-coding genomic regions. This non-canonical category of antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry to enable the discovery of non-canonical MHC-associated peptides (ncMAPs) from non-coding regions. Considering that the emergence of tumor antigens can also involve post-translational modifications, we included an open search component in our pipeline. Leveraging the wealth of mass spectrometry-based immunopeptidomics, we analyzed 26 MHC class I immunopeptidomic studies of 9 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant post-translational modifications, using spectral matching and controlled their false discovery rate (FDR) to 1%. Interestingly, the non-canonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 54.85%. Here, we reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targeting agents for T-cell therapies or vaccine development.Publisher PDFPeer reviewe