Representation of Jews and Anti-Jewish Bias in 19th-Century French Public Discourse: Distant and Close Reading

We explore through the lens of distant reading the evolution of discourse on Jews in France during the XIX century. We analyze a large textual corpus including heterogeneous sources-literary works, periodicals, songs, essays, historical narratives-to trace how Jews are associated to different semantic domains, and how such associations shift over time. Our analysis deals with three key aspects of such changes: the overall transformation of embedding spaces, the trajectories of word associations, and the comparative projection of different religious groups over different, historically relevant semantic dimensions or streams of discourse. This allows to show changes in the association between words and semantic domains (referring e.g. to economic and moral behaviors), the evolution of stereotypes, and the dynamics of bias over a long time span characterized by major historical transformations. We suggest that the analysis of large textual corpora can be fruitfully used in a dialogue with more traditional close reading approaches-by pointing to opportunities of in-depth analyses that mobilize more qualitative approaches and a detailed inspection of the sources that distant reading inevitably tends to aggregate. We offer a short example of such a dialogue between different approaches in our discussion of the Second Empire transformations, where we mobilize the historian's tools to start disentangling the complex interactions between changes in French society, the nature of sources, and representations of Jews. While our example is limited in scope, we foresee large potential payoffs in the cooperative interaction between distant and close reading

Open System Quantum Thermodynamics of Time Varying Graphs

In this article, we present a novel analysis of time-evolving networks, based on a thermodynamic representation of graph structure. We show how to characterize the evolution of time-varying complex networks by relating major structural changes to thermodynamic phase transitions. In particular, we derive expressions for a number of different thermodynamic quantities (specifically energy, entropy and temperature), which we use to describe the evolutionary behaviour of the network system over time. Since in the real world no system is truly closed and interactions with the environment are usually strong, we assume an open nature of the system. We adopt the Schrödinger picture as the dynamical representation of the quantum system over time. First, we compute the network entropy using a recent quantum mechanical representation of graph structure, connecting the graph Laplacian to a density operator. Then, we assume the system evolves according to the Schrödinger representation, but we allow for decoherence due to the interaction with the environment in a model akin to Environment-Induced Decoherence. We simplify the model by separating its dynamics into (a) an unknown time-dependent unitary evolution plus (b) an observation/interaction process, and this is the sole cause of the changes in the eigenvalues of the density matrix of the system. This allows us to obtain a measure of energy exchange with the environment through the estimation of the hidden time-varying Hamiltonian responsible for the unitary part of the evolution. Using the thermodynamic relationship between changes in energy, entropy, pressure and volume, we recover the thermodynamic temperature. We assess the utility of the method on real-world time-varying networks representing complex systems in the financial and biological domains. We also compare and contrast the different characterizations provided by the thermodynamic variables (energy, entropy, temperature and pressure). The study shows that the estimation of the time-varying energy operator strongly characterizes different states of a time-evolving system and successfully detects critical events occurring during network evolution

Un benchmark per il topic modeling sulle origini dell’antisemitismo moderno

The pace of digitized collective knowledge accumulation has become increasingly rapid in the last few years. That means we have tremendous amounts of information content to be organized, searched, and understood that can be arranged only by employing automatic methods. In the case of textual data analysis, topic modeling, a machine learning method, is definitely the most famous framework to uncover latent topics from text documents. Adopting topic modeling approaches for studying textual sources is a well-established practice in many scientific and humanities studies fields, including the historical research scope. In this paper, we present a benchmark corpus for topic models, a dataset containing an annotated real-world collection of texts focused on the antisemitism theme in 19th century France. The benchmark corpus has been developed to address a specific machine learning task but it can also support the enhancement of other natural language processing-based studies, in particular, those concerning the historical sphere.Negli ultimi anni il ritmo di accumulazione della conoscenza collettiva digitalizzata è divenuto sempre più rapido. Ciò significa che abbiamo enormi quantità di contenuto informativo da organizzare, ricercare e analizzare: una serie di compiti che possono essere svolti soltanto impiegando metodi automatici. Nel caso dell'analisi dei dati testuali, il topic modeling, un metodo di apprendimento automatico, è sicuramente la via più nota per cogliere gli argomenti latenti all’interno dei testi. L'adozione di approcci di topic modeling per lo studio delle fonti testuali è una pratica consolidata in molti campi di studi scientifici e umanistici, incluso quello della ricerca storica. In questo articolo presentiamo un benchmark per il topic modeling, un dataset contenente una collezione di testi annotati incentrati sul tema dell'antisemitismo nella Francia del XIX secolo. Il benchmark è stato sviluppato per affrontare un compito specifico di apprendimento automatico, ma può anche consentire il miglioramento di altri studi basati sull'elaborazione del linguaggio naturale, in particolare, quelli riguardanti l’ambito storico

Non-Parametric Spectral Model for Shape Retrieval

Non-rigid 3D shape retrieval is an active and important research topic in content based object retrieval. This problem is often cast in terms of the shapes intrinsic geometry due to its invariance to a wide range of non-rigid deformations. In this paper, we devise a novel generative model for shape retrieval based on the spectral representation of the Laplacian of a mesh. Contrary to common use, our approach avoids the ubiquitous correspondence problem by transforming the eigenvectors of the Laplacian to a density in the spectral-embedding space which is estimated non-parametrically. We show that this model can efficiently be learned from a set of 3D meshes. The experimental results on the SHREC'14 benchmark show the effectiveness of the approach compared to the state-of-the-art

A Non-parametric Spectral Model for Graph Classification

Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective spectral approach to graph learning. In particular, we define a novel model of structural representation based on the spectral decomposition of graph Laplacian of a set of graphs, but which make away with the need of one-to-one node-correspondences at the base of several previous approaches, and handles directly a set of other invariants of the representation which are often neglected. An experimental evaluation shows that the approach significantly improves over the state of the art.Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective spectral approach to graph learning. In particular, we define a novel model of structural representation based on the spectral decomposition of graph Laplacian of a set of graphs, but which make away with the need of one-to-one node-correspondences at the base of several previous approaches, and handles directly a set of other invariants of the representation which are often neglected. An experimental evaluation shows that the approach significantly improves over the state of the ar

On the von Neumann entropy of graphs

The von Neumann entropy of a graph is a spectral complexity measure that has recently found applications in complex networks analysis and pattern recognition. Two variants of the von Neumann entropy exist based on the graph Laplacian and normalized graph Laplacian, respectively. Due to its computational complexity, previous works have proposed to approximate the von Neumann entropy, effectively reducing it to the computation of simple node degree statistics. Unfortunately, a number of issues surrounding the von Neumann entropy remain unsolved to date, including the interpretation of this spectral measure in terms of structural patterns, understanding the relation between its two variants and evaluating the quality of the corresponding approximations. In this article, we aim to answer these questions by first analysing and comparing the quadratic approximations of the two variants and then performing an extensive set of experiments on both synthetic and real-world graphs. We find that (1) the two entropies lead to the emergence of similar structures, but with some significant differences; (2) the correlation between them ranges from weakly positive to strongly negative, depending on the topology of the underlying graph; (3) the quadratic approximations fail to capture the presence of non-trivial structural patterns that seem to influence the value of the exact entropies; and (4) the quality of the approximations, as well as which variant of the von Neumann entropy is better approximated, depends on the topology of the underlying graph

LDA2Net: Digging under the surface of COVID-19 topics in scientific literature

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically, both in terms of the number of publications and of its impact on people's life. This literature encompasses a varied set of sensible topics, ranging from vaccination, to protective equipment efficacy, to lockdown policy evaluation. Up to now, hundreds of thousands of papers have been uploaded on online repositories and published in scientific journals. As a result, the development of digital methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis to investigate topics under their surface. Specifically, LDA2Net exploits the frequencies of pairs of consecutive words to reconstruct the network structure of topics discussed in the Cord-19 corpus. The results suggest that the effectiveness of topic models can be magnified by enriching them with word network representations, and by using the latter to display, analyse, and explore COVID-related topics at different levels of granularity

Quantum Thermodynamics of Time Evolving Networks

In this paper, we present a novel thermodynamic framework for graphs that can be used to analyze time evolving networks, relating the thermodynamics variables to macroscopic changes in network topology, and linking major structural transition to phase changes in the thermodynamic picture. We start from a recent quantum-mechanical characterization of the structure of a network relating the graph Laplacian to a density operator and resulting in a characterization of the network's entropy. Then we adopt a Schrodinger picture of the dynamics of the network, resulting in an estimation of a hidden time-varying Hamiltonian from the data, from which we derive a measure of Energy exchange. From these variables, using the thermodynamic identity, we obtain temperature under the assumption of constant volume of the system. Evaluation of real-world data shows that the thermodynamic variables thus extracted are effective in detecting critical events occurring during network evolution

Degradation of Pyrethrin Residues on Stored Durum Wheat after Postharvest Treatment

In this paper, pyrethrin levels during a postharvest treatment on stored durum wheat were studied. Two experiments were carried out at single and double the dose recommended by the manufacturer. In all trials, the initial deposition of pyrethrins levels was below the fixed maximum residue level of 3 mg/kg. The fate of pyrethrins in the two experiments was similar, and the total content of pyrethrins remained unchanged for 22 days with a complete dissipation in 8 months. In the single dose experiment, half-life times of pyrethrins I and II were 46 and 72 days, while for the double dose, pyrethrins I and II were 41 and 53 days, respectively

Can a Quantum Walk Tell Which Is Which?A Study of Quantum Walk-Based Graph Similarity

We consider the problem of measuring the similarity between two graphs using continuous-time quantum walks and comparing their time-evolution by means of the quantum Jensen-Shannon divergence. Contrary to previous works that focused solely on undirected graphs, here we consider the case of both directed and undirected graphs. We also consider the use of alternative Hamiltonians as well as the possibility of integrating additional node-level topological information into the proposed framework. We set up a graph classification task and we provide empirical evidence that: (1) our similarity measure can effectively incorporate the edge directionality information, leading to a significant improvement in classification accuracy; (2) the choice of the quantum walk Hamiltonian does not have a significant effect on the classification accuracy; (3) the addition of node-level topological information improves the classification accuracy in some but not all cases. We also theoretically prove that under certain constraints, the proposed similarity measure is positive definite and thus a valid kernel measure. Finally, we describe a fully quantum procedure to compute the kernel