Cryptocurrency Turmoil: Unraveling the Collapse of a Unified Stablecoin (USTC) through Twitter as a Passive Sensor

This study sought to explore whether Twitter, as a passive sensor, could have foreseen the collapse of the Unified Stablecoin (USTC). In May 2022, in just a few days, the cryptocurrency went to near-zero valuation. Analyzing 244,312 tweets from 89,449 distinct accounts between April and June 2022, this study delved into the correlation between personal sentiments in tweets and the USTC market value, revealing a moderate correlation with polarity. While sentiment analysis has often been used to predict market prices, the results suggest the challenge of foreseeing sudden catastrophic events like the USTC collapse solely through sentiment analysis. The analysis uncovered unexpected global interest and noted positive sentiments during the collapse. Additionally, it identified events such as the launch of the new Terra blockchain (referred to as “Terra 2.0”) that triggered positive surges. Leveraging machine learning clustering techniques, this study also identified distinct user behaviors, providing valuable insights into influential figures in the cryptocurrency space. This comprehensive analysis marks an initial step toward understanding sudden and catastrophic phenomena in the cryptocurrency market

Informing Clients through Multimedia Communications: An approach to provide interactivity

One of the key problems in informing clients through multimedia streaming applications over the Internet is to customize the stream of information according to the client’s requests. This is achievable only if client and server can interact along the application lifetime, which is possible only if the communication system supports the rigid timing constraints imposed by these interactive applications on their traffic. In the Internet scenario, these applications are very difficult to support, as the Internet provides a best-effort service to the traffic it carries, which means that the Internet does not make any promises about the end-to-end delay for an individual packet and about the variation of packet delay (network jitter) within a packet stream. These problems are confirmed by several experiments we performed over the Internet, which highlight that interactive applications achieve a quality that is frustrating. The contribution of this paper is the proposal of a novel mechanism to support interactive multimedia streaming applications over the Internet. Our mechanism adapts the multimedia stream transmission to the network conditions, by intentionally and slightly acting on the video QoS. Our mechanism has been validated through severalexperiments performed over the Internet. Results confirm that the supported interactive applications achieve a satisfactory quality and the user perceives a video quality only slightly affected by the QoS modification introduced by our mechanism

A branch-and-price algorithm for the temporal bin packing problem

We study an extension of the classical Bin Packing Problem, where each item consumes the bin capacity during a given time window that depends on the item itself. The problem asks for finding the minimum number of bins to pack all the items while respecting the bin capacity at any time instant. A polynomial-size formulation, an exponential-size formulation, and a number of lower and upper bounds are studied. A branch-and-price algorithm for solving the exponential-size formulation is introduced. An overall algorithm combining the different methods is then proposed and tested through extensive computational experiments

On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

What happened on social media during the recent pandemic? Who was the opinion leader of the conversations? Who influenced whom? Were they medical doctors, ordinary people, scientific experts? Did health institutions play an important role in informing and updating citizens? Identifying opinion leaders within social platforms is of particular importance and, in this paper, we introduce the idea of a time sensitive interaction graph to identify opinion leaders within Twitter conversations. To evaluate our proposal, we focused on all the tweets posted on Twitter in the period 2020-21 and we considered just the ones that were Italian-written and were related to COVID-19. After mapping these tweets into the graph, we applied the PageRank algorithm to extract the opinion leaders of these conversations. Results show that our approach is effective in identifying opinion leaders and therefore it might be used to monitor the role that specific accounts (i.e., health authorities, politicians, city administrators) have within specific conversations

Identification via numerical computation of transcriptional determinants of a cell phenotype decision making

Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers

accuracy of cultural heritage 3d models by rpas and terrestrial photogrammetry

The combined use of high-resolution digital images taken from ground as well as from RPAS (Remotely Piloted Aircraft Systems) have significantly increased the potential of close range digital photogrammetry applications in Cultural Heritage surveying and modeling. It is in fact possible, thanks to SfM (Structure from Motion), to simultaneously process great numbers of aerial and terrestrial images for the production of a dense point cloud of an object. In order to analyze the accuracy of results, we started numerous tests based on the comparison between 3D digital models of a monumental complex realized by the integration of aerial and terrestrial photogrammetry and an accurate TLS (Terrestrial Laser Scanner) reference model of the same object. A lot of digital images of a renaissance castle, assumed as test site, have been taken both by ground level and by RPAS at different distances and flight altitudes and with different flight patterns. As first step of the experimentation, the images were previously processed with Agisoft PhotoScan, one of the most popular photogrammetric software. The comparison between the photogrammetric DSM of the monument and a TLS reference one was carried out by evaluating the average deviation between the points belonging to the two entities, both globally and locally, on individual façades and architectural elements (sections and particular). In this paper the results of the first test are presented. A good agreement between photogrammetric and TLS digital models of the castle is pointed out

CliSAT: A new exact algorithm for hard maximum clique problems

Given a graph, the maximum clique problem (MCP) asks for determining a complete subgraph with the largest possible number of vertices. We propose a new exact algorithm, called CliSAT , to solve the MCP to proven optimality. This problem is of fundamental importance in graph theory and combinatorial optimization due to its practical relevance for a wide range of applications. The newly developed exact approach is a combinatorial branch-and-bound algorithm that exploits the state-of-the-art branching scheme enhanced by two new bounding techniques with the goal of reducing the branching tree. The first one is based on graph colouring procedures and partial maximum satisfiability problems arising in the branching scheme. The second one is a filtering phase based on constraint programming and domain propagation techniques. CliSAT is designed for structured MCP instances which are computationally difficult to solve since they are dense and contain many interconnected large cliques. Extensive experiments on hard benchmark instances, as well as new hard instances arising from different applications, show that CliSAT outperforms the state-of-the-art MCP algorithms, in some cases by several orders of magnitude

VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering

In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video Summaries generated by VSCAN are compared with summaries generated by other approaches found in the literature and those created by users. Experimental results indicate that the video summaries generated by VSCAN have a higher quality than those generated by other approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1401.3590 by other authors without attributio

Endomembrane reorganization induced by heavy metals

Plant cells maintain plasmatic concentrations of essential heavy metal ions, such as iron, zinc, and copper, within the optimal functional range. To do so, several molecular mechanisms have to be committed to maintain concentrations of non-essential heavy metals and metalloids, such as cadmium, mercury and arsenic below their toxicity threshold levels. Compartmentalization is central to heavy metals homeostasis and secretory compartments, finely interconnected by traffic mechanisms, are determinant. Endomembrane reorganization can have unexpected effects on heavy metals tolerance altering in a complex way membrane permeability, storage, and detoxification ability beyond gene\u2019s expression regulation. The full understanding of endomembrane role is propaedeutic to the comprehension of translocation and hyper-accumulation mechanisms and their applicative employment. It is evident that further studies on dynamic localization of these and many more proteins may significantly contribute to the understanding of heavy metals tolerance mechanisms. The aim of this review is to provide an overview about the endomembrane alterations involved in heavy metals compartmentalization and tolerance in plants