Gauge fixing the Standard Model Effective Field Theory

We gauge fix the Standard Model Effective Field Theory in a manner invariant under background field gauge transformations using a geometric description of the field connections.Comment: 4 pages. Accepted in PR

Energy Disaggregation Using Elastic Matching Algorithms

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio

Explosive Percolation: Unusual Transitions of a Simple Model

In this paper we review the recent advances on explosive percolation, a very sharp phase transition first observed by Achlioptas et al. (Science, 2009). There a simple model was proposed, which changed slightly the classical percolation process so that the emergence of the spanning cluster is delayed. This slight modification turns out to have a great impact on the percolation phase transition. The resulting transition is so sharp that it was termed explosive, and it was at first considered to be discontinuous. This surprising fact stimulated considerable interest in "Achlioptas processes". Later work, however, showed that the transition is continuous (at least for Achlioptas processes on Erdos networks), but with very unusual finite size scaling. We present a review of the field, indicate open "problems" and propose directions for future research.Comment: 27 pages, 4 figures, Review pape

Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments

© 2020 World Scientific Publishing Company. Electronic version of an article published as International Journal on Artificial Intelligence Tools, Vol. 29, No. 02, 2040004 (2020): https://doi.org/10.1142/S0218213020400047.Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.Peer reviewe

A Comparative Performance Evaluation of Algorithms for the Analysis and Recognition of Emotional Content

Sentiment Analysis is highly valuable in Natural Language Processing (NLP) across domains, processing and evaluating sentiment in text for emotional understanding. This technology has diverse applications, including social media monitoring, brand management, market research, and customer feedback analysis. Sentiment Analysis identifies positive, negative, or neutral sentiments, providing insights into decision-making, customer experiences, and business strategies. With advanced machine learning models like Transformers, Sentiment Analysis achieves remarkable progress in sentiment classification. These models capture nuances, context, and variations for more accurate results. In the digital age, Sentiment Analysis is indispensable for businesses, organizations, and researchers, offering deep insights into opinions, sentiments, and trends. It impacts customer service, reputation management, brand perception, market research, and social impact analysis. In the following experimental research, we will examine the Zero-Shot technique on pre-trained Transformers and observe that, depending on the Model we use, we can achieve up to 83% in terms of the model’s ability to distinguish between classes in this Sentiment Analysis problem

Accountable Storage

We introduce Accountable Storage, a framework allowing a client with small local space to outsource n file blocks to an untrusted server and be able (at any point in time after outsourcing) to provably compute how many bits have been discarded by the server. Such protocols offer ``provable storage insurance to a client: In case of a data loss, the client can be compensated with a dollar amount proportional to the damage that has occurred, forcing the server to be more ``accountable for his behavior. The insurance can be captured in the SLA between the client and the server. Although applying existing techniques (e.g., proof-of-storage protocols) could address the problem, the related costs of such approaches are prohibitive. Instead, our protocols can provably compute the damage that has occurred through an efficient recovery process of the lost or corrupted file blocks, which requires only sublinear $O(\delta\log n)$ communication, computation and local space, where $\delta$ is the maximum number of corrupted file blocks that can be tolerated. Our technique is based on an extension of invertible Bloom filters, a data structure used to quickly compute the distance between two sets. Finally, we show how our protocol can be integrated with Bitcoin, to support automatic compensations proportional to the number of corrupted bits at the server. We also build and evaluate our protocols showing that they perform well in practice

Cerebrospinal fluid HIV-1 escape in patients with neurocognitive symptoms: pooled data from a neuro-HIV platform and the NAMACO study.

BACKGROUND Despite modern antiretroviral therapy, HIV-1 RNA escape into the cerebrospinal fluid (CSF) may occur. We examined the prevalence of and factors associated with CSF HIV-1 escape among people living with HIV (PLWH) in Switzerland. SETTING The Neurocognitive Assessment in the Metabolic and Aging Cohort (NAMACO) study is an ongoing, prospective, longitudinal, multicenter study within the Swiss HIV Cohort Study. The neuro-HIV platform is a multi-disciplinary, single-day outpatient consultation at Lausanne University Hospital. METHODS We pooled data from the NAMACO study and the neuro-HIV platform participants who underwent lumbar puncture (LP) between 2011 and 2019. Both patient groups had neurocognitive symptoms. CSF HIV-1 escape was defined as the presence of quantifiable CSF HIV-1 RNA when plasma HIV-1 RNA was suppressed or CSF HIV-1 RNA greater than plasma HIV-1 RNA when the latter was detectable. RESULTS Of 1166 PLWH assessed, 288 underwent LP. CSF HIV-1 escape was observed in 25 PLWH (8.7%) of whom 19 (76%) had supressed plasma HIV-1 RNA. Characteristics of PLWH were comparable whether they had CSF HIV-1 escape or not, including comorbidities, time since HIV diagnosis (15 vs 16 years, p=0.9), median CD4 nadir (158.5/mm3 vs 171/mm3, p=0.6), antiretroviral CSF-Penetration-Effectiveness score (7 vs 7 points, p=0.8), neurocognitive diagnosis based on Frascati criteria and radiological findings. CONCLUSIONS In this large pooled sample of PLWH with neurocognitive symptoms, CSF HIV-1 escape occurred in 8.7% of PLWH. PLWH with CSF HIV-1 escape presented no distinctive clinical or paraclinical characteristics. We conclude that LP is unavoidable in confirming CSF HIV-1 escape

Performance and Safety of EUS Ablation Techniques for Pancreatic Cystic Lesions: A Systematic Review and Meta-Analysis

Background: Pancreatic cystic lesions (PCL) represent an increasingly diagnosed condition with significant burden to patients’ lives and medical resources. Endoscopic ultrasound (EUS) ablation techniques have been utilized to treat focal pancreatic lesions. This systematic review with meta-analysis aims to assess the efficacy of EUS ablation on PCL in terms of complete or partial response and safety. Methods: A systematic search in Medline, Cochrane and Scopus databases was performed in April 2023 for studies assessing the performance of the various EUS ablation techniques. The primary outcome was complete cyst resolution, defined as cyst disappearance in follow-up imaging. Secondary outcomes included partial resolution (reduction in PCL size), and adverse events rate. A subgroup analysis was planned to evaluate the impact of the available ablation techniques (ethanol, ethanol/paclitaxel, radiofrequency ablation (RFA), and lauromacrogol) on the results. Meta-analyses using a random effects model were conducted and the results were reported as percentages with 95% confidence intervals (95%CI). Results: Fifteen studies (840 patients) were eligible for analysis. Complete cyst resolution after EUS ablation was achieved in 44% of cases (95%CI: 31–57; 352/767; I2 = 93.7%), and the respective partial response rate was 30% (95%CI: 20–39; 206/767; I2 = 86.1%). Adverse events were recorded in 14% (95%CI: 8–20; 164/840; I2 = 87.2%) of cases, rated as mild in 10% (95%CI: 5–15; 128/840; I2 = 86.7%), and severe in 4% (95%CI: 3–5; 36/840; I2 = 0%). The subgroup analysis for the primary outcome revealed rates of 70% (95%CI: 64–76; I2 = 42.3%) for ethanol/paclitaxel, 44% (95%CI: 33–54; I2= 0%) for lauromacrogol, 32% (95%CI: 27–36; I2 = 88.4%) for ethanol, and 13% (95%CI: 4–22; I2 = 95.8%) for RFA. Considering adverse events, the ethanol-based subgroup rated the highest percentage (16%; 95%CI: 13–20; I2 = 91.0%). Conclusion: EUS ablation of pancreatic cysts provides acceptable rates of complete resolution and a low incidence of severe adverse events, with chemoablative agents yielding higher performance rates