arXiv subjects

Ariel Rosenfeld

Publications and source records attributed to Ariel Rosenfeld.

At least 19 recordsLinked to original sources

arXiv2026

Bi-National Academic Funding and Collaboration Dynamics: The Case of the German-Israeli Foundation

Academic grant programs are widely used to motivate international research collaboration and boost scientific impact across borders. Among these, bi-national funding schemes -- pairing researchers from just two designated countries -- are common yet understudied compared with national and multinational funding. In this study, we explore whether bi-national programs are associated with new collaborations and lasting partnerships. To this end, we conducted an observational bibliometric case study of the German--Israeli Foundation (GIF), covering 642 grants, 2,386 researchers, and 52,847 publications. Our results show that GIF funding is associated with increased co-authorship behavior during, and even slightly before, the grant period, but it is rarely linked with long-lasting co-authorship patterns that persist once the funding concludes. By tracing co-authorship before, during, and after the funding period, clustering collaboration trajectories with temporally-aware K-means, and predicting cluster membership with ML models, we find that 45% of teams with no prior co-authored publications become active while funded, yet activity declines rapidly post-award; roughly one-third sustain longer-term co-authorship activity, whereas only a small subset sustains high, lasting co-authorship activity. Moreover, the team's composition and bibliometrics are found to be largely insufficient in predicting co-authorship activity during and after the grant.

cs.DL↗

arXiv2026

The Rising Dominance of Methods Across Science

Scientific progress is traditionally narrated through the interplay of theoretical insights and experimental findings. Yet this view of science underplays a third and central pillar of progress: the methods that underlie both conceptual advances and empirical evidence. By analysing more than 3 million articles across science published between 1980 and 2019, we find that science has undergone a fundamental structural transition. The share of papers that primarily contribute new methods-methods papers-has doubled across science over the past four decades, rising universally across disciplines and citation impact levels. Rather than a gradual evolution, this transition marks a pivotal shift beginning in the early 1990s, aligning with the computational revolution and the emergence of data-intensive science. The surge in methodological research is not confined to the most cited, elite publications; it spans the full spectrum of scientific output. These findings reveal a systemic reorientation of the scientific ecosystem where reusable methods increasingly serve as the essential infrastructure of scientific advances, challenging the traditional dichotomy of theory and experimental research. As science becomes increasingly methods-driven, our results call for rethinking how research is evaluated, funded and organised-towards better incentivising method innovations. This is especially the case as expanding AI must be effectively integrated with scientific instruments to realise its full potential.

cs.DL↗

arXiv2025

Whose LLM is it Anyway? Linguistic Comparison and LLM Attribution for GPT-3.5, GPT-4 and Bard

Large Language Models (LLMs) are capable of generating text that is similar to or surpasses human quality. However, it is unclear whether LLMs tend to exhibit distinctive linguistic styles akin to how human authors do. Through a comprehensive linguistic analysis, we compare the vocabulary, Part-Of-Speech (POS) distribution, dependency distribution, and sentiment of texts generated by three of the most popular LLMS today (GPT-3.5, GPT-4, and Bard) to diverse inputs. The results point to significant linguistic variations which, in turn, enable us to attribute a given text to its LLM origin with a favorable 88\% accuracy using a simple off-the-shelf classification model. Theoretical and practical implications of this intriguing finding are discussed.

cs.CL↗

arXiv2025

Authorship conflicts in academia: an international cross-discipline survey

Collaboration among scholars has emerged as a significant characteristic of contemporary science. As a result, the number of authors listed in publications continues to rise steadily. Unfortunately, determining the authors to be included in the byline and their respective order entails multiple difficulties which often lead to conflicts. Despite the large volume of literature about conflicts in academia, it remains unclear how exactly these are distributed over the main socio-demographic properties, as well as the different types of interactions academics experience. To address this gap, we conducted an international and cross-disciplinary survey answered by 752 academics from 41 fields of research and 93 countries that statistically well-represent the overall academic workforce. Our findings are concerning and suggest that conflicts over authorship credit arise very early in one's academic career, even at the level of Master and Ph.D., and become increasingly common over time.

cs.DL↗

arXiv2025

Publishing Instincts: An Exploration-Exploitation Framework for Studying Academic Publishing Behavior and "Home Venues"

Scholarly communication is vital to scientific advancement, enabling the exchange of ideas and knowledge. When selecting publication venues, scholars consider various factors, such as journal relevance, reputation, outreach, and editorial standards and practices. However, some of these factors are inconspicuous or inconsistent across venues and individual publications. This study proposes that scholars' decision-making process can be conceptualized and explored through the biologically inspired exploration-exploitation (EE) framework, which posits that scholars balance between familiar and under-explored publication venues. Building on the EE framework, we introduce a grounded definition for "Home Venues" (HVs) - an informal concept used to describe the set of venues where a scholar consistently publishes - and investigate their emergence and key characteristics. Our analysis reveals that the publication patterns of roughly three-quarters of computer science scholars align with the expectations of the EE framework. For these scholars, HVs typically emerge and stabilize after approximately 15-20 publications. Additionally, scholars with higher h-indexes, greater number of publications, or higher academic age tend to have higher-ranking journals as their HVs.

cs.DL↗

arXiv2025

Multi-Species Prey-Predator Dynamics During a Multi-Strain Pandemic

Small and large scale pandemics are a natural phenomenon repeatably appearing throughout history, causing ecological and biological shifts in ecosystems and a wide range of their habitats. These pandemics usually start with a single strain but shortly become multi-strain due to a mutation process of the pathogen causing the epidemic. In this study, we propose a novel eco-epidemiological model that captures multi-species prey-predator dynamics with a multi-strain pandemic. The proposed model extends and combines the Lotka-Volterra prey-predator model and the Susceptible-Infectious-Recovered epidemiological model. We investigate the ecosystem's sensitivity and stability during such a multi-strain pandemic through extensive simulation relying on both synthetic cases as well as two real-world configurations. Our results are aligned with known ecological and epidemiological findings, thus supporting the adequacy of the proposed model in realistically capturing the complex eco-epidemiological properties of the multi-species multi-strain pandemic dynamics.

q-bio.PE↗

arXiv2025

Detecting LLM-assisted writing in scientific communication: Are we there yet?

Large Language Models (LLMs), exemplified by ChatGPT, have significantly reshaped text generation, particularly in the realm of writing assistance. While ethical considerations underscore the importance of transparently acknowledging LLM use, especially in scientific communication, genuine acknowledgment remains infrequent. A potential avenue to encourage accurate acknowledging of LLM-assisted writing involves employing automated detectors. Our evaluation of four cutting-edge LLM-generated text detectors reveals their suboptimal performance compared to a simple ad-hoc detector designed to identify abrupt writing style changes around the time of LLM proliferation. We contend that the development of specialized detectors exclusively dedicated to LLM-assisted writing detection is necessary. Such detectors could play a crucial role in fostering more authentic recognition of LLM involvement in scientific communication, addressing the current challenges in acknowledgment practices.

cs.IR↗

arXiv2025

Advising Agent for Supporting Human-Multi-Drone Team Collaboration

Multi-drone systems have become transformative technologies across various industries, offering innovative applications. However, despite significant advancements, their autonomous capabilities remain inherently limited. As a result, human operators are often essential for supervising and controlling these systems, creating what is referred to as a human-multi-drone team. In realistic settings, human operators must make real-time decisions while addressing a variety of signals, such as drone statuses and sensor readings, and adapting to dynamic conditions and uncertainty. This complexity may lead to suboptimal operations, potentially compromising the overall effectiveness of the team. In critical contexts like Search And Rescue (SAR) missions, such inefficiencies can have costly consequences. This work introduces an advising agent designed to enhance collaboration in human-multi-drone teams, with a specific focus on SAR scenarios. The advising agent is designed to assist the human operator by suggesting contextual actions worth taking. To that end, the agent employs a novel computation technique that relies on a small set of human demonstrations to generate varying realistic human-like trajectories. These trajectories are then generalized using machine learning for fast and accurate predictions of the long-term effects of different advice. Through human evaluations, we demonstrate that our approach delivers high-quality assistance, resulting in significantly improved performance compared to baseline conditions.

cs.HC↗

arXiv2025

The Einstein Test: Towards a Practical Test of a Machine's Ability to Exhibit Superintelligence

Creative and disruptive insights (CDIs), such as the development of the theory of relativity, have punctuated human history, marking pivotal shifts in our intellectual trajectory. Recent advancements in artificial intelligence (AI) have sparked debates over whether state of the art models possess the capacity to generate CDIs. We argue that the ability to create CDIs should be regarded as a significant feature of machine superintelligence (SI).To this end, we propose a practical test to evaluate whether an approach to AI targeting SI can yield novel insights of this kind. We propose the Einstein test: given the data available prior to the emergence of a known CDI, can an AI independently reproduce that insight (or one that is formally equivalent)? By achieving such a milestone, a machine can be considered to at least match humanity's past top intellectual achievements, and therefore to have the potential to surpass them.

cs.AI↗

arXiv2024

Economical-Epidemiological Analysis of the Coffee Trees Rust Pandemic

Coffee leaf rust is a prevalent botanical disease that causes a worldwide reduction in coffee supply and its quality, leading to immense economic losses. While several pandemic intervention policies (PIPs) for tackling this rust pandemic are commercially available, they seem to provide only partial epidemiological relief for farmers. In this work, we develop a high-resolution economical-epidemiological model that captures the rust pandemic's spread in coffee tree farms and its associated economic impact. Through extensive simulations for the case of Colombia, a country that consists mostly of small-size coffee farms and is the second-largest coffee producer in the world, our results show that it is economically impractical to sustain any profit without directly tackling the rust pandemic. Furthermore, even in the hypothetical case where farmers perfectly know their farm's epidemiological state and the weather in advance, any rust pandemic-related efforts can only amount to a limited profit of roughly 4% on investment. In the more realistic case, any rust pandemic-related efforts are expected to result in economic losses, indicating that major disturbances in the coffee market are anticipated.

physics.soc-ph↗

arXiv2023

The Scientometrics and Reciprocality Underlying Co-Authorship Panels in Google Scholar Profiles

Online academic profiles are used by scholars to reflect a desired image to their online audience. In Google Scholar, scholars can select a subset of co-authors for presentation in a central location on their profile using a social feature called the Co-authroship panel. In this work, we examine whether scientometrics and reciprocality can explain the observed selections. To this end, we scrape and thoroughly analyze a novel set of 120,000 Google Scholar profiles, ranging across four disciplines and various academic institutions. Our results suggest that scholars tend to favor co-authors with higher scientometrics over others for inclusion in their co-authorship panels. Interestingly, as one's own scientometrics are higher, the tendency to include co-authors with high scientometrics is diminishing. Furthermore, we find that reciprocality is central to explaining scholars' selections.

cs.DL↗

arXiv2023

Mathematical Modeling of BCG-based Bladder Cancer Treatment Using Socio-Demographics

Cancer is one of the most widespread diseases around the world with millions of new patients each year. Bladder cancer is one of the most prevalent types of cancer affecting all individuals alike with no obvious prototypical patient. The current standard treatment for BC follows a routine weekly Bacillus Calmette-Guerin (BCG) immunotherapy-based therapy protocol which is applied to all patients alike. The clinical outcomes associated with BCG treatment vary significantly among patients due to the biological and clinical complexity of the interaction between the immune system, treatments, and cancer cells. In this study, we take advantage of the patient's socio-demographics to offer a personalized mathematical model that describes the clinical dynamics associated with BCG-based treatment. To this end, we adopt a well-established BCG treatment model and integrate a machine learning component to temporally adjust and reconfigure key parameters within the model thus promoting its personalization. Using real clinical data, we show that our personalized model favorably compares with the original one in predicting the number of cancer cells at the end of the treatment, with 14.8% improvement, on average.

cs.LG↗

arXiv2023

Optimizing SMS Reminder Campaigns for Pre- and Post-Diagnosis Cancer Check-Ups using Socio-Demographics: An In-Silco Investigation Into Bladder Cancer

Timely pre- and post-diagnosis check-ups are critical for cancer patients, across all cancer types, as these often lead to better outcomes. Several socio-demographic properties have been identified as strongly connected with both cancer's clinical dynamics and (indirectly) with different individual check-up behaviors. Unfortunately, existing check-up policies typically consider only the former association explicitly. In this work, we propose a novel framework, accompanied by a high-resolution computer simulation, to investigate and optimize socio-demographic-based SMS reminder campaigns for cancer check-ups. We instantiate our framework and simulation for the case of bladder cancer, the 10th most prevalent cancer today, using extensive real-world data. Our results indicate that optimizing an SMS reminder campaign based solely on simple socio-demographic features can bring about a statistically significant reduction in mortality rate compared to alternative campaigns by up to 5.8%.

stat.AP↗

arXiv2023

A Computational Model For Individual Scholars' Writing Style Dynamics

A manuscript's writing style is central in determining its readership, influence, and impact. Past research has shown that, in many cases, scholars present a unique writing style that is manifested in their manuscripts. In this work, we report a comprehensive investigation into how scholars' writing styles evolve throughout their careers focusing on their academic relations with their advisors and peers. Our results show that scholars' writing styles tend to stabilize early on in their careers -- roughly their 13th publication. Around the same time, scholars' departures from their advisors' writing styles seem to converge as well. Last, collaborations involving fewer scholars, scholars from the same gender, or from the same field of study seem to bring about greater change in their co-authors' writing styles with younger scholars being especially influenceable.

cs.DL↗

arXiv2023

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies

Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological substrates could be associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning has shown promise in predicting treatment response in MDD, but one limitation has been the lack of clinical interpretability of machine learning models. We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a neural network model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Model validity and clinical utility were measured based on area under the curve (AUC) and expected improvement in sample remission rate with model-guided treatment, respectively. Post-hoc analyses yielded clusters (subgroups) based on patient prototypes learned during training. Prototypes were evaluated for interpretability by assessing differences in feature distributions and treatment-specific outcomes. A 3-prototype model achieved an AUC of 0.66 and an expected absolute improvement in population remission rate compared to the sample remission rate. We identified three treatment-relevant patient clusters which were clinically interpretable. It is possible to produce novel treatment-relevant patient profiles using machine learning models; doing so may improve precision medicine for depression. Note: This model is not currently the subject of any active clinical trials and is not intended for clinical use.

cs.LG↗

arXiv2022

A logical set theory approach to journal subject classification analysis: intra-system irregularities and inter-system discrepancies in Web of Science and Scopus

Journal classification into subject categories is an important aspect in scholarly research evaluation as well as in bibliometric analysis. However, this classification is not standardized, resulting in several different journal subject classification systems. In this study, we adopt a logical set theory-based definition of irregularities within a given classification system and discrepancies between systems and investigate their prevalence in the two most widely used indexing services of Web of Science (WoS) and Scopus. In both systems, we identify unusually sized categories, high overlap and incohesiveness between categories. In addition, across the two systems, journals are systematically classified to a different number of categories and most categories in either system are not adequately represented in the other system. Our findings suggest that these irregularities and discrepancies are, in fact, non-anecdotal and thus cannot be easily disregarded. Consequently, potentially misguided and/or inconsistent outcomes may be encountered when relying on these subject classification systems.

cs.DL↗

arXiv2022

Should Young Computer Scientists Stop Collaborating with their Doctoral Advisors?

One of the first steps in an academic career, and perhaps the pillar thereof, is completing a PhD under the supervision of a doctoral advisor. While prior work has examined the advisor-advisee relationship and its potential effects on the prospective academic success of the advisee, very little is known on the possibly continued relationship after the advisee has graduated. We harnessed three genealogical and scientometric datasets to identify 3 distinct groups of computer scientists: Highly independent, who cease collaborating with their advisors (almost) instantly upon graduation; Moderately independent, who (quickly) reduce the collaboration rate over ~5 years; and Weakly independent who continue collaborating with their advisors for at least 10 years post-graduation. We find that highly independent researchers are more academically successful than their peers in terms of H-index, i10-index and total number of citations throughout their careers. Moderately independent researchers perform, on average, better than weakly independent researchers, yet the differences are not found to be statistically significant. In addition, both highly and moderately independent researchers are found to have longer academic careers. Interestingly, weakly independent researchers tend to be supervised by more academically successful advisors.

cs.CY↗

arXiv2022

Proportional Ranking in Primary Elections: A Case Study

Many democratic political parties hold primary elections, which nicely reflects their democratic nature and promote, among other things, the democratic value of inclusiveness. However, the methods currently used for holding such primary elections may not be the most suitable, especially if some form of proportional ranking is desired. In this paper, we compare different algorithmic methods for holding primaries (i.e., different aggregation methods for voters' ballots), by evaluating the degree of proportional ranking that is achieved by each of them using real-world data. In particular, we compare six different algorithms by analyzing real-world data from a recent primary election conducted by the Israeli Democratit party. Technically, we analyze unique voter data and evaluate the proportionality achieved by means of cluster analysis, aiming at pinpointing the representation that is granted to different voter groups under each of the algorithmic methods considered. Our finding suggest that, contrary to the most-prominent primaries algorithm used (i.e., Approval), other methods such as Sequential Proportional Approval or Phragmen can bring about better proportional ranking and thus may be better suited for primary elections in practice.

cs.MA↗