ethicalabs/Research-Intent-Collab huggingface dataset:
Several Studies of Weakly Supervised Learning in Text Classification

Text classification is one of the most fundamental tasks in Natural Language Processing. How to effectually utilize the unlabeled dataset in text classification and apply weakly supervised learning methods to further improve the performance based on the existing labeled dataset, especially for supervision-starved tasks (hard to obtain high-quality labeled data), is challenging. In this PhD thesis, we show several studies of weakly supervised learning methods in text classification. We first focus on improving the accuracy and interpretability in text classification tasks using weakly supervised learning methods with the help of unlabeled dataset. More specifically, we proposed several new methods to further improve the accuracy and interpretability on both of two main research directions in weakly supervised learning methods: learning with noisy labels and semi-supervised learning. For learning with noisy labels, we proposed two weakly supervised learning aided methods on the special supervision-starved text classification task: Research Replication Prediction. For semi-supervised learning, we presented a new weakly interpretable model to improve the interpretability on the long text classification tasks. We also proposed a new ensemble method to assign better pseudo or noisy labels to the samples in the unlabeled dataset for semi-supervise learning methods. Furthermore, we conducted the research on fairness on weakly supervised learning. More specifically, we reveal the disparate impacts in different sub-populations (e.g., race and gender) when applying the semi-supervised learning methods. Finally, we also contribute a weakly supervised learning benchmark (Research Replication Prediction) to the community.
Intertemporal Pricing and Price Discrimination: A Semiparametric Hedonic Analysis of the Personal Computer Market

We apply a smooth coefficient semiparametric model to a unique high frequency data set to examine the intertemporal pricing of personal computers. Furthermore, we test (a) whether firms charge differential component prices for their top performance personal computers and (b) whether premium firms charge both a premium for all their computers and a premium for their top performance ones. We find nonlinear effects in the pricing of personal components. We also find that firms in general do not charge differential prices for the components of their top performance computers. In addition, high quality firms charge higher premia only for their most advanced products
ROME STATUTE OF INTERNATIONAL CRIMINAL COURT AND LEGAL STATUS OF THE RUSSIAN FEDERATION

Introduction. This article examines the legal consequences attached to the Russia’s notification of its intention not to become a party to the ICC. The authors point out the confusing language employed by Russian Ministry for Foreign Affairs in its attendant declarations and highlight the necessity to strictly stick to legal terminology especially in documents and speeches intended as a message to foreign States. These materials express the official position of the Russian Federation and need to be unequivocal and not subject to interpretation. Otherwise, a State taking liberty with terminology in its official declarations takes a risk of assuming in the eyes of international community obligations that it did not intend to assume. The authors’ remarks may be used in the future interactions with the ICC and generally in similar situations.Materials and methods. The article uses treaties, declarations of States and doctrinal research on the law of treaties. The authors rely on various methods to reach their conclusions, among which treaty interpretation is most frequently used.Research results. In the article the authors show that on 16 November 2016, Russia disclosed its intention not to become a party to the 1998 Rome Statute of the International Criminal Court. The Ministry for Foreign Affairs stressed that although fighting against impunity, including collectively through international tribunals, is of the utmost importance to Russia, in its view, the ICC has not lived up to the expectations held at the Rome diplomatic conference. The Ministry pointed out the disproportionality between the finances spent by the Court in fourteen years of work and only four verdicts rendered. It also indicated understanding the decision of several States-parties to initiate proceedings to withdraw from the Statute.Discussion and conclusions. In this article the authors draw attention to potential adverse effect of such declarations and notifications on the legal status of the Russian Federation. The article concludes that no substantial changes have affected the status of the Russian Federation as a result of its decision not to become a party to the Rome Statute. Russian officials simply have had recourse to common language to express a legal concept thereby creating a confusion and distorting the reality.
Agricultural Adaptation to Climate Change in Morocco: Between Vulnerability and Resilience.

Abstract  This paper investigates the trajectory of agricultural revenues in Morocco between 1995 and 2021, with the aim of elucidating the intricate relationships among vulnerability and resilience to climate change and agricultural economic performance. Agricultural revenues are analysed in relation to two main sets of independent variables: vulnerability and resilience. The assessment of resilience is grounded in a range of indicators, encompassing economic, social, and governance aspects. Conversely, vulnerability is examined through key dimensions such as the sensitivity of health, food security, ecosystems, human habitation, water resources, and infrastructure. The methodological approach employs an ARDL (Autoregressive Distributed Lag) and vector error correction model (VECM) to capture the long-term interactions and dynamics among these variables. The results reveal that the effects of vulnerability are significantly more pronounced than those of resilience. The agricultural sector is more susceptible to vulnerability, with a relatively lower capacity for resilience. The insights derived from this research are expected to contribute to a deeper understanding of the factors affecting agricultural revenues in Morocco. Furthermore, the findings hold potential for informing policy recommendations aimed at enhancing the resilience of the agricultural sector within the unique context of the country.  Keywords: Climate change; Vulnerability; Resilience; Agricultural revenues; Morocco
Galaxies: From Kinematics to Dynamics

These lectures are intended to provide an introduction to the rich interplay between N-body simulations and stellar-kinematic observations of galaxies. The first section describes the kinematic properties of galaxies that are accessible to observation, and gives a brief introduction to the stellar dynamics that one might wish to infer from such observations. In the next section, the use of N-body simulations in the study of galaxy dynamics is placed in its historical context. The next two sections provide case studies of the stellar-dynamical properties of elliptical and disk galaxies where N-body simulations have been used to explain kinematic data, to test new analysis techniques, and to motivate further observations. The final section contains some speculation as to how the symbiotic relation between observations and N-body simulations may develop in future studies of galaxy dynamics.
Supplementary Table S1 from Blood-Based Genomic Alteration Signature for Predicting Progression-Free Survival in &lt;i&gt;De Novo&lt;/i&gt; Metastatic Hormone-Sensitive Prostate Cancer: A Real-World Study

&lt;p&gt;Analysis for b.TRPC as an Independent Prognostic Factor&lt;/p&gt;
Current trends in the application of causal inference methods to pooled longitudinal observational infectious disease studies—A protocol for a methodological systematic review

Introduction Pooling (or combining) and analysing observational, longitudinal data at the individual level facilitates inference through increased sample sizes, allowing for joint estimation of study- and individual-level exposure variables, and better enabling the assessment of rare exposures and diseases. Empirical studies leveraging such methods when randomization is unethical or impractical have grown in the health sciences in recent years. The adoption of so-called “causal” methods to account for both/either measured and/or unmeasured confounders is an important addition to the methodological toolkit for understanding the distribution, progression, and consequences of infectious diseases (IDs) and interventions on IDs. In the face of the Covid-19 pandemic and in the absence of systematic randomization of exposures or interventions, the value of these methods is even more apparent. Yet to our knowledge, no studies have assessed how causal methods involving pooling individual-level, observational, longitudinal data are being applied in ID-related research. In this systematic review, we assess how these methods are used and reported in ID-related research over the last 10 years. Findings will facilitate evaluation of trends of causal methods for ID research and lead to concrete recommendations for how to apply these methods where gaps in methodological rigor are identified.   Methods and analysis We will apply MeSH and text terms to identify relevant studies from EBSCO (Academic Search Complete, Business Source Premier, CINAHL, EconLit with Full Text, PsychINFO), EMBASE, PubMed, and Web of Science. Eligible studies are those that apply causal methods to account for confounding when assessing the effects of an intervention or exposure on an ID-related outcome using pooled, individual-level data from 2 or more longitudinal, observational studies. Titles, abstracts, and full-text articles, will be independently screened by two reviewers using Covidence software. Discrepancies will be resolved by a third reviewer. This systematic review protocol has been registered with PROSPERO (CRD42020204104).
A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.
The Right to Sanitation: Time to Delink from the Right to Water

Within the human rights arena, water and sanitation are very often presented as linked together. This article examines the historical roots of this linkage as well as its manifestation at both the international and domestic level in countries that have formally recognized a right to water and sanitation. The analysis leads to a conclusion that a continuation of the linkage is not historically warranted, nor does it offer clear advantages for realization of a right to water or a right to sanitation.
Preventing Misappropriation of Trade Secrets in Chinese Workplace

Protecting trade secrets from employee’s misappropriation is a crucial but significantly challenging task for many companies in China. Chinese legal regulations, especially the labour laws, have provided enterprises with a set of legal tools to prevent workplace misappropriation of trade secrets. However, this article suggests that those legal instruments would not be effective unless the appropriate prevention mechanisms have been implemented, and therefore, it is rather crucial to introduce a number of information management of “good practice” recommendations.
Community-Aware Graph Signal Processing

The emerging field of graph signal processing (GSP) allows to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role to study graph properties and measure graph signal smoothness. Here instead, we propose the graph modularity matrix as the centerpiece of GSP, in order to incorporate knowledge about graph community structure when processing signals on the graph, but without the need for community detection. We study this approach in several generic settings such as filtering, optimal sampling and reconstruction, surrogate data generation, and denoising. Feasibility is illustrated by a small-scale example and a transportation network dataset, as well as one application in human neuroimaging where community-aware GSP reveals relationships between behavior and brain features that are not shown by Laplacian-based GSP. This work demonstrates how concepts from network science can lead to new meaningful operations on graph signals.
The Role of the United Nations in Conflict Resolution and Peace-building in Timor-Leste

This chapter shares views on the contribution made by the United Nations Mission of Support in East Timor (UNMISET) towards conflict resolution and peace-building in Timor-Leste as East Timor is now officially called. Since the end of the Cold War, both the concept and practice of United Nations (UN) peacekeeping operations have changed substantially. UN peacekeeping is a form of conflict resolution as its activities are based on third-party intervention. During the year following the establishment of UNMISET, the military component continued to provide support for the external security and territorial integrity of Timor-Leste, while ensuring timely transfer of security responsibilities to the national defence force. In Timor-Leste, United Nations Development Programme (UNDP), the UN system organs and development partners have been committed to supporting the Government's efforts. The chapter concludes that the United Nations has completed one of its most comprehensive and successful peacekeeping and peace-building missions in Timor-Leste.  Keywords: conflict resolution; peace-building mission; peacekeeping mission; Timor-Leste; United Nations Development Programme (UNDP)
Supplementary Table S5 from Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

&lt;p&gt;Supplementary Table S5.&lt;/p&gt;
Optimizing serverless computing: A comparative analysis of multi-output regression models for predictive function invocations

En el dominio en rápida evolución de la computación sin servidor, la necesidad de métodos predictivos eficientes y precisos de invocación de funciones se vuelve primordial. Este estudio presenta un conjunto completo de innovaciones para mejorar la previsibilidad y la eficiencia de la invocación de funciones dentro de las arquitecturas sin servidor. Al emplear modelos de regresión de múltiples salidas, realizamos un análisis multinivel de los patrones de invocación de funciones en los niveles de usuario, aplicación y función, revelando información sobre los comportamientos granulares de la carga de trabajo. Investigamos rigurosamente el impacto de las técnicas de ventanas y la reducción de la dimensionalidad en el rendimiento del modelo a través del Análisis de Componentes Principales (PCA), ofreciendo una comprensión matizada de las complejidades de los datos y las implicaciones computacionales. Nuestro novedoso marco de análisis comparativo evalúa meticulosamente el rendimiento de estos métodos frente a varias configuraciones de ventanas, utilizando el conjunto de datos de Azure Functions para la aplicabilidad en el mundo real. Además, valoramos la estabilidad temporal de los modelos y la variación del desempeño diario, proporcionando una visión holística de su viabilidad operativa. Nuestras contribuciones abordan las brechas críticas en el modelado predictivo de la computación sin servidor y establecen un nuevo punto de referencia para la eficiencia operativa y la toma de decisiones basada en datos en entornos de nube. Este estudio está preparado para guiar los futuros avances en la informática sin servidor, impulsando innovaciones teóricamente sólidas y prácticamente viables.
Generative modelling with jump-diffusions

Score-based diffusion models generate samples from an unknown target distribution using a time-reversed diffusion process. While such models represent state-of-the-art approaches in industrial applications such as artificial image generation, it has recently been noted that their performance can be further improved by considering injection noise with heavy tailed characteristics. Here, I present a generalization of generative diffusion processes to a wide class of non-Gaussian noise processes. I consider forward processes driven by standard Gaussian noise with super-imposed Poisson jumps representing a finite activity Levy process. The generative process is shown to be governed by a generalized score function that depends on the jump amplitude distribution and can be estimated by minimizing a simple MSE loss as in conventional Gaussian models. Both probability flow ODE and SDE formulations are derived using basic technical effort. A detailed implementation for a pure jump process with Laplace distributed amplitudes yields a generalized score function in closed analytical form and is shown to outperform the equivalent Gaussian model in specific parameter regimes.
Digital Platform Regulation: Global Perspectives on Internet Governance, Terry Flew and Fiona R. Martin (eds) (2022)

Review of: Digital Platform Regulation: Global Perspectives on Internet Governance, Terry Flew and Fiona R. Martin (eds) (2022)                Cham: Springer International Publishing, 319 pp.,                ISBN 978-3-03095-220-4, e-book, USD 49.99
The role of genomic and transcriptomic profiling in predicting survival and diagnosing non-invasive and invasive urinary bladder cancer

Urinary bladder cancer (UBC) is a prevalent malignancy worldwide, exhibiting high recurrence rates and significant morbidity and mortality. Traditional diagnostic and prognostic methods often fall short in providing the precision required for effective patient stratification and personalized treatment. Genomic and transcriptomic studies have revolutionized our understanding of UBC by unveiling molecular alterations that drive tumor initiation, progression, and therapeutic response. This systematic review explores the role and application of genomic and transcriptomic analyses in the diagnostics and survival prediction of non-invasive and invasive UBC. We conducted a comprehensive literature search in MEDLINE, Web of Science, and Scopus up to October 2023, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search yielded 1,256 records (412 in MEDLINE, 378 in Web of Science, and 466 in Scopus), and 356 duplicates were removed. Our findings highlight key mechanisms of action, including mutations in FGFR3, TP53, and RB1 genes, and alterations in pathways such as PI3K/AKT/mTOR and MAPK/ERK, which are pivotal in UBC pathogenesis. Recent research advances, including liquid biopsies and single-cell sequencing, offer promising non-invasive diagnostic tools and deeper insights into tumor heterogeneity. This review underscores the critical importance of integrating genomic and transcriptomic data into clinical practice to improve diagnostics, prognostic assessments, and personalized treatment strategies for UBC patients. Future research should focus on integrating multi-omics data and validating molecular biomarkers in large clinical trials to further enhance patient outcomes.
Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’

AbstractThis paper critically explores the research and development of ‘digital phenotyping’, which broadly refers to the idea that digital data can measure and predict people’s mental health as well as their potential risk for mental ill health. Despite increasing research and efforts to digitally track and predict ill mental health, there has been little sociological and critical engagement with this field. This paper aims to fill this gap by introducing digital phenotyping to the social sciences. We explore the origins of digital phenotyping, the concept of the digital phenotype and its potential for benefit, linking these to broader developments within the field of ‘mental health sensing’. We then critically discuss the technology, offering three critiques. First, that there may be assumptions of normality and bias present in the use of algorithms; second, we critique the idea that digital data can act as a proxy for social life; and third that the often biological language employed in this field risks reifying mental health problems. Our aim is not to discredit the scientific work in this area, but rather to call for scientists to remain reflexive in their work, and for more social science engagement in this area.
Automated Rare Event Simulation for Fault Tree Analysis via Minimal Cut Sets

Monte Carlo simulation is a common technique to estimate dependability metrics for fault trees. A bottleneck in this technique is the number of samples needed, especially when the interesting events are rare and occur with low probability. Rare Event Simulation (RES) reduces the number of samples when analysing rare events. Importance splitting is a RES method that spawns more simulation runs from promising system states. How promising a state is, is indicated by an importance function, which concentrates the information that makes this method efficient. Importance functions are given by domain and RES experts. This hinders re-utilisation and involves decisions entailing potential human error. Focusing in (general) fault trees, in this paper we automatically derive importance functions based on the tree structure. For this we exploit a common fault tree concept, namely cut sets: the more elements from a cut set have failed, the higher the importance. We show that the cut-set-derived importance function is an easy-to-implement and simple concept, that can nonetheless compete against another (more involved) automatic importance function for RES.