ethicalabs/Research-Intent-Collab huggingface dataset:
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning

We present Transformation Invariance and Covariance Contrast (TiCo) for self-supervised visual representation learning. Similar to other recent self-supervised learning methods, our method is based on maximizing the agreement among embeddings of different distorted versions of the same image, which pushes the encoder to produce transformation invariant representations. To avoid the trivial solution where the encoder generates constant vectors, we regularize the covariance matrix of the embeddings from different images by penalizing low rank solutions. By jointly minimizing the transformation invariance loss and covariance contrast loss, we get an encoder that is able to produce useful representations for downstream tasks. We analyze our method and show that it can be viewed as a variant of MoCo with an implicit memory bank of unlimited size at no extra memory cost. This makes our method perform better than alternative methods when using small batch sizes. TiCo can also be seen as a modification of Barlow Twins. By connecting the contrastive and redundancy-reduction methods together, TiCo gives us new insights into how joint embedding methods work.
Management of autoscaling serverless functions in edge computing via Q-Learning

Serverless computing is a recently introduced deployment model to provide cloud services. The autoscaling of function instances allows adapting allocated resources to workload, so as to reduce latency and improve resource usage efficiency. However, autoscaling mechanisms could be affected by undesired ‘cold starts’ events, causing latency peaks due to spawning of new instances, which can be critical in edge deployments where applications are typically sensitive to latency. In order to regulate autoscaling of functions and mitigate the latency for accessing services, which may hinder the adoption of the serverless model in edge computing, we resort to the usage of reinforcement learning. Our experimental system is based on OpenFaaS, the most popular open-source Kubernetes-based serverless platform. In this system, we introduce a Q-Learning (QL) agent to dynamically configure the Kubernetes Horizontal Pod Autoscaler (HPA). This is accomplished via a QL model state space and a reward function definition that enforce service level agreement (SLA) compliance, in terms of latency, without allocating excessive resources. The agent is trained and tested using real serverless function invocation patterns, made available by Microsoft Azure. The experimental results show the benefits provided by the proposed solution over state-of-the-art in terms of compliance to the SLA, while limiting resource consumption and service request losses.
From Error Correction to Engineered Noise: A Bio-Inspired Path to Scalable Quantum Computing

The von Neumann bottleneck and the fragility of qubits represent fundamental roadblocks to efficient and scalable computation.Confronting these twin crises requires more than incremental fixes; it demands a bio-inspired path forward that treats noise,dissipation and networked dynamics as design resources rather than enemies. Bio-inspired quantum computing adaptsprinciples from neuroscience and biology to quantum hardware and algorithms, embracing neuromorphic architectures in which qubits act as artificial neurons, learning paradigms from quantum reservoir and spiking networks, and design lessons from quantum biology that show how coherence and decoherence can be co-opted for function. Recent experimental progress across superconducting, semiconducting and photonic platforms has yielded prototypes of quantum neurons and tunablesynapses, and our comparative analysis of hardware and theory identifies a practical near-term trajectory—harness fixed high-dimensional dynamics with engineered couplers and classical readouts while developing autonomous, physics-embeddedlearning for the longer term. This convergence points beyond co-processors towards a new form of intelligent systems, capableof autonomous learning in real-world environments.
Life stressors and mental health: Depressive symptoms, anxiety, and suicidal ideation or intent during and after the COVID-19 pandemic

Objective                     This study aimed to investigate the associations between life stressors and depressive symptoms, anxiety, and suicidal ideation or intent, while also examining how COVID-19 pandemic influenced these associations.                                                           Methods                     This study used data from 19,950 participants in the Mental Health Research Canada cohort. Participants who were under the age of 20 or missing data for key mental health variables were excluded from the analyses. Primary outcomes included depressive symptoms, anxiety, and suicidal ideation or intent. Their associations with life stressors (concerns about catching COVID-19, economic downturn and inflation, job loss, challenges in paying household bills, and social factors) were analyzed using logistic regressions models.                                                           Results                     Participants experiencing negative impacts from life stressors had significantly higher odds of having depressive symptoms (aOR: 1.46; 95% CI: 1.28–1.66), moderate to severe anxiety (aOR: 1.33; 95% CI: 1.15–1.53) and developing suicidal ideation or intent (aOR: 1.54; 95% CI: 1.36–1.74) compared to those experiencing positive impacts. The odds were higher after COVID‑19 than during COVID‑19 for depressive symptoms (aOR = 1.62 vs 1.29; p = 0.08) and moderate to severe anxiety (aOR = 1.52 vs 1.12; p = 0.03) among participants with negative impacts from life stressors. Participants who reported negative impacts from certain stressors had higher odds of adverse mental health outcomes. Challenges in paying household bills were most strongly associated with depressive symptoms (aOR: 1.96; 95% CI: 1.62–2.36), moderate to severe anxiety (aOR: 1.79; 95% CI: 1.45–2.21), and suicidal ideation or intent (aOR: 1.52; 95% CI: 1.28–1.82).                                                           Conclusions                     This study highlights the significant impact of life stressors on mental health, with challenges related to paying household bills being a key factor linked to depressive symptoms, anxiety, and suicidal ideation or intent. The COVID-19 pandemic exacerbated these stressors, emphasizing the need for targeted mental health interventions. Findings are subject to limitations of cross-sectional, self-reported data and unvalidated suicidality measures.
Equilibrium statistical mechanics for kinetic phase transitions

On considere des modeles cinetiques pour des processus irreversibles qui presentent des etats stationnaires non triviaux, et des transitions de phases entre ces etats. On etudie les etats stationnaires de ces systemes hors d'equilibre, en utilisant les methodes de la mecanique statistique a l'equilibre
The effects of diagenesis on the trace element and stable isotope geochemistry of non-marine ostracod valves

The trace-element and stable-isotope geochemistry of non-marine ostracod valves is a valuable tool in palaeolimnology. However, the potential effects of early diagenesis on geochemical composition are poorly documented. In this study, the effect of partial dissolution on the trace-element (Sr and Mg) and isotopic (18O/16O and 13C/12C) composition of late Quaternary non-marine ostracod valves was investigated. Both unaltered and extremely degraded valves of the species Cypretta brevisaepta recovered from the same core level had trace-element and stable-isotope ratios that were drawn from the same statistical population, suggesting that the geochemical effects of early diagenesis are minimal. However, since the possibility remains that diagenesis could under certain circumstances alter valve chemistry, only valves in pristine condition should be used in palaeoenvironmental studies whenever feasible.
A Semantic Clinical Decision Support System: conceptual architecture and implementation guidelines

Clinical Decision Support Systems (CDSS) are computer applications that focus on assisting medical decisions required during clinical tasks. Although CDSS have been extensively studied for more than 30 years, their use is not broadly extended yet in daily clinical practice. Identified challenges of CDSS include (i) automating decision support, (ii) clinical workflow integration, (iii) ability of the system to be maintained and extended, (iv) timely advice and (v) evaluation of decisions effects and costs. In this paper, we hypothesize that Knowledge Engineering techniques and semantic technologies could be applied to CDSS in order to overcome the current identified challenges. We present a generic architecture for a Semantic CDSS, which we call SCDSS, and implementation guidelines for the breast cancer domain. Our approach follows a cyclic-federated paradigm allowing the reutilization of knowledge gathered at every stage of the clinical cycle.
Where is the cosmic-ray modulation boundary of the heliosphere?

The intensity of Galactic cosmic rays in the heliosphere is modulated by solar activities. The outer boundary where the solar modulation begins has always been a subject matter of debate in the cosmic-ray and heliophysics community. Various experimental methods and theoretical model calculations have been used to determine the boundary. Although the heliopause was always suspected to be the boundary, it is only until very recently after Voyager 1 had crossed the heliopause did we confirm that the boundary is indeed the heliopause. In this paper, we use a model simulation and detailed Voyager observation of cosmic rays at the heliopause crossing to show that the modulation boundary, in fact, is a fraction of an AU beyond the heliopause. Such a conclusion requires a very low turbulence level of the interstellar magnetic field in the outer heliosheath. According to the quasi-linear theory, a low level of turbulence should result in a very large diffusion coefficient parallel to the magnetic field and a very small perpendicular diffusion coefficient. For the first time, we are confident that Voyager 1 has obtained the truly pristine local interstellar cosmic-ray spectra down to the energies below 1 MeV. The cosmic-ray intensity is rapidly filtered by a thin layer of the interstellar magnetic field immediately outside of the heliopause. Its filtration amount depends on the conditions of magnetic field turbulence on the both sides of the heliopause, thus making it solar-cycle dependent as well.
Dynamic Self-Organization Pathways in Complex Systems for the Emergence of Sub-Boundaries, Resilience, Connectivity, and Energy Efficiency

Self-organization is common in complex systems, especially those in a metastable state at critical tipping points. This article examines the concept of energy storage and sub-regions formation during self-organization pathways within a specific region of interest known as the control volume. Spontaneous self-organization typically occurs in response to events that trigger a need to respond to an imbalance in the rate of entropy production within the control volume. All entropy-producing processes generate anti-work, enabling the creation of storable power and new sub-boundary pat-terns. A new concept, called anti-work (and related anti-power), is introduced to explain the stability of sub-regions. Examples of triggers include exceeding a local warming rate, earthquakes, or uniquely shaped bird flight formations aimed at energy efficiency, or supercooling, which is the primary example studied in this article to illustrate self-organization. The main conclusion is that the product of temperature and entropy density rate production remains constant during an entropy-producing process. Self-organization leads to a new order and new sub-boundaries. The resilience of this new order is examined in the context of energy partitioning and sub-boundary formation rates. It is observed that self-organization often follows sigmoidal (S-curve) patterns when selecting process pathways. This feature enables the maximization of entropy generation rate and the corresponding availability anti-power. This seemingly universal mechanism for pathway selection in entropy-producing transformations is closely related to the main findings of this article, namely the maximization of the entropy generation rate coupled with energy storage, which helps establish connections across discrete events.
Rethinking Statehood in Palestine: Self-Determination and Decolonization Beyond Partition

The quest for an inclusive and independent state has been at the center of the Palestinian national struggle for a very long time. This book critically explores the meaning of Palestinian statehood and the challenges that face alternative models to it. Giving prominence to a young set of diverse Palestinian scholars, this groundbreaking book shows how notions of citizenship, sovereignty, and nationhood are being rethought within the broader context of decolonization. Bringing forth critical and multifaceted engagements with what modern Palestinian self-determination entails, Rethinking Statehood sets the terms of debate for the future of Palestine beyond partition.
Modified Approach for Robotic Retroauricular Thyroidectomy

Background. Recently, a single remote access retroauricular robotic technique has been described for thyroid lobectomy. We aimed to explore the feasibility and safety of modifying this novel approach using preclinical cadaver model followed by performing the same operation in a real patient. Methods. The modified retroauricular approach was performed by creating a working space between the 2 heads of the sternocleidomastoid muscle, instead of that anterior to muscle. This was performed to create a wider working space. Two operations were initially performed in human cadavers. Subsequently, robotic-assisted thyroid lobectomy was performed using this novel modified retroauricular approach. Results. Robotic-assisted hemithyroidectomy was performed successfully in 2 cadavers and subsequently in one patient using modified approach. The patient was discharged on the same day of surgery and had no complications. Conclusions. The modified retroauricular approach with creation of a working space between the 2 heads of sternocleidomastoid muscle is safe and feasible, and offers a wider working space for robotic thyroid surgery.
Humanitarian Intervention and Responsibility to Protect

Chapter 3 explains the evolution of the responsibility to protect (R2P) norm, as a distinct model but still contingent to the notion of humanitarian intervention. It also discusses how the R2P is articulated in separate discursive coalitions, which makes its meaning contested and unfixed. In this part, a literature review of both critical and pro-R2P is presented. The chapter also makes references to the discursive practices. Moreover, the chapter introduces how the term entered into the Turkish discourse.
Application Root Cause Analysis Technique in Investigating the Causes of a Fatal Sentinel Event: Case Report

Abstract Background & Aim: Medical errors are among the most challenging threats to health systems in all countries. Thus, it is essential to take actions to reduce the risk of sentinel events reoccurrence. Root cause analysis is one of the risk management models, used for retrospective analysis of the root cause or causes of errors and weaknesses in a system or its related processes systematically. This study aimed to analyze the root causes of a sentinel event led to death in one of the hospitals in Isfahan in 2015. Materials & Methods: This is a case report study that analyzes root causes of medical errors.  The study was consisted of seven steps including: determining an event that must be analyzed, organizing a team to run it, gathering relevant data, identifying problems, searching for the causes of the incident, providing solutions, implementing solutions, and assessing and writing research report that lasted for 9 months. Results: The results showed that the first reason was lack of policy and protocol, developed for how to triage patients from one service to other services in the hospital, which caused problems in managing and assuming the responsibility of the patient's administration. The second fundamental problem was the patient’s examination by different specialists regardless of the status and progression of the clinical symptoms of the patient that caused loss of key information in the process of the patient clinical symptoms. Conclusion: Due to the benefits of this technique in identifying the root causes of errors, it can be used to prevent similar errors, eliminate organization defects, correct processes in the organization, and improve patient safety.
Genetic pre-employment test: the sociological aspects

info:eu-repo/semantics/published
Towards a More Legitimate United Nations Security Council: Reform Initiatives and Lessons from the Syrian Conflict

This thesis explores the pressing need for reform within the United Nations Security Council, a key organization in global governance, in the form of a case study. The main purpose of the research is to understand how its reform could enhance its legitimacy while attending to the possible opposition from the USA, China, and Russia. It encompasses historical information, from the League of Nations to the reform proposals throughout history, culminating in the present debates, as well as useful theoretical approaches for the analysis. Through the Syrian Civil War, it describes the Council struggle to handle contemporary geopolitical issues. The insights of the Intergovernmental Negotiations Framework and the Elements Paper are crucial to research the current reform debates, and other literature from scholars, International Organizations and media are also employed. The analysis investigates the research question and hypothesis, and the conclusion helps summarize the findings. The thesis serves as a call for a better articulated Security Council that can better confront the complexities of our multipolar world with effectivity and effectiveness.
ENERGY ESTIMATION OF ULTRA-HIGH ENERGY COSMIC RAY HADRONS BY MONTE CARLO SIMULATIONS OF EXTENSIVE AIR SHOWERS

Ultra-High Energy Cosmic Rays (E ≥ 5 ×1019 eV ) are detected through Extensive Air Showers that are created when a primary cosmic ray particle interacts with the atmosphere of the Earth. The energy of the primary particle can be estimated experimentally based on simulations. In this paper we attempt to estimate the energy of UHECR hadrons ( He, Li, C, Fe ) by applying a Monte Carlo simulation code and we compare the results with the ones derived in our previous paper for proton initiated showers. The scenario of simulations is adapted to the P. Auger Observatory site.
DIGITAL CURRENCY OF THE CENTRAL BANK OF SINGAPORE: FEATURES OF IMPLEMENTATION AND COMPARISON WITH THE DIGITAL RUBLE

The article discusses the features of the introduction of the digital currency of the central bank of Singapore as one of the leaders in the Asian region in the development of digital payment technologies. The main characteristics of the developed models for the introduction of the digital ruble and the Singapore dollar are compared. The main differences between the digital currencies of the analyzed countries are revealed: the different legal nature of the Central Bank’s digital currency, the opposite view of the monetary authorities on the level of confidentiality of transactions and openness of the payment infrastructure, different expectations of the monetary authorities from the introduction of the central bank’s digital currency. The main results of the development of financial mechanisms for the introduction of the digital Singapore dollar are determined.
A Software Defined approach to the Internet of Things: From Wireless Sensor Networks to Network Operating Systems

Di recente il mondo delle reti di telecomunicazioni è stato caratterizzato dall’emergere del paradigma di Networking Software Defined (SDN). Grazie a questo approccio è possibile fornire un’interfaccia standard per lo sviluppo di software in grado di gestire i dispositivi di networking e i flussi di traffico che li attraversano, disaccoppiando il management della rete dal forwarding dei dati. Questa soluzione, di cui il protocollo di comunicazione OpenFlow è tra i maggiori esponenti, ha riscosso un notevole successo nelle reti cablate permettendo di superare il vendor lock-in e permettendo la gestione di dispositivi eterogenei tramite un unico punto di accesso logicamente centralizzato. Un ulteriore ambito che potrebbe trarre notevole beneficio da un simile approccio è quello dell’Internet of Things (IoT), cioè l’insieme di quei dispositivi embedded di uso quotidiano interconnessi tramite Internet, di cui le reti wireless di sensori rappresentano un importante caso d’uso. Anche in questo ambiente, l’eterogeneità dei dispositivi e la necessità di soluzioni cucite attorno alla singola applicazione ha creato una moltitudine di protocolli e soluzioni che difficilmente riescono a cooperare, creando così una moltitudine di Intranet of Things invece di un unica Internet of Things. Di recente, sono state presentate alcune soluzioni per implementare un approccio Software Defined anche nelle reti di sensori wireless. Il lavoro presentato in questa dissertazione si colloca in questo quadro presentando un framework completo per la gestione di reti di sensori tramite un’interfaccia OpenFlow-like chiamata SDN-WISE e la sua integrazione all’interno del sistema operativo per networking ONOS. In questa dissertazione sono presentati i dettagli architetturali e le conclusioni maturate durante la progettazione della soluzione in questione.
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation

3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats. Triangular meshes are an efficient representation for irregular, non-uniform 3D objects. However, meshes are often challenging to annotate due to their high geometrical complexity. Specifically, creating segmentation masks for meshes is tedious and time-consuming. Therefore, it is desirable to train segmentation networks with limited-labeled data. Self-supervised learning (SSL), a form of unsupervised representation learning, is a growing alternative to fully-supervised learning which can decrease the burden of supervision for training. We propose SSL-MeshCNN, a self-supervised contrastive learning method for pre-training CNNs for mesh segmentation. We take inspiration from traditional contrastive learning frameworks to design a novel contrastive learning algorithm specifically for meshes. Our preliminary experiments show promising results in reducing the heavy labeled data requirement needed for mesh segmentation by at least 33%.