ethicalabs/Research-Intent-Collab huggingface dataset:
Artificial intelligence in adolescents mental health disorder diagnosis, prognosis, and treatment

Social, psychological, and emotional wellbeing are all considered to be components of one's mental health. It affects how someone thinks, feels, and responds to circumstances. When one has good mental health, it is easier to perform efficiently and reach their full potential (1). Preschool, adolescence, and adulthood are all included in the definition of mental health. Anxiety, social phobia, depression, panic disorder, substance dependence, and specific illnesses are factors that contribute to mental health problems that result in mental illness. The mental health status of adolescents in India is a topic of great concern and importance. Adolescents are children within the age group of 10&ndash;19. According to the National Mental Health Survey of India (2015&ndash;2016) (2), the prevalence of psychiatric disorders among adolescents of ages 13&ndash;17 is 7.3% and in the US it is 27.9% (3). This problem is further aggravated by stigma and lack of awareness surrounding mental health and a treatment gap of 95% in common mental disorders which is greater than the treatment gap for severe disorders (76%).
Fault-Tolerance on Near-Term Quantum Computers and Subsystem Quantum Error Correcting Codes.

Large-scale fault-tolerant quantum computers are supposed to provide exponential speedup over many classical algorithms for solving realistic computationally intensive problems. Given that practical quantum computers are extremely sensitive to noise, error correction protocols have to be employed in order to suppress noises in the quantum system and maintain the fidelity of computations. As quantum computing experiments are progressing into the regime where active quantum error correction can soon be implemented, it is becoming more important to understand the properties of small quantum error correcting codes and how to efficiently implement them in actual experiments given realistic near-term quantum device specifications. In this dissertation, we present our work on suppressing errors in quantum computing systems in two directions: realizing fault-tolerance with several small quantum error-correcting codes under realistic device assumptions, and developing large-scale code families with subsystem quantum error correcting codes. For small codes such as the Bare [[7,1,3]] code, the Bacon-Shor code, and the rotated 17-qubit surface code, we show designs for implementing them in realistic ion trap quantum computers while minimizing the amount of resources needed and optimizing fidelity of the logical system. For families of subsystem quantum error correcting codes, we investigate the compass codes, Bravyi-Bacon-Shor codes and subsystem hypergraph product codes, and show that these codes can be advantageous in terms of handling biased noises, having good code parameters, and exhibiting efficient decoding performance. ; Ph.D.
Optimizing transportation systems and logistics network configurations: from biased-randomized algorithms to fuzzy simheuristics

El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cualquier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, independientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones alternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima —por ejemplo, una solución con el mínimo costo o la máxima utilidad— es un desafío realmente difícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumentan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos; sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convierten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. En primer lugar, se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver problemas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica.
Learning to Predict Gradients for Semi-Supervised Continual Learning

A key challenge for machine intelligence is to learn new visual concepts without forgetting the previously acquired knowledge. Continual learning is aimed towards addressing this challenge. However, there is a gap between existing supervised continual learning and human-like intelligence, where human is able to learn from both labeled and unlabeled data. How unlabeled data affects learning and catastrophic forgetting in the continual learning process remains unknown. To explore these issues, we formulate a new semi-supervised continual learning method, which can be generically applied to existing continual learning models. Specifically, a novel gradient learner learns from labeled data to predict gradients on unlabeled data. Hence, the unlabeled data could fit into the supervised continual learning method. Different from conventional semi-supervised settings, we do not hypothesize that the underlying classes, which are associated to the unlabeled data, are known to the learning process. In other words, the unlabeled data could be very distinct from the labeled data. We evaluate the proposed method on mainstream continual learning, adversarial continual learning, and semi-supervised learning tasks. The proposed method achieves state-of-the-art performance on classification accuracy and backward transfer in the continual learning setting while achieving desired performance on classification accuracy in the semi-supervised learning setting. This implies that the unlabeled images can enhance the generalizability of continual learning models on the predictive ability on unseen data and significantly alleviate catastrophic forgetting. The code is available at \url{https://github.com/luoyan407/grad_prediction.git}.
Integrated semiconductor optical amplifiers based photonic cross-connect for deep neural networks

We perform weight addition of four 10 Gbit/s channels employing a photonic integrated indium phosphide (InP) chip based on arrayed waveguide gratings (AWGs) and semiconductor optical amplifiers (SOAs) technology. Continuous weight tuning for positive and negative weight matrix numbers is demonstrated with a maximum reading error of 0.04. The utilized chip is already designed to perform cross-connectivity between two layers populated with 8 neurons each, for the complete integration of one input layer and one hidden layer for photonic multilayer perceptron neural network.
Function Offloading and Data Migration for Stateful Serverless Edge Computing

Serverless computing and, in particular, Function-as-a-Service (FaaS) have emerged as valuable paradigms to deploy applications without the burden of managing the computing infrastructure. While initially limited to the execution of stateless functions in the cloud, serverless computing is steadily evolving. The paradigm has been increasingly adopted at the edge of the network to support latency-sensitive services. Moreover, it is not limited to stateless applications, with functions often recurring to external data stores to exchange partial computation outcomes or to persist their internal state. To the best of our knowledge, several policies to schedule function instances to distributed hosts have been proposed, but they do not explicitly model the data dependency of functions and its impact on performance.In this paper, we study the allocation of functions and associated key-value state in geographically distributed environments. Our contribution is twofold. First, we design a heuristic for function offloading that satisfies performance requirements. Then, we formulate the state migration problem via Integer Linear Programming, taking into account the heterogeneity of data, its access patterns by functions, and the network resources. Extensive simulations demonstrate that our policies allow FaaS providers to effectively support stateful functions and also lead to improved response times.
Organizational Knowledge Management as A Mediator on the Relationship Between Headmaster’s Sustainable Leadership and Teacher Innovative Work Behavior at Pahang Primary School (TS25)

The main aim of this study was to test the effect of the Headmasters’ Sustainable Leadership (SL) practices, Organizational Knowledge Management (OKM), and Teachers’ Innovative Work Behaviour (TIWB). This study also tests the role of Organizational Knowledge Management practices as a mediator in the relationship between Headmasters’ Sustainable Leadership practices and Teachers’ Innovative Work Behaviour in the primary schools of Transformation School 2025 (TS25). The design of this study utilized a quantitative approach with the survey method involving a total of 318 teachers who were serving in 30 primary schools (TS25) cohorts 1, 2, and 3 in the state of Pahang. The data has been analysed by descriptive and inferential using SPSS-Amos software. The observed data met the requirements of the fit indices (RMSEA=0.06, CFI=0.94, TLI=0.93, Chisq=2.28). The findings showed that significant effect of SL practices on OKM practices (β=.69, p&lt;.05), SL practices on TIWB practices (β=.25, p&lt;.05), and OKM practices on TIWB practices (β=.63, p&lt;.05). Organizational Knowledge Management practices has been identified as a full mediator in the relationship between Headmasters’ Sustainable Leadership practices and Teachers’ Innovative Work Behaviour in (TS25) schools. As a conclusion, it has been identified that the role of headmasters is crucial in practicing knowledge that relevant to the organization to support the implementation of innovative work behaviour of teachers in schools. In terms of implications, the structural equation model that has been suggested shows that Headmasters’ Sustainable Leadership practices may affect Teachers’ Innovative Work Behaviour through the application of organizational knowledge management practices in schools. This provides an indicator that courses, training, and educational leadership modules for headmasters and teachers should be planned holistically to improve the quality as well as to transform the education system in Malaysia to achieve the goals of the Malaysia Education Blueprint 2013-2025.
Impact of Social Media on Adolescent Mental Health: A Comprehensive Study

In the past decade, the use of social media among teenagers has continued to gain tremendous momentum and increasingly grown to concern the potential impact on their mental well-being. Herein, we look into the positive and negative psychological outcomes associated with the use of social media by adolescents aged 13–18. Using a mixed-method approach, we view recent empirical data which emphasize the relationships between excessive social media exposure and symptoms of depression, anxiety, sleep disturbance, and body dissatisfaction. On the other side of things, we also look at studies demonstrating the use of social media for social connection, peer support, creativity, and relevant online coping strategies for vulnerable youths.  Moreover, about half of teens now say that social media detracts from their age group, and this is a big increase from before, whereas others continue to see social media as a way to promote creativity and connectedness. Emerging evidence of maladaptive use behaviours-again symptoms reminiscent of addiction and inability to regulate use-are also examined, these seems to have a higher occurrence among adolescents who carry pre-existent mental conditions. The study attempts to reconcile the two extant narratives by attempting to identify the types of moderating factors, such as type of content, usage patterns, and individual vulnerabilities, that separate positive outcomes from negative ones.  Through an exhaustive study, these results have proven that among parents, educators, policymakers, and platform designers attempting to nurture digital environments for adolescent mental health, more nuanced implications arise. Finally, it suggests interventional and longitudinal avenues that would better clarify cause-effect pathways and safety mechanisms.
Epigenetic Regulation of Chromatin during Mitosis in Embryos

Recent studies show that mitosis does not involve complete eradication of the past chromatin states but the memory of active and repressed chromatin is maintained to the next generation. This epigenetic regulation is critical for embryonic development and is also required for normal physiological functions. Further, impairment in this gene regulation can result in disease conditions such as cancer. Yet, the mechanisms by which regulatory components remain bound to the compacted mitotic chromosomes is not clearly understood. Here we review the process of gene bookmarking during mitosis in the developing embryo and its impact on the transcriptional dynamics during embryogenesis. The phenomenon of bookmarking is pivotal to normal embryonic development as it ensures that the transcriptional program is faithfully inherited to maintain cell fates. We review the mechanisms which control the accessibility of regulatory regions in the genome during cell division in the developing embryo. These include regulation by transcription factors, chromatin regulators and epigenetic histone marks such as histone tail modifications. For example, the Polycomb group of genes (PcG) regulate long term memory of the repressed chromatin state in the Drosophila embryo. We will also deliberate on the latest tools and techniques available to study the phenomenon of gene bookmarking such as ATAC seq and chromosome conformation capture technique. Finally, we will also discuss some of the unanswered questions and possible future studies in this field.
Modelling railway freight traffic using the methods of graph theory and combinatorial optimization

zbMATH Open Web Interface contents unavailable due to conflicting licenses.
The Lateral Trigger Probability function for the Ultra-High Energy Cosmic Ray showers detected by the Pierre Auger Observatory

22 pages, 8 figures. Published in Astroparticle Physics
Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets

17 pages, 3 figures
Nanocrystalline iron oxide based electroactive materials in lithium ion batteries: the critical role of crystallite size, morphology, and electrode heterostructure on battery relevant electrochemistry

The whole versus the sum of its parts; contributions of nanoscale iron-containing materials to the bulk electrochemistry of composite electrodes.
Analysis of the Ethical, Legal and Economic Domains of Corporate Social Responsibility: A Business Case

The tendency of organizations is to achieve positioning and legitimacy through strategies of corporate social responsibility (CSR). This article focuses on the analysis of CSR practices and it seeks, through a business case, to define a method to examine the benefits of its application in society and in organizations. From the proposal of Schwartz (2011) on the domains of corporate social responsibility, where the ethical, economic, and legal dimensions of philanthropic organizations converge, financial reports and social balances of a company, were studied, where we identified, financial, ethical, and tax variables which allowed us to establish the orientation and balance between social responsibility practices and corporate image.
Legal Interpretation Issues Involving Dispute Settlement Body in the World Trade Organization

The article attempts to explore the background of the establishment of the dispute settlement body under the World Trade Organization’s dispute settlement system, and analyze whether the existing legal interpretation power of the dispute settlement body is suspected of exceeding its authority through the legal interpretation disputes arising out of the DS379 case and the laws of relevant countries. Simultaneously, in analyzing the interpretations of the panel and the Appellate Body in the DS379 case report, possible solutions are proposed for the current legal interpretation issues of the dispute settlement body and the current status of the suspension of the Appellate Body. The Appellate Body has been shut down for several years until now, making it impossible to deal with the appeals and fulfil the mandate of the WTO in facilitating inter-member trade disputes. Trade turmoil now is darkening the world horizon, seeking new solutions within the structure of the past mechanisms is infinitely more possible than the restoration of the old ones. Through the analysis of the DS379 case, it is not only to explore whether the DSB is suspected of overstepping its authority, but also to seek how to leverage the advantages of the existing dispute resolution mechanism in the current context of global conflicts.
Automated Generation of High-Performance Computational Fluid Dynamics Codes

Domain-Specific Languages (DSLs) improve programmers productivity by decoupling problem descriptions from algorithmic implementations. However, DSLs for High-Performance Computing (HPC) have two additional critical requirements: performance and scalability. This paper presents the automated process of generating, from abstract mathematical specifications of Computational Fluid Dynamics (CFD) problems, optimised parallel codes that perform and scale as manually optimised ones. We consciously combine within Saiph, a DSL for solving CFD problems, low-level optimisations and parallelisation strategies, enabling high-performance single-core executions which effectively scale to multi-core and distributed environments. Our results demonstrate how high-level DSLs can offer competitive performance by transparently leveraging state-of-the-art HPC techniques.
KubeFold: Kubernetes-native automation of AlphaFold protein structure prediction workflows

KubeFold is a Kubernetes-native software platform for automating AlphaFold-based protein structure prediction workflows in cloud and cluster environments.The system introduces a declarative model for managing reference databases and prediction jobs through custom Kubernetes resources.It automates database preparation, workflow orchestration, storage integration, and artifact handling, reducing the operational complexity of large-scale prediction workloads. \emph{KubeFold} is intended for research institutions, bioinformatics centers, and enterprise environments that require scalable and reproducible execution of protein structure prediction pipelines.The software is intended to bridge the gap between state-of-the-art prediction methods and practical cloud-native deployment.
The role of gender-responsive participation in water and sanitation rights adjudication

This thesis critically examines the justifications, implications and possibilities for strengthening gender participatory considerations in the adjudication of the separate but critically interlinked rights to basic water and adequate sanitation services. The thesis first examines how the lack of women’s full, free and meaningful participation as key stakeholders both causes and contributes to gendered deprivation by examining developments and obligations at the international human rights law level. It then selects as its focus, the selected leading jurisdiction of South Africa and assesses three early principal jurisprudential examples to demonstrate the persistent gaps; Mazibuko and Others v City of Johannesburg (right to water), Nokotyana and Others v Ekurhuleni Municipality (right to sanitation) and Beja and Others v Premier of the Western Cape (right to sanitation). Employing the literature on principles of adjudication and the discourse on equality, gender and dignity, the conceptual framework of ‘gender-responsive participatory justice’ is then put forth as a flexible, analytical and strategic tool to mitigate the ad hoc gender-sensitive considerations that remain wholly inadequate to guaranteeing equality and non-discrimination commitments. In doing so, the inquiry in seeking to engender participation, emphasises the critical nexus between procedure and the substantive values and functions of these rights in the form of autonomy-based equality and agency-based dignity. In the final instance, this examination is operationalised by an applied analysis of how gender-responsive deliberative principles of participation and their accompanying values can be utilised and applied in adjudication to ultimately pursue proactive ways of guaranteeing that women’s participation becomes fully integrated in water and sanitation rights implementation in practice.
Tight-frame-like Analysis-Sparse Recovery Using Non-tight Sensing Matrices

The choice of the sensing matrix is crucial in compressed sensing. Random Gaussian sensing matrices satisfy the restricted isometry property, which is crucial for solving the sparse recovery problem using convex optimization techniques. However, tight-frame sensing matrices result in minimum mean-squared-error recovery given oracle knowledge of the support of the sparse vector. If the sensing matrix is not tight, could one achieve the recovery performance assured by a tight frame by suitably designing the recovery strategy? -- This is the key question addressed in this paper. We consider the analysis-sparse l1-minimization problem with a generalized l2-norm-based data-fidelity and show that it effectively corresponds to using a tight-frame sensing matrix. The new formulation offers improved performance bounds when the number of non-zeros is large. One could develop a tight-frame variant of a known sparse recovery algorithm using the proposed formalism. We solve the analysis-sparse recovery problem in an unconstrained setting using proximal methods. Within the tight-frame sensing framework, we rescale the gradients of the data-fidelity loss in the iterative updates to further improve the accuracy of analysis-sparse recovery. Experimental results show that the proposed algorithms offer superior analysis-sparse recovery performance. Proceeding further, we also develop deep-unfolded variants, with a convolutional neural network as the sparsifying operator. On the application front, we consider compressed sensing image recovery. Experimental results on Set11, BSD68, Urban100, and DIV2K datasets show that the proposed techniques outperform the state-of-the-art techniques, with performance measured in terms of peak signal-to-noise ratio and structural similarity index metric.