ethicalabs/Research-Intent-Collab huggingface dataset:
Consistency is the key to further mitigating catastrophic forgetting in continual learning

Deep neural networks struggle to continually learn multiple sequential tasks due to catastrophic forgetting of previously learned tasks. Rehearsal-based methods which explicitly store previous task samples in the buffer and interleave them with the current task samples have proven to be the most effective in mitigating forgetting. However, Experience Replay (ER) does not perform well under low-buffer regimes and longer task sequences as its performance is commensurate with the buffer size. Consistency in predictions of soft-targets can assist ER in preserving information pertaining to previous tasks better as soft-targets capture the rich similarity structure of the data. Therefore, we examine the role of consistency regularization in ER framework under various continual learning scenarios. We also propose to cast consistency regularization as a self-supervised pretext task thereby enabling the use of a wide variety of self-supervised learning methods as regularizers. While simultaneously enhancing model calibration and robustness to natural corruptions, regularizing consistency in predictions results in lesser forgetting across all continual learning scenarios. Among the different families of regularizers, we find that stricter consistency constraints preserve previous task information in ER better.
Topological Gapless Matters in Three-dimensional Ultracold Atomic Gases

Three-dimensional topological gapless matters with gapless degeneracies protected by a topological invariant defined over a closed manifold in momentum space have attracted considerable interest in various fields ranging from condensed matter materials to ultracold atomic gases. As a highly controllable and disorder free system, ultracold atomic gases provide a versatile platform to simulate topological gapless matters. Here, the current progress in studies of topological gapless phenomena in three-dimensional cold atom systems is summarized in the review. It is mainly focused on Weyl points, structured (type-II) Weyl points, Dirac points, nodal rings and Weyl exceptional rings in cold atoms. Since interactions in cold atoms can be controlled via Feshbach resonances, the progress in both superfluids for attractive interactions and non-interacting cold atom gases is reviewed.
Can Hospital Doctors Provide Quality Palliative Care Informed by End-of-Life Care Legislation

Background and Aim:             Approximately 50% of deaths in Australia occur in hospitals, and this number is growing. Studies consistently show that doctors have poor knowledge of end-of-life decision making; however, this has not been examined in specific groups of hospital doctors. We examined hospital doctors' knowledge of key elements of end-of-life care legislation.                                   Materials and Methods:             We conducted a prospective, observational, cross-sectional study of doctors from a large Australian public tertiary health network using six questions formulated on basic key elements of the legislation. Demographic data collected included years of work experience, clinical unit, and proportion of work hours spent with dying patients.                                   Results:             Of the 201 doctors censored, senior doctors (&gt;10 years' experience) were the least knowledgeable group. Only approximately 20% of doctors correctly answered all questions. Thirty-two percent would potentially provide futile treatment if demanded by a competent patient. Fifty percent did not know how to locate an advance directive in the hospital record. There was confusion regarding the role of the substitute decision-maker.                                   Conclusions:             Approximately a quarter of hospital doctors practise with a poor understanding of the law over the various domains. The urgent call for education is further highlighted not only for students and junior doctors but also for senior doctors who scored poorly. Educational efforts could begin from addressing the simple key areas of legislation covered in the survey.
Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on clod start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence (AI)/Machine Learning (ML)-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.
Improving Molecule Generation and Drug Discovery With a Knowledge-Enhanced Generative Model

Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative models and the utilization of extensive biomedical knowledge, often systematized within knowledge graphs, whose potential to inform and enhance generative processes has not been realized. In this paper, we present a novel approach that bridges this divide by developing a framework for knowledge-enhanced generative models called KARL. We develop a scalable methodology to extend the functionality of knowledge graphs while preserving semantic integrity, and incorporate this contextual information into a generative framework to guide a diffusion-based model. The integration of knowledge graph embeddings with our generative model furnishes a robust mechanism for producing novel drug candidates possessing specific characteristics while ensuring validity and synthesizability. KARL outperforms state-of-the-art generative models on both unconditional and targeted generation tasks.
AI-GENERATED VISUAL ART AND ITS ETHICAL IMPLICATIONS IN ACADEMIA

The swift growth of the artificial intelligence (AI) has transformed the world of art, especially in the shape of AI-generated visual art. In academic life this phenomenon presents very profound concerns about authorship, creativity and social responsibility. In this paper, the author discusses the intersection of AI-generated art and education, and addresses the potential and the possible social implications of the intersection. It starts with significant technical foundations of the art of AI like neural networks and generative adversarial networks (GANs), and signals how this dichotomy of human authorship and machine authorship has changed. On academic grounds, this argument concerns the question of whether AI, in fact, can be called an artist, or merely a tool that can facilitate human creativity. This study is focused on ethical question. Authorship and intellectual property questions are also disruptive to the conventional academic practices, because AI systems frequently produce works, which lack a clear human provenance. Also, creativity and imitation appear to be significant issues in the area of education, in which students are able to produce art with minimal human involvement via AI tools. The necessity of transparency (the explanation of how AI helped in schoolwork and research) reveals the fact that academic ethics should be preserved even more. The discussion continues to address more about the higher contribution to imagination and learning. With the introduction of AI art into school education, the dilemma of how to add it without entering the trap of students who over-depend on technologies and become responsible innovators will continue to gain topicality. This research provides a mechanism through which AI art can be socialized and made productive by universities through the examination of both controversies and successful collaborations. In summary, it supports a moderate position that is receptive to creative innocence, but also concedes with technological innovation as a teaching incentive.
Lowering of Cholesterol Hampers Glioblastoma Stem Cell Proliferation in Spheroids Through impaired Hh signaling: upregulation of epigenetic chromatin modifiers and down regulation CAV1 and Stem Cell markers

Abstract                 Glioblastoma multiform (GBM) harbours tumor heterogeneity and shows little efficacy to the current treatment strategies. Persistence of glioma stem cells (GSCs) are the root cause of tumor recurrence and drug resistance. So, targeting GSCs can be a better therapeutic strategy to tackle GBM. To mimic the tumor microenvironment, we have developed tumor spheroids by hanging drop method using U87MG and Ln229 cells with an enriched GSC population. Compared to monolayer cells spheroids had higher expression of stemness markers like CD133, CD44, PAX6 and reduced expression of differentiation marker. Cancer cells rewire the metabolic pathways to sustain high proliferation. Among the metabolic pathways, cholesterol biosynthetic pathways are mostly dysregulated in cancers including GBM. The spheroids showed high expression of cholesterol biosynthetic gene (HMGCR, DHCR24), and Caveolin1 (Cav1). Targeting cholesterol metabolism by lovastatin resulted in depletion of cellular cholesterol levels, including in plasma membrane. Lowering of cholesterol affected membrane fluidity and hampered Hh signaling by lowering Gli1; consequently, causing downregulation of HMGCR, DHCR24, Cav1, and IDH3A, as well as loss of the stemness factors. However, there are enhanced expression of epigenetic chromatin modification enzymes, including DNMT1 and KDM5A. Tracking into the root cause of silencing of Cav1 gene, we found CAV1 gene promoter is methylated by DNMT1, and H3K4me3 level depleted due to enhanced KDM5A mediated demethylation. CAV1 gene silencing by siRNA validated its role in stemness maintenance and metabolic reprogramming of GSCs. Our findings suggest that, lovastatin has therapeutic potential and illustrates the importance of tumor spheroid models better understanding molecular mechanisms of GBM.
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version

With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this setting, outlier detection is frequently important, and while solutions based on neural networks exist, they leave room for improvement in terms of both accuracy and efficiency. With the objective of achieving such improvements, we propose a diversity-driven, convolutional ensemble. To improve accuracy, the ensemble employs multiple basic outlier detection models built on convolutional sequence-to-sequence autoencoders that can capture temporal dependencies in time series. Further, a novel diversity-driven training method maintains diversity among the basic models, with the aim of improving the ensemble's accuracy. To improve efficiency, the approach enables a high degree of parallelism during training. In addition, it is able to transfer some model parameters from one basic model to another, which reduces training time. We report on extensive experiments using real-world multivariate time series that offer insight into the design choices underlying the new approach and offer evidence that it is capable of improved accuracy and efficiency. This is an extended version of "Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles", to appear in PVLDB 2022.
Rights and Realities: The Legal Protection of Unaccompanied Migrant Minors - A focus on Tunisian Minors Arriving in Italy

A child is a child before being a migrant, a refugee, or an asylum seeker. He must be treated like one in the case of child migration. Unfortunately, this is not true for some children, especially Tunisian children. The migration of unaccompanied Tunisian minors to Italy has significantly increased in recent years. Many factors, such as high unemployment, economic and political crises contribute to this phenomenon. When Tunisian minors arrive in Italy all alone, they face some challenges, such as difficulties in legalizing their stay, language and cultural barriers, and limited access to education and healthcare. Therefore, the existing international, European, and national protection frameworks in place for unaccompanied Tunisian migrant minors are often not effectively enforced, resulting in adverse implications for these vulnerable children. This socio-legal research explores the existing international, European, and national protection frameworks for unaccompanied Tunisian minors who migrate irregularly to Italy and to what extent these frameworks are effectively implemented, considering that their inadequate application can negatively affect these minors. This research presents a comprehensive examination of the protection frameworks for unaccompanied migrant minors, drawing on various sources. Based on extensive research incorporating analysis of academic journals, insightful essays, and books, in addition to qualitative methods, specifically interviews with legal experts, the findings point towards notable shortcomings in the implementation of the existing protection frameworks for unaccompanied Tunisian migrant minors. By examining the challenges and highlighting the specific instances where the frameworks fall short, the thesis will explain the underlying factors responsible for the insufficiency of protection. In addition, it will showcase the need for improved application and conclude with recommendations on how to improve the application and enforcement of these frameworks, ensuring their proper implementation and safeguarding the rights of Tunisian unaccompanied migrant minors
Water and Sanitation in the Context of HIV/AIDS: The Right of Access in Resource-Poor Countries

Because the HIV/AIDS epidemic in resource-poor countries is concentrated in the most productive age group, it affects all development sectors and threatens the protection and safeguarding of human rights. While access to safe water and sanitation is a human right, it is particularly important for people infected and affected by HIV/AIDS that these rights are respected, protected, and fulfilled. Yet, the impact of the epidemic jeopardizes the ability of the water and sanitation sectors to fulfill these duties. This article reviews the linkages between these sectors and HIV/AIDS from a rights perspective and gives suggestions for strategies to be undertaken by state and non-state actors to promote access to water and sanitation as a right in an HIV/AIDS context.
Smong as a cultural early warning system: strengthening community awareness in tsunami disaster risk reduction

Abstract                   Smong, a tsunami narrative from Simeulue Island in Aceh, Indonesia, has long served as a cultural early warning system, enabling communities to survive the 2004 Indian Ocean tsunami with remarkably few casualties. This article examines how Smong operates not only as local knowledge for disaster risk reduction but also as a resource for community development. Drawing on a critical desk-based review of thirty-seven academic publications and policy texts, complemented by three semi-structured interviews with researchers involved in drafting Aceh’s disaster education Qanun (regional regulation), this study analyses the ways in which Smong has been reproduced, institutionalized, and translated into formal education and policy. It shows how academic engagement with Smong has helped to sustain collective memory, strengthen intergenerational transmission of knowledge in schools and Islamic boarding schools, and support collaborative governance between local communities, universities, and government agencies. A comparative discussion with Central Sulawesi, where local oral traditions related to tsunamis and liquefaction have not been similarly institutionalized, highlights the risks that arise when such knowledge remains fragmented and unsupported. The article argues that recognizing and co-producing local knowledge, such as Smong, within community development processes can enhance disaster preparedness, promote socially embedded early warning systems, and contribute to more equitable and resilient coastal development.
Changes in Health Information Exchange Use Behavior After Introduction of a Fast Healthcare Interoperability Resources (FHIR) Application.

The aim of our study was to characterize emergency department clinicians' health information exchange (HIE) use patterns after the implementation of a Fast Healthcare Interoperability Resources (FHIR) application. Using longitudinal electronic health record log data, we categorized HIE use behavior as: no HIE use (0), Web-based viewer use only (1), FHIR application use only (2), or Web-based viewer and FHIR application use (3). We sequenced HIE use behavior from September 2019 to February 2023, then employed hierarchical agglomerative clustering to identify clinician characteristics associated with each HIE use pattern. Our results showed four usage patterns representing (1) clinicians who "lagged" in HIE use and continued as sporadic HIE users (n=66, 46.1%), (2) "late adopters" who had more consistent usage over time (n=32, 22.4%), (3) "legacy users" whose preferred modality was the Web-based viewer (n=25, 17.5%), and (4) "mixed modality users" who displayed frequent changes in HIE access modality (n=20, 14.0%).
Mitigating Catastrophic Forgetting in Mathematical Reasoning Finetuning through Mixed Training

When finetuning large language models for specialized tasks such as mathematical reasoning, models exhibit catastrophic forgetting, losing previously learned capabilities. We investigate this by finetuning Flan-T5-Base (250M parameters) on the DeepMind Mathematics dataset and measuring forgetting on MultiNLI. Math-only training improves mathematical accuracy from 3.1\% to 12.0\% but causes NLI accuracy to collapse from 81.0\% to 16.5\%--a 64.5 percentage point drop occurring within the first 1,000 training steps. We propose mixed training strategies that interleave mathematical and NLI examples during training. Our results demonstrate that mixed training completely eliminates catastrophic forgetting while maintaining equivalent mathematical performance: the balanced 1:1 ratio achieves 12.0\% math accuracy (matching math-only) while preserving 86.2\% NLI accuracy. We systematically explore mixing ratios from 1:1 to 15:1, finding that even minimal NLI exposure (6.2\%) provides effective regularization. These findings demonstrate that specialization need not require forgetting general capabilities, with implications for scaling to larger models where mixed training may confer additional benefits beyond forgetting prevention.
Relativistic Accretion Disk Models of High‐State Black Hole X‐Ray Binary Spectra

We present calculations of non-LTE, relativistic accretion disk models applicable to the high/soft state of black hole X-ray binaries. We include the effects of thermal Comptonization and bound-free and free-free opacities of all abundant ion species. We present spectra calculated for a variety of accretion rates, black hole spin parameters, disk inclinations, and stress prescriptions. We also consider nonzero inner torques on the disk, and explore different vertical dissipation profiles, including some which are motivated by recent radiation MHD simulations of magnetorotational turbulence. Bound-free metal opacity generally produces significantly less spectral hardening than previous models which only considered Compton scattering and free-free opacity. It also tends to keep the effective photosphere near the surface, resulting in spectra which are remarkably independent of the stress prescription and vertical dissipation profile, provided little dissipation occurs above the effective photosphere. We provide detailed comparisons between our models and the widely used multicolor disk model. Frequency dependent discrepancies exist that may affect the parameters of other spectral components when this simpler disk model is used to fit modern X-ray data. For a given source, our models predict that the luminosity in the high/soft state should approximately scale with the fourth power of the empirically inferred maximum temperature, but with a slight hardening at high luminosities. This is in good agreement with observations. (abridged)
ALTERNATIVE DISPUTE RESOLUTION IN INDIA: A DETAILED ANALYSIS OF ARBITRATION & MEDIATION AS A TOOL OF EFFICIENT JUSTICE DELIVERY

The effective administration of justice is a basic requirement of the maintenance of rule of law in any democratic society. In India, the judiciary has been commissioned the task of protecting the values of the Constitution, protecting the rights of individuals and resolving disputes in a fair impartial manner. However, the Indian judicial system has been faced with a severe caseload backlog of cases for several decades. Millions of cases are still pending in the courts, both at the trial courts and the courts of appeals, causing long delays in litigation and poor justice. The maxim 'justice delayed is justice denied' has come to obtain with special significance in this regard. The traditional process for litigation is often described as one of complicated process, frequent adjournments, high costs and extended timelines. These challenges have shattered the limitations of the conventional justice delivery system and the need for alternative justice delivery mechanisms that are capable of resolving disputes in an efficient manner. Alternative Dispute Resolution (ADR) mechanisms have grown to be a viable solution to these concerns. ADR is a set of processes that enable parties to conduct a resolution of the disputes outside the formal court system in a less adversarial and collaborative manner. Among the different types of ADR, arbitration and mediation has become an important variant in India with the help of effective legislative support and rising judicial support. Arbitration provides a way to settle disputes that is binding and enforceable, and is especially used for commercial disputes, whereas mediation focuses more on the settlement of disputes based on consensus and maintaining relationships. Over the years, India has implemented extensive legal reforms in strengthening its ADR framework and bringing it closer to international standards. This article aims to offer a detailed analysis on arbitration and mediation in India, taking into consideration...
Analysis and Design of Dashboard Information System Results of Nutrition Status Monitoring Year 2016 As a Supporter of Community Nutrition Policy

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