allenai/openscilm_queries huggingface dataset:
Query: I am considering a research project for modeling urban dynamics, bringing together interactions and feedback mechanisms between businesses and jobs, households, buildings (residential and commercial), transportation, education, and various infrastructure. I want to build a model that not only assesses long-term tradeoffs coming from interventions in each sector with their ripple effects across other sectors, but also captures the dynamics of change in response to those interventions. With this background, I am interested in learning about data sources that would allow me to calibrate this model to different cities in the USA. Specifically, I am interested in what data sources exist that offer data over time (the longer, the better) with at least city-level resolution, on factors spanning these sectors, from demographics, to jobs, businesses, incomes, savings, education, migration patterns, car ownership, travel times, quality of education, and various infrastructure levels. Please provide me with a list of data sources, what variables I may be able to get from each, and what timeline and resolution is available in each dataset
Subject: environmental science
Intent: Literature Understanding
Query: Please discuss any notable initiatives (in academia, government, consortia, and industry), large investments, partnerships, project and product launches, and commercialization efforts in 2024 related to your materials group that you see affecting the AM landscape. related to a.    Thermoplastic (1. Printing of resins with short fibers) b.    Integration of AM processes with conventional (1. Printing of resins with short fibers)
Subject: engineering
Intent: Literature Understanding
Query: Since the release of AlphaFold 2, optimizing the performance of protein structure prediction models has become a focus of computational biologists. In the model training phase, the core objectives are to maximize the prediction accuracy and generalization ability. This stage mainly includes data preparation and preprocessing, forward propagation and feature learning, loss calculation, back propagation, parameter update and iterative optimization.
Feature extraction is one of the core steps of model training. Position specificity score matrix (PSSM) provides an important indicator of sequence similarity by calculating the conservation of amino acids in multiple sequence alignments. In addition, ESM (Evolutionary Scale Modeling) uses Transformer architecture to learn from large protein sequence databases, which can effectively capture local and long-range dependencies in sequences, which is particularly important for template-free prediction. The computational requirements for high-dimensional structural data are simplified. Autoencoders and multi-scale feature extraction significantly improve the accuracy and adaptability of the model by combining local and global features.
In the validation and evaluation stage, objective indicators were relied on to measure the prediction accuracy of the model. TM-score and predictive registration error (PAE) are commonly used to evaluate protein topological similarity, and the latter is used to quantify the prediction error between residues. The local distance difference test (lDDT) is particularly important in assessing the quality of local models, especially in the case of domain shifts, and its supernation-free score can provide robust evaluation results. In addition, pLDDT, as a per-residue confidence indicator in AlphaFold, has become a key tool for assessing model confidence.
Subject: biology
Intent: Other
Query: Traditionally, physical invariants have been manually defined by system engineers during the design phase of industrial control systems. These experts leverage their domain knowledge to formulate rules that express the expected relationships between different process variables based on physical laws (e.g., conservation of mass, energy balances) and operational constraints~\cite{manual-invariants}. For example, in a water distribution system, engineers might define an invariant stating that the water level in a tank must increase when the inflow rate exceeds the outflow rate, adhering to basic principles of fluid dynamics.
Subject: engineering
Intent: Other
Query: Reviewers asked: Why did not detect the weight loss rate and decay rate of persimmon storage at different times?

When referring to relevant authoritative studies, we found that the research methods of many literatures were similar to our experimental design. For example, according to the study of [author of Reference 1] in [Title of Reference 1] (published in [Journal 1], Impact factor [X]), they also only measured the weight loss rate and decay rate at the end of storage, because in the early storage stage, the physiological changes of fruits are relatively slow, and the difference or change between weight loss rate and decay rate is not obvious. It is difficult to obtain statistically significant data. In addition, [Reference 2 authors] adopted a similar approach in [Reference 2 Title] (published in [Journal 2], Impact Factor [X]), focusing on key indicators at the end of storage.
Help me recommend some SCI articles that explain the past
Subject: food science
Intent: Paper Finding
Query: Find similar works as this idea: We introduce Recursive Uncertainty Decomposition (RUD), a hierarchical approach to uncertainty
estimation. RUD works as follows: (1) Given an input, the model first generates a high-level response
and an initial uncertainty estimate. (2) The model then recursively breaks down its response into
smaller components (e.g., individual claims or reasoning steps) and estimates uncertainty for each. (3)
At each level of decomposition, the model also identifies the type of uncertainty (e.g., factual, logical,
linguistic) using a learned classifier. (4) The process continues until a pre-defined depth is reached or
the uncertainty of a component falls below a threshold. (5) Finally, a graph neural network aggregates
the hierarchical uncertainty estimates to produce a final, nuanced uncertainty score. The entire process
is trained end-to-end using a multi-task objective that combines uncertainty estimation accuracy with
decomposition quality.
Subject: computer science
Intent: Paper Finding
Query: Relative Humidity

How does relative humidity affect solar PV power generation, particularly for monocrystalline or polycrystalline modules?

Which type of PV technology is used in the system—monocrystalline or polycrystalline modules?

What is the environmental factor being analysed (e.g., relative humidity)?

Is the impact of relative humidity on PV performance explicitly mentioned or inferred in the publication?

What method or model is used in the analysis (e.g., regression analysis, simulation model)?

What are the key findings regarding the effect of relative humidity on PV performance (e.g., efficiency, output)?

If the impact of relative humidity is explicitly mentioned, what is the exact quote from the source?
Subject: engineering
Intent: Literature Understanding
Query: "Experimental studies have shown that estrogen exerts significant neuroprotective and immunomodulatory effects, reducing the extent of lesions and infarct volume in ischemic stroke models. Estrogen also regulates both innate and adaptive immune responses, decreasing neutrophil infiltration, inhibiting the release of pro-inflammatory cytokines, and promoting the expression of anti-inflammatory cytokines such as IL-10, thereby alleviating immune damage following brain ischemia. However, in clinical trials, estrogen replacement therapy has not shown similar protective effects and may even increase the risk of stroke
Subject: medicine
Intent: Other
Query: For HCI Research:
Children’s Educational Apps:
"Usability of educational apps for children"
"Designing for young users in digital learning tools"
"Children's interaction patterns with mobile and tablet applications"
Nature Exploration and Learning:
"Digital tools for nature exploration in children"
"Mobile applications for environmental education"
"Technology in outdoor learning for children"
Engagement and Accessibility:
"Improving engagement in children’s apps"
"Accessibility challenges in children’s user interfaces"
"Cognitive load in educational apps for children"
For HCI Theory:
Design Principles for Children:
"Affordances in educational app design"
"Feedback mechanisms in children’s interfaces"
"Consistency and constraints in child-friendly applications"
User Experience and Interaction Design:
"HCI guidelines for designing for young children"
"Touch interaction patterns in children's applications"
"Designing for cognitive and physical development in children"
Gamification and Motivation:
"Gamification in children's educational apps"
"Reward systems in child-focused digital learning tools"
"Motivational design principles for children"
Subject: computer science
Intent: Ideation
Query: Furthermore, we draw an analogy between this issue and the working principle of the human brain. Suppose that the various layers of the language model can be analogized to the human brain, and the tokenization results of the tokenizer are similar to the information input into the brain. If these input information itself contains errors and the brain (i.e., the model) fails to make effective corrections, then the output results will inevitably be affected. 
Subject: computer science
Intent: Other
Query: "I devised the Integrated Disaster Knowledge Transformation Model (IDKPT) to systematize the digital transformation process of disaster prevention knowledge and establish an integrated approach. The IDKPT is based on the knowledge transformation process of the SECI model and consists of three layers: tacit knowledge layer, explicit knowledge layer, and machine learning layer."

I am writing a paper with the above thesis, but has a similar concept already been proposed? If there is similar research, please present it.
Subject: disaster management
Intent: Literature Understanding
Query: Please give me a detailed literature review for the following research proposal.

This research proposal focuses on an efficient framework for multimodal learning in healthcare, aimed at integrating diverse medical data, including images, text, and protein sequences, for improved diagnosis and treatment. The challenge lies in combining spatial data from images, contextual information from text, and molecular insights from protein sequences in a scalable and robust manner. Existing fusion techniques are limited by their inability to handle large, heterogeneous datasets effectively. To address this, the proposed framework leverages low-rank approximations and graph neural networks (GNNs) for enhanced cross-modal integration.

The framework processes three primary modalities: images, text, and protein sequences. For images, it uses NIH-CXR (X-ray images) and HPA (microscopy images), applying normalization and resizing during preprocessing. For text, MIMIC-CXR (clinical reports) is processed using Clinical BERT embeddings. Protein sequences from UniProt are encoded with ESM-1b. The data from these modalities are processed and encoded into feature representations to be used in the model.

The methodology involves extracting features from each modality using state-of-the-art models. Vision Transformers (ViT) are used for images, Clinical BERT for text, and ESM-1b for protein sequences. To manage the high-dimensional data, a low-rank approximation is applied, reducing the computational burden by approximating the concatenated feature matrix. The multimodal data is then modeled as a heterogeneous graph, with nodes representing the different feature embeddings and edges capturing the relationships between them. A GNN is employed to refine these embeddings through message passing.

The training of the model is done by minimizing a total loss function, which includes the task-specific loss, an alignment term that brings image and text embeddings closer, and a regularization term that enforces smoothness across the graph. The proposed framework aims to improve efficiency and scalability while integrating multimodal data for advanced healthcare applications.
Subject: computer science
Intent: Literature Understanding
Query: Figure 1 illustrates how the tokenization process works. Imagine you have a text, which can be anything from a simple sentence to a full paragraph of a document. This text, representing the input to the process, is symbolized by the blue box at the top of the image. For the computer to be able to understand and analyse this text, it needs to be broken down into smaller parts, a process called tokenization, represented by the orange box. These smaller parts, resulting from the division, are called tokens and are illustrated by the smaller boxes below. Each token can correspond to a single word or even a complete sentence, as indicated by the green box. The way this division is carried out depends on the type of tokenization applied, which varies according to the purpose of the analysis. In short, Figure 1 shows that tokenization works as a process of “breaking down” a text into smaller units, the tokens, so that the computer can then understand and process it more effectively.
Subject: computer science
Intent: Other
Query: 精简一下：1. While the authors showcase an IIoT Gateway implementation in a manufacturing workshop, it’s important to clarify the concept of a Digital Twin. A key characteristic of a Digital Twin is bidirectional communication, allowing for real-time feedback and updates between the physical and virtual models. The authors' current setup, with its unidirectional communication, doesn't fully meet this criterion.
To ensure a deeper understanding, I recommend exploring further scientific literature on Digital Twins, which can offer valuable insights into their defining characteristics and applications.
For instance, could the authors elaborate on how incorporating bidirectional communication might enhance their current IIoT Gateway setup and potentially achieve a true Digital Twin representation?

2. The depiction of a 'Virtual Space' in Figure 8 raises questions about the scope and methodology of this study. Initially, the authors focused on connecting disparate communication protocols within the manufacturing workshop. Developing a virtual space, however, implies a more complex undertaking, involving 3D modeling, data visualization, and potentially simulation capabilities.
Given the limited details provided in the article, Could the authors elaborate on the development process of the virtual space, including the tools and technologies used, and clarify how it contributes to their primary research goal of protocol integration?

3. The authors state that their IIoT gateway leads to a 57.3% reduction in access time for the laser quenching equipment compared to traditional methods. However, this claim lacks sufficient substantiation. To validate this improvement, a more detailed analysis is required.
Could the authors please provide further information on how access time was measured, both with and without the IIoT gateway? Additionally, presenting data on sample size, data collection methods, and statistical analysis used to arrive at the 57.3% reduction would strengthen the credibility of their findings.

4. While the authors highlight the advantages of their IIoT gateway compared to traditional methods, it's crucial for authors also state the limitations of this study?

5. The article mentions the use of an API (Application Programming Interface) within the IIoT gateway framework. Understanding the deployment environment for this API is crucial for assessing the system's scalability and reliability.
Could the authors please elaborate on where this API was deployed? Did they utilize any cloud infrastructure, such as AWS, Azure, or Google Cloud Platform? If so, what specific services were employed for hosting and managing the API? If not, what alternative deployment strategy was used?
Subject: computer science
Intent: Paper Understanding
Query: In Chen Huang, Peixin Qin, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua. Concept - An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factors. 2024. arXiv.org., do they break down the utterances into information nuggets?
Subject: computer science
Intent: Paper Understanding
Query: Five-fold cross-validation is a resampling procedure employed to evaluate machine learning models when the data sample is limited. In this approach, the dataset is divided into five equally sized subsets or “folds.” The model is trained on four of these folds and validated on the remaining fold, and this process is repeated five times, ensuring that each fold serves as the validation set once. The resulting performance metrics are then averaged across all five iterations, offering a more reliable assessment of the model’s predictive capabilities. This method helps reduce overfitting by providing a more general evaluation, makes efficient use of the data by allowing all observations to serve in both training and validation roles, and yields a more robust estimate of model performance through the aggregation of multiple validation results 
Subject: computer science
Intent: Other
Query: To address the challenges of Natural Language Processing (NLP), various approaches have been proposed, combining modern machine learning techniques with advancements in neural network architectures. Semantic ambiguity, for instance, can be mitigated using pre-trained models like BERT, which analyze words in both directions within a context, enabling more accurate interpretations. Additionally, advanced frameworks such as Transformer-XL are effective in handling long texts, as they allow the learning of dependencies beyond the fixed length of traditional sequences. Linguistic and cultural diversity can also be tackled through modular multilingual systems capable of processing dialectal variations and building adaptable contextual representations. Finally, the use of recurrent neural networks and attention mechanisms, such as those found in transformational attention models, aids in capturing long-term interactions between words and sentences, enhancing the contextual and semantic understanding of machines (Khurana et al., 2023).
Subject: computer science
Intent: Other
Query: The Moral Data City Hunt (MDCH) is an innovative research method aiming to map the moral positions of citizens regarding techno-moral dilemmas in urban environments.

The MDCH consists of two main components:
1. Mobile Moral Lab: A chatbot-based system that presents citizens with urban dilemmas and allows them to interact with and set preferences for moral programming of smart city technologies.
2. Personal Values Dictionary: A linguistic analysis tool that categorizes the language used by participants to identify the values they consider important.

What research methods align with the MDCH?
Subject: research methodology
Intent: Literature Understanding
Query: find info and write based on the content below:Conceptual and Theoretical Framework for AI in Intangible Cultural Heritage (ICH) Music Video Production
Building on the uploaded paper “An AI-Driven Workflow for Preserving Traditional Malay Music Videos”​An AI-Driven Workflow f…, we will develop a novel contribution focused on AI-assisted video production for cultural heritage music, particularly using AI-generated videos (AIGV). The main innovation lies in proposing a refined AI-driven process model that balances automation with cultural authenticity. Below is an outline of the research paper:

Proposed Structure and Key Novel Contributions
I. Introduction
Context & Significance

Intangible Cultural Heritage (ICH) music is at risk due to modernization and declining documentation.
AI-generated videos (AIGV) offer a new paradigm for preserving and revitalizing cultural heritage music.
The challenge: Ensuring cultural authenticity while leveraging AI-driven automation.
Research Gap

Prior studies focus on AI tools separately (e.g., face synthesis, voice cloning, lip-sync AI, etc.), but lack an integrated workflow for ICH music production.
Existing workflows do not fully preserve traditional performance nuances, emotional expression, and visual fidelity.
Objectives

Develop an end-to-end AI-driven process model for cultural heritage music video production.
Ensure authenticity, historical accuracy, and engagement while minimizing production costs.
Subject: computer science
Intent: Literature Understanding
Query: The rapid evolution of generative AI tools, such as GitHub Copilot and OpenAI Codex, has introduced transformative possibilities in software engineering. These tools promise to revolutionize traditional development processes by automating repetitive coding tasks, reducing errors, and accelerating project timelines (Vaithilingam et al., 2022). For students, generative AI provides a valuable learning aid, enhancing their ability to grasp programming concepts and experiment with code generation (Finnie-Ansley et al., 2022). For professionals, these tools hold the potential to boost productivity and enable more efficient workflows. Despite their benefits, the adoption of generative AI tools in software engineering remains uneven, raising critical questions about how users perceive and integrate these technologies into their practices (Zhang et al., 2023).
Subject: computer science
Intent: Other
Query: Find papers that support these claims: In the modern era of scientific research, the volume of academic publications is expanding at an unprecedented rate, making it increasingly difficult for researchers to keep pace with advancements in their fields. Staying informed often requires synthesizing knowledge from a multitude of papers, which is both time-consuming and prone to errors of omission or misinterpretation. Multi-document summarization emerges as a critical tool to alleviate this burden, enabling researchers to distill key insights from multiple sources into a single, cohesive narrative.
Subject: computer science
Intent: Paper Finding
Query: Social Identity Theory (SIT) provides a lens for interpreting how individuals align themselves with particular groups—be they residents, tourists, or other regionally affiliated cohorts—when communicating online (Tajfel & Turner, 1979. Jiang et al. (2021) argue that social media can intensify in-group solidarity or out-group distinctions by enabling quick formation of community-driven narratives. In Montenegrin and Dalmatian contexts, in-group sentiments might surface as expressions of cultural pride, while out-group perspectives, often held by tourists or new residents, can yield critical reflections on environmental problems or local hospitality practices (Molnar, 2023). Such identity-based distinctions regularly emerge in sentiment analysis. Positive remarks from in-group members can reinforce a shared sense of heritage, whereas out-group users may mix enthusiasm with critique, highlighting infrastructural shortcomings. Understanding these nuanced identity dynamics can help local authorities and marketers engage more effectively with diverse stakeholder groups. By extending SIT into digital sentiment research, scholars can better understand the intersections between group membership, regional representation, and emotional discourse.
Subject: psychology
Intent: Other
Query: Clinical skills training for T2D encompasses several key categories:
1. Diagnosis and Monitoring: Training on interpreting lab results (e.g., HbA1c levels), understanding risk factors, and recognizing the clinical signs of T2D.
2. Patient Communication: Teaching how to effectively communicate with patients about lifestyle changes, treatment adherence, and managing the psychosocial aspects of chronic disease.
3. Clinical Decision Making: Training on selecting the appropriate pharmacologic interventions, insulin management, and patient-specific treatment plans.
4. Multidisciplinary Collaboration: Emphasizing the importance of working with dietitians, nurses, and endocrinologists in the management of T2D.
Subject: medicine
Intent: Other
Query: Can we isolate "harmful" and "safety" vector representations in LLMs across different safety taxonomies? How would jailbreak prompts behave differently?
How does multilingual content affect jailbreak success rates and harmful output generation?
How do different safety alignment methods compare in terms of compliance rates and unintended model degradation? (so like we get previous works as baselines)

Give me 10 specific method to test these answer with current technology. Backup each method with 1 or 2 specific paper.
Subject: computer science
Intent: Literature Understanding
Query: Kaur and Jain (2017) corroborate the importance of text mining by exploring the use of machine learning algorithms to extract keywords. The authors point out that the vastness of data in large organizations, which can easily exceed 500 terabytes, requires efficient techniques to deal with the information. In this context, text mining emerges as an effective solution, using techniques such as text fragmentation, tokenization and keyword extraction to make data analysis more agile and accurate. Automated keyword identification allows users to navigate and understand large volumes of data more quickly, revealing insights and patterns that would be practically impossible to obtain through manual analysis.
Subject: computer science
Intent: Other
Query: Growing reliance on AI for recruitment, performance management, and employee engagement . Emphasis on both efficiency gains and ethical responsibilities in designing AI-enabled HR processes 
Global Trends
Organizations worldwide are considering AI to optimize HR functions . Heightened scrutiny of fairness, confidentiality, and legal compliance in AI-driven practices.

Emerging interest in AI-based HR solutions among Nigerian enterprises. Need to assess socio-cultural variables that may influence responsible AI deployment 

Ethical considerations extend beyond technology to national regulations, organizational climates, and employees’ rights . Integrating ethical guidelines for AI in Nigerian HRM may offer insights for the global community

reformulate the below problem statement related to this research :

Ethical Dilemmas in AI-Driven HRM 
Algorithmic bias, data privacy, transparency, accountability
Impact of Ethical Concerns 
Legal risks, organizational reputation, employee trust 
Need for an Ethical Framework 
Lack of comprehensive guidelines tailored to HRM



Subject: business
Intent: Ideation
Query: Temporal action segmentation plays a key role in long
untrimmed video understanding. Most previous works adopt
frame-wise supervision, which inevitably falls into the oversegmentation
 issue. Although some strategies try to solve it,
such as boundary smoothing, gaussian smoothing, and multistage
 reffnement, it could be tough to achieve balance results.
In this paper, for the desirable trade-off between accuracy
and over-segmentation, we provide a novel perspective that
models action segmentation as a domain adaptation task. Our
framework can be easily plugged into existing action segmentation
 models via frame-segment domain adaptation, called
FSDA. Speciffcally, we optimize the model with frame-wise
supervision and segment-wise pseudo-labels. In addition, we
design a speciffc optimal transport loss to align the feature
distribution among frame-segment domains. It enhances the
discriminative ability of target segment features. Afterward,
a novel discrepancy mini-max training strategy is proposed
to achieve more accurate and less over-segmentation results.
We verify the effectiveness of FSDA on GTEA, 50Salads, and
Breakfast benchmark datasets, observing consistent improvements
 in different evaluation metrics over popular action segmentation
 approaches. We will release the code soon.
Subject: computer science
Intent: Other
Query: can you access the paper with this title: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation

and

Analysis Requirements:
1. Reference Classification:
   - Characterize the nature and depth of mission reference
   - Distinguish between superficial mention and substantive engagement

2. Research Contribution Assessment:
   Evaluate mission's role in:
   a) Research framework development
   b) Methodological approach
   c) Theoretical foundations
   d) Data utilization
   e) Analytical implementation

3. Scholarly Significance:
   - Estimate depth of mission/model integration
   - Assess scholarly impact and citation potential
Subject: earth sciences
Intent: Paper Understanding
Query: Böhm et al. 2000 established a formula that calculates temperature from d18O of the seawater and the theoretical d18O of aragonite  in equilibrium with the seawater. Is there a newer formular, that does the same, especially in the southern ocean and in a temperature range of down to -2 °C in the water? I am mostly interested in the d18O of aragonite in equilibrium with the seawater. 
Subject: earth sciences
Intent: Literature Understanding
Query: what are some open problems in any of the following: neural/differentiable rendering, classic rendering, neural graphics, view synthesis, neural rerendering, implicit neural representation, photo synthesis, texture embedding or mapping. Here are some of my current ideas: "Nonlinear raytracing (light transport) for rendering black holes 
Rendering mesh fracturing and destruction 
High-dimensional or non euclidean light transport formulation
Rendering scenes using echolocation data
Rendering challenger deep
Unblurring fast objects in photos/scenes
Neural/differentiable volume rendering of galaxies (view synthesis) 
Generating meshes and scenes from shadows
Editing avatar poses based on gestures lines from artists
Rendering crowd perspectives of 3d scene
Rendering effects of hallucinations and drugs
Restoring broken objects based on images
Transfer objects from one lighting condition to another
Rendering kaleidoscopes
Generate/approximate lighting for 3D scenes using from art directors" 
Can I get some other ideas for research problems based on what I have down so far? I heard it's easier to write a paper solving a new problem than improving an existing solution, so try to find a unique and interesting problem
Subject: computer science
Intent: Ideation
Query: In contrast, SBS-modified bitumen demonstrated substantial improvements in stiffness and fatigue resistance, with the 5% SBS sample showing the highest A-parameter and significantly slower modulus degradation over time in the Linear Amplitude Sweep (LAS) test (Figures 14 and 15). SBS enhances the elasticity and stiffness of bitumen by forming a polymeric network within the binder, improving its resistance to deformation underload. However, the moderate B-parameter suggests that while SBS improves the initial stiffness, further enhancements are needed to optimize fatigue resistance under prolonged cyclic loading.

find me studies that found like above result
Subject: engineering
Intent: Paper Finding
Query: The 5-year overall survival (OS) rate in adult AML patients remains suboptimal at approximately 24%, while pediatric cases demonstrate more favorable outcomes with 5-year OS rates ranging from 65% to 70%. However, significant clinical challenges persist in childhood AML, including high relapse rates (30-40%), development of chemoresistance (particularly in FLT3-ITD and TP53-mutated subtypes), and treatment-related toxicities (such as anthracycline-induced cardiotoxicity and hematopoietic stem cell transplantation-associated complications).
Subject: medicine
Intent: Other
Query: The ISO/IEC 40180 framework consists of three main components: the Process Model, the Description Model, and the Reference Quality Criteria (RQC). The Process Model is a guide to the relevant processes for developing learning scenarios within the whole life cycle of online education. It consists of seven process categories and 38 sub-processes, which can be applied to assess ICT capabilities and challenges in e-government initiatives.  I would like to clarify these components and how to benefit from them in the evaluation process by creating questionnaires that can be submitted to Faculty members, students, and admins.
Subject: computer science
Intent: Literature Understanding
Query: Apart from that, inter-offloading can be further broken down into complete inter-offloading and partial inter-offloading approach depending on the offloading characteristic where complete inter-offloading describing scenarios where all computational tasks are transferred from local device to a remote processing unit such that the entire workload is executed remotely, with no local processing taking place while the later represent approach where the computational tasks are splited between local execution and remote processing unit. 

provide literature review that supports this idea
Subject: computer science
Intent: Literature Understanding
Query: The noteworthy aspect of miR-1202 levels in Major Depressive Disorder
(MDD) is its impact on the RNA that encodes the metabotropic glutamate receptor 4
(GRM4). The presence of the G-allele in the GRM4 SNP rs2229901, which is
situated within the miR-1202 binding site of the GRM4 coding sequence, is linked to
a higher susceptibility to MDD.
Subject: neuroscience
Intent: Other
Query: In the domain of multi-label text classification, it has been shown that high accuracy performance can only be achieved using complex and computationally expensive LLM architectures. To overcome this challenge, a knowledge distillation approach is proposed as an alternative, where a large model (teacher) is used to train a small model (student) without significantly compromising accuracy. However, the initial approach of distillation with a single teacher still has drawbacks in terms of generalization due to the limited representation that a single teacher model has.
Subject: computer science
Intent: Other
Query: Is this calculation correct? The top_keywords_df and top_keywords_tfidf should be a set of all unique keywords that are common to both the reviewer-provided keywords and the ADG keywords associated with the email for each email.

```
def process_profile(email: str, top_10_keywords_df: set, top_10_tfidf_keywords: set,
                    reviewer_keywords: list, adg_keywords: set) -> dict:
    """
    Process a single profile using precomputed TF-IDF scores.

    :param email: Email of the profile.
    :param top_10_keywords_df: Keywords from the database for the reviewer email.
    :param top_10_tfidf_keywords: Dictionary of precomputed TF-IDF scores for all unique keywords.
    :param reviewer_keywords: Reviewer-provided keywords.
    :param adg_keywords: All ADG keywords associated with the email.
    :return: Dictionary containing processed data for the profile.
    """
    overlap_percentage_frequency = calculate_overlap(top_10_keywords_df, reviewer_keywords)
    overlap_percentage_tfidf = calculate_overlap(top_10_tfidf_keywords, reviewer_keywords)
    adg_reviewer_overlap = calculate_overlap(adg_keywords, reviewer_keywords)

    return {
        'email': email,
        'overlap_percentage_frequency': overlap_percentage_frequency,
        'overlap_percentage_tfidf': overlap_percentage_tfidf,
        'adg_reviewer_overlap': adg_reviewer_overlap,
    }

    def fetch_and_process_reviewer_keywords(args: CliArgs, reviewer_profiles: dict):
    """
    Fetch ADG profiles associated with provided emails from PostgreSQL, process the data to calculate keyword
    rankings and overlaps, and save the results to CSV files.

    :param args: CLI parsed arguments.
    :param reviewer_profiles: Dictionary of reviewer emails and keywords.
    """
    adg_profiles, keywords_df, total_documents = fetch_data_from_db(args, reviewer_profiles)
    tfidf_scores = calculate_tf_idf(adg_profiles, keywords_df, total_documents)

    output_data = []

    for email, reviewer_keywords in reviewer_profiles.items():
        email_keywords_df = {
            keyword: freq
            for keyword, freq in keywords_df.items() if keyword in reviewer_keywords
        }
        top_keywords_df = {
            keyword
            for keyword in email_keywords_df.keys() if keyword in adg_keywords
        }
        email_tfidf_scores = tfidf_scores.get(email, {})
        top_keywords_tfidf = {
            keyword
            for keyword in email_tfidf_scores.keys() if keyword in adg_keywords
        }
        adg_keywords = set(
            next((keywords for adg_email, keywords in adg_profiles if adg_email == email), [])
        )
        processed_data = process_profile(email, top_keywords_df, top_keywords_tfidf, reviewer_keywords, adg_keywords)
        output_data.append(processed_data)

    rank_evaluation = evaluate_rank_ordering(output_data)

    for data in output_data:
        email = data['email']
        data['rank_evaluation'] = rank_evaluation[email]

    save_results(args, output_data)

    conclusion = analyze_results(output_data, reviewer_profiles)
    logger.info(conclusion)

```
Subject: computer science
Intent: Other
Query: Great choice! Let’s start with the literature review, focusing on two aspects: HCI Research and HCI Theory.

Step 1: HCI Research
We'll identify relevant academic literature related to:

Rock climbing apps: Explore existing applications or studies focusing on similar user needs (e.g., activity tracking, social features, weather integration).
Outdoor activity applications: Broaden the scope to apps designed for outdoor enthusiasts, focusing on interaction design and usability for such contexts.
Wearables in sports: Research how wearables are integrated with mobile apps for outdoor activities.
Step 2: HCI Theory
We'll cover concepts such as:

Interaction Design:
Touch and gesture interactions for ease of use.
Design patterns for logging and tracking progress.
Cognitive Load Theory:
Simplify navigation and interfaces for users in challenging outdoor conditions.
Feedback and Visibility:
Immediate feedback for logged activities or weather updates.
Social Features:
Interaction design theory for enhancing community engagement.
Action Plan:
Search for literature or case studies focusing on:
Mobile apps for climbing or similar sports.
HCI principles applied to outdoor apps.
Usability and accessibility challenges for climbers.
Collect and summarise findings under two headings:
Insights for conceptual design and features.
Theories to guide user-friendly interactions.
Would you like me to help locate or summarise specific research papers for these aspects? Or guide you in building a structured search strategy?






You said:
give me some more specific esearch papers just give me some keyword the rest will I will do it with ai open scholar ai 

2/2

ChatGPT said:

ChatGPT
To assist with your literature review on designing a digital application for outdoor rock climbers in the UK, consider using the following keywords in your searches:

Rock Climbing Mobile Applications: Explore existing apps tailored for rock climbers, focusing on features like climb logging, social connectivity, and weather integration.
Usability in Outdoor Activity Apps: Investigate studies on user interface design and user experience specific to outdoor recreational applications.
Wearable Technology in Sports: Examine the integration of wearable devices in sports applications, emphasizing data tracking and user interaction.
Social Features in Sports Apps: Look into how social functionalities are implemented in sports-related applications to enhance community engagement.
Weather Forecast Integration in Mobile Apps: Research methods and best practices for incorporating real-time weather data into mobile applications.
Utilizing these keywords should help you find relevant academic papers and articles to inform your project's design and development.


Sources


can you find 10 related paper which could be helpful for me to write an build my project 
Subject: computer science
Intent: Paper Finding
Query: I am familiar with smooth manifold and geometry, things over real and complex field. I know p-adic fields are very important, yet I never get to really feel the "geometry" in algebraic geometry and number theories, mostly because the lack of understanding of the basic concepts. Would you walk me through the basics such as the topology, the structure fields, the schemes and homology and stuffs, on the mean time keep reminding me of  their smooth categorical "relatives"  
Subject: math
Intent: Literature Understanding
Query: ```
def calculate_tf_idf(profiles: list[tuple[str, list[str]]], keyword_df: dict, total_documents: int) -> dict:
    """
    Calculates the TF-IDF scores for keywords in provided profiles.

    :param profiles: Profiles list mapping emails to lists of keywords.
    :param keyword_df: Dictionary mapping ADG keywords to their document frequencies.
    :param total_documents: Total number of documents in the dataset.
    :return: Dictionary mapping emails to dictionaries of keywords and their TF-IDF scores.
    """
    idf = {
        keyword: math.log((1 + total_documents) / (1 + df)) + 1
        for keyword, df in keyword_df.items()
    }

    tf_idf = defaultdict(dict)

    for email, keywords in profiles:
        keyword_counts = Counter(keywords)
        total_keywords = len(keywords)

        if total_keywords == 0:
            continue

        for keyword, count in keyword_counts.items():
            tf = count / total_keywords
            tf_idf[email][keyword] = tf * idf[keyword.lower()]

    return tf_idf

```
is this correct calculation for tf-idf?
Subject: computer science
Intent: Literature Understanding
Query: Please provide papers related to the content of the following text. If possible, present those written in Japanese.

The tacit knowledge referred to here is positioned as an accumulation of empirical and cultural knowledge related to disaster prevention in Japan. It includes practical knowledge cultivated through experiences such as the Great Hanshin-Awaji Earthquake and the Great East Japan Earthquake, and particularly, implicit understandings concerning Japan's unique disaster prevention systems, such as the activities of voluntary disaster prevention organizations and fire brigades, and the strengthening of disaster prevention capabilities in local communities, are important elements. This knowledge is closely tied not only to technical countermeasures but also to cultural values such as social cohesion and the spirit of mutual aid. Notably, there are rules of thumb and implicit rules cultivated in the field of disaster response. For example, there exists a wealth of undocumented important knowledge, such as considerations for the elderly and disabled in shelter management, identifying and establishing support systems for those needing assistance in local communities, and cooperation systems among neighboring residents during disasters. These tacit knowledge elements form the core of Japan's disaster prevention system, yet systematic sharing has been challenging until now.
Subject: disaster management
Intent: Paper Finding
Query: I'm writing a Research Grant Application on Combining Small Language Models and Decentralized Federated Learning support for Differential Diagnosis of Vertebral Compression Fractures Caused by Primary Osteoporosis and Multiple Myeloma. This Research Grant Application aims to develop a novel approach by combining small language models and decentralized federated learning to improve the differential diagnosis of VCFs caused by PO and MM. This approach addresses the limitations of traditional diagnostic methods and offers a more accurate, patient-centered, and cost-effective solution. Please summarize the papers on RESEARCH STRATEGies and Approachs, and rephrase for clarity, coherence and conciseness, ensuring each paragraph flows into the next. Remove jargon. Use a professional tone.
Subject: computer science
Intent: Literature Understanding
Query: What are some related works to this: We introduce Multi-Modal Coherence Networks (MMCN), a novel architecture for assessing
visual-linguistic coherence and making abstention decisions in multi-modal tasks. MMCN consists of
several innovative components: (1) A cross-modal embedding module that learns joint representations
of visual and linguistic features, capturing deep semantic relationships between the two modalities. This
module uses a novel attention mechanism that allows for fine-grained alignment between visual regions
and textual segments. (2) A coherence evaluation network that assesses the alignment and
consistency between the visual and linguistic components. This network is trained on a large dataset of
coherent and incoherent visual-linguistic pairs, learning to identify subtle misalignments or
contradictions. (3) An abstention policy network that decides whether to generate content, abstain, or
request clarification based on the coherence evaluation. This network is trained using reinforcement
learning, with rewards based on human judgments of multi-modal coherence. (4) A multi-modal
refinement module that, when triggered, attempts to reconcile inconsistencies between the visual and
linguistic components by generating alternative textual descriptions or suggesting visual edits.
Subject: computer science
Intent: Literature Understanding
Query: 1. How can TopProject Inc. calculate the expected value (EV) of the investment in the project management software product if there is a 40% chance of earning 3 million euros, a 40% chance of earning 1.5 million euros, and a 20% chance of losing 500,000 euros. (0,5 points)
EV = (3,000,000 * 0.4)+(1,500,000 * 0.4) + (500,000 * 0.2) = 1,900,000 euros
2. In the context of investment decisions, what is the significance of the expected value? (0,5 points)
It provides the expected average value of an investment in the future. Thus, helps to compare different investment options.
3. Calculate the variance and standard deviation of the possible returns for TopProject’s investment, given the following probabilities and outcomes (1 point):
● 40% chance of earning 3 million euros
● 40% chance of earning 1.5 million euros
● 20% chance of losing 500,000 euros

α2= 0.4(3,000,000 - EV)2 + 0.4(1,500,000-EV)2+ 0.2(-500,000-EV)2 
α2 = 1,700,000,000,000
α= √1,700,000,000,000 = 1,303,840.48
4. Based on the calculated standard deviation of the previous exercise, does it make sense for TopProject to proceed with the investment?? (0,5 points)

I dont know what to answer on 4
Subject: finance
Intent: Literature Understanding
Query: 4	Advanced and Future Perspectives
4.1 Clinical Translation Challenges and Opportunities
*   VI.A.1.  Scale-up and Manufacturing Considerations (Challenges in large-scale production of nano-quercetin formulations)
*   VI.A.2.  Regulatory and Safety Aspects (Addressing regulatory pathways and safety evaluations for clinical use)
*   VI.A.3.  Potential for Personalized Medicine (Tailoring nano-quercetin delivery based on individual patient and tumor characteristics)
write review content in context of quercetin 
Subject: medicine
Intent: Literature Understanding
Query: The proliferation of Internet of Things (IoT) devices has catalyzed the growth of data-driven applications, necessitating efficient data processing frameworks. The Cloud-to-Thing Continuum, encompassing cloud, fog, and edge computing, offers a distributed architecture to handle massive data while meeting latency, energy efficiency, and resource utilization demands.However, the scale and complexity of such systems pose significant challenges in resource management.
Subject: computer science
Intent: Other