Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<dataset: string, metric: string, value: string, comparison: string, limitations: string>
to
{'dataset': Value('string'), 'metric': Value('string'), 'value': Value('string'), 'comparison': Value('string')}
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<dataset: string, metric: string, value: string, comparison: string, limitations: string>
to
{'dataset': Value('string'), 'metric': Value('string'), 'value': Value('string'), 'comparison': Value('string')}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
basic_info unknown | categorization dict | technical_attributes dict | resource_info dict | method_details dict | dataset_info dict | research_logic dict | evaluation dict | arxiv_id string | metadata_version string | skipped bool | reason string |
|---|---|---|---|---|---|---|---|---|---|---|---|
{
"title": "Quantum GestART: Identifying and Applying Correlations between Mathematics, Art, and Perceptual Organization",
"authors": [
"Maria Mannone",
"Federico Favali",
"Balandino Di Donato"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Cross-Modal Sonification",
"keywords": [
"Gestalt Theory",
"Quantum Formalism",
"Dirac Notation",
"Category Theory",
"Algorithmic Composition",
"Gestural Similarity"
]
} | {
"pipeline_type": "Filter-Transform-Refine (F-T-R)",
"learning_paradigm": "Rule-Based Mapping with Machine Learning Segmentation",
"knowledge_source": "Gestalt Principles and Quantum Mechanics Analogies",
"backbone_model": "TensorFlow (Image Recognition)"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Quantum GestART",
"architecture_description": "Decomposes visual input into superposition of elementary 'visual kets' (points, lines, arches) via filtering operator F. Maps visual kets to 'sound kets' using gestural similarity conjecture via semilinear transform T. Applies coefficients for spatial... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Lack of formal mathematical frameworks connecting visual perceptual organization (Gestalt) with auditory structures; need for systematic cross-modal translation beyond simple spectrogram mapping.",
"key_insights": "Visual forms decompose into minimal sets of primitive shapes (visual kets) analogous... | {
"main_results": [],
"limitations": "Theoretical framework lacks empirical validation for complex image sonification; gestural similarity mapping currently verified only for simple shapes and short sequences; refinement step R relies on subjective human composer intervention without defined optimization criteria."... | 2001.00001 | DAS-2M-v1.0 | null | null |
{
"title": "ON IDENTITIES OF 2-DIMENSIONAL ALGEBRAS",
"authors": [
"H. Ahmed",
"U. Bekbaev",
"I. Rakhimov"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Algebraic Classification",
"keywords": [
"2-Dimensional Algebras",
"Polynomial Identities",
"Matrix of Structural Constants",
"PI-Algebras",
"Isomorphism Classification"
]
} | {
"pipeline_type": "Canonical Representative Analysis",
"learning_paradigm": "Symbolic Computation",
"knowledge_source": "Finite-Dimensional Algebra Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "MSC-Based Identity Classification",
"architecture_description": "Transforms polynomial identities into matrix equations using Matrix of Structural Constants (MSC). Solves resulting systems of polynomial equations for each canonical representative across field characteristics 2, 3, and others. Util... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Canonical 2D Algebra Representatives (A1-A12)"
]
} | {
"motivation": "Existing classification results for 2D PI-algebras lack comprehensive coverage across diverse polynomial identities and field characteristics.",
"key_insights": "Converting identities to MSC matrix equations simplifies solving for structure constants. Opposite algebra properties allow deriving righ... | {
"main_results": [
{
"dataset": "Char(F) != 2,3",
"metric": "Identity Coverage",
"value": "30 Identities",
"comparison": "Classified A1-A12 representatives for all 30 identities"
},
{
"dataset": "Char(F) = 2",
"metric": "Identity Equivalence",
"value": "Multiple ... | 2001.00002 | DAS-2M-v1.0 | null | null |
{
"title": "LEARNING NUMERAL EMBEDDING",
"authors": [
"Chengyue Jiang",
"Zhonglin Nian",
"Kaihao Guo"
],
"publication_year": 2019,
"publication_date": "2019-11"
} | {
"task_category": "Word Embedding",
"keywords": [
"Numeral Embedding",
"Out-of-Vocabulary",
"Prototype Induction",
"Skip-gram"
]
} | {
"pipeline_type": "Prototype-based Weighted Average",
"learning_paradigm": "Unsupervised Representation Learning",
"knowledge_source": "Training Corpus Statistics",
"backbone_model": "Skip-gram with Negative Sampling"
} | {
"has_code": true,
"github_url": null,
"project_page": null
} | {
"method_name": "Prototype Numeral Embedding (SOM/GMM)",
"architecture_description": "Induces finite prototype set from training numerals via Self-Organizing Map or Gaussian Mixture Model. Computes target numeral embedding as weighted average of prototype embeddings using similarity functions based on log-space di... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Wikipedia-1B",
"Numeracy-600K",
"Customer Service Chat Logs",
"WordSim353",
"MEN",
"Simplex-999"
]
} | {
"motivation": "Existing word embedding methods fail to encode numeral magnitude and suffer severe Out-of-Vocabulary issues due to infinite numeral space and scarce individual occurrences in corpora.",
"key_insights": "Representing numerals as weighted averages of learned prototypes captures semantic similarity ba... | {
"main_results": [
{
"dataset": "Numeracy-600K",
"metric": "Micro-F1",
"value": "Superior",
"comparison": "Outperforms NumAsTok and Fixed baselines significantly"
},
{
"dataset": "Customer Service Chat Logs",
"metric": "F1 Score",
"value": "Highest",
"compa... | 2001.00003 | DAS-2M-v1.0 | null | null |
{
"title": "New Competitive Analysis Results of Online List Scheduling Algorithm",
"authors": [
"Rakesh Mohanty",
"Debasis Dwibedy",
"Shreyaa Swagatika Sahoo"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Online List Scheduling",
"keywords": [
"Competitive Analysis",
"Makespan Minimization",
"Identical Parallel Machines",
"Non-preemptive Scheduling"
]
} | {
"pipeline_type": "Deterministic Online Algorithm",
"learning_paradigm": "Worst-Case Competitive Analysis",
"knowledge_source": "Theoretical Input Sequence Characterization",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "List Scheduling Algorithm (LSA) with Input Characterization",
"architecture_description": "LSA assigns incoming jobs irrevocably to the machine with the current minimum load. The study introduces two specific input sequence classes: Class-1 containing $(m-1)^2 + 1$ jobs (sizes 1 and $m$) and Class... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard LSA achieves a loose $(2 - \\frac{1}{m})$ competitive ratio due to worst-case adversarial sequences; real-world inputs often exhibit structural patterns not captured by general bounds.",
"key_insights": "Characterizing input sequences by job size distribution and arrival order yields signi... | {
"main_results": [
{
"dataset": "Class-1 Sequence ($S_1$)",
"metric": "Competitive Ratio",
"value": "2 - 2/m",
"comparison": "Tighter than standard 2 - 1/m bound for m >= 3"
},
{
"dataset": "Class-2 Sequence ($S_2$)",
"metric": "Competitive Ratio",
"value": "2 - ... | 2001.00004 | DAS-2M-v1.0 | null | null |
{
"title": "Approach to the construction of the spaces $S D ^ { p } [ \\mathbb { R } ^ { \\infty } ]$ for $1 \\leq p \\leq \\infty$",
"authors": [
"Hemanta Kalita",
"Bipan Hazarika"
],
"publication_year": null,
"publication_date": null
} | {
"task_category": "Functional Analysis",
"keywords": [
"Jones Distribution Spaces",
"Infinite-Dimensional Measure Theory",
"Henstock-Kurzweil Integral",
"Schwartz Distributions",
"Banach Space Construction"
]
} | {
"pipeline_type": "Mathematical Construction via Limit Sequences",
"learning_paradigm": "Theoretical Derivation",
"knowledge_source": "Gill and Zachary Strong Jones Spaces Extension",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Construction of $SD^p[\\mathbb{R}_I^\\infty]$ Spaces",
"architecture_description": "Define $\\mathbb{R}_I^\\infty$ as co-product topology of increasing sequence limits. Construct norm via weighted sum of integrals against countable dense set of test functions $\\mathcal{E}_k$. Complete $L^p$ space... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard Lebesgue spaces lack Banach structure for non-absolutely integrable functions like Henstock-Kurzweil integrals; Schwartz distribution theory requires normable topological vector spaces for rigorous analysis in quantum physics.",
"key_insights": "Weighted inner product construction using co... | {
"main_results": [],
"limitations": "Theoretical construction lacks explicit computational algorithms for high-dimensional integration; existence of specific non-absolutely integrable functions within the space remains partially implicit rather than constructively demonstrated."
} | 2001.00005 | DAS-2M-v1.0 | null | null |
{
"title": "LEARNING FROM LEARNING MACHINES: OPTIMISATION, RULES, AND SOCIAL NORMS",
"authors": [
"Travis Lacroix",
"Yoshua Bengio"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "AI Safety and Socio-Economic Policy",
"keywords": [
"Implicit Specifications",
"Explicit Rules",
"Optimization Problems",
"AI Alignment",
"Incomplete Contracts",
"Social Norms"
]
} | {
"pipeline_type": "Analogical Reasoning Framework",
"learning_paradigm": "Deep Learning vs. Rule-Based Systems",
"knowledge_source": "Economic Theory and Machine Learning Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Hypothetical Analogy between ML and Economic Entities",
"architecture_description": "Constructs a formal analogical argument mapping machine learning systems (source) to economic entities (target). Identifies positive analogies in adaptive optimization under constraints. Leverages deep learning su... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Explicit rule-based constraints in optimization problems drive agents to boundary solutions, exploiting loopholes in laws or reward functions. Formalizing moral behavior via rigid rules fails due to the exponential complexity of required constraints in high-dimensional action spaces.",
"key_insight... | {
"main_results": [
{
"dataset": "Theoretical Case Study",
"metric": "Analogy Strength",
"value": "Structural Correspondence Established",
"comparison": "Maps ML implicit specification success to economic principle-based regulation"
},
{
"dataset": "Legal Framework Analysis",... | 2001.00006 | DAS-2M-v1.0 | null | null |
{
"title": "A generalization of the symmetrical and optimal probability-to-possibility transformations",
"authors": [
"Esteve del Acebo",
"Yousef Alizadeh-Q",
"Sayyed Ali Hossayn"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Uncertainty Modeling",
"keywords": [
"Probability-Possibility Transformation",
"Fuzzy Specificity",
"Authorship Attribution",
"Parametric Generalization"
]
} | {
"pipeline_type": "Parametric Mathematical Transformation",
"learning_paradigm": "Analytical Derivation",
"knowledge_source": "Dubois-Prade Uncertainty Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Generalized Probability-to-Possibility Transformation",
"architecture_description": "Defines parametric family $\\pi_{G}(w_i) = \\sum p(w_j) \\cdot min(1, (p(w_i)/p(w_j))^n)$ bridging symmetrical ($n=1$) and optimal ($n \\to \\infty$) cases. Controls trade-off between continuity and specificity vi... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Custom Mail Database (800 mails, 40 authors)"
]
} | {
"motivation": "Symmetrical transformation lacks specificity while optimal transformation suffers from discontinuity causing counter-intuitive jumps under small probability perturbations.",
"key_insights": "Increasing parameter $n$ monotonically increases specificity without sacrificing continuity. Power-law distr... | {
"main_results": [
{
"dataset": "Mail Database (Ridge Regression)",
"metric": "Accuracy",
"value": "Highest Overall",
"comparison": "Optimal range $n \\in [4, 10]$ outperforms $n=1$ and $n \\to \\infty$"
},
{
"dataset": "Mail Database (Passive-Aggressive)",
"metric": "... | 2001.00007 | DAS-2M-v1.0 | null | null |
{
"title": "COMPUTATIONAL MODEL DISCOVERY WITH REINFORCEMENT LEARNING",
"authors": [
"Maxime Bassenne",
"Adrián Lozano-Durán"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Scientific Model Discovery",
"keywords": [
"Reinforcement Learning",
"Symbolic Regression",
"Reduced-Order Modeling",
"Computational Physics",
"Domain-Specific Language"
]
} | {
"pipeline_type": "RL-Guided Symbolic Search",
"learning_paradigm": "Deep Reinforcement Learning (Policy Gradient)",
"knowledge_source": "Integral Quantities from Reference Solutions",
"backbone_model": "Deep Deterministic Policy Gradients (DDPG)"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Model Discovery with Reinforcement Learning (MDRL)",
"architecture_description": "MDRL employs a Random Model Generator (RMG) parameterized by a neural network policy to sample mathematical expressions encoded in a Domain-Specific Language (DSL). The agent samples models, evaluates them a posterio... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Burgers' Equation Simulation Data"
]
} | {
"motivation": "Traditional reduced-order modeling relies on human intuition and phenomenological understanding, limiting exploration of the vast model space and introducing biases that constrain solutions to suboptimal forms.",
"key_insights": "Reinforcement learning agents can efficiently navigate the combinator... | {
"main_results": [
{
"dataset": "Burgers' Equation",
"metric": "Probability of Exact Solution Discovery",
"value": "99%",
"comparison": "Achieved in ~220 iterations vs O(10^9) for random search"
},
{
"dataset": "Burgers' Equation",
"metric": "Discovered Term",
"v... | 2001.00008 | DAS-2M-v1.0 | null | null |
{
"title": "Deep Reinforced Self-Attention Masks for Abstractive Summarization (DR.SAS)",
"authors": [
"Ankit Chadha",
"Mohamed Masoud"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Abstractive Summarization",
"keywords": [
"Reinforcement Learning",
"Self-Attention Masks",
"Actor-Critic",
"UniLM"
]
} | {
"pipeline_type": "Reinforcement Learning Policy Gradient",
"learning_paradigm": "Fine-tuning with RLHF-style Rewards",
"knowledge_source": "CNN/Daily Mail Dataset",
"backbone_model": "UniLM"
} | {
"has_code": true,
"github_url": "https://github.com/example/drsas",
"project_page": null
} | {
"method_name": "DR.SAS",
"architecture_description": "Modifies UniLM encoder to integrate Advantage Actor-Critic (A2C) agent. Agent observes intermediate attention score distributions as states. Actor network samples binary attention masks (attend/mask) per token. Critic estimates state value to reduce variance. ... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"CNN/Daily Mail"
]
} | {
"motivation": "Standard abstractive models suffer from exposure bias and generate superfluous details due to lack of human quality assessment during training. Global attention mechanisms process redundant information inefficiently.",
"key_insights": "Learning dynamic attention masks via RL allows the model to ign... | {
"main_results": [
{
"dataset": "CNN/Daily Mail",
"metric": "ROUGE-1/2/L",
"value": "Marginal Improvement",
"comparison": "Outperforms Fine-tuned UniLM and TextRank baseline"
}
],
"limitations": "High computational cost and GPU memory requirements hinder convergence; ROUGE reward ... | 2001.00009 | DAS-2M-v1.0 | null | null |
{
"title": "Traversable Wormholes in R + αRn Gravity",
"authors": [
"Nisha Godani",
"Gauranga C. Samanta"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Modified Gravity Cosmology",
"keywords": [
"f(R) Gravity",
"Traversable Wormhole",
"Energy Conditions",
"Exotic Matter",
"Shape Function"
]
} | {
"pipeline_type": "Analytical Solution Derivation",
"learning_paradigm": "Theoretical Physics Modeling",
"knowledge_source": "Einstein-Hilbert Action Modification",
"backbone_model": "f(R) = R + alpha*R^n"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "f(R) Gravity Wormhole Analysis",
"architecture_description": "Modifies Einstein-Hilbert action to f(R) = R + alpha*R^n. Adopts spherically symmetric static metric with shape function b(r) = r*exp(r-r0) and redshift functions Phi(r) = constant or Phi(r) = ln(r/r0 + 1). Derives effective field equat... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "General Relativity requires exotic matter violating Null Energy Condition (NEC) to sustain traversable wormholes. Modified f(R) gravity offers potential to minimize or eliminate exotic matter requirements through curvature corrections.",
"key_insights": "Variable redshift function Phi(r) = ln(r/r0 ... | {
"main_results": [
{
"dataset": "Constant Redshift Case",
"metric": "Energy Condition Validity",
"value": "r > 2",
"comparison": "Achieved for n=1, alpha < 0; avoids exotic matter beyond throat"
},
{
"dataset": "Variable Redshift Case",
"metric": "Energy Condition Vali... | 2001.00010 | DAS-2M-v1.0 | null | null |
{
"title": "Dark Energy and Modified Scale Covariant Theory of Gravitation",
"authors": [
"Koijam Manihar Singh",
"Sanjay Mandal",
"Longjam Parbati Devi"
],
"publication_year": 2019,
"publication_date": "2019-12"
} | {
"task_category": "Theoretical Cosmology",
"keywords": [
"Scale Covariant Theory",
"Dark Energy",
"Modified Gravity",
"Big Rip Singularity",
"Quintessence"
]
} | {
"pipeline_type": "Analytical Solution of Field Equations",
"learning_paradigm": "N/A",
"knowledge_source": "Canuto et al. (1977) Scale Covariant Formalism",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Modified Scale Covariant Gravitation with Interaction Term Q",
"architecture_description": "Implements Canuto's scale covariant theory using conformal transformation $g _ { i j } ^ { \\prime } = \\phi ^ { 2 } g _ { i j }$. Constructs Friedmann-Robertson-Walker (FRW) field equations incorporating v... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard General Relativity requires dark energy to explain accelerated expansion; modified gravity theories offer alternative mechanisms but often neglect explicit dark sector interactions within scale covariant frameworks.",
"key_insights": "Gauge function $\\phi(t)$ actively enhances energy exch... | {
"main_results": [
{
"dataset": "Model II ($\\alpha=1/2$)",
"metric": "Statefinder Parameters",
"value": "{r=1, s=0}",
"comparison": "Matches standard $\\Lambda$CDM model trajectory"
},
{
"dataset": "Model IV",
"metric": "Singularity Type",
"value": "Big Rip at $... | 2001.00011 | DAS-2M-v1.0 | null | null |
{
"title": "Differentially Private M-band Wavelet-Based Mechanisms in Machine Learning Environments",
"authors": [
"Kenneth Choi",
"Tony Lee",
"Xiaodi Wang"
],
"publication_year": 2019,
"publication_date": "2019-08"
} | {
"task_category": "Differential Privacy",
"keywords": [
"M-band Wavelet Transform",
"Input Perturbation",
"Laplace-Sigmoid Distribution",
"Pseudo-Quantum Steganography",
"Machine Learning Privacy"
]
} | {
"pipeline_type": "Wavelet Domain Noise Embedding",
"learning_paradigm": "Supervised Learning with Input Perturbation",
"knowledge_source": "IPUMS Census Data, MNIST Image Dataset",
"backbone_model": "Logistic Regression, SVM, CNN, Classical ANN"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "LS, LS+, and Pseudo-Quantum Mechanisms",
"architecture_description": "Transforms datasets via Discrete M-band Wavelet Transform (DMWT) into approximation and detail subspaces. LS/LS+ mechanisms inject Laplace-Sigmoid distributed noise into approximation coefficients scaled by data-sensitive bounds... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"IPUMS",
"MNIST"
]
} | {
"motivation": "Standard Laplace mechanisms introduce excessive noise hindering statistical utility; Haar wavelets allow easier adversarial de-noising; need for privacy mechanisms compatible with complex ML models like CNNs.",
"key_insights": "M-band wavelets provide smoother decomposition than Haar, resisting de-... | {
"main_results": [
{
"dataset": "IPUMS",
"metric": "Classification Accuracy",
"value": "100%",
"comparison": "Achieved by LS/LS+ at epsilon=8 in Logistic Regression and Classical ANN"
},
{
"dataset": "MNIST",
"metric": "Classification Accuracy",
"value": "99.14%"... | 2001.00012 | DAS-2M-v1.0 | null | null |
{
"title": "An Extension of the Cardioid Distributions on Circle",
"authors": [
"Erfan Salavati"
],
"publication_year": null,
"publication_date": null
} | {
"task_category": "Circular Statistics",
"keywords": [
"Quadratic Cardioid Distribution",
"Fourier Series",
"Probability Density Function",
"Von Mises Distribution"
]
} | {
"pipeline_type": "Analytical Derivation",
"learning_paradigm": "Theoretical Probability",
"knowledge_source": "Mathematical Analysis",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Quadratic Cardioid (QC) Distribution",
"architecture_description": "Defines probability density function via squared modulus of complex polynomial $1 + r_1 e^{i(\theta - \\mu_1)} + r_2 e^{-i(\theta - \\mu_2)}$. Parameters include two location angles and two non-negative radii. Normalization consta... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard Cardioid distributions limited to unimodal symmetric shapes; Von Mises distributions require numerical integration for normalization. Need flexible analytical family supporting bimodality and asymmetry.",
"key_insights": "QC distribution constitutes a subset of measures with Fourier series... | {
"main_results": [],
"limitations": "Completeness of parametrization for entire space $\\mathcal{T}_2^\\pi$ remains unproven; analytical mode calculation requires solving quartic equations."
} | 2001.00013 | DAS-2M-v1.0 | null | null |
{
"title": "Quantum algorithms for the Goldreich-Levin learning problem",
"authors": [
"Hongwei Li"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Quantum Learning Theory",
"keywords": [
"Goldreich-Levin Algorithm",
"Walsh Spectrum",
"Boolean Function",
"Deutsch-Jozsa Algorithm",
"Query Complexity"
]
} | {
"pipeline_type": "Sampling-based Spectral Estimation",
"learning_paradigm": "Quantum Query Model",
"knowledge_source": "Oracle Access to Boolean Functions",
"backbone_model": "Deutsch-Jozsa Circuit"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Quantum Goldreich-Levin Algorithm",
"architecture_description": "Algorithm iteratively executes Deutsch-Jozsa circuit to sample vectors w from probability distribution P(w) = S_f^2(w). Maintains frequency counters for observed vectors across l = O(epsilon^-4 log delta^-1) trials. Selects vectors e... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Classical Goldreich-Levin algorithms incur polynomial dependence on input dimension n and higher epsilon powers. Existing quantum approaches utilize Grover-like amplitude amplification or divide-and-conquer strategies still scaling with n.",
"key_insights": "Repeated Deutsch-Jozsa execution generat... | {
"main_results": [
{
"dataset": "Theoretical Analysis",
"metric": "Query Complexity",
"value": "O(log(1/delta)/epsilon^4)",
"comparison": "Independent of n vs O(n/epsilon^2) classical"
},
{
"dataset": "Multi-output Extension",
"metric": "Query Complexity",
"value... | 2001.00014 | DAS-2M-v1.0 | null | null |
{
"title": "Average angles of triangle in regular polygons",
"authors": [
"Herman Muzychko"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Computational Geometry",
"keywords": [
"Regular Polygons",
"Average Angle Calculation",
"Numerical Integration",
"Geometric Symmetry"
]
} | {
"pipeline_type": "Monte Carlo Numerical Simulation",
"learning_paradigm": "Analytical Derivation and Empirical Verification",
"knowledge_source": "Euclidean Geometry and Calculus",
"backbone_model": null
} | {
"has_code": true,
"github_url": null,
"project_page": null
} | {
"method_name": "Discrete Summation Approximation",
"architecture_description": "Formulates average angles via double integrals over unit square domain using law of cosines. Replaces analytical integration with discrete summation algorithm iterating 1000x1000 grid points. Computes side lengths $a, b$ from Cartesia... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Analytical evaluation of double integrals for average angles in regular polygons proves computationally intractable using standard Taylor series expansions.",
"key_insights": "Spatial averaging of triangle angles within regular polygons yields values identical to those measured at the geometric cen... | {
"main_results": [
{
"dataset": "Unit Square Simulation",
"metric": "Average Angle Convergence",
"value": "Matches Center Geometry",
"comparison": "Validates symmetry hypothesis numerically"
}
],
"limitations": "Relies on numerical approximation rather than rigorous analytical pro... | 2001.00015 | DAS-2M-v1.0 | null | null |
{
"title": "Proof of the tree module property for exceptional representations of tame quivers",
"authors": [
"Szabolcs Lénárt",
"Ábel Lőrinczi",
"Csaba Szántó"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Representation Theory Verification",
"keywords": [
"Tame Quivers",
"Tree Modules",
"Exceptional Representations",
"Computer-Assisted Proof",
"Field Independence"
]
} | {
"pipeline_type": "Symbolic Block-Matrix Computation",
"learning_paradigm": "Formal Verification via Induction",
"knowledge_source": "Auslander-Reiten Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Field-Independent Proof Assistant",
"architecture_description": "Software parses LaTeX-defined block matrices (zero, identity, secondary diagonal) to verify tree module properties. Executes field-independent Gaussian echelonization ensuring operations yield identical formal results across all fiel... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"\\widetilde{\\mathbb{E}}_6 quiver representations",
"\\widetilde{\\mathbb{D}}_6 quiver representations"
]
} | {
"motivation": "Existing proofs for tree representations lack explicit general formulas and often rely on algebraically closed fields, limiting applicability. Manual verification of sparse matrix forms for infinite families of exceptional modules is error-prone and computationally infeasible.",
"key_insights": "Tr... | {
"main_results": [
{
"dataset": "\\widetilde{\\mathbb{E}}_6",
"metric": "Proof Completeness",
"value": "All exceptional families verified",
"comparison": "Extends previous partial results to full canonical orientation"
},
{
"dataset": "\\widetilde{\\mathbb{D}}_6",
"met... | 2001.00016 | DAS-2M-v1.0 | null | null |
{
"title": "Loops and trees in generic EFTs",
"authors": [
"Nathaniel Craig",
"Minyuan Jiang",
"Ying-Ying Li"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Effective Field Theory Classification",
"keywords": [
"Standard Model EFT",
"Helicity Amplitudes",
"Tree-Loop Classification",
"Non-Renormalization Theorems",
"Dimension 8 Operators"
]
} | {
"pipeline_type": "Functional Matching and Generalized Unitarity",
"learning_paradigm": "Analytical Derivation",
"knowledge_source": "Generic 4d EFT of Massless Scalars, Fermions, and Vectors",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Tree/Loop Classification via Functional Methods and Helicity Coordinates",
"architecture_description": "Utilizes functional matching to integrate out heavy fields in generic renormalizable UV completions, deriving tree-level operator coefficients up to dimension 8. Classifies operators and amplitu... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Existing understanding of non-interference and non-renormalization theorems remains incomplete beyond dimension 6, limiting precision interpretation of LHC data and future collider signals in the high-energy limit.",
"key_insights": "Tree-level generated operators at dimension 6 and 8 mirror the co... | {
"main_results": [
{
"dataset": "Analytical Calculation",
"metric": "Rational Part Contribution",
"value": "Zero",
"comparison": "Vanishes for $|h|=0$ amplitudes with tree-level dimension 6 operator insertions"
},
{
"dataset": "Operator Classification",
"metric": "Tree... | 2001.00017 | DAS-2M-v1.0 | null | null |
{
"title": "Connecting Optical Morphology, Environment, and H I Mass Fraction for Low-Redshift Galaxies Using Deep Learning",
"authors": [
"John F. Wu"
],
"publication_year": 2021,
"publication_date": "2021-01"
} | {
"task_category": "Galaxy Property Regression",
"keywords": [
"Deep Learning",
"Convolutional Neural Networks",
"H I Mass Fraction",
"Optical Morphology",
"Pattern Recognition",
"Grad-CAM"
]
} | {
"pipeline_type": "Dual-Stage CNN (Regression + Classification)",
"learning_paradigm": "Supervised Learning",
"knowledge_source": "SDSS gri Imaging",
"backbone_model": "xresnet-34"
} | {
"has_code": true,
"github_url": "https://github.com/jwuphysics/HI-convnets",
"project_page": null
} | {
"method_name": "CNN-based H I Estimation with Pattern Recognition Filtering",
"architecture_description": "Implements xresnet-34 with Mish activation and self-attention layers to regress logarithmic H I mass fraction from SDSS gri cutouts. Deploys a secondary binary classification CNN for pattern recognition to f... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"ALFALFA alpha.40",
"ALFALFA alpha.100",
"NIBLES",
"xGASS"
]
} | {
"motivation": "Direct H I measurement via 21-cm emission remains observationally expensive and limited to low redshifts, necessitating robust proxies linking optical morphology to gas content.",
"key_insights": "Deep CNNs extract morphological cues correlating with H I mass fraction more effectively than traditio... | {
"main_results": [
{
"dataset": "ALFALFA alpha.100",
"metric": "RMSE",
"value": "0.20 dex",
"comparison": "Outperforms FCNN baseline (0.27 dex) with p_CNN > 0.9 cut"
},
{
"dataset": "xGASS",
"metric": "RMSE",
"value": "0.22 dex",
"comparison": "Significant ... | 2001.00018 | DAS-2M-v1.0 | null | null |
{
"title": "Not all doped Mott insulators have a pseudogap: key role of van Hove singularities",
"authors": [
"Wei Wu",
"Mathias S. Scheurer",
"Michel Ferrero"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Condensed Matter Theory",
"keywords": [
"Pseudogap",
"Mott Insulator",
"van Hove Singularity",
"Hubbard Model",
"Dynamical Cluster Approximation"
]
} | {
"pipeline_type": "Numerical Many-Body Simulation",
"learning_paradigm": "First-Principles Numerical Calculation",
"knowledge_source": "Hubbard Model (Square and Triangular Lattices)",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Dynamical Cluster Approximation (DCA)",
"architecture_description": "Employ DCA to solve Hubbard models on square and triangular lattices with varying hopping parameters. Calculate spectral functions A(k,w) and self-energies Sigma(k,iw_n). Analyze proximity of van Hove singularities to the Hartree... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Existing theories attribute pseudogap formation primarily to short-range antiferromagnetic correlations or Mottness, failing to explain particle-hole asymmetry and lattice-dependence in doped cuprates.",
"key_insights": "Pseudogap existence depends sensitively on non-interacting band parameters rat... | {
"main_results": [
{
"dataset": "Triangular Lattice (Phi=3pi/4)",
"metric": "Spectral Function Gap",
"value": "Present",
"comparison": "Absent at Phi=0 despite identical magnetic correlations"
},
{
"dataset": "Square Lattice (t'=-0.2)",
"metric": "Antinodal Scattering"... | 2001.00019 | DAS-2M-v1.0 | null | null |
{
"title": "Warped Flat Spacetimes in Three-Dimensional Topologically Massive Gravity",
"authors": [
"Stéphane Detournay",
"Wout Merbis",
"Gim Seng Ng"
],
"publication_year": 2019,
"publication_date": "2019-04"
} | {
"task_category": "Holographic Entropy Derivation",
"keywords": [
"Topologically Massive Gravity",
"Warped Conformal Field Theory",
"Asymptotic Symmetry Analysis",
"Cosmological Horizons"
]
} | {
"pipeline_type": "Chern-Simons-like First-Order Formalism",
"learning_paradigm": "Analytical Derivation",
"knowledge_source": "Warped Flat Quotient Geometries",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Centerless Warped CFT Entropy Matching",
"architecture_description": "Constructs consistent boundary conditions for warped flat quotients within Topologically Massive Gravity using first-order Chern-Simons-like formulation. Computes asymptotic charges via covariant phase space methods to derive a ... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Microscopic origin of gravitational entropy for cosmological horizons remains elusive compared to black hole microstates; existing holographic duals rely on non-vanishing central extensions.",
"key_insights": "Warped flat quotients possess asymptotic symmetry algebra of centerless Warped CFT. Gener... | {
"main_results": [
{
"dataset": "Warped Flat Quotient Geometry",
"metric": "Entropy Matching",
"value": "Exact Match",
"comparison": "Reproduces Bekenstein-Hawking entropy S = 2π√(P0 L0) via centerless Cardy formula"
},
{
"dataset": "Asymptotic Symmetry Algebra",
"metr... | 2001.00020 | DAS-2M-v1.0 | null | null |
{
"title": "Efficient classical simulation of random shallow 2D quantum circuits",
"authors": [
"John Napp",
"Rolando L. La Placa",
"Alexander M. Dalzell"
],
"publication_year": 2020,
"publication_date": "2020-03"
} | {
"task_category": "Quantum Circuit Simulation",
"keywords": [
"Random Quantum Circuits",
"Classical Simulation",
"Entanglement Phase Transition",
"Matrix Product States",
"Statistical Mechanics Mapping"
]
} | {
"pipeline_type": "Space-Evolving Block Decimation (SEBD) and Patching",
"learning_paradigm": "Tensor Network Contraction with Truncation",
"knowledge_source": "Effective 1D Unitary-and-Measurement Dynamics",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Space-Evolving Block Decimation (SEBD)",
"architecture_description": "SEBD reduces 2D shallow circuit simulation to 1D dynamics by sweeping column-wise, maintaining a Matrix Product State (MPS) within the causal lightcone. The algorithm alternates between applying unitary gates in the lightcone an... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Brickwork Architecture (Depth-3)",
"Cluster State with Haar-Random Measurements (CHR)"
]
} | {
"motivation": "Prevailing conjectures suggest random quantum circuit simulation is as hard as worst-case instances, underpinning quantum supremacy claims. Existing tensor network methods scale exponentially with treewidth, rendering deep 2D circuits intractable.",
"key_insights": "Shallow random 2D circuits map t... | {
"main_results": [
{
"dataset": "Brickwork Architecture (409x409)",
"metric": "Runtime per Sample",
"value": "~1 minute",
"comparison": "Intractable for standard tensor network contraction (estimated > Summit supercomputer capacity)"
},
{
"dataset": "CHR Model (50x50)",
... | 2001.00021 | DAS-2M-v1.0 | null | null |
{
"title": "Rademacher Expansions and the Spectrum of 2d CFT",
"authors": [
"Luis F. Alday",
"Jin-Beom Bae"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Spectral Density Derivation",
"keywords": [
"2d CFT",
"Rademacher Expansion",
"Modular Invariance",
"Pure Gravity",
"Negative Norm States"
]
} | {
"pipeline_type": "Analytic Number Theory Application",
"learning_paradigm": "Exact Analytical Derivation",
"knowledge_source": "Modular Constraints and Polar Terms",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Rademacher Expansion for Non-Holomorphic Forms",
"architecture_description": "Constructs spectral density via Rademacher series summing over PSL(2,Z) images of polar terms. Deforms integration contour along Ford circles to isolate essential singularities at rational points. Derives convergent expr... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Full modular invariance constraints on 2d CFT spectrum with c > 1 remain incompletely characterized by asymptotic formulas like Cardy's. Existing Poincare constructions for pure gravity duals suffer from negative norm states and continuous spectra.",
"key_insights": "Rademacher expansion yields con... | {
"main_results": [
{
"dataset": "Pure Gravity Dual",
"metric": "Spectral Density Sign",
"value": "Negative for odd spins",
"comparison": "Matches MWK Poincare construction negativity"
},
{
"dataset": "Large Spin Limit",
"metric": "Asymptotic Behavior",
"value": "... | 2001.00022 | DAS-2M-v1.0 | null | null |
{
"title": "Evidence for a Non-Supersymmetric 5d CFT from Deformations of 5d SU(2) SYM",
"authors": [
"Pietro Benetti Genolini",
"Masazumi Honda",
"Hee-Cheol Kim"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "High-Energy Theory / Conformal Field Theory",
"keywords": [
"5d CFT",
"Supersymmetry Breaking",
"Chern-Simons Terms",
"RG Flow",
"Topological Phases"
]
} | {
"pipeline_type": "Theoretical Derivation via Anomaly Matching",
"learning_paradigm": "Analytical Field Theory",
"knowledge_source": "Seiberg-Witten Theory and Brane Constructions",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Supersymmetry-Breaking Deformation Analysis",
"architecture_description": "Deforms the N=1 5d E1 fixed point using relevant scalar operators M and mu to break supersymmetry while preserving U(1) global symmetries. Computes induced background Chern-Simons levels for U(1)_R and U(1)_I by integrating... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Discovery of interacting strongly coupled fixed points in d=4+1 dimensions remains challenging due to lack of relevant operators in free theories; existing examples predominantly rely on supersymmetry.",
"key_insights": "Discontinuities in background Chern-Simons levels across the h=0 axis imply em... | {
"main_results": [
{
"dataset": "Theoretical Phase Diagram",
"metric": "Chern-Simons Level k_I",
"value": "-2 sign(h)",
"comparison": "Jump indicates phase transition at h=0"
},
{
"dataset": "Theoretical Phase Diagram",
"metric": "Chern-Simons Level k_R",
"value"... | 2001.00023 | DAS-2M-v1.0 | null | null |
{
"title": "Reliability of lattice gauge theories",
"authors": [
"Jad C. Halimeh",
"Philipp Hauke"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Quantum Simulation Reliability",
"keywords": [
"Lattice Gauge Theory",
"Gauge Invariance Violation",
"Quantum Simulator",
"Emergent Symmetry",
"Out-of-Equilibrium Dynamics"
]
} | {
"pipeline_type": "Exact Diagonalization and Perturbation Theory",
"learning_paradigm": "Analytic Proof and Numerical Simulation",
"knowledge_source": "Z2 and U(1) Lattice Gauge Models",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Energy Penalty Protection Scheme",
"architecture_description": "Introduces protection term V*H_G to Hamiltonian H_0 + lambda*H_1. Term energetically penalizes states violating local Gauss's law G_j. Creates large energy gap Delta proportional to V between gauge-invariant sector and violating secto... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Z2 Lattice Gauge Theory",
"U(1) Quantum Link Model"
]
} | {
"motivation": "Practical quantum simulators cannot achieve perfect local gauge invariance due to experimental imperfections. Unchecked gauge-violating errors cause dynamics to proliferate into incorrect Hilbert space sectors, invalidating simulation results for large-scale systems.",
"key_insights": "Gauge violat... | {
"main_results": [
{
"dataset": "Z2 Gauge Theory",
"metric": "Infinite-time Gauge Violation",
"value": "Scales as (lambda/V)^2",
"comparison": "Suppressed indefinitely in controlled-violation regime"
},
{
"dataset": "U(1) Gauge Theory",
"metric": "Observable Deviation ... | 2001.00024 | DAS-2M-v1.0 | null | null |
{
"title": "Over 78 000 RR Lyrae Stars in the Galactic Bulge and Disk from the OGLE Survey",
"authors": [
"I. Soszynski",
"A. Udalski",
"M. Wrona"
],
"publication_year": 2019,
"publication_date": "2019-12"
} | {
"task_category": "Stellar Variability Cataloging",
"keywords": [
"RR Lyrae Stars",
"Galactic Bulge",
"Galactic Disk",
"OGLE Survey",
"Petersen Diagram"
]
} | {
"pipeline_type": "Photometric Time-Series Analysis",
"learning_paradigm": "Template Fitting and Fourier Decomposition",
"knowledge_source": "OGLE Galaxy Variability Survey (GVS)",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": "http://ogle.astrouw.edu.pl"
} | {
"method_name": "OGLE Collection of Variable Stars (OCVS) Upgrade",
"architecture_description": "Pipeline executes Discrete Fourier Transform via FNPEAKS on billion-star I-band light curves. Candidates filtered by period limits (<50 d) and signal-to-noise ratios. Classification employs template fitting against cur... | {
"contributed_new_dataset": true,
"new_dataset_name": "OGLE Collection of Galactic RR Lyr Stars (78,350 entries)",
"datasets_used_for_eval": [
"Gaia DR2",
"Pan-STARRS",
"ASAS-SN",
"VVV Survey"
]
} | {
"motivation": "Previous wide-field surveys avoided Galactic bulge and disk due to crowding and blending, resulting in incomplete catalogs for old stellar populations in these critical regions.",
"key_insights": "Double-mode RRd incidence rate correlates strongly with metallicity, increasing from 0.5% in bulge to ... | {
"main_results": [
{
"dataset": "OGLE GVS Fields",
"metric": "Total Sample Size",
"value": "78,350",
"comparison": "Doubles previous OCVS count; 40% are new detections"
},
{
"dataset": "Overlap Regions",
"metric": "Completeness",
"value": "94%",
"comparison... | 2001.00025 | DAS-2M-v1.0 | null | null |
{
"title": "The 2020 Skyrmionics Roadmap",
"authors": [
"C. Back",
"V. Cros",
"H. Ebert"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Topological Spintronics Review",
"keywords": [
"Magnetic Skyrmions",
"Dzyaloshinskii-Moriya Interaction",
"Spintronics",
"Topological Hall Effect",
"Racetrack Memory"
]
} | {
"pipeline_type": "Multi-Section Expert Review",
"learning_paradigm": "Theoretical Modeling and Experimental Synthesis",
"knowledge_source": "Condensed Matter Physics Literature (2009-2019)",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Skyrmionics Roadmap Framework",
"architecture_description": "Synthesizes status, challenges, and advances across 14 domains: bulk materials, frustrated magnets, non-equilibrium dynamics, creation/annihilation mechanisms, collective excitations, emergent electrodynamics, particle models, DFT calcul... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"MnSi",
"FeGe",
"Cu2OSeO3",
"Co-Zn-Mn alloys",
"GaV4S8",
"Fe/Ir(111)"
]
} | {
"motivation": "Transition skyrmion research from fundamental discovery to practical spintronic applications requires resolving conflicts between thermal stability, small size, and low-current manipulability.",
"key_insights": "Interfacial DMI in multilayers enables room-temperature stability but introduces grain ... | {
"main_results": [
{
"dataset": "Pt/Co/Ir Multilayers",
"metric": "Skyrmion Diameter",
"value": "<100 nm",
"comparison": "Stable at Room Temperature"
},
{
"dataset": "MnSi Nanowires",
"metric": "Magnetoresistance",
"value": "Quantized Jumps",
"comparison": ... | 2001.00026 | DAS-2M-v1.0 | null | null |
{
"title": "Universal bounds on the size of a black hole",
"authors": [
"Run-Qiu Yang",
"H. Lü"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Theoretical General Relativity",
"keywords": [
"Black Hole Thermodynamics",
"Photon Sphere",
"Shadow Area",
"Penrose Inequality",
"Variational Method"
]
} | {
"pipeline_type": "Analytical Proof via Variational Calculus",
"learning_paradigm": "N/A",
"knowledge_source": "Einstein Gravity and Energy Conditions",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Bondi-Sachs Variational Formalism",
"architecture_description": "Utilizes Bondi-Sachs null foliation to parameterize static spacetimes without spherical symmetry. Constructs auxiliary functionals bounding shadow and photon sphere areas. Applies variational calculus with Lagrange multipliers enforc... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Horizon area unobservable directly; photon spheres and shadows provide observable proxies for black hole size. Existing bounds limited to spherical symmetry or specific matter fields. Need universal inequalities valid for general static configurations to interpret astrophysical shadow observations.",... | {
"main_results": [
{
"dataset": "Spherically Symmetric Case",
"metric": "Inequality Proof",
"value": "Complete",
"comparison": "Extends Hod's bound $r _ { \\mathrm { p h } } \\leq 3 M$ to area relations"
},
{
"dataset": "General Static Case",
"metric": "Partial Inequal... | 2001.00027 | DAS-2M-v1.0 | null | null |
{
"title": "Cyclic reduction densities for elliptic curves",
"authors": [
"Francesco Campagna",
"Peter Stevenhagen"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Arithmetic Geometry",
"keywords": [
"Elliptic Curves",
"Cyclic Reduction",
"Artin Primitive Root Problem",
"Division Fields",
"Complex Multiplication",
"Chebotarev Density Theorem"
]
} | {
"pipeline_type": "Theoretical Proof and Factorization",
"learning_paradigm": "Analytic Number Theory",
"knowledge_source": "Galois Representations and Class Field Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Inclusion-Exclusion Density Factorization",
"architecture_description": "Decomposes heuristic density δE/K into finite rational sum δE/K(n) and infinite Artin-type product. Distinguishes non-CM, CM-over-K, and CM-not-over-K cases. Utilizes Serre's Open Image Theorem for finite entanglement in non-... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"LMFDB (L-functions and Modular Forms Database)"
]
} | {
"motivation": "Heuristic inclusion-exclusion sums for cyclic reduction density converge slowly and obscure vanishing conditions. Determining infinitude of cyclic reduction primes unconditionally remains open when density is positive.",
"key_insights": "Finite entanglement of division fields allows factorization i... | {
"main_results": [
{
"dataset": "Non-CM Curves over Q",
"metric": "Density Approximation Error",
"value": "< 0.0006",
"comparison": "Theoretical density matches numerical count of 78,498 primes below 10^6"
},
{
"dataset": "CM Curve y^2=x^3+x",
"metric": "Cyclic Reducti... | 2001.00028 | DAS-2M-v1.0 | null | null |
{
"title": "A general formulation of time-optimal quantum control and optimality of singular protocols",
"authors": [
"Hiroaki Wakamura",
"Tatsuhiko Koike"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Time-Optimal Quantum Control",
"keywords": [
"Pontryagin's Maximum Principle",
"Singular Controls",
"Quantum Brachistochrone",
"Inequality Constraints",
"Drift Hamiltonian"
]
} | {
"pipeline_type": "Variational Optimization with Inequality Constraints",
"learning_paradigm": "Optimal Control Theory (Analytical)",
"knowledge_source": "Pontryagin's Maximum Principle and Generalized Legendre-Clebsch Condition",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "MP-QB (Maximum Principle-Quantum Brachistochrone)",
"architecture_description": "Formulates time-optimal control via Pontryagin's Maximum Principle on augmented system $\\mathbb{R} \\times \\mathrm{SU}(N)$. Defines Pontryagin Hamiltonian $H_{\\mathrm{MP}} = -1 + \\mathrm{tr}[HF]$. Derives necessar... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Landau-Zener Model",
"One-Qubit System",
"Symmetric Two-Qubit System"
]
} | {
"motivation": "Existing Quantum Brachistochrone (QB) theory handles only equality constraints, failing when drift fields prevent reducing inequality constraints (e.g., finite energy bandwidth) to equalities.",
"key_insights": "Constraints classified as lollipop-type ($H_d \\in \\mathcal{C}$) admit no optimal sing... | {
"main_results": [
{
"dataset": "Landau-Zener Model",
"metric": "Optimality Verification",
"value": "Bang-off-bang control confirmed optimal",
"comparison": "Singular off-control ($u=0$) satisfies GLC conditions"
},
{
"dataset": "One-Qubit System",
"metric": "Optimalit... | 2001.00029 | DAS-2M-v1.0 | null | null |
{
"title": "Quantum Adversarial Machine Learning",
"authors": [
"Sirui Lu",
"Lu-Ming Duan",
"Dong-Ling Deng"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Adversarial Robustness in Quantum Machine Learning",
"keywords": [
"Quantum Classifiers",
"Adversarial Attacks",
"Adversarial Training",
"Topological Phases",
"Quantum Data Classification"
]
} | {
"pipeline_type": "Variational Quantum Circuit Classifier",
"learning_paradigm": "Supervised Learning with Adversarial Optimization",
"knowledge_source": "Classical Images (MNIST), Simulated Time-of-Flight Images, Quantum Ground States",
"backbone_model": "Hardware-efficient Variational Quantum Circuit"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Quantum Adversarial Learning Framework",
"architecture_description": "Utilizes amplitude encoding to map input data into $n$-qubit states. Employs a variational quantum circuit with interleaved rotation units ($Z$ and $X$ gates) and entangler units (CNOT gates). Optimizes parameters via Adam optim... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"MNIST",
"Simulated Time-of-Flight Images (QAH Model)",
"Transverse Field Ising Model Ground States"
]
} | {
"motivation": "Quantum machine learning systems face security risks analogous to classical deep learning, yet vulnerability to crafted adversarial perturbations remains unexplored despite rapid algorithmic development.",
"key_insights": "Quantum classifiers suffer from high susceptibility to imperceptible perturb... | {
"main_results": [
{
"dataset": "MNIST (Binary)",
"metric": "Accuracy under BIM Attack",
"value": "0%",
"comparison": "Drops from 98% clean accuracy after 5 iterations"
},
{
"dataset": "MNIST (Transfer Attack)",
"metric": "Accuracy on CNN-generated Adversarial Examples... | 2001.00030 | DAS-2M-v1.0 | null | null |
{
"title": "Effective LQC model for $k = + 1$ isotropic cosmologies from spatial discretisations",
"authors": [
"Klaus Liegener",
"Stefan Andreas Weigl"
],
"publication_year": 2021,
"publication_date": "2021-03"
} | {
"task_category": "Loop Quantum Cosmology",
"keywords": [
"Thiemann Regularisation",
"Closed FLRW Universe",
"Spatial Discretisation",
"Asymmetric Big Bounce",
"Effective Hamiltonian"
]
} | {
"pipeline_type": "Classical Discretisation with Effective Dynamics",
"learning_paradigm": "Semi-Classical Approximation",
"knowledge_source": "Ashtekar-Barbero Variables on Hyperspherical Lattice",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Thiemann-Regularised Effective Constraint",
"architecture_description": "Discretises scalar constraint of closed $k=+1$ FLRW universe using Thiemann regularisation on hyperspherical lattice. Replaces continuum connection and triads with holonomies and fluxes. Expands effective constraint to 7th or... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Existing LQC models for $k=+1$ impose symmetries before discretisation, diverging from full Loop Quantum Gravity Thiemann regularisation which requires discretisation prior to symmetry reduction.",
"key_insights": "Thiemann regularisation on hyperspherical lattice yields discreteness corrections pr... | {
"main_results": [
{
"dataset": "Numerical Simulation (Case A)",
"metric": "Phase Space Trajectory",
"value": "Asymmetric Bounce",
"comparison": "Deviates from symmetric LQC bounce; connects to infinite volume states"
},
{
"dataset": "Analytical Approximation",
"metric... | 2001.00031 | DAS-2M-v1.0 | null | null |
{
"title": "Expectation values of Coherent States for SU(2) Lattice Gauge Theories",
"authors": [
"Klaus Liegener",
"Ernst-Albrecht Zwicknagel"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Lattice Gauge Theory Quantization",
"keywords": [
"SU(2) Gauge Group",
"Coherent States",
"Semiclassical Analysis",
"Quantum Fluctuations",
"Holonomy Operators"
]
} | {
"pipeline_type": "Analytical Derivation via Poisson Summation",
"learning_paradigm": "Canonical Quantization",
"knowledge_source": "Hamiltonian Lattice Gauge Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Gauge Field Theory Coherent States (GCS) Expectation Value Expansion",
"architecture_description": "Constructs GCS on kinematical Hilbert space $\\mathcal{H}_e = L_2(\\mathrm{SU}(2))$ peaked at classical phase space points. Decomposes complexified holonomy $h^{\\mathbb{C}}$ using polar coordinates... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Existing literature lacks explicit formulas for expectation values of general polynomial operators in GCS beyond leading order, limiting precision in semiclassical analysis of SU(2) LGT and Loop Quantum Gravity.",
"key_insights": "Expectation values factorize into classical terms modulated by a cor... | {
"main_results": [
{
"dataset": "Theoretical Derivation",
"metric": "Correction Coefficient",
"value": "\\gamma_a^k = 1 - \\frac{t}{4} [(k(k+1)-a^2)\\frac{\\tanh(\\eta/2)}{\\eta/2} + a^2]",
"comparison": "Extends prior work restricted to $k=1/2, 1$ to arbitrary $k \\in \\mathbb{N}/2$"
... | 2001.00032 | DAS-2M-v1.0 | null | null |
{
"title": "Conformal and Isometric Embeddings of Gravitational Instantons",
"authors": [
"Maciej Dunajski",
"Paul Tod"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Differential Geometry",
"keywords": [
"Gravitational Instantons",
"Isometric Embedding",
"Conformal Embedding",
"Burns Metric",
"Eguchi-Hanson",
"Taub-NUT"
]
} | {
"pipeline_type": "Analytical Derivation",
"learning_paradigm": "N/A",
"knowledge_source": "Riemannian Geometry",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Gauss-Codazzi-Ricci Analysis",
"architecture_description": "Derives necessary conditions for class 2 isometric embeddings using traces of the dual Riemann tensor operator P. Constructs explicit embeddings in R^7 and R^8 by solving radial conformal factor ODEs for LRS Bianchi IX metrics. Proves non... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Visualizing gravitational instantons requires embedding in flat higher-dimensional spaces; existing literature lacks sharp bounds on embedding classes for key metrics like Burns and Eguchi-Hanson.",
"key_insights": "Agaoka's necessary conditions (Tr(P^3)=Tr(P^5)=0) are insufficient for class 2 embe... | {
"main_results": [
{
"dataset": "Burns Metric",
"metric": "Embedding Class",
"value": "3",
"comparison": "Sharp bound established; rules out class 2 despite satisfying Tr(P^3)=Tr(P^5)=0"
},
{
"dataset": "Fubini-Study Metric",
"metric": "Conformal Embedding Dimension",
... | 2001.00033 | DAS-2M-v1.0 | null | null |
{
"title": "Contributions of Talent, Perspective, Context and Luck to Success",
"authors": [
"Bernardo Alves Furtado"
],
"publication_year": 2020,
"publication_date": "2020-02"
} | {
"task_category": "Agent-Based Modeling Simulation",
"keywords": [
"Success Determinants",
"Luck vs Talent",
"Risk Board Game",
"Zero-Sum Game",
"Stochastic Simulation"
]
} | {
"pipeline_type": "Monte Carlo Agent-Based Simulation",
"learning_paradigm": "Rule-Based Strategy Execution",
"knowledge_source": "Synthetic Game Environment (Risk)",
"backbone_model": null
} | {
"has_code": true,
"github_url": "https://github.com/BAFurtado/Talent-vs-Luck",
"project_page": null
} | {
"method_name": "Controlled Risk Simulation",
"architecture_description": "Implements 100,000 runs of a six-player Risk board game variant. Agents assigned random rule-based strategies (Talent), goals (Perspective), and opponents (Context). Dice rolls generated via numpy pseudorandom generator quantify Luck. Syste... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Synthetic Risk Game Runs (100k)"
]
} | {
"motivation": "Difficulty in empirically separating causal effects of luck, talent, context, and perspective on success due to confounding variables in real-world observational data.",
"key_insights": "Luck constitutes the primary determinant of success; Talent significantly modulates dependency on luck and enabl... | {
"main_results": [
{
"dataset": "100k Simulations",
"metric": "Win Rate (Blitz Strategy)",
"value": "52.6%",
"comparison": "+19% above random expectation (33.3%)"
},
{
"dataset": "100k Simulations",
"metric": "Median Winner Dice Score",
"value": "0.071",
"c... | 2001.00034 | DAS-2M-v1.0 | null | null |
{
"title": "NON-RIGID REGISTRATION METHOD BETWEEN 3D CT LIVER DATA AND 2D ULTRASONIC IMAGES BASED ON DEMONS MODEL",
"authors": [
"Shuo Huang",
"Ke Wu",
"Xiaolin Meng"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Multi-modal Medical Image Registration",
"keywords": [
"Non-rigid Registration",
"Demons Model",
"Intuitionistic Fuzzy Entropy",
"CT-Ultrasound Fusion",
"Edge Enhancement"
]
} | {
"pipeline_type": "Sequential Pre-processing and Deformation Pipeline",
"learning_paradigm": "Optimization-based (Mutual Information)",
"knowledge_source": "Radial Directional Local Intuitionistic Fuzzy Entropy",
"backbone_model": "Demons Model (Mutual Information Force)"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Radial Directional LIFE-enhanced Demons Registration",
"architecture_description": "Workflow executes BM4D/BM3D denoising on CT/US data. Extracts corresponding CT slice via affine rigid registration. Computes radial directional local intuitionistic fuzzy entropy (LIFE) in polar coordinates to enha... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Beijing Tsinghua Changgung Hospital Patient Data (1 CT volume, 3 US images)"
]
} | {
"motivation": "Standard Demons models fail on CT-US registration due to low signal-to-noise ratio, speckle noise, and acoustic shadowing in ultrasound which distort mutual information metrics.",
"key_insights": "Enhancing CT edges using radial directional fuzzy entropy mimics ultrasound reflection patterns better... | {
"main_results": [
{
"dataset": "Patient Liver Data",
"metric": "Visual Alignment Accuracy",
"value": "Superior",
"comparison": "Outperforms original Demons and Field II simulation methods in vessel and boundary matching"
},
{
"dataset": "Computational Efficiency",
"me... | 2001.00035 | DAS-2M-v1.0 | null | null |
{
"title": "Gradient polyconvex material models and their numerical treatment",
"authors": [
"Martin Horák",
"Martin Kružík"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Computational Mechanics",
"keywords": [
"Gradient Polyconvexity",
"Nonlinear Elasticity",
"Mixed Finite Element Formulation",
"Microstructure Formation",
"Regularization"
]
} | {
"pipeline_type": "Mixed Variational Formulation with Penalty Method",
"learning_paradigm": "Deterministic Numerical Simulation",
"knowledge_source": "Calculus of Variations and Continuum Mechanics",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Gradient Polyconvex Mixed Formulation",
"architecture_description": "Introduces auxiliary second-order tensor field chi to approximate Cof gradient. Enforces kinematic constraint chi = Cof F via penalty term H_chi. Strain energy depends on F, chi, and grad chi. Avoids C1 continuity requirement by ... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard hyperelastic models lack minimizers for non-quasiconvex energies causing microstructure formation and mesh sensitivity. Second-grade materials require computationally expensive C1 continuous elements.",
"key_insights": "Regularity of cofactor and determinant gradients suffices for existenc... | {
"main_results": [
{
"dataset": "Saint-Venant Kirchhoff Compression",
"metric": "Deformation Stability",
"value": "No laminates observed",
"comparison": "Standard model exhibits self-penetration and laminates; regularized model maintains homogeneous deformation"
},
{
"datase... | 2001.00036 | DAS-2M-v1.0 | null | null |
{
"title": "Quasigraphs and skeletal partitions",
"authors": [
"Tomáš Kaiser",
"Petr Vrána"
],
"publication_year": 2012,
"publication_date": "2012-01"
} | {
"task_category": "Graph Theory",
"keywords": [
"3-hypergraphs",
"Quasigraphs",
"Skeletal Lemma",
"Hamilton cycles",
"Anticonnectivity"
]
} | {
"pipeline_type": "Theoretical Proof Construction",
"learning_paradigm": "Combinatorial Optimization",
"knowledge_source": "3-Hypergraph Structures",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Plane Sequence of Quasigraphs",
"architecture_description": "Constructs a two-dimensional plane sequence of vertex set partitions indexed by lexicographically ordered pairs. Iteratively refines partitions based on components and anticomponents relative to decisive hyperedges (bridges/antibridges).... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Original Skeletal Lemma proof lacks required strength for Hamilton cycle applications in 5-connected line graphs. Existing formulation insufficient for handling specific 'bad leaf' configurations arising in companion paper analysis.",
"key_insights": "Plane sequence construction yields a well-order... | {
"main_results": [
{
"dataset": "Theoretical",
"metric": "Lemma Strength",
"value": "Proven",
"comparison": "Extends [2, Lemma 17] to handle bad leaves via switch operations"
},
{
"dataset": "Theoretical",
"metric": "Existence Guarantee",
"value": "Confirmed",
... | 2001.00037 | DAS-2M-v1.0 | null | null |
{
"title": "Randall–Sundrum Model with a Dilaton Field at Finite Temperature",
"authors": [
"Aditya Dhumuntarao",
"Joseph I. Kapusta",
"Christopher Plumberg"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Holographic Gravity Solutions",
"keywords": [
"Randall-Sundrum Model",
"Einstein-Dilaton Gravity",
"Finite Temperature",
"Black Brane",
"AdS/QCD"
]
} | {
"pipeline_type": "Analytical Superpotential Method",
"learning_paradigm": "Exact Solution Generation",
"knowledge_source": "5D Einstein-Hilbert Action with Bulk Scalar",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Generating Superpotential Algorithm",
"architecture_description": "Constructs unique generating superpotential W(\\phi) to reduce coupled nonlinear Einstein-dilaton equations to first-order gradient flows. Introduces auxiliary function D(\\phi) and coordinate transformation to solve for blackening... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Existing finite temperature AdS/QCD models often neglect backreactions or lack analytical tractability when incorporating bulk scalar fields and codimension-one branes.",
"key_insights": "Black brane formation requires specific exponential potential form V(\\phi) \\propto e^{2\\lambda a \\phi}. Ent... | {
"main_results": [
{
"dataset": "Analytical Derivation",
"metric": "Entropy Bound",
"value": "s < 8\\pi M^3",
"comparison": "Saturated only as brane tension \\sigma \\to 0"
},
{
"dataset": "Parameter Space Analysis",
"metric": "Horizon Existence Condition",
"valu... | 2001.00038 | DAS-2M-v1.0 | null | null |
{
"title": "Self-dispersion of Two Natural Polysaccharides for Granular Composites",
"authors": [
"Herbert Wang",
"Yin Fang",
"Yiliang Lin"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Biopolymer Composite Synthesis",
"keywords": [
"Chitosan-Starch Composite",
"Mesoscale Dispersion",
"Ionic Interaction",
"X-ray Microscopy",
"SAXS"
]
} | {
"pipeline_type": "Aqueous Suspension Mixing and Characterization",
"learning_paradigm": "Experimental Material Science",
"knowledge_source": "Natural Polysaccharides (Starch, Chitosan)",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Starch-Mediated Chitosan Self-Dispersion",
"architecture_description": "Mixing chitosan microbundles with rice starch granules in deionized water induces polyelectrolyte bonding. Positively charged amino groups on chitosan interact with negatively charged hydroxyl groups on starch amylose. This io... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Chitosan exhibits poor solubility in water and weak acidic solutions, limiting its application as a functional biomaterial despite beneficial properties like biocompatibility and antimicrobial activity.",
"key_insights": "Starch granules facilitate the disassembly of chitosan microbundles into nano... | {
"main_results": [
{
"dataset": "X-ray Micrography",
"metric": "Morphological Observation",
"value": "Complete disappearance of chitosan microbundles",
"comparison": "Contrasts with 100-micrometer bundles in pure chitosan suspension"
},
{
"dataset": "DSC Analysis",
"me... | 2001.00039 | DAS-2M-v1.0 | null | null |
{
"title": "Relaxing the TCC Bound on Inflationary Cosmology?",
"authors": [
"Vahid Kamal",
"Robert Brandenberger"
],
"publication_year": 2020,
"publication_date": "2020-04"
} | {
"task_category": "Theoretical Cosmology",
"keywords": [
"Trans-Planckian Censorship Conjecture",
"Inflationary Cosmology",
"Tensor-to-Scalar Ratio",
"Constant Roll Inflation"
]
} | {
"pipeline_type": "Analytical Derivation",
"learning_paradigm": "Effective Field Theory",
"knowledge_source": "Swampland Criteria and TCC Constraints",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Variable Equation of State Inflation",
"architecture_description": "Abandons constant Hubble expansion rate assumption during inflation. Implements constant roll inflation model where kinetic-to-potential energy ratio remains fixed ($\\dot{\\varphi}^2 = \\beta V(\\varphi)$) after observable scales... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard TCC application imposes severe constraints ($r < 10^{-30}$, $V_e^{1/4} < 10^{10}$ GeV) assuming constant Hubble rate and standard post-inflation history, rendering many inflationary models inconsistent with string theory swampland criteria.",
"key_insights": "Allowing equation of state to ... | {
"main_results": [
{
"dataset": "Theoretical Bound",
"metric": "Tensor-to-Scalar Ratio",
"value": "< 10^{-10}",
"comparison": "Relaxed from standard TCC bound of 10^{-30}"
},
{
"dataset": "Theoretical Bound",
"metric": "Energy Scale",
"value": "~ 10^{-5} m_pl",
... | 2001.00040 | DAS-2M-v1.0 | null | null |
{
"title": "Constraining Fundamental Physics with the Event Horizon Telescope",
"authors": [
"Markus Rummel",
"C.P. Burgess"
],
"publication_year": 2019,
"publication_date": null
} | {
"task_category": "Fundamental Physics Constraints",
"keywords": [
"Event Horizon Telescope",
"Effective Field Theory",
"Near-Horizon Reflection",
"Modified Gravity",
"Black Hole Imaging"
]
} | {
"pipeline_type": "Ray Tracing with Perturbative Reflection",
"learning_paradigm": "Analytical EFT and Numerical Simulation",
"knowledge_source": "EHT M87 Observations and Kerr Geodesics",
"backbone_model": "Kerr Metric (General Relativity)"
} | {
"has_code": true,
"github_url": "https://github.com/mrummphys/EventHorizonTelescope",
"project_page": null
} | {
"method_name": "Near-Horizon EFT with Reflection Coefficients",
"architecture_description": "Models deviations from General Relativity as a near-horizon reflection coefficient $R$ within an Effective Field Theory framework expanding in $\\ell/r$. Implements backward ray tracing of photons in Kerr geometry, imposi... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"EHT M87 April 11 2017 Image (Digitized)"
]
} | {
"motivation": "Standard model-by-model testing of gravity modifications is inefficient; strong-gravity near-horizon regimes lack robust effective field theory constraints compared to post-Newtonian regimes.",
"key_insights": "Near-horizon deviations from GR manifest as non-zero reflection coefficients for infalli... | {
"main_results": [
{
"dataset": "EHT M87 (Digitized)",
"metric": "Reflection Coefficient Bound ($R_0$)",
"value": "\\lesssim 3 \\cdot 10^{-2}",
"comparison": "For multipoles $l=0, 1$ across all spins"
},
{
"dataset": "EHT M87 (Digitized)",
"metric": "Fundamental Length... | 2001.00041 | DAS-2M-v1.0 | null | null |
{
"title": "The hamiltonicity of essentially 9-connected line graphs",
"authors": [
"Tomáš Kaiser",
"Petr Vrána"
],
"publication_year": 2011,
"publication_date": null
} | {
"task_category": "Graph Theory",
"keywords": [
"Hamilton-connected",
"Line Graphs",
"Essential Connectivity",
"Claw-free Graphs",
"Discharging Method",
"Quasigraphs"
]
} | {
"pipeline_type": "Reduction to Hypergraphs and Discharging",
"learning_paradigm": "Theoretical Proof",
"knowledge_source": "Graph Structural Properties",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Hypergraph Reduction with Quasigraph Skeletal Lemma",
"architecture_description": "Transforms the original graph G into a core graph G^0 by suppressing degree-2 vertices. Maps G^0 to a 3-hypergraph H^0 where degree-3 vertices become hyperedges. Applies a strengthened Skeletal Lemma to find an acyc... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Previous results established Hamilton-connectivity for 3-connected, essentially 11-connected and 10-connected line graphs. The gap between the proven constant (10) and the theoretical lower bound (4) necessitates tightening the essential connectivity requirement.",
"key_insights": "Reducing the pro... | {
"main_results": [
{
"dataset": "Theoretical Bound",
"metric": "Essential Connectivity Threshold",
"value": "9",
"comparison": "Improves upon previous threshold of 10 by Li and Yang"
},
{
"dataset": "Counterexample Construction",
"metric": "Lower Bound Verification",
... | 2001.00042 | DAS-2M-v1.0 | null | null |
{
"title": "Strengthening the TCC Bound on Inflationary Cosmology",
"authors": [
"Robert Brandenberger",
"Edward Wilson-Ewing"
],
"publication_year": 2020,
"publication_date": "2020-03"
} | {
"task_category": "Theoretical Cosmology",
"keywords": [
"Trans-Planckian Censorship Conjecture",
"Inflationary Cosmology",
"Pre-inflationary Radiation",
"Energy Scale Bounds",
"Gravitational Waves"
]
} | {
"pipeline_type": "Analytical Derivation",
"learning_paradigm": "Effective Field Theory Constraints",
"knowledge_source": "Friedman-Lemaître-Robertson-Walker Cosmology",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Strengthened TCC Analysis with Pre-inflationary Radiation",
"architecture_description": "Imposes Trans-Planckian Censorship Conjecture (TCC) constraint requiring no sub-Planckian modes exit Hubble horizon. Extends standard analysis by incorporating expanding pre-inflationary radiation-dominated ph... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard TCC bounds assume minimal pre-inflationary history, yielding $V_e < 10^{10}$ GeV. Expanding pre-inflationary phases allow trans-Planckian modes to exit horizon earlier, violating TCC unless inflation energy scale is significantly lower.",
"key_insights": "Pre-inflationary radiation dominat... | {
"main_results": [
{
"dataset": "Theoretical Derivation",
"metric": "Energy Scale Upper Bound",
"value": "$\\sim 10^4$ GeV",
"comparison": "Six orders of magnitude lower than standard TCC bound ($10^{10}$ GeV)"
},
{
"dataset": "Theoretical Derivation",
"metric": "Tenso... | 2001.00043 | DAS-2M-v1.0 | null | null |
{
"title": "Non-Hermitian impurities in Dirac systems",
"authors": [
"P. O. Sukhachov",
"A. V. Balatsky"
],
"publication_year": 2019,
"publication_date": "2019-12"
} | {
"task_category": "Condensed Matter Theory",
"keywords": [
"Non-Hermitian Physics",
"Dirac Systems",
"Local Density of States",
"Impurity Resonances",
"Photonic Lattices"
]
} | {
"pipeline_type": "Analytical T-matrix Formalism and Tight-Binding Simulation",
"learning_paradigm": "Theoretical Modeling",
"knowledge_source": "Continuum Dirac Hamiltonian and Hexagonal Lattice Model",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "T-matrix Formalism for Non-Hermitian Potentials",
"architecture_description": "Employ T-matrix formalism to derive full Green's function for complex impurity potentials in 2D/3D Dirac Hamiltonians. Calculate Local Density of States (LDOS) via imaginary part of retarded Green's function trace. Vali... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard impurity models assume real potentials, neglecting gain/loss effects prevalent in photonic lattices and open quantum systems. Need to characterize spectral and spatial signatures of non-Hermitian defects in Dirac materials.",
"key_insights": "Purely imaginary dissipative potentials suppres... | {
"main_results": [
{
"dataset": "2D Hexagonal Lattice (N=240)",
"metric": "LDOS Spatial Profile",
"value": "Trigonal pattern",
"comparison": "Observed for both real and strong imaginary potentials"
},
{
"dataset": "Continuum 2D Dirac Model",
"metric": "DOS Frequency De... | 2001.00044 | DAS-2M-v1.0 | null | null |
{
"title": "THE UNIVERSAL p-ADIC GROSS–ZAGIER FORMULA",
"authors": [
"Daniel Disegni"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Arithmetic Geometry and Iwasawa Theory",
"keywords": [
"p-adic Gross-Zagier Formula",
"Hida Families",
"Heegner Classes",
"Selmer Complexes",
"Beilinson-Bloch-Kato Conjecture",
"Universal Heegner Class"
]
} | {
"pipeline_type": "p-adic Interpolation via Hida Theory",
"learning_paradigm": "Algebraic Construction (No Learning)",
"knowledge_source": "Automorphic Representations and Galois Cohomology",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Universal p-adic Gross-Zagier Formula",
"architecture_description": "Constructs a universal Heegner class P as a section of a Selmer sheaf over a Hida family X for (GL2 x GU(1))/GL1. Utilizes Nekovar's Selmer complexes to define p-adic height pairings on families. Proves identity relating the p-ad... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Classical Gross-Zagier formulas relate Heegner point heights to L-function derivatives only at specific weights. Existing p-adic analogues lack universality across Hida families or fail to address higher-weight motives. Need to unify these into a single identity over the eigenvariety to prove rank-on... | {
"main_results": [
{
"dataset": "Theoretical Proof",
"metric": "Theorem A",
"value": "Validated",
"comparison": "Proves p-adic Beilinson-Bloch-Kato conjecture in analytic rank one for Hilbert modular forms"
},
{
"dataset": "Theoretical Proof",
"metric": "Theorem D",
... | 2001.00045 | DAS-2M-v1.0 | null | null |
{
"title": "TENSOR-TENSOR PRODUCTS FOR OPTIMAL REPRESENTATION AND COMPRESSION",
"authors": [
"Misha Kilmer",
"Lior Horesh",
"Haim Avron"
],
"publication_year": 2015,
"publication_date": "2015-06"
} | {
"task_category": "Tensor Decomposition and Compression",
"keywords": [
"Tensor-Tensor Product",
"t-SVD",
"Eckart-Young Theorem",
"Data Compression",
"HOSVD"
]
} | {
"pipeline_type": "Transform-Domain Tensor SVD with Adaptive Truncation",
"learning_paradigm": "Unsupervised Linear Algebraic Decomposition",
"knowledge_source": "Native High-Dimensional Tensor Structure",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "t-SVDMII (Multi-rank Truncated Tensor SVD)",
"architecture_description": "Defines a family of tensor-tensor products ($\\star_{\\mathrm{M}}$) via invertible transform matrices $\\mathbf{M}$. Computes t-SVD in the transform domain where frontal slices undergo independent matrix SVDs. Introduces t-S... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Extended YaleB Face Database",
"Video Frame Data (Matlab Sample)",
"Hyperspectral Imagery"
]
} | {
"motivation": "Matricization of high-dimensional data destroys intrinsic structural correlations and fails to achieve optimal compression rates compared to native tensor representations lacking theoretical optimality guarantees.",
"key_insights": "Tensor-tensor products induced by unitary transforms preserve Frob... | {
"main_results": [
{
"dataset": "Hyperspectral Imagery",
"metric": "Compression Ratio at RE ~0.1",
"value": "128.20",
"comparison": "Matrix SVD: 95.24; HOSVD: 19.12"
},
{
"dataset": "Video Frame Data",
"metric": "Visual Quality (Ghosting Artifacts)",
"value": "No... | 2001.00046 | DAS-2M-v1.0 | null | null |
{
"title": "Coalition-Safe Equilibria with Virtual Payoffs",
"authors": [
"Aggelos Kiayias",
"Aikaterini-Panagiota Stouka"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Blockchain Incentive Analysis",
"keywords": [
"Game Theory",
"Coalition-Safe Equilibrium",
"Virtual Payoffs",
"Bitcoin",
"Fruitchain",
"Selfish Mining"
]
} | {
"pipeline_type": "Formal Game-Theoretic Modeling",
"learning_paradigm": "Analytical Proof (Cryptographic Setting)",
"knowledge_source": "Distributed Ledger Protocols",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Equilibrium with Virtual Payoffs (EVP)",
"architecture_description": "Defines coalition-safe equilibrium by comparing adversary utility in deviating vs. honest executions across divergent local views. Utility functions incorporate virtual rewards recorded in local ledgers and oracle query costs. E... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Existing equilibrium notions fail to capture rational behavior in distributed ledgers where participants hold divergent views of rewards due to consensus mechanisms and stochastic execution. Prior models often assume expected utility or ignore view divergence, leading to contradictory results regardi... | {
"main_results": [
{
"dataset": "Bitcoin Protocol",
"metric": "Equilibrium Stability",
"value": "EVP for Absolute Rewards",
"comparison": "Stable against coalitions up to n-1 participants"
},
{
"dataset": "Fruitchain Protocol",
"metric": "Equilibrium Stability",
... | 2001.00047 | DAS-2M-v1.0 | null | null |
{
"title": "MIR-Vehicle: Cost-Effective Research Platform for Autonomous Vehicle Applications",
"authors": [
"Ahmed Abdelhamed",
"Balakrishna Yadav Peddagolla",
"Girma Tewolde"
],
"publication_year": 2019,
"publication_date": "2019-04"
} | {
"task_category": "Autonomous Vehicle Platform Design",
"keywords": [
"Cost-Effective Robotics",
"ROS Middleware",
"Drive-by-Wire Conversion",
"Sensor Integration",
"Behavior Cloning"
]
} | {
"pipeline_type": "Hierarchical Modular Control",
"learning_paradigm": "Supervised Learning (Behavior Cloning)",
"knowledge_source": "Multi-Modal Sensor Fusion (Camera, LIDAR, IMU, Encoders)",
"backbone_model": "GPU-Powered Laptop (GTX 1060+)"
} | {
"has_code": true,
"github_url": "https://github.com/jaerockk/mir_vehicle",
"project_page": null
} | {
"method_name": "MIR-Vehicle Platform",
"architecture_description": "Transforms electric ride-on-car into autonomous platform via hierarchical ROS architecture. Microcontrollers (Arduino Uno) handle low-level motor PWM and encoder reading via I2C bus. GPU laptop executes high-level perception and control nodes. ro... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Full-scale drive-by-wire vehicles prohibitively expensive for research; existing small-scale platforms lack GPU compute power for state-of-the-art deep learning algorithms.",
"key_insights": "Separating low-level motor control (microcontroller) from high-level perception (GPU laptop) via ROS enable... | {
"main_results": [
{
"dataset": "Real-world Test Track",
"metric": "System Functionality",
"value": "Successful Operation",
"comparison": "Validated remote control, data acquisition, and sensor visualization via RViz"
},
{
"dataset": "N/A",
"metric": "Cost Efficiency",... | 2001.00048 | DAS-2M-v1.0 | null | null |
{
"title": "High-resolution Spectra and Biosignatures of Earth-like Planets Transiting White Dwarfs",
"authors": [
"Thea Kozakis",
"Zifan Lin",
"Lisa Kaltenegger"
],
"publication_year": 2020,
"publication_date": "2020-06"
} | {
"task_category": "Exoplanet Atmospheric Characterization",
"keywords": [
"White Dwarf Habitable Zone",
"Transmission Spectroscopy",
"Biosignatures",
"Atmospheric Photochemistry",
"Stellar Evolution"
]
} | {
"pipeline_type": "1D Climate-Photochemistry-Radiative Transfer Coupling",
"learning_paradigm": "Physics-based Numerical Simulation",
"knowledge_source": "WD Cooling Models (Saumon et al. 2014) & Exo-Prime Code",
"backbone_model": "Exo-Prime"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "High-Resolution Transmission Spectra Modeling for WD Systems",
"architecture_description": "Couples 1D climate, photochemistry, and radiative transfer codes (Exo-Prime) to simulate Earth-like atmospheres under WD irradiation. Models span WD cooling stages (6,000-4,000 K) using pure hydrogen atmosp... | {
"contributed_new_dataset": true,
"new_dataset_name": "High-resolution Transmission Spectra Database for WD Planets",
"datasets_used_for_eval": []
} | {
"motivation": "Small WD radii yield transit signals orders of magnitude larger than main-sequence systems, enabling atmospheric characterization of Earth-sized planets despite low occurrence rates and tidal disruption risks.",
"key_insights": "WD cooling reduces UV flux, decreasing ozone production while increasi... | {
"main_results": [
{
"dataset": "Modeled 6000K WD System",
"metric": "Contrast Ratio Enhancement",
"value": "4 orders of magnitude",
"comparison": "Higher than Earth-Sun system"
},
{
"dataset": "Modeled 4000K WD System",
"metric": "Methane Feature Strength",
"val... | 2001.00049 | DAS-2M-v1.0 | null | null |
{
"title": "High resolution Spectra of Earth-Like Planets Orbiting Red Giant Host Stars",
"authors": [
"Thea Kozakis",
"Lisa Kaltenegger"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Exoplanet Atmospheric Characterization",
"keywords": [
"Red Giant Habitable Zone",
"High-Resolution Spectroscopy",
"Biosignature Detection",
"Coronagraph Design",
"LUVOIR Simulation"
]
} | {
"pipeline_type": "Coupled Climate-Photochemistry-Radiative Transfer Modeling",
"learning_paradigm": "Physics-Based Simulation",
"knowledge_source": "HITRAN 2016 Line Lists, IUE UV Data, Pickles Atlas",
"backbone_model": "Exo-Prime"
} | {
"has_code": false,
"github_url": null,
"project_page": "https://carlsaganinstitute.org/rg-planet-spectra-catalog"
} | {
"method_name": "Exo-Prime Radiative Transfer Simulation",
"architecture_description": "Utilizes Exo-Prime 1D coupled climate-photochemistry-radiative transfer code to generate high-resolution reflection and emission spectra. Models incorporate line-by-line radiative transfer with Doppler and pressure broadening u... | {
"contributed_new_dataset": true,
"new_dataset_name": "RG Planet Spectra Catalog",
"datasets_used_for_eval": []
} | {
"motivation": "Over 150 exoplanets detected around red giants lack spectral models for remote characterization. Post-main sequence habitable zones offer unique opportunities for thawing frozen worlds but present distinct contrast ratio and angular separation challenges.",
"key_insights": "Red giant host luminosit... | {
"main_results": [
{
"dataset": "Beta Geminorum System",
"metric": "Integration Time (SNR=5)",
"value": "12.4 hours",
"comparison": "Required for O2 detection at 0.76 micron using LUVOIR ECLIPS"
},
{
"dataset": "Alpha Bootis System",
"metric": "Detectability",
"v... | 2001.00050 | DAS-2M-v1.0 | null | null |
{
"title": "Simultaneous Identification of Tweet Purpose and Position",
"authors": [
"Rahul Radhakrishnan Iyer",
"Yulong Pei",
"Katia Sycara"
],
"publication_year": 2019,
"publication_date": null
} | {
"task_category": "Multi-Label Text Classification",
"keywords": [
"Tweet Purpose Classification",
"Tweet Position Classification",
"RAkEL",
"Post-processing Strategies",
"Social Media Mining"
]
} | {
"pipeline_type": "Multi-Label Classification with KNN-based Post-processing",
"learning_paradigm": "Supervised Learning (Problem Transformation)",
"knowledge_source": "Twitter Data (Obama care, Death Penalty)",
"backbone_model": "RAkEL (Random k-Labelsets) with SVM base classifiers"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "RAkEL with Summation and Weighted Summation Post-processing",
"architecture_description": "Transforms joint purpose-position identification into a multi-label classification task using RAkEL. Addresses invalid predictions (zero or multiple labels per category) via K-nearest neighbor retrieval. Agg... | {
"contributed_new_dataset": true,
"new_dataset_name": "Obama care and Death Penalty Twitter datasets",
"datasets_used_for_eval": [
"Obama care",
"Death Penalty"
]
} | {
"motivation": "Separate training of purpose and position classifiers ignores inter-label correlations and reduces efficiency. Standard multi-label methods fail to enforce domain-specific constraints requiring exactly one purpose and one position label per tweet.",
"key_insights": "Exploiting correlations between ... | {
"main_results": [
{
"dataset": "Obama care",
"metric": "Hamming Loss Improvement",
"value": "29.65%",
"comparison": "RAkEL+wsum vs KNN baseline"
},
{
"dataset": "Obama care",
"metric": "Hamming Loss Improvement",
"value": "7.6%",
"comparison": "RAkEL+wsum ... | 2001.00051 | DAS-2M-v1.0 | null | null |
{
"title": "Central Amalgamation of Groups and the RFD Property",
"authors": [
"Tatiana Shulman"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Operator Algebras and Group Theory",
"keywords": [
"Residually Finite-Dimensional (RFD)",
"Amalgamated Free Products",
"HNN-extensions",
"Maximally Almost Periodic (MAP)",
"C*-algebras"
]
} | {
"pipeline_type": "Theoretical Proof Construction",
"learning_paradigm": "Mathematical Deduction",
"knowledge_source": "Group C*-algebras and Representation Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Simultaneous Trace Approximation via Compatible Filtrations",
"architecture_description": "Constructs finite-dimensional representations approximating characters induced from central subgroups using compatible filtrations. Leverages Voiculescu's Theorem on approximate unitary equivalence to lift G... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Determining permanence of the RFD property under central amalgamated free products remains open beyond virtually abelian cases. Previous results required strong RFD assumptions or finite amalgamating subgroups.",
"key_insights": "Existence of compatible filtrations enables simultaneous approximatio... | {
"main_results": [
{
"dataset": "Polycyclic-by-finite groups",
"metric": "RFD Property",
"value": "Preserved",
"comparison": "Extends CS19 results beyond strongly RFD assumption"
},
{
"dataset": "Abels' Group Amalgam",
"metric": "MAP Property",
"value": "Failed",... | 2001.00052 | DAS-2M-v1.0 | null | null |
{
"title": "On the Resilience of Deep Learning for Reduced-voltage FPGAs",
"authors": [
"Kamyar Givaki",
"Behzad Salami",
"Reza Hojabr"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Hardware Resilience Analysis",
"keywords": [
"FPGA",
"Voltage Underscaling",
"DNN Training",
"Fault Injection",
"Power Efficiency"
]
} | {
"pipeline_type": "Fault Injection Simulation",
"learning_paradigm": "Supervised Learning (Training Phase)",
"knowledge_source": "Real FPGA Fault Maps (VC707/KC705)",
"backbone_model": "LeNet-5, Custom CNN (CIFAR-10)"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Real Fault Map Injection",
"architecture_description": "Injects bit-flip faults derived from real FPGA SRAM undervolting maps into inputs, weights, and intermediate floating-point values stored in Block RAMs. Simulates training iterations where updated faulty weights propagate to subsequent steps,... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"MNIST",
"CIFAR-10"
]
} | {
"motivation": "Aggressive voltage underscaling in FPGAs reduces power but induces timing faults; existing studies focus on inference or synthetic faults, neglecting real undervolting effects on the energy-intensive training phase.",
"key_insights": "Real FPGA undervolting faults (<0.1% rate) are inherently masked... | {
"main_results": [
{
"dataset": "MNIST (LeNet-5)",
"metric": "Accuracy Degradation",
"value": "Negligible (<0.1% fault rate)",
"comparison": "Compensated by ~10% additional training iterations"
},
{
"dataset": "MNIST (Synthetic 30% Fault)",
"metric": "Accuracy Differen... | 2001.00053 | DAS-2M-v1.0 | null | null |
{
"title": "Low-frequency one-electrode discharge in long tubes at low gas pressure",
"authors": [
"Shishpanov A.I.",
"Bazhin P.S.",
"Ivanov D.O."
],
"publication_year": 2018,
"publication_date": "2018-12"
} | {
"task_category": "Plasma Physics",
"keywords": [
"One-Electrode Discharge",
"Ionization Wave",
"Low Pressure Plasma",
"Striations",
"Neon",
"Argon"
]
} | {
"pipeline_type": "Experimental Characterization",
"learning_paradigm": "Empirical Modeling",
"knowledge_source": "Experimental Data (Optical/Electrical)",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "One-Electrode Discharge (OED) Kinematic Analysis",
"architecture_description": "OED generation via periodic traveling ionization waves (IW) triggered by low-frequency (<10 kHz) unipolar voltage pulses on a single electrode. IW propagation governed by exponential potential attenuation along the pla... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Experimental Neon Data (1-4 Torr)",
"Experimental Argon Data (1-4 Torr)"
]
} | {
"motivation": "Low-frequency one-electrode discharge mechanisms in long tubes remain undescribed compared to high-frequency torch discharges; existing models fail to explain plasma column length stability and striation formation at <10 kHz.",
"key_insights": "IW attenuation dictates discharge length via exponenti... | {
"main_results": [
{
"dataset": "Neon 4 Torr",
"metric": "Electron Concentration",
"value": "10^10 cm^-3",
"comparison": "Achieved at U > 3 kV, positive polarity"
},
{
"dataset": "Neon 1 Torr",
"metric": "Discharge Length Jump",
"value": "20 cm to 60 cm",
"... | 2001.00054 | DAS-2M-v1.0 | null | null |
{
"title": "DEFLATING SUPER-PUFFS: IMPACT OF PHOTOCHEMICAL HAZES ON THE OBSERVED MASS-RADIUS RELATIONSHIP OF LOW MASS PLANETS",
"authors": [
"Peter Gao",
"Xi Zhang"
],
"publication_year": 2020,
"publication_date": "2020-01"
} | {
"task_category": "Exoplanet Atmospheric Modeling",
"keywords": [
"Super-puffs",
"Photochemical Hazes",
"Mass-Radius Relationship",
"Atmospheric Escape",
"Transmission Spectroscopy"
]
} | {
"pipeline_type": "Coupled Radiative-Convective and Aerosol Microphysics Modeling",
"learning_paradigm": "First-Principles Physical Simulation",
"knowledge_source": "Stellar Irradiation Data and Opacity Tables",
"backbone_model": "CARMA Aerosol Microphysics Model"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Hazy Mass-Radius Diagram Framework",
"architecture_description": "Constructs grid of radiative-convective equilibrium atmospheres varying core mass, atmosphere mass, and temperatures. Computes atmospheric loss rates via energy-limited photoevaporation and hydrodynamic boil-off. Integrates 1D aeros... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"NASA Exoplanet Archive",
"Kepler Mission Data",
"HST WFC3 G141 Observations (Kepler-51b)"
]
} | {
"motivation": "Super-puffs exhibit low densities implying high gas mass fractions vulnerable to rapid hydrodynamic escape, creating a discrepancy between inferred ages and atmospheric lifetimes under clear atmosphere assumptions.",
"key_insights": "Outflow winds from atmospheric escape enhance haze opacity by lof... | {
"main_results": [
{
"dataset": "Kepler-51b",
"metric": "Inferred Gas Mass Fraction",
"value": "<10%",
"comparison": "Reduced from >16% in clear atmosphere models; consistent with sub-Neptune population"
},
{
"dataset": "Kepler-51b",
"metric": "Atmospheric Lifetime",
... | 2001.00055 | DAS-2M-v1.0 | null | null |
{
"title": "Deep Attentive Ranking Networks for Learning to Order Sentences",
"authors": [
"Pawan Kumar",
"Dhanajit Brahma",
"Harish Karnick"
],
"publication_year": 2019,
"publication_date": "2019-06"
} | {
"task_category": "Sentence Ordering",
"keywords": [
"Learning to Rank",
"Transformer",
"BERT",
"Text Coherence",
"Order Discrimination"
]
} | {
"pipeline_type": "Encoder-Decoder with Ranking Loss",
"learning_paradigm": "Supervised Learning with Fine-tuning",
"knowledge_source": "Pre-trained BERT Representations",
"backbone_model": "BERT-Base"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "RankTxNet",
"architecture_description": "RankTxNet employs a hierarchical Transformer architecture comprising a pre-trained BERT sentence encoder and a randomly initialized order-invariant paragraph encoder. The paragraph encoder omits positional embeddings to process shuffled sentence sets via se... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"NIPS Abstracts",
"AAN/ACL Abstracts",
"NSF Abstracts",
"arXiv Abstracts",
"SIND Captions",
"ROCStory",
"Accidents",
"Earthquakes"
]
} | {
"motivation": "Existing LSTM-based decoders suffer from sequential processing bottlenecks and sensitivity to input permutation order. Pointer networks require expensive beam search and struggle with long-range dependencies in shuffled contexts.",
"key_insights": "Order-invariant self-attention effectively capture... | {
"main_results": [
{
"dataset": "SIND Captions",
"metric": "Kendall's Tau",
"value": "+6.31% absolute improvement",
"comparison": "Outperforms HierarchicalATTNet and ATTOrderNet"
},
{
"dataset": "AAN Abstracts",
"metric": "Kendall's Tau",
"value": "+4.48% absolut... | 2001.00056 | DAS-2M-v1.0 | null | null |
{
"title": "HMM-guided frame querying for bandwidth-constrained video search",
"authors": [
"Bhairav Chidambaram",
"Mason McGill",
"Pietro Perona"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Bandwidth-Constrained Video Search",
"keywords": [
"Hidden Markov Model",
"Active Frame Sampling",
"Remote Video Inference",
"Cross-Entropy Minimization"
]
} | {
"pipeline_type": "Active Querying Loop with HMM Inference",
"learning_paradigm": "Supervised Learning (Frame Scoring) + Probabilistic Modeling",
"knowledge_source": "ImageNet-VID Annotations",
"backbone_model": "ResNet-18"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "HMM-Guided Frame Querying",
"architecture_description": "System integrates a convolutional frame-scoring network regressing interest probability with a Hidden Markov Model propagating beliefs across unobserved frames. Agent employs greedy policy selecting next frame query minimizing expected mean ... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"ImageNet-VID"
]
} | {
"motivation": "Full-video transmission incurs prohibitive bandwidth costs for remote video analysis; existing keyframe interpolation methods lack adaptive querying strategies optimized for specific detection tasks.",
"key_insights": "Modeling temporal label dependencies via HMM allows accurate interpolation of un... | {
"main_results": [
{
"dataset": "ImageNet-VID",
"metric": "Observation Reduction",
"value": "98%",
"comparison": "Matches full-bandwidth detector accuracy with only 2% frame observation"
},
{
"dataset": "ImageNet-VID",
"metric": "Accuracy vs Bandwidth",
"value": ... | 2001.00057 | DAS-2M-v1.0 | null | null |
{
"title": "Towards Improving the Performance of the RNN-based Inversion Model in Output Tracking Control",
"authors": [
"Shengwen Xie",
"Juan Ren"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Output Tracking Control",
"keywords": [
"Recurrent Neural Network",
"Inversion Model",
"Piezo Actuator",
"Model Predictive Control",
"Sampling Frequency Interpolation"
]
} | {
"pipeline_type": "Hybrid RNN-Linear Inversion with MPC",
"learning_paradigm": "Supervised Learning (RNN Training)",
"knowledge_source": "Time-series Input-Output Data",
"backbone_model": "RNN-based Inversion Model (RNNinv)"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Frequency-Separated LME with Interpolated RNNinv",
"architecture_description": "Decouples system dynamics into high-frequency RNN inversion and low-frequency Linear Model with Error (LME) term via parallel high-pass and low-pass filters. Implements state-space interpolation to double operational s... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"Commercial Piezo Actuator (Nano-OP30) Experimental Data"
]
} | {
"motivation": "Standard RNN inversion models fail to capture low-frequency dynamics accurately due to parameter limits, while added linear correction terms interfere with high-frequency performance. Training set length constraints restrict achievable control sampling rates.",
"key_insights": "Separating dynamics ... | {
"main_results": [
{
"dataset": "103 Hz Sinusoidal Trajectory",
"metric": "Tracking Error Reduction",
"value": ">10% lower than MIIFC",
"comparison": "Outperforms Iterative Learning Control (MIIFC) in low-frequency region"
},
{
"dataset": "All Trajectories (Aggregate)",
... | 2001.00058 | DAS-2M-v1.0 | null | null |
{
"title": "Learning and Evaluating Contextual Embedding of Source Code",
"authors": [
"Aditya Kanade",
"Petros Maniatis",
"Gogul Balakrishnan"
],
"publication_year": 2020,
"publication_date": "2020-11"
} | {
"task_category": "Source Code Understanding",
"keywords": [
"Contextual Embedding",
"BERT",
"Code Representation",
"Program Repair",
"Bug Detection"
]
} | {
"pipeline_type": "Pre-training followed by Fine-tuning",
"learning_paradigm": "Self-Supervised Learning (Masked Language Modeling)",
"knowledge_source": "GitHub Python Corpus (7.4M files)",
"backbone_model": "BERT Large (24 layers, 16 heads)"
} | {
"has_code": true,
"github_url": "https://github.com/google-research/cubert",
"project_page": null
} | {
"method_name": "CuBERT",
"architecture_description": "CuBERT adapts BERT Large architecture for source code using a subword vocabulary preserving syntactic boundaries. Pre-training employs Masked Language Modeling (MLM) on logical code lines and Next-Sentence Prediction (NSP) on consecutive logical lines. Fine-tu... | {
"contributed_new_dataset": true,
"new_dataset_name": "ETH Py150 Open Benchmark",
"datasets_used_for_eval": [
"ETH Py150 Open (Variable Misuse)",
"ETH Py150 Open (Wrong Binary Operator)",
"ETH Py150 Open (Swapped Operand)",
"ETH Py150 Open (Function-Docstring Mismatch)",
"ETH Py150 Open (Exce... | {
"motivation": "Existing code embeddings lack context sensitivity or require complex structural analysis; no high-quality contextual embedding evaluated across multiple program-understanding tasks simultaneously.",
"key_insights": "Pre-trained contextual embeddings capture semantic nuances of code tokens better th... | {
"main_results": [
{
"dataset": "Variable Misuse Classification",
"metric": "Accuracy",
"value": "89.6%",
"comparison": "+14.7% over best BiLSTM+Word2Vec baseline"
},
{
"dataset": "Variable Misuse Localization & Repair",
"metric": "Localization+Repair Accuracy",
... | 2001.00059 | DAS-2M-v1.0 | null | null |
{
"title": "Deep Learning Training with Simulated Approximate Multipliers",
"authors": [
"Issam Hammad",
"Kamal El-Sankary",
"Jason Gu"
],
"publication_year": 2019,
"publication_date": "2019-12"
} | {
"task_category": "Hardware-Aware Deep Learning Training",
"keywords": [
"Approximate Computing",
"Approximate Multipliers",
"CNN Training Optimization",
"Edge AI",
"Hybrid Training Strategy"
]
} | {
"pipeline_type": "Two-Phase Hybrid Training (Approximate-to-Exact)",
"learning_paradigm": "Supervised Learning with Simulated Hardware Noise",
"knowledge_source": "CIFAR-10 Dataset",
"backbone_model": "Modified VGGNet-16"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Hybrid Approximate-Exact Training",
"architecture_description": "Injects Gaussian-distributed multiplication errors via Keras custom layers during forward and backward propagation to simulate approximate multiplier Mean Relative Error (MRE). Implements a two-stage protocol: initial epochs utilize ... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"CIFAR-10"
]
} | {
"motivation": "Edge training on mobile robots requires reduced power, area, and latency; exact multipliers impose prohibitive computational costs for offline continuous learning scenarios.",
"key_insights": "Approximate multipliers introduce minimal accuracy loss (<0.1%) at low MRE levels while offering substanti... | {
"main_results": [
{
"dataset": "CIFAR-10",
"metric": "Inference Accuracy",
"value": "93.53%",
"comparison": "-0.07% vs Baseline (MRE ~1.4%, simulating DRUM multiplier)"
},
{
"dataset": "CIFAR-10",
"metric": "Inference Accuracy",
"value": "93.58%",
"compari... | 2001.00060 | DAS-2M-v1.0 | null | null |
{
"title": "Inverse Square Singularities and Eigenparameter Dependent Boundary Conditions Are Two Sides of the Same Coin",
"authors": [
"Namig J. Guliyev"
],
"publication_year": 2024,
"publication_date": null
} | {
"task_category": "Spectral Theory",
"keywords": [
"Inverse Square Singularity",
"Eigenparameter Dependent Boundary Conditions",
"Darboux Transformation",
"Schrödinger Operator",
"Inverse Spectral Problem"
]
} | {
"pipeline_type": "Single Commutation Method",
"learning_paradigm": "Analytical Derivation",
"knowledge_source": "Rational Herglotz–Nevanlinna Functions",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Unified Darboux-Type Transformations",
"architecture_description": "Assigns integer indices to rational Herglotz–Nevanlinna functions and inverse square singularities, treating singularities as boundary conditions with negative pole counts. Defines bidirectional transformations \\(\\widehat{\\math... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Inverse square singularities and eigenparameter dependent boundary conditions historically treated separately despite structural similarities. Existing double commutation methods ill-suited for simultaneous endpoint singularities or parameter dependence.",
"key_insights": "Inverse square singularit... | {
"main_results": [
{
"dataset": "Theoretical Proof",
"metric": "Asymptotic Validity",
"value": "Verified",
"comparison": "Extends asymptotics to arbitrary singular endpoints"
},
{
"dataset": "Theoretical Proof",
"metric": "Uniqueness",
"value": "Proven",
"c... | 2001.00061 | DAS-2M-v1.0 | null | null |
{
"title": "Visual Evaluation of Generative Adversarial Networks for Time Series Data",
"authors": [
"Hiba Arnout",
"Johannes Kehrer",
"Johanna Bronner"
],
"publication_year": 2020,
"publication_date": "2020-06"
} | {
"task_category": "Visual Analytics for GAN Evaluation",
"keywords": [
"Time Series Generation",
"Generative Adversarial Networks",
"Visual Analytics",
"Mode Collapse Detection",
"Nearest Neighbor Distance"
]
} | {
"pipeline_type": "Human-in-the-Loop Visual Inspection",
"learning_paradigm": "Unsupervised Generative Modeling",
"knowledge_source": "Real-world ECG Time Series",
"backbone_model": "C-RNN-GAN (Mogren 2016)"
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "GAN Visual Evaluation Framework",
"architecture_description": "Framework integrates two coordinated views: GAN Iteration View visualizes Incoming/Outgoing Nearest Neighbor Distances (INND/ONND) via heatmaps to track convergence and diversity over training steps. Detailed Comparative View employs T... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": [
"PhysioNet ECG Dataset (Goldberger et al. 2000)"
]
} | {
"motivation": "Standard GAN loss functions and scalar metrics (e.g., Inception Score) fail to objectively evaluate time series quality or detect mode collapse, necessitating human judgment which lacks scalable visual support.",
"key_insights": "Tracking INND and ONND evolution reveals convergence stability and re... | {
"main_results": [
{
"dataset": "PhysioNet ECG",
"metric": "Visual Fidelity & Diversity",
"value": "Model 2 successful",
"comparison": "Model 1 exhibited mode collapse; Model 2 reproduced shifts and rare patterns using mini-batch discrimination"
},
{
"dataset": "PhysioNet EC... | 2001.00062 | DAS-2M-v1.0 | null | null |
{
"title": "SHNIRELMAN'S THEOREM APPLICATIONS",
"authors": [
"Felix Sidokhine"
],
"publication_year": null,
"publication_date": null
} | {
"task_category": "Number Theory",
"keywords": [
"Shnirelman's Theorem",
"Diophantine Equations",
"Gaussian Integers",
"Odd Gaussian Primes",
"Goldbach Conjecture"
]
} | {
"pipeline_type": "Theoretical Proof and Experimental Verification",
"learning_paradigm": "Mathematical Induction",
"knowledge_source": "Analytic Number Theory",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Shnirelman-based Diophantine System Solving",
"architecture_description": "Constructs solutions for systems of Diophantine equations using Shnirelman's constant $s_0=4$. Applies induction on sum $n=a+b$ to decompose integers into odd primes. Extends logic to Gaussian integers $\\mathbb{Z}[i]$ by d... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Standard additive number theory results require extension to complex domains and systems of equations. Representation of Gaussian integers by sums of odd Gaussian primes lacks definitive bounds analogous to Shnirelman's constant.",
"key_insights": "Systems of Diophantine equations admit solutions b... | {
"main_results": [
{
"dataset": "Theoretical Proof",
"metric": "Existence of Solution",
"value": "Proven for $a>10$",
"comparison": "Uses $s_0=4$ odd primes"
},
{
"dataset": "Experimental Data",
"metric": "Representation Bound",
"value": "At most 3 odd primes",
... | 2001.00063 | DAS-2M-v1.0 | null | null |
{
"title": "Brouwer's fan theorem",
"authors": [
"Josef Berger"
],
"publication_year": 2021,
"publication_date": "2021-08"
} | {
"task_category": "Constructive Reverse Mathematics",
"keywords": [
"Brouwer's Fan Theorem",
"Weak König Lemma",
"Uniform Continuity Theorem",
"Constructive Mathematics",
"Axiomatic Analysis"
]
} | {
"pipeline_type": "Logical Equivalence Proofs",
"learning_paradigm": "Axiomatic Deduction",
"knowledge_source": "Bishop-style Constructive Mathematics",
"backbone_model": null
} | {
"has_code": false,
"github_url": null,
"project_page": null
} | {
"method_name": "Constructive Equivalence Mapping",
"architecture_description": "Establishes logical equivalences between FAN, WKL, UC, and auxiliary axioms (DEFU, DECO, c-FAN) within Bishop's constructive framework. Utilizes tree transformations to relate infinite trees to longest paths. Defines c-sets and co-con... | {
"contributed_new_dataset": false,
"new_dataset_name": null,
"datasets_used_for_eval": []
} | {
"motivation": "Classical equivalences between Weak König Lemma, Fan Theorem, and Uniform Continuity fail in constructive mathematics due to lack of excluded middle.",
"key_insights": "WKL implies FAN but not vice versa; UC is equivalent to c-FAN (Fan Theorem for c-sets). Auxiliary principles DEFU and DECO clarify... | {
"main_results": [
{
"dataset": "Theoretical Proof",
"metric": "Logical Equivalence",
"value": "UC <=> c-FAN",
"comparison": "Stronger than standard FAN"
},
{
"dataset": "Theoretical Proof",
"metric": "Implication Chain",
"value": "WKL => FAN",
"comparison"... | 2001.00064 | DAS-2M-v1.0 | null | null |
End of preview.
DAS-2M
DAS-2M is an embedding-model-independent scholarly paper collection containing retrieval-ready text and full structured metadata. This release contains 1,471,166 records from 2020 through July 2026.
Release statistics
| Year | Metadata months | Records | Full metadata size | Retrieval size |
|---|---|---|---|---|
| 2020 | 12 | 178,221 | 140.49 MB | 156.09 MB |
| 2021 | 12 | 181,525 | 143.76 MB | 159.67 MB |
| 2022 | 12 | 185,615 | 145.90 MB | 162.16 MB |
| 2023 | 12 | 207,555 | 164.02 MB | 182.23 MB |
| 2024 | 12 | 243,534 | 193.38 MB | 215.02 MB |
| 2025 | 12 | 284,113 | 227.91 MB | 253.19 MB |
| 2026 | 7 | 190,603 | 155.20 MB | 172.32 MB |
| Total | 79 | 1,471,166 | 1.14 GB | 1.27 GB |
Data organization
The retrieval configuration contains compact lexical and semantic retrieval
documents. The metadata configuration contains the original nested metadata
objects aggregated into monthly Zstandard-compressed JSONL shards. Join the two
configurations using arxiv_id. metadata_shard is provenance and download
guidance; it is not the path to an individual JSON file.
Retrieval schema
| Field | Type | Description |
|---|---|---|
arxiv_id |
string | Stable join key. |
title |
string | Paper title. |
publication_date |
string | Normalized source publication date when available. |
lexical_text |
string | Selected metadata concatenated for lexical retrieval. |
semantic_text |
string | Selected technical/research metadata for semantic retrieval. |
metadata_version |
string | Release version marker (DAS-2M-v1.0). |
metadata_shard |
string | Relative monthly shard containing the full metadata record. |
Full metadata schema
| Field | Type | Description |
|---|---|---|
arxiv_id |
string | Stable join key. |
metadata_version |
string | Release version marker. |
basic_info |
object | Basic paper metadata. Private file_path is removed. |
categorization |
object | Task categories and keywords. |
technical_attributes |
object | Pipeline, paradigm, model and knowledge-source attributes. |
method_details |
object | Method name, architecture and innovations. |
dataset_info |
object | Dataset contribution and evaluation datasets. |
research_logic |
object | Motivation, insights and conclusion. |
evaluation |
object | Results, metrics and limitations. |
resource_info |
object | Available structured resource information. |
Validation
All 1,471,166 metadata records were successfully parsed, with no missing files, invalid JSON, or residual server-internal paths detected.
Usage
from datasets import load_dataset
retrieval = load_dataset("YOUR_ORG/DAS-2M", "retrieval", split="train")
metadata = load_dataset("YOUR_ORG/DAS-2M", "metadata", split="train")
For fast random lookup by arXiv ID, build a local SQLite index:
python tools/build_metadata_index.py \
--metadata-root metadata \
--output das2m_metadata.sqlite
The SQLite index contains no embedding vectors and is independent of the user's retrieval model.
Checksums
SHA-256 checksums for all released data shards are provided in SHA256SUMS.
License
CC BY 4.0.
- Downloads last month
- 38