Datasets:

sapienzanlp
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propsearch

id stringlengths 2 7	text stringlengths 56 1.51k
d0	Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differential gene expression.
d1	However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved.
d2	Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis.
d3	Correlated clustering and projection (CCP) was recently introduced as an effective method for preprocessing scRNA-seq data.
d4	CCP utilizes gene-gene correlations to partition the genes and, based on the partition, employs cell-cell interactions to obtain super-genes.
d5	Because CCP is a data-domain approach that does not require matrix diagonalization, it can be used in many downstream machine learning tasks.
d6	In this work, we utilize CCP as an initialization tool for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE).
d7	By using eight publicly available datasets, we have found that CCP significantly improves UMAP and t-SNE visualization and dramatically improve their accuracy.
d8	The number of studies on modeling, optimization, simulation, and data analysis of transportation systems is on the rise.
d9	Many of these studies utilize transportation test networks to represent real-world transportation systems in urban areas, examining the efficacy of their proposed approaches.
d10	Each of these networks exhibits unique characteristics in their topology, making their applications distinct for various study objectives.
d11	Despite their widespread use in research, there is a lack of comprehensive study addressing the classification of these networks based on their topological characteristics.
d12	Additionally, we employ two dimensionality reduction techniques, namely Principal Component Analysis (PCA) and Isometric Feature Mapping (ISOMAP), to reduce overlaps of highly correlated features and enhance the interpretability of the subsequent classification results.
d13	The PCA method, followed by the K-means clustering approach, outperforms other alternatives with a Silhouette score of $0.510$, enabling the classification of transportation networks into five clusters.
d14	Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks.
d15	In this paper, we present SceneCalib, a novel method for simultaneous self-calibration of extrinsic and intrinsic parameters in a system containing multiple cameras and a lidar sensor.
d16	Existing methods typically require specially designed calibration targets and human operators, or they only attempt to solve for a subset of calibration parameters.
d17	We resolve these issues with a fully automatic method that requires no explicit correspondences between camera images and lidar point clouds, allowing for robustness to many outdoor environments.
d18	Furthermore, the full system is jointly calibrated with explicit cross-camera constraints to ensure that camera-to-camera and camera-to-lidar extrinsic parameters are consistent.
d19	However, most previous work has focused on explaining an individual recommendation of an item to a user.
d20	In this paper, we propose summary explanations, i.e., explanations that highlight why a user or a group of users receive a set of item recommendations and why an item, or a group of items, is recommended to a set of users as an effective means to provide insights into the collective behavior of the recommender.
d21	We also present a novel method to summarize explanations using efficient graph algorithms, specifically the Steiner Tree and the Prize-Collecting Steiner Tree.
d22	Our approach reduces the size and complexity of summary explanations while preserving essential information, making explanations more comprehensible for users and more useful to model developers.
d23	Evaluations across multiple metrics demonstrate that our summaries outperform baseline explanation methods in most scenarios, in a variety of quality aspects.
d25	However, we found that, from the view of causal inference, the result of the original model has been influenced by spurious statistical relationships.
d26	In this work, we consider counterfactual methods to avoid the confounder in the original model.
d27	The experiment results show proposed models can surpass previous confounded models and can still make use of additional structures to improve the performance.
d28	We also design a simple structure based on Graph Convolutional Networks (GCN) in this work.
d29	The erosion of chloride ions in concrete bridges will accelerate the corrosion of reinforcement, which is an important reason for the decline of bridge durability.
d30	The erosion process of chloride ion, especially deicing salt solution in cold regions, is complex and has many influencing factors.
d32	In this study, the pre-stressed concrete bridge retired in the cold region was taken as the research object, and the specimens from the whole bridge are obtained by the method of core drilling sampling.
d33	The process of chloride ion erosion was simulated in two-dimensional space through COMSOL multi-physical field simulation, and compared with the measured results.
d34	Despite recent success in discriminative approaches in monocular depth estimation its quality remains limited by training datasets.
d35	Generative approaches mitigate this issue by leveraging strong priors derived from training on internet-scale datasets.
d36	Recent studies have demonstrated that large text-to-image diffusion models achieve state-of-the-art results in depth estimation when fine-tuned on small depth datasets.
d37	Concurrently, autoregressive generative approaches, such as the Visual AutoRegressive modeling~(VAR), have shown promising results in conditioned image synthesis.
d38	Following the visual autoregressive modeling paradigm, we introduce the first autoregressive depth estimation model based on the visual autoregressive transformer.
d39	Our primary contribution is DepthART -- a novel training method formulated as Depth Autoregressive Refinement Task.
d40	Unlike the original VAR training procedure, which employs static targets, our method utilizes a dynamic target formulation that enables model self-refinement and incorporates multi-modal guidance during training.
d41	Specifically, we use model predictions as inputs instead of ground truth token maps during training, framing the objective as residual minimization.
d43	The Visual Autoregressive Transformer trained with our approach on Hypersim achieves superior results on a set of unseen benchmarks compared to other generative and discriminative baselines.
d44	Understanding the genetic underpinnings of complex traits and diseases has been greatly advanced by genome-wide association studies (GWAS).
d45	Integrating GWAS with functional genomic data, such as expression quantitative trait loci (eQTLs), can bridge this gap.
d46	This study introduces a novel approach to discover candidate genes regulated by GWAS signals in both cis and trans.
d47	Unlike existing eQTL studies that focus solely on cis-eQTLs or consider cis- and trans-QTLs separately, we utilize adaptive statistical metrics that can reflect both the strong, sparse effects of cis-eQTLs and the weak, dense effects of trans-eQTLs.
d48	We demonstrate the efficiency of our method through theoretical and numerical analyses and apply it to adipose eQTL data from the METabolic Syndrome in Men (METSIM) study, uncovering genes playing important roles in the regulatory networks influencing cardiometabolic traits.
d49	Our findings offer new insights into the genetic regulation of complex traits and present a practical framework for identifying key regulatory genes based on joint eQTL effects.
d50	Hyperspectral unmixing is the analytical process of determining the pure materials and estimating the proportions of such materials composed within an observed mixed pixel spectrum.
d51	Ordinary least squares (OLS) regression serves as the foundation for many linear mixture models employed in Hyperspectral Image analysis.
d52	Though variations of OLS are implemented, studies rarely address the underlying assumptions that affect results.
d53	This paper provides an in depth discussion on the assumptions inherently endorsed by the application of OLS regression.
d54	We also examine variations of OLS models stemming from highly effective approaches in spectral unmixing -- sparse regression, iterative feature search strategies and Mathematical programming.
d55	We evaluated each approach's performance by computing the average error and precision of each model.
d56	Additionally, we provide a taxonomy of the molecular structure of each mineral to derive further understanding into the detection of the target materials.
d57	Large, self-supervised transformer-based language representation models have recently received significant amounts of attention, and have produced state-of-the-art results across a variety of tasks simply by scaling up pre-training on larger and larger corpora.
d58	Such models usually produce high dimensional vectors, on top of which additional task-specific layers and architectural modifications are added to adapt them to specific downstream tasks.
d59	Though there exists ample evidence that such models work well, we aim to understand what happens when they work well.
d60	We analyze the redundancy and location of information contained in output vectors for one such language representation model -- BERT.
d61	We show empirical evidence that the [CLS] embedding in BERT contains highly redundant information, and can be compressed with minimal loss of accuracy, especially for finetuned models, dovetailing into open threads in the field about the role of over-parameterization in learning.
d62	We also shed light on the existence of specific output dimensions which alone give very competitive results when compared to using all dimensions of output vectors.
d63	We examine future trajectories of the social cost of carbon, global temperatures, and carbon concentrations using the cost-benefit Dynamic Integrated Climate-Economy (DICE) model calibrated to the five Shared Socioeconomic Pathways (SSPs) under two mitigation scenarios: achieving net-zero carbon emissions by 2050 and b...
d64	The DICE model is calibrated to align industrial and land-use carbon emissions with projections from six leading process-based integrated assessment models (IAMs): IMAGE, MESSAGE--GLOBIOM, AIM/CGE, GCAM, REMIND--MAgPIE and WITCH--GLOBIOM.
d65	We find that even with aggressive mitigation (net-zero by 2050), global temperatures are projected to exceed $2^\circ\text{C}$ above preindustrial levels by 2100, with estimates ranging from $2.5^\circ\text{C}$ to $2.7^\circ\text{C}$ across all SSPs and IAMs considered.
d66	Under the more lenient mitigation scenario (net-zero by 2100), global temperatures are projected to rise to between $3^\circ\text{C}$ and $3.7^\circ\text{C}$ by 2100.
d67	Additionally, the social cost of carbon is estimated to increase from approximately USD 30--50 in 2025 to USD 250--400 in 2100.
d68	In recent years there has been significant effort to adapt the key tools and ideas in convex optimization to the Riemannian setting.
d69	Is there a Nesterov-like accelerated gradient method for geodesically convex functions on a Riemannian manifold?
d70	Recent work has given partial answers and the hope was that this ought to be possible.
d71	We prove that in a noisy setting, there is no analogue of accelerated gradient descent for geodesically convex functions on the hyperbolic plane.
d72	In negatively curved spaces, the volume of a ball grows so fast that information about the past gradients is not useful in the future.
d73	Convolution is the core component within deep neural networks and it is computationally intensive and time consuming.
d74	Tensor data layouts significantly impact convolution operations in terms of memory access and computational efficiency.
d75	Yet, there is still a lack of comprehensive performance characterization on data layouts on SIMD architectures concerning convolution methods.
d76	This paper proposes three novel data layouts for im2win convolution: NHWC, CHWN, and CHWN8, and introduces a set of general optimization techniques for both direct and im2win convolutions.
d77	We compare the optimized im2win convolution with the direct convolution and PyTorch's im2col-based convolution across the aforementioned layouts on SIMD machines.
d78	The experiments demonstrated that the im2win convolution with the new NHWC layout achieved up to 355% performance speedup over NCHW layout.
d79	Our optimized im2win and direct convolutions achieved up to 95% and 94% of machine's theoretical peak performance, respectively.
d80	Data augmentation has been shown to be effective in providing more training data for machine learning and resulting in more robust classifiers.
d81	However, for some problems, there may be multiple augmentation heuristics, and the choices of which one to use may significantly impact the success of the training.
d82	In this work, we propose a metric for evaluating augmentation heuristics; specifically, we quantify the extent to which an example is “hard to distinguish” by considering the difference between the distribution of the augmented samples of different classes.
d83	Experimenting with multiple heuristics in two prediction tasks (positive/negative sentiment and verbosity/conciseness) validates our claims by revealing the connection between the distribution difference of different classes and the classification accuracy.
d84	We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate.
d85	This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain.
d86	The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills.
d87	We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites.
d88	The controller displays the ability to handle obstructing obstacles, slopes up to 27$^\circ$, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions.
d89	The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.
d91	While existing TTS methods work well, they often rely on long decoding paths or require a large number of samples to be generated, increasing the token usage and inference latency.
d92	We observe the surprising fact that for reasoning tasks, shorter traces are much more likely to be correct than longer ones.
d93	Motivated by this, we introduce First Finish Search (FFS), a training-free parallel decoding strategy that launches $n$ independent samples and returns as soon as any one completes.
d94	We evaluate FFS alongside simple decoding, beam search, majority voting, and budget forcing on four reasoning models (DeepSeek-R1, R1-Distill-Qwen-32B, QwQ-32B and Phi-4-Reasoning-Plus) and across four datasets (AIME24, AIME25-I, AIME25-II and GPQA Diamond).
d95	With DeepSeek-R1, FFS achieves $82.23\%$ accuracy on the AIME datasets, a $15\%$ improvement over DeepSeek-R1's standalone accuracy, nearly matching OpenAI's o4-mini performance.
d96	Our theoretical analysis explains why stopping at the shortest trace is likely to yield a correct answer and identifies the conditions under which early stopping may be suboptimal.
d97	The elegance and simplicity of FFS demonstrate that straightforward TTS strategies can perform remarkably well, revealing the untapped potential of simple approaches at inference time.
d98	The advancement of autonomous drones, essential for sectors such as remote sensing and emergency services, is hindered by the absence of training datasets that fully capture the environmental challenges present in real-world scenarios, particularly operations in non-optimal weather conditions and the detection of thin ...
d99	We present the Drone Depth and Obstacle Segmentation (DDOS) dataset to fill this critical gap with a collection of synthetic aerial images, created to provide comprehensive training samples for semantic segmentation and depth estimation.
d100	Specifically designed to enhance the identification of thin structures, DDOS allows drones to navigate a wide range of weather conditions, significantly elevating drone training and operational safety.
d101	Additionally, this work introduces innovative drone-specific metrics aimed at refining the evaluation of algorithms in depth estimation, with a focus on thin structure detection.
d102	These contributions not only pave the way for substantial improvements in autonomous drone technology but also set a new benchmark for future research, opening avenues for further advancements in drone navigation and safety.
d103	Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions.

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PropSearch

This benchmark measures the ability of proposition-level embedding models (as well as fixed-size sentence encoders) to retrieve a source sentence starting from an extracted atomic proposition. The benchmark is built on sentences extracted from scientific abstracts (arXiv abstracts and papers-with-abstracts).

Atomic propositions are extracted by Claude Sonnet 4.6 using the following system prompt:

Extract atomic propositions from the given sentence. The propositions need to be independent (for instance, no pronouns that refer to entities outside the proposition itself) and connectives between propositions need to be discarded.

If the sentence only has one atomic proposition, return a singleton.

Return the output as a Python list.

In total, the task has 14,483 source sentences, 36,337 query propositions and a corpus of 300,000 confounding sentences.

For the full data creation and validation procedure, check our EMNLP 2026 paper Variable-size Discovery and Representation of Atomic Propositions.

We release this data for evaluation purposes. In the creation of this benchmark, we use Claude Sonnet 4.6 for data generation. Therefore, as to Anthropic's Privacy and Legal Policies, this data must not be used to train models that are competitive with Anthropic's own.

Subsets

The dataset contains four subsets:

source_sentences: The source sentences, along with an identifier and their original number of atomic propositions (n_atoms, used to stratify results)
queries: The extracted propositions, each labelled with the correct source sentence ID
corpus: A large number of confounder sentences, with no corresponding atomic propositions
counting: Sentences with their full list of atomic propositions, used to evaluate proposition counting

Data Distribution

Source Sentences by Atom Count

Atom Count	Total Sentences
1	2,089
2	5,233
3	3,516
4	2,116
5	961
6+	952

Query Atoms by Source-sentence Atom Count

Atom Count	Total Query Atoms
1	1,962
2	9,195
3	8,850
4	7,040
5	3,921
6+	5,369

Citation

If you use this task, please cite us as:

@inproceedings{ortame-navigli-2026-variable,
    title = "Variable-size Discovery and Representation of Atomic Propositions",
    author = "Ortame, Francesco  and
      Navigli, Roberto",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.2302/",
    pages = "52330--52348",
    ISBN = "979-8-89176-459-0"
}

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Variable-size Atomic Proposition Embeddings • 4 items • Updated about 2 hours ago