Une approche computationnelle du cadastre napoléonien de Venise

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Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and ...

File formats are described in read_me files in the concerning folder. For plotting and data evaluation, Python 2.7 was used. Example scripts for plotting are provided. ...

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Previous studies have shown that access to a satisfactory view to the outside with sufficient daylight is essential for building occupants' health and well-being. It has also been suggested that certain features of visual content improve view-out quality, ...

PurposeTo perform a systematic review of literature specific to single-fraction stereotactic radiosurgery (SRS) for large vestibular schwannomas (VS), maximum diameter >= 2.5 cm and/or classified as Koos Grade IV, and to present consensus recommendations o ...

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages ...

The drive to develop cartilage implants for the treatment of major defects in the musculoskeletal sys-tem has resulted in a major research thrust towards developing biomaterial devices for cartilage repair. Investigational devices for the restoration of ar ...

Perioptic meningiomas, defined as those that are less than 3 mm from the optic apparatus, are challenging to treat with stereotactic radiosurgery (SRS). Tumor control must be weighed against the risk of radiation-induced optic neuropathy (RION), as both tu ...

BACKGROUND: Stereotactic radiosurgery (SRS) is one of the main treatment options in the management of small to medium size vestibular schwannomas (VSs), because of high tumor control rate and low cranial nerves morbidity. Series reporting long-term hearing ...

If automatic document processing techniques have achieved a certain maturity for present time documents, the transformation of hand-written documents into well-represented, structured and connected data which can satisfactorily be used for historical study ...

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

Language has shaped human evolution and led to the desire to endow machines with language abilities. Recent advancements in natural language processing enable us to achieve this breakthrough in human-machine interaction. However, introducing conversational ...

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...

Diagonal linear networks (DLNs) are a toy simplification of artificial neural networks; they consist in a quadratic reparametrization of linear regression inducing a sparse implicit regularization. In this paper, we describe the trajectory of the gradient ...

We prove that every online learnable class of functions of Littlestone dimension d admits a learning algorithm with finite information complexity. Towards this end, we use the notion of a globally stable algorithm. Generally, the information complexity of ...

We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

Une approche computationnelle du cadastre napoléonien de Venise

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Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

From scattered sources to comprehensive technology landscape : A recommendation-based retrieval approach

Periodic and aperiodic NiFe nanomagnet/ferrimagnet hybrid structures for 2D magnon steering and interferometry with high extinction ratio

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Dynamism in the context of views out: A literature review

Single-fraction radiosurgery outcomes for large vestibular schwannomas in the upfront or post-surgical setting: a systematic review and International Stereotactic Radiosurgery Society (ISRS) Practice Guidelines

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Lausanne Historical Censuses Dataset HTR 35k

A guide to preclinical evaluation of hydrogel-based devices for treatment of cartilage lesions

Single fraction and hypofractionated radiosurgery for perioptic meningiomas-tumor control and visual outcomes: a systematic review and meta-analysis

Long-Term Hearing Outcome After Radiosurgery for Vestibular Schwannoma: A Systematic Review and Meta-Analysis

From Archival Sources to Structured Historical Information: Annotating and Exploring the "Accordi dei Garzoni"

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Towards Novel Evaluation Methods for Social Dialog Systems

A new age in protein design empowered by deep learning

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Incremental Learning in Diagonal Linear Networks

Finite Littlestone Dimension Implies Finite Information Complexity

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning