Sequential Proximity: Towards Provably Scalable Concurrent Search Algorithms

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Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

Formally verifying the correctness of software is necessary to merit the trust people put in software systems. Currently, formal verification requires human effort to prove that a piece of code matches its specification and code changes to improve verifiab ...

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in r ...

Context. A novel high-performance exact pair-counting toolkit called fast correlation function calculator (FCFC) is presented.Aims. With the rapid growth of modern cosmological datasets, the evaluation of correlation functions with observational and simula ...

The design and implementation of efficient concurrent data structures has seen significant attention. However, most of this work has focused on concurrent data structures providing good worst-case guarantees, although, in real workloads, objects are often ...

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validat ...

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly refe ...

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains. Integrated into an off-the-shelf vision transformer backbone, our adapters can simultaneously solve mu ...

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

The problem of learning graphons has attracted considerable attention across several scientific communities, with significant progress over the re-cent years in sparser regimes. Yet, the current techniques still require diverg-ing degrees in order to succe ...

Type systems usually characterize the shape of values but not their free variables. However, many desirable safety properties could be guaranteed if one knew the free variables captured by values. We describe CC

Macromolecular mass fingerprinting using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is a quick tool for biochemical analysis. In combination with algorithms for automated data analysis and interpretation, it has wide appli ...

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 ...

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re- main unclear. Previous works only compare decoding algorithms in narrow sc ...

Sequential Proximity: Towards Provably Scalable Concurrent Search Algorithms

Chattez avec Graph Search

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

A ride time-oriented scheduling algorithm for dial-a-ride problems

Automated Formal Verification of Software Network Functions

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

Fast correlation function calculator A high-performance pair-counting toolkit

The splay-list: a distribution-adaptive concurrent skip-list

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots

Bayesian reconstruction of memories stored in neural networks from their connectivity

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Vision Transformer Adapters for Generalizable Multitask Learning

Results on Sparse Integer Programming and Geometric Independent Sets

Fairness and Explainability in Clustering Problems

Learning Sparse Graphons And The Generalized Kesten-Stigum Threshold

Capturing Types

Algorithms push forward the application of MALDI-TOF mass fingerprinting in rapid precise diagnosis

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Language Model Decoding as Likelihood–Utility Alignment