MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

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Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Leukocyte count is associated with coronary artery disease (CAD) events in the general population. Here we show that leukocytes are independently associated with CAD events in people with HIV in Switzerland, after adjusting for traditional and HIVrelated r ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

A search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state is presented. The analysis is based on proton-proton collision data recorded by the CMS detector in 2016-2018 at a centre-of-mass energy of 13 TeV a ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

Visual decisions are attracted toward features of previous stimuli. This phenomenon, termed serial dependence, has been related to a mechanism that integrates present visual input with stimuli seen up to 10 to 15 s in the past. It is believed that this mec ...

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour tow ...

This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonconvex program aiming to maximize the Va ...

Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

Investigating the dynamic activities of protein expression and signaling in living organisms is a crucial focus of intense research aimed at elucidating the processes that underlie disease progression and improving treatments and drug development. Resolvin ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

A search for the standard model Higgs boson decaying to a charm quark-antiquark pair, H ? cc-, produced in association with a leptonically decaying V (W or Z) boson is presented. The search is performed with proton-proton collisions at vs = 13 TeV collecte ...

Calibration between robots and cameras is critical in automated robot vision systems. However, conventional manually conducted image-based calibration techniques are often limited by their accuracy sensitivity and poor adaptability to dynamic or unstructur ...

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

Chattez avec Graph Search

Person Re-Identification and its Application to Multi-Object Tracking

Leukocyte Count and Coronary Artery Disease Events in People With Human Immunodeficiency Virus: A Longitudinal Study

A comparative analysis of gene expression profiling by statistical and machine learning approaches

An Unbiased Method of Measuring the Ratio of Two Data Sets

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state in proton-proton collisions at √s=13 TeV

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Attractive and repulsive serial dependence: The role of task relevance, the passage of time, and the number of stimuli

Expectation consistency for calibration of neural networks

Peak Value-at-Risk Estimation for Stochastic Differential Equations using Occupation Measures

A phase transition in diffusion models reveals the hierarchical nature of data

Sequential Reasoning with Socially Caused Beliefs

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Polynomial-time universality and limitations of deep learning

Fair Decentralized Learning

Label-free plasmonic microarray for multiplexed analysis of cells and tumor organoids

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Search for Higgs Boson Decay to a Charm Quark-Antiquark Pair in Proton-Proton Collisions at √s=13 TeV

A data-efficient and general-purpose hand–eye calibration method for robotic systems using next best view