Recovering Dantzig–Wolfe Bounds by Cutting Planes
Rui Chen
Oktay Günlük
Andrea Lodi
Leveraging Dantzig–Wolfe Decomposition in the Original Variable Space for Mixed-Integer Programming Dantzig–Wolfe decomposition has been… (see more) extensively applied to solve large-scale mixed-integer programs with decomposable structures, leading to exact solution approaches, such as branch and price. However, these approaches would require solving the problem in an extended variable space and are not readily present in off-the-shelf solvers. In “Recovering Dantzig–Wolfe Bounds by Cutting Planes,” Chen, Günlük, and Lodi propose a computational effective approach for generating cutting planes from Dantzig–Wolfe decomposition to enhance branch and cut in the space of original variables. The proposed approach requires a relatively small number of cutting planes to recover the strength of the Dantzig–Wolfe dual bound and should be easy to implement in general-purpose mixed-integer programming solvers. The authors show that these cutting planes typically lead to a formulation with lower dual degeneracy and hence, a better computational performance than naïve approaches, such as the objective function cut.
Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization
Rose Guay-Hottin
Lison Kardassevitch
Hugo Pham
Is sharing always caring? Entropy, boundaries and the plurality of psychotherapeutic process.
Lena Adel
Ana Gómez-Carrillo
Jonas Mago
Michael Lifshitz
Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy
Abdul Al-Shawwa
Michael Craig
Kalum Ost
David Anderson
Steven Casha
W. Bradley Jacobs
Nathan Evaniew
Saswati Tripathy
Jacques Bouchard
Peter Lewkonia
Fred Nicholls
Alex Soroceanu
Ganesh Swamy
Kenneth C. Thomas
Stephan duPlessis
Michael M.H. Yang
Nicholas Dea
Jefferson R. Wilson
David W. Cadotte
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Guy Desaulniers
Towards contrast-agnostic soft segmentation of the spinal cord
Sandrine Bédard
Enamundram Naga Karthik
Charidimos Tsagkas
Emanuele Pravatà
Cristina Granziera
Andrew C. Smith
Kenneth Arnold Weber
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (see more)monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants (
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training
Brian R. Bartoldson
Siddarth Venkatraman
James Diffenderfer
Moksh J. Jain
Tal Ben-Nun
Seanie Lee
Minsu Kim
Johan Samir Obando Ceron
Bhavya Kailkhura
Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images.
Pedro Vianna
Paria Mehrbod
Muawiz Chaudhary
Michael Eickenberg
An Tang
Guy Cloutier
Ultrasound is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its non-invasiveness and… (see more) availability. Deep learning methods have attracted considerable interest in this field, as they are capable of learning patterns in a collection of images and achieve clinically comparable levels of accuracy in steatosis grading. However, variations in patient populations, acquisition protocols, equipment, and operator expertise across clinical sites can introduce domain shifts that reduce model performance when applied outside the original training setting. In response, unsupervised domain adaptation techniques are being investigated to address these shifts, allowing models to generalize more effectively across diverse clinical environments. In this work, we propose a test-time batch normalization technique designed to handle domain shift, especially for changes in label distribution, by adapting selected features of batch normalization layers in a trained convolutional neural network model. This approach operates in an unsupervised manner, allowing robust adaptation to new distributions without access to label data. The method was evaluated on two abdominal ultrasound datasets collected at different institutions, assessing its capability in mitigating domain shift for hepatic steatosis classification. The proposed method reduced the mean absolute error in steatosis grading by 37% and improved the area under the receiver operating characteristic curve for steatosis detection from 0.78 to 0.97, compared to non-adapted models. These findings demonstrate the potential of the proposed method to address domain shift in ultrasound-based hepatic steatosis diagnosis, minimizing risks associated with deploying trained models in various clinical settings.
The Romantic Historicism and The Rise of the Historical Novel in the 19th Century Romanian Literature
Medium-scale flexible integrated circuits based on 2D semiconductors
Yalin Peng
Chenyang Cui
Lu Li
Yuchen Wang
Qinqin Wang
Jinpeng Tian
Zhiheng Huang
Biying Huang
Yangkun Zhang
Xiuzhen Li
Yanbang Chu
Wei Yang
Dongxia Shi
Luojun Du
Na Li
Guangyu Zhang
Sliding ferroelectric memories and synapses based on rhombohedral-stacked bilayer MoS2
Xiuzhen Li
Biao Qin
Yaxian Wang
Yue Xi
Zhiheng Huang
Mengze Zhao
Yalin Peng
Zitao Chen
Zitian Pan
Jundong Zhu
Chenyang Cui
Rong Yang
Wei Yang
Sheng Meng
Dongxia Shi
Xuedong Bai
Can Liu
Na Li
Kaihui Liu … (see 3 more)
Kai-Wen Liu
Luojun Du
Guangyu Zhang
AfriHG: News headline generation for African Languages
Toyib Ogunremi
Serah Akojenu
Anthony Soronnadi
Olubayo Adekanmbi
This paper introduces AfriHG -- a news headline generation dataset created by combining from XLSum and MasakhaNEWS datasets focusing on 16 l… (see more)anguages widely spoken by Africa. We experimented with two seq2eq models (mT5-base and AfriTeVa V2), and Aya-101 LLM. Our results show that Africa-centric seq2seq models such as AfriTeVa V2 outperform the massively multilingual mT5-base model. Finally, we show that the performance of fine-tuning AfriTeVa V2 with 313M parameters is competitive to prompting Aya-101 LLM with more than 13B parameters.