Publications

R3Design: deep tertiary structure-based RNA sequence design and beyond
Cheng Tan
Yijie Zhang
Zhangyang Gao
Hanqun Cao
Siyuan Li
Siqi Ma
Mathieu Blanchette
Stan Z. Li
The rational design of Ribonucleic acid (RNA) molecules is crucial for advancing therapeutic applications, synthetic biology, and understand… (voir plus)ing the fundamental principles of life. Traditional RNA design methods have predominantly focused on secondary structure-based sequence design, often neglecting the intricate and essential tertiary interactions. We introduce R3Design, a tertiary structure-based RNA sequence design method that shifts the paradigm to prioritize tertiary structure in the RNA sequence design. R3Design significantly enhances sequence design on native RNA backbones, achieving high sequence recovery and Macro-F1 score, and outperforming traditional secondary structure-based approaches by substantial margins. We demonstrate that R3Design can design RNA sequences that fold into the desired tertiary structures by validating these predictions using advanced structure prediction models. This method, which is available through standalone software, provides a comprehensive toolkit for designing, folding, and evaluating RNA at the tertiary level. Our findings demonstrate R3Design’s superior capability in designing RNA sequences, which achieves around \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document}
2024-12-31
Briefings in Bioinformatics (publié)
doi.org
REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
Le Zhang
Bo Wang
Xipeng Qiu
Siva Reddy
Aishwarya Agrawal
We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, sign… (voir plus)ificantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular information retrieval benchmarks, notably requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT benchmarks. These results underscore the effectiveness of our approach and highlight how reinforcement learning can enhance LLM reasoning capabilities in reranking.
2024-12-31
EMNLP (publié)
doi.org
arxiv.org
Reassessing Speech Translation for Low-Resource Languages: Do LLMs Redefine the State-of-the-Art Against Cascaded Models?
Jonah Dauvet
Min Ma
Jessica Ojo
David Ifeoluwa Adelani
2024-12-31
Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025) (publié)
doi.org
Recovering Dantzig–Wolfe Bounds by Cutting Planes
Ying Chen
Oktay Günlük
Andrea Lodi
Leveraging Dantzig–Wolfe Decomposition in the Original Variable Space for Mixed-Integer Programming Dantzig–Wolfe decomposition has been… (voir plus) 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.
2024-12-31
Oper. Res. (publié)
doi.org
arxiv.org
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
Zhongyu Li
Xue Bin Peng
Pieter Abbeel
Sergey Levine
Glen Berseth
Koushil Sreenath
This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal rob… (voir plus)ots. Going beyond focusing on a single locomotion skill, we develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. Our RL-based controller incorporates a novel dual-history architecture, utilizing both a long-term and short-term input/output (I/O) history of the robot. This control architecture, when trained through the proposed end-to-end RL approach, consistently outperforms other methods across a diverse range of skills in both simulation and the real world. The study also delves into the adaptivity and robustness introduced by the proposed RL system in developing locomotion controllers. We demonstrate that the proposed architecture can adapt to both time-invariant dynamics shifts and time-variant changes, such as contact events, by effectively using the robot's I/O history. Additionally, we identify task randomization as another key source of robustness, fostering better task generalization and compliance to disturbances. The resulting control policies can be successfully deployed on Cassie, a torque-controlled human-sized bipedal robot. This work pushes the limits of agility for bipedal robots through extensive real-world experiments. We demonstrate a diverse range of locomotion skills, including: robust standing, versatile walking, fast running with a demonstration of a 400-meter dash, and a diverse set of jumping skills, such as standing long jumps and high jumps.
2024-12-31
Int. J. Robotics Res. (publié)
doi.org
arxiv.org
Reliability Assessment of Distribution Systems With Microgrids Using Discrete-Time Markov Chains
Jean-William Lauzon
Ilhan Kocar
Antoine Lesage-Landry
Microgrids can improve the reliability and resiliency of modern distribution systems. The stochasticity of local non-dispatchable distribute… (voir plus)d energy resources (NDDERs), combined with the time-dependency of battery energy storage systems (BESSs) and load shedding strategies (LSSs), complicates the reliability assessment of distribution networks embedded with microgrids. In this work, we propose a minimal cut-set method using a discrete-time Markov chain to perform the time-series adequacy assessment. Our method offers an alternative to sequential Monte Carlo simulations (SMCSs) to account for the stochasticity of NDDERs and the time-dependency of BESSs and LSSs. Case studies on modified IEEE-RTBS Bus2 and IEEE 123-Test Feeder systems assess the accuracy of the method when compared with SMCSs.
2024-12-31
IEEE Transactions on Power Delivery (publié)
doi.org
Representing Positional Information in Generative World Models for Object Manipulation
Stefano Ferraro
Pietro Mazzaglia
Tim Verbelen
Bart Dhoedt
Sai Rajeswar
Object manipulation capabilities are essential skills that set apart embodied agents engaging with the world, especially in the realm of rob… (voir plus)otics. The ability to predict outcomes of interactions with objects is paramount in this setting. While model-based control methods have started to be employed for tackling manipulation tasks, they have faced challenges in accurately manipulating objects. As we analyze the causes of this limitation, we identify the cause of underperformance in the way current world models represent crucial positional information, especially about the target's goal specification for object positioning tasks. We introduce a general approach that empowers world model-based agents to effectively solve object-positioning tasks. We propose two declinations of this approach for generative world models: position-conditioned (PCP) and latent-conditioned (LCP) policy learning. In particular, LCP employs object-centric latent representations that explicitly capture object positional information for goal specification. This naturally leads to the emergence of multimodal capabilities, enabling the specification of goals through spatial coordinates or a visual goal. Our methods are rigorously evaluated across several manipulation environments, showing favorable performance compared to current model-based control approaches.
2024-12-31
ECAI (publié)
doi.org
arxiv.org
Retreever: Tree-Based Coarse-to-Fine Representations for Retrieval
Shubham Gupta
Zichao Li
Tianyi Chen
Cem Subakan
Siva Reddy
Perouz Taslakian
Valentina Zantedeschi
Document retrieval is a core component of question-answering systems, as it enables conditioning answer generation on new and large-scale co… (voir plus)rpora. While effective, the standard practice of encoding documents into high-dimensional embeddings for similarity search entails large memory and compute footprints, and also makes it hard to inspect the inner workings of the system. In this paper, we propose a tree-based method for organizing and representing reference documents at various granular levels, which offers the flexibility to balance cost and utility, and eases the inspection of the corpus content and retrieval operations. Our method, called ReTreever, jointly learns a routing function per internal node of a binary tree such that query and reference documents are assigned to similar tree branches, hence directly optimizing for retrieval performance. Our evaluations show that ReTreever generally preserves full representation accuracy. Its hierarchical structure further provides strong coarse representations and enhances transparency by indirectly learning meaningful semantic groupings. Among hierarchical retrieval methods, ReTreever achieves the best retrieval accuracy at the lowest latency, proving that this family of techniques can be viable in practical applications.
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
Revisiting Data Augmentation for Ultrasound Images
Adam Tupper
Christian Gagné
Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite o… (voir plus)ften facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.
2024-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination
Jerry Huang
Prasanna Parthasarathi
Mehdi Rezagholizadeh
Boxing Chen
A. Chandar
The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some … (voir plus)of this is also owed to the risks and costs associated with their use. On one front is their tendency to \textit{hallucinate} false or misleading information, limiting their reliability. On another is the increasing focus on the computational limitations associated with traditional self-attention based LLMs, which has brought about new alternatives, in particular recurrent models, meant to overcome them. Yet it remains uncommon to consider these two concerns simultaneously. Do changes in architecture exacerbate/alleviate existing concerns about hallucinations? Do they affect how and where they occur? Through an extensive evaluation, we study how these architecture-based inductive biases affect the propensity to hallucinate. While hallucination remains a general phenomenon not limited to specific architectures, the situations in which they occur and the ease with which specific types of hallucinations can be induced can significantly differ based on the model architecture. These findings highlight the need for better understanding both these problems in conjunction with each other, as well as consider how to design more universal techniques for handling hallucinations.
2024-12-31
ACL (Findings) (publié)
doi.org
arxiv.org
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling
Jonas Ngnawe
Maxime Heuillet
Sabyasachi Sahoo
Yann Pequignot
Ola Ahmad
Audrey Durand
Frédéric Precioso
Christian Gagné
Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims… (voir plus) to simultaneously achieve adaptation to a downstream task and robustness to adversarial examples, remains challenging. Despite the abundance of non-robust pretrained models in open-source repositories, their potential for RFT is less understood. We address this knowledge gap by systematically examining RFT from such non-robust models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub \emph{suboptimal transfer}. In challenging scenarios (eg, difficult tasks, high perturbation), the resulting performance can be so low that it may be considered a transfer failure. We find that fine-tuning using a robust objective impedes task adaptation at the beginning of training and eventually prevents optimal transfer. However, we propose a novel heuristic, \emph{Epsilon-Scheduling}, a schedule over perturbation strength used during training that promotes optimal transfer. Additionally, we introduce \emph{expected robustness}, a metric that captures performance across a range of perturbations, providing a more comprehensive evaluation of the accuracy-robustness trade-off for diverse models at test time. Extensive experiments on a wide range of configurations (six pretrained models and five datasets) show that \emph{Epsilon-Scheduling} successfully prevents \emph{suboptimal transfer} and consistently improves expected robustness.
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
Robust Model Predictive Control for the Optimal Operation of the Indoor Environment of a Cluster of Smart Greenhouses
Ehsan Ghorbani
Ahmed Ouammi
Hanane Dagdougui
Smart greenhouses can be defined as cutting-edge technological systems that efficiently control indoor climate conditions to protect crops f… (voir plus)rom harsh outdoor conditions to increase their productivity. In this article, we developed and implemented a robust model predictive control approach that relies on a recursive state estimation method to cope with the impact of measurement and process signal errors. The aim of this approach is to optimally control the internal environment of intelligent greenhouses. A feedback policy problem is decomposing signals for the accessibility of uncertainties. Then, a robust feasibility set can be defined by determining the ellipsoid set on uncertainty to obtain solvable constrained optimization in the CPLEX solver. In the overall formulation, each greenhouse is considered as an independent element. This method can improve the quality of set-point tracking while reducing the computation time required to arrive at a solution. Extensive numerical simulations involving the application of an innovative and robust algorithm to a cluster of greenhouses were conducted to demonstrate the algorithm’s performance and effectiveness.
2024-12-31
IEEE Transactions on AgriFood Electronics (publié)
doi.org