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Publications
Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck
The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle. In reality, however, co… (voir plus)de reuse practices lack proper control, resulting in issues such as vulnerability propagation and intellectual property infringements. Assembly clone search, a critical shift-right defence mechanism, has been effective in identifying vulnerable code resulting from reuse in released executables. Recent studies on assembly clone search demonstrate a trend towards using machine learning-based methods to match assembly code variants produced by different toolchains. However, these methods are limited to what they learn from a small number of toolchain variants used in training, rendering them inapplicable to unseen architectures and their corresponding compilation toolchain variants. This paper presents the first study on the problem of assembly clone search with unseen architectures and libraries. We propose incorporating human common knowledge through large-scale pre-trained natural language models, in the form of transfer learning, into current learning-based approaches for assembly clone search. Transfer learning can aid in addressing the limitations of the existing approaches, as it can bring in broader knowledge from human experts in assembly code. We further address the sequence limit issue by proposing a reinforcement learning agent to remove unnecessary and redundant tokens. Coupled with a new Variational Information Bottleneck learning strategy, the proposed system minimizes the reliance on potential indicators of architectures and optimization settings, for a better generalization of unseen architectures. We simulate the unseen architecture clone search scenarios and the experimental results show the effectiveness of the proposed approach against the state-of-the-art solutions.
When has an agent converged? Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence:… (voir plus) An agent converges when its behavior or performance in each environment state stops changing. However, as we shift the focus of our learning problem from the environment's state to the agent's state, the concept of an agent's convergence becomes significantly less clear. In this paper, we propose two complementary accounts of agent convergence in a framing of the reinforcement learning problem that centers around bounded agents. The first view says that a bounded agent has converged when the minimal number of states needed to describe the agent's future behavior cannot decrease. The second view says that a bounded agent has converged just when the agent's performance only changes if the agent's internal state changes. We establish basic properties of these two definitions, show that they accommodate typical views of convergence in standard settings, and prove several facts about their nature and relationship. We take these perspectives, definitions, and analysis to bring clarity to a central idea of the field.
Grouped convolution has been observed to be an effective approximation for convolution in many DNN applications. For example, SqueezeBERT, w… (voir plus)hich is a light and fast BERT language processing model, utilizes 1D grouped convolutions. Though SqueezeBERT is well-optimized for inference on edge devices, it suffers from poor memory management during fine-tuning (training). This results in longer fine-tuning time on resource-limited GPUs compared to the original BERT model, BERT-base, despite being specifically designed for edge devices. We study this behavior and show that this poor memory management originates from the use of 1D grouped convolutions in SqueezeBERT. We re-implement 1D grouped convolutions using fully-connected layers, addressing the poor memory allocation and data locality of 1D grouped convolutions. We show that our method is well-suited for edge devices with limited memory; further, it has a negligible effect on inference speed. When utilizing our method, we observe a 42 % reduction in fine-tuning time for SqueezeBERT on edge devices.
2023-07-19
2023 IEEE 34th International Conference on Application-specific Systems, Architectures and Processors (ASAP) (publié)
Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptatio… (voir plus)n are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We find that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we find that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time.
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well de… (voir plus)scribed by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat “neural manifold”. Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey motor cortex, mouse motor cortex, mouse striatum, and human motor cortex, we show that: 1) neural manifolds are intrinsically nonlinear; 2) the degree of their nonlinearity varies across architecturally distinct brain regions; and 3) manifold nonlinearity becomes more evident during complex tasks that require more varied activity patterns. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented w… (voir plus)ith a sequence of related tasks with limited interactions per task. The agent can use its experience in each task and across tasks to estimate both the transition model and the distribution over tasks. We propose an algorithm to meta-learn the underlying structure across tasks, utilize it to plan in each task, and upper-bound the regret of the planning loss. Our bound suggests that the average regret over tasks decreases as the number of tasks increases and as the tasks are more similar. In the classical single-task setting, it is known that the planning horizon should depend on the estimated model's accuracy, that is, on the number of samples within task. We generalize this finding to meta-RL and study this dependence of planning horizons on the number of tasks. Based on our theoretical findings, we derive heuristics for selecting slowly increasing discount factors, and we validate its significance empirically.
This paper first presents a time-series impact analysis of charging electric vehicles (EVs) to loading levels of power network equipment con… (voir plus)sidering stochasticity in charging habits of EV owners. A novel incentive-based mitigation strategy is then designed to shift the EV charging from the peak hours when the equipment is overloaded to the off-peak hours and maintain equipment service lifetime. The incentive level and corresponding contributions from customers to alter their EV charging habits are determined by a search algorithm and a constrained optimization problem. The strategy is illustrated on a modified version of the IEEE 8500 feeder with a high EV penetration to mitigate overloads on the substation transformer.
2023-07-16
2023 IEEE Power & Energy Society General Meeting (PESGM) (publié)
The role of a decision support system is to gather, synthesize and present information in order to make informed decisions. In this project,… (voir plus) a mapping platform and a decision support system are proposed to present beekeeping data in Quebec. A complete review of the data and factors influencing honey production must first be carried out. The decision support system will be designed according to the nature of the data and access to available technologies. Continuous and real-time data management must be configured to make data interoperable. Multi-dimensional data loading tools will need to be configured to display data and analyses in a dashboard. Beekeepers will be able to optimize or move their hives according to their interpretation of the results displayed in the decision support system.
2023-07-16
IEEE International Geoscience and Remote Sensing Symposium (publié)
Structured Pruning of Neural Networks for Constraints Learning
Matteo Cacciola
Antonio Frangioni
Andrea Lodi
In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse app… (voir plus)lications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this domain, the combination of ML and OR often relies on representing the ML model output using Mixed Integer Programming (MIP) formulations. Numerous studies in the literature have developed such formulations for many ML predictors, with a particular emphasis on Artificial Neural Networks (ANNs) due to their significant interest in many applications. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations that are impractical to solve, thereby impeding scalability. In fact, the ML community has already introduced several techniques to reduce the parameter count of ANNs without compromising their performance, since the substantial size of modern ANNs presents challenges for ML applications as it significantly impacts computational efforts during training and necessitates significant memory resources for storage. In this paper, we showcase the effectiveness of pruning, one of these techniques, when applied to ANNs prior to their integration into MIPs. By pruning the ANN, we achieve significant improvements in the speed of the solution process. We discuss why pruning is more suitable in this context compared to other ML compression techniques, and we identify the most appropriate pruning strategies. To highlight the potential of this approach, we conduct experiments using feed-forward neural networks with multiple layers to construct adversarial examples. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.
The quality of cervical spinal cord images can be improved by the use of tailored radiofrequency (RF) coil solutions for ultrahigh field ima… (voir plus)ging; however, very few commercial and research 7‐T RF coils currently exist for the spinal cord, and in particular, those with parallel transmission (pTx) capabilities. This work presents the design, testing, and validation of a pTx/Rx coil for the human neck and cervical/upper thoracic spinal cord. The pTx portion is composed of eight dipoles to ensure high homogeneity over this large region of the spinal cord. The Rx portion is made up of twenty semiadaptable overlapping loops to produce high signal‐to‐noise ratio (SNR) across the patient population. The coil housing is designed to facilitate patient positioning and comfort, while also being tight fitting to ensure high sensitivity. We demonstrate RF shimming capabilities to optimize B1+ uniformity, power efficiency, and/or specific absorption rate efficiency. B1+ homogeneity, SNR, and g‐factor were evaluated in adult volunteers and demonstrated excellent performance from the occipital lobe down to the T4‐T5 level. We compared the proposed coil with two state‐of‐the‐art head and head/neck coils, confirming its superiority in the cervical and upper thoracic regions of the spinal cord. This coil solution therefore provides a convincing platform for producing the high image quality necessary for clinical and research scanning of the upper spinal cord.
