OpenRaiser/Intern-Atlas huggingface dataset:
Improving computation efficiency using input and architecture features for a virtual screening application
Venue: arXiv cs.CE 2023
Year: 2023

Virtual screening is an early stage of the drug discovery process that selects the most promising candidates. In the urgent computing scenario it is critical to find a solution in a short time frame. In this paper, we focus on a real-world virtual screening application to evaluate out-of-kernel optimizations, that consider input and architecture features to improve the computation efficiency on GPU. Experiment results on a modern supercomputer node show that we can almost double the performance. Moreover, we implemented the optimization using SYCL and it provides a consistent benefit with the CUDA optimization. A virtual screening campaign can use this gain in performance to increase the number of evaluated candidates, improving the probability of finding a drug.
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset. To overcome the challenge of train the big teacher model in resource limited user devices, the digital twin (DT) is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources. Then, during model distillation, each user can update the parameters of its model at either the physical entity or the digital agent. The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming (MIP) problem. To solve the problem, Q-learning and optimization are jointly used, where Q-learning selects models for users and determines whether to train locally or on the server, and optimization is used to allocate resources for users based on the output of Q-learning. Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.
MOELA: A Multi-Objective Evolutionary/Learning Design Space Exploration Framework for 3D Heterogeneous Manycore Platforms
Venue: arXiv cs.LG 2023
Year: 2023

To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many processing elements (PEs) are needed. However, designing such complex systems with multiple objectives can be challenging due to the huge associated design space and long evaluation times. To optimize such systems, we propose a new multi-objective design space exploration framework called MOELA that combines the benefits of evolutionary-based search with a learning-based local search to quickly determine PE and communication link placement to optimize multiple objectives (e.g., latency, throughput, and energy) in 3D NoC enabled heterogeneous manycore systems. Compared to state-of-the-art approaches, MOELA increases the speed of finding solutions by up to 128x, leads to a better Pareto Hypervolume (PHV) by up to 12.14x and improves energy-delay-product (EDP) by up to 7.7% in a 5-objective scenario.
Force Feedback Control For Dexterous Robotic Hands Using Conditional Postural Synergies
Venue: arXiv cs.RO 2023
Year: 2023

We present a force feedback controller for a dexterous robotic hand equipped with force sensors on its fingertips. Our controller uses the conditional postural synergies framework to generate the grasp postures, i.e. the finger configuration of the robot, at each time step based on forces measured on the robot's fingertips. Using this framework we are able to control the hand during different grasp types using only one variable, the grasp size, which we define as the distance between the tip of the thumb and the index finger. Instead of controlling the finger limbs independently, our controller generates control signals for all the hand joints in a (low-dimensional) shared space (i.e. synergy space). In addition, our approach is modular, which allows to execute various types of precision grips, by changing the synergy space according to the type of grasp. We show that our controller is able to lift objects of various weights and materials, adjust the grasp configuration during changes in the object's weight, and perform object placements and object handovers.
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Venue: arXiv cs.DC 2023
Year: 2023

Mixture-of-Experts (MoE) models have gained popularity in achieving state-of-the-art performance in a wide range of tasks in computer vision and natural language processing. They effectively expand the model capacity while incurring a minimal increase in computation cost during training. However, deploying such models for inference is difficult due to their large size and complex communication pattern. In this work, we provide a characterization of two MoE workloads, namely Language Modeling (LM) and Machine Translation (MT) and identify their sources of inefficiencies at deployment. We propose three optimization techniques to mitigate sources of inefficiencies, namely (1) Dynamic gating, (2) Expert Buffering, and (3) Expert load balancing. We show that dynamic gating improves maximum throughput by 6.21-11.23$\times$ for LM, 5.75-10.98$\times$ for MT Encoder and 2.58-5.71$\times$ for MT Decoder. It also reduces memory usage by up to 1.36$\times$ for LM and up to 1.1$\times$ for MT. We further propose Expert Buffering, a new caching mechanism that only keeps hot, active experts in GPU memory while buffering the rest in CPU memory. This reduces static memory allocation by up to 1.47$\times$. We finally propose a load balancing methodology that provides additional scalability to the workload.
A New Super-Resolution Measurement of Perceptual Quality and Fidelity
Venue: arXiv cs.CV 2023
Year: 2023

Super-resolution results are usually measured by full-reference image quality metrics or human rating scores. However, these evaluation methods are general image quality measurement, and do not account for the nature of the super-resolution problem. In this work, we analyze the evaluation problem based on the one-to-many mapping nature of super-resolution, and propose a novel distribution-based metric for super-resolution. Starting from the distribution distance, we derive the proposed metric to make it accessible and easy to compute. Through a human subject study on super-resolution, we show that the proposed metric is highly correlated with the human perceptual quality, and better than most existing metrics. Moreover, the proposed metric has a higher correlation with the fidelity measure compared to the perception-based metrics. To understand the properties of the proposed metric, we conduct extensive evaluation in terms of its design choices, and show that the metric is robust to its design choices. Finally, we show that the metric can be used to train super-resolution networks for better perceptual quality.
Fast computation of permutation equivariant layers with the partition algebra
Venue: arXiv cs.LG 2023
Year: 2023

Linear neural network layers that are either equivariant or invariant to permutations of their inputs form core building blocks of modern deep learning architectures. Examples include the layers of DeepSets, as well as linear layers occurring in attention blocks of transformers and some graph neural networks. The space of permutation equivariant linear layers can be identified as the invariant subspace of a certain symmetric group representation, and recent work parameterized this space by exhibiting a basis whose vectors are sums over orbits of standard basis elements with respect to the symmetric group action. A parameterization opens up the possibility of learning the weights of permutation equivariant linear layers via gradient descent. The space of permutation equivariant linear layers is a generalization of the partition algebra, an object first discovered in statistical physics with deep connections to the representation theory of the symmetric group, and the basis described above generalizes the so-called orbit basis of the partition algebra. We exhibit an alternative basis, generalizing the diagram basis of the partition algebra, with computational benefits stemming from the fact that the tensors making up the basis are low rank in the sense that they naturally factorize into Kronecker products. Just as multiplication by a rank one matrix is far less expensive than multiplication by an arbitrary matrix, multiplication with these low rank tensors is far less expensive than multiplication with elements of the orbit basis. Finally, we describe an algorithm implementing multiplication with these basis elements.
Generating Query Focused Summaries without Fine-tuning the Transformer-based Pre-trained Models
Venue: arXiv cs.CL 2023
Year: 2023

Fine-tuning the Natural Language Processing (NLP) models for each new data set requires higher computational time associated with increased carbon footprint and cost. However, fine-tuning helps the pre-trained models adapt to the latest data sets; what if we avoid the fine-tuning steps and attempt to generate summaries using just the pre-trained models to reduce computational time and cost. In this paper, we tried to omit the fine-tuning steps and investigate whether the Marginal Maximum Relevance (MMR)-based approach can help the pre-trained models to obtain query-focused summaries directly from a new data set that was not used to pre-train the models. First, we used topic modelling on Wikipedia Current Events Portal (WCEP) and Debatepedia datasets to generate queries for summarization tasks. Then, using MMR, we ranked the sentences of the documents according to the queries. Next, we passed the ranked sentences to seven transformer-based pre-trained models to perform the summarization tasks. Finally, we used the MMR approach again to select the query relevant sentences from the generated summaries of individual pre-trained models and constructed the final summary. As indicated by the experimental results, our MMR-based approach successfully ranked and selected the most relevant sentences as summaries and showed better performance than the individual pre-trained models.
Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models
Venue: arXiv cs.SE 2023
Year: 2023

Pre-trained Programming Language Models (PPLMs) achieved many recent states of the art results for many code-related software engineering tasks. Though some studies use data flow or propose tree-based models that utilize Abstract Syntax Tree (AST), most PPLMs do not fully utilize the rich syntactical information in source code. Still, the input is considered a sequence of tokens. There are two issues; the first is computational inefficiency due to the quadratic relationship between input length and attention complexity. Second, any syntactical information, when needed as an extra input to the current PPLMs, requires the model to be pre-trained from scratch, wasting all the computational resources already used for pre-training the current models. In this work, we propose Named Entity Recognition (NER) adapters, lightweight modules that can be inserted into Transformer blocks to learn type information extracted from the AST. These adapters can be used with current PPLMs such as CodeBERT, GraphCodeBERT, and CodeT5. We train the NER adapters using a novel Token Type Classification objective function (TTC). We insert our proposed work in CodeBERT, building CodeBERTER, and evaluate the performance on two tasks of code refinement and code summarization. CodeBERTER improves the accuracy of code refinement from 16.4 to 17.8 while using 20% of training parameter budget compared to the fully fine-tuning approach, and the BLEU score of code summarization from 14.75 to 15.90 while reducing 77% of training parameters compared to the fully fine-tuning approach.
Do we need entire training data for adversarial training?
Venue: arXiv cs.CV 2023
Year: 2023

Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suffer from vulnerability against adversarial attacks. In the past few years, numerous approaches have been proposed to tackle this problem by training networks using adversarial training. Almost all the approaches generate adversarial examples for the entire training dataset, thus increasing the training time drastically. We show that we can decrease the training time for any adversarial training algorithm by using only a subset of training data for adversarial training. To select the subset, we filter the adversarially-prone samples from the training data. We perform a simple adversarial attack on all training examples to filter this subset. In this attack, we add a small perturbation to each pixel and a few grid lines to the input image. We perform adversarial training on the adversarially-prone subset and mix it with vanilla training performed on the entire dataset. Our results show that when our method-agnostic approach is plugged into FGSM, we achieve a speedup of 3.52x on MNIST and 1.98x on the CIFAR-10 dataset with comparable robust accuracy. We also test our approach on state-of-the-art Free adversarial training and achieve a speedup of 1.2x in training time with a marginal drop in robust accuracy on the ImageNet dataset.
ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, supervised learning has become the dominant paradigm for training deep-learning based methods for 3D object detection. Lately, the academic community has studied 3D object detection in the context of autonomous vehicles (AVs) using publicly available datasets such as nuScenes and Argoverse 2.0. However, these datasets may have incomplete annotations, often only labeling a small subset of objects in a scene. Although commercial services exists for 3D bounding box annotation, these are often prohibitively expensive. To address these limitations, we propose ReBound, an open-source 3D visualization and dataset re-annotation tool that works across different datasets. In this paper, we detail the design of our tool and present survey results that highlight the usability of our software. Further, we show that ReBound is effective for exploratory data analysis and can facilitate active-learning. Our code and documentation is available at https://github.com/ajedgley/ReBound
Predicting risk of delirium from ambient noise and light information in the ICU
Venue: arXiv cs.LG 2023
Year: 2023

Existing Intensive Care Unit (ICU) delirium prediction models do not consider environmental factors despite strong evidence of their influence on delirium. This study reports the first deep-learning based delirium prediction model for ICU patients using only ambient noise and light information. Ambient light and noise intensities were measured from ICU rooms of 102 patients from May 2021 to September 2022 using Thunderboard, ActiGraph sensors and an iPod with AudioTools application. These measurements were divided into daytime (0700 to 1859) and nighttime (1900 to 0659). Deep learning models were trained using this data to predict the incidence of delirium during ICU stay or within 4 days of discharge. Finally, outcome scores were analyzed to evaluate the importance and directionality of every feature. Daytime noise levels were significantly higher than nighttime noise levels. When using only noise features or a combination of noise and light features 1-D convolutional neural networks (CNN) achieved the strongest performance: AUC=0.77, 0.74; Sensitivity=0.60, 0.56; Specificity=0.74, 0.74; Precision=0.46, 0.40 respectively. Using only light features, Long Short-Term Memory (LSTM) networks performed best: AUC=0.80, Sensitivity=0.60, Specificity=0.77, Precision=0.37. Maximum nighttime and minimum daytime noise levels were the strongest positive and negative predictors of delirium respectively. Nighttime light level was a stronger predictor of delirium than daytime light level. Total influence of light features outweighed that of noise features on the second and fourth day of ICU stay. This study shows that ambient light and noise intensities are strong predictors of long-term delirium incidence in the ICU. It reveals that daytime and nighttime environmental factors might influence delirium differently and that the importance of light and noise levels vary over the course of an ICU stay.
Consistency Analysis of ChatGPT
Venue: arXiv cs.CL 2023
Year: 2023

ChatGPT has gained a huge popularity since its introduction. Its positive aspects have been reported through many media platforms, and some analyses even showed that ChatGPT achieved a decent grade in professional exams, adding extra support to the claim that AI can now assist and even replace humans in industrial fields. Others, however, doubt its reliability and trustworthiness. This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour, focusing specifically on semantic consistency and the properties of negation, symmetric, and transitive consistency. Our findings suggest that while both models appear to show an enhanced language understanding and reasoning ability, they still frequently fall short of generating logically consistent predictions. We also ascertain via experiments that prompt designing, few-shot learning and employing larger large language models (LLMs) are unlikely to be the ultimate solution to resolve the inconsistency issue of LLMs.
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Venue: arXiv cs.LG 2023
Year: 2023

Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computational costs. However, current distributed deep learning frameworks are limited in their ability to train high-quality MoE models with large base models. In this work, we present DeepSpeed-TED, a novel, three-dimensional, hybrid parallel algorithm that combines data, tensor, and expert parallelism to enable the training of MoE models with 4 to 8x larger base models than the current state-of-the-art. We also describe memory optimizations in the optimizer step, and communication optimizations that eliminate unnecessary data movement. We implement our approach in DeepSpeed and achieve speedups of 26% over a baseline (i.e. without our communication optimizations) when training a 40 billion parameter MoE model (6.7 billion base model with 16 experts) on 128 V100 GPUs.
PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, self-supervised learning has attracted widespread academic debate and addressed many of the key issues of computer vision. The present research focus is on how to construct a good agent task that allows for improved network learning of advanced semantic information on images so that model reasoning is accelerated during pre-training of the current task. In order to solve the problem that existing feature extraction networks are pre-trained on the ImageNet dataset and cannot extract the fine-grained information in pedestrian images well, and the existing pre-task of contrast self-supervised learning may destroy the original properties of pedestrian images, this paper designs a pre-task of mask reconstruction to obtain a pre-training model with strong robustness and uses it for the pedestrian re-identification task. The training optimization of the network is performed by improving the triplet loss based on the centroid, and the mask image is added as an additional sample to the loss calculation, so that the network can better cope with the pedestrian matching in practical applications after the training is completed. This method achieves about 5% higher mAP on Marker1501 and CUHK03 data than existing self-supervised learning pedestrian re-identification methods, and about 1% higher for Rank1, and ablation experiments are conducted to demonstrate the feasibility of this method. Our model code is located at https://github.com/ZJieX/prsnet.
Betti Number for Point Sets
Venue: arXiv cs.CG 2023
Year: 2023

Topology is the foundation for many industrial applications ranging from CAD to simulation analysis. Computational topology mostly focuses on structured data such as mesh, however unstructured dataset such as point set remains a virgin land for topology scientists. The significance of point-based topology can never be overemphasized, especially in the area of reverse engineering, geometric modeling and algorithmic analysis. In this paper, we propose a novel approach to compute the Betti number for point set data and illustrate its usefulness in real world examples. To the best of our knowledge, our work is pioneering and first of its kind in the fields of computational topology.
Automatic Detection of Signalling Behaviour from Assistance Dogs as they Forecast the Onset of Epileptic Seizures in Humans
Venue: arXiv cs.LG 2023
Year: 2023

Epilepsy or the occurrence of epileptic seizures, is one of the world's most well-known neurological disorders affecting millions of people. Seizures mostly occur due to non-coordinated electrical discharges in the human brain and may cause damage, including collapse and loss of consciousness. If the onset of a seizure can be forecast then the subject can be placed into a safe environment or position so that self-injury as a result of a collapse can be minimised. However there are no definitive methods to predict seizures in an everyday, uncontrolled environment. Previous studies have shown that pet dogs have the ability to detect the onset of an epileptic seizure by scenting the characteristic volatile organic compounds exuded through the skin by a subject prior a seizure occurring and there are cases where assistance dogs, trained to scent the onset of a seizure, can signal this to their owner/trainer. In this work we identify how we can automatically detect the signalling behaviours of trained assistance dogs and use this to alert their owner. Using data from an accelerometer worn on the collar of a dog we describe how we gathered movement data from 11 trained dogs for a total of 107 days as they exhibited signalling behaviour on command. We present the machine learning techniques used to accurately detect signalling from routine dog behaviour. This work is a step towards automatic alerting of the likely onset of an epileptic seizure from the signalling behaviour of a trained assistance dog.