pwc-archive/papers-with-abstracts huggingface dataset:
Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
Authors: Bo Qiang, Yiran Zhou, Yuheng Ding, Ningfeng Liu, Song Song, Liangren Zhang, Bo Huang, Zhenming Liu
Date: 2023-03-13 00:00:00
Tasks: Drug Design, Representation Learning

Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning framework that can efficiently capture the basic rules of chemical reactions. In this paper, we have proposed a unified framework that addresses both the reaction representation learning and molecule generation tasks, which allows for a more holistic approach. Inspired by the organic chemistry mechanism, we develop a novel pretraining framework that enables us to incorporate inductive biases into the model. Our framework achieves state-of-the-art results on challenging downstream tasks. By possessing chemical knowledge, our generative framework overcome the limitations of current molecule generation models that rely on a small number of reaction templates. In the extensive experiments, our model generates synthesizable drug-like structures of high quality. Overall, our work presents a significant step toward a large-scale deep-learning framework for a variety of reaction-based applications.
A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments
Authors: Lorenzo Cazzella, Francesco Linsalata, Maurizio Magarini, Matteo Matteucci, Umberto Spagnolini
Date: 2023-03-13 00:00:00

Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment that can enable multi-layer decisions at the physical communication equipment. At high-frequency bands, DTs can help to overcome the challenges emerging in high mobility conditions featuring vehicular environments. In this paper, we propose a novel data-driven workflow for the creation of the DT of a Vehicle-to-Everything (V2X) communication scenario and a multi-modal simulation framework for the generation of realistic sensor data and accurate mmWave/sub-THz wireless channels. The proposed method leverages an automotive simulation and testing framework and an accurate ray-tracing channel simulator. Simulations over an urban scenario show the achievable realistic sensor and channel modelling both at the infrastructure and at ego-vehicles. We showcase the proposed framework on the DT-aided blockage handover task for V2X link restoration, leveraging the framework's dynamic channel generation capabilities for realistic vehicular blockage simulation.
CoGANPPIS: A Coevolution-enhanced Global Attention Neural Network for Protein-Protein Interaction Site Prediction
Authors: Jiaxing Guo, Xuening Zhu, Zixin Hu, Xiaoxi Hu
Date: 2023-03-13 00:00:00
Tasks: Prediction, Representation Learning

Protein-protein interactions are of great importance in biochemical processes. Accurate prediction of protein-protein interaction sites (PPIs) is crucial for our understanding of biological mechanism. Although numerous approaches have been developed recently and achieved gratifying results, there are still two limitations: (1) Most existing models have excavated a number of useful input features, but failed to take coevolutionary features into account, which could provide clues for inter-residue relationships; (2) The attention-based models only allocate attention weights for neighboring residues, instead of doing it globally, which may limit the model's prediction performance since some residues being far away from the target residues might also matter. We propose a coevolution-enhanced global attention neural network, a sequence-based deep learning model for PPIs prediction, called CoGANPPIS. Specifically, CoGANPPIS utilizes three layers in parallel for feature extraction: (1) Local-level representation aggregation layer, which aggregates the neighboring residues' features as the local feature representation; (2) Global-level representation learning layer, which employs a novel coevolution-enhanced global attention mechanism to allocate attention weights to all residues on the same protein sequences; (3) Coevolutionary information learning layer, which applies CNN & pooling to coevolutionary information to obtain the coevolutionary profile representation. Then, the three outputs are concatenated and passed into several fully connected layers for the final prediction. Extensive experiments on two benchmark datasets have been conducted, demonstrating that our proposed model achieves the state-of-the-art performance.
DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment
Authors: Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik, Masoud Daneshtalab, Maksim Jenihhin
Date: 2023-03-13 00:00:00

Deep Neural Networks (DNNs) and their accelerators are being deployed ever more frequently in safety-critical applications leading to increasing reliability concerns. A traditional and accurate method for assessing DNNs' reliability has been resorting to fault injection, which, however, suffers from prohibitive time complexity. While analytical and hybrid fault injection-/analytical-based methods have been proposed, they are either inaccurate or specific to particular accelerator architectures. In this work, we propose a novel accurate, fine-grain, metric-oriented, and accelerator-agnostic method called DeepVigor that provides vulnerability value ranges for DNN neurons' outputs. An outcome of DeepVigor is an analytical model representing vulnerable and non-vulnerable ranges for each neuron that can be exploited to develop different techniques for improving DNNs' reliability. Moreover, DeepVigor provides reliability assessment metrics based on vulnerability factors for bits, neurons, and layers using the vulnerability ranges. The proposed method is not only faster than fault injection but also provides extensive and accurate information about the reliability of DNNs, independent from the accelerator. The experimental evaluations in the paper indicate that the proposed vulnerability ranges are 99.9% to 100% accurate even when evaluated on previously unseen test data. Also, it is shown that the obtained vulnerability factors represent the criticality of bits, neurons, and layers proficiently. DeepVigor is implemented in the PyTorch framework and validated on complex DNN benchmarks.
Molecular Property Prediction by Semantic-invariant Contrastive Learning
Authors: Ziqiao Zhang, Ailin Xie, Jihong Guan, Shuigeng Zhou
Date: 2023-03-13 00:00:00
Tasks: Contrastive Learning, Drug Design, Molecular Property Prediction, molecular representation, Prediction, Property Prediction, Representation Learning

Contrastive learning have been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, exiting methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs and consequently poor prediction performance. To address this problem, in this paper we first propose a semantic-invariant view generation method by properly breaking molecular graphs into fragment pairs. Then, we develop a Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on this view generation method for molecular property prediction. The FraSICL model consists of two branches to generate representations of views for contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity loss are introduced to make better use of the information contained in different fragment-pair views. Extensive experiments on various benchmark datasets show that with the least number of pre-training samples, FraSICL can achieve state-of-the-art performance, compared with major existing counterpart models.
Physics-driven machine learning models coupling PyTorch and Firedrake
Authors: Nacime Bouziani, David A. Ham
Date: 2023-03-13 00:00:00
Tasks: Inductive Bias

Partial differential equations (PDEs) are central to describing and modelling complex physical systems that arise in many disciplines across science and engineering. However, in many realistic applications PDE modelling provides an incomplete description of the physics of interest. PDE-based machine learning techniques are designed to address this limitation. In this approach, the PDE is used as an inductive bias enabling the coupled model to rely on fundamental physical laws while requiring less training data. The deployment of high-performance simulations coupling PDEs and machine learning to complex problems necessitates the composition of capabilities provided by machine learning and PDE-based frameworks. We present a simple yet effective coupling between the machine learning framework PyTorch and the PDE system Firedrake that provides researchers, engineers and domain specialists with a high productive way of specifying coupled models while only requiring trivial changes to existing code.
On the Regret of Online Edge Service Hosting
Authors: R Sri Prakash, Nikhil Karamchandani, Sharayu Moharir
Date: 2023-03-13 00:00:00

We consider the problem of service hosting where a service provider can dynamically rent edge resources via short term contracts to ensure better quality of service to its customers. The service can also be partially hosted at the edge, in which case, customers' requests can be partially served at the edge. The total cost incurred by the system is modeled as a combination of the rent cost, the service cost incurred due to latency in serving customers, and the fetch cost incurred as a result of the bandwidth used to fetch the code/databases of the service from the cloud servers to host the service at the edge. In this paper, we compare multiple hosting policies with regret as a metric, defined as the difference in the cost incurred by the policy and the optimal policy over some time horizon $T$. In particular we consider the Retro Renting (RR) and Follow The Perturbed Leader (FTPL) policies proposed in the literature and provide performance guarantees on the regret of these policies. We show that under i.i.d stochastic arrivals, RR policy has linear regret while FTPL policy has constant regret. Next, we propose a variant of FTPL, namely Wait then FTPL (W-FTPL), which also has constant regret while demonstrating much better dependence on the fetch cost. We also show that under adversarial arrivals, RR policy has linear regret while both FTPL and W-FTPL have regret $\mathrm{O}(\sqrt{T})$ which is order-optimal.
Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals
Authors: Zhenyuan Lu, Burcu Ozek, Sagar Kamarthi
Date: 2023-03-13 00:00:00
Tasks: Management

Pain is a serious worldwide health problem that affects a vast proportion of the population. For efficient pain management and treatment, accurate classification and evaluation of pain severity are necessary. However, this can be challenging as pain is a subjective sensation-driven experience. Traditional techniques for measuring pain intensity, e.g. self-report scales, are susceptible to bias and unreliable in some instances. Consequently, there is a need for more objective and automatic pain intensity assessment strategies. In this paper, we develop PainAttnNet (PAN), a novel transfomer-encoder deep-learning framework for classifying pain intensities with physiological signals as input. The proposed approach is comprised of three feature extraction architectures: multiscale convolutional networks (MSCN), a squeeze-and-excitation residual network (SEResNet), and a transformer encoder block. On the basis of pain stimuli, MSCN extracts short- and long-window information as well as sequential features. SEResNet highlights relevant extracted features by mapping the interdependencies among features. The third module employs a transformer encoder consisting of three temporal convolutional networks (TCN) with three multi-head attention (MHA) layers to extract temporal dependencies from the features. Using the publicly available BioVid pain dataset, we test the proposed PainAttnNet model and demonstrate that our outcomes outperform state-of-the-art models. These results confirm that our approach can be utilized for automated classification of pain intensity using physiological signals to improve pain management and treatment.
Label Information Bottleneck for Label Enhancement
Authors: Qinghai Zheng, Jihua Zhu, Haoyu Tang
Date: 2023-03-13 00:00:00
Proceeding: CVPR 2023 1

In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE. For the recovery process of label distributions, the label irrelevant information contained in the dataset may lead to unsatisfactory recovery performance. To address this limitation, we make efforts to excavate the essential label relevant information to improve the recovery performance. Our method formulates the LE problem as the following two joint processes: 1) learning the representation with the essential label relevant information, 2) recovering label distributions based on the learned representation. The label relevant information can be excavated based on the "bottleneck" formed by the learned representation. Significantly, both the label relevant information about the label assignments and the label relevant information about the label gaps can be explored in our method. Evaluation experiments conducted on several benchmark label distribution learning datasets verify the effectiveness and competitiveness of LIB. Our source codes are available https://github.com/qinghai-zheng/LIBLE
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm
Authors: Fang Kong, Canzhe Zhao, Shuai Li
Date: 2023-03-13 00:00:00
Tasks: Vocal Bursts Type Prediction

The linear bandit problem has been studied for many years in both stochastic and adversarial settings. Designing an algorithm that can optimize the environment without knowing the loss type attracts lots of interest. \citet{LeeLWZ021} propose an algorithm that actively detects the loss type and then switches between different algorithms specially designed for specific settings. However, such an approach requires meticulous designs to perform well in all environments. Follow-the-regularized-leader (FTRL) is another type of popular algorithm that can adapt to different environments. This algorithm is of simple design and the regret bounds are shown to be optimal in traditional multi-armed bandit problems compared with the detect-switch type. Designing an FTRL-type algorithm for linear bandits is an important question that has been open for a long time. In this paper, we prove that the FTRL algorithm with a negative entropy regularizer can achieve the best-of-three-world results for the linear bandit problem. Our regret bounds achieve the same or nearly the same order as the previous detect-switch type algorithm but with a much simpler algorithmic design.
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee
Authors: Chenyang Li, Jihoon Chung, Mengnan Du, Haimin Wang, Xianlian Zhou, Bo Shen
Date: 2023-03-13 00:00:00
Tasks: Image Classification, Model Compression, Tensor Decomposition

Model compression is a crucial part of deploying neural networks (NNs), especially when the memory and storage of computing devices are limited in many applications. This paper focuses on two model compression techniques: low-rank approximation and weight pruning in neural networks, which are very popular nowadays. However, training NN with low-rank approximation and weight pruning always suffers significant accuracy loss and convergence issues. In this paper, a holistic framework is proposed for model compression from a novel perspective of nonconvex optimization by designing an appropriate objective function. Then, we introduce NN-BCD, a block coordinate descent (BCD) algorithm to solve the nonconvex optimization. One advantage of our algorithm is that an efficient iteration scheme can be derived with closed-form, which is gradient-free. Therefore, our algorithm will not suffer from vanishing/exploding gradient problems. Furthermore, with the Kurdyka-{\L}ojasiewicz (K{\L}) property of our objective function, we show that our algorithm globally converges to a critical point at the rate of O(1/k), where k denotes the number of iterations. Lastly, extensive experiments with tensor train decomposition and weight pruning demonstrate the efficiency and superior performance of the proposed framework. Our code implementation is available at https://github.com/ChenyangLi-97/NN-BCD
Gaussian Process on the Product of Directional Manifolds
Authors: Ziyu Cao, Kailai Li
Date: 2023-03-13 00:00:00
Tasks: Gaussian Processes, Hyperparameter Optimization, regression

We present a principled study on defining Gaussian processes (GPs) with inputs on the product of directional manifolds. A circular kernel is first presented according to the von Mises distribution. Based thereon, the hypertoroidal von Mises (HvM) kernel is proposed to establish GPs on hypertori with consideration of correlated circular components. The proposed HvM kernel is demonstrated with multi-output GP regression for learning vector-valued functions on hypertori using the intrinsic coregionalization model. Analytic derivatives for hyperparameter optimization are provided for runtime-critical applications. For evaluation, we synthesize a ranging-based sensor network and employ the HvM-based GPs for data-driven recursive localization. Numerical results show that the HvM-based GP achieves superior tracking accuracy compared to parametric model and GPs of conventional kernel designs.
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative Study
Authors: Salah Zaiem, Robin Algayres, Titouan Parcollet, Slim Essid, Mirco Ravanelli
Date: 2023-03-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings. In this context, it has been demonstrated that larger self-supervised feature extractors are crucial for achieving lower downstream ASR error rates. Thus, better performance might be sanctioned with longer inferences. This article explores different approaches that may be deployed during the fine-tuning to reduce the computations needed in the SSL encoder, leading to faster inferences. We adapt a number of existing techniques to common ASR settings and benchmark them, displaying performance drops and gains in inference times. Interestingly, we found that given enough downstream data, a simple downsampling of the input sequences outperforms the other methods with both low performance drops and high computational savings, reducing computations by 61.3% with an WER increase of only 0.81. Finally, we analyze the robustness of the comparison to changes in dataset conditions, revealing sensitivity to dataset size.
Global Optimality of Elman-type RNN in the Mean-Field Regime
Authors: Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee
Date: 2023-03-12 00:00:00
Tasks: Vocal Bursts Type Prediction

We analyze Elman-type Recurrent Reural Networks (RNNs) and their training in the mean-field regime. Specifically, we show convergence of gradient descent training dynamics of the RNN to the corresponding mean-field formulation in the large width limit. We also show that the fixed points of the limiting infinite-width dynamics are globally optimal, under some assumptions on the initialization of the weights. Our results establish optimality for feature-learning with wide RNNs in the mean-field regime
Knowledge-integrated AutoEncoder Model
Authors: Teddy Lazebnik, Liron Simon-Keren
Date: 2023-03-12 00:00:00
Tasks: model

Data encoding is a common and central operation in most data analysis tasks. The performance of other models, downstream in the computational process, highly depends on the quality of data encoding. One of the most powerful ways to encode data is using the neural network AutoEncoder (AE) architecture. However, the developers of AE are not able to easily influence the produced embedding space, as it is usually treated as a \textit{black box} technique, which makes it uncontrollable and not necessarily has desired properties for downstream tasks. In this paper, we introduce a novel approach for developing AE models that can integrate external knowledge sources into the learning process, possibly leading to more accurate results. The proposed \methodNamefull{} (\methodName{}) model is able to leverage domain-specific information to make sure the desired distance and neighborhood properties between samples are preservative in the embedding space. The proposed model is evaluated on three large-scale datasets from three different scientific fields and is compared to nine existing encoding models. The results demonstrate that the \methodName{} model effectively captures the underlying structures and relationships between the input data and external knowledge, meaning it generates a more useful representation. This leads to outperforming the rest of the models in terms of reconstruction accuracy.
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension
Authors: Shima Foolad, Kourosh Kiani
Date: 2023-03-12 00:00:00
Tasks: Common Sense Reasoning, Graph Attention, Knowledge Graphs, Machine Reading Comprehension, Question Answering, Reading Comprehension

Incorporating prior knowledge can improve existing pre-training models in cloze-style machine reading and has become a new trend in recent studies. Notably, most of the existing models have integrated external knowledge graphs (KG) and transformer-based models, such as BERT into a unified data structure. However, selecting the most relevant ambiguous entities in KG and extracting the best subgraph remains a challenge. In this paper, we propose the LUKE-Graph, a model that builds a heterogeneous graph based on the intuitive relationships between entities in a document without using any external KG. We then use a Relational Graph Attention (RGAT) network to fuse the graph's reasoning information and the contextual representation encoded by the pre-trained LUKE model. In this way, we can take advantage of LUKE, to derive an entity-aware representation; and a graph model - to exploit relation-aware representation. Moreover, we propose Gated-RGAT by augmenting RGAT with a gating mechanism that regulates the question information for the graph convolution operation. This is very similar to human reasoning processing because they always choose the best entity candidate based on the question information. Experimental results demonstrate that the LUKE-Graph achieves state-of-the-art performance on the ReCoRD dataset with commonsense reasoning.