pwc-archive/papers-with-abstracts huggingface dataset:
Diagnosis of Prostate Cancer and Prostatitis Using near Infra-Red Fluorescent AgInSe/ZnS Quantum Dots
Authors: Vuyelwa Ncapayi, Neethu Ninan, Thabang C. Lebepe, Sundararajan Parani, Aswathy Ravindran, Girija, Richard Bright, Krasimir Vasilev, Rodney Maluleke, Ncediwe Tsolekile, Tetsuya Kodama And Oluwatobi S. Oluwafemi
Date: 2021-11-19 00:00:00
Proceeding: International Journal of Molecular Sciences 2021 11
Tasks: Diagnostic

The link between the microbiome and cancer has led researchers to search for a potential probe for intracellular targeting of bacteria and cancer. Herein, we developed near infrared-emitting ternary AgInSe/ZnS quantum dots (QDs) for dual bacterial and cancer imaging. Briefly, water-soluble AgInSe/ZnS QDs were synthesized in a commercial kitchen pressure cooker. The as-synthesized QDs exhibited a spherical shape with a particle diameter of 4.5 ± 0.5 nm, and they were brightly fluorescent with a photoluminescence maximum at 705 nm. The QDs showed low toxicity against mouse mammary carcinoma (FM3A-Luc), mouse colon carcinoma (C26), malignant fibrous histiocytoma-like (KM-Luc/GFP) and prostate cancer cells, a greater number of accumulations in Staphylococcus aureus, and good cellular uptake in prostate cancer cells. This work is an excellent step towards using ternary QDs for diagnostic and guided therapy for prostate cancer.
Neural Airport Ground Handling
Authors: Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang
Date: 2023-03-04 00:00:00
Tasks: Combinatorial Optimization, Reinforcement Learning (RL), Scheduling

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at: https://github.com/RoyalSkye/AGH.
Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training
Authors: Zhenning Yang, Luoxi Meng, Jae-Won Chung, Mosharaf Chowdhury
Date: 2023-03-04 00:00:00

Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networks (DNNs). Answering the call for sustainability, conventional solutions have attempted to move training jobs to locations or time frames with lower carbon intensity. However, moving jobs to other locations may not always be feasible due to large dataset sizes or data regulations. Moreover, postponing training can negatively impact application service quality because the DNNs backing the service are not updated in a timely fashion. In this work, we present a practical solution that reduces the carbon footprint of DNN training without migrating or postponing jobs. Specifically, our solution observes real-time carbon intensity shifts during training and controls the energy consumption of GPUs, thereby reducing carbon footprint while maintaining training performance. Furthermore, in order to proactively adapt to shifting carbon intensity, we propose a lightweight machine learning algorithm that predicts the carbon intensity of the upcoming time frame. Our solution, Chase, reduces the total carbon footprint of training ResNet-50 on ImageNet by 13.6% while only increasing training time by 2.5%.
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
Authors: Haoteng Yin, Muhan Zhang, Jianguo Wang, Pan Li
Date: 2023-03-06 00:00:00
Tasks: Graph Representation Learning, Prediction, Representation Learning

Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability. Most previous SGRL models face computational challenges associated with the high cost of subgraph extraction for each training or test query. Recently, SUREL was proposed to accelerate SGRL, which samples random walks offline and joins these walks online as a proxy of subgraph for representation learning. Thanks to the reusability of sampled walks across different queries, SUREL achieves state-of-the-art performance in terms of scalability and prediction accuracy. However, SUREL still suffers from high computational overhead caused by node duplication in sampled walks. In this work, we propose a novel framework SUREL+ that upgrades SUREL by using node sets instead of walks to represent subgraphs. This set-based representation eliminates repeated nodes by definition but can also be irregular in size. To address this issue, we design a customized sparse data structure to efficiently store and access node sets and provide a specialized operator to join them in parallel batches. SUREL+ is modularized to support multiple types of set samplers, structural features, and neural encoders to complement the structural information loss after the reduction from walks to sets. Extensive experiments have been performed to validate SUREL+ in the prediction tasks of links, relation types, and higher-order patterns. SUREL+ achieves 3-11$\times$ speedups of SUREL while maintaining comparable or even better prediction performance; compared to other SGRL baselines, SUREL+ achieves $\sim$20$\times$ speedups and significantly improves the prediction accuracy.
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning
Authors: Zaiyan Xu, Kishan Panaganti, Dileep Kalathil
Date: 2023-03-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of learning a control policy that is robust against the parameter mismatches between the training environment and testing environment. We formulate this as a distributionally robust reinforcement learning (DR-RL) problem where the objective is to learn the policy which maximizes the value function against the worst possible stochastic model of the environment in an uncertainty set. We focus on the tabular episodic learning setting where the algorithm has access to a generative model of the nominal (training) environment around which the uncertainty set is defined. We propose the Robust Phased Value Learning (RPVL) algorithm to solve this problem for the uncertainty sets specified by four different divergences: total variation, chi-square, Kullback-Leibler, and Wasserstein. We show that our algorithm achieves $\tilde{\mathcal{O}}(|\mathcal{S}||\mathcal{A}| H^{5})$ sample complexity, which is uniformly better than the existing results by a factor of $|\mathcal{S}|$, where $|\mathcal{S}|$ is number of states, $|\mathcal{A}|$ is the number of actions, and $H$ is the horizon length. We also provide the first-ever sample complexity result for the Wasserstein uncertainty set. Finally, we demonstrate the performance of our algorithm using simulation experiments.
Learned Lossless Compression for JPEG via Frequency-Domain Prediction
Authors: Jixiang Luo, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Date: 2023-03-05 00:00:00
Tasks: Prediction

JPEG images can be further compressed to enhance the storage and transmission of large-scale image datasets. Existing learned lossless compressors for RGB images cannot be well transferred to JPEG images due to the distinguishing distribution of DCT coefficients and raw pixels. In this paper, we propose a novel framework for learned lossless compression of JPEG images that achieves end-to-end optimized prediction of the distribution of decoded DCT coefficients. To enable learning in the frequency domain, DCT coefficients are partitioned into groups to utilize implicit local redundancy. An autoencoder-like architecture is designed based on the weight-shared blocks to realize entropy modeling of grouped DCT coefficients and independently compress the priors. We attempt to realize learned lossless compression of JPEG images in the frequency domain. Experimental results demonstrate that the proposed framework achieves superior or comparable performance in comparison to most recent lossless compressors with handcrafted context modeling for JPEG images.
EXIT-chart aided near-capacity Irregular Bit-Interleaved Coded Modulation design
Authors: Ronald Y. S. Tee; Robert G. Maunder; Lajos Hanzo
Date: 2009-01-01 00:00:00
Proceeding: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=649929&isnumber=14173 2009 1
Tasks: Unity

A near-capacity irregular bit-interleaved coded modulation based iterative decoding (Ir-BICM-ID) aided scheme is proposed. The irregular design of the scheme pervades the three basic components of BICM-ID, namely the encoder, the unity-rate precoder and the bit-to-symbol mapper. As a result, irregular BICM-ID schemes constituted by irregular components are created, which are capable of approaching the capacity of coded modulation. This is achieved by creating a marginally open extrinsic information transfer (EXIT) chart tunnel, and exploiting the theorem that the open tunnel's area is characteristic of how closely the scheme operates to the channel's capacity. The proposed Ir-BICM-ID scheme employs irregular convolutional codes (IrCC), irregular unity-rate codes (IrURC) and irregular mappers (IrMapper).
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
Authors: Zhiwei Wang, Fa Zhang, Cong Zheng, Xianghong Hu, Mingxuan Cai, Can Yang
Date: 2023-03-05 00:00:00
Tasks: Missing Values, Variational Inference

In various practical situations, matrix factorization methods suffer from poor data quality, such as high data sparsity and low signal-to-noise ratio (SNR). Here, we consider a matrix factorization problem by utilizing auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality. Unlike existing methods that mainly rely on simple linear models to combine auxiliary information with the main data matrix, we propose to integrate gradient boosted trees in the probabilistic matrix factorization framework to effectively leverage auxiliary information (MFAI). Thus, MFAI naturally inherits several salient features of gradient boosted trees, such as the capability of flexibly modeling nonlinear relationships and robustness to irrelevant features and missing values in auxiliary information. The parameters in MFAI can be automatically determined under the empirical Bayes framework, making it adaptive to the utilization of auxiliary information and immune to overfitting. Moreover, MFAI is computationally efficient and scalable to large datasets by exploiting variational inference. We demonstrate the advantages of MFAI through comprehensive numerical results from simulation studies and real data analyses. Our approach is implemented in the R package mfair available at https://github.com/YangLabHKUST/mfair.
Reparameterization through Spatial Gradient Scaling
Authors: Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh, Jialin Zhang, Wei Lui, Shangling Jui, Di Niu
Date: 2023-03-05 00:00:00

Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training. However, there exists a gap in understanding how reparameterization may change and benefit the learning process of neural networks. In this paper, we present a novel spatial gradient scaling method to redistribute learning focus among weights in convolutional networks. We prove that spatial gradient scaling achieves the same learning dynamics as a branched reparameterization yet without introducing structural changes into the network. We further propose an analytical approach that dynamically learns scalings for each convolutional layer based on the spatial characteristics of its input feature map gauged by mutual information. Experiments on CIFAR-10, CIFAR-100, and ImageNet show that without searching for reparameterized structures, our proposed scaling method outperforms the state-of-the-art reparameterization strategies at a lower computational cost.
Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery
Authors: Dejun Feng, Hongyu Chen, Suning Liu, Ziyang Liao, Xingyu Shen, Yakun Xie, Jun Zhu
Date: 2023-03-06 00:00:00
Tasks: Decoder, object-detection, Object Detection, Salient Object Detection

With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.
PaLM-E: An Embodied Multimodal Language Model
Authors: Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence
Date: 2023-03-06 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model, Question Answering, State Estimation, Visual Question Answering, Visual Question Answering (VQA)

Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link between words and percepts. Input to our embodied language model are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks including sequential robotic manipulation planning, visual question answering, and captioning. Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits positive transfer: the model benefits from diverse joint training across internet-scale language, vision, and visual-language domains. Our largest model, PaLM-E-562B with 562B parameters, in addition to being trained on robotics tasks, is a visual-language generalist with state-of-the-art performance on OK-VQA, and retains generalist language capabilities with increasing scale.
Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach
Authors: Yunfei Ge, Tao Li, Quanyan Zhu
Date: 2023-03-06 00:00:00
Tasks: Decision Making, Meta-Learning

The increasing connectivity and intricate remote access environment have made traditional perimeter-based network defense vulnerable. Zero trust becomes a promising approach to provide defense policies based on agent-centric trust evaluation. However, the limited observations of the agent's trace bring information asymmetry in the decision-making. To facilitate the human understanding of the policy and the technology adoption, one needs to create a zero-trust defense that is explainable to humans and adaptable to different attack scenarios. To this end, we propose a scenario-agnostic zero-trust defense based on Partially Observable Markov Decision Processes (POMDP) and first-order Meta-Learning using only a handful of sample scenarios. The framework leads to an explainable and generalizable trust-threshold defense policy. To address the distribution shift between empirical security datasets and reality, we extend the model to a robust zero-trust defense minimizing the worst-case loss. We use case studies and real-world attacks to corroborate the results.
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors
Authors: Björn Lindenberg, Karl-Olof Lindahl
Date: 2023-03-06 00:00:00
Tasks: Thompson Sampling

We consider Thompson sampling for linear bandit problems with finitely many independent arms, where rewards are sampled from normal distributions that are linearly dependent on unknown parameter vectors and with unknown variance. Specifically, with a Bayesian formulation we consider multivariate normal-gamma priors to represent environment uncertainty for all involved parameters. We show that our chosen sampling prior is a conjugate prior to the reward model and derive a Bayesian regret bound for Thompson sampling under the condition that the 5/2-moment of the variance distribution exist.
Deep Age-Invariant Fingerprint Segmentation System
Authors: M. G. Sarwar Murshed, Keivan Bahmani, Stephanie Schuckers, Faraz Hussain
Date: 2023-03-06 00:00:00
Tasks: Segmentation

Fingerprint-based identification systems achieve higher accuracy when a slap containing multiple fingerprints of a subject is used instead of a single fingerprint. However, segmenting or auto-localizing all fingerprints in a slap image is a challenging task due to the different orientations of fingerprints, noisy backgrounds, and the smaller size of fingertip components. The presence of slap images in a real-world dataset where one or more fingerprints are rotated makes it challenging for a biometric recognition system to localize and label the fingerprints automatically. Improper fingerprint localization and finger labeling errors lead to poor matching performance. In this paper, we introduce a method to generate arbitrary angled bounding boxes using a deep learning-based algorithm that precisely localizes and labels fingerprints from both axis-aligned and over-rotated slap images. We built a fingerprint segmentation model named CRFSEG (Clarkson Rotated Fingerprint segmentation Model) by updating the previously proposed CFSEG model which was based on traditional Faster R-CNN architecture [21]. CRFSEG improves upon the Faster R-CNN algorithm with arbitrarily angled bounding boxes that allow the CRFSEG to perform better in challenging slap images. After training the CRFSEG algorithm on a new dataset containing slap images collected from both adult and children subjects, our results suggest that the CRFSEG model was invariant across different age groups and can handle over-rotated slap images successfully. In the Combined dataset containing both normal and rotated images of adult and children subjects, we achieved a matching accuracy of 97.17%, which outperformed state-of-the-art VeriFinger (94.25%) and NFSEG segmentation systems (80.58%).
Optimizing L1 cache for embedded systems through grammatical evolution
Authors: Josefa Díaz Álvarez, J. Manuel Colmenar, José L. Risco-Martín, Juan Lanchares, Oscar Garnica
Date: 2023-03-06 00:00:00

Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting its configuration according to the memory access patterns of the applications being run. However, given that cache memories have many parameters which may be set to a high number of different values, designers face to a wide and time-consuming exploration space. In this paper we propose an optimization framework based on Grammatical Evolution (GE) which is able to efficiently find the best cache configurations for a given set of benchmark applications. This metaheuristic allows an important reduction of the optimization runtime obtaining good results in a low number of generations. Besides, this reduction is also increased due to the efficient storage of evaluated caches. Moreover, we selected GE because the plasticity of the grammar eases the creation of phenotypes that form the call to the cache simulator required for the evaluation of the different configurations. Experimental results for the Mediabench suite show that our proposal is able to find cache configurations that obtain an average improvement of $62\%$ versus a real world baseline configuration.
Time series anomaly detection with reconstruction-based state-space models
Authors: Fan Wang, Keli Wang, Boyu Yao
Date: 2023-03-06 00:00:00
Tasks: Anomaly Detection, Decoder, State Space Models, Time Series, Time Series Analysis, Time Series Anomaly Detection, Unsupervised Anomaly Detection

Recent advances in digitization have led to the availability of multivariate time series data in various domains, enabling real-time monitoring of operations. Identifying abnormal data patterns and detecting potential failures in these scenarios are important yet rather challenging. In this work, we propose a novel unsupervised anomaly detection method for time series data. The proposed framework jointly learns the observation model and the dynamic model, and model uncertainty is estimated from normal samples. Specifically, a long short-term memory (LSTM)-based encoder-decoder is adopted to represent the mapping between the observation space and the latent space. Bidirectional transitions of states are simultaneously modeled by leveraging backward and forward temporal information. Regularization of the latent space places constraints on the states of normal samples, and Mahalanobis distance is used to evaluate the abnormality level. Empirical studies on synthetic and real-world datasets demonstrate the superior performance of the proposed method in anomaly detection tasks.