pwc-archive/papers-with-abstracts huggingface dataset:
Low-Complexity Soft-Decision Detection for Combating DFE Burst Errors in IM/DD Links
Authors: Kaiquan Wu, Gabriele Liga, Jamal Riani, Alex Alvarado
Date: 2023-06-13 00:00:00

The deployment of non-binary pulse amplitude modulation (PAM) and soft decision (SD)-forward error correction (FEC) in future intensity-modulation (IM)/direct-detection (DD) links is inevitable. However, high-speed IM/DD links suffer from inter-symbol interference (ISI) due to bandwidth-limited hardware. Traditional approaches to mitigate the effects of ISI are filters and trellis-based algorithms targeting symbol-wise maximum a posteriori (MAP) detection. The former approach includes decision-feedback equalizer (DFE), and the latter includes Max-Log-MAP (MLM) and soft-output Viterbi algorithm (SOVA). Although DFE is easy to implement, it introduces error propagation. Such burst errors distort the log-likelihood ratios (LLRs) required by SD-FEC, causing performance degradation. On the other hand, MLM and SOVA provide near-optimum performance, but their complexity is very high for high-order PAM. In this paper, we consider a one-tap partial response channel model, which is relevant for high-speed IM/DD links. We propose to combine DFE with either MLM or SOVA in a low-complexity architecture. The key idea is to allow MLM or SOVA to detect only 3 typical DFE symbol errors, and use the detected error information to generate LLRs in a modified demapper. The proposed structure enables a tradeoff between complexity and performance: (i) the complexity of MLM or SOVA is reduced and (ii) the decoding penalty due to error propagation is mitigated. Compared to SOVA detection, the proposed scheme can achieve a significant complexity reduction of up to 94% for PAM-8 transmission. Simulation and experimental results show that the resulting SNR loss is roughly 0.3 to 0.4 dB for PAM-4, and becomes marginal 0.18 dB for PAM-8.
Multi-Target Localization in Multi-Static Integrated Sensing and Communication Deployments
Authors: Maximilian Bauhofer, Silvio Mandelli, Marcus Henninger, Thorsten Wild, Stephan ten Brink
Date: 2023-06-13 00:00:00
Tasks: Integrated sensing and communication

In future wireless communication networks, existing active localization will gradually evolve into more sophisticated (passive) sensing functionalities. One main enabler for this process is the merging of information collected from the network's nodes, sensing the environment in a multi-static deployment. The current literature considers single sensing node systems and/or single target scenarios, mainly focusing on specific issues pertaining to hardware impairments or algorithmic challenges. In contrast, in this work we propose an ensemble of techniques for processing the information gathered from multiple sensing nodes, jointly observing an environment with multiple targets. A scattering model is used within a flexibly configurable framework to highlight the challenges and issues with algorithms used in this distributed sensing task. We validate our approach by supporting it with detailed link budget evaluations, considering practical millimeter-wave systems' capabilities. Our numerical evaluations are performed in an indoor scenario, sweeping a variety of parameter to analyze the KPIs sensitivity with respect to each of them. The proposed algorithms to fuse information by multiple nodes show significant gains in terms of targets' localization performance, with up to 35\% for the probability of detection, compared to the baseline with a mono-static setup.
Approximate Maximum-Likelihood RIS-Aided Positioning
Authors: Wei zhang, Zhenni Wang, Wee Peng Tay
Date: 2023-06-13 00:00:00

A reconfigurable intelligent surface (RIS) allows a reflection transmission path between a base station (BS) and user equipment (UE). In wireless localization, this reflection path aids in positioning accuracy, especially when the line-of-sight (LOS) path is subject to severe blockage and fading. In this paper, we develop a RIS-aided positioning framework to locate a UE in environments where the LOS path may or may not be available. We first estimate the RIS-aided channel parameters from the received signals at the UE. To infer the UE position and clock bias from the estimated channel parameters, we propose a fusion method consisting of weighted least squares over the estimates of the LOS and reflection paths. We show that this approximates the maximum likelihood estimator under the large-sample regime and when the estimates from different paths are independent. We then optimize the RIS phase shifts to improve the positioning accuracy and extend the proposed approach to the case with multiple BSs and UEs. We derive Cramer-Rao bound (CRB) and demonstrate numerically that our proposed positioning method approaches the CRB.
SMART: Spatial Modeling Algorithms for Reaction and Transport
Authors: Justin G. Laughlin, Jørgen S. Dokken, Henrik N. T. Finsberg, Emmet A. Francis, Christopher T. Lee, Marie E. Rognes, Padmini Rangamani
Date: 2023-06-12 00:00:00
Tasks: 3D Reconstruction

Recent advances in microscopy and 3D reconstruction methods have allowed for characterization of cellular morphology in unprecedented detail, including the irregular geometries of intracellular subcompartments such as membrane-bound organelles. These geometries are now compatible with predictive modeling of cellular function. Biological cells respond to stimuli through sequences of chemical reactions generally referred to as cell signaling pathways. The propagation and reaction of chemical substances in cell signaling pathways can be represented by coupled nonlinear systems of reaction-transport equations. These reaction pathways include numerous chemical species that react across boundaries or interfaces (e.g., the cell membrane and membranes of organelles within the cell) and domains (e.g., the bulk cell volume and the interior of organelles). Such systems of multi-dimensional partial differential equations (PDEs) are notoriously difficult to solve because of their high dimensionality, non-linearities, strong coupling, stiffness, and potential instabilities. In this work, we describe Spatial Modeling Algorithms for Reactions and Transport (SMART), a high-performance finite-element-based simulation package for model specification and numerical simulation of spatially-varying reaction-transport processes. SMART is based on the FEniCS finite element library, provides a symbolic representation framework for specifying reaction pathways, and supports geometries in 2D and 3D including large and irregular cell geometries obtained from modern ultrastructural characterization methods.
Nonlinear slow-timescale mechanisms in synaptic plasticity
Authors: Cian O'Donnell
Date: 2023-06-13 00:00:00

Learning and memory relies on synapses changing their strengths in response to neural activity. However there is a substantial gap between the timescales of neural electrical dynamics (1-100 ms) and organism behaviour during learning (seconds-minutes). What mechanisms bridge this timescale gap? What are the implications for theories of brain learning? Here I first cover experimental evidence for slow-timescale factors in plasticity induction. Then I review possible underlying cellular and synaptic mechanisms, and insights from recent computational models that incorporate such slow-timescale variables. I conclude that future progress on understanding brain learning across timescales will require both experimental and computational modelling studies that map out the nonlinearities implemented by both fast and slow plasticity mechanisms at synapses, and crucially, their joint interactions.
Exact sharp-fronted solutions for nonlinear diffusion on evolving domains
Authors: Stuart T. Johnston, Matthew J. Simpson
Date: 2023-06-13 00:00:00
Tasks: valid

Models of diffusive processes that occur on evolving domains are frequently employed to describe biological and physical phenomena, such as diffusion within expanding tissues or substrates. Previous investigations into these models either report numerical solutions or require an assumption of linear diffusion to determine exact solutions. Unfortunately, numerical solutions do not reveal the relationship between the model parameters and the solution features. Additionally, experimental observations typically report the presence of sharp fronts, which are not captured by linear diffusion. Here we address both limitations by presenting exact sharp-fronted solutions to a model of degenerate nonlinear diffusion on a growing domain. We obtain the solution by identifying a series of transformations that converts the model of a nonlinear diffusive process on an evolving domain to a nonlinear diffusion equation on a fixed domain, which admits known exact solutions for certain choices of diffusivity functions. We determine expressions for critical time scales and domain growth rates such that the diffusive population never reaches the domain boundaries and hence the solution remains valid.
Stability and Fluctuations in Complex Ecological Systems
Authors: Eric Forgoston, Sarah Day, Peter C. de Ruiter, Arjen Doelman, Nienke Hartemink, Alan Hastings, Lia Hemerik, Alexandru Hening, Josef Hofbauer, Sonia Kefi, David A. Kessler, Toni Klauschies, Christian Kuehn, Xiaoxiao Li, John C. Moore, Elly Morrien, Anje-Margriet Neutel, Jelena Pantel, Sebastian J. Schreiber, Leah B. Shaw, Nadav Shnerb, Eric Siero, Laura S. Storch, Michael A. S. Thorne, Ingrid van de Leemput, Ellen van Velzen, Els Weinans
Date: 2023-06-12 00:00:00

From 08-12 August, 2022, 32 individuals participated in a workshop, Stability and Fluctuations in Complex Ecological Systems, at the Lorentz Center, located in Leiden, The Netherlands. An interdisciplinary dialogue between ecologists, mathematicians, and physicists provided a foundation of important problems to consider over the next 5-10 years. This paper outlines eight areas including (1) improving our understanding of the effect of scale, both temporal and spatial, for both deterministic and stochastic problems; (2) clarifying the different terminologies and definitions used in different scientific fields; (3) developing a comprehensive set of data analysis techniques arising from different fields but which can be used together to improve our understanding of existing data sets; (4) having theoreticians/computational scientists collaborate closely with empirical ecologists to determine what new data should be collected; (5) improving our knowledge of how to protect and/or restore ecosystems; (6) incorporating socio-economic effects into models of ecosystems; (7) improving our understanding of the role of deterministic and stochastic fluctuations; (8) studying the current state of biodiversity at the functional level, taxa level and genome level.
A Comparative Study of Factor Models for Different Periods of the Electricity Spot Price Market
Authors: Christian Laudagé, Florian Aichinger, Sascha Desmettre
Date: 2023-06-13 00:00:00

Due to major shifts in European energy supply, a structural change can be observed in Austrian electricity spot price data starting from the second quarter of the year 2021 onward. In this work we study the performance of two different factor models for the electricity spot price in three different time periods. To this end, we consider three samples of EEX data for the Austrian base load electricity spot price, one from the pre-crises from 2018 to 2021, the second from the time of the crisis from 2021 to 2023 and the whole data from 2018 to 2023. For each of these samples, we investigate the fit of a classical 3-factor model with a Gaussian base signal and one positive and one negative jump signal and compare it with a 4-factor model to assess the effect of adding a second Gaussian base signal to the model. For the calibration of the models we develop a tailor-made Markov Chain Monte Carlo method based on Gibbs sampling. To evaluate the model adequacy, we provide simulations of the spot price as well as a posterior predictive check for the 3- and the 4-factor model. We find that the 4-factor model outperforms the 3-factor model in times of non-crises. In times of crises, the second Gaussian base signal does not lead to a better fit of the model. To the best of our knowledge, this is the first study regarding stochastic electricity spot price models in this new market environment. Hence, it serves as a solid base for future research.
ATT3D: Amortized Text-to-3D Object Synthesis
Authors: Jonathan Lorraine, Kevin Xie, Xiaohui Zeng, Chen-Hsuan Lin, Towaki Takikawa, Nicholas Sharp, Tsung-Yi Lin, Ming-Yu Liu, Sanja Fidler, James Lucas
Date: 2023-06-06 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image to 3D, Object, Text to 3D

Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently achieved high-quality results but requires a lengthy, per-prompt optimization to create 3D objects. To address this, we amortize optimization over text prompts by training on many prompts simultaneously with a unified model, instead of separately. With this, we share computation across a prompt set, training in less time than per-prompt optimization. Our framework - Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to generalize to unseen setups and smooth interpolations between text for novel assets and simple animations.
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
Authors: Qiushi Sun, Nuo Chen, Jianing Wang, Xiang Li, Ming Gao
Date: 2023-05-23 00:00:00
Tasks: Clone Detection, Code Summarization, Code Translation, Representation Learning

Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and code summarization. The current mainstream method that deploys these models to downstream tasks is to fine-tune them on individual tasks, which is generally costly and needs sufficient data for large models. To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning. Inspired by human inherent skills of knowledge generalization, TransCoder drives the model to learn better code-related meta-knowledge like human programmers. Specifically, we employ a tunable prefix encoder as the meta-learner to capture cross-task and cross-language transferable knowledge, respectively. Besides, tasks with minor training sample sizes and languages with small corpus can be remarkably benefited from our approach. Extensive experiments conducted on benchmark datasets clearly demonstrate that our method can lead to superior performance on various code-related tasks and encourage mutual reinforcement. We also show that TransCoder is applicable in low-resource scenarios. Our codes are available at https://github.com/QiushiSun/TransCoder.
A 3D grain-based reconstruction method from a 2D surface image for the Distinct Lattice Spring Model
Authors: Xin-DongWei, Zhi-Qiang Deng, Qin Li, Yan Huang, Gao-Feng Zhao
Date: 2023-05-06 00:00:00
Proceeding: Numerical and Analytical Methods in Geomechanics 2023 5
Tasks: Computational Efficiency

Abstract In this work, a surface image-based 3D grain-basedmodel (GBM) reconstruction method based on the digital image processing (DIP) technique, periodic random packing and the simulated annealing algorithm is proposed. Taking a surface digital image of Beishan granite as an example, a K-means clustering algorithm is employed to extract the mineral compositions, the distribution ofwhich is quantified by the two-point probability function (TPPF).Given the 3D volume fraction ofeach mineral composition, their corresponding 3D two-point probability functions can be evaluated from their 2D forms by the linear interpolation method. The 3D GBM reconstruction is generated by a simulated annealing algorithm, with the Monte Carlo algorithm extending the calculation of the two-point probability function as the target function to the random particle model. To improve the computational efficiency, the periodic boundary condition is applied to both the random particle generator and the evaluation of the two-point probability function, allowing large-scale GBM to be generated for the Distinct Lattice Spring Model (DLSM). The elastic response and advantages of the random particle model for the DLSM are investigated. Finally, the DLSM is further enriched with a tension-cutoffMohr-Coulomb failure criterion, and the GBM reconstruction method is successfully tested for reproducing the mechanical behaviours of granite and interpreting the failure mechanism at the mesoscale under different
loading conditions.
Radiological image synthesis using cycle-consistent generative adversarial network
Authors: Firoozeh Shomal Zadeh, Sevda Molani, Maysam Orouskhani, Marziyeh Rezaei, Mehrzad Shafiei, Hossein Abbasi
Date: 2022-01-15 00:00:00
Proceeding: Proceedings of International Conference on Recent Trends in Computing 2022 1
Tasks: Deep Learning, Generative Adversarial Network, Image Generation

In medical imaging, image synthesis is the estimation process of one image (sequence, modality) from another image (sequence, modality). Since images with different modalities provide diverse biomarkers and capture various features, multi-modality imaging is crucial in medicine. While multi-screening is expensive, costly, and time-consuming to report to radiologists, image synthesis methods are capable of artificially generating missing modalities. Deep learning models can automatically capture and extract the high dimensional features. Especially, generative adversarial network (GAN) is one of the most popular generative-based deep learning methods, uses convolutional networks as generators, and estimated images are discriminated as true or false based on a discriminator network. This review provides brain image synthesis via GANs. We summarized the recent developments of GANs for cross-modality brain image synthesis including CT to PET, CT to MRI, MRI to PET, and vice versa.
Hand Pose Estimation in the Task of Egocentric Actions
Authors: Marek Hrúz, Jakub Kanis, Zdenĕk Krňoul
Date: 2021-01-11 00:00:00
Proceeding: IEEE Access 2021 1
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation, Task 2

In this article we tackle the problem of hand pose estimation when the hand is interacting with various objects from egocentric viewpoint. This entails a frequent occlusion of parts of the hand by the object and also self-occlusions of the hand. We use a Voxel-to-Voxel approach to obtain hypotheses of the hand joint locations, ensemble the hypotheses and use several post-processing strategies to improve on the results. We utilize models of prior hand pose in the form of Truncated Singular Value Decomposition (SVD) and the temporal context to produce refined hand joint locations. We present an ablation study of the methods to show the influence of individual features of the post-processing. With our method we were able to constitute state-of-the-art results on the HANDS19 Challenge: Task 2 - Depth-Based 3D Hand Pose Estimation while Interacting with Objects, with precision on unseen test data of 33.09 mm.
MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image
Authors: Jakub Kanis, Ivan Gruber, Zdeněk Krňoul, Matyáš Boháček, Jakub Straka, Marek Hrúz
Date: 2023-06-12 00:00:00
Proceeding: Sensors 2023 6
Tasks: Hand Pose Estimation, Pose Estimation

This work presents a novel transformer-based method for hand pose estimation—DePOTR. We test the DePOTR method on four benchmark datasets, where DePOTR outperforms other transformer-based methods while achieving results on par with other state-of-the-art methods. To further demonstrate the strength of DePOTR, we propose a novel multi-stage approach from full-scene depth image—MuTr. MuTr removes the necessity of having two different models in the hand pose estimation pipeline—one for hand localization and one for pose estimation—while maintaining promising results. To the best of our knowledge, this is the first successful attempt to use the same model architecture in standard and simultaneously in full-scene image setup while achieving competitive results in both of them. On the NYU dataset, DePOTR and MuTr reach precision equal to 7.85 mm and 8.71 mm, respectively.
One Model is Not Enough: Ensembles for Isolated Sign Language Recognition
Authors: Marek Hrúz, Ivan Gruber, Jakub Kanis, Matyáš Boháček, Miroslav Hlaváč, Zdeněk Krňoul
Date: 2022-07-04 00:00:00
Proceeding: Sensors 2022 7
Tasks: Sign Language Recognition

In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler.
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification
Authors: Quanwei Liu; Yanni Dong; Yuxiang Zhang; Hui Luo
Date: 2022-05-30 00:00:00
Proceeding: journal 2022 5
Tasks: Hyperspectral Image Classification, image-classification, Image Classification

Deep learning has achieved impressive results on hyperspectral image (HSI) classiﬁcation. Among them, both convolutional neural networks (CNNs) and graph neural networks (GNNs) have great potential for HSI classiﬁcation. Supervised CNNs can efﬁciently extract hierarchical spatial–spectral features of HSIs, but these methods face the problem of high time complexity as the number of network layers increases. Semisupervised GNNs can rapidly capture the structural information of HSIs, while they cannot be well extended to HSI applications because of the process of adjacency matrix consuming large amount of memory resources. In this article, we propose a fast dynamic graph convolutional network and CNN (FDGC) parallel network for HSI classiﬁcation. We ﬁrst obtain two classiﬁcation features by ﬂattening and pooling operations on the results of the convolution layers, which fully exploits the spatial–spectral information contained in the hyperspectral data cube. Then, a dynamic graph convolution module is applied to extract the intrinsic structural information of each patch. Finally, we can obtain the HSI classiﬁcation results based on these spatial, spectral, and structural features. By using three branches, FDGC can parallelly process multiple features of HSI in a supervised learning manner. In addition, regularization techniques, such as DropBlock and label smoothing, are applied to further improve the generalization capability of the model. Experimental results on three datasets show that our proposed algorithm is comparable with the state-of-the-art supervised learning models in terms of accuracy while also signiﬁcantly outperforming in terms of training and inference time.