pwc-archive/papers-with-abstracts huggingface dataset:
NEnv: Neural Environment Maps for Global Illumination
Authors: Carlos Rodriguez-Pardo, Javier Fabre, Elena Garces, Jorge Lopez-Moreno
Date: 2023-07-26 00:00:00
Proceeding: Computer Graphics Forum 2023 7
Tasks: Neural Rendering, Normalising Flows

Environment maps are commonly used to represent and compute far-field illumination in virtual scenes. However, they are expensive to evaluate and sample from, limiting their applicability to real-time rendering. Previous methods have focused on compression through spherical-domain approximations, or on learning priors for natural, day-light illumination. These hinder both accuracy and generality, and do not provide the probability information required for importance-sampling Monte Carlo integration. We propose NEnv, a deep-learning fully-differentiable method, capable of compressing and learning to sample from a single environment map. NEnv is composed of two different neural networks: A normalizing flow, able to map samples from uniform distributions to the probability density of the illumination, also providing their corresponding probabilities; and an implicit neural representation which compresses the environment map into an efficient differentiable function. The computation time of environment samples with NEnv is two orders of magnitude less than with traditional methods. NEnv makes no assumptions regarding the content (i.e. natural illumination), thus achieving higher generality than previous learning-based approaches. We share our implementation and a diverse dataset of trained neural environment maps, which can be easily integrated into existing rendering engines.
From Words to Sound: Neural Audio Synthesis of Guitar Sounds with Timbral Descriptors
Authors: The Sound of AI Community
Date: 2022-09-17 00:00:00
Proceeding: Proceedings of the 3rd Conference on AI Music Creativity, AIMC 2022 9
Tasks: Audio Synthesis

Interest in neural audio synthesis has been growing lately both in academia and industry. Deep Learning (DL) synthesisers enable musicians to generate fresh, often completely unconventional sounds. However, most of these applications present a drawback. It is difficult for musicians to generate sounds which reflect the timbral properties they have in mind, because of the nature of the latent spaces of such systems. These spaces generally have large dimensionality and cannot easily be mapped to semantically meaningful timbral properties. Navigation of such timbral spaces is therefore impractical. In this paper, we introduce a DL-powered instrument that generates guitar sounds from vocal commands. The system analyses vocal instructions to extract timbral descriptors which condition the sound generation.
The Weights Reset Technique for Deep Neural Networks Implicit Regularization
Authors: Grigoriy Plusch, Sergey Arsenyev-Obraztsov, Olga Kochueva
Date: 2023-08-01 00:00:00
Proceeding: Computation 2023 8
Tasks: Classification

We present a new regularization method called Weights Reset, which includes periodically resetting a random portion of layer weights during the training process using predefined probability distributions. This technique was applied and tested on several popular classification datasets, Caltech-101, CIFAR-100 and Imagenette. We compare these results with other traditional regularization methods. The subsequent test results demonstrate that the Weights Reset method is competitive, achieving the best performance on Imagenette dataset and the challenging and unbalanced Caltech-101 dataset. This method also has sufficient potential to prevent vanishing and exploding gradients. However, this analysis is of a brief nature. Further comprehensive studies are needed in order to gain a deep understanding of the computing potential and limitations of the Weights Reset method. The observed results show that the Weights Reset method can be estimated as an effective extension of the traditional regularization methods and can help to improve model performance and generalization.
Design and Implementation of an Efficient Onboard Computer System for CanSat Atmosphere Monitoring
Authors: Abhijit Gadekar
Date: 2023-08-07 00:00:00

With advancements in technology, the smaller versions of satellites have gained momentum in the space industry for earth monitoring and communication-based applications. The rise of CanSat technology has significantly impacted the space industry by providing a cost-effective solution for space exploration. CanSat is a simulation model of a real satellite and plays a crucial role in collecting and transmitting atmospheric data. This paper discusses the design of an Onboard Computer System forCanSat, used to study various environmental parameters by monitoring the concentrations of gases in the atmosphere. The Onboard Computer System uses GPS, accelerometer, altitude, temperature, pressure, gyroscope, magnetometer, UV radiation, and air quality sensors for atmospheric sensing. A highly efficient and low-power ESP32 microcontroller and a transceiver module are used to acquire data, facilitate seamless communication and transmit the collected data to the ground station.
Design Tasks and Their Complexity for the European Train Control System with Hybrid Train Detection
Authors: Stefan Engels, Tom Peham, Judith Przigoda, Nils Przigoda, Robert Wille
Date: 2023-08-03 00:00:00

Railway networks have become increasingly important in recent times, especially in moving freight and public transportation from road traffic and planes to more environmentally friendly trains. Since expanding the global railway network is time- and resource-consuming, maximizing the rail capacity of the existing infrastructure is desirable. However, simply running more trains is infeasible as certain constraints enforced by the train control system must be satisfied. The capacity of a network depends (amongst others) on the distance between trains allowed by this safety system. While most signaling systems rely on fixed blocks defined by costly hardware, new specifications provided by Level 2 with Hybrid Train Detection of the European Train Control System (ETCS L2 HTD), formerly known as ETCS Hybrid Level 3, allow the usage of virtual subsections. This additional degree of freedom allows for shorter train following times and, thus, more trains on existing railway tracks. On the other hand, new design tasks arise on which automated methods might be helpful for designers of modern railway networks. However, although first approaches exist that solve design problems arising within ETCS L2 HTD, neither formal descriptions nor results on the computational complexity of the corresponding design tasks exist. In this paper, we fill this gap by providing a formal description of design tasks for ETCS L2 HTD and proof that these tasks are NP-complete or NP-hard, respectively. By that, we are providing a solid basis for the future development of methods to solve those tasks, which will be integrated into the Munich Train Control Toolkit available open-source on GitHub at https://github.com/cda-tum/mtct.
Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity
Authors: Savina Kim, Stefan Lessmann, Galina Andreeva, Michael Rovatsos
Date: 2023-08-01 00:00:00
Tasks: Decision Making, Fairness

The increasing usage of new data sources and machine learning (ML) technology in credit modeling raises concerns with regards to potentially unfair decision-making that rely on protected characteristics (e.g., race, sex, age) or other socio-economic and demographic data. The authors demonstrate the impact of such algorithmic bias in the microfinance context. Difficulties in assessing credit are disproportionately experienced among vulnerable groups, however, very little is known about inequities in credit allocation between groups defined, not only by single, but by multiple and intersecting social categories. Drawing from the intersectionality paradigm, the study examines intersectional horizontal inequities in credit access by gender, age, marital status, single parent status and number of children. This paper utilizes data from the Spanish microfinance market as its context to demonstrate how pluralistic realities and intersectional identities can shape patterns of credit allocation when using automated decision-making systems. With ML technology being oblivious to societal good or bad, we find that a more thorough examination of intersectionality can enhance the algorithmic fairness lens to more authentically empower action for equitable outcomes and present a fairer path forward. We demonstrate that while on a high-level, fairness may exist superficially, unfairness can exacerbate at lower levels given combinatorial effects; in other words, the core fairness problem may be more complicated than current literature demonstrates. We find that in addition to legally protected characteristics, sensitive attributes such as single parent status and number of children can result in imbalanced harm. We discuss the implications of these findings for the financial services industry.
Evaluating ChatGPT and GPT-4 for Visual Programming
Authors: Adish Singla
Date: 2023-07-30 00:00:00

Generative AI and large language models have the potential to drastically improve the landscape of computing education by automatically generating personalized feedback and content. Recent works have studied the capabilities of these models for different programming education scenarios; however, these works considered only text-based programming, in particular, Python programming. Consequently, they leave open the question of how well these models would perform in visual programming domains popularly used for K-8 programming education. The main research question we study is: Do state-of-the-art generative models show advanced capabilities in visual programming on par with their capabilities in text-based Python programming? In our work, we evaluate two models, ChatGPT (based on GPT-3.5) and GPT-4, in visual programming domains for various scenarios and assess performance using expert-based annotations. In particular, we base our evaluation using reference tasks from the domains of Hour of Code: Maze Challenge by Code-dot-org and Karel. Our results show that these models perform poorly and struggle to combine spatial, logical, and programming skills crucial for visual programming. These results also provide exciting directions for future work on developing techniques to improve the performance of generative models in visual programming.
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
Authors: Skylar Wolfgang Wurster, Tianyu Xiong, Han-Wei Shen, Hanqi Guo, Tom Peterka
Date: 2023-07-16 00:00:00
Tasks: Data Visualization, GPU

Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems. We also release an open-source neural volume rendering application that allows plug-and-play rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses multiple spatially adaptive feature grids that learn where to be placed within the domain to dynamically allocate more neural network resources where error is high in the volume, improving state-of-the-art reconstruction accuracy of SRNs for scientific data without requiring expensive octree refining, pruning, and traversal like previous adaptive models. In our domain decomposition approach for representing large-scale data, we train an set of APMGSRNs in parallel on separate bricks of the volume to reduce training time while avoiding overhead necessary for an out-of-core solution for volumes too large to fit in GPU memory. After training, the lightweight SRNs are used for realtime neural volume rendering in our open-source renderer, where arbitrary view angles and transfer functions can be explored. A copy of this paper, all code, all models used in our experiments, and all supplemental materials and videos are available at https://github.com/skywolf829/APMGSRN.
Feature Reweighting for EEG-based Motor Imagery Classification
Authors: Taveena Lotey, Prateek Keserwani, Debi Prosad Dogra, Partha Pratim Roy
Date: 2023-07-29 00:00:00
Tasks: Classification, EEG, Motor Imagery

Classification of motor imagery (MI) using non-invasive electroencephalographic (EEG) signals is a critical objective as it is used to predict the intention of limb movements of a subject. In recent research, convolutional neural network (CNN) based methods have been widely utilized for MI-EEG classification. The challenges of training neural networks for MI-EEG signals classification include low signal-to-noise ratio, non-stationarity, non-linearity, and high complexity of EEG signals. The features computed by CNN-based networks on the highly noisy MI-EEG signals contain irrelevant information. Subsequently, the feature maps of the CNN-based network computed from the noisy and irrelevant features contain irrelevant information. Thus, many non-contributing features often mislead the neural network training and degrade the classification performance. Hence, a novel feature reweighting approach is proposed to address this issue. The proposed method gives a noise reduction mechanism named feature reweighting module that suppresses irrelevant temporal and channel feature maps. The feature reweighting module of the proposed method generates scores that reweight the feature maps to reduce the impact of irrelevant information. Experimental results show that the proposed method significantly improved the classification of MI-EEG signals of Physionet EEG-MMIDB and BCI Competition IV 2a datasets by a margin of 9.34% and 3.82%, respectively, compared to the state-of-the-art methods.
A Tractable Handoff-aware Rate Outage Approximation with Applications to THz-enabled Vehicular Network Optimization
Authors: Mohammad Amin Saeidi, Haider Shoaib, Hina Tabassum
Date: 2023-08-07 00:00:00
Tasks: Collision Avoidance

In this paper, we first develop a tractable mathematical model of the handoff (HO)-aware rate outage experienced by a typical connected and autonomous vehicle (CAV) in a given THz vehicular network. The derived model captures the impact of line-of-sight (LOS) Nakagami-m fading channels, interference, and molecular absorption effects. We first derive the statistics of the interference-plus-molecular absorption noise ratio and demonstrate that it can be approximated by Gamma distribution using Welch-Satterthwaite approximation. Then, we show that the distribution of signal-to-interference-plus-molecular absorption noise ratio (SINR) follows a generalized Beta prime distribution. Based on this, a closed-form HO-aware rate outage expression is derived. Finally, we formulate and solve a CAVs' traffic flow maximization problem to optimize the base-stations (BSs) density and speed of CAVs with collision avoidance, rate outage, and CAVs' minimum traffic flow constraint. The CAVs' traffic flow is modeled using Log-Normal distribution. Our numerical results validate the accuracy of the derived expressions using Monte-Carlo simulations and discuss useful insights related to optimal BS density and CAVs' speed as a function of crash intensity level, THz molecular absorption effects, minimum road-traffic flow and rate requirements, and maximum speed and rate outage limits.
Control-Oriented Deep Space Communications For Unmanned Space Exploration
Authors: Xinran Fang, Wei Feng, Yunfei Chen, Ning Ge, Gan Zheng
Date: 2023-08-07 00:00:00

In unmanned space exploration, the cooperation among space robots requires advanced communication techniques. In this paper, we propose a communication optimization scheme for a specific cooperation system named the "mother-daughter system". In this setup, the mother spacecraft orbits the planet, while daughter probes are distributed across the planetary surface. During each control cycle, the mother spacecraft senses the environment, computes control commands and distributes them to daughter probes for actions. They synergistically form sensing-communication-computing-control ($\mathbf{SC^3}$) loops. Given the indivisibility of the $\mathbf{SC^3}$ loop, we optimize the mother-daughter downlink for closed-loop control. The optimization objective is the linear quadratic regulator (LQR) cost, and the optimization parameters are the block length and transmit power. To solve the nonlinear mixed-integer problem, we first identify the optimal block length and then transform the power allocation problem into a tractable convex problem. We further derive the approximate closed-form solutions for the proposed scheme and two communication-oriented schemes: the max-sum rate scheme and the max-min rate scheme. On this basis, we analyze their power allocation principles. In particular, for time-insensitive control tasks, we find that the proposed scheme demonstrates equivalence to the max-min rate scheme. These findings are verified through simulations.
Joint Precoding and Fronthaul Compression for Cell-Free MIMO Downlink With Radio Stripes
Authors: Sangwon Jo, Hoon Lee, Seok-Hwan Park
Date: 2023-08-07 00:00:00

A sequential fronthaul network, referred to as radio stripes, is a promising fronthaul topology of cell-free MIMO systems. In this setup, a single cable suffices to connect access points (APs) to a central processor (CP). Thus, radio stripes are more effective than conventional star fronthaul topology which requires dedicated cables for each of APs. Most of works on radio stripes focused on the uplink communication or downlink energy transfer. This work tackles the design of the downlink data transmission for the first time. The CP sends compressed information of linearly precoded signals to the APs on fronthaul. Due to the serial transfer on radio stripes, each AP has an access to all the compressed blocks which pass through it. Thus, an advanced compression technique, called Wyner-Ziv (WZ) compression, can be applied in which each AP decompresses all the received blocks to exploit them for the reconstruction of its desired precoded signal as side information. The problem of maximizing the sum-rate is tackled under the standard point-to-point (P2P) and WZ compression strategies. Numerical results validate the performance gains of the proposed scheme.
Improving Probabilistic Bisimulation for MDPs Using Machine Learning
Authors: Mohammadsadegh Mohaghegh, Khayyam Salehi
Date: 2023-07-30 00:00:00

The utilization of model checking has been suggested as a formal verification technique for analyzing critical systems. However, the primary challenge in applying to complex systems is state space explosion problem. To address this issue, bisimulation minimization has emerged as a prominent method for reducing the number of states in a labeled transition system, aiming to overcome the difficulties associated with the state space explosion problem. In the case of systems exhibiting stochastic behaviors, probabilistic bisimulation is employed to minimize a given model, obtaining its equivalent form with fewer states. Recently, various techniques have been introduced to decrease the time complexity of the iterative methods used to compute probabilistic bisimulation for stochastic systems that display nondeterministic behaviors. In this paper, we propose a new technique to partition the state space of a given probabilistic model to its bisimulation classes. This technique uses the PRISM program of a given model and constructs some small versions of the model to train a classifier. It then applies machine learning classification techniques to approximate the related partition. The resulting partition is used as an initial one for the standard bisimulation technique in order to reduce the running time of the method. The experimental results show that the approach can decrease significantly the running time compared to state-of-the-art tools.
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Authors: Yu-Ting Lan, Kan Ren, Yansen Wang, Wei-Long Zheng, Dongsheng Li, Bao-liang Lu, Lili Qiu
Date: 2023-07-27 00:00:00
Tasks: EEG, Image Reconstruction, Time Series

Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery. Thanks to the recent advances in both neuroscience and artificial intelligence, we have been able to record the visually evoked brain activities and mimic the visual perception ability through computational approaches. In this paper, we pay attention to visual stimuli reconstruction by reconstructing the observed images based on portably accessible brain signals, i.e., electroencephalography (EEG) data. Since EEG signals are dynamic in the time-series format and are notorious to be noisy, processing and extracting useful information requires more dedicated efforts; In this paper, we propose a comprehensive pipeline, named NeuroImagen, for reconstructing visual stimuli images from EEG signals. Specifically, we incorporate a novel multi-level perceptual information decoding to draw multi-grained outputs from the given EEG data. A latent diffusion model will then leverage the extracted information to reconstruct the high-resolution visual stimuli images. The experimental results have illustrated the effectiveness of image reconstruction and superior quantitative performance of our proposed method.
Active Noise Control based on the Momentum Multichannel Normalized Filtered-x Least Mean Square Algorithm
Authors: Dongyuan Shi, Woon-Seng Gan, Bhan Lam, Shulin Wen, Xiaoyi Shen
Date: 2023-08-07 00:00:00

Multichannel active noise control (MCANC) is widely utilized to achieve significant noise cancellation area in the complicated acoustic field. Meanwhile, the filter-x least mean square (FxLMS) algorithm gradually becomes the benchmark solution for the implementation of MCANC due to its low computational complexity. However, its slow convergence speed more or less undermines the performance of dealing with quickly varying disturbances, such as piling noise. Furthermore, the noise power variation also deteriorates the robustness of the algorithm when it adopts the fixed step size. To solve these issues, we integrated the normalized multichannel FxLMS with the momentum method, which hence, effectively avoids the interference of the primary noise power and accelerates the convergence of the algorithm. To validate its effectiveness, we deployed this algorithm in a multichannel noise control window to control the real machine noise.
AudioVMAF: Audio Quality Prediction with VMAF
Authors: Arijit Biswas, Harald Mundt
Date: 2023-08-07 00:00:00
Tasks: Prediction

Video Multimethod Assessment Fusion (VMAF) [1], [2], [3] is a popular tool in the industry for measuring coded video quality. In this study, we propose an auditory-inspired frontend in existing VMAF for creating videos of reference and coded spectrograms, and extended VMAF for measuring coded audio quality. We name our system AudioVMAF. We demonstrate that image replication is capable of further enhancing prediction accuracy, especially when band-limited anchors are present. The proposed method significantly outperforms all existing visual quality features repurposed for audio, and even demonstrates a significant overall improvement of 7.8% and 2.0% of Pearson and Spearman rank correlation coefficient, respectively, over a dedicated audio quality metric (ViSQOL-v3 [4]) also inspired from the image domain.