pwc-archive/papers-with-abstracts huggingface dataset:
Modeling the Material-Inventory Transportation Problem Using Multi-Objective Optimization
Authors: Issarapong Khuankrue, Sudchai Boonto, Yasuhiro Tsujimura
Date: 2022-06-06 00:00:00
Tasks: Management, Multiobjective Optimization

In the era of industry 4.0, procurement in supply chain management is the key to developing information management systems. It directly affects production planning failure. In this case, it is the process to prepare and confirming the material inventory is in the ordinal stages and be able to produce the products in any production line. In terms of industrial informatics, it can provide information management approaches for leveraging data sharing between factories. The multiobjective optimization will be enabled by integrating material inventory, production planning and monitoring, and transportation planning collaboration. The material-inventory transportation problem is the virtual factory situation when production plan failure occurs. It becomes the cost to transport material between each factory and the distribution to clients. In this study, the question of the material-inventory transportation problem is: How can we transport other materials from one factory into another factory? This study proposed a model to find out about the adjustment of material inventory through transportation. The objective of this model is to minimize the whole production cost and total transportation cost.
OmicSelector: automatic feature selection and deep learning modeling for omic experiments
Authors: Konrad Stawiski, Marcin Kaszkowiak, Damian Mikulski, Piotr Hogendorf, Adam Durczyński, Janusz Strzelczyk, Dipanjan Chowdhury, Wojciech Fendler
Date: 2022-06-02 00:00:00
Proceeding: bioRxiv 2022 6
Tasks: Classification, Diagnostic, feature selection, GPU, Hyperparameter Optimization, Variable Selection

A crucial phase of modern biomarker discovery studies is selecting the most promising features from high-throughput screening assays. Here, we present the OmicSelector - Docker-based web application and R package that facilitates the analysis of such experiments. OmicSelector provides a consistent and overfitting-resilient pipeline that integrates 94 feature selection approaches based on 25 distinct variable selection methods. It identifies and then ranks the best feature sets using 11 modeling techniques with hyperparameter optimization in hold-out or cross-validation. OmicSelector provides classification performance metrics for proposed feature sets, allowing researchers to choose the overfitting-resistant biomarker set with the highest diagnostic potential. Finally, it performs GPU-accelerated development, validation, and implementation of deep learning feedforward neural networks (up to 3 hidden layers, with or without autoencoders) on selected signatures. The application performs an extensive grid search of hyperparameters, including balancing and preprocessing of next-generation sequencing (e.g. RNA-seq, miRNA-seq) oraz qPCR data. The pipeline is applicable for determining candidate circulating or tissue miRNAs, gene expression data and methylomic, metabolomic or proteomic analyses. As a case study, we use OmicSelector to develop a diagnostic test for pancreatic and biliary tract cancer based on serum small RNA next-generation sequencing (miRNA-seq) data. The tool is open-source and available at https://biostat.umed.pl/OmicSelector/ .
Dynamic Modality Interaction Modeling for Image-Text Retrieval
Authors: Leigang Qu, Meng Liu, Jianlong Wu, Zan Gao, Liqiang Nie
Date: 2021-07-11 00:00:00
Proceeding: ACM Special Interest Group on Information Retrieval 2021 7
Tasks: cross-modal alignment, Cross-Modal Retrieval, Image-text Retrieval, Information Retrieval, multimodal interaction, Retrieval, Text Retrieval

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although much progress has been made in bridging vision and language, it remains challenging because of the difficult intra-modal reasoning and cross-modal alignment. Existing modality interaction methods have achieved impressive results on public datasets. However, they heavily rely on expert experience and empirical feedback towards the design of interaction patterns, therefore, lacking flexibility. To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval. In particular, we first design four types of cells as basic units to explore different levels of modality interactions, and then connect them in a dense strategy to construct a routing space. To endow the model with the capability of path decision, we integrate a dynamic router in each cell for pattern exploration. As the routers are conditioned on inputs, our model can dynamically learn different activated paths for different data. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, verify the superiority of our model compared with several state-of-the-art baselines.
Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs
Authors: Kwantae Kim, Shih-Chii Liu
Date: 2022-06-06 00:00:00
Tasks: Keyword Spotting

Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure always-on operation, their stringent power constraints pose several design challenges and force IC designers to look for solutions that use low standby power. One promising bio-inspired approach is to combine the continuous-time analog filter channels with a small memory footprint deep neural network that is trained on edge tasks such as keyword spotting, thereby allowing all blocks to be embedded in an IC. This paper reviews the historical background of the continuous-time analog filter circuits that have been used as feature extractors for current edge audio devices. Starting from the interpretation of a basic biquad filter as a two-integrator-loop topology, we introduce the progression in the design of second-order low-pass and band-pass filters ranging from OTA-based to source-follower-based architectures. We also derive and analyze the small-signal transfer function and discuss their usage in edge audio applications.
Wigner-Smith Time Delay Matrix for Electromagnetics: Guiding and Periodic Systems with Evanescent Modes
Authors: Yiqian Mao, Utkarsh R. Patel, Eric Michielssen
Date: 2022-06-03 00:00:00

The Wigner-Smith (WS) time delay matrix relates an electromagnetic system's scattering matrix and its frequency derivative. Previous work showed that the entries of WS time delay matrices of systems excited by propagating waves consist of volume integrals of energy-like field quantities. This paper introduces a generalized WS relationship that applies to systems excited by mixtures of propagating and evanescent fields. Just like its predecessor, the generalized WS relationship allows for the identification of so-called WS modes that interact with the system with well-defined time delays. Furthermore, a technique is developed to compute the WS time delay matrix of a composite system from the WS time delay matrices of its subsystems. Numerical examples demonstrate the usefulness of the generalized WS method when characterizing time delays experienced by fields interacting with guiding and periodic structures that have ports supporting evanescent modes.
Optimal Stopping Theory for a Distributionally Robust Seller
Authors: Pieter Kleer, Johan van Leeuwaarden
Date: 2022-06-06 00:00:00

Sellers in online markets face the challenge of determining the right time to sell in view of uncertain future offers. Classical stopping theory assumes that sellers have full knowledge of the value distributions, and leverage this knowledge to determine stopping rules that maximize expected welfare. In practice, however, stopping rules must often be determined under partial information, based on scarce data or expert predictions. Consider a seller that has one item for sale and receives successive offers drawn from some value distributions. The decision on whether or not to accept an offer is irrevocable, and the value distributions are only partially known. We therefore let the seller adopt a robust maximin strategy, assuming that value distributions are chosen adversarially by nature to minimize the value of the accepted offer. We provide a general maximin solution to this stopping problem that identifies the optimal (threshold-based) stopping rule for the seller for all possible statistical information structures. We then perform a detailed analysis for various ambiguity sets relying on knowledge about the common mean, dispersion (variance or mean absolute deviation) and support of the distributions. We show for these information structures that the seller's stopping rule consists of decreasing thresholds converging to the common mean, and that nature's adversarial response, in the long run, is to always create an all-or-nothing scenario. The maximin solutions also reveal what happens as dispersion or the number of offers grows large.
Low Power Neuromorphic EMG Gesture Classification
Authors: Sai Sukruth Bezugam, Ahmed Shaban, Manan Suri
Date: 2022-06-04 00:00:00
Tasks: Classification, EMG Gesture Recognition, Gesture Recognition, GPU

EMG (Electromyograph) signal based gesture recognition can prove vital for applications such as smart wearables and bio-medical neuro-prosthetic control. Spiking Neural Networks (SNNs) are promising for low-power, real-time EMG gesture recognition, owing to their inherent spike/event driven spatio-temporal dynamics. In literature, there are limited demonstrations of neuromorphic hardware implementation (at full chip/board/system scale) for EMG gesture classification. Moreover, most literature attempts exploit primitive SNNs based on LIF (Leaky Integrate and Fire) neurons. In this work, we address the aforementioned gaps with following key contributions: (1) Low-power, high accuracy demonstration of EMG-signal based gesture recognition using neuromorphic Recurrent Spiking Neural Networks (RSNN). In particular, we propose a multi-time scale recurrent neuromorphic system based on special double-exponential adaptive threshold (DEXAT) neurons. Our network achieves state-of-the-art classification accuracy (90%) while using ~53% lesser neurons than best reported prior art on Roshambo EMG dataset. (2) A new multi-channel spike encoder scheme for efficient processing of real-valued EMG data on neuromorphic systems. (3) Unique multi-compartment methodology to implement complex adaptive neurons on Intel's dedicated neuromorphic Loihi chip is shown. (4) RSNN implementation on Loihi (Nahuku 32) achieves significant energy/latency benefits of ~983X/19X compared to GPU for batch size as 50.
Collaborative search and autonomous task allocation in organizations of learning agents
Authors: Stephan Leitner
Date: 2022-06-05 00:00:00

This paper introduces a model of multi-unit organizations with either static structures, i.e., they are designed top-down following classical approaches to organizational design, or dynamic structures, i.e., the structures emerge over time from micro-level decisions. In the latter case, the units are capable of learning about the technical interdependencies of the task they face, and they use their knowledge by adapting the task allocation from time to time. In both static and dynamic organizations, searching for actions to increase the performance can either be carried out individually or collaboratively. The results indicate that (i) collaborative search processes can help overcome the adverse effects of inefficient task allocations as long as there is an internal fit with other organizational design elements, and (ii) for dynamic organizations, the emergent task allocation does not necessarily mirror the technical interdependencies of the task the organizations face, even though the same (or even higher) performances are achieved.
Coding of volumetric content with MIV using VVC subpictures
Authors: Maria Santamaria, Vinod Kumar Malamal Vadakital, Lukasz Kondrad, Antti Hallapuro, Miska M. Hannuksela
Date: 2022-06-06 00:00:00
Tasks: Decoder

Storage and transport of six degrees of freedom (6DoF) dynamic volumetric visual content for immersive applications requires efficient compression. ISO/IEC MPEG has recently been working on a standard that aims to efficiently code and deliver 6DoF immersive visual experiences. This standard is called the MIV. MIV uses regular 2D video codecs to code the visual data. MPEG jointly with ITU-T VCEG, has also specified the VVC standard. VVC introduced recently the concept of subpicture. This tool was specifically designed to provide independent accessibility and decodability of sub-bitstreams for omnidirectional applications. This paper shows the benefit of using subpictures in the MIV use-case. While different ways in which subpictures could be used in MIV are discussed, a particular case study is selected. Namely, subpictures are used for parallel encoding and to reduce the number of decoder instances. Experimental results show that the cost of using subpictures in terms of bitrate overhead is negligible (0.1% to 0.4%), when compared to the overall bitrate. The number of decoder instances on the other hand decreases by a factor of two.
A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks
Authors: Yuxin Sun, Dong Lao, Ganesh Sundaramoorthi, Anthony Yezzi
Date: 2022-06-04 00:00:00

We discover restrained numerical instabilities in current training practices of deep networks with stochastic gradient descent (SGD), and its variants. We show numerical error (on the order of the smallest floating point bit and thus the most extreme or limiting numerical perturbations induced from floating point arithmetic in training deep nets can be amplified significantly and result in significant test accuracy variance (sensitivities), comparable to the test accuracy variance due to stochasticity in SGD. We show how this is likely traced to instabilities of the optimization dynamics that are restrained, i.e., localized over iterations and regions of the weight tensor space. We do this by presenting a theoretical framework using numerical analysis of partial differential equations (PDE), and analyzing the gradient descent PDE of convolutional neural networks (CNNs). We show that it is stable only under certain conditions on the learning rate and weight decay. We show that rather than blowing up when the conditions are violated, the instability can be restrained. We show this is a consequence of the non-linear PDE associated with the gradient descent of the CNN, whose local linearization changes when over-driving the step size of the discretization, resulting in a stabilizing effect. We link restrained instabilities to the recently discovered Edge of Stability (EoS) phenomena, in which the stable step size predicted by classical theory is exceeded while continuing to optimize the loss and still converging. Because restrained instabilities occur at the EoS, our theory provides new insights and predictions about the EoS, in particular, the role of regularization and the dependence on the network complexity.
On Crossover Distance for Optical Wireless Satellite Networks and Optical Fiber Terrestrial Networks
Authors: Aizaz U. Chaudhry, Halim Yanikomeroglu
Date: 2022-06-06 00:00:00

Optical wireless satellite networks (OWSNs) can provide lower latency data communications compared to optical fiber terrestrial networks (OFTNs). The crossover function enables to calculate the crossover distance for an OWSN and an OFTN. If the distance between two points on Earth is greater than the crossover distance, then switching or crossing over from the OFTN to the OWSN results in lower latency for data communications between these points. In this work, we extend the previously proposed crossover function for a scenario such that intermediate satellites (or hops) are incorporated between ingress and egress satellites in the OWSN for a more realistic calculation of the crossover distance in this scenario. We consider different OWSNs with different satellite altitudes and different OFTNs with different optical fiber refractive indexes, and we study the effect of the number of hops on the crossover distance and length of a laser inter-satellite link (LISL). It is observed from the numerical results that the crossover distance increases with an increase in the number of hops, and this increase is higher at higher satellite altitudes in OWSNs and lower refractive indexes in OFTNs. Furthermore, an inverse relationship between the crossover distance and length of a LISL is observed. With an increase in the number of hops, the length of a LISL decreases as opposed to the crossover distance.
Geometrically-Motivated Primary-Ambient Decomposition With Center-Channel Extraction
Authors: Jouni Paulus, Matteo Torcoli
Date: 2022-06-05 00:00:00

A geometrically-motivated method for primary-ambient decomposition is proposed and evaluated in an up-mixing application. The method consists of two steps, accommodating a particularly intuitive explanation. The first step consists of signal-adaptive rotations applied on the input stereo scene, which translate the primary sound sources into the center of the rotated scene. The second step applies a center-channel extraction method, based on a simple signal model and optimal in the mean-squared-error sense. The performance is evaluated by using the estimated ambient component to enable surround sound starting from real-world stereo signals. The participants in the reported listening test are asked to adjust the audio scene envelopment and find the audio settings that pleases them the most. The possibility for up-mixing enabled by the proposed method is used extensively, and the user satisfaction is significantly increased compared to the original stereo mix.
Sampling Frequency Independent Dialogue Separation
Authors: Jouni Paulus, Matteo Torcoli
Date: 2022-06-05 00:00:00
Tasks: Audio Source Separation

In some DNNs for audio source separation, the relevant model parameters are independent of the sampling frequency of the audio used for training. Considering the application of dialogue separation, this is shown for two DNN architectures: a U-Net and a fully-convolutional model. The models are trained with audio sampled at 8 kHz. The learned parameters are transferred to models for processing audio at 48 kHz. The separated audio sources are compared with the ones produced by the same model architectures trained with 48 kHz versions of the same training data. A listening test and computational measures show that there is no significant perceptual difference between the models trained with 8 kHz or with 48 kHz. This transferability of the learned parameters allows for a faster and computationally less costly training. It also enables using training datasets available at a lower sampling frequency than the one needed by the application at hand, or using data collections with multiple sampling frequencies.
STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events
Authors: Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Virtanen
Date: 2022-06-04 00:00:00
Tasks: Sound Event Localization and Detection

This report presents the Sony-TAu Realistic Spatial Soundscapes 2022 (STARS22) dataset for sound event localization and detection, comprised of spatial recordings of real scenes collected in various interiors of two different sites. The dataset is captured with a high resolution spherical microphone array and delivered in two 4-channel formats, first-order Ambisonics and tetrahedral microphone array. Sound events in the dataset belonging to 13 target sound classes are annotated both temporally and spatially through a combination of human annotation and optical tracking. The dataset serves as the development and evaluation dataset for the Task 3 of the DCASE2022 Challenge on Sound Event Localization and Detection and introduces significant new challenges for the task compared to the previous iterations, which were based on synthetic spatialized sound scene recordings. Dataset specifications are detailed including recording and annotation process, target classes and their presence, and details on the development and evaluation splits. Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format. Results of the baseline indicate that with a suitable training strategy a reasonable detection and localization performance can be achieved on real sound scene recordings. The dataset is available in https://zenodo.org/record/6387880.
Multi-model assessment of heat decarbonisation options in the UK using electricity and hydrogen
Authors: Marko Aunedi, Maria Yliruka, Shahab Dehghan, Antonio Marco Pantaleo, Nilay Shah, Goran Strbac
Date: 2022-06-06 00:00:00

Delivering low-carbon heat will require the substitution of natural gas with low-carbon alternatives such as electricity and hydrogen. The objective of this paper is to develop a method to soft-link two advanced, investment-optimising energy system models, RTN (Resource-Technology Network) and WeSIM (Whole-electricity System Investment Model), in order to assess cost-efficient heat decarbonisation pathways for the UK while utilising the respective strengths of the two models. The linking procedure included passing on hourly electricity prices from WeSIM as input to RTN, and returning capacities and locations of hydrogen generation and shares of electricity and hydrogen in heat supply from RTN to WeSIM. The outputs demonstrate that soft-linking can improve the quality of the solution, while providing useful insights into the cost-efficient pathways for zero-carbon heating. Quantitative results point to the cost-effectiveness of using a mix of electricity and hydrogen technologies for delivering zero-carbon heat, also demonstrating a high level of interaction between electricity and hydrogen infrastructure in a zero-carbon system. Hydrogen from gas reforming with carbon capture and storage can play a significant role in the medium term, while remaining a cost-efficient option for supplying peak heat demand in the longer term, with the bulk of heat demand being supplied by electric heat pumps.
Model predictive eco-driving control for heavy-duty trucks using Branch and Bound optimization
Authors: B. Wingelaar, G. R. Gonçalves da Silva, M. Lazar
Date: 2022-06-06 00:00:00
Tasks: Model Predictive Control

Eco-driving (ED) can be used for fuel savings in existing vehicles, requiring only a few hardware modifications. For this technology to be successful in a dynamic environment, ED requires an online real-time implementable policy. In this work, a dedicated Branch and Bound (BnB) model predictive control (MPC) algorithm is proposed to solve the optimization part of an ED optimal control problem. The developed MPC solution for ED is based on the following ingredients. As a prediction model, the velocity dynamics as a function of distance is modeled by a finite number of driving modes and gear positions. Then we formulate an optimization problem that minimizes a cost function with two terms: one penalizing the fuel consumption and one penalizing the trip duration. We exploit contextual elements and use a warm-started solution to make the BnB solver run in real-time. The results are evaluated in numerical simulations on two routes in Israel and France and the long haul cycle of the Vehicle Energy consumption Calculation Tool (VECTO). In comparison with a human driver and a Pontryagin's Minimum Principle (PMP) solution, 25.8% and 12.9% fuel savings, respectively, are achieved on average.