pwc-archive/papers-with-abstracts huggingface dataset:
Integrated Sensing and Communications Enabled Low Earth Orbit Satellite Systems
Authors: Longfei Yin, Ziang Liu, Bhavani Shankar M. R., Mohammad Alaee-Kerahroodi, Bruno Clerckx
Date: 2023-04-03 00:00:00
Tasks: ISAC

Extreme crowding of electromagnetic spectrum in recent years has led to the challenges in designing sensing and communications systems. Both systems require a broad range of bandwidth, thus resulting in competing interests in exploiting the spectrum. Efficient spectrum and hardware utilization have led to the emergence of integrated sensing and communications (ISAC) systems, which have recently emerged as a candidate 6G technology. In particular, we provide potential techniques, namely the opportunistic ISAC and optimized ISAC. Rate-splitting multiple access (RSMA) is highlighted as an optimized ISAC technique for LEO-ISAC systems due to its effectiveness in simultaneously managing interference and enabling better communication-sensing trade-off performance.
Optimal Semantic-aware Sampling and Transmission in Energy Harvesting Systems Through the AoII
Authors: Abolfazl Zakeri, Mohammad Moltafet, Marian Codreanu
Date: 2023-04-03 00:00:00
Tasks: Deep Reinforcement Learning, Stochastic Optimization

We study a real-time tracking problem in an energy harvesting status update system with a Markov source and an imperfect channel, considering both sampling and transmission costs. The problem primary challenge stems from the non-observability of the source due to the sampling cost. By using the age of incorrect information (AoII) as a semantic-aware performance metric, our main goal is to find an optimal policy that minimizes the time average AoII subject to an energy-causality constraint. To this end, a stochastic optimization problem is formulated and solved by modeling it as a partially observable Markov decision process (POMDP). More specifically, to solve the main problem, we use the notion of a belief state and cast the problem as a belief MDP problem. Then, for the perfect channel setup, we effectively truncate the corresponding belief space and solve the MDP problem using the relative value iteration method. For the general setup, a deep reinforcement learning policy is proposed. The simulation results show the efficacy of the derived policies in comparison to an AoI-optimal policy and an opportunistic baseline policy.
Stationarity Evaluation of High-mobility sub-6 GHz and mmWave non-WSSUS Channels
Authors: Danilo Radovic, Faruk Pasic, Markus Hofer, Herbert Groll, Christoph F. Mecklenbräuker, Thomas Zemen
Date: 2023-04-03 00:00:00

Analysis and modeling of wireless communication systems are dependent on the validity of the wide-sense stationarity uncorrelated scattering (WSSUS) assumption. However, in high-mobility scenarios, the WSSUS assumption is approximately fulfilled just over a short time period. This paper focuses on the stationarity evaluation of high-mobility multi-band channels. We evaluate the stationarity time, the time over which WSSUS is fulfilled approximately. The investigation is performed over real, measured high-mobility channels for two frequency bands, 2.55 and 25.5 GHz. Furthermore, we demonstrate the influence of the user velocity on the stationarity time. We show that the stationarity time decreases with increased relative velocity between the transmitter and the receiver. Furthermore, we show the similarity of the stationarity regions between sub-6 GHz and mmWave channels. Finally, we demonstrate that the sub-6 GHz channels are characterized by longer stationarity time.
JrCUP: Joint RIS Calibration and User Positioning for 6G Wireless Systems
Authors: Pinjun Zheng, Hui Chen, Tarig Ballal, Mikko Valkama, Henk Wymeersch, Tareq Y. Al-Naffouri
Date: 2023-04-02 00:00:00

Reconfigurable intelligent surface (RIS)-assisted localization has attracted extensive attention as it can enable and enhance localization services in extreme scenarios. However, most existing works treat RISs as anchors with known positions and orientations, which is not realistic in applications with mobile or uncalibrated RISs. This work considers the joint RIS calibration and user positioning (JrCUP) problem with an active RIS. We propose a novel two-stage method to solve the considered JrCUP problem. The first stage comprises a tensor-estimation of signal parameters via rotational invariance techniques (tensorESPRIT), followed by a channel parameters refinement using least-squares. In the second stage, a two-dimensional search algorithm is proposed to estimate the three-dimensional user and RIS positions, one-dimensional RIS orientation, and clock bias from the estimated channel parameters. The Cramer-Rao lower bounds of the channel parameters and localization parameters are derived to verify the effectiveness of the proposed tensorESPRIT-based algorithms. In addition, simulation results reveal that the active RIS can significantly improve the localization performance compared to the passive case under the same system power supply in practical regions. Moreover, we observe the presence of blind areas with limited JrCUP localization performance, which can be mitigated by either leveraging more prior information or deploying extra base stations.
Real-Time Prediction of Gas Flow Dynamics in Diesel Engines using a Deep Neural Operator Framework
Authors: Varun Kumar, Somdatta Goswami, Daniel J. Smith, George Em Karniadakis
Date: 2023-04-02 00:00:00

We develop a data-driven deep neural operator framework to approximate multiple output states for a diesel engine and generate real-time predictions with reasonable accuracy. As emission norms become more stringent, the need for fast and accurate models that enable analysis of system behavior have become an essential requirement for system development. The fast transient processes involved in the operation of a combustion engine make it difficult to develop accurate physics-based models for such systems. As an alternative to physics based models, we develop an operator-based regression model (DeepONet) to learn the relevant output states for a mean-value gas flow engine model using the engine operating conditions as input variables. We have adopted a mean-value model as a benchmark for comparison, simulated using Simulink. The developed approach necessitates using the initial conditions of the output states to predict the accurate sequence over the temporal domain. To this end, a sequence-to-sequence approach is embedded into the proposed framework. The accuracy of the model is evaluated by comparing the prediction output to ground truth generated from Simulink model. The maximum $\mathcal L_2$ relative error observed was approximately $6.5\%$. The sensitivity of the DeepONet model is evaluated under simulated noise conditions and the model shows relatively low sensitivity to noise. The uncertainty in model prediction is further assessed by using a mean ensemble approach. The worst-case error at the $(\mu + 2\sigma)$ boundary was found to be $12\%$. The proposed framework provides the ability to predict output states in real-time and enables data-driven learning of complex input-output operator mapping. As a result, this model can be applied during initial development stages, where accurate models may not be available.
Linear approximation of CPM signals for a reduced-complexity, multi-mode telemetry transmitter
Authors: Francesco Silino, Fabio Dell'Acqua, Pietro Savazzi, Anna Vizziello, Diego Biz, Federico Brega
Date: 2023-04-02 00:00:00

In space applications, hardware (HW) implementation is made more expensive not only by the levels of performance required, but also by complex and rigorous HW qualification tests. Reducing qualification cost and time is thus a key design requirement. In this paper, a new versatile transmitter is proposed for space telemetry, capable of soft-switching across different linear and continuous phase modulation schemes while maintaining the same hardware structure. This permits a single HW qualification to ``cover'' diverse uses of the same hardware, and thus avoid re-qualification in case of configuration changes. The envisaged solution foresees the use of a single filter, suitable not only for linear modulations such as M-QAM, but also for continuous phase modulation methods. At this stage, we focus on pulse code modulation/frequency modulation (PCM/FM), for which we propose a minimum mean square error (MMSE) algorithm. The proposed algorithm, which adds to the system flexibility and effectiveness, may use a single first filter based on Laurent decomposition for initialization, if needed. Performances are assessed using the mean square error (MSE) measure between the proposed MMSE-modulated signal and the completely modulated signal. Simulation results confirm that the proposed algorithm leads to MSE values that are lower than the case of Laurent decomposition using the first component only.
Performance Analysis and Low-Complexity Design for XL-MIMO with Near-Field Spatial Non-Stationarities
Authors: Kangda Zhi, Cunhua Pan, Hong Ren, Kok Keong Chai, Cheng-Xiang Wang, Robert Schober, Xiaohu You
Date: 2023-03-31 00:00:00

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.
An Evaluation of Link Performance Based on Rainfall Attenuation for a LEO Communication Satellite Constellation Over Africa
Authors: Osoro B. Ogutu
Date: 2023-03-31 00:00:00

In this paper, we apply the ITU-R P.618-8 model with data from the ITU-R P.837-7, Tropical Rain Measuring Mission (TRMM) and Global Precipitation Mission (GPM) satellite to determine the level of attenuation and available link margin for a LEO system such as Telesat. The specific and predicted attenuation for chosen six candidate ground stations (Abuja, Hartbeesthoek, Cairo, Longonot, Port Louis and Praia) is computed and results presented for 0.001%-1% unavailability time in a year. Setting a link margin of 0.36dB, the available link margin and the best candidate ground station for a LEO system such as Telesat is determined. The approach used can be implemented for other potential ground stations and LEO communication systems over Africa.
Model-Free Reconstruction of Capacity Degradation Trajectory of Lithium-Ion Batteries Using Early Cycle Data
Authors: Seongyoon Kim, Hangsoon Jung, Minho Lee, Yun Young Choi, Jung-Il Choi
Date: 2023-03-31 00:00:00
Tasks: Bayesian Optimization, Diagnostic, Trajectory Prediction

Early degradation prediction of lithium-ion batteries is crucial for ensuring safety and preventing unexpected failure in manufacturing and diagnostic processes. Long-term capacity trajectory predictions can fail due to cumulative errors and noise. To address this issue, this study proposes a data-centric method that uses early single-cycle data to predict the capacity degradation trajectory of lithium-ion cells. The method involves predicting a few knots at specific retention levels using a deep learning-based model and interpolating them to reconstruct the trajectory. Two approaches are used to identify the retention levels of two to four knots: uniformly dividing the retention up to the end of life and finding optimal locations using Bayesian optimization. The proposed model is validated with experimental data from 169 cells using five-fold cross-validation. The results show that mean absolute percentage errors in trajectory prediction are less than 1.60% for all cases of knots. By predicting only the cycle numbers of at least two knots based on early single-cycle charge and discharge data, the model can directly estimate the overall capacity degradation trajectory. Further experiments suggest using three-cycle input data to achieve robust and efficient predictions, even in the presence of noise. The method is then applied to predict various shapes of capacity degradation patterns using additional experimental data from 82 cells. The study demonstrates that collecting only the cycle information of a few knots during model training and a few early cycle data points for predictions is sufficient for predicting capacity degradation. This can help establish appropriate warranties or replacement cycles in battery manufacturing and diagnosis processes.
HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation
Authors: Haozhen Li, Xinyu Gu, Boyuan Zhang, Dongliang Li, Zhenyu Liu, Lin Zhang
Date: 2023-03-30 00:00:00
Tasks: Domain Adaptation, Transfer Learning

Deep Learning (DL)-based channel state information (CSI) feedback is a promising technique for the transmitter to accurately acquire the CSI of massive multiple-input multiple-output (MIMO) systems. As critical concerns about DL-based physical layer applications, the intra-domain generalizability affected by dataset bias and inter-domain robustness in data drift remain challenging. Therefore, we build on a Hybrid Complex-Valued Lightweight framework, namely the HybridCVLNet, capable of overcoming the dataset bias with regularized hybrid structure and codeword. Meanwhile, a corresponding transductive-based hybrid domain adaptation scheme is proposed to tackle the inter-domain data drift. The experiment verifies that HybridCVLNet achieves stable generalizability and performance gain over the state-of-the-art (SOTA) feedback schemes in an intra-domain heterogeneous dataset. In addition, its transductive-based hybrid domain adaptation scheme is more efficient and superior to the inductive-based transfer learning methods under two inter-domain online re-optimization settings.
A high-efficiency model indicating the role of inhibition in the resilience of neuronal networks to damage resulting from traumatic injury
Authors: Brian L. Frost, Stanislav M. Mintchev
Date: 2023-04-01 00:00:00
Tasks: Physical Simulations

Recent investigations of traumatic brain injuries have shown that these injuries can result in conformational changes at the level of individual neurons in the cerebral cortex. Focal axonal swelling is one consequence of such injuries and leads to a variable width along the cell axon. Simulations of the electrical properties of axons impacted in such a way show that this damage may have a nonlinear deleterious effect on spike-encoded signal transmission. The computational cost of these simulations complicates the investigation of the effects of such damage at a network level. We have developed an efficient algorithm that faithfully reproduces the spike train filtering properties seen in physical simulations. We use this algorithm to explore the impact of focal axonal swelling on small networks of integrate and fire neurons. We explore also the effects of architecture modifications to networks impacted in this manner. In all tested networks, our results indicate that the addition of presynaptic inhibitory neurons either increases or leaves unchanged the fidelity of the network's processing properties with respect to this damage.
Rough Randomness and its Application
Authors: Mani A
Date: 2023-03-21 00:00:00
Tasks: Explainable artificial intelligence

A number of generalizations of stochastic and information-theoretic randomness are known in the literature. However, they are not compatible with handling meaning in vague and dynamic contexts of rough reasoning (and therefore explainable artificial intelligence and machine learning). In this research, new concepts of rough randomness that are neither stochastic nor based on properties of strings are introduced by the present author. Her concepts are intended to capture a wide variety of rough processes (applicable to both static and dynamic data), construct related models, and explore the validity of other machine learning algorithms. The last mentioned is restricted to soft/hard clustering algorithms in this paper. Two new computationally efficient algebraically-justified algorithms for soft and hard cluster validation that involve rough random functions are additionally proposed in this research. A class of rough random functions termed large-minded reasoners have a central role in these.
Learning fragment self-attention embeddings for image-text matching
Authors: Yiling Wu, Shuhui Wang, Guoli Song, Qingming Huang
Date: 2019-10-01 00:00:00
Proceeding: ACMMM 2019 10
Tasks: Image-text matching, Sentence, Text Matching

In image-text matching task, the key to good matching quality is to capture the rich contextual dependencies between fragments of image and text. However, previous works either simply aggregate the similarity of all possible pairs of image regions and words, or take multi-step cross attention to attend to image regions and words with each other as context, which requires exhaustive similarity computation between all image region and word pairs. In this paper, we propose Self-Attention Embeddings (SAEM) to exploit fragment relations in images or texts by self-attention mechanism, and aggregate fragment information into visual and textual embeddings. Specifically, SAEM extracts salient image regions based on bottom-up attention, and takes WordPiece tokens as sentence fragments. The self-attention layers are built to model subtle and fine-grained fragment relation in image and text respectively, which consists of multi-head self-attention sub-layer and position-wise feed-forward network sub-layer. Consequently, the fragment self-attention mechanism can discover the fragment relations and identify the semantically salient regions in images or words in sentences, and capture their interaction more accurately. By simultaneously exploiting the fine-grained fragment relation in both visual and textual modalities, our method produces more semantically consistent embeddings for representing images and texts, and demonstrates promising image-text matching accuracy and high efficiency on Flickr30K and MSCOCO datasets.
Improving Resolution and Resolvability of Single Particle CryoEM using Gaussian Mixture Models
Authors: Muyuan Chen, Michael F. Schmid, Wah Chiu
Date: 2023-03-31 00:00:00

Cryogenic electron microscopy is widely used in structural biology, but its resolution is often limited by the dynamics of the macromolecule. Here, we developed a refinement protocol based on Gaussian mixture models that integrates particle orientation and conformation estimation, and improves the alignment for flexible domains of protein structures. We demonstrated this protocol on multiple datasets, resulting in improved resolution and resolvability, locally and globally, by visual and quantitative measures.
Investigation of In Vitro Apocarotenoid Expression in Perianth of Saffron (Crocus sativus L.) Under Different Soil EC
Authors: Mandana Mirbakhsh, Zahra Zahed, Sepideh Mashayekhi, Monire Jafari
Date: 2023-04-03 00:00:00

Crocus sativus is a triploid sterile plant with red stigmas belonging to the family of Iridaceae, and sub-family Crocoideae. Crocin, picrocrocin, and safranal are three major carotenoid derivatives that are responsible for the color, taste, and specific aroma of Crocus. Saffron flowers are harvested manually and used as spice, dye, or medicinal applications. The natural propagation rate of most geophytes including saffron is relatively low. An in vitro multiplication technique like micropropagation has been used for the propagation of saffron. To understand the efficiency of this alternative and study the molecular basis of apocarotenoid biosynthesis/accumulation, the RT-PCR method was performed on perianth explants that were cultured on MS medium to observe the level of expression of zeaxanthin cleavage dioxygenase (CsZCD) gene during stigma development, and also the impact of soil EC on its expression. The present study was conducted at Plant molecular and physiology Lab, Alzahra University, Tehran, Iran during 2011-2013. Stigma-like structures (SLSs) on calli were collected from immature perianth explants from floral buds of corms that were collected from Ghaen city, and compared to (Torbat-e Haidariye, Mardabad, and Shahroud cities) for investigating the impact of different soil EC on CsZCD expression. The results indicated that the CsZCD gene was highly expressed in fully developed red SLSs in perianth of cultured samples of Shahroud with the highest salinity. In this research, a close relationship between soil EC and second metabolites regulation is studied. Overall, these results will pave the way for understanding the molecular basis of apocarotenoid biosynthesis and other aspects of stigma development in C. sativus.
Resistance Management for Cancer: Lessons from Farmers
Authors: Sareh Seyedi, Valerie K. Harris, Stefania E. Kapsetaki, Daniel Saha, Zachary Compton, Rezvan Yousefi, Alexander May, Efe Fakir, Amy M. Boddy, Marco Gerlinger, Christina Wu, Lida Mina, Silvie Huijben, Dawn H. Gouge, Luis Cisneros, Peter C. Ellsworth, Carlo C. Maley
Date: 2023-04-01 00:00:00
Tasks: Management

One of the main reasons we have not been able to cure cancers is that drugs select for drug-resistant cancer cells. Pest managers face similar challenges with pesticides selecting for pesticide-resistant organisms. Lessons in pest management have led to four heuristics that can be translated to controlling cancers 1. limit use (of chemical controls or modes of action to the lowest practical levels) 2. diversify use (of modes of action largely through rotations of chemical controls) 3. partition chemistry (modes of action through space and time, which in effect is a refuge management strategy) and 4. include non-chemical methods. These principles are general to all cancers and cancer drugs, and thus should be employed to improve oncology. We review the parallel difficulties in controlling the evolution of drug resistance in pests and cancer cells, and describe the results of single- and multi-drug strategies in agriculture and oncology. We dissect the methods that pest managers use to prevent the evolution of pesticide resistance, showing how integrated pest management inspired the development of adaptive therapy in oncology to stabilize tumor size, and increase progression-free survival and quality of life of patients. Finally, we demonstrate these principles in a proposal for clinical trials in colorectal cancer.