pwc-archive/papers-with-abstracts huggingface dataset:
Channel Sparsity Variation and Model-Based Analysis on 6, 26, and 132 GHz Measurements
Authors: Ximan Liu, Jianhua Zhang, Pan Tang, Lei Tian, Harsh Tataria, Shu Sun, Mansoor Shafi
Date: 2023-02-17 00:00:00

In this paper, the level of sparsity is examined at 6, 26, and 132 GHz carrier frequencies by conducting channel measurements in an indoor office environment. By using the Gini index (value between 0 and 1) as a metric for characterizing sparsity, we show that increasing carrier frequency leads to increased levels of sparsity. The measured channel impulse responses are used to derive a Third-Generation Partnership Project (3GPP)-style propagation model, used to calculate the Gini index for the comparison of the channel sparsity between the measurement and simulation based on the 3GPP model. Our results show that the mean value of the Gini index in measurement is over twice the value in simulation, implying that the 3GPP channel model does not capture the effects of sparsity in the delay domain as frequency increases. In addition, a new intra-cluster power allocation model based on measurements is proposed to characterize the effects of sparsity in the delay domain of the 3GPP channel model. The accuracy of the proposed model is analyzed using theoretical derivations and simulations. Using the derived intra-cluster power allocation model, the mean value of the Gini index is 0.97, while the spread of variability is restricted to 0.01, demonstrating that the proposed model is suitable for 3GPP-type channels. To our best knowledge, this paper is the first to perform measurements and analysis at three different frequencies for the evaluation of channel sparsity in the same environment.
Improved Recursive Algorithms for V-BLAST to Save Computations and Memories
Authors: Hufei Zhu, Yanyang Liang, Fuqin Deng, Genquan Chen, Jiaming Zhong
Date: 2023-02-17 00:00:00
Tasks: LEMMA

For vertical Bell Laboratories layered space-time architecture (V-BLAST), the original fast recursive algorithm was proposed, and then Improvements I-IV were introduced to further reduce the complexity. The existing recursive algorithm with speed advantage and that with memory saving incorporate Improvements I-IV and only Improvements III-IV into the original algorithm, respectively. This paper proposes Improvements V and VI to replace Improvements I and II, respectively. Instead of the lemma for inversion of partitioned matrix applied in Improvement I, Improvement V uses another lemma to speed up the matrix inversion step by 167%. Then the formulas adopted in our Improvement V are applied to deduce Improvement VI for interference cancellation, which saves memories without sacrificing speed compared to Improvement II. In the existing algorithm with speed advantage, the proposed algorithm I with speed advantage replaces Improvement I with Improvement V, while the proposed algorithm II with both speed advantage and memory saving replaces Improvements I and II with Improvements V and VI, respectively. Both proposed algorithms speed up the existing algorithm with speed advantage by 130%, while the proposed algorithm II achieves the speedup of 186% and saves about half memories, compared to the existing algorithm with memory saving.
Topological Signal Processing over Weighted Simplicial Complexes
Authors: Claudio Battiloro, Stefania Sardellitti, Sergio Barbarossa, Paolo Di Lorenzo
Date: 2023-02-16 00:00:00

Weighing the topological domain over which data can be represented and analysed is a key strategy in many signal processing and machine learning applications, enabling the extraction and exploitation of meaningful data features and their (higher order) relationships. Our goal in this paper is to present topological signal processing tools for weighted simplicial complexes. Specifically, relying on the weighted Hodge Laplacian theory, we propose efficient strategies to jointly learn the weights of the complex and the filters for the solenoidal, irrotational and harmonic components of the signals defined over the complex. We numerically asses the effectiveness of the proposed procedures.
A New 22 nm ULPLS Architecture to Detect 70 mV Minimum Input, Suitable for IOT Applications
Authors: Jehan Taraporewalla, Dipankar Saha
Date: 2023-02-16 00:00:00

Modern applications such as energy harvesting, signal monitoring in bio-medical sensing, portable point of care devices, etc. which involve state of the art mixed signal subsystems require robust ultra low power operation. Here in this work, a novel ultra low power level shifter (ULPLS) is proposed for sensing voltage signals in sub-threshold region. The proposed architecture is implemented in 22 nm technology using a dual power supply. The high and low supply voltages (VddH & VddL) are set as 0.8 V and 0.4 V respectively. The key design features of ULPLS include a current limiting PMOS diode, a voltage divider, and an enhanced pull up network. The ULPLS exhibits a low power dissipation of ~ 22.84 nW with a minimum ~ 70 mV detection of input signal. The robustness of the design has been examined via worst case and Monte Carlo analyses.
Computation and Privacy Protection for Satellite-Ground Digital Twin Networks
Authors: Yongkang Gong, Haipeng Yao Xiaonan Liu, Mehdi Bennis, Arumugam Nallanathan, Zhu Han
Date: 2023-02-16 00:00:00
Tasks: channel selection, CPU, Scheduling

Satellite-ground integrated digital twin networks (SGIDTNs) are regarded as innovative network architectures for reducing network congestion, enabling nearly-instant data mapping from the physical world to digital systems, and offering ubiquitous intelligence services to terrestrial users. However, the challenges, such as the pricing policy, the stochastic task arrivals, the time-varying satellite locations, mutual channel interference, and resource scheduling mechanisms between the users and cloud servers, are critical for improving quality of service in SGIDTNs. Hence, we establish a blockchain-aided Stackelberg game model for maximizing the pricing profits and network throughput in terms of minimizing overhead of privacy protection, thus performing computation offloading, decreasing channel interference, and improving privacy protection. Next, we propose a Lyapunov stability theory-based model-agnostic metalearning aided multi-agent deep federated reinforcement learning (MAML-MADFRL) framework for optimizing the CPU cycle frequency, channel selection, task-offloading decision, block size, and cloud server price, which facilitate the integration of communication, computation, and block resources. Subsequently, the extensive performance analyses show that the proposed MAMLMADFRL algorithm can strengthen the privacy protection via the transaction verification mechanism, approach the optimal time average penalty, and fulfill the long-term average queue size via lower computational complexity. Finally, our simulation results indicate that the proposed MAML-MADFRL learning framework is superior to the existing baseline methods in terms of network throughput, channel interference, cloud server profits, and privacy overhead.
Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation
Authors: Wenshuai Liu, Bin Li, Wancheng Xie, Yueyue Dai, Zesong Fei
Date: 2023-02-14 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Scheduling

With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.
A Countable-Type Branching Process Model for the Tug-of-War Cancer Cell Dynamics
Authors: Ren-Yi Wang, Marek Kimmel
Date: 2023-02-17 00:00:00

We consider a time-continuous Markov branching process of proliferating cells with a countable collection of types. Among-type transitions are inspired by the Tug-of-War process introduced in McFarland et al. as a mathematical model for competition of advantageous driver mutations and deleterious passenger mutations in cancer cells. We introduce a version of the model in which a driver mutation pushes the type of the cell $L$-units up, while a passenger mutation pulls it $1$-unit down. The distribution of time to divisions depends on the type (fitness) of cell, which is an integer. The extinction probability given any initial cell type is strictly less than $1$, which allows us to investigate the transition between types (type transition) in an infinitely long cell lineage of cells. The analysis leads to the result that under driver dominance, the type transition process escapes to infinity, while under passenger dominance, it leads to a limit distribution. Implications in cancer cell dynamics and population genetics are discussed.
Index analysis: an approach to understand signal transduction with application to the EGFR signalling pathway
Authors: Jane Knöchel, Charlotte Kloft, Wilhelm Huisinga
Date: 2023-02-17 00:00:00
Tasks: Drug Discovery

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. We envision that index analysis will be beneficial in comparing different model scenarios (e.g., healthy and diseased conditions), in designing more informed model reduction approaches, and in translating large-scale systems biology models from early to late phase in drug discovery and development.
SPX, VIX and scale-invariant LSV\footnote{Local Stochastic Volatility}
Authors: Alexander Lipton, Adil Reghai
Date: 2023-02-17 00:00:00
Tasks: Management

Local Stochastic Volatility (LSV) models have been used for pricing and hedging derivatives positions for over twenty years. An enormous body of literature covers analytical and numerical techniques for calibrating the model to market data. However, the literature misses a potent approach commonly used in physics and works with absolute (dimensional) variables rather than with relative (non-dimensional) ones. While model parameters defined in absolute terms are counter-intuitive for trading desks and tend to be heavily time-dependent, relative parameters are intuitive and stable, making it easy to steer the model adequately and consistently with its Profit and Loss (PnL) explanation power. We propose a specification that first explores historical data and uses physically well-defined relative quantities to design the model. We then develop an efficient hybrid method to price derivatives under this specification. We also show how our method can be used for robust scenario generation purposes - an important risk management task vital for buy-side firms.\footnote{The authors would like to thank Prof. Marcos Lopez de Prado and Dr. Vincent Davy Zoonekynd for valuable comments.}
Tighter 'uniform bounds for Black-Scholes implied volatility' and the applications to root-finding
Authors: Jaehyuk Choi, Jeonggyu Huh, Nan Su
Date: 2023-02-17 00:00:00
Tasks: Math

Using the option delta systematically, we derive tighter lower and upper bounds of the Black-Scholes implied volatility than those in Tehranchi [SIAM J. Financ. Math. 7 (2016), 893-916]. As an application, we propose a Newton-Raphson algorithm on the log price that converges rapidly for all price ranges when using a new lower bound as an initial guess. Our new algorithm is a better alternative to the widely used naive Newton-Raphson algorithm, whose convergence is slow for extreme option prices.
A study on Non-Performing Assets Cases and Cryptocurrency in Japan
Authors: Burina Fujiwara
Date: 2023-02-15 00:00:00

The economic bubble bursting resulted in a large number of non-performing loans in Japanese financial institutions, which weakened their functions and prevented them from extending credit for normal economic activities. However, cryptocurrency operations are thriving in Japan. In this way, this paper focuses on non-performing assets and cryptocurrencies. The goal is to use literature analysis methods to summarise the development process, types of issuance, mechanisms, evaluation models, application scenarios, and trends in how cryptocurrencies are supervised.
Measuring distribution risk in discrete models
Authors: Roberto Fontana, Patrizia Semeraro
Date: 2023-02-17 00:00:00

Model risk measures consequences of choosing a model in a class of possible alternatives. We find analytical and simulated bounds for payoff functions on classes of plausible alternatives of a given discrete model. We measure the impact of choosing a risk-neutral measure on convex derivative pricing in incomplete markets. We find analytical bounds for prices of European and American options in the class of all risk-neutral measures, and we also find simulated bounds for given classes of perturbations of the minimal martingale equivalent measure.
Optimal management of DB pension fund under both underfunded and overfunded cases
Authors: Guohui Guan, Zongxia Liang, Yi Xia
Date: 2023-02-17 00:00:00
Tasks: Management

This paper investigates the optimal management of an aggregated defined benefit pension plan in a stochastic environment. The interest rate follows the Ornstein-Uhlenbeck model, the benefits follow the geometric Brownian motion while the contribution rate is determined by the spread method of fund amortization. The pension manager invests in the financial market with three assets: cash, bond and stock. Regardless of the initial status of the plan, we suppose that the pension fund may become underfunded or overfunded in the planning horizon. The optimization goal of the manager is to maximize the expected utility in the overfunded region minus the weighted solvency risk in the underfunded region. By introducing an auxiliary process and related equivalent optimization problems and using the martingale method, the optimal wealth process, optimal portfolio and efficient frontier are obtained under four cases (high tolerance towards solvency risk, low tolerance towards solvency risk, a specific lower bound, and high lower bound). Moreover, we also obtain the probabilities that the optimal terminal wealth falls in the overfunded and underfunded regions. At last, we present numerical analyses to illustrate the manager's economic behaviors.
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?
Authors: Mostafa R. Sarkandiz
Date: 2023-02-12 00:00:00
Tasks: Time Series, Time Series Analysis

Throughout the past year, Turkey's central bank policy to decrease the nominal interest rate has caused episodes of severe fluctuations in Turkish lira exchange rates. According to these conditions, the daily return of the USD/TRY have attracted the risk-taker investors' attention. Therefore, the uncertainty about the rates has pushed algorithmic traders toward finding the best forecasting model. While there is a growing tendency to employ sophisticated models to forecast financial time series, in most cases, simple models can provide more precise forecasts. To examine that claim, present study has utilized several models to predict daily exchange rates for a short horizon. Interestingly, the simple exponential smoothing model outperformed all other alternatives. Besides, in contrast to the initial inferences, the time series neither had structural break nor exhibited signs of the ARCH and leverage effects. Despite that behavior, there was undeniable evidence of a long-memory trend. That means the series tends to keep a movement, at least for a short period. Finally, the study concluded the simple models provide better forecasts for exchange rates than the complicated approaches.
Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images
Authors: Gabriel Jimenez, Anuradha Kar, Mehdi Ounissi, Léa Ingrassia, Susana Boluda, Benoît Delatour, Lev Stimmer, Daniel Racoceanu
Date: 2023-01-13 00:00:00
Tasks: Morphological Analysis, Segmentation, Semantic Segmentation, whole slide images

Quantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL) models such as UNet have been successfully used for automatic segmentation of histopathological whole slide images (WSI) of biological tissues. In this study, we propose a DL-based methodology for semantic segmentation of tau lesions (i.e., neuritic plaques) in WSI of postmortem patients with AD. The state of the art in semantic segmentation of neuritic plaques in human WSI is very limited. Our study proposes a baseline able to generate a significant advantage for morphological analysis of these tauopathies for further stratification of AD patients. Essential discussions concerning biomarkers (ALZ50 versus AT8 tau antibodies), the imaging modality (different slide scanner resolutions), and the challenge of weak annotations are addressed within this seminal study. The analysis of the impact of context in plaque segmentation is important to understand the role of the micro-environment for reliable tau protein segmentation. In addition, by integrating visual interpretability, we are able to explain how the network focuses on a region of interest (ROI), giving additional insights to pathologists. Finally, the release of a new expert-annotated database and the code (\url{https://github.com/aramis-lab/miccai2022-stratifiad.git}) will be helpful for the scientific community to accelerate the development of new pipelines for human WSI processing in AD.
Referential communication in heterogeneous communities of pre-trained visual deep networks
Authors: Matéo Mahaut, Francesca Franzon, Roberto Dessì, Marco Baroni
Date: 2023-02-04 00:00:00
Tasks: Self-Driving Cars

As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the surrounding world, despite their different architectures and training regimes. As a first step in this direction, we systematically explore the task of referential communication in a community of heterogeneous state-of-the-art pre-trained visual networks, showing that they can develop, in a self-supervised way, a shared protocol to refer to a target object among a set of candidates. This shared protocol can also be used, to some extent, to communicate about previously unseen object categories of different granularity. Moreover, a visual network that was not initially part of an existing community can learn the community's protocol with remarkable ease. Finally, we study, both qualitatively and quantitatively, the properties of the emergent protocol, providing some evidence that it is capturing high-level semantic features of objects.
Exploring External Knowledge for Accurate modeling of Visual and Language Problems
Authors: Xuewen Yang
Date: 2023-01-27 00:00:00
Tasks: Image Captioning, Machine Translation, Translation

The interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. The success can be partly attributed to the advancements of deep neural networks made in the sub-fields of AI such as Computer Vision (CV) and Natural Language Processing (NLP). The promising research area that this dissertation focuses on is visual and language understanding which involves many challenging tasks, i.e., classification, detection, segmentation, machine translation and captioning, etc. The state-of-the-art methods for solving these problems usually involves only two parts: source data and target labels, which is rather insufficient especially when the dataset is small. Meanwhile, many external tools or sources can provide extra useful information (external knowledge) that can help improve the performance of these methods. For example, a detection model has been applied to provide better object features than state-of-the-art ResNet for image captioning models. Inspired by this observation, we developed a methodology that we can first extract external knowledge and then integrate it with the original models. The external knowledge has to be extracted from the dataset, or can directly come from external, e.g., grammar rules or scene graphs. We apply this methodology to different AI tasks, including machine translation and image captioning and improve the original state-of-the-art models by a large margin.
SeeFar: Vehicle Speed Estimation and Flow Analysis from a Moving UAV
Authors: Mang Ning, Xiaoliang Ma, Yao Lu, Simone Calderara, Rita Cucchiara
Date: 2022-05-15 00:00:00
Proceeding: ICIAP 2022 5
Tasks: Vehicle Speed Estimation

Visual perception from drones has been largely investigated for Intelligent Traffic Monitoring System (ITMS) recently. In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone. SeeFar differs from previous works in three key ways: the speed estimation and flow analysis components are integrated into a unified framework; our method of predicting car speed has the least constraints while maintaining a high accuracy; our flow analysor is direction-aware and outlier-aware. Specifically, we design the speed estimator only using the camera imaging geometry, where the transformation between world space and image space is completed by the variable Ground Sampling Distance. Besides, previous papers do not evaluate their speed estimators at scale due to the difficulty of obtaining the ground truth, we therefore propose a simple yet efficient approach to estimate the true speeds of vehicles via the prior size of the road signs. We evaluate SeeFar on our ten videos that contain 929 vehicle samples. Experiments on these sequences demonstrate the effectiveness of SeeFar by achieving 98.0% accuracy in speed estimation and 99.1% accuracy in traffic volume prediction, respectively. The code is available at https://github.com/forever208/SeeFar