pwc-archive/papers-with-abstracts huggingface dataset:
Vapor Cloud Delayed-DPPM Modulation Technique for nonlinear Optoacoustic Communication
Authors: Muntasir Mahmud, Mohamed Younis, Md Shafiqul Islam, Fow-Sen Choa, Gary Carter
Date: 2022-08-29 00:00:00

The optoacoustic process can solve the longstanding challenge of wireless information transmission from an airborne unit to an underwater node (UWN). The nonlinear optoacoustic signal generated by proper laser parameters can propagate long distances in water. However, forming such a signal requires a high-power laser, and the buildup of a vapor cloud precludes the subsequent acoustic signal generation. Therefore, pursuing the traditional on-off keying (OOK) modulation technique will limit the data rate and power efficiency. In this paper, we analyze different modulation techniques and propose a vapor cloud delayed-differential pulse position modulation (VCD-DPPM) technique to improve the data rate and achieve high power efficiency for a single stationary laser transmitter. The symbol rate of VCD-DPPM is approximately 6.9 times and 1.69 times higher than OOK in our text communication simulation using a laser repetition rate of 10 kHz and 40 Hz, respectively. Furthermore, VCD-DPPM is 137% more power efficient than the OOK technique for both cases. We have generated different acoustic signal levels in laboratory conditions and simulated the bit error rate (BER) for different depths and positions of the UWN, while considering ambient underwater noises. Our results indicate that VCD-DPPM enables efficient data transmission.
Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing
Authors: Shuaifei Chen, Jiayi Zhang, Emil Björnson, Özlem Tuğfe Demir, Bo Ai
Date: 2022-08-29 00:00:00

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling.
Dynamic RF Beam Codebook Reduction for Cost-Efficient mmWave Full-Duplex Systems
Authors: Gee Yong Suk, Jongwoo Kwak, Jaeyoung Choi, Chan-Byoung Chae
Date: 2022-08-29 00:00:00

The recent attempts to realize full-duplex (FD) communications in millimeter wave (mmWave) systems have garnered a significant amount of interest for its potential. In this paper, we present a cost-efficient design of mmWave FD systems, where the system dynamically reduces the RF beam codebook in a computationally efficient manner, so that it is comprised of the RF beams that will prevent the Rx receive chain from saturating due to the self-interference (SI). The analog beamformer will suppress the SI to the level that the residual SI can be completely removed with digital SI cancellation, allowing the digital beamformer to concentrate on the desired channel, free of the SI. To reduce the computation required for the proposed method, we propose two sufficient conditions that prevent the Rx side saturations, which are practically tight. Through performance valuations conducted in realistically modeled mmWave FD scenarios, we demonstrate that the proposed design achieves comparable performance with the ideal FD and other benchmarks with significantly lower costs.
An AFDM-Based Integrated Sensing and Communications
Authors: Yuanhan Ni, Zulin Wang, Peng Yuan, Qin Huang
Date: 2022-08-29 00:00:00
Tasks: ISAC, parameter estimation

This paper considers an affine frequency division multiplexing (AFDM)-based integrated sensing and communications (ISAC) system, where the AFDM waveform is used to simultaneously carry communications information and sense targets. To realize AFDM-based sensing functionality, two parameter estimation methods are designed to process echoes in the time domain and the discrete affine Fourier transform (DAFT) domain, respectively. It allows us to decouple delay and Doppler shift in the fast time axis and can maintain good sensing performance even in large Doppler shift scenarios. Numerical results verify the effectiveness of our proposed AFDM-based system in high mobility scenarios.
An Energy Activity Dataset for Smart Homes
Authors: Chen Li
Date: 2022-08-29 00:00:00
Tasks: Diversity, Miscellaneous, Optical Character Recognition, Optical Character Recognition (OCR)

A smart home energy dataset that records miscellaneous energy consumption data is publicly offered. The proposed energy activity dataset (EAD) has a high data type diversity in contrast to existing load monitoring datasets. In EAD, a simple data point is labeled with the appliance, brand, and event information, whereas a complex data point has an extra application label. Several discoveries have been made on the energy consumption patterns of many appliances. Load curves of the appliances are measured when different events and applications are triggered and utilized. A revised longest-common-subsequence (LCS) similarity measurement algorithm is proposed to calculate energy dataset similarities. Thus, the data quality prior information becomes available before training machine learning models. In addition, a subsample convolutional neural network (SCNN) is put forward. It serves as a non-intrusive optical character recognition (OCR) approach to obtain energy data directly from monitors of power meters. The link for the EAD dataset is: https://drive.google.com/drive/folders/1zn0V6Q8eXXSKxKgcs8ZRValL5VEn3anD
Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise
Authors: Antos Cheeramban Varghese, Anamitra Pal, Gautam Dasarathy
Date: 2022-08-28 00:00:00
Tasks: parameter estimation

Accurate knowledge of transmission line parameters is essential for a variety of power system monitoring, protection, and control applications. The use of phasor measurement unit (PMU) data for transmission line parameter estimation (TLPE) is well-documented. However, existing literature on PMU-based TLPE implicitly assumes the measurement noise to be Gaussian. Recently, it has been shown that the noise in PMU measurements (especially in the current phasors) is better represented by Gaussian mixture models (GMMs), i.e., the noises are non-Gaussian. We present a novel approach for TLPE that can handle non-Gaussian noise in the PMU measurements. The measurement noise is expressed as a GMM, whose components are identified using the expectation-maximization (EM) algorithm. Subsequently, noise and parameter estimation is carried out by solving a maximum likelihood estimation problem iteratively until convergence. The superior performance of the proposed approach over traditional approaches such as least squares and total least squares as well as the more recently proposed minimum total error entropy approach is demonstrated by performing simulations using the IEEE 118-bus system as well as proprietary PMU data obtained from a U.S. power utility.
On the Correspondence and the Risk Contribution for Conditional Coherent and Deviation Risk Measures
Authors: Guangyan Jia, Mengjin Zhao
Date: 2022-08-29 00:00:00

We give an axiomatic framework for conditional generalized deviation measures. Under financially reasonable assumptions, we give the correspondence between conditional coherent risk measures and generalized deviation measures. Moreover, we establish the notion of continuous-time risk contribution for conditional coherent risk measures and generalized deviation measures. With the help of the correspondence between these two different types of risk measures, we give a microscopic interpretation of their risk contributions. Particularly, we show that the risk contributions of time-consistent risk measures are still time-consistent. We also demonstrate that the second element of the BSDE solution $(Y, Z)$ associated with $g$-expectation has the meaning of risk contribution.
Mapache: a flexible pipeline to map ancient DNA
Authors: Samuel Neuenschwander, Diana I. Cruz Dávalos, Lucas Anchieri, Bárbara Sousa da Mota, Davide Bozzi, Simone Rubinacci, Olivier Delaneau, Simon Rasmussen, Anna-Sapfo Malaspinas
Date: 2022-08-28 00:00:00

Summary: Mapping ancient DNA to a reference genome is challenging as it involves numerous steps, is time-consuming and has to be repeated within a study to assess the quality of extracts and libraries; as a result, the mapping needs to be automatized to handle large amounts of data in a reproducible way. We present mapache, a flexible, robust, and scalable pipeline to map, quantify and impute ancient and present-day DNA in a reproducible way. Mapache is implemented in the workflow manager Snakemake and is optimized for low-space consumption, allowing to efficiently (re)map large data sets such as reference panels and multiple extracts and libraries.
In vitro micropropagation and apocarotenoid gene expression in saffron
Authors: Mandana Mirbakhsh
Date: 2022-08-28 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Saffron (Crocus sativus L.) is a triploid, sterile, monocot plant belonging to the family Iridaceae, sub-family Crocoideae. C.sativus only blooms once a year and should be collected within a very short duration, the stigmas of Saffron flowers are harvested manually and subjected to desiccation then have been used as a spice. It has been also used as a drug to treat tumors, cancer, chronic uterine hemorrhage, insomnia, scarlet fever, smallpox, colds, and cardiovascular disorders. It has been shown that saffron is a protective agent against chromosomal damage. Saffron has been vegetatively propagated by corm, each mother corm produces 7-8 cormlets each year. The main colors of saffron, crocetin, and crocetin glycosides, and the main flavors, picrocrocin, Safranal is the main component of aroma and Its bitter taste is related to Glycosidepicrocrocin that is derived from the oxidative cleavage of the carotenoid, zeaxanthin.by zeaxanthin cleavage dioxygenase (ZCD). We investigated gene expression of ZCD in vitro by using tissue culture of perianth obtained from immature flora buds of Ghaen of Khorasan province, Iran, on MS medium supplemented by 10 mg/L NAA and BAP. RNA of each sample was extracted using RNx method; followed by RT-PCR techniques. The results indicated that ZCD was present in perianth of all cultured samples of mentioned areas. Investigation of this pathway which controls the saffron apocarotenoid pigments in perianth is important to produce saffron with high quality and quantity. The results showed that the expression of ZCD in perianth whether through mevalonic acid or non-mevalonic acid needs more investigation
Juggling too many balls at once: Qualitatively different effects when measuring priming and masking in single, dual, and triple tasks
Authors: Melanie Biafora, Thomas Schmidt
Date: 2022-08-29 00:00:00

Dissociation paradigms examine dissociations between indirect measures of prime processing and direct measures of prime awareness. It is debated whether direct measures should be objective or subjective, and whether these measures should be obtained on the same or separate trials. In two metacontrast experiments, we measured prime discrimination, PAS ratings, and response priming either separately or in multiple tasks. Single tasks show the fastest responses in priming and therefore most likely meet the assumption of feedforward processing as assumed under Rapid-Chase Theory. Similarly, dual tasks allow for a fast response activation by the prime; nevertheless, prolonged responses and slower errors occur more often. In contrast, triple tasks have a negative effect on response activation: responses are massively slowed and fast prime-locked errors are lost. Moreover, decreasing priming effects and prime identification performance result in a loss of a double dissociation. Here, a necessary condition for unconscious response priming, feedforward processing, is violated.
The Role of Valence and Meta-awareness in Mirror Self-recognition Using Hierarchical Active Inference
Authors: Jonathan Bauermeister, Pablo Lanillos
Date: 2022-08-28 00:00:00

The underlying processes that enable self-perception are crucial for understanding multisensory integration, body perception and action, and the development of the self. Previous computational models have overlooked an essential aspect: affective or emotional components cannot be uncoupled from the self-recognition process. Hence, here we propose a computational approach to study self-recognition that incorporates affect using state-of-the-art hierarchical active inference. We evaluated our model in a synthetic experiment inspired by the mirror self-recognition test, a benchmark for evaluating self-recognition in animals and humans alike. Results show that i) negative valence arises when the agent recognizes itself and learns something unexpected about its internal states. Furthermore, ii) the agent in the presence of strong prior expectations of a negative affective state will avoid the mirror altogether in anticipation of an undesired learning process. Both results are in line with current literature on human self-recognition.
Grid Cell Percolation
Authors: Yuri Dabaghian
Date: 2022-08-26 00:00:00

Grid cells play a principal role in enabling mammalian cognitive representations of ambient environments. The key property of these cells -- the regular arrangement of their firing fields -- is commonly viewed as means for establishing spatial scales or encoding specific locations. However, using grid cells' spiking outputs for deducing spatial orderliness proves to be a strenuous task, due to fairly irregular activation patterns triggered by the animal's sporadic visits to the grid fields. The following discussion addresses statistical mechanisms enabling emergent regularity of grid cell firing activity, from the perspective of percolation theory. In particular, it is shown that the range of neurophysiological parameters required for spiking percolation phenomena matches experimental data, which points at biological viability of the percolation approach and casts a new light on the role of grid cells in organizing the hippocampal map.
Nitrogen-induced hysteresis in grassland biodiversity: a theoretical test of litter-mediated mechanisms
Authors: Katherine Meyer, James Broda, Andrew Brettin, María Sánchez Muñiz, Sarah Gorman, Forest Isbell, Sarah E. Hobbie, Mary Lou Zeeman, Richard McGehee
Date: 2022-08-26 00:00:00
Tasks: Diversity, Management

The global rise in anthropogenic reactive nitrogen (N) and the negative impacts of N deposition on terrestrial plant diversity are well-documented. The R* theory of resource competition predicts reversible decreases in plant diversity in response to N loading. However, empirical evidence for the reversibility of N-induced biodiversity loss is mixed. In a long-term N-enrichment experiment in Minnesota, a low-diversity state that emerged during N addition has persisted for decades after additions ceased. Hypothesized mechanisms preventing recovery of biodiversity include nutrient recycling, insufficient external seed supply, and litter inhibition of plant growth. Here we present an ODE model that unifies these mechanisms, produces bistability at intermediate N inputs, and qualitatively matches the observed hysteresis at Cedar Creek. Key features of the model, including native species' growth advantage in low-N conditions and limitation by litter accumulation, generalize from Cedar Creek to North American grasslands. Our results suggest that effective biodiversity restoration in these systems may require management beyond reducing N inputs, such as burning, grazing, haying, and seed additions. By coupling resource competition with an additional inter-specific inhibitory process, the model also illustrates a general mechanism for bistability and hysteresis that may occur in multiple ecosystem types.
Pasture Intake Protects Against Commercial Diet-induced Lipopolysaccharide Production Facilitated by Gut Microbiota through Activating Intestinal Alkaline Phosphatase Enzyme in Meat Geese
Authors: Qasim Ali, Sen Ma, Umar Farooq, Jiakuan Niu, Fen Li, Muhammad Abaidullah, Boshuai Liu, Shaokai La, Defeng Li, Zhichang Wang, Hao Sun, Yalei Cui, Yinghua Shi
Date: 2022-08-29 00:00:00

In-house feeding system (IHF, a low dietary fiber source) may cause altered cecal microbiota composition and inflammatory responses in meat geese via increased endotoxemia (lipopolysaccharides) with reduced intestinal alkaline phosphatase (ALP) production. The effects of artificial pasture grazing system (AGF, a high dietary fiber source) on modulating gut microbiota architecture and gut barrier functions have not been investigated in meat geese. The intestinal ALP functions to regulate gut microbial homeostasis and barrier function appears to inhibit pro-inflammatory cytokines by reducing LPS-induced reactive oxygen species (ROS) production. The purpose of our study was to investigate whether this enzyme could play a critical role in attenuating ROS generation and then ROS facilitated NF-\k{appa}B pathway-induced systemic inflammation in meat geese. First, we assessed the impacts of IHF and AGF on gut microbial composition via 16 sRNA sequencing in meat geese. In the gut microbiota analysis, meat geese supplemented with pasture demonstrated a significant reduction in microbial richness and diversity compared to IHF meat geese demonstrating antimicrobial, antioxidation, and anti-inflammatory ability of AGF system. Second host markers analysis through protein expression of serum and cecal tissues and quantitative PCR of cecal tissues were evaluated. We confirmed a significant increase in intestinal ALP-induced Nrf2 signaling pathway representing LPS dephosphorylation mediated TLR4/MyD88 induced ROS reduction mechanisms in AGF meat geese. Further, the correlation analysis of top 44 host markers with gut microbiota shows that artificial pasture intake induced gut barrier functions via reducing ROS-mediated NF-\k{appa}B pathway-induced gut permeability, systemic inflammation, and aging phenotypes.
Modeling the Mitral Valve
Authors: Alexander D. Kaiser
Date: 2022-08-29 00:00:00
Tasks: Anatomy

This thesis is concerned with modeling and simulation of the mitral valve, one of the four valves in the human heart. The valve is composed of leaflets attached to a ring, the free edges of which are supported by a system of chordae, which themselves are anchored to muscles inside the heart. First, we examine valve anatomy and show the results of original dissections. These display the gross anatomy and information on fiber structure of the mitral valve. Next, we build a model valve following a design-based approach to elasticity. We incorporate information from the dissections to specify the fiber topology of this model. We assume the valve achieves mechanical equilibrium while supporting a static pressure load. The solution to the resulting differential equations determines the pressurized configuration of the valve model. To complete the model we then specify a constitutive law based on experimental stress-strain relations from the literature. Finally, using the immersed boundary method, we simulate the model valve in fluid in a computer test chamber. The aim of this work is to determine the basic principles and mechanisms underlying the anatomy and function of the mitral valve.
High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach
Authors: Kang Gao, Perukrishnen Vytelingum, Stephen Weston, Wayne Luk, Ce Guo
Date: 2022-08-29 00:00:00
Tasks: Time Series Analysis

This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, aiming at precipitating mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers.
Out-of-Model Adjustments of Variable Annuities
Authors: Zhiyi Shen
Date: 2022-08-26 00:00:00
Tasks: model

This paper studies the model risk of the Black-Scholes (BS) model in pricing and risk-managing variable annuities motivated by its wide usage in the insurance industry. Specifically, we derive a model-free decomposition of the no-arbitrage price of the variable annuity into the BS model price in conjunction with three out-of-model adjustment terms. This sheds light on all risk drivers behind the product, that is, spot price, realized volatility, future smile, and sub-optimal withdrawal. We further investigate the efficacy of the BS-based hedging strategy given the market diverges from the model assumptions. We disclose that the spot price risk can always be eliminated by the strategy and the hedger's cumulative P\&L exhibits gradual slippage and instantaneous leakage. We finally show that the pricing, risk and hedging models can be separated from each other in managing the risks of variable annuities.
PECAN: A Product-Quantized Content Addressable Memory Network
Authors: Jie Ran, Rui Lin, Jason Chun Lok Li, Jiajun Zhou, Ngai Wong
Date: 2022-08-13 00:00:00
Tasks: Quantization

A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ). This results in a natural implementation via content addressable memory (CAM), which transcends regular DNN layer operations and requires only simple table lookup. Two schemes are developed for the end-to-end PQ prototype training, namely, through angle- and distance-based similarities, which differ in their multiplicative and additive natures with different complexity-accuracy tradeoffs. Even more, the distance-based scheme constitutes a truly multiplier-free DNN solution. Experiments confirm the feasibility of such Product-Quantized Content Addressable Memory Network (PECAN), which has strong implication on hardware-efficient deployments especially for in-memory computing.