pwc-archive/papers-with-abstracts huggingface dataset:
Dual-Polarized IRS-Assisted MIMO Network
Authors: Muteen Munawar, Kyungchun Lee
Date: 2023-07-11 00:00:00

This study considers a dual-polarized intelligent reflecting surface (DP-IRS)-assisted multiple-input multiple-output (MIMO) single-user wireless communication system. The transmitter and receiver are equipped with DP antennas, and each antenna features a separate phase shifter for each polarization. We attempt to maximize the system's spectral efficiency (SE) by optimizing the operations of the reflecting elements at the DP-IRS, precoder/combiner at the transmitter/receiver, and vertical/horizontal phase shifters at the DP antennas. To address this problem, we propose a three-step alternating optimization (AO) algorithm based on the semi-definite relaxation method. Next, we consider asymptotically low/high signal-to-noise ratio (SNR) regimes and propose low-complexity algorithms. In particular, for the low-SNR regime, we derive computationally low-cost closed-form solutions. According to the obtained numerical results, the proposed algorithm outperforms the various benchmark schemes. Specifically, our main algorithm exhibits a 65.6 \% increase in the SE performance compared to random operations. In addition, we compare the SE performance of DP-IRS with that of simple IRS (S-IRS). For \(N = 50\), DP-IRS achieves 24.8 \%, 28.2 \%, and 30.3 \% improvements in SE for \({4} \times {4}\), \({8} \times {8}\), and \({16} \times {16}\) MIMO, respectively, compared to S-IRS.
Thin-Film Lithium Niobate Acoustic Resonator with High Q of 237 and k2 of 5.1% at 50.74 GHz
Authors: Jack Kramer, Vakhtang Chulukhadze, Kenny Huynh, Omar Barrera, Michael Liao, Sinwoo Cho, Lezli Matto, Mark S. Goorsky, Ruochen Lu
Date: 2023-07-11 00:00:00

This work reports a 50.74 GHz lithium niobate (LiNbO3) acoustic resonator with a high quality factor (Q) of 237 and an electromechanical coupling (k2) of 5.17% resulting in a figure of merit (FoM, Q x k2) of 12.2. The LiNbO3 resonator employs a novel bilayer periodically poled piezoelectric film (P3F) 128 Y-cut LiNbO3 on amorphous silicon (a-Si) on sapphire stack to achieve low losses and high coupling at millimeter wave (mm-wave). The device also shows a Q of 159, k2 of 65.06%, and FoM of 103.4 for the 16.99 GHz tone. This result shows promising prospects of P3F LiNbO3 towards mm-wave front-end filters.
UAV Trajectory Optimization for Directional THz Links Using Deep Reinforcement Learning
Authors: Mohammad Taghi Dabiri, Mazen Hasna
Date: 2023-07-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning

As an alternative solution for quick disaster recovery of backhaul/fronthaul links, in this paper, a dynamic unmanned aerial vehicles (UAV)-assisted heterogeneous (HetNet) network equipped with directional terahertz (THz) antennas is studied to solve the problem of transferring traffic of distributed small cells. To this end, we first characterize a detailed three-dimensional modeling of the dynamic UAV-assisted HetNet, and then, we formulate the problem for UAV trajectory to minimize the maximum outage probability of directional THz links. Then, using deep reinforcement learning (DRL) method, we propose an efficient algorithm to learn the optimal trajectory. Finally, using simulations, we investigate the performance of the proposed DRL-based trajectory method.
Reaction Engineering and Comparison of Electroenzymatic and Enzymatic ATP Regeneration Systems
Authors: Regine Siedentop, Tobias Prenzel, Siegfried R. Waldvogel, Katrin Rosenthal, Stephan Lütz
Date: 2023-07-12 00:00:00

Adenosine-5'-triphosphate (ATP) plays a crucial role in many biocatalytic reactions and its regeneration can influence the performance of a related enzymatic reaction significantly. Here, we established an electrochemically coupled ATP regeneration by pyruvate oxidase and acetate kinase (ACK) for the phosphorylation of mevalonate catalyzed by mevalonate kinase. A yield of 84% for the product mevalonate phosphate was reached and a total turnover number for ADP of 68. These metrics are promising for the development of an economic feasible bioprocess and surpass many other enzymatic ATP regeneration systems. A comparison was made to polyphosphate kinases (PPKs), ACK, pyruvate kinase, and creatine kinase in terms of the phosphate donor properties and biocatalytic metrics of exemplary reactions. Furthermore, our system was expanded by a PPK that enables the phosphorylation of AMP, which can broaden the spectrum of applications even further.
NLP Meets RNA: Unsupervised Embedding Learning for Ribozymes with Word2Vec
Authors: Andrew Kean Gao
Date: 2023-07-08 00:00:00

Ribozymes, RNA molecules with distinct 3D structures and catalytic activity, have widespread applications in synthetic biology and therapeutics. However, relatively little research has focused on leveraging deep learning to enhance our understanding of ribozymes. This study implements Word2Vec, an unsupervised learning technique for natural language processing, to learn ribozyme embeddings. Ribo2Vec was trained on over 9,000 diverse ribozymes, learning to map sequences to 128 and 256-dimensional vector spaces. Using Ribo2Vec, sequence embeddings for five classes of ribozymes (hatchet, pistol, hairpin, hovlinc, and twister sister) were calculated. Principal component analysis demonstrated the ability of these embeddings to distinguish between ribozyme classes. Furthermore, a simple SVM classifier trained on ribozyme embeddings showed promising results in accurately classifying ribozyme types. Our results suggest that the embedding vectors contained meaningful information about ribozymes. Interestingly, 256-dimensional embeddings behaved similarly to 128-dimensional embeddings, suggesting that a lower dimension vector space is generally sufficient to capture ribozyme features. This approach demonstrates the potential of Word2Vec for bioinformatics, opening new avenues for ribozyme research. Future research includes using a Transformer-based method to learn RNA embeddings, which can capture long-range interactions between nucleotides.
Coupled environmental and demographic fluctuations shape the evolution of cooperative antimicrobial resistance
Authors: Lluís Hernández-Navarro, Matthew Asker, Alastair M. Rucklidge, Mauro Mobilia
Date: 2023-07-12 00:00:00

There is a pressing need to better understand how microbial populations respond to antimicrobial drugs, and to find mechanisms to possibly eradicate antimicrobial-resistant cells. The inactivation of antimicrobials by resistant microbes can often be viewed as a cooperative behavior leading to the coexistence of resistant and sensitive cells in large populations and static environments. This picture is however greatly altered by the fluctuations arising in volatile environments, in which microbial communities commonly evolve. Here, we study the eco-evolutionary dynamics of a population consisting of an antimicrobial resistant strain and microbes sensitive to antimicrobial drugs in a time-fluctuating environment, modeled by a carrying capacity randomly switching between states of abundance and scarcity. We assume that antimicrobial resistance is a shared public good when the number of resistant cells exceeds a certain threshold. Eco-evolutionary dynamics is thus characterised by demographic noise (birth and death events) coupled to environmental fluctuations which can cause population bottlenecks. By combining analytical and computational means, we determine the environmental conditions for the long-lived coexistence and fixation of both strains, and characterise a fluctuation-driven antimicrobial resistance eradication mechanism, where resistant microbes experience bottlenecks leading to extinction. We also discuss the possible applications of our findings to laboratory-controlled experiments.
Forward hysteresis and Hopf bifurcation in an NPZD model with application to harmful algal blooms
Authors: Joshua C. Macdonald, Hayriye Gulbudak
Date: 2023-07-12 00:00:00

Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) models, describing the interactions between phytoplankton, zooplankton systems, and their ecosystem, are used to predict their ecological and evolutionary population dynamics. These organisms form the base two trophic levels of aquatic ecosystems. Hence understanding their population dynamics and how disturbances can affect these systems is crucial. Here, starting from a base NPZ modeling framework, we incorporate the harmful effects of phytoplankton overpopulation on zooplankton - representing a crucial next step in harmful algal bloom (HAB) modeling - and split the nutrient compartment to formulate an NPZD model. We then mathematically analyze the NPZ system upon which this new model is based, including local and global stability of equilibria, Hopf bifurcation condition, and forward hysteresis, where the bi-stability occurs with multiple attractors. Finally, we extend the threshold analysis to the NPZD model, which displays both forward hysteresis with bi-stability and Hopf bifurcation under different parameter regimes, and examine ecological implications after incorporating seasonality and ecological disturbances. Ultimately, we quantify ecosystem health in terms of the relative values of the robust persistence thresholds for phytoplankton and zooplankton and find (i) ecosystems sufficiently favoring phytoplankton, as quantified by the relative values of the plankton persistence numbers, are vulnerable to both HABs and (local) zooplankton extinction (ii) even healthy ecosystems are extremely sensitive to nutrient depletion over relatively short time scales.
Mathematical Modeling Insights into Improving CAR T cell Therapy for Solid Tumors: Antigen Heterogeneity and Bystander Effects
Authors: Erdi Kara, T. L. Jackson, Chartese Jones, Reginald L. McGee II, Rockford Sison
Date: 2023-07-10 00:00:00

As an adoptive cellular therapy, Chimeric Antigen Receptor T-cell (CAR T-cell) therapy has shown remarkable success in hematological malignancies, but only limited efficacy against solid tumors. Compared with blood cancers, solid tumors present a unique set of challenges that ultimately neutralize the function of CAR T-cells. One such barrier is antigen heterogeneity - variability in the expression of the antigen on tumor cells. Success of CAR T-cell therapy in solid tumors is unlikely unless almost all the tumor cells express the specific antigen that CAR T-cells target. A critical question for solving the heterogeneity problem is whether CAR T therapy induces bystander effects, such as antigen spreading. Antigen spreading occurs when CAR T-cells activate other endogenous antitumor CD8 T cells against antigens that were not originally targeted. In this work, we develop a mathematical model of CAR T-cell therapy for solid tumors that takes into consideration both antigen heterogeneity and bystander effects. Our model is based on in vivo treatment data that includes a mixture of target antigen-positive and target antigen-negative tumor cells. We use our model to simulate large cohorts of virtual patients to gain a better understanding of the relationship between bystander killing. We also investigate several strategies for enhancing the bystander effect and thus increasing the overall efficacy of CAR T-cell therapy for solid tumor.
Systemic risk indicator based on implied and realized volatility
Authors: Paweł Sakowski, Rafał Sieradzki, Robert Ślepaczuk
Date: 2023-07-10 00:00:00
Tasks: valid

We propose a new measure of systemic risk to analyze the impact of the major financial market turmoils in the stock markets from 2000 to 2023 in the USA, Europe, Brazil, and Japan. Our Implied Volatility Realized Volatility Systemic Risk Indicator (IVRVSRI) shows that the reaction of stock markets varies across different geographical locations and the persistence of the shocks depends on the historical volatility and long-term average volatility level in a given market. The methodology applied is based on the logic that the simpler is always better than the more complex if it leads to the same results. Such an approach significantly limits model risk and substantially decreases computational burden. Robustness checks show that IVRVSRI is a precise and valid measure of the current systemic risk in the stock markets. Moreover, it can be used for other types of assets and high-frequency data. The forecasting ability of various SRIs (including CATFIN, CISS, IVRVSRI, SRISK, and Cleveland FED) with regard to weekly returns of S&P 500 index is evaluated based on the simple linear, quasi-quantile, and quantile regressions. We show that IVRVSRI has the strongest predicting power among them.
Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies
Authors: Tom Liu, Stephen Roberts, Stefan Zohren
Date: 2023-07-07 00:00:00
Tasks: Time Series, Variable Selection

We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time series (TS) and cross sectional (CS) features directly from daily price returns. This removes the need for handcrafted features, and allows the model to learn from TS and CS information simultaneously. DINs benefit from a fully data-driven approach to feature extraction, whilst avoiding overfitting. Extending prior work on Deep Momentum Networks, DIN models directly output position sizes that optimise Sharpe ratio, but for the entire portfolio instead of individual assets. We propose a novel loss term to balance turnover regularisation against increased systemic risk from high correlation to the overall market. Using futures data, we show that DIN models outperform traditional TS and CS benchmarks, are robust to a range of transaction costs and perform consistently across random seeds. To balance the general nature of DIN models, we provide examples of how attention and Variable Selection Networks can aid the interpretability of investment decisions. These model-specific methods are particularly useful when the dimensionality of the input is high and variable importance fluctuates dynamically over time. Finally, we compare the performance of DIN models on other asset classes, and show how the space of potential features can be customised.
Towards Environmentally Equitable AI via Geographical Load Balancing
Authors: Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren
Date: 2023-06-20 00:00:00

Fueled by the soaring popularity of large language and foundation models, the accelerated growth of artificial intelligence (AI) models' enormous environmental footprint has come under increased scrutiny. While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity -- the fact that AI's environmental footprint can be disproportionately higher in certain regions than in others -- has emerged, raising social-ecological justice concerns. This paper takes a first step toward addressing AI's environmental inequity by balancing its regional negative environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (GLB) to explicitly address AI's environmental impacts on the most disadvantaged regions. We run trace-based simulations by considering a set of 10 geographically-distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.
Adaptive Graph Convolution Networks for Traffic Flow Forecasting
Authors: Zhengdao Li, Wei Li, Kai Hwang
Date: 2023-07-07 00:00:00

Traffic flow forecasting is a highly challenging task due to the dynamic spatial-temporal road conditions. Graph neural networks (GNN) has been widely applied in this task. However, most of these GNNs ignore the effects of time-varying road conditions due to the fixed range of the convolution receptive field. In this paper, we propose a novel Adaptive Graph Convolution Networks (AGC-net) to address this issue in GNN. The AGC-net is constructed by the Adaptive Graph Convolution (AGC) based on a novel context attention mechanism, which consists of a set of graph wavelets with various learnable scales. The AGC transforms the spatial graph representations into time-sensitive features considering the temporal context. Moreover, a shifted graph convolution kernel is designed to enhance the AGC, which attempts to correct the deviations caused by inaccurate topology. Experimental results on two public traffic datasets demonstrate the effectiveness of the AGC-net\footnote{Code is available at: https://github.com/zhengdaoli/AGC-net} which outperforms other baseline models significantly.
Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing
Authors: Marc Duquesnoy, Chaoyue Liu, Vishank Kumar, Elixabete Ayerbe, Alejandro A. Franco
Date: 2023-07-07 00:00:00

The optimization of the electrode manufacturing process is important for upscaling the application of Lithium Ion Batteries (LIBs) to cater for growing energy demand. In particular, LIB manufacturing is very important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery application conditions by proposing a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. This ML pipeline allows the inverse design of the process parameters to adopt in order to manufacture electrodes for energy or power applications. The latter work is an analogy to our previous work that supported the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical pseudo-two-dimensional model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic ML model to implement fast bi-objective optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.
Importance of equivariant and invariant symmetries for fluid flow modeling
Authors: Varun Shankar, Shivam Barwey, Zico Kolter, Romit Maulik, Venkatasubramanian Viswanathan
Date: 2023-05-03 00:00:00

Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics. In tandem, geometric deep learning principles have informed the development of equivariant architectures respecting underlying physical symmetries. However, the effect of rotational equivariance in modeling fluids remains unclear. We build a multi-scale equivariant GNN to forecast fluid flow and study the effect of modeling invariant and non-invariant representations of the flow state. We evaluate the model performance of several equivariant and non-equivariant architectures on predicting the evolution of two fluid flows, flow around a cylinder and buoyancy-driven shear flow, to understand the effect of equivariance and invariance on data-driven modeling approaches. Our results show that modeling invariant quantities produces more accurate long-term predictions and that these invariant quantities may be learned from the velocity field using a data-driven encoder.
LimSim: A Long-term Interactive Multi-scenario Traffic Simulator
Authors: Licheng Wen, Daocheng Fu, Song Mao, Pinlong Cai, Min Dou, Yikang Li, Yu Qiao
Date: 2023-07-13 00:00:00
Tasks: Autonomous Driving

With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing. Existing simulation systems suffer from a lack of support for different types of scenarios, and the vehicle models used in these systems are too simplistic. Thus, such systems fail to represent driving styles and multi-vehicle interactions, and struggle to handle corner cases in the dataset. In this paper, we propose LimSim, the Long-term Interactive Multi-scenario traffic Simulator, which aims to provide a long-term continuous simulation capability under the urban road network. LimSim can simulate fine-grained dynamic scenarios and focus on the diverse interactions between multiple vehicles in the traffic flow. This paper provides a detailed introduction to the framework and features of the LimSim, and demonstrates its performance through case studies and experiments. LimSim is now open source on GitHub: https://www.github.com/PJLab-ADG/LimSim .
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
Authors: Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Wei Wei, Tingbo Hou, Yael Pritch, Neal Wadhwa, Michael Rubinstein, Kfir Aberman
Date: 2023-07-13 00:00:00
Proceeding: CVPR 2024 1
Tasks: Diffusion Personalization, Diffusion Personalization Tuning Free, Diversity, GPU

Personalization has emerged as a prominent aspect within the field of generative AI, enabling the synthesis of individuals in diverse contexts and styles, while retaining high-fidelity to their identities. However, the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment, and storing a personalized model per subject can be demanding in terms of storage capacity. To overcome these challenges, we propose HyperDreamBooth - a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications. Our method achieves personalization on faces in roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual Inversion, using as few as one reference image, with the same quality and style diversity as DreamBooth. Also our method yields a model that is 10,000x smaller than a normal DreamBooth model. Project page: https://hyperdreambooth.github.io