pwc-archive/papers-with-abstracts huggingface dataset:
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Authors: Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Ranković, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Herck, Christoph Völker, Logan Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, KJ Schmidt, Ian Foster, Andrew D. White, Ben Blaiszik
Date: 2023-06-09 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
A framework of windowed octonion linear canonical transform
Authors: Manish Kumar, Bhawna
Date: 2023-06-08 00:00:00

The uncertainty principle is a fundamental principle in theoretical physics, such as quantum mechanics and classical mechanics. It plays a prime role in signal processing, including optics, where a signal is to be analyzed simultaneously in both domains; for instance, in harmonic analysis, both time and frequency domains, and in quantum mechanics, both time and momentum. On the other hand, many mathematicians, physicists, and other related domain researchers have paid more attention to the octonion-related integral transforms in recent years. In this paper, we define important properties of the windowed octonion linear canonical transform (WOCLCT), such as inversion, linearity, parity, shifting, and the relationship between OCLCT and WOCLCT. Further, we derived sharp Pitt's and sharp Young-Hausdorff inequalities for 3D WOCLCT. We obtain the logarithmic uncertainty principle for the 3D WOCLCT. Furthermore, Heisenberg's and Donoho-Stark's uncertainty principles are derived for WOCLCT, and the potential applications of WOCLCT are also discussed.
Pattern Synthesizing of the Concentric Ring Arrays Using Recursive Least Square Method
Authors: Atefe Akbari-Bardaskan
Date: 2023-06-08 00:00:00

This letter presents a recursive technique to synthesize the array factor (AF) of a concentric ring array. In this method, first, the problem is modeled using the traditional least square method (LSM). In the second step, a recursive technique is applied to the defined problem. It is shown that for many practical ring arrays, only a few iteration number is sufficient to obtain a solution with high accuracy. To evaluate the performance and benefits of the proposed method, various concentric ring arrays with different array factor, including equi-ripple pattern, pattern with deep nulls, flat-top array factor, are examined. It is shown that the introduced procedure is a very noble candidate for this purpose.
Movement bias in asymmetric landscapes and its impact on population distribution and critical habitat size
Authors: Vivian Dornelas, Pablo de Castro, Justin M. Calabrese, William F. Fagan, Ricardo Martinez-Garcia
Date: 2023-06-10 00:00:00

Ecologists have long investigated how demographic and movement parameters determine the spatial distribution and critical habitat size of a population. However, most models oversimplify movement behavior, neglecting how landscape heterogeneity influences individual movement. We relax this assumption and introduce a reaction-advection-diffusion equation that describes population dynamics when individuals exhibit space-dependent movement bias toward preferred regions. Our model incorporates two types of these preferred regions: a high-quality habitat patch, termed `habitat', which is included to model avoidance of degraded habitats like deforested regions; and a preferred location, such as a chemoattractant source or a watering hole, that we allow to be asymmetrically located with respect to habitat edges. In this scenario, the critical habitat size depends on both the relative position of the preferred location and the movement bias intensities. When preferred locations are near habitat edges, the critical habitat size can decrease when diffusion increases, a phenomenon called the drift paradox. Also, ecological traps arise when the habitat overcrowds due to excessive attractiveness or the preferred location is near a low-quality region. Our results highlight the importance of species-specific movement behavior and habitat preference as drivers of population dynamics in fragmented landscapes and, therefore, in the design of protected areas.
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
Authors: Ziqiao Meng, Peilin Zhao, Yang Yu, Irwin King
Date: 2023-06-05 00:00:00
Tasks: Decoder, Drug Discovery, Prediction

Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of electrons, resulting in state-of-the-art top-1 accuracy, and enabling parallel sampling. However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule. This violation of the physical constraints of chemical reactions impairs model performance. In this work, we propose a new framework called that combines two doubly stochastic self-attention mappings to obtain electron redistribution predictions that follow both constraints. We further extend our solution to a general multi-head attention mechanism with augmented constraints. To achieve this, we apply Sinkhorn's algorithm to iteratively update self-attention mappings, which imposes doubly conservative constraints as additional informative priors on electron redistribution modeling. We theoretically demonstrate that our can simultaneously satisfy both rules, which the current decoder mechanism cannot do. Empirical results show that our approach consistently improves the predictive performance of non-autoregressive models and does not bring an unbearable additional computational cost.
Towards clinical translation of deep-learning based classification of DSA image sequences for stroke treatment
Authors: Timo Baumgärtner, Benjamin J. Mittmann, Till Malzacher, Johannes Roßkopf, Michael Braun, Bernd Schmitz, Alfred M. Franz
Date: 2023-05-23 00:00:00

In the event of stroke, a catheter-guided procedure (thrombectomy) is used to remove blood clots. Feasibility of machine learning based automatic classifications for thrombus detection on digital substraction angiography (DSA) sequences has been demonstrated. It was however not used live in the clinic, yet. We present an open-source tool for automatic thrombus classification and test it on three selected clinical cases regarding functionality and classification runtime. With our trained model all large vessel occlusions in the M1 segment were correctly classified. One small remaining M3 thrombus was not detected. Runtime was in the range from 1 to 10 seconds depending on the used hardware. We conclude that our open-source software tool enables clinical staff to classify DSA sequences in (close to) realtime and can be used for further studies in clinics.
Towards using utility data to quantify how investments would have increased the wind resilience of distribution systems
Authors: Arslan Ahmad, Ian Dobson
Date: 2023-06-10 00:00:00

We quantify resilience with metrics extracted from the historical outage data that is routinely recorded by many distribution utilities. The outage data is coordinated with wind data to relate average outage rates in an area to wind speed measured at a nearby weather station. A past investment in wind hardening would have reduced the outage rates, and the effect of this on metrics can be calculated by sampling a reduced number of the historical outages and recomputing the metrics. This quantifies the impact that the hardening would have had on customers. This is a tangible way to relate an investment in wind resilience to the benefits it would have had on the lived experience of customers that could help make the case for the investment to the public and regulators. We also quantify the impact of earlier or faster restoration on customer metrics and compare this to the impact of investment in hardening. Overall this is a new and straightforward approach to quantify resilience and justify resilience investments to stakeholders that is directly driven by utility data. The approach driven by data avoids complicated models or modeling assumptions.
Instrument-based estimation of full treatment effects with movers
Authors: Didier Nibbering, Matthijs Oosterveen
Date: 2023-06-12 00:00:00

The effect of the full treatment is a primary parameter of interest in policy evaluation, while often only the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into only a subset of treatment (movers). We show that movers violate the standard exclusion restriction, necessary conditions on the presence of movers are testable, and partial identification holds under a double exclusion restriction. We identify movers in four empirical applications and estimate informative bounds on the LAFTE in three of them.
Auctioning Corporate Bonds: A Uniform-Price under Investment Mandates
Authors: Labrini Zarpala
Date: 2023-06-12 00:00:00

This paper examines how risk and budget limits on investment mandates affect the bidding strategy in a uniform-price auction for issuing corporate bonds. I prove the existence of symmetric Bayesian Nash equilibrium and explore how the risk limits imposed on the mandate may mitigate severe underpricing, as the symmetric equilibrium's yield positively relates to the risk limit. Investment mandates with low-risk acceptance inversely affect the equilibrium bid. The equilibrium bid provides insights into the optimal mechanism for pricing corporate bonds conveying information about the bond's valuation, market power, and the number of bidders. These findings contribute to auction theory and have implications for empirical research in the corporate bond market.
Surname Order and Revaccination Intentions: The Effect of Mixed-Gender Lists on Gender Differences during the COVID-19 Pandemic
Authors: Eiji Yamamura, Yoshiro Tsutsui, Fumio Ohtake
Date: 2023-06-10 00:00:00

This study probes the effects of Japan's traditional alphabetical surname-based call system on students' experiences and long-term behavior. It reveals that early listed surnames enhance cognitive and non-cognitive skill development. The adoption of mixed-gender lists since the 1980s has amplified this effect, particularly for females. Furthermore, the study uncovers a strong correlation between childhood surname order and individuals' intention for COVID-19 revaccination, while changes in adulthood surnames do not exhibit the same influence. The implications for societal behaviors and policy are substantial and wide-ranging.
Investigation User Reviews FRO to Determine the Level of Customer Loyalty Model Shahrvand Chain Stores
Authors: Mohammad Heydari, Matineh Moghaddam, Khadijeh Gholami, Habibollah Danai
Date: 2023-06-09 00:00:00

In this study, focusing on organizations in a rapid-response component model (FRO), the relative importance of each one, from the point of view of customers and their impact on the purchase of Shahrvand chain stores determined to directors and managers of the shops, according to customer needs and their priorities in order to satisfy the customers and take steps to strengthen their competitiveness. For this purpose, all shahrvand chain stores in Tehran currently have 10 stores in different parts of Tehran that have been studied are that of the 10 branches; Five branches were selected. The sampling method is used in this study population with a confidence level of 95% and 8% error; 150 are more specifically typically 30 were studied in each branch. In this study, a standard questionnaire of 26 questions which is used FRO validity using Cronbach's alpha values of "0/95" is obtained. The results showed that each of the six factors on customer loyalty model FRO effective Shahrvand chain stores. The effect of each of the six Foundation FRO customer loyalty model shahrvand is different chain stores.
Video Decoding Energy Reduction Using Temporal-Domain Filtering
Authors: Christian Herglotz, Matthias Kränzler, Robert Ludwig, André Kaup
Date: 2023-06-12 00:00:00
Tasks: Sensitivity

In this paper, we study decoding energy reduction opportunities using temporal-domain filtering and subsampling methods. In particular, we study spatiotemporal filtering using a contrast sensitivity function and temporal downscaling, i.e., frame rate reduction. We apply these concepts as a pre-filtering to the video before compression and evaluate the bitrate, the decoding energy, and the visual quality with a dedicated metric targeting temporally down-scaled sequences. We find that decoding energy savings yield 35% when halving the frame rate and that spatiotemporal filtering can lead to up to 5% of additional savings, depending on the content.
Estimation of River Water Surface Elevation Using UAV Photogrammetry and Machine Learning
Authors: Radosław Szostak, Marcin Pietroń, Przemysław Wachniew, Mirosław Zimnoch, Paweł Ćwiąkała
Date: 2023-06-05 00:00:00

Unmanned aerial vehicle (UAV) photogrammetry allows for the creation of orthophotos and digital surface models (DSMs) of a terrain. However, DSMs of water bodies mapped with this technique reveal water surface distortions, preventing the use of photogrammetric data for accurate determination of water surface elevation (WSE). Firstly, we propose a new solution in which a convolutional neural network (CNN) is used as a WSE estimator from photogrammetric DSMs and orthophotos. Second, we improved the previously known "water-edge" method by filtering the outliers using a forward-backwards exponential weighted moving average. Further improvement in these two methods was achieved by performing a linear regression of the WSE values against chainage. The solutions estimate the uncertainty of the predictions. This is the first approach in which DL was used for this task. A brand new machine learning data set has been created. It was collected on a small lowland river in winter and summer conditions. It consists of 322 samples, each corresponding to a 10 by 10 meter area of the river channel and adjacent land. Each data set sample contains orthophoto and DSM arrays as input, along with a single ground-truth WSE value as output. The data set was supplemented with data collected by other researchers that compared the state-of-the-art methods for determining WSE using an UAV. The results of the DL solution were verified using k-fold cross-validation method. This provided an in-depth examination of the model's ability to perform on previously unseen data. The WSE RMSEs differ for each k-fold cross-validation subset and range from 1.7 cm up to 17.2 cm. The RMSE results of the improved "water-edge" method are at least six times lower than the RMSE results achieved by the conventional "water-edge" method. The results obtained by new methods are predominantly outperforming existing ones.
Strengths and Weaknesses of 3D Pose Estimation and Inertial Motion Capture System for Movement Therapy
Authors: Shawan Mohammed, Hannah Siebers, Ted Preuß
Date: 2023-06-01 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation, Position

3D pose estimation offers the opportunity for fast, non-invasive, and accurate motion analysis. This is of special interest also for clinical use. Currently, motion capture systems are used, as they offer robust and precise data acquisition, which is essential in the case of clinical applications. In this study, we investigate the accuracy of the state-of-the-art 3D position estimation approach MeTrabs, compared to the established inertial sensor system MTw Awinda for specific motion exercises. The study uses and provides an evaluation dataset of parallel recordings from 10 subjects during various movement therapy exercises. The information from the Awinda system and the frames for monocular pose estimation are synchronized. For the comparison, clinically relevant parameters for joint angles of ankle, knee, back, and elbow flexion-extension were estimated and evaluated using mean, median, and maximum deviation between the calculated joint angles for the different exercises, camera positions, and clothing items. The results of the analysis indicate that the mean and median deviations can be kept below 5{\deg} for some of the studied angles. These joints could be considered for medical applications even considering the maximum deviations of 15{\deg}. However, caution should be applied to certain particularly problematic joints. In particular, elbow flexions, which showed high maximum deviations of up to 50{\deg} in our analysis. Furthermore, the type of exercise plays a crucial role in the reliable and safe application of the 3D position estimation method. For example, all joint angles showed a significant deterioration in performance during exercises near the ground.
Overview of Deep Learning Methods for Retinal Vessel Segmentation
Authors: Gorana Gojić, Ognjen Kundačina, Dragiša Mišković, Dinu Dragan
Date: 2023-06-01 00:00:00
Tasks: Deep Learning, Retinal Vessel Segmentation, Segmentation

Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye and systemic diseases. With the fast development of deep learning methods, more and more retinal vessel segmentation methods are implemented as deep neural networks. In this paper, we provide a brief review of recent deep learning methods from highly influential journals and conferences. The review objectives are: (1) to assess the design characteristics of the latest methods, (2) to report and analyze quantitative values of performance evaluation metrics, and (3) to analyze the advantages and disadvantages of the recent solutions.
Quality of Service Based Radar Resource Management for Navigation and Positioning
Authors: Tobias Müller, Sebastian Durst, Pascal Marquardt, Stefan Brüggenwirth
Date: 2023-06-12 00:00:00
Tasks: Management

In hostile environments, GNSS is a potentially unreliable solution for self-localization and navigation. Many systems only use an IMU as a backup system, resulting in integration errors which can dramatically increase during mission execution. We suggest using a fighter radar to illuminate satellites with known trajectories to enhance the self-localization information. This technique is time-consuming and resource-demanding but necessary as other tasks depend on the self-localization accuracy. Therefore an adaption of classical resource management frameworks is required. We propose a quality of service based resource manager with capabilities to account for inter-task dependencies to optimize the self-localization update strategy. Our results show that this leads to adaptive navigation update strategies, mastering the trade-off between self-localization and the requirements of other tasks.
Transformer-based GAN for Terahertz Spatial-Temporal Channel Modeling and Generating
Authors: Zhengdong Hu, Yuanbo Li, Chong Han
Date: 2023-06-12 00:00:00
Tasks: Generative Adversarial Network

Terahertz (THz) communications are envisioned as a promising technology for 6G and beyond wireless systems, providing ultra-broad continuous bandwidth and thus Terabit-per-second (Tbps) data rates. However, as foundation of designing THz communications, channel modeling and characterization are fundamental to scrutinize the potential of the new spectrum. Relied on time-consuming and costly physical measurements, traditional statistical channel modeling methods suffer from the problem of low accuracy with the assumed certain distributions and empirical parameters. In this paper, a transformer-based generative adversarial network modeling method (T-GAN) is proposed in the THz band, which exploits the advantage of GAN in modeling the complex distribution, and the powerful expressive capability of transformer structure. Experimental results reveal that the distribution of channels generated by the proposed T-GAN method shows good agreement with the original channels in terms of the delay spread and angular spread. Moreover, T-GAN achieves good performance in modeling the power delay angular profile, with 2.18 dB root-mean-square error (RMSE).
A Survey of NOMA: State of the Art, Key Techniques, Open Challenges, Security Issues and Future Trends
Authors: Syed Agha Hassnain Mohsan, Yanlong Li
Date: 2023-06-11 00:00:00
Tasks: Edge-computing, Fairness

Non-orthogonal multiple access (NOMA) systems can serve multiple users in contrast to orthogonal multiple-access (OMA), which makes use of the limited time or frequency domain resources. It can help to address the unprecedented technological advancements of the sixth generation (6G) network, which include high spectral efficiency, high flexibility, low transmission latency, massive connectivity, higher cell-edge throughput, and user fairness. NOMA has gained widespread recognition as a viable technology for future wireless networks. The main characteristic that sets NOMA apart from the conventional orthogonal multiple access (OMA) techniques is its ability to handle more users than orthogonal resource slots. NOMA techniques can serve multiple users in the same resource block by multiplexing users in power or code domain. The purpose of this paper is to provide a thorough overview of the promising NOMA systems. Initially, we discuss the state-of-the-art and existing literature on NOMA systems. This study also examines the practical deployment of NOMA implementation and key performance indicators. An overview of the most recent NOMA advancements and applications is also given in this survey. We also briefly discuss that multiple-input multiple-output (MIMO), visible light communications, cognitive and cooperative communications, intelligent reflecting surfaces (IRS), unmanned aerial vehicles (UAV), HetNets, backscatter communication, mobile edge computing (MEC), deep learning (DL), and other emerging and existing wireless technologies can all be flexibly combined with NOMA. This study surveys a thorough analysis of the interactions between NOMA and the aforementioned technologies. Lastly, we will highlight a number of difficult open problems and security issues that need to be resolved for NOMA, along with pertinent possibilities and potential future research directions.