pwc-archive/papers-with-abstracts huggingface dataset:
Effect of seaweed, moringa leaf extract and biofertilizer on growth, yield and fruit quality of cucumber (Cucumis sativus L.) under greenhouse condition
Authors: Roshna Faeq Kakbra
Date: 2024-03-25 00:00:00

This factorial experiment was conducted in a greenhouse during the period of May 3, 2021 to August 5, 2021 at the research farm belongs to the Horticulture Department, College of Agricultural Engineering Sciences, University of Sulaimani, Sulaimani, Iraq. The experiment was designed to study the effect of some biostimulants, individually and their combinations, on cucumber plants performance under greenhouse conditions; in addition to compare the results with of chemical fertilizers application. The treatments consisted of control (without adding any kinds of biostimulants) recommended dose of 100% chemical fertilizers (RDCF), seaweed extracts (SE), moringa leaf extract (MLE), bacterial-based biostimulant of Fulzym-plus (FP), that contains Bacillus subtilis and Pseudomonas putida, (SE+MLE), (SE+FP), (MLE+FP), and (SE+MLE+FP). The experiment was laid out in simple RCBD with 3 replications. The results showed that the application of different biostimulants, individually and their combinations, significantly improved the root growth characteristics. However, the highest values of lateral roots number per plant, lateral root length, lateral root diameter and root system dry weight were recorded by the application of recommended dose of chemical fertilizer (RDCF). While, this treatment was not different substantially with the triple combination of the tested biostimulants (SE+FP+MLE) in all studied root characteristics. In addition, untreated plants registered the minimum value of all the mentioned characters.
Optimal Rebalancing in Dynamic AMMs
Authors: Matthew Willetts, Christian Harrington
Date: 2024-03-27 00:00:00

Dynamic AMM pools, as found in Temporal Function Market Making, rebalance their holdings to a new desired ratio (e.g. moving from being 50-50 between two assets to being 90-10 in favour of one of them) by introducing an arbitrage opportunity that disappears when their holdings are in line with their target. Structuring this arbitrage opportunity reduces to the problem of choosing the sequence of portfolio weights the pool exposes to the market via its trading function. Linear interpolation from start weights to end weights has been used to reduce the cost paid by pools to arbitrageurs to rebalance. Here we obtain the $\textit{optimal}$ interpolation in the limit of small weight changes (which has the downside of requiring a call to a transcendental function) and then obtain a cheap-to-compute approximation to that optimal approach that gives almost the same performance improvement. We then demonstrate this method on a range of market backtests, including simulating pool performance when trading fees are present, finding that the new approximately-optimal method of changing weights gives robust increases in pool performance. For a BTC-ETH-DAI pool from July 2022 to June 2023, the increases of pool P\&L from approximately-optimal weight changes is $\sim25\%$ for a range of different strategies and trading fees.
Growth rate of liquidity provider's wealth in G3Ms
Authors: Cheuk Yin Lee, Shen-Ning Tung, Tai-Ho Wang
Date: 2024-03-27 00:00:00

We study how trading fees and continuous-time arbitrage affect the profitability of liquidity providers (LPs) in Geometric Mean Market Makers (G3Ms). We use stochastic reflected diffusion processes to analyze the dynamics of a G3M model under the arbitrage-driven market. Our research focuses on calculating LP wealth and extends the findings of Tassy and White related to the constant product market maker (Uniswap v2) to a wider range of G3Ms, including Balancer. This allows us to calculate the long-term expected logarithmic growth of LP wealth, offering new insights into the complex dynamics of AMMs and their implications for LPs in decentralized finance.
Revisiting Elastic String Models of Forward Interest Rates
Authors: Victor Le Coz, Jean-Philippe Bouchaud
Date: 2024-03-26 00:00:00

Twenty five years ago, several authors proposed to describe the forward interest rate curve (FRC) as an elastic string along which idiosyncratic shocks propagate, accounting for the peculiar structure of the return correlation across different maturities. In this paper, we revisit the specific "stiff'' elastic string field theory of Baaquie and Bouchaud (2004) in a way that makes its micro-foundation more transparent. Our model can be interpreted as capturing the effect of market forces that set the rates of nearby tenors in a self-referential fashion. The model is parsimonious and accurately reproduces the whole correlation structure of the FRC over the time period 1994-2023, with an error around 1% and with only one adjustable parameter, the value of which being very stable across the last three decades. The dependence of correlation on time resolution (also called the Epps effect) is also faithfully reproduced within the model and leads to a cross-tenor information propagation time on the order of 30 minutes. Finally, we confirm that the perceived time in interest rate markets is a strongly sub-linear function of real time, as surmised by Baaquie and Bouchaud (2004). In fact, our results are fully compatible with hyperbolic discounting, in line with the recent behavioral Finance literature (Farmer and Geanakoplos, 2009).
Circuit-centric Genetic Algorithm (CGA) for Analog and Radio-Frequency Circuit Optimization
Authors: Mingi Kwon, Yeonjun Lee, Ickhyun Song
Date: 2023-11-19 00:00:00

This paper presents an automated method for optimizing parameters in analog/high-frequency circuits, aiming to maximize performance parameters of a radio-frequency (RF) receiver. The design target includes a reduction of power consumption and noise figure and an increase in conversion gain. This study investigates the use of an artificial algorithm for the optimization of a receiver, illustrating how to fulfill the performance parameters with diverse circuit parameters. To overcome issues observed in the traditional Genetic Algorithm (GA), the concept of the Circuit-centric Genetic Algorithm (CGA) is proposed as a viable approach. The new method adopts an inference process that is simpler and computationally more efficient than the existing deep learning models. In addition, CGA offers significant advantages over manual design of finding optimal points and the conventional GA, mitigating the designer's workload while searching for superior optimum points.
Matrix Domination: Convergence of a Genetic Algorithm Metaheuristic with the Wisdom of Crowds to Solve the NP-Complete Problem
Authors: Shane Storm Strachan
Date: 2023-12-14 00:00:00
Tasks: Decision Making

This research explores the application of a genetic algorithm metaheuristic enriched by the wisdom of crowds in order to address the NP-Complete matrix domination problem (henceforth: TMDP) which is itself a constraint on related problems applied in graphs. Matrix domination involves accurately placing a subset of cells, referred to as dominators, within a matrix with the goal of their dominating the remainder of the cells. This research integrates the exploratory nature of a genetic algorithm with the wisdom of crowds to find more optimal solutions with user-defined parameters to work within computational complexity considerations and gauge performance mainly with a fitness evaluation function and a constraining function to combat the stochastic nature of genetic algorithms. With this, I propose a novel approach to MDP with a genetic algorithm that incorporates the wisdom of crowds, emphasizing collective decision-making in the selection process, and by exploring concepts of matrix permutations and their relevance in finding optimal solutions. Results demonstrate the potential of this convergence to generate efficient solutions, optimizing the trade-off between the number of dominators and their strategic placements within the matrices while efficiently ensuring consistent and complete matrix domination.
Enriching Word Usage Graphs with Cluster Definitions
Authors: Mariia Fedorova, Andrey Kutuzov, Nikolay Arefyev, Dominik Schlechtweg
Date: 2024-03-26 00:00:00
Tasks: Decoder

We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions. They are generated from scratch by fine-tuned encoder-decoder language models. The conducted human evaluation has shown that these definitions match the existing clusters in WUGs better than the definitions chosen from WordNet by two baseline systems. At the same time, the method is straightforward to use and easy to extend to new languages. The resulting enriched datasets can be extremely helpful for moving on to explainable semantic change modeling.
The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation
Authors: Nicolas Guerin, Shane Steinert-Threlkeld, Emmanuel Chemla
Date: 2024-03-26 00:00:00
Tasks: Machine Translation, Translation, Unsupervised Machine Translation

Unsupervised on-the-fly back-translation, in conjunction with multilingual pretraining, is the dominant method for unsupervised neural machine translation. Theoretically, however, the method should not work in general. We therefore conduct controlled experiments with artificial languages to determine what properties of languages make back-translation an effective training method, covering lexical, syntactic, and semantic properties. We find, contrary to popular belief, that (i) parallel word frequency distributions, (ii) partially shared vocabulary, and (iii) similar syntactic structure across languages are not sufficient to explain the success of back-translation. We show however that even crude semantic signal (similar lexical fields across languages) does improve alignment of two languages through back-translation. We conjecture that rich semantic dependencies, parallel across languages, are at the root of the success of unsupervised methods based on back-translation. Overall, the success of unsupervised machine translation was far from being analytically guaranteed. Instead, it is another proof that languages of the world share deep similarities, and we hope to show how to identify which of these similarities can serve the development of unsupervised, cross-linguistic tools.
For those who don't know (how) to ask: Building a dataset of technology questions for digital newcomers
Authors: Evan Lucas, Kelly S. Steelman, Leo C. Ureel, Charles Wallace
Date: 2024-03-26 00:00:00

While the rise of large language models (LLMs) has created rich new opportunities to learn about digital technology, many on the margins of this technology struggle to gain and maintain competency due to lexical or conceptual barriers that prevent them from asking appropriate questions. Although there have been many efforts to understand factuality of LLM-created content and ability of LLMs to answer questions, it is not well understood how unclear or nonstandard language queries affect the model outputs. We propose the creation of a dataset that captures questions of digital newcomers and outsiders, utilizing data we have compiled from a decade's worth of one-on-one tutoring. In this paper we lay out our planned efforts and some potential uses of this dataset.
RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees
Authors: Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding
Date: 2024-01-23 00:00:00
Tasks: Decoder

Safeguarding intellectual property and preventing potential misuse of AI-generated images are of paramount importance. This paper introduces a robust and agile plug-and-play watermark detection framework, dubbed as RAW. As a departure from traditional encoder-decoder methods, which incorporate fixed binary codes as watermarks within latent representations, our approach introduces learnable watermarks directly into the original image data. Subsequently, we employ a classifier that is jointly trained with the watermark to detect the presence of the watermark. The proposed framework is compatible with various generative architectures and supports on-the-fly watermark injection after training. By incorporating state-of-the-art smoothing techniques, we show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image, even in the presence of certain adversarial attacks targeting watermark removal. Experiments on a diverse range of images generated by state-of-the-art diffusion models reveal substantial performance enhancements compared to existing approaches. For instance, our method demonstrates a notable increase in AUROC, from 0.48 to 0.82, when compared to state-of-the-art approaches in detecting watermarked images under adversarial attacks, while maintaining image quality, as indicated by closely aligned FID and CLIP scores.
Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition
Authors: Xiaozhou Ye, Kevin I-Kai Wang
Date: 2024-03-12 00:00:00
Tasks: Activity Recognition, Domain Adaptation, Human Activity Recognition, Relation, Time Series

In Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed ($\displaystyle i.i.d.$). Contrarily, practical implementations often challenge this notion, manifesting data distribution discrepancies, especially in scenarios such as cross-user HAR. Domain adaptation is the promising approach to address these challenges inherent in cross-user HAR tasks. However, a clear gap in domain adaptation techniques is the neglect of the temporal relation embedded within time series data during the phase of aligning data distributions. Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method. This novel method uniquely recognises and integrates temporal relations during the domain adaptation process. By synergizing the capabilities of generative models with the Temporal Relation Attention mechanism, our method improves the classification performance in cross-user HAR. A comprehensive evaluation has been conducted on three public sensor-based HAR datasets targeting different scenarios and applications to demonstrate the efficacy of the proposed DGDATA method.
A Note On Lookahead In Real Life And Computing
Authors: Burle Sharma, Rakesh Mohanty, Sucheta Panda
Date: 2024-02-02 00:00:00
Tasks: Future prediction, World Knowledge

Past, Present and Future are considered to be three temporal and logical concepts which are well defined by human beings for their existence and growth. We, as human beings, have the privilege of using our intelligence to mentally execute an activity before physical occurrence of the same in the real world. Knowledge of the past, aplomb of present and visualisation for the future correspond to three concepts such as look-back, look-at and look-ahead respectively in real life as well as in diversified domains of computing. Look-Ahead(LA) deals with the future prediction of information and processing of input to produce the output in advance. In this article, our main objective is to learn, understand and explore the concept of LA and design novel models as solution for real world problems. We present three well known algorithmic frameworks used in practice based on availability of input information such as offline, online and semi-online. We introduce interesting real life applications and well known computing problems where LA plays a significant role for making a process, system or algorithm efficient. We define new types of LA and propose a taxonomy for LA based on literature review for designing novel LA models in future. Using the concept of LA, We identify and present many interesting and non-trivial research challenges as future potential research directions. Intuitively, we observe that LA can be used as a powerful tool and framework for future researchers in design of efficient computational models and algorithms for solving non-trivial and challenging optimization problems.
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
Authors: Yongyi Yang, Jiaming Yang, Wei Hu, Michał Dereziński
Date: 2024-03-26 00:00:00

As a variant of Graph Neural Networks (GNNs), Unfolded GNNs offer enhanced interpretability and flexibility over traditional designs. Nevertheless, they still suffer from scalability challenges when it comes to the training cost. Although many methods have been proposed to address the scalability issues, they mostly focus on per-iteration efficiency, without worst-case convergence guarantees. Moreover, those methods typically add components to or modify the original model, thus possibly breaking the interpretability of Unfolded GNNs. In this paper, we propose HERTA: a High-Efficiency and Rigorous Training Algorithm for Unfolded GNNs that accelerates the whole training process, achieving a nearly-linear time worst-case training guarantee. Crucially, HERTA converges to the optimum of the original model, thus preserving the interpretability of Unfolded GNNs. Additionally, as a byproduct of HERTA, we propose a new spectral sparsification method applicable to normalized and regularized graph Laplacians that ensures tighter bounds for our algorithm than existing spectral sparsifiers do. Experiments on real-world datasets verify the superiority of HERTA as well as its adaptability to various loss functions and optimizers.
Divide, Conquer, Combine Bayesian Decision Tree Sampling
Authors: Jodie A. Cochrane, Adrian Wills, Sarah J. Johnson
Date: 2024-03-26 00:00:00
Tasks: Bayesian Inference

Decision trees are commonly used predictive models due to their flexibility and interpretability. This paper is directed at quantifying the uncertainty of decision tree predictions by employing a Bayesian inference approach. This is challenging because these approaches need to explore both the tree structure space and the space of decision parameters associated with each tree structure. This has been handled by using Markov Chain Monte Carlo (MCMC) methods, where a Markov Chain is constructed to provide samples from the desired Bayesian estimate. Importantly, the structure and the decision parameters are tightly coupled; small changes in the tree structure can demand vastly different decision parameters to provide accurate predictions. A challenge for existing MCMC approaches is proposing joint changes in both the tree structure and the decision parameters that result in efficient sampling. This paper takes a different approach, where each distinct tree structure is associated with a unique set of decision parameters. The proposed approach, entitled DCC-Tree, is inspired by the work in Zhou et al. [23] for probabilistic programs and Cochrane et al. [4] for Hamiltonian Monte Carlo (HMC) based sampling for decision trees. Results show that DCC-Tree performs comparably to other HMC-based methods and better than existing Bayesian tree methods while improving on consistency and reducing the per-proposal complexity.
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
Authors: Ao Zhou, Bin Liu, Jin Wang, Grigorios Tsoumakas
Date: 2024-03-27 00:00:00

Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-batches with optimizers, where each sample is randomly selected with equal probability when constructing mini-batches. However, the intrinsic class imbalance in multi-label data may bias the model towards majority labels, since samples relevant to minority labels may be underrepresented in each mini-batch. Meanwhile, during the training process, we observe that instances associated with minority labels tend to induce greater losses. Existing heuristic batch selection methods, such as priority selection of samples with high contribution to the objective function, i.e., samples with high loss, have been proven to accelerate convergence while reducing the loss and test error in single-label data. However, batch selection methods have not yet been applied and validated in multi-label data. In this study, we introduce a simple yet effective adaptive batch selection algorithm tailored to multi-label deep learning models. It adaptively selects each batch by prioritizing hard samples related to minority labels. A variant of our method also takes informative label correlations into consideration. Comprehensive experiments combining five multi-label deep learning models on thirteen benchmark datasets show that our method converges faster and performs better than random batch selection.
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
Authors: Fabian Baldenweg, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Date: 2024-03-27 00:00:00
Tasks: Contrastive Learning, Time Series

Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities in supervised settings, we apply advanced self-supervised multi-modal contrastive learning techniques to ICU data, specifically focusing on clinical notes and time-series for clinically relevant online prediction tasks. We introduce a loss function Multi-Modal Neighborhood Contrastive Loss (MM-NCL), a soft neighborhood function, and showcase the excellent linear probe and zero-shot performance of our approach.
Macroscale fracture surface segmentation via semi-supervised learning considering the structural similarity
Authors: Johannes Rosenberger, Johannes Tlatlik, Sebastian Münstermann
Date: 2024-03-27 00:00:00

To this date the safety assessment of materials, used for example in the nuclear power sector, commonly relies on a fracture mechanical analysis utilizing macroscopic concepts, where a global load quantity K or J is compared to the materials fracture toughness curve. Part of the experimental effort involved in these concepts is dedicated to the quantitative analysis of fracture surfaces. Within the scope of this study a methodology for the semi-supervised training of deep learning models for fracture surface segmentation on a macroscopic level was established. Therefore, three distinct and unique datasets were created to analyze the influence of structural similarity on the segmentation capability. The structural similarity differs due to the assessed materials and specimen, as well as imaging-induced variance due to fluctuations in image acquisition in different laboratories. The datasets correspond to typical isolated laboratory conditions, complex real-world circumstances, and a curated subset of the two. We implemented a weak-to-strong consistency regularization for semi-supervised learning. On the heterogeneous dataset we were able to train robust and well-generalizing models that learned feature representations from images across different domains without observing a significant drop in prediction quality. Furthermore, our approach reduced the number of labeled images required for training by a factor of 6. To demonstrate the success of our method and the benefit of our approach for the fracture mechanics assessment, we utilized the models for initial crack size measurements with the area average method. For the laboratory setting, the deep learning assisted measurements proved to have the same quality as manual measurements. For models trained on the heterogeneous dataset, very good measurement accuracies with mean deviations smaller than 1 % could be achieved...