pwc-archive/papers-with-abstracts huggingface dataset:
Neuropsychology and Explainability of AI: A Distributional Approach to the Relationship Between Activation Similarity of Neural Categories in Synthetic Cognition
Authors: Michael Pichat, Enola Campoli, William Pogrund, Jourdan Wilson, Michael Veillet-Guillem, Anton Melkozerov, Paloma Pichat, Armanush Gasparian, Samuel Demarchi, Judicael Poumay
Date: 2024-10-23 00:00:00

We propose a neuropsychological approach to the explainability of artificial neural networks, which involves using concepts from human cognitive psychology as relevant heuristic references for developing synthetic explanatory frameworks that align with human modes of thought. The analogical concepts mobilized here, which are intended to create such an epistemological bridge, are those of categorization and similarity, as these notions are particularly suited to the categorical "nature" of the reconstructive information processing performed by artificial neural networks. Our study aims to reveal a unique process of synthetic cognition, that of the categorical convergence of highly activated tokens. We attempt to explain this process with the idea that the categorical segment created by a neuron is actually the result of a superposition of categorical sub-dimensions within its input vector space.
Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
Authors: Stefano Favaro, Sandra Fortini
Date: 2024-11-12 00:00:00

The Poisson compound decision problem is a long-standing problem in statistics, where empirical Bayes methodologies are commonly used to estimate Poisson's means in static or batch domains. In this paper, we study the Poisson compound decision problem in a streaming or online domain. Adopting a quasi-Bayesian approach, referred to as Newton's algorithm, we obtain a sequential estimate that is easy to evaluate, computationally efficient, and maintain a constant per-observation computational cost as data accumulate. Asymptotic frequentist guarantees of this estimate are established, showing consistency and asymptotic optimality, where the latter is understood as vanishing excess Bayes risk or regret. We demonstrate the effectiveness of our methodology through empirical analysis on synthetic and real data, with comparisons to existing approaches.
PICZL: Image-based Photometric Redshifts for AGN
Authors: William Roster, Mara Salvato, Sven Krippendorf, Aman Saxena, Raphael Shirley, Johannes Buchner, Julien Wolf, Tom Dwelly, Franz E. Bauer, James Aird, Claudio Ricci, Roberto J. Assef, Scott F. Anderson, Xin Liu, Andrea Merloni, Jochen Weller, Kirpal Nandra
Date: 2024-11-11 00:00:00

Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $\sigma_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $\eta$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.
A Theoretical Analysis of Recommendation Loss Functions under Negative Sampling
Authors: Giulia Di Teodoro, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri
Date: 2024-11-12 00:00:00
Tasks: Recommendation Systems

Loss functions like Categorical Cross Entropy (CCE), Binary Cross Entropy (BCE), and Bayesian Personalized Ranking (BPR) are commonly used in training Recommender Systems (RSs) to differentiate positive items - those interacted with by users - and negative items. While prior works empirically showed that CCE outperforms BCE and BPR when using the full set of negative items, we provide a theoretical explanation for this by proving that CCE offers the tightest lower bound on ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR), followed by BPR and BCE. However, using the full set of negative items is computationally infeasible for large-scale RSs, prompting the use of negative sampling techniques. Under negative sampling, we reveal that BPR and CCE are equivalent when a single negative sample is drawn, and all three losses converge to the same global minimum. We further demonstrate that the sampled losses remain lower bounds for NDCG (MRR), albeit in a probabilistic sense. Our worst-case analysis shows that BCE offers the strongest bound on NDCG (MRR). Experiments on five datasets and four models empirically support these theoretical findings. Our code and supplementary material are available at https://github.com/federicosiciliano/recsys_losses.git.
Advancing Sustainability via Recommender Systems: A Survey
Authors: Xin Zhou, Lei Zhang, Honglei Zhang, Yixin Zhang, Xiaoxiong Zhang, Jie Zhang, Zhiqi Shen
Date: 2024-11-12 00:00:00
Tasks: Recommendation Systems, Survey

Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to transportation, energy utilization, and resource consumption collectively precipitating substantial ecological impacts. Recommender systems, which generate personalized suggestions based on user preferences and historical interaction data, exert considerable influence on individual behavioral trajectories. However, conventional recommender systems predominantly optimize for user engagement and economic metrics, inadvertently neglecting the environmental and societal ramifications of their recommendations, potentially catalyzing over-consumption and reinforcing unsustainable behavioral patterns. Given their instrumental role in shaping user decisions, there exists an imperative need for sustainable recommender systems that incorporate sustainability principles to foster eco-conscious and socially responsible choices. This comprehensive survey addresses this critical research gap by presenting a systematic analysis of sustainable recommender systems. As these systems can simultaneously advance multiple sustainability objectives--including resource conservation, sustainable consumer behavior, and social impact enhancement--examining their implementations across distinct application domains provides a more rigorous analytical framework. Through a methodological analysis of domain-specific implementations encompassing transportation, food, buildings, and auxiliary sectors, we can better elucidate how these systems holistically advance sustainability objectives while addressing sector-specific constraints and opportunities. Moreover, we delineate future research directions for evolving recommender systems beyond sustainability advocacy toward fostering environmental resilience and social consciousness in society.
Towards Automated Model Design on Recommender Systems
Authors: Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Yudong Liu, Feng Cheng, Yufan Cao, Feng Yan, Hai Li, Yiran Chen, Wei Wen
Date: 2024-11-12 00:00:00
Tasks: AutoML, Click-Through Rate Prediction, model, Recommendation Systems

The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model-hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multi-modality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space's scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rates (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2x FLOPs efficiency, 1.8x energy efficiency, and 1.5x performance improvements in recommender models.
Feature Interaction Fusion Self-Distillation Network For CTR Prediction
Authors: Lei Sang, Qiuze Ru, Honghao Li, Yiwen Zhang, Qian Cao, Xindong Wu
Date: 2024-11-12 00:00:00
Tasks: Click-Through Rate Prediction, Knowledge Distillation, Model Compression, Prediction, Recommendation Systems, Transfer Learning

Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions through stacked or parallel structures, with some employing knowledge distillation for model compression. However, we observe some limitations with these approaches: (1) In parallel structure models, the explicit and implicit components are executed independently and simultaneously, which leads to insufficient information sharing within the feature set. (2) The introduction of knowledge distillation technology brings about the problems of complex teacher-student framework design and low knowledge transfer efficiency. (3) The dataset and the process of constructing high-order feature interactions contain significant noise, which limits the model's effectiveness. To address these limitations, we propose FSDNet, a CTR prediction framework incorporating a plug-and-play fusion self-distillation module. Specifically, FSDNet forms connections between explicit and implicit feature interactions at each layer, enhancing the sharing of information between different features. The deepest fusion layer is then used as the teacher model, utilizing self-distillation to guide the training of shallow layers. Empirical evaluation across four benchmark datasets validates the framework's efficacy and generalization capabilities. The code is available on https://anonymous.4open.science/r/FSDNet.
Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models
Authors: Seungheon Doh, Keunwoo Choi, Daeyong Kwon, Taesu Kim, Juhan Nam
Date: 2024-11-11 00:00:00
Tasks: Attribute, Dialogue Generation, Language Modeling, Language Modelling, Large Language Model, Retrieval

A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system should seamlessly engage in multi-turn conversation by 1) understanding user queries and 2) responding with natural language and retrieved music. A straightforward solution would be a data-driven approach utilizing such conversation logs. However, few datasets are available for the research and are limited in terms of volume and quality. In this paper, we present a data generation framework for rich music discovery dialogue using a large language model (LLM) and user intents, system actions, and musical attributes. This is done by i) dialogue intent analysis using grounded theory, ii) generating attribute sequences via cascading database filtering, and iii) generating utterances using large language models. By applying this framework to the Million Song dataset, we create LP-MusicDialog, a Large Language Model based Pseudo Music Dialogue dataset, containing over 288k music conversations using more than 319k music items. Our evaluation shows that the synthetic dataset is competitive with an existing, small human dialogue dataset in terms of dialogue consistency, item relevance, and naturalness. Furthermore, using the dataset, we train a conversational music retrieval model and show promising results.
Impact of R&D and AI Investments on Economic Growth and Credit Rating
Authors: Davit Gondauri, Ekaterine Mikautadze
Date: 2024-11-12 00:00:00
Tasks: regression

The research and development (R&D) phase is essential for fostering innovation and aligns with long-term strategies in both public and private sectors. This study addresses two primary research questions: (1) assessing the relationship between R&D investments and GDP through regression analysis, and (2) estimating the economic value added (EVA) that Georgia must generate to progress from a BB to a BBB credit rating. Using World Bank data from 2014-2022, this analysis found that increasing R&D, with an emphasis on AI, by 30-35% has a measurable impact on GDP. Regression results reveal a coefficient of 7.02%, indicating a 10% increase in R&D leads to a 0.70% GDP rise, with an 81.1% determination coefficient and a strong 90.1% correlation. Georgia's EVA model was calculated to determine the additional value needed for a BBB rating, comparing indicators from Greece, Hungary, India, and Kazakhstan as benchmarks. Key economic indicators considered were nominal GDP, GDP per capita, real GDP growth, and fiscal indicators (government balance/GDP, debt/GDP). The EVA model projects that to achieve a BBB rating within nine years, Georgia requires $61.7 billion in investments. Utilizing EVA and comprehensive economic indicators will support informed decision-making and enhance the analysis of Georgia's economic trajectory.
Adaptive remanufacturing for freeform surface parts based on linear laser scanner and robotic laser cladding
Authors: Wei Ma, TianliangHu, Chengrui Zhang, Qizhi Chen
Date: 2024-08-10 00:00:00
Proceeding: Robotics and Computer-Integrated Manufacturing 2024 8

Freeform surface parts play a significant role in the aerospace industry, the mold- manufacturing industry and the automobile industry, and it is energy-saving, material-saving, time-saving and environmentally beneficial to remanufacture the damaged components to restore their functionality and performance. Due to the complex geometry of the freeform surface wear, the adaptive remanufacturing of freeform surface parts is confronted with challenges. In this paper, an adaptive remanufacturing method for freeform surface parts based on linear laser scanner and robotic laser cladding is proposed to realize the precise freeform surface measurement and optimized remanufacturing path generation. On the one hand, a systematic wear measurement and assessment method is proposed to precisely locate and quantify the wear. With the noncontact calibration of the laser scanner and industrial robot, the contour of the target surface is real-timely measured and the reverse model is efficiently constructed, which provides detailed 3D morphological information of the worn freeform surface for the latter wear analysis. Next, considering the considerable difference between the reverse model and the nominal model, a refined model aligning method weighted by surface wear segmentation is proposed to minimize the alignment error and, further, the difference entity to be additively manufactured is obtained by discrete model comparison. On the other hand, to cope with the unsatisfactory binding strength over the freeform surface basis and small fragments of the working path for the traditional plane or cylinder slicing method, a novel remanufacturing path generation method is proposed. Considering the curvature distribution of the freeform surface, an optimized equidistant freeform surface slicing method is especially proposed for the difference entity to realize the adaptive fitting to the freeform basin. Furthermore, based on the equivalent volume overlapping model of laser cladding, the cladding track filling method for the freeform surface slicing is designed with the optimized track-to-track distance, which can reduce surface waviness and improve remanufacturing efficiency. Finally, simulations and experiments for the remanufacturing scenario of the steam turbine blade are conducted to verify the validity and feasibility of the proposed adaptive remanufacturing method for freeform surface parts based on linear laser scanner and robotic laser cladding.
AGE: Amharic, Ge’ez and English Parallel Dataset
Authors: Henok Biadglign Ademtew, Mikiyas Girma Birbo
Date: 2024-08-12 00:00:00
Proceeding: Association for Computational Linguistics 2024 8
Tasks: Language Modeling, Language Modelling

African languages are not well-represented in Natural Language Processing (NLP). The main reason is a lack of resources for training models. Low-resource languages, such as Amharic and Ge’ez, cannot benefit from modern NLP methods because of the lack of high-quality datasets. This paper presents AGE, an open-source tripartite alignment of Amharic, Ge’ez, and English parallel dataset. Additionally, we introduced a novel, 1,000 Ge’ez-centered sentences sourced from areas such as news and novels. Furthermore, we developed a model from a multilingual pre-trained language model, which brings 12.29 and 30.66 for English-Ge’ez and Ge’ez to English, respectively, and 9.39 and 12.29 for Amharic-Ge’ez and Ge’ez-Amharic respectively.
SPF-Net: Solar panel fault detection using U-Net based deep learning image classification
Authors: Rifat Al Mamun Rudro a, Kamruddin Nur a, Md. Faruk Abdullah Al Sohan a, M.F. Mridha a, ∗, Sultan Alfarhood b, Mejdl Safran b, Karthick Kanagarathinam c
Date: 2024-12-01 00:00:00
Proceeding: Energy Reports 2024 12
Tasks: Deep Learning, Fault Detection, image-classification, Image Classification, Image Segmentation, Semantic Segmentation

The detection of faults in solar panels is essential for generating increased amounts of renewable green energy.
Solar panels degrade over time due to physical damage, dust, or other faults. Numerous studies have been
conducted to detect and monitor solar panel faults in real-time. This research examines the deployment of
deep learning models for identifying these faults. In this research, we propose a novel deep learning model
combining the InceptionV3-Net with U-Net architecture. The proposed architecture applies the InceptionV3
base with ImageNet weights, enhanced by convolutional layers, squeeze-and-excitation (SE) blocks, residual
connections, and global average pooling. The model includes two dense layers with LeakyReLU and batch
normalization, ending with a Soft-Max output layer. Incorporating image segmentation into deep learning
models significantly improves the precision and test accuracy of identifying issues in solar panels. The proposed
model achieves exceptional performance, having a validation accuracy of 98.34%, a test accuracy of 94.35%
with an F1 score of 0.94, a precision of 0.94, and a Recall of 0.94.
Node Reliability: Approximation, Upper Bounds, and Applications to Network Robustness
Authors: Xinhan Liu, Robert Kooij, Piet Van Mieghem
Date: 2024-11-12 00:00:00

This paper discusses the reliability of a graph in which the links are perfectly reliable but the nodes may fail with certain probability p. Calculating graph node reliability is an NP-Hard problem. We introduce an efficient and accurate Monte Carlo method and a stochastic approximation for the node reliability polynomial based solely on the degree distribution. We provide the formulas for the node reliability polynomial of both Erdos-Renyi graphs and Random Geometric graphs. The phase transition in the node reliability of Erdos-Renyi graphs is also discussed. Additionally, we propose two increasingly accurate upper bounds for the node reliability polynomial solely based on the graph's degree distributions. The advantages and disadvantages of these two upper bounds are thoroughly compared. Beyond the computation of node reliability polynomials, we also estimate the number of cut sets and present a solution to the reliability-based network enhancement problem.
A Fully Analog Pipeline for Portfolio Optimization
Authors: James S. Cummins, Natalia G. Berloff
Date: 2024-11-10 00:00:00
Tasks: Portfolio Optimization

Portfolio optimization is a ubiquitous problem in financial mathematics that relies on accurate estimates of covariance matrices for asset returns. However, estimates of pairwise covariance could be better and calculating time-sensitive optimal portfolios is energy-intensive for digital computers. We present an energy-efficient, fast, and fully analog pipeline for solving portfolio optimization problems that overcomes these limitations. The analog paradigm leverages the fundamental principles of physics to recover accurate optimal portfolios in a two-step process. Firstly, we utilize equilibrium propagation, an analog alternative to backpropagation, to train linear autoencoder neural networks to calculate low-rank covariance matrices. Then, analog continuous Hopfield networks output the minimum variance portfolio for a given desired expected return. The entire efficient frontier may then be recovered, and an optimal portfolio selected based on risk appetite.
Approaching multifractal complexity in decentralized cryptocurrency trading
Authors: Marcin Wątorek, Marcin Królczyk, Jarosław Kwapień, Tomasz Stanisz, Stanisław Drożdż
Date: 2024-11-08 00:00:00

Multifractality is a concept that helps compactly grasping the most essential features of the financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on the centralized exchanges. A new element that adds complexity to cryptocurrency markets is the possibility of decentralized trading. Based on the extracted tick-by-tick transaction data from the Universal Router contract of the Uniswap decentralized exchange, from June 6, 2023, to June 30, 2024, the present study using Multifractal Detrended Fluctuation Analysis (MFDFA) shows that even though liquidity on these new exchanges is still much lower compared to centralized exchanges convincing traces of multifractality are already emerging on this new trading as well. The resulting multifractal spectra are however strongly left-side asymmetric which indicates that this multifractality comes primarily from large fluctuations and small ones are more of the uncorrelated noise type. What is particularly interesting here is the fact that multifractality is more developed for time series representing transaction volumes than rates of return. On the level of these larger events a trace of multifractal cross-correlations between the two characteristics is also observed.