pwc-archive/papers-with-abstracts huggingface dataset:
Neural Encoding and Decoding at Scale
Authors: Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, The International Brain Laboratory, Eva Dyer, Liam Paninski, Cole Hurwitz
Date: 2025-04-11 00:00:00

Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting neural activity from behavior (encoding) or predicting behavior from neural activity (decoding), limiting their ability to capture the bidirectional relationship between neural activity and behavior. To bridge this gap, we introduce a multimodal, multi-task model that enables simultaneous Neural Encoding and Decoding at Scale (NEDS). Central to our approach is a novel multi-task-masking strategy, which alternates between neural, behavioral, within-modality, and cross-modality masking. We pretrain our method on the International Brain Laboratory (IBL) repeated site dataset, which includes recordings from 83 animals performing the same visual decision-making task. In comparison to other large-scale models, we demonstrate that NEDS achieves state-of-the-art performance for both encoding and decoding when pretrained on multi-animal data and then fine-tuned on new animals. Surprisingly, NEDS's learned embeddings exhibit emergent properties: even without explicit training, they are highly predictive of the brain regions in each recording. Altogether, our approach is a step towards a foundation model of the brain that enables seamless translation between neural activity and behavior.
Emergence of psychopathological computations in large language models
Authors: Soo Yong Lee, Hyunjin Hwang, Taekwan Kim, Yuyeong Kim, Kyuri Park, Jaemin Yoo, Denny Borsboom, Kijung Shin
Date: 2025-04-10 00:00:00

Can large language models (LLMs) implement computations of psychopathology? An effective approach to the question hinges on addressing two factors. First, for conceptual validity, we require a general and computational account of psychopathology that is applicable to computational entities without biological embodiment or subjective experience. Second, mechanisms underlying LLM behaviors need to be studied for better methodological validity. Thus, we establish a computational-theoretical framework to provide an account of psychopathology applicable to LLMs. To ground the theory for empirical analysis, we also propose a novel mechanistic interpretability method alongside a tailored empirical analytic framework. Based on the frameworks, we conduct experiments demonstrating three key claims: first, that distinct dysfunctional and problematic representational states are implemented in LLMs; second, that their activations can spread and self-sustain to trap LLMs; and third, that dynamic, cyclic structural causal models encoded in the LLMs underpin these patterns. In concert, the empirical results corroborate our hypothesis that network-theoretic computations of psychopathology have already emerged in LLMs. This suggests that certain LLM behaviors mirroring psychopathology may not be a superficial mimicry but a feature of their internal processing. Thus, our work alludes to the possibility of AI systems with psychopathological behaviors in the near future.
Lemons and Learning: Adult and Juvenile Free-Ranging Dogs Navigate Aversive Foraging Challenges Differently
Authors: Tuhin Subhra Pal, Prithwiraj Debnath, Sagarika Biswas, Anindita Bhadra
Date: 2025-04-10 00:00:00
Tasks: Decision Making, Navigate

Understanding how animals make foraging decisions in challenging or unpleasant contexts sheds light on the processes that underlie cognitive development and the evolution of adaptive foraging techniques in complex ecological settings. In order to investigate how age, gender, and environmental cues impact decision-making and scavenging behaviour, this study explores how juvenile and adult free-ranging dogs (FRDs) respond to aversive stimuli, specifically different concentrations of lemon juice. We conducted a three-bowl choice experiment using lemon juice at concentrations of 25%, 33.3%, and 50% with 73 juvenile FRDs. The juvenile FRD data were compared with adult behavioural data from a prior experiment. The findings indicated that juvenile dogs' foraging behaviour was consistent under provided circumstances, suggesting that their capacity to adapt to aversive acidic stimuli was limited, possibly due to their inexperience and developing cognitive abilities. Adult dogs, on the other hand, exhibited selective foraging by preferring lower acidity and demonstrating more deliberate foraging strategies. Age, sex, and acidity all had significant impact on eating behaviour, with males displaying a higher tolerance to unpleasant stimuli. Markov chain analysis showed repetitive decision-making patterns, especially in adults, indicating continuous sensory evaluation before food consumption, while strategy-making behaviour increased the likelihood of food consumption in juveniles, suggesting juveniles are possibly beginning to build adaptive skills. These findings demonstrate the dynamic interaction of developmental stage, sensory processing, and individual diversity, in developing of foraging strategies of FRDs. This offers new perspectives to understand the cognitive basis of scavenging behaviour in aversive ecological conditions like urban and human-dominated landscapes.
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers
Authors: Dawn L. Sanderson, Richard L. Smith
Date: 2025-04-09 00:00:00
Tasks: Survival Analysis, Uncertainty Quantification

This study investigates the incidence and age at diagnosis of Non-Hodgkin Lymphoma (NHL) among missileers stationed at Malmstrom Air Force Base (MAFB) compared to national benchmarks. The analysis was motivated by reports of elevated cancer diagnoses within the Intercontinental Ballistic Missile (ICBM) community, specifically targeting NHL cases due to initial media focus and data collection through the Torchlight Initiative. The methodology integrates simulation-based estimation of expected diagnoses using incomplete data and expert knowledge on the underlying population. Statistical tests, including the Standardized Incidence Ratio (SIR) and a nonparametric Sign Test, were used to evaluate both the rate of diagnosis and age at diagnosis. The results demonstrate a statistically significant increase in NHL diagnoses among missileers in the later decades, with observed rates surpassing expected benchmarks. The study also finds that the median age of diagnosis is significantly younger for the study population compared to national averages. Key methodological contributions include estimating the population size and service start ages when comprehensive cohort data is unavailable, incorporating uncertainty quantification, and applying multiple hypothesis testing to identify temporal patterns. While the study acknowledges limitations such as small sample size and estimation uncertainty, as well as recognizing the study subjects as self-reported diagnoses, the findings highlight the effectiveness of these statistical techniques in identifying significant deviations from expected rates. Future studies should refine and build upon these methods, incorporating survival analysis and additional covariates to improve the robustness and scope of the results.
International Financial Markets Through 150 Years: Evaluating Stylized Facts
Authors: Sara A. Safari, Maximilian Janisch, Thomas Lehéricy
Date: 2025-04-11 00:00:00

In the theory of financial markets, a stylized fact is a qualitative summary of a pattern in financial market data that is observed across multiple assets, asset classes and time horizons. In this article, we test a set of eleven stylized facts for financial market data. Our main contribution is to consider a broad range of geographical regions across Asia, continental Europe, and the US over a time period of 150 years, as well as two of the most traded cryptocurrencies, thus providing insights into the robustness and generalizability of commonly known stylized facts.
Optimal Investment in Equity and Credit Default Swaps in the Presence of Default
Authors: Zhe Fei, Scott Robertson
Date: 2025-04-10 00:00:00

We consider an equity market subject to risk from both unhedgeable shocks and default. The novelty of our work is that to partially offset default risk, investors may dynamically trade in a credit default swap (CDS) market. Assuming investment opportunities are driven by functions of an underlying diffusive factor process, we identify the certainty equivalent for a constant absolute risk aversion investor with a semi-linear partial differential equation (PDE) which has quadratic growth in both the function and gradient coefficients. For general model specifications, we prove existence of a solution to the PDE which is also the certainty equivalent. We show the optimal policy in the CDS market covers not only equity losses upon default (as one would expect), but also losses due to restricted future trading opportunities. We use our results to price default dependent claims though the principal of utility indifference, and we show that provided the underlying equity market is complete absent the possibility of default, the equity-CDS market is complete accounting for default. Lastly, through a numerical application, we show the optimal CDS policies are essentially static (and hence easily implementable) and that investing in CDS dramatically increases investor indirect utility.
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction
Authors: Jinfeng Zhuang, Yinrui Li, Runze Su, Ke Xu, Zhixuan Shao, Kungang Li, Ling Leng, Han Sun, Meng Qi, Yixiong Meng, Yang Tang, Zhifang Liu, Qifei Shen, Aayush Mudgal, Caleb Lu, Jie Liu, Hongda Shen
Date: 2025-04-10 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems

The predictions of click through rate (CTR) and conversion rate (CVR) play a crucial role in the success of ad-recommendation systems. A Deep Hierarchical Ensemble Network (DHEN) has been proposed to integrate multiple feature crossing modules and has achieved great success in CTR prediction. However, its performance for CVR prediction is unclear in the conversion ads setting, where an ad bids for the probability of a user's off-site actions on a third party website or app, including purchase, add to cart, sign up, etc. A few challenges in DHEN: 1) What feature-crossing modules (MLP, DCN, Transformer, to name a few) should be included in DHEN? 2) How deep and wide should DHEN be to achieve the best trade-off between efficiency and efficacy? 3) What hyper-parameters to choose in each feature-crossing module? Orthogonal to the model architecture, the input personalization features also significantly impact model performance with a high degree of freedom. In this paper, we attack this problem and present our contributions biased to the applied data science side, including: First, we propose a multitask learning framework with DHEN as the single backbone model architecture to predict all CVR tasks, with a detailed study on how to make DHEN work effectively in practice; Second, we build both on-site real-time user behavior sequences and off-site conversion event sequences for CVR prediction purposes, and conduct ablation study on its importance; Last but not least, we propose a self-supervised auxiliary loss to predict future actions in the input sequence, to help resolve the label sparseness issue in CVR prediction. Our method achieves state-of-the-art performance compared to previous single feature crossing modules with pre-trained user personalization features.
How Good Are Large Language Models for Course Recommendation in MOOCs?
Authors: Boxuan Ma, Md Akib Zabed Khan, Tianyuan Yang, Agoritsa Polyzou, Shin'ichi Konomi
Date: 2025-04-11 00:00:00
Tasks: Diversity, Recommendation Systems

Large Language Models (LLMs) have made significant strides in natural language processing and are increasingly being integrated into recommendation systems. However, their potential in educational recommendation systems has yet to be fully explored. This paper investigates the use of LLMs as a general-purpose recommendation model, leveraging their vast knowledge derived from large-scale corpora for course recommendation tasks. We explore a variety of approaches, ranging from prompt-based methods to more advanced fine-tuning techniques, and compare their performance against traditional recommendation models. Extensive experiments were conducted on a real-world MOOC dataset, evaluating using LLMs as course recommendation systems across key dimensions such as accuracy, diversity, and novelty. Our results demonstrate that LLMs can achieve good performance comparable to traditional models, highlighting their potential to enhance educational recommendation systems. These findings pave the way for further exploration and development of LLM-based approaches in the context of educational recommendations.
OnSET: Ontology and Semantic Exploration Toolkit
Authors: Benedikt Kantz, Kevin Innerebner, Peter Waldert, Stefan Lengauer, Elisabeth Lex, Tobias Schreck
Date: 2025-04-11 00:00:00
Tasks: Knowledge Graphs, Retrieval

Retrieval over knowledge graphs is usually performed using dedicated, complex query languages like SPARQL. We propose a novel system, Ontology and Semantic Exploration Toolkit (OnSET) that allows non-expert users to easily build queries with visual user guidance provided by topic modelling and semantic search throughout the application. OnSET allows users without any prior information about the ontology or networked knowledge to start exploring topics of interest over knowledge graphs, including the retrieval and detailed exploration of prototypical sub-graphs and their instances. Existing systems either focus on direct graph explorations or do not foster further exploration of the result set. We, however, provide a node-based editor that can extend on these missing properties of existing systems to support the search over big ontologies with sub-graph instances. Furthermore, OnSET combines efficient and open platforms to deploy the system on commodity hardware.
A Reproducibility Study of Graph-Based Legal Case Retrieval
Authors: Gregor Donabauer, Udo Kruschwitz
Date: 2025-04-11 00:00:00
Tasks: Information Retrieval, Large Language Model, Retrieval

Legal retrieval is a widely studied area in Information Retrieval (IR) and a key task in this domain is retrieving relevant cases based on a given query case, often done by applying language models as encoders to model case similarity. Recently, Tang et al. proposed CaseLink, a novel graph-based method for legal case retrieval, which models both cases and legal charges as nodes in a network, with edges representing relationships such as references and shared semantics. This approach offers a new perspective on the task by capturing higher-order relationships of cases going beyond the stand-alone level of documents. However, while this shift in approaching legal case retrieval is a promising direction in an understudied area of graph-based legal IR, challenges in reproducing novel results have recently been highlighted, with multiple studies reporting difficulties in reproducing previous findings. Thus, in this work we reproduce CaseLink, a graph-based legal case retrieval method, to support future research in this area of IR. In particular, we aim to assess its reliability and generalizability by (i) first reproducing the original study setup and (ii) applying the approach to an additional dataset. We then build upon the original implementations by (iii) evaluating the approach's performance when using a more sophisticated graph data representation and (iv) using an open large language model (LLM) in the pipeline to address limitations that are known to result from using closed models accessed via an API. Our findings aim to improve the understanding of graph-based approaches in legal IR and contribute to improving reproducibility in the field. To achieve this, we share all our implementations and experimental artifacts with the community.
Topic mining based on fine-tuning Sentence-BERT and LDA
Authors: Jianheng Li, Lirong Chen
Date: 2025-04-07 00:00:00
Tasks: Sentence

Research background: With the continuous development of society, consumers pay more attention to the key information of product fine-grained attributes when shopping. Research purposes: This study will fine tune the Sentence-BERT word embedding model and LDA model, mine the subject characteristics in online reviews of goods, and show consumers the details of various aspects of goods. Research methods: First, the Sentence-BERT model was fine tuned in the field of e-commerce online reviews, and the online review text was converted into a word vector set with richer semantic information; Secondly, the vectorized word set is input into the LDA model for topic feature extraction; Finally, focus on the key functions of the product through keyword analysis under the theme. Results: This study compared this model with other word embedding models and LDA models, and compared it with common topic extraction methods. The theme consistency of this model is 0.5 higher than that of other models, which improves the accuracy of theme extraction
Scholar Inbox: Personalized Paper Recommendations for Scientists
Authors: Markus Flicke, Glenn Angrabeit, Madhav Iyengar, Vitalii Protsenko, Illia Shakun, Jovan Cicvaric, Bora Kargi, Haoyu He, Lukas Schuler, Lewin Scholz, Kavyanjali Agnihotri, Yong Cao, Andreas Geiger
Date: 2025-04-11 00:00:00
Tasks: Active Learning, Recommendation Systems

Scholar Inbox is a new open-access platform designed to address the challenges researchers face in staying current with the rapidly expanding volume of scientific literature. We provide personalized recommendations, continuous updates from open-access archives (arXiv, bioRxiv, etc.), visual paper summaries, semantic search, and a range of tools to streamline research workflows and promote open research access. The platform's personalized recommendation system is trained on user ratings, ensuring that recommendations are tailored to individual researchers' interests. To further enhance the user experience, Scholar Inbox also offers a map of science that provides an overview of research across domains, enabling users to easily explore specific topics. We use this map to address the cold start problem common in recommender systems, as well as an active learning strategy that iteratively prompts users to rate a selection of papers, allowing the system to learn user preferences quickly. We evaluate the quality of our recommendation system on a novel dataset of 800k user ratings, which we make publicly available, as well as via an extensive user study. https://www.scholar-inbox.com/
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation
Authors: Arman Khaledian, Amirreza Ghadiridehkordi, Nariman Khaledian
Date: 2025-04-11 00:00:00
Tasks: Dimensionality Reduction, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for grounding large language models in external knowledge sources, improving the precision of agents responses. However, high-dimensional language model embeddings, often in the range of hundreds to thousands of dimensions, can present scalability challenges in terms of storage and latency, especially when processing massive financial text corpora. This paper investigates the use of Principal Component Analysis (PCA) to reduce embedding dimensionality, thereby mitigating computational bottlenecks without incurring large accuracy losses. We experiment with a real-world dataset and compare different similarity and distance metrics under both full-dimensional and PCA-compressed embeddings. Our results show that reducing vectors from 3,072 to 110 dimensions provides a sizeable (up to $60\times$) speedup in retrieval operations and a $\sim 28.6\times$ reduction in index size, with only moderate declines in correlation metrics relative to human-annotated similarity scores. These findings demonstrate that PCA-based compression offers a viable balance between retrieval fidelity and resource efficiency, essential for real-time systems such as Zanista AI's \textit{Newswitch} platform. Ultimately, our study underscores the practicality of leveraging classical dimensionality reduction techniques to scale RAG architectures for knowledge-intensive applications in finance and trading, where speed, memory efficiency, and accuracy must jointly be optimized.
Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning
Authors: Jiahua Lan, Sen Zhang, Haixia Pan, Ruijun Liu, Li Shen, DaCheng Tao
Date: 2025-04-09 00:00:00
Tasks: Deep Reinforcement Learning

In contrast to the human ability to continuously acquire knowledge, agents struggle with the stability-plasticity dilemma in deep reinforcement learning (DRL), which refers to the trade-off between retaining existing skills (stability) and learning new knowledge (plasticity). Current methods focus on balancing these two aspects at the network level, lacking sufficient differentiation and fine-grained control of individual neurons. To overcome this limitation, we propose Neuron-level Balance between Stability and Plasticity (NBSP) method, by taking inspiration from the observation that specific neurons are strongly relevant to task-relevant skills. Specifically, NBSP first (1) defines and identifies RL skill neurons that are crucial for knowledge retention through a goal-oriented method, and then (2) introduces a framework by employing gradient masking and experience replay techniques targeting these neurons to preserve the encoded existing skills while enabling adaptation to new tasks. Numerous experimental results on the Meta-World and Atari benchmarks demonstrate that NBSP significantly outperforms existing approaches in balancing stability and plasticity.
A Python toolkit for dealing with Petri nets over ontological graphs
Authors: Krzysztof Pancerz
Date: 2025-04-09 00:00:00

We present theoretical rudiments of Petri nets over ontological graphs as well as the designed and implemented Python toolkit for dealing with such nets. In Petri nets over ontological graphs, the domain knowledge is enclosed in a form of ontologies. In this way, some valuable knowledge (especially in terms of semantic relations) can be added to model reasoning and control processes by means of Petri nets. In the implemented approach, ontological graphs are obtained from ontologies built in accordance with the OWL 2 Web Ontology Language. The implemented tool enables the users to define the structure and dynamics of Petri nets over ontological graphs.