pwc-archive/papers-with-abstracts huggingface dataset:
Is Relevance Propagated from Retriever to Generator in RAG?
Authors: Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Date: 2025-02-20 00:00:00
Tasks: Large Language Model, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval Augmented Generation (RAG) is a framework for incorporating external knowledge, usually in the form of a set of documents retrieved from a collection, as a part of a prompt to a large language model (LLM) to potentially improve the performance of a downstream task, such as question answering. Different from a standard retrieval task's objective of maximising the relevance of a set of top-ranked documents, a RAG system's objective is rather to maximise their total utility, where the utility of a document indicates whether including it as a part of the additional contextual information in an LLM prompt improves a downstream task. Existing studies investigate the role of the relevance of a RAG context for knowledge-intensive language tasks (KILT), where relevance essentially takes the form of answer containment. In contrast, in our work, relevance corresponds to that of topical overlap between a query and a document for an information seeking task. Specifically, we make use of an IR test collection to empirically investigate whether a RAG context comprised of topically relevant documents leads to improved downstream performance. Our experiments lead to the following findings: (a) there is a small positive correlation between relevance and utility; (b) this correlation decreases with increasing context sizes (higher values of k in k-shot); and (c) a more effective retrieval model generally leads to better downstream RAG performance.
Orientation selectivity properties for integrated affine quasi quadrature models of complex cells
Authors: Tony Lindeberg
Date: 2025-03-27 00:00:00

This paper presents an analysis of the orientation selectivity properties of idealized models of complex cells in terms of affine quasi quadrature measures, which combine the responses of idealized models of simple cells in terms of affine Gaussian derivatives by (i) pointwise squaring, (ii) summation of responses for different orders of spatial derivation and (iii) spatial integration. Specifically, this paper explores the consequences of assuming that the family of spatial receptive fields should be covariant under spatial affine transformations, thereby implying that the receptive fields ought to span a variability over the degree of elongation. We investigate the theoretical properties of three main ways of defining idealized models of complex cells and compare the predictions from these models to neurophysiologically obtained receptive field histograms over the resultant of biological orientation selectivity curves. It is shown that the extended modelling mechanism lead to more uniform behaviour and a wider span over the values of the resultat that are covered, compared to earlier presented idealized models of complex cells without spatial integration. More generally, we propose (i) to include a variability over the degree of elongation of the receptive fields in functional models of complex cells, and that (ii) the presented methodology with comparisons to biological orientation selectivity curves and orientation selectivity histograms could be used as a new tool to evaluate other computational models of complex cells in relation to biological measurements.
Barrier-Free Microhabitats: Self-Organized Seclusion in Microbial Communities
Authors: Valentin Slepukhin, Víctor Peris Yagüe, Christian Westendorf, Birgit Koch, Oskar Hallatschek
Date: 2025-03-27 00:00:00
Tasks: Diversity, Friction

Bacteria frequently colonize natural microcavities such as gut crypts, plant apoplasts, and soil pores. Recent studies have shown that the physical structure of these spaces plays a crucial role in shaping the stability and resilience of microbial populations (Karita et al., PNAS 2022, Postek et al. PNAS 2024). Here, we demonstrate that protected microhabitats can emerge dynamically, even in the absence of physical barriers. Interactions with surface features -- such as roughness or friction -- lead microbial populations to self-organize into effectively segregated subpopulations. Our numerical and analytical models reveal that this self-organization persists even when strains have different growth rates, allowing slower-growing strains to avoid competitive exclusion. These findings suggest that emergent spatial structuring can serve as a fundamental mechanism for maintaining microbial diversity, despite selection pressures, competition, and genetic drift.
Fungicides vs mycoinsecticides in the management of corn leafhopper: physicochemical, in vitro and in vivo compatibilities, and degradation kinetics in maize plants
Authors: Matheus Rakes, Maíra Chagas Morais, Maria Eduarda Sperotto, Odimar Zanuzo Zanardi, Gabriel Rodrigues Palma, Luana Floriano, Renato Zanella, Osmar Damian Prestes, Daniel Bernardi, Anderson Dionei Grützmacher, Leandro do Prado Ribeiro
Date: 2025-03-27 00:00:00
Tasks: Management

The present study investigates the compatibility of mycoinsecticides based on isolates IBCB66 and Simbi BB15 of Beauveria bassiana and Esalq-1296 of Cordyceps javanica, which are registered for the management of Dalbulus maidis in Brazil, with synthetic fungicides. Irrespective of the fungicide, a total inhibition in the number of colony-forming units (CFUs), vegetative growth, conidiogenesis, and conidial viability of the three tested isolates was observed, with their incompatibility being indicated in the in vitro bioassays. However, the use of formulated mycoinsecticides mitigated the impact of these xenobiotics on the number of CFUs, with the commercial mycoinsecticide FlyControl (B. bassiana isolate Simbi BB15) being the least sensitive to the fungicides propiconazole + difenoconazole, bixafem + prothioconazole + trifloxystrobin and trifloxystrobin + tebuconazole. Nevertheless, an increase in exposure time (from 1.5 to 3 hours) generally led to an increase in the toxicity of fungicides towards entomopathogens. Physical-chemical compatibility assessments indicated that physical incompatibilities were observed, depending on the mycoinsecticide formulation. In addition, in vivo bioassays employing D. maidis adults demonstrated that, despite a synergistic effect on mortality in certain binary mixtures, no cadavers exposed to such mixtures exhibited fungal extrusion. Furthermore, analyses using UHPLC/MS/MS revealed alterations in the degradation kinetics (k) of the active ingredient (a.i.) pyraclostrobin, with changes greater than tenfold being observed in the different formulations of the fungicides that were tested. Consequently, given the diminished degradation kinetics of the active ingredients in maize plants, the implementation of mycoinsecticides should precede, in isolation, the application of synthetic fungicides within the framework of phytosanitary management of maize crops.
Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining
Authors: Zhenya Zang, David A Dorward, Katherine E Quiohilag, Andrew DJ Wood, James R Hopgood, Ahsan R Akram, Qiang Wang
Date: 2025-03-25 00:00:00
Tasks: Diagnostic, Lung Cancer Diagnosis, Multi-class Classification, Prognosis

The differentiation between pathological subtypes of non-small cell lung cancer (NSCLC) is an essential step in guiding treatment options and prognosis. However, current clinical practice relies on multi-step staining and labelling processes that are time-intensive and costly, requiring highly specialised expertise. In this study, we propose a label-free methodology that facilitates autofluorescence imaging of unstained NSCLC samples and deep learning (DL) techniques to distinguish between non-cancerous tissue, adenocarcinoma (AC), squamous cell carcinoma (SqCC), and other subtypes (OS). We conducted DL-based classification and generated virtual immunohistochemical (IHC) stains, including thyroid transcription factor-1 (TTF-1) for AC and p40 for SqCC, and evaluated these methods using two types of autofluorescence imaging: intensity imaging and lifetime imaging. The results demonstrate the exceptional ability of this approach for NSCLC subtype differentiation, achieving an area under the curve above 0.981 and 0.996 for binary- and multi-class classification. Furthermore, this approach produces clinical-grade virtual IHC staining which was blind-evaluated by three experienced thoracic pathologists. Our label-free NSCLC subtyping approach enables rapid and accurate diagnosis without conventional tissue processing and staining. Both strategies can significantly accelerate diagnostic workflows and support efficient lung cancer diagnosis, without compromising clinical decision-making.
A Causal Perspective of Stock Prediction Models
Authors: Songci Xu, Qiangqiang Cheng, Chi-Guhn Lee
Date: 2025-03-26 00:00:00
Tasks: Causal Discovery, Domain Generalization, Econometrics, Prediction, Representation Learning, Stock Prediction

In the realm of stock prediction, machine learning models encounter considerable obstacles due to the inherent low signal-to-noise ratio and the nonstationary nature of financial markets. These challenges often result in spurious correlations and unstable predictive relationships, leading to poor performance of models when applied to out-of-sample (OOS) domains. To address these issues, we investigate \textit{Domain Generalization} techniques, with a particular focus on causal representation learning to improve a prediction model's generalizability to OOS domains. By leveraging multi-factor models from econometrics, we introduce a novel error bound that explicitly incorporates causal relationships. In addition, we present the connection between the proposed error bound and market nonstationarity. We also develop a \textit{Causal Discovery} technique to discover invariant feature representations, which effectively mitigates the proposed error bound, and the influence of spurious correlations on causal discovery is rigorously examined. Our theoretical findings are substantiated by numerical results, showcasing the effectiveness of our approach in enhancing the generalizability of stock prediction models.
Advanced simulation paradigm of human behaviour unveils complex financial systemic projection
Authors: Cheng Wang, Chuwen Wang, Shirong Zeng, Jianguo Liu, Changjun Jiang
Date: 2025-02-18 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

The high-order complexity of human behaviour is likely the root cause of extreme difficulty in financial market projections. We consider that behavioural simulation can unveil systemic dynamics to support analysis. Simulating diverse human groups must account for the behavioural heterogeneity, especially in finance. To address the fidelity of simulated agents, on the basis of agent-based modeling, we propose a new paradigm of behavioural simulation where each agent is supported and driven by a hierarchical knowledge architecture. This architecture, integrating language and professional models, imitates behavioural processes in specific scenarios. Evaluated on futures markets, our simulator achieves a 13.29% deviation in simulating crisis scenarios whose price increase rate reaches 285.34%. Under normal conditions, our simulator also exhibits lower mean square error in predicting futures price of specific commodities. This technique bridges non-quantitative information with diverse market behaviour, offering a promising platform to simulate investor behaviour and its impact on market dynamics.
Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach
Authors: Yunuo Chen, Junli Cao, Anil Kag, Vidit Goel, Sergei Korolev, Chenfanfu Jiang, Sergey Tulyakov, Jian Ren
Date: 2025-02-05 00:00:00
Tasks: Video Generation

We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space. The resulting 3D-aware video dataset, PointVid, is then used to fine-tune a latent diffusion model, enabling it to track 2D objects with 3D Cartesian coordinates. Building on this, we regularize the shape and motion of objects in the video to eliminate undesired artifacts, \eg, nonphysical deformation. Consequently, we enhance the quality of generated RGB videos and alleviate common issues like object morphing, which are prevalent in current video models due to a lack of shape awareness. With our 3D augmentation and regularization, our model is capable of handling contact-rich scenarios such as task-oriented videos. These videos involve complex interactions of solids, where 3D information is essential for perceiving deformation and contact. Furthermore, our model improves the overall quality of video generation by promoting the 3D consistency of moving objects and reducing abrupt changes in shape and motion.
Motion Modes: What Could Happen Next?
Authors: Karran Pandey, Matheus Gadelha, Yannick Hold-Geoffroy, Karan Singh, Niloy J. Mitra, Paul Guerrero
Date: 2024-11-29 00:00:00
Proceeding: CVPR 2025 1
Tasks: Diversity, Object, Video Generation

Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific motions from motion arrow input, they rely on synthetic data and predefined motions, limiting their application to complex scenes. We introduce Motion Modes, a training-free approach that explores a pre-trained image-to-video generator's latent distribution to discover various distinct and plausible motions focused on selected objects in static images. We achieve this by employing a flow generator guided by energy functions designed to disentangle object and camera motion. Additionally, we use an energy inspired by particle guidance to diversify the generated motions, without requiring explicit training data. Experimental results demonstrate that Motion Modes generates realistic and varied object animations, surpassing previous methods and even human predictions regarding plausibility and diversity. Project Webpage: https://motionmodes.github.io/
FluidNexus: 3D Fluid Reconstruction and Prediction from a Single Video
Authors: Yue Gao, Hong-Xing Yu, Bo Zhu, Jiajun Wu
Date: 2025-03-06 00:00:00
Proceeding: CVPR 2025 1
Tasks: Future prediction, Novel View Synthesis, Video Generation

We study reconstructing and predicting 3D fluid appearance and velocity from a single video. Current methods require multi-view videos for fluid reconstruction. We present FluidNexus, a novel framework that bridges video generation and physics simulation to tackle this task. Our key insight is to synthesize multiple novel-view videos as references for reconstruction. FluidNexus consists of two key components: (1) a novel-view video synthesizer that combines frame-wise view synthesis with video diffusion refinement for generating realistic videos, and (2) a physics-integrated particle representation coupling differentiable simulation and rendering to simultaneously facilitate 3D fluid reconstruction and prediction. To evaluate our approach, we collect two new real-world fluid datasets featuring textured backgrounds and object interactions. Our method enables dynamic novel view synthesis, future prediction, and interaction simulation from a single fluid video. Project website: https://yuegao.me/FluidNexus.
WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generation
Authors: Jing Wang, Ao Ma, Ke Cao, Jun Zheng, Zhanjie Zhang, Jiasong Feng, Shanyuan Liu, Yuhang Ma, Bo Cheng, Dawei Leng, Yuhui Yin, Xiaodan Liang
Date: 2025-03-11 00:00:00
Tasks: Text-to-Video Generation, Video Generation

Recent rapid advancements in text-to-video (T2V) generation, such as SoRA and Kling, have shown great potential for building world simulators. However, current T2V models struggle to grasp abstract physical principles and generate videos that adhere to physical laws. This challenge arises primarily from a lack of clear guidance on physical information due to a significant gap between abstract physical principles and generation models. To this end, we introduce the World Simulator Assistant (WISA), an effective framework for decomposing and incorporating physical principles into T2V models. Specifically, WISA decomposes physical principles into textual physical descriptions, qualitative physical categories, and quantitative physical properties. To effectively embed these physical attributes into the generation process, WISA incorporates several key designs, including Mixture-of-Physical-Experts Attention (MoPA) and a Physical Classifier, enhancing the model's physics awareness. Furthermore, most existing datasets feature videos where physical phenomena are either weakly represented or entangled with multiple co-occurring processes, limiting their suitability as dedicated resources for learning explicit physical principles. We propose a novel video dataset, WISA-32K, collected based on qualitative physical categories. It consists of 32,000 videos, representing 17 physical laws across three domains of physics: dynamics, thermodynamics, and optics. Experimental results demonstrate that WISA can effectively enhance the compatibility of T2V models with real-world physical laws, achieving a considerable improvement on the VideoPhy benchmark. The visual exhibitions of WISA and WISA-32K are available in the https://360cvgroup.github.io/WISA/.
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
Authors: Alexandrin Popescul, Lyle H. Ungar, David M Pennock, Steve Lawrence
Date: 2013-01-10 00:00:00
Tasks: Recommendation Systems

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's [1999] aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. Global probabilistic models coupled with standard Expectation Maximization (EM) learning algorithms tend to drastically overfit in sparse-data situations, as is typical in recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the ResearchIndex library of Computer Science publications show that appropriate mixture models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN). Global probabilistic models also allow more general inferences than local methods like k-NN.
Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval
Authors: Yuanmin Tang, Jing Yu, Keke Gai, Jiamin Zhuang, Gang Xiong, Gaopeng Gou, Qi Wu
Date: 2025-03-21 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Image Retrieval, Zero-Shot Composed Image Retrieval (ZS-CIR)

Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to modify a reference image according to manipulation text to accurately retrieve a target image, especially when the reference image is missing essential target content. In this paper, we propose a novel prediction-based mapping network, named PrediCIR, to adaptively predict the missing target visual content in reference images in the latent space before mapping for accurate ZS-CIR. Specifically, a world view generation module first constructs a source view by omitting certain visual content of a target view, coupled with an action that includes the manipulation intent derived from existing image-caption pairs. Then, a target content prediction module trains a world model as a predictor to adaptively predict the missing visual information guided by user intention in manipulating text at the latent space. The two modules map an image with the predicted relevant information to a pseudo-word token without extra supervision. Our model shows strong generalization ability on six ZS-CIR tasks. It obtains consistent and significant performance boosts ranging from 1.73% to 4.45% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/predicir.
ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
Authors: Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma, Akash Gupta, Samuel Roberts, Ioana Croitoru, Simion-Vlad Bogolin, Jialu Tang, Florian Langer, Vyas Raina, Vatsal Raina, Hanyi Xiong, Vishaal Udandarao, Jingyi Lu, Shiyang Chen, Sam Purkis, Tianshuo Yan, Wenye Lin, Gyungin Shin, Qiaochu Yang, Anh Totti Nguyen, David I. Atkinson, Aaditya Baranwal, Alexandru Coca, Mikah Dang, Sebastian Dziadzio, Jakob D. Kunz, Kaiqu Liang, Alexander Lo, Brian Pulfer, Steven Walton, Charig Yang, Kai Han, Samuel Albanie
Date: 2025-02-13 00:00:00
Tasks: Visual Reasoning

Large Multimodal Models (LMMs) exhibit major shortfalls when interpreting images and, by some measures, have poorer spatial cognition than small children or animals. Despite this, they attain high scores on many popular visual benchmarks, with headroom rapidly eroded by an ongoing surge of model progress. To address this, there is a pressing need for difficult benchmarks that remain relevant for longer. We take this idea to its limit by introducing ZeroBench-a lightweight visual reasoning benchmark that is entirely impossible for contemporary frontier LMMs. Our benchmark consists of 100 manually curated questions and 334 less difficult subquestions. We evaluate 20 LMMs on ZeroBench, all of which score 0.0%, and rigorously analyse the errors. To encourage progress in visual understanding, we publicly release ZeroBench.