Epilepsy is a widespread neurological disorder characterized by highly patient-specific EEG patterns. Existing EEG-based seizure detection methods either train individualized models for each patient or adapt models pre-trained on known patients to new ones. However, when encountering previously unseen patients, these methods typically require retraining or fine-tuning, which limits their practical utility in clinical settings. This limitation can be linked to biases caused by patient-specific variations, which obscure the underlying pathological patterns of seizures. To address this, we propose an evidential multi-view framework that reinforces the learning of core epileptic features by promoting consistency across multiple views and reducing reliance on high-uncertainty, patient-specific segments. Specifically, we introduce Bias-guided Fisher-Evidential Multi-View Learning (BF-EML) to guide the model toward discovering intrinsic seizure patterns. BF-EML employs a two-stage training architecture: In Stage 1, we use the Fisher Information Matrix to reorder EEG segments by uncertainty and deliberately train a biased feature generator on low-evidence segments. In Stage 2, we design a dual-branch network where the biased and unbiased branches are alternately trained, encouraging the unbiased branch to reduce its reliance on patient-specific biases. Finally, we introduce a shift-calibrated fusion strategy to enhance the consistency of pathogenic feature integration. Extensive experiments on public datasets and a clinical dataset demonstrate that our method achieves superior performance in both single- and multi-patient scenarios. Importantly, it generalizes well to unseen patients without the need for retraining.

Multi-modal fusion effectively integrates diverse data sources, i.e., prices and financial news, to capture complementary information, significantly enhancing stock market prediction. This survey presents a comprehensive survey of multi-modal fusion approaches in stock prediction, proposing a generalized pipeline and a novel hierarchical taxonomy. First, we identify five typical data modalities, detail their feature extraction methods, and summarize publicly available datasets. Second, we review existing fusion methods under a unified taxonomy comprising two main paradigms: Intra-stock Multi-modal Fusion (covering early feature-level to late decision-level integration) and Inter-stock Multi-modal Fusion (capturing inter-stock relational modalities via graph-based modeling). We comprehensively compare their working mechanisms, strengths, and limitations. Finally, we highlight persistent challenges and promising directions for future researches.

Recent works in domain adaptive object detection (DAOD) have demonstrated integrating domain adaptation modules into detectors can improve the models' transferability. Nevertheless, given the presence of multiple objects within an image and uncertainties in their localizations, adapting features to diverse objects faces a potential challenge in distinguishing foreground from background. This may hurt the detector's discriminability. Furthermore, traditional methods solely rely on category information for foreground object identification, thereby overlooking the significant category-agnostic information, namely objectness features, and leading to a negative transfer. In this paper, we propose a novel collaborative architecture named Progressively Decoupled adaptation with Objectness Information (PDOI), which individually adapts proposals and semantics, and decouples them from the training of the detector. PDOI sequentially cascades three parts: a category-agnostic adaptor, a category-conditional adaptor, and a detector. The first adaptor focuses on learning category-agnostic objectness features to enhance cross-domain proposal alignments. Subsequently, the category-conditional adaptor is designed to achieve cross-domain semantic alignments by adaptively learning the semantic attributes within objectness features. Finally, we train the detector using pseudo-proposal/category labels generated by two adaptors. We develop a self-feedback optimization mechanism enabling mutual enhancement between the two adapters and detectors. Our approach consistently outperforms state-of-the-art methods across several DAOD benchmarks.

Text style transfer (TST) aims to vary the style polarity of text while preserving the semantic content.Although recent advancements have demonstrated remarkable progress in short TST, it remains a relatively straightforward task with limited practical applications.The more comprehensive long TST task presents two challenges: (1) existing methods encounter difficulties in accurately evaluating content attributes in multiple words, leading to content degradation; (2) the conventional vanilla style classifier loss encounters obstacles in maintaining consistent style across multiple generated sentences.In this paper, we propose a novel method SC2, where a multilayer Joint Style-Content Weighed (JSCW) module and a Style Consistency loss are designed to address the two issues.The JSCW simultaneously assesses the amounts of style and content attributes within a token, aiming to acquire a lossless content representation and thereby enhancing content preservation.The multiple JSCW layers further progressively refine content representations.We design a style consistency loss to ensure the generated multiple sentences consistently reflect the target style polarity.Moreover, we incorporate a denoising non-autoregressive decoder to accelerate the training.We conduct plentiful experiments and the results show significant improvements of SC2 over competitive baselines.Our code:

Criminal case matching endeavors to determine the relevance between different criminal cases.Conventional methods predict the relevance solely based on instance-level semantic features and neglect the diverse legal factors (LFs), which are associated with diverse court judgments.Consequently, comprehensively representing a criminal case remains a challenge for these approaches.Moreover, extracting and utilizing these LFs for criminal case matching face two challenges: (1) the manual annotations of LFs rely heavily on specialized legal knowledge; (2) overlaps among LFs may potentially harm the model's performance.In this paper, we propose a two-stage framework named Diverse Legal Factor-enhanced Criminal Case Matching (DLF-CCM).Firstly, DLF-CCM employs a multi-task learning framework to pre-train an LF extraction network on a large-scale legal judgment prediction dataset.In stage two, DLF-CCM introduces an LF de-redundancy module to learn shared LF and exclusive LFs.Moreover, an entropy-weighted fusion strategy is introduced to dynamically fuse the multiple relevance generated by all LFs.Experimental results validate the effectiveness of DLF-CCM and show its significant improvements over competitive baselines.Code: https://github.com/jiezhao6/DLF-CCM.

Charge prediction is to automatically predict the judgemental charges for legal cases. To convict a person/unit of a charge, the case description must contain matching instances of the constitutive elements (CEs) of that charge. This knowledge of CEs is a valuable guide for the judge in making final decisions. However, it is far from fully exploited for charge prediction in the literature. In this paper we propose a novel method named Constitutive Elements-guided Charge Prediction (CECP). CECP mimics human's charge identification process to extract potential instances of CEs and generate predictions accordingly. It avoids laborious labeling of matching instances of CEs by a novel reinforcement learning module which progressively selects potentially matching sentences for CEs and evaluates their relevance. The final prediction is generated based on the selected sentences and their relevant CEs. Experiments on two real-world datasets show the superiority of CECP over competitive baselines.

Routing newly posted questions (a.k.a cold questions) to potential answerers with suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task. The existing methods either focus only on embedding the graph structural information and are less effective for newly posted questions, or adopt manually engineered feature vectors that are not as representative as the graph embedding methods. Therefore, we propose to address the challenge of leveraging heterogeneous graph and textual information for cold question routing by designing an end-to-end framework that jointly learns CQA node embeddings and finds best answerers for cold questions. We conducted extensive experiments to confirm the usefulness of incorporating the textual information from question tags and demonstrate that an end-2-end framework can achieve promising performances on routing newly posted questions asked by both existing users and newly registered users.

This work studies product question answering (PQA) which aims to answer product-related questions based on customer reviews. Most recent PQA approaches adopt end2end semantic matching methodologies, which map questions and answers to a latent vector space to measure their relevance. Such methods often achieve superior performance but it tends to be difficult to interpret why. On the other hand, simple keyword-based search methods exhibit natural interpretability through matched keywords, but often suffer from the lexical gap problem. In this work, we develop a new PQA framework (named Riker) that enjoys the benefits of both interpretability and effectiveness. Riker mines rich keyword representations of a question with two major components, internal word re-weighting and external word association, which predict the importance of each question word and associate the question with outside relevant keywords respectively, and can be jointly trained under weak supervision with large-scale QA pairs. The keyword representations from Riker can be directly used as input to a keyword-based search module, enabling the whole process to be effective while preserving good interpretability. We conduct extensive experiments using Amazon QA and review datasets from 5 different departments, and our results show that Riker substantially outperforms previous state-of-the-art methods in both synthetic settings and real user evaluations. In addition, we compare keyword representations from Riker and those from attention mechanisms popularly used for deep neural networks through case studies, showing that the former are more effective and interpretable.

Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers.If yes, it then outputs a correct one.In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance.Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner.Experimental results on the WIKIQA benchmark show that our framework outperforms the state of the arts by a 6.6% absolute gain under F 1 measure 1 .

Femtocells (small cells) augment the current mobile network by providing users short-range radio access at home and small-business settings. They have rapidly emerged as a promising scheme to alleviate capacity and coverage shortage by offloading traffic from the conventional Macrocells (large cells). Despite its increasing popularity, the real-world Femtocell performance has remained largely unexplored. In this paper, we conduct an in-depth study to assess Femtocell performance and diagnose identified issues in operational carrier networks. We focus on user-deployed Femtocells in a top-tier US mobile network. While the Femtocell generally works well, unanticipated performance degradations and even failures still occur. Contrary to conventional wisdom in the research community, we find that, radio link quality and interference is not the main bottleneck of Femtocells in many real-life usage scenarios. For instance, while Femtocell deployment at blind-zones with no radio coverage is desirable, not all deployments have succeeded; Compared with their Macrocell counterparts, Femtocells exhibit lower speed and larger speed variations, and induce larger delay for data services. Moreover, mobility support for femtocells is incomplete and no seamless migration is available under certain usage scenarios. We pinpoint their root causes, quantify the potential impacts, and share the learned lessons.