Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancements in Dueling Bandits

The dueling bandits problem is an online learning framework where learning happens ``on-the-fly'' through preference feedback, i.e., from comparisons between a pair of actions. Unlike conventional online learning settings that require absolute feedback for each action, the dueling bandits framework assumes only the presence of (noisy) binary feedback about the relative quality of each pair of actions. The dueling bandits problem is well-suited for modeling settings that elicit subjective or implicit human feedback, which is typically more reliable in preference form. In this survey, we review recent results in the theories, algorithms, and applications of the dueling bandits problem. As an emerging domain, the theories and algorithms of dueling bandits have been intensively studied during the past few years. We provide an overview of recent advancements, including algorithmic advances and applications. We discuss extensions to standard problem formulation and novel application areas, highlighting key open research questions in our discussion.
Cross-modal Bidirectional Translation via Reinforcement Learning

The inconsistent distribution and representation of image and text make it quite challenging to measure their similarity, and construct correlation between them. Inspired by neural machine translation to establish a corresponding relationship between two entirely different languages, we attempt to treat images as a special kind of language to provide visual descriptions, so that translation can be conduct between bilingual pair of image and text to effectively explore cross-modal correlation. Thus, we propose Cross-modal Bidirectional Translation (CBT) approach, and further explore the utilization of reinforcement learning to improve the translation process. First, a cross-modal translation mechanism is proposed, where image and text are treated as bilingual pairs, and cross-modal correlation can be effectively captured in both feature spaces of image and text by bidirectional translation training. Second, cross-modal reinforcement learning is proposed to perform a bidirectional game between image and text, which is played as a round to promote the bidirectional translation process. Besides, both inter-modality and intra-modality reward signals can be extracted to provide complementary clues for boosting cross-modal correlation learning. Experiments are conducted to verify the performance of our proposed approach on cross-modal retrieval, compared with 11 state-of-the-art methods on 3 datasets.
Collaborative and Attentive Learning for Personalized Image Aesthetic Assessment

The ever-increasing volume of visual images has stimulated the demand for organizing such data by aesthetic quality. Automatic and especially learning based aesthetic assessment methods have shown potential by recent works. Existing image aesthetic prediction is often user-agnostic which may ignore the fact that the rating to an image can be inherently individual. We fill this gap by formulating the personalized image aesthetic assessment problem with a novel learning method. Specifically, we collect user-image textual reviews in addition with visual images from the public dataset to organize a review-augmented benchmark. Using this enriched dataset, we devise a deep neural network with a user/image relation encoding input for collaborative filtering. Meanwhile an attentive mechanism is designed to capture the user-specific taste for image semantic tags and regions of interest by fusing the image and user's review. Extensive and promising experimental results on the review-augmented benchmark corroborate the efficacy of our approach.
Densely Connected CNN with Multi-scale Feature Attention for Text Classification

Text classification is a fundamental problem in natural language processing. As a popular deep learning model, convolutional neural network (CNN) has demonstrated great success in this task. However, most existing CNN models apply convolution filters of fixed window size, thereby unable to learn variable n-gram features flexibly. In this paper, we present a densely connected CNN with multi-scale feature attention for text classification. The dense connections build short-cut paths between upstream and downstream convolutional blocks, which enable the model to compose features of larger scale from those of smaller scale, and thus produce variable n-gram features. Furthermore, a multi-scale feature attention is developed to adaptively select multi-scale features for classification. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on five benchmark datasets. Attention visualization further reveals the model's ability to select proper n-gram features for text classification.
Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning

Pedestrian attributes recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset. It is observed that semantic pedestrian attributes to be recognised tend to show semantic or visual spatial correlation. Attributes can be grouped by the correlation while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)'s super capability of learning context correlations, this paper proposes an end-to-end Grouping Recurrent Learning (GRL) model that takes advantage of the intra-group mutual exclusion and inter-group correlation to improve the performance of pedestrian attribute recognition. Our GRL method starts with the detection of precise body region via Body Region Proposal followed by feature extraction from detected regions. These features, along with the semantic groups, are fed into RNN for recurrent grouping attribute recognition, where intra group correlations can be learned. Extensive empirical evidence shows that our GRL model achieves state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.
Filter‐based isolation, enrichment, and characterization of circulating tumor cells

Isolation of circulating tumor cells (CTCs) from blood has long been a challenge due to the rarity and heterogeneity of these cells. Detection technologies have predominantly focused on different molecular or physical properties of CTCs. Size‐based isolation approach using microfilters have been widely used to capture CTCs because of the difference in size and stiffness of the cells compared to other hemocytes. Isolation of rare cells based on their size was the original CTC enrichment technique and it demonstrated a simple yet rapid method that enhanced the recovery of cells with high throughput. In this review, we highlight key technical aspects of filter‐based isolation, detection, and characterization of CTCs, and compare the clinical performance of filter‐based devices with the approved platforms and immunoassays used for the analysis of CTCs. We have also discussed future prospective and incorporation of advances in immunochemistry technique into the filter‐based platforms for enhancing the utility in clinical settings.
PBRM1 bromodomains variably influence nucleosome interactions and cellular function

Chromatin remodelers use bromodomains (BDs) to recognize histones. Polybromo 1 (PBRM1 or BAF180) is hypothesized to function as the nucleosome-recognition subunit of the PBAF chromatin-remodeling complex and is frequently mutated in clear cell renal cell carcinoma (ccRCC). Previous studies have applied in vitro methods to explore the binding specificities of the six individual PBRM1 BDs. However, BD targeting to histones and the influence of neighboring BD on nucleosome recognition have not been well characterized. Here, using histone microarrays and intact nucleosomes to investigate the histone-binding characteristics of the six PBRM1 BDs individually and combined, we demonstrate that BD2 and BD4 of PBRM1 mediate binding to acetylated histone peptides and to modified recombinant and cellular nucleosomes. Moreover, we show that neighboring BDs variably modulate these chromatin interactions, with BD1 and BD5 enhancing nucleosome interactions of BD2 and BD4, respectively, whereas BD3 attenuated these interactions. We also found that binding pocket missense mutations in BD4 observed in ccRCC disrupt PBRM1–chromatin interactions and that these mutations in BD4, but not similar mutations in BD2, in the context of full-length PBRM1, accelerate ccRCC cell proliferation. Taken together, our biochemical and mutational analyses have identified BD4 as being critically important for maintaining proper PBRM1 function and demonstrate that BD4 mutations increase ccRCC cell growth. Because of the link between PBRM1 status and sensitivity to immune checkpoint inhibitor treatment, these data also suggest the relevance of BD4 as a potential clinical target.
Feature Hashing for Network Representation Learning

The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework. There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. The second is a decoder to hash these vectors into a lower dimensional feature space. More specifically, we firstly derive a proximity measurement called expected distance as target which combines position distribution and co-occurrence statistics of nodes over random walks so as to build a proximity matrix, then introduce a set of T different hash functions into feature hashing to generate uniformly distributed vector representations of nodes from the proximity matrix. Compared with the existing state-of-the-art network representation learning approaches, node2hash shows a competitive performance on multi-class node classification and link prediction tasks on three real-world networks from various domains.
Combining Opinion Pooling and Evidential Updating for Multi-Agent Consensus

The evidence available to a multi-agent system can take at least two distinct forms. There can be direct evidence from the environment resulting, for example, from sensor measurements or from running tests or experiments. In addition, agents also gain evidence from other individuals in the population with whom they are interacting. We, therefore, envisage an agent's beliefs as a probability distribution over a set of hypotheses of interest, which are updated either on the basis of direct evidence using Bayesian updating, or by taking account of the probabilities of other agents using opinion pooling. This paper investigates the relationship between these two processes in a multi-agent setting. We consider a possible Bayesian interpretation of probability pooling and then explore properties for pooling operators governing the extent to which direct evidence is diluted, preserved or amplified by the pooling process. We then use simulation experiments to show that pooling operators can provide a mechanism by which a limited amount of direct evidence can be efficiently propagated through a population of agents so that an appropriate consensus is reached. In particular, we explore the convergence properties of a parameterised family of operators with a range of evidence propagation strengths.
Advances and Challenges in Privacy Preserving Planning

Collaborative privacy-preserving planning (CPPP) is a multi-agent planning task in which agents need to achieve a common set of goals without revealing certain private information. CPPP has gained attention in recent years as an important sub area of multi agent planning, presenting new challenges to the planning community. In this paper we describe recent advancements, and outline open problems and future directions in this field. We begin with describing different models of privacy, such as weak and strong privacy, agent privacy, and cardinality preserving privacy. We then discuss different solution approaches, focusing on the two prominent methods --- joint creation of a global coordination scheme first, followed by independent planning to extend the global scheme with private actions; and collaborative local planning where agents communicate information concerning their planning process. In both cases a heuristic is needed to guide the search process. We describe several adaptations of well known classical planning heuristic to CPPP, focusing on the difficulties in computing the heuristic without disclosing private information.
The use of the international classification of functioning, disability and health in an interactive perspective: the assessment and intervention of students’ additional support needs in Portugal

Abstract Purpose: Evaluating the influence of person-environment interactions on students’ performance is a fundamental requirement for planning individualized educational interventions. Such understanding grounded the use of the International Classification of Functioning, Disability and Health as a reference framework to support special needs assessment in the Portuguese educational system. This study sought to investigate the extent to which special education teams reported relationships between Body Functions, Activities and Participation, and Environmental Factors in Individualized Education Programmes for students with additional support needs and what types of relations were mostly described. Materials and methods: Using content analysis, 176 Individualized Education Programmes were examined. A coding scheme based on the International Classification of Functioning, Disability and Health was developed to categorize and quantify code-relations. Results and conclusions: Code-relations consisted in 6.1% out of the total of meaning units found in textual segments concerning assessment and intervention processes. Code-relations were chiefly focused on mental functions, learning and applying knowledge, and products and technology. Intervention plans were predominantly presented as separate lists of goals and strategies, focusing Activities and Participation (67.8%), Body Functions (16.1%) and Environmental Factors (16.2%). Within the reduced amount of contents in which there was a match between goals and strategies, only 8.2% were directly connected with assessment data. Recommendations are made for the implementation of an interactive approach when using the International Classification of Functioning, Disability and Health in educational contexts. Implications for rehabilitation The adoption of the International Classification of Functioning, Disability and Health in the educational context goes beyond the use of a universal language including, as well, the potential to foster a multidimensional and comprehensive approach to students’ needs. Professionals’ approach in special needs assessment is often partial and segmented, suggesting a narrow understanding of the relationships between body functions, activities and participation, and environmental factors. Our findings support the need for an expanded focus on person–environment interactions, considering students’ participation in different domains of life – besides learning – as well as the impact of environmental barriers over students’ participation; Training programmes centred on a biopsychosocial understanding of human functioning, the establishment of a transdisciplinary collaborative culture and the use of dynamic assessment tools may equip professionals with appropriate conditions to use the International Classification of Functioning, Disability and Health within an interactive perspective.
An Appearance-and-Structure Fusion Network for Object Viewpoint Estimation

Automatic object viewpoint estimation from a single image is an important but challenging problem in machine intelligence community. Although impressive performance has been achieved, current state-of-the-art methods still have difficulty to deal with the visual ambiguity and structure ambiguity in real world images. To tackle these problems, a novel Appearance-and-Structure Fusion network, which we call it ASFnet that estimates viewpoint by fusing both appearance and structure information, is proposed in this paper. The structure information is encoded by precise semantic keypoints and can help address the visual ambiguity. Meanwhile, distinguishable appearance features contribute to overcoming the structure ambiguity. Our ASFnet integrates an appearance path and a structure path to an end-to-end network and allows deep features effectively share supervision from both the two complementary aspects. A convolutional layer is learned to fuse the two path results adaptively. To balance the influence from the two supervision sources, a piecewise loss weight strategy is employed during training. Experimentally, our proposed network outperforms state-of-the-art methods on a public PASCAL 3D+ dataset, which verifies the effectiveness of our method and further corroborates the above proposition.
Adaboost with Auto-Evaluation for Conversational Models

We propose a boosting method for conversational models to encourage them to generate more human-like dialogs. In our method, we consider existing conversational models as weak generators and apply Adaboost to update those models. However, conventional Adaboost cannot be directly applied on conversational models. Because for conversational models, conventional Adaboost cannot adaptively adjust the weight on the instance for subsequent learning, result from the simple comparison between the true output y (to an input x) and its corresponding predicted output y' cannot directly evaluate the learning performance on x. To address this issue, we develop the Adaboost with Auto-Evaluation (called AwE). In AwE, an auto-evaluator is proposed to evaluate the predicted results, which makes it applicable to conversational models. Furthermore, we present the theoretical analysis that the training error drops exponentially fast only if certain assumption over the proposed auto-evaluator holds. Finally, we empirically show that AwE visibly boosts the performance of existing single conversational models and also outperforms the other ensemble methods for conversational models.
Low Interleukin-7 Receptor Messenger RNA Expression Is Independently Associated With Day 28 Mortality in Septic Shock Patients*

Objectives: Septic shock is the primary cause of death in ICUs. A better comprehension of its pathophysiology, in particular, the immune alteration mechanisms, opened new therapeutic perspectives such as the recombinant interleukin-7. The use of biomarkers could improve the identification of eligible patients for this therapy. The soluble form of the interleukin-7 appears as a promising candidate in this regard since an association between its high plasmatic level and mortality in critically ill patients has been demonstrated. Because there are no data available on the transcriptional regulation of the interleukin-7 receptor in such patients, this study aimed to explore the expression level of different interleukin-7 receptor transcripts after septic shock and evaluate their association with mortality. Design: Retrospective discovery cohort (30 patients) and validation cohort (177 patients). Setting: Two French ICUs (discovery study) and six French ICUs (validation study). Patients: Adult septic shock patients. Interventions: None. Measurements and Main Results: The quantification of several interleukin-7 receptor transcripts using specific reverse transcription quantitative polymerase chain reaction designs allowed for global evaluation of interleukin-7 receptor gene expression in whole blood. In the discovery cohort, all interleukin-7 receptor transcripts studied were expressed at lower levels in septic shock patients than in healthy volunteers. Interleukin-7 receptor gene expression at day 3 after septic shock diagnosis was associated with day 28 mortality. Patients at a lower risk of death showed higher expression levels. These results were confirmed in the independent validation cohort. Interestingly, using a threshold obtained on the discovery cohort, we observed in the validation cohort a high negative predictive value for day 28 mortality for the transcript encoding the membrane form of interleukin-7 receptor (0.86; 95% CI, 0.79–0.93). Conclusions: Interleukin-7 receptor transcripts appear as biomarkers of impaired adaptive immune response in septic shock patients and as a promising tool for patient stratification in clinical trials evaluating immunoadjuvant therapies.
Interactive Learning and Decision Making: Foundations, Insights & Challenges

Designing "teams of intelligent agents that successfully coordinate and learn about their complex environments inhabited by other agents (such as humans)" is one of the major goals of AI, and it is the challenge that I aim to address in my research. In this paper I give an overview of some of the foundations, insights and challenges in this field of Interactive Learning and Decision Making.
Dilated Convolutional Network with Iterative Optimization for Continuous Sign Language Recognition

This paper presents a novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition. Generally, a continuous sign language recognition system consists of visual input encoder for feature extraction and a sequence learning model to learn the correspondence between the input sequence and the output sentence-level labels. We use a 3D residual convolutional network (3D-ResNet) to extract visual features. After that, a stacked dilated convolutional network with Connectionist Temporal Classification (CTC) is applied for learning the mapping between the sequential features and the text sentence. The deep network is hard to train since the CTC loss has limited contribution to early CNN parameters. To alleviate this problem, we design an iterative optimization strategy to train our architecture. We generate pseudo-labels for video clips from sequence learning model with CTC, and fine-tune the 3D-ResNet with the supervision of pseudo-labels for a better feature representation. We alternately optimize feature extractor and sequence learning model with iterative steps. Experimental results on RWTH-PHOENIX-Weather, a large real-world continuous sign language recognition benchmark, demonstrate the advantages and effectiveness of our proposed method.
Synergistic cross‐talk of hedgehog and interleukin‐6 signaling drives growth of basal cell carcinoma

Persistent activation of hedgehog (HH)/GLI signaling accounts for the development of basal cell carcinoma (BCC), a very frequent nonmelanoma skin cancer with rising incidence. Targeting HH/GLI signaling by approved pathway inhibitors can provide significant therapeutic benefit to BCC patients. However, limited response rates, development of drug resistance, and severe side effects of HH pathway inhibitors call for improved treatment strategies such as rational combination therapies simultaneously inhibiting HH/GLI and cooperative signals promoting the oncogenic activity of HH/GLI. In this study, we identified the interleukin‐6 (IL6) pathway as a novel synergistic signal promoting oncogenic HH/GLI via STAT3 activation. Mechanistically, we provide evidence that signal integration of IL6 and HH/GLI occurs at the level of cis‐regulatory sequences by co‐binding of GLI and STAT3 to common HH‐IL6 target gene promoters. Genetic inactivation of Il6 signaling in a mouse model of BCC significantly reduced in vivo tumor growth by interfering with HH/GLI‐driven BCC proliferation. Our genetic and pharmacologic data suggest that combinatorial HH‐IL6 pathway blockade is a promising approach to efficiently arrest cancer growth in BCC patients.