Miracle-dz/UniFAR-FTUs huggingface dataset:
HCR-Net: A Hybrid of Classification and Regression Network for Object Pose Estimation

Object pose estimation from a single image is a fundamental and challenging problem in computer vision and robotics. Generally, current methods treat pose estimation as a classification or a regression problem. However, regression based methods usually suffer from the issue of imbalanced training data, while classification methods are difficult to discriminate nearby poses. In this paper, a hybrid CNN model, which we call it HCR-Net that integrates both a classification network and a regression network, is proposed to deal with these issues. Our model is inspired by that regression methods can get better accuracy on homogeneously distributed datasets while classification methods are more effective for coarse quantization of the poses even if the dataset is not well balanced. The classification methods and the regression methods essentially complement each other. Thus we integrate both them into a neural network in a hybrid fashion and train it end-to-end with two novel loss functions. As a result, our method surpass the state-of-the-art methods, even with imbalanced training data and much less data augmentation. The experimental results on the challenging Pascal3D+ database demonstrate that our method outperforms the state-of-the-arts significantly, achieving improvements on ACC and AVP metrics up to 4% and 6%, respectively.
Local Minima, Heavy Tails, and Search Effort for GBFS

Problem difficulty for greedy best first search (GBFS) is not entirely understood, though existing work points to deep local minima and poor correlation between the h-values and the distance to goal as factors that have significant negative effect on the search effort. In this work, we show that there is a very strong exponential correlation between the depth of the single deepest local minima encountered in a search and the overall search effort. Furthermore, we find that the distribution of local minima depth changes dramatically based on the constrainedness of problems, suggesting an explanation for the previously observed heavy-tailed behavior in GBFS. In combinatorial search, a similar result led to the use of randomized restarts to escape deep subtrees with no solution and corresponding significant speed-ups. We adapt this method and propose a randomized restarting GBFS variant that improves GBFS performance by escaping deep local minima, and does so even in the presence of other, randomization-based, search enhancements.
Periorbital desmoplastic squamous cell carcinoma

ABSTRACT Desmoplasia is the formation of a dense collagenous stroma around a neoplasm. It occurs in a variety of malignancies including squamous cell carcinoma (SCC). While desmoplasia is uncommonly seen in cutaneous SCC, it is an independent risk factor for recurrence and metastasis. We report a case series of desmoplastic SCC in the periorbital region. Seven cases were identified: the median age was 68, four were men. The mean follow-up was 48 months. Two patients (29%) had aggressive local recurrence: one required salvage surgery including orbital exenteration, parotidectomy, and neck dissection to excise involved parotid and cervical lymph nodes; the other required repeat excision and adjuvant radiotherapy. Desmoplastic SCC is an uncommon but highly aggressive subtype. In the periorbital region, due to the high risk of orbital invasion, it is potentially sight and life-threatening.
Stochastic Fractional Hamiltonian Monte Carlo

In this paper, we propose a novel stochastic fractional Hamiltonian Monte Carlo approach which generalizes the Hamiltonian Monte Carlo method within the framework of fractional calculus and L\'evy diffusion. Due to the large ``jumps'' introduced by L\'evy noise and momentum term, the proposed dynamics is capable of exploring the parameter space more efficiently and effectively. We have shown that the fractional Hamiltonian Monte Carlo could sample the multi-modal and high-dimensional target distribution more efficiently than the existing methods driven by Brownian diffusion. We further extend our method for optimizing deep neural networks. The experimental results show that the proposed stochastic fractional Hamiltonian Monte Carlo for training deep neural networks could converge faster than other popular optimization schemes and generalize better.
Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks

Modeling the evolution of user feedback and social links in dynamic social networks is of considerable significance, because it is the basis of many applications, including recommendation systems and user behavior analyses. Most of the existing methods in this area model user behaviors separately and consider only certain aspects of this problem, such as dynamic preferences of users, dynamic attributes of items, evolutions of social networks, and their partial integration. This work proposes a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user feedback and social links. The framework considers the dynamic user preferences, dynamic item attributes, and time-dependent social links in time evolving social networks. Experimental results conducted on two real-world datasets demonstrate that our proposed model performs remarkably better than state-of-the-art methods.
Iterative Metric Learning for Imbalance Data Classification

In many classification applications, the amount of data from different categories usually vary significantly, such as software defect predication and medical diagnosis. Under such circumstances, it is essential to propose a proper method to solve the imbalance issue among the data. However, most of the existing methods mainly focus on improving the performance of classifiers rather than searching for an appropriate way to find an effective data space for classification. In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and construct an effective data space for classification. Given the imbalance training data, it is important to select a subset of training samples for each testing data. Thus, we aim to find a more stable neighborhood for testing data using the iterative metric learning strategy. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.
Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective

A job title usually implies the responsibility and the rank of a job position. While traditional job title analysis has been focused on studying the responsibilities of job titles, this paper attempts to reveal the rank of job titles. Specifically, we propose to extract job title hierarchy from employees' career trajectories. Along this line, we first quantify the Difficulty of Promotion (DOP) from one job title to another by a monotonic transformation of the length of tenure based on the assumption that a longer tenure usually implies a greater difficulty to be promoted. Then, the difference of two job title ranks is defined as a mapping of the DOP observed from job transitions. A Gaussian Bayesian Network (GBN) is adopted to model the joint distribution of the job title ranks and the DOPs in a career trajectory. Furthermore, a stochastic algorithm is developed for inferring the posterior job title rank by a given collection of DOPs in the GBN. Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method.
Finite Model Reasoning in Hybrid Classes of Existential Rules

Two paradigmatic restrictions that have been studied for ensuring the decidability of query answering under existential rules are guardedness and stickiness. With the aim of consolidating these restrictions, a flexible condition, called tameness, has been proposed a few years ago, which relies on hybrid reasoning, i.e., a combination of forward and backward procedures. The complexity of query answering under this hybrid class of existential rules is by now well-understood. However, the complexity of finite query answering, i.e., query answering under finite models, has remained an open problem. Closing this problem is the main goal of this work.
DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation

Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this paper proposes a dual-embedding based deep latent factor model named DELF for recommendation with implicit feedback. In addition to learning a single embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users). We employ an attentive neural method to discriminate the importance of interacted users/items for dual-embedding learning. We further introduce a neural network architecture to incorporate dual embeddings for recommendation. A novel attempt of DELF is to model each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of DELF on item recommendation.
Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone

Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).
Whole-Body MRI with Diffusion-Weighted Imaging in Bone Metastases: A Narrative Review

Whole body magnetic resonance imaging (MRI) with diffusion-weighted imaging (WB-MRI-DWI) is currently emerging as a diagnostic technique in the evaluation of bone metastases from breast, prostate, lung, thyroid, and melanoma tumors. The most relevant articles regarding the detection of solid tumor bone metastases with MRI have been reviewed and cited. The imaging methods currently used in the detection of bone metastases are bone scintigraphy, computed tomography (CT), and positron emission tomography (PET/CT) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose (18F-FDG PET/CT). WB-MRI-DWI allows qualitative and quantitative evaluation of focal lesions through signal intensity evaluation on DWI images and the reconstruction of the apparent diffusion coefficient (ADC) map. In prostate and breast cancer, WB-MRI-DWI is useful in assessing the response of bone lesions to therapy and to detecting early non-responders, while in lung cancer the method shows a similar sensitivity to 18F-FDG PET/CT in the detection of bone metastases. In bone metastases of thyroid tumors and melanoma, the WB-MRI-DWI shows a higher sensitivity when compared to 18F-FDG PET/CT. With a standardization of the WB-MRI-DWI protocol, this method seems to play an important role in the diagnosis of bone solid tumor metastases.
Learning Sequential Correlation for User Generated Textual Content Popularity Prediction

Popularity prediction of user generated textual content is critical for prioritizing information in the web, which alleviates heavy information overload for ordinary readers. Most previous studies model each content instance separately for prediction and thus overlook the sequential correlations between instances of a specific user. In this paper, we go deeper into this problem based on the two observations for each user, i.e., sequential content correlation and sequential popularity correlation. We propose a novel deep sequential model called User Memory-augmented recurrent Attention Network (UMAN). This model encodes the two correlations by updating external user memories which is further leveraged for target text representation learning and popularity prediction. The experimental results on several real-world datasets validate the benefits of considering these correlations and demonstrate UMAN achieves best performance among several strong competitors.
Label-Free Quantitative Proteomics Combined with Biological Validation Reveals Activation of Wnt/β-Catenin Pathway Contributing to Trastuzumab Resistance in Gastric Cancer

Resistance to trastuzumab, which specifically target HER2-positive breast and gastric cancer, can develop ultimately in cancer patients. However, the underlying mechanisms of resistance in gastric cancer have not been fully elucidated. Here, we established trastuzumab-resistant MKN45 and NCI N87 gastric cancer sublines from their parental cells. The resistant cells exhibited characteristics of epithelial-mesenchymal transition (EMT) and acquired higher migratory and invasive capacities. To exploit the activated pathways and develop new strategies to overcome trastuzumab resistance, we investigated MKN45 and MKN45/R cells via label-free quantitative proteomics, and found pathways that were altered significantly in MKN45/R cells, with the Wnt/β-catenin pathway being the most significant. We further confirmed the activation of this pathway by detecting its key molecules in MKN45/R and NCI N87/R cells via Western blot, in which Wnt3A, FZD6, and CTNNB1 increased, whereas GSK-3β decreased, manifesting the activation of the Wnt/β-catenin pathway. Correspondingly, inhibition of Wnt/β-catenin pathway by ICG-001, a specific Wnt/β-catenin inhibitor, preferentially reduced proliferation and invasion of trastuzumab-resistant cells and reversed EMT. Concurringly, CTNNB1 knockdown in stable cell lines potently sensitized cells to trastuzumab and induced more apoptosis. Taken together, our study demonstrates that the Wnt/β-catenin pathway mediates trastuzumab resistance, and the combination of Wnt/β-catenin inhibitors with trastuzumab may be an effective treatment option.
Generalization-Aware Structured Regression towards Balancing Bias and Variance

Attaining the proper balance between underfitting and overfitting is one of the central challenges in machine learning. It has been approached mostly by deriving bounds on generalization risks of learning algorithms. Such bounds are, however, rarely controllable. In this study, a novel bias-variance balancing objective function is introduced in order to improve generalization performance. By utilizing distance correlation, this objective function is able to indirectly control a stability-based upper bound on a model's expected true risk. In addition, the Generalization-Aware Collaborative Ensemble Regressor (GLACER) is developed, a model that bags a crowd of structured regression models, while allowing them to collaborate in a fashion that minimizes the proposed objective function. The experimental results on both synthetic and real-world data indicate that such an objective enhances the overall model's predictive performance. When compared against a broad range of both traditional and structured regression models GLACER was ~10-56% and ~49-99% more accurate for the task of predicting housing prices and hospital readmissions, respectively.
Comment on “Sedimentary DNA from a submerged site reveals wheat in the British Isles 8000 years ago”

Smith et al. (Reports, 27 February 2015, p. 998) identify wheat DNA from an 8000-calendar-years-before-the-present archaeological site in southern England and conclude that wheat was traded to Britain 2000 years before the arrival of agriculture. The DNA samples are not dated, either directly or from circumstantial evidence, so there is no chronological evidence to support the claim.
Service Exchange Problem

In this paper, we study the service exchange problem where each agent is willing to provide her service in order to receive in exchange the service of someone else. We assume that agent's preference depends both on the service that she receives and the person who receives her service. This framework is an extension of the housing market problem to preferences including a degree of externalities. We investigate the complexity of computing an individually rational and Pareto efficient allocation of services to agents for ordinal preferences, and the complexity of computing an allocation which maximizes either the utility sum or the utility of the least served agent for cardinal preferences.
Regulation of translation initiation factor eIF2B at the hub of the integrated stress response

Phosphorylation of the translation initiation factor eIF2 is one of the most widely used and well‐studied mechanisms cells use to respond to diverse cellular stresses. Known as the integrated stress response (ISR), the control pathway uses modulation of protein synthesis to reprogram gene expression and restore homeostasis. Here the current knowledge of the molecular mechanisms of eIF2 activation and its control by phosphorylation at a single‐conserved phosphorylation site, serine 51 are discussed with a major focus on the regulatory roles of eIF2B and eIF5 where a current molecular view of ISR control of eIF2B activity is presented. How genetic disorders affect eIF2 or eIF2B is discussed, as are syndromes where excess signaling through the ISR is a component. Finally, studies into the action of recently identified compounds that modulate the ISR in experimental systems are discussed; these suggest that eIF2B is a potential therapeutic target for a wide range of conditions.
Fuzzy Adaptation Algorithms' Control for Robot Manipulators with Uncertainty Modelling Errors

A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.
Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks

Aspect sentiment classification, a challenging task in sentiment analysis, has been attracting more and more attention in recent years. In this paper, we highlight the need for incorporating the importance degrees of both words and clauses inside a sentence and propose a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification. Specifically, we first adopt sentence-level discourse segmentation to segment a sentence into several clauses. Then, we leverage multiple Bi-directional LSTM layers to encode all clauses and propose a word-level attention layer to capture the importance degrees of words in each clause. Third and finally, we leverage another Bi-directional LSTM layer to encode the outputs from the former layers and propose a clause-level attention layer to capture the importance degrees of all the clauses inside a sentence. Experimental results on the laptop and restaurant datasets from SemEval-2015 demonstrate the effectiveness of our proposed approach to aspect sentiment classification.
Grounded Language Learning: Where Robotics and NLP Meet

Grounded language acquisition is concerned with learning the meaning of language as it applies to the physical world. As robots become more capable and ubiquitous, there is an increasing need for non-specialists to interact with and control them, and natural language is an intuitive, flexible, and customizable mechanism for such communication. At the same time, physically embodied agents offer a way to learn to understand natural language in the context of the world to which it refers. This paper gives an overview of the research area, selected recent advances, and some future directions and challenges that remain.
Women’s prepregnancy lipid levels and number of children: a Norwegian prospective population-based cohort study

Objective To study prepregnancy serum lipid levels and the association with the number of children. Design Prospective, population-based cohort. Setting Linked data from the Cohort of Norway and the Medical Birth Registry of Norway. Participants 2645 women giving birth to their first child during 1994–2003 (488 one-child mothers and 2157 women with ≥2 births) and 1677 nulliparous women. Main outcome measures ORs for no and one lifetime pregnancy (relative to ≥2 pregnancies) obtained by multinomial logistic regression, adjusted for age at examination, education, body mass index (BMI), smoking, time since last meal and oral contraceptive use. Results Assessed in quintiles, higher prepregnant triglyceride (TG) and TG to high-density lipoprotein (TG:HDL-c) ratio levels were associated with increased risk of one lifetime pregnancy compared with having ≥2 children. Compared with the highest quintile, women in the lowest quintile of HDL cholesterol levels had an increased risk of one lifetime pregnancy (OR 1.7, 95% CI 1.2 to 2.4), as were women with the highest low-density lipoprotein (LDL) cholesterol, TG and TG:HDL-c ratio quintiles (compared with the lowest) (OR 1.2, 95% CI 0.8 to 1.7; OR 2.2, 95% CI 1.5 to 3.2; and OR 2.2, 95% CI 1.5 to 3.2, respectively). Similar effects were found in women with BMI≥25 and the highest LDL and total cholesterol levels in risk of lifetime nulliparity. Conclusion Women with unfavourable prepregnant lipid profile had higher risk of having no or only one child. These findings substantiate an association between prepregnant serum lipid levels and number of children. Previously observed associations between low parity and increased cardiovascular mortality may in part be due to pre-existing cardiovascular disease lipid risk factors.