Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymmetric distances for binary embeddings

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique — PCA Embedding (PCAE) — which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.
Cybertwin: An Origin of Next Generation Network Architecture

With the fast development of IoE and its applications, the ever increasing mobile Internet traffic and services bring unprecedented challenges, including scalability, mobility, availability, and security, which cannot be addressed by the current clean-slate network architecture. In this article, a cybertwin based next generation network architecture is proposed to accommodate the evolution from end-to-end connection to cloud-toend connection in the future network. As a digital representation of humans or things in the virtual cyberspace, cybertwin serves in multiple capacities, such as communications assistant, network behavior logger, and digital asset owner. The new and unique characteristics of the cybertwin make the proposed network flexible, scalable, reliable, and secure. Further, we advocate a new cloud network operating system which can work in a distributed way through a real-time multi-agent trading platform to allocate 3C (computing, caching, communication) resources. We also propose cloud operator, a new operator that can provide and manage the resources to the end users and offer location and authentication services for humans and things in cyberspace. Some promising and open research topics are discussed to envision the challenges and opportunities of the cybertwin in the future network architecture.
A Deeper Look at Facial Expression Dataset Bias

Datasets play an important role in the progress of facial expression recognition algorithms, but they may suffer from obvious biases caused by different cultures and collection conditions. To look deeper into this bias, we first conduct comprehensive experiments on dataset recognition and cross-dataset generalization tasks, and for the first time, explore the intrinsic causes of the dataset discrepancy. The results quantitatively verify that current datasets have a strong build-in bias, and corresponding analyses indicate that the conditional probability distributions between source and target datasets are different. However, previous researches are mainly based on shallow features with limited discriminative ability under the assumption that the conditional distribution remains unchanged across domains. To address these issues, we further propose a novel deep Emotion-Conditional Adaption Network (ECAN) to learn domain-invariant and discriminative feature representations, which can match not only the marginal distribution but also the class-conditional distribution across domains by exploring the underlying label information of the target dataset. Moreover, the largely ignored expression class distribution bias is also addressed so that the training and testing domains can share similar class distribution. Extensive cross-database experiments on both lab-controlled datasets (CK+, JAFFE, MMI, and Oulu-CASIA) and real-world databases (AffectNet, FER2013, RAF-DB 2.0, and SFEW 2.0) demonstrate that our ECAN can yield competitive performances across various cross-dataset facial expression recognition tasks and outperform the state-of-the-art methods.
Electronic structure and magnetic properties of few-layer Cr2Ge2Te6: the key role of nonlocal electron–electron interaction effects

Atomically-thin magnetic crystals have been recently isolated experimentally, greatly expanding the family of two-dimensional materials. In this Article we present an extensive comparative analysis of the electronic and magnetic properties of , based on density functional theory (DFT). We first show that the often-used approaches fail in predicting the ground-state properties of this material in both its monolayer and bilayer forms, and even more spectacularly in its bulk form. In the latter case, the fundamental gap decreases by increasing the Hubbard-U parameter, eventually leading to a metallic ground state for physically relevant values of U, in stark contrast with experimental data. On the contrary, the use of hybrid functionals, which naturally take into account nonlocal exchange interactions between all orbitals, yields good account of the electronic gap as measured by ARPES. We then calculate all the relevant exchange couplings (and the magneto-crystalline anisotropy energy) for monolayer, bilayer, and bulk with a hybrid functional, with super-cells containing up to 270 atoms, commenting on existing calculations with much smaller super-cell sizes. In the case of bilayer , we show that two distinct intra-layer second-neighbor exchange couplings emerge, a result which, to the best of our knowledge, has not been noticed in the literature.
An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection

Agriculture is a major part of the world economy as it provides food safety. However, in recent years, it has been noted that plants are extensively infected by different diseases. This causes enormous economic losses in agriculture industry around the world. The manual inspection of fruit diseases is a difficult process which can be minimized by using automated methods for detection of plant diseases at the earlier stage. In this paper, a new method is implemented for apple diseases identification and recognition. Three pipeline procedures are followed by preprocessing, spot segmentation, and features extraction, and classification. In the first step, the apple leaf spots are enhanced by a hybrid method which is the conjunction of 3D box filtering, de-correlation, 3D-Gaussian filter, and 3D-median filter. After that, the lesion spots are segmented by the strong correlation-based method and optimized their results by fusion of expectation maximization (EM) segmentation. Finally, the color, color histogram, and local binary pattern (LBP) features are fused by comparison-based parallel fusion. The extracted features are optimized by genetic algorithm and classified by One-vs-All M-SVM. The experimental results are performed on plant village dataset. The proposed methodology is tested for four types of apple disease classes including healthy leaves, Blackrot, Rust, and Scab. The classification accuracy shows the improvement of our method on selected apple diseases. Moreover, the good preprocessing step always produced prominent features which later achieved significant classification accuracy.
Sensitivity analysis : multilinearity and beyond

Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensively studied and implemented in different software packages. These methods usually focus on the study of sensitivity functions and on the impact of a parameter change to the Chan-Darwiche distance. Although not fully recognized, the majority of these results heavily rely on the multilinear structure of atomic probabilities in terms of the conditional probability parameters associated with this type of network. By defining a statistical model through the polynomial expression of its associated defining conditional probabilities, we develop a unifying approach to sensitivity methods applicable to a large suite of models including extensions of Bayesian networks, for instance context-specific and dynamic ones, and chain event graphs. By then focusing on models whose defining polynomial is multilinear, our algebraic approach enables us to prove that the Chan-Darwiche distance is minimized for a certain class of multi-parameter contemporaneous variations when parameters are proportionally covaried.
Adaptation of the Childbirth Experience Questionnaire (CEQ) in China: A multisite cross-sectional study

Background The childbirth experience of women represents a significant aspect of quality care. Due to the lack of a reliable Chinese language tool for assessing childbirth experiences, examples must be adapted from other countries. The aim of this study was to translate an English version of the Childbirth Experience Questionnaire (CEQ) into Chinese and adapt this tool to the Chinese context. Methods A questionnaire validation study was conducted. A forward-backward translation procedure involving the developer of the CEQ was conducted. The data were collected in postnatal wards at 50 birth facilities in 4 regions of Zhejiang Province, China. Women who gave birth vaginally at the investigated facilities during the study period completed an online questionnaire that included the Chinese version of the CEQ (CEQ-C), demographic information and clinical information. Psychometric analyses were performed to assess the internal and content consistency. After subdividing the sample into subsamples, an exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were applied to examine the structural validity. Known-group comparisons were performed to assess the discriminant validity. Results Overall, 1747 women participated in this study. The content validity index (CVI) of the CEQ was 0.92. Based on the comments of the experts combined with the statistical results, we removed 3 items related to pain, sense of control and sense of security and changed 3 items to different dimensions. The CFA supported the four dimensions of the CEQ-C (standard root mean square residual (SRMR) = 0.037, root mean square error of approximation (RMSEA) = 0.036, comparative fit index (CFI) = 0.966, and Tucker-Lewis index (TLI) = 0.959). Cronbach’s alpha of the CEQ-C was 0.88, and McDonald’s omega value was 0.91. The duration of labor, delivery mode, parity, oxytocin augmentation, pain management, companionship, prenatal education and pain experienced exerted significant effects on the women’s childbirth experiences. Conclusions Although some items performed differently in our analysis comparing the English and Chinese versions of the CEQ, the CEQ-C is reliable and valid. Additionally, the CEQ-C is an easy-to-use and promising tool for measuring childbirth experiences among Chinese women in facility settings that can be used to improve the quality of intrapartum care. Efforts are needed to provide women with respectful, evidence-based intrapartum care to facilitate positive childbirth experiences.
Testing allostatic load factor structures among adolescents: A structural equation modeling approach

Allostatic load (AL) represents cumulative biological “wear and tear” that results from chronic stress exposure over time, ultimately increasing risk for chronic disease. A consensus is lacking regarding the best operationalization of AL, particularly for younger, less studied populations. The purpose of this study was to test multiple hypothesized factor structures for AL to determine the best measurement approach for adolescents.
Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields

The visual world is vast and varied, but its variations divide into structured and unstructured factors. We compose free-form filters and structured Gaussian filters, optimized end-to-end, to factorize deep representations and learn both local features and their degree of locality. Our semi-structured composition is strictly more expressive than free-form filtering, and changes in its structured parameters would require changes in free-form architecture. In effect this optimizes over receptive field size and shape, tuning locality to the data and task. Dynamic inference, in which the Gaussian structure varies with the input, adapts receptive field size to compensate for local scale variation. Optimizing receptive field size improves semantic segmentation accuracy on Cityscapes by 1-2 points for strong dilated and skip architectures and by up to 10 points for suboptimal designs. Adapting receptive fields by dynamic Gaussian structure further improves results, equaling the accuracy of free-form deformation while improving efficiency.
Boosting Arabic Named-Entity Recognition With Multi-Attention Layer

Sequence labeling models with recurrent neural network variants, such as long short-term memory (LSTM) and gated recurrent unit (GRU), show promising performance on several natural language processing (NLP) problems, including named-entity recognition (NER). Most existing models utilize word embeddings for capturing similarities between words. However, they lag when handling previously unobserved or infrequent words. Moreover, the attention mechanism has been used to improve sequence labeling tasks. In this paper, we propose an efficient multi-attention layer system for the Arabic named-entity recognition (ANER) task. In addition to word-level embeddings, we adopt character-level embeddings and combine them via an embedding-level attention mechanism. The output is fed into an encoder unit with bidirectional-LSTM, followed by another self-attention layer that is used to boost the system performance. Our model achieves approximately matched F1 score of 91% on the “ANERCorpus. ” The overall experimental results demonstrate that our method is superior to other systems. Our approach using multi-layer attention mechanism yields a new state-of-the-art result for the ANER.
Diagnosing the remnants of binary neutron star merger from GW170817/GRB170817A event

The event GW170817/GRB 170817A, discovered via the successful joint observation of its gravitational wave radiation and its multi-wavelength electromagnetic counterparts, was the first definite "smoking-gun" from the merger of two neutron stars (NSs). However, the remnant of the merger remains unknown. Piro et al. recently claimed that a low-significance X-ray variability in GRB 170817A. By systematically comparing the properties of variability in the afterglow of GRB 170817A and X-ray flares in GRB afterglows, we find that this X-ray variability seems to share similar statistical correlations with X-ray flares in GRB afterglows. We further investigate several possible merger product scenarios to see whether they can produce the observed X-ray variability in GRB 170817A. The first scenario invokes a stable magnetar as the central engine producing the later X-ray variability via differential rotation or fall-back accretion onto the NS. The second scenario invokes a black hole as the central engine with a fall-back accretion process. The final scenario is a central engine with a long-lived supra-massive NS. We find that the first two scenarios have difficulty producing the later X-ray variability, which requires either an impractical NS magnetic field or an extraordinarily large stellar envelope and an extremely long accretion timescale. However, the third scenario seems to be consistent with observations, and the later X-ray variability can be produced by the magnetosphere which is expelled following the collapse of the NS with a $B_p\in(3.6, 13.5)\times10^{13}$ G.
Balance Midpoint Potential Control of Three-Level Boost Converter for Rail Transit Application

The application of the three-level boost converter (TLBC) in a high-frequency isolated auxiliary inverter system is able to reduce the voltage stress of power switches and diodes in the back-end circuit, reduce the volume and weight of energy storage components, and improve the overall power density of the converter system. However, the TLBC topology has its own disadvantage which is the capacitor midpoint potential offset problem. In order to solve this problem, this paper analyzes the limitations of the traditional independent duty control strategy first and then proposes the pulse phase delay control strategy to balance the midpoint potential of TLBC. After the relevant theoretical analysis based on the ac small-signal model of the TLBC, which is controlled by the pulse phase delay control strategy, this paper completes its modeling and simulation by using PSIM & Matlab. Finally, this paper presents some experimental results and combines with the waveforms of the simulation to verify that the proposed control strategy can effectively adjust the bias voltage without changing the TLBC output characteristics.
Effects of sexual/gender minority- and race-based enacted stigma on mental health and substance use in female assigned at birth sexual minority youth.

OBJECTIVE People of color who are also sexual and gender minorities (SGM) experience forms of enacted stigma based on both their racial/ethnic identity and their SGM status. We set out to test the effects of enacted stigma specific to race/ethnicity and SGM identity on mental health and substance use problems among female assigned at birth (FAB) SGM of color. METHOD Data come from a community-based sample of FAB SGM who also identified as racial/ethnic minorities (N = 352). The effects of racial discrimination, SGM victimization, and sexual orientation microaggressions on depression symptoms, anxiety symptoms, alcohol-related problems, and marijuana-related problems were tested using linear regression and negative binomial models. RESULTS Enacted stigma based on both race/ethnicity and SGM status were significant predictors of mental health outcomes and alcohol-related problems within the same model, which suggested that both uniquely contributed to poorer health. There was little support for interactive effects between the multiple forms of enacted stigma. Marijuana-related problems were best explained by enacted stigma based on race/ethnicity only. CONCLUSIONS Racially diverse FAB SGM are at unique risk of experiencing multiple forms of discrimination and aggression based on their minority identities that each contribute negatively to their wellbeing. Consideration of the multiple forms of enacted stigma they face is necessary for understanding health disparities in these populations. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Factorial models to estimate isoleucine requirements for broilers.

The objective of this work was to determine the efficiency of utilization (EU) and produce factorial models for optimal isoleucine (Ile) intake. Six dose-response trials were carried out, three for males and three for females, with 640 Ross 308 in each studied phase. The initial (1-14 days), grower (15-28 days) and finisher (29-42 days) phases were evaluated to cover the growing phase of the broiler chicken. In total, eight treatments were randomly distributed to four replicates of 20 birds each. The treatments consisted of seven crescent levels of Ile and one counter proof to ensure that Ile was the first limiting amino acid in the diet. Dilution technique was applied to produce the levels of Ile and keep the amino acid ratio with lysine. The EU was determined to account for whole body or partitioned for feather-free body (Bff) and feather. Two distinct factorial models were adjusted, M1 and M2. The M2 model was evaluated for one or two EU, being denominated as M2 and M3. When the efficiency was partitioned, the values of 53% and 69% for feather and Bff were determined. The optimal Ile intake estimated for each model were of 275, 908, 1,412 mg of Ile/bird/day (M1); 258, 829, 1,321 mg of Ile/bird/day (M2); and 284, 835, 1,288 mg of Ile/bird/day (M3) for initial, grower and finisher phases respectively. The EU partitioned for feather-free body and feather reduced the biased of the model M3. Overall, higher values of Ile intake are estimated when model M1 is used, which may be the difference in account for body weight gain (M1) or only protein gain (M2 and M3) to estimate the amount of amino acid required for broiler.
Continuous-Time Mean-Variance Portfolio Optimization via Reinforcement Learning

We consider continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting. The problem falls into the entropy-regularized relaxed stochastic control framework recently introduced in Wang et al. (2019). We derive the feedback exploration policy as the Gaussian distribution, with time-decaying variance. Close connections between the entropy-regularized MV and the classical MV are also discussed, including the solvability equivalence and the convergence as exploration decays. Finally, we prove a policy improvement theorem (PIT) for the continuous-time MV problem under both entropy regularization and control relaxation. The PIT leads to an implementable RL algorithm for the continuous-time MV problem. Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors

As the manufacturing industry is heading toward the fourth industrial revolution, smart manufacturing is born at the right moment. By integrating new information technologies, such as the Internet of Things, the cyber-physical system (CPS), big data, and artificial intelligence, smart manufacturing has endowed the factories and shop floors with much intelligence, which is characterized by the organic cooperation among workers, machines, unmanufactured products, and other physical assets. In this situation, endowing these smart physical assets with self-X intelligence and autonomy to make manufacturing resources allocation decisions autonomously has been a vital problem that needs prompt solutions. To solve this problem, this paper deals with training a reasonable knowledge model from the historical shop floor data using a hidden Markov model (HMM) theory. In this model, the unmanufactured product’s machining feature/process flow is considered as an observation sequence and the corresponding smart manufacturing resources (SMRs) sequence is considered as a hidden state sequence. The solving method to train the HMM-based knowledge model for autonomous manufacturing resources allocation (A-MRA) is further described in a step-by-step manner. Thereafter, a demonstrative case is studied to verify the proposed model and method. First, 123 pairs of historical data (i.e., process flow and SMR sequence) are used to learn the HMM-based knowledge model and another 5 pairs of historical data are used to test the feasibility and accuracy of the proposed model. The results show that only three elements (total $5\times 9$ elements) in the predicted SMR sequences are different from those in the historical SMR sequences, and the average vector angle between the five predicted and historical SMR sequences is 11.68°, which is relatively low considering that only nine elements exists in each SMR sequence.