arXiv subjects

Bo Wang

Publications and source records attributed to Bo Wang.

At least 577 records · Page 32Linked to original sources

arXiv2021

Prospects for Constraining Interacting Dark Energy Models with 21 cm Intensity Mapping Experiments

We forecast constraints on cosmological parameters in the interacting dark energy models using the mock data generated for neutral hydrogen intensity mapping (IM) experiments. In this work, we only consider the interacting dark energy models with energy transfer rate $Q=βHρ_{\rm c}$, and take BINGO, FAST, SKA1-MID, and Tianlai as typical examples of the 21 cm IM experiments. We find that the Tianlai cylinder array will play an important role in constraining the interacting dark energy model. Assuming perfect foreground removal and calibration, and using the Tianlai-alone data, we obtain $σ(H_0)=0.19$ km s$^{-1}$ Mpc$^{-1}$, $σ(Ω_{\rm m})=0.0033$ and $σ(σ_8)=0.0033$ in the I$Λ$CDM model, which are much better than the results of Planck+optical BAO (i.e. optical galaxy surveys). However, the Tianlai-alone data cannot provide a very tight constraint on the coupling parameter $β$ compared with Planck+optical BAO, while the Planck+Tianlai data can give a rather tight constraint of $σ(β)=0.00023$ due to the parameter degeneracies being well broken by the data combination. In the I$w$CDM model, we obtain $σ(β)=0.00079$ and $σ(w)=0.013$ from Planck+Tianlai. In addition, we also make a detailed comparison among BINGO, FAST, SKA1-MID, and Tianlai in constraining the interacting dark energy models. We show that future 21 cm IM experiments will provide a useful tool for exploring the nature of dark energy and play a significant role in measuring the coupling between dark energy and dark matter.

astro-ph.CO↗

arXiv2021

Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction

Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. To address this problem, recent works fine-tune pre-trained Transformer encoders for ALSC to extract an aspect-centric dependency tree that can locate the opinion words. However, the induced opinion words only provide an intuitive cue far below human-level interpretability. Besides, the pre-trained encoder tends to internalize an aspect's intrinsic sentiment, causing sentiment bias and thus affecting model performance. In this paper, we propose a span-based anti-bias aspect representation learning framework. It first eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects' prior sentiment. Then, it aligns the distilled opinion candidates with the aspect by span-based dependency modeling to highlight the interpretable opinion terms. Our method achieves new state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.

cs.CL↗

arXiv2021

Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events

With the continuous upgrade of detectors, more and more gravitational wave (GW) events were captured by the LIGO Scientific Collaboration and Virgo Collaboration (LVC), which offers a new avenue to test General Relativity and explore the nature of gravity. Although, various model-independent tests have been performed by LVC in previous works, it is still interesting to ask what constraints on specific models can be placed by current GW observations. In this work, we focus on three models of scalar-tensor theories, the Brans-Dicke theory (BD), the theory with scalarization phenomena proposed by Damour and Esposito-Farèse (DEF), and Screened Modified Gravity (SMG). From all 4 possible NSBH events so far, we use two of them to place the constraints. The other two are excluded in this work due to the possible unphysical deviations. We consider the inspiral range with the cutoff frequency at the innermost stable circular orbit and add a modification of dipole radiation into the waveform template. The scalar charges of neutron stars in the dipole term are derived by solving the Tolman-Oppenheimer-Volkoff equations for different equations-of-states. The constraints are obtained by performing the full Bayesian inference with the help of the open source software \texttt{Bilby}. The results show that the constraints given by GWs are comparable with those given by pulsar timing experiments for DEF theory, but are not competitive with the current solar system constraints for BD and SMG theories.

gr-qc↗

arXiv2021

Ultra-compact X-ray binaries with He star companions

Ultra-compact X-ray binaries (UCXBs) are low-mass X-ray binaries with hydrogen-deficient mass-donors and ultra-short orbital periods. They have been suggested to be the potential Laser Interferometer Space Antenna (LISA) sources in the low-frequency region. Several channels for the formation of UCXBs have been proposed so far. In this article, we carried out a systematic study on the He star donor channel, in which a neutron star (NS) accretes matter from a He main-sequence star through Roche-lobe overflow, where the mass-transfer is driven by gravitational wave radiation. Firstly, we followed the long-term evolution of the NS+He main-sequence star binaries by employing the stellar evolution code Modules for Experiments in Stellar Astrophysics, and thereby obtained the initial parameter spaces for the production of UCXBs. We then used these results to perform a detailed binary population synthesis approach to obtain the Galactic rates of UCXBs through this channel. We estimate the Galactic rates of UCXBs appearing as LISA sources to be $\sim3.1-11.9\, \rm Myr^{-1}$ through this channel, and the number of such UCXB-LISA sources in the Galaxy can reach about $1-26$ calibrated by observations. The present work indicates that the He star donor channel may contribute significantly to the Galactic UCXB formation rate. We found that the evolutionary tracks of UCXBs through this channel can account for the location of the five transient sources with relatively long orbital periods quite well. We also found that such UCXBs can be identified by their locations in the mass-transfer rate versus the orbital period diagram.

astro-ph.HE↗

arXiv2021

Evaluation of Thematic Coherence in Microblogs

Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence. We provide annotation guidelines and human annotations of thematic coherence by journalist experts. We subsequently investigate the efficacy of different automated evaluation metrics for the task. We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs). While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs. TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.

cs.CL↗

arXiv2021

SynGuar: Guaranteeing Generalization in Programming by Example

Programming by Example (PBE) is a program synthesis paradigm in which the synthesizer creates a program that matches a set of given examples. In many applications of such synthesis (e.g., program repair or reverse engineering), we are to reconstruct a program that is close to a specific target program, not merely to produce some program that satisfies the seen examples. In such settings, we wish that the synthesized program generalizes well, i.e., has as few errors as possible on the unobserved examples capturing the target function behavior. In this paper, we propose the first framework (called SynGuar) for PBE synthesizers that guarantees to achieve low generalization error with high probability. Our main contribution is a procedure to dynamically calculate how many additional examples suffice to theoretically guarantee generalization. We show how our techniques can be used in 2 well-known synthesis approaches: PROSE and STUN (synthesis through unification), for common string-manipulation program benchmarks. We find that often a few hundred examples suffice to provably bound generalization error below $5\%$ with high ($\geq 98\%$) probability on these benchmarks. Further, we confirm this empirically: SynGuar significantly improves the accuracy of existing synthesizers in generating the right target programs. But with fewer examples chosen arbitrarily, the same baseline synthesizers (without SynGuar) overfit and lose accuracy.

cs.PL↗

arXiv2021

Implications of the $Z_{cs}(3985)$ and $Z_{cs}(4000)$ as two different states

Recently, the hidden charm tetraquark states $Z_{cs}(3985)$ and $Z_{cs}(4000)$ with strangeness were observed by the BESIII and LHCb collaborations, respectively, which are great breakthroughs for exploring exotic QCD structures. The first and foremost question is whether they are the same state. In this work, we explore the implications of the narrower state $Z_{cs}(3985)$ in BESIII and the wider one $Z_{cs}(4000)$ in LHCb as two different states. Within a solvable nonrelativistic effective field theory, we include the possible violations of heavy quark spin symmetry and SU(3) flavor symmetry in a comprehensive approach. If $Z_{cs}(3985)$ and $Z_{cs}(4000)$ are two different states, our results show that $Z_{cs}(4000)/Z_{cs}(3985)$ is the pure $(|\bar{D}_s^*D\rangle+/- |\bar{D}_sD^*\rangle)/\sqrt{2}$ state, and the SU(3) flavor partner of $Z_{c}(3900)$ is $Z_{cs}(4000)$ rather than the $Z_{cs}(3985)$. Another two important consequences are the existence of a tensor $\bar{D}_s^*D^*$ resonance with mass about 4126 MeV and width 13 MeV, and the suppression of the decay mode $Z_{cs}(3985) \to J/ψK$. The two consequences can be tested in experiments and distinguish the two-state interpretation from the one-state scheme.

hep-ph↗

arXiv2021

The axisymmetric $σ_k$-Nirenberg problem

We study the problem of prescribing $σ_k$-curvature for a conformal metric on the standard sphere $\mathbb{S}^n$ with $2 \leq k < n/2$ and $n \geq 5$ in axisymmetry. Compactness, non-compactness, existence and non-existence results are proved in terms of the behaviors of the prescribed curvature function $K$ near the north and the south poles. For example, consider the case when the north and the south poles are local maximum points of $K$ of flatness order $β\in [2,n)$. We prove among other things the following statements. (1) When $β>n-2k$, the solution set is compact, has a nonzero total degree counting and is therefore non-empty. (2) When $ β= n-2k$, there is an explicit positive constant $C(K)$ associated with $K$. If $C(K)>1$, the solution set is compact with a nonzero total degree counting and is therefore non-empty. If $C(K)<1$, the solution set is compact but the total degree counting is $0$, and the solution set is sometimes empty and sometimes non-empty. (3) When $\frac{2}{n-2k}\le β< n-2k$, the solution set is compact, but the total degree counting is zero, and the solution set is sometimes empty and sometimes non-empty. (4) When $β< \frac{n-2k}{2}$, there exists $K$ for which there exists a blow-up sequence of solutions with unbounded energy. In this same range of $β$, there exists also some $K$ for which the solution set is empty.

math.AP↗

arXiv2021

Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Control

While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs), we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most "informative" data collection, and improve production stability.

stat.ML↗

arXiv2021

Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews

Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. In this work, we first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic . We investigate the automatic detection of the two conditions, and demonstrate a good linear classifier that can be learnt using a down-selected set of features from the different aspects of the interviews and a novel approach of summarising these features. Finally, we find that different sets of features characterise BD and BPD, thus providing insights into the difference between the automatic screening of the two conditions.

cs.LG↗

arXiv2021

DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text.

cs.CL↗

arXiv2021

Helium enrichment during classical nova outbursts

Nova outbursts play an important role in the chemical evolution of galaxies, especially they are the main source of synthetic $^{13}\rm C$, $^{15}\rm N$, $^{17}\rm O$ and some radioactive isotopes like $^{22}\rm Na$ and $^{26}\rm Al$. The enrichment of He in nova ejecta indicates that the accreted material may mix with the He-shell (He-mixing). The purpose of this work is to investigate how the He-mixing affects the nova outbursts in a systematic way. We evolved a series of accreting WD models, and found that the mass fraction of H and He in nova ejecta can be influenced by different He-mixing fractions significantly. We also found that both the nova cycle duration and ejected mass increase with the He-mixing fractions. Meanwhile, the nuclear energy production from $p$-$p$ chains decreases with the He-mixing fraction during the nova outbursts, whereas the CNO-cycle increases. The present work can reproduce the chemical abundances in the ejecta of some novae, such as GQ Mus, ASASSN-18fv, HR Del, T Aur and V443 Sct. This implies that the He-mixing process cannot be neglected when studying nova outbursts. This study also develops a He-mixing meter (i.e. $\rm He/H$) that can be used to estimate the He-mixing fraction in classical nova systems.

astro-ph.SR↗

arXiv2021

Two-stage Training for Learning from Label Proportions

Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utilize end-to-end pipelines to obtain the proportional loss with Kullback-Leibler divergence between the bag-level prior and posterior class distributions. However, the unconstrained optimization on this objective can hardly reach a solution in accordance with the given proportions. Besides, concerning the probabilistic classifier, this strategy unavoidably results in high-entropy conditional class distributions at the instance level. These issues further degrade the performance of the instance-level classification. In this paper, we regard these problems as noisy pseudo labeling, and instead impose the strict proportion consistency on the classifier with a constrained optimization as a continuous training stage for existing LLP classifiers. In addition, we introduce the mixup strategy and symmetric crossentropy to further reduce the label noise. Our framework is model-agnostic, and demonstrates compelling performance improvement in extensive experiments, when incorporated into other deep LLP models as a post-hoc phase.

cs.LG↗

arXiv2021

The fractions of post-binary-interaction stars and evolved blue straggler stars on the red giant branch of globular clusters

The red giant branch (RGB) of globular clusters (GCs) is home to some exotic stars, which may provide clues on the formation of multiple stellar populations in GCs. It is well known that binary interactions are responsible for many exotic stars. Thus, it is important to understand what fraction of stars on the RGB of GCs is the result of binary interactions. In this paper, we performed a binary population synthesis study to track the number of post-binary-interaction (post-BI) stars that appear on the RGB, with particular emphasis on the evolved blue straggler stars (E-BSSs). Assuming an initial binary fraction of nearly 50%, we find that about half of the objects on the RGB (called giants) underwent the binary interactions, and that E-BSSs account for around 10% of the giants in our standard simulation. We also compare the properties of post-BI giants that evolved from different channels. We find that the initial orbital period and mass ratio distributions significantly affect the fraction of post-BI giants. Our results imply that the non-standard stars from binary interactions provide a non-negligible contribution to the RGB stars in GCs, which should be considered in future investigations of the origin of multiple stellar populations.

astro-ph.SR↗

arXiv2021

Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos

Despite the recent progress, 3D multi-person pose estimation from monocular videos is still challenging due to the commonly encountered problem of missing information caused by occlusion, partially out-of-frame target persons, and inaccurate person detection. To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that do not require camera parameters. In particular, we introduce a human-joint GCN, which, unlike the existing GCN, is based on a directed graph that employs the 2D pose estimator's confidence scores to improve the pose estimation results. We also introduce a human-bone GCN, which models the bone connections and provides more information beyond human joints. The two GCNs work together to estimate the spatial frame-wise 3D poses and can make use of both visible joint and bone information in the target frame to estimate the occluded or missing human-part information. To further refine the 3D pose estimation, we use our temporal convolutional networks (TCNs) to enforce the temporal and human-dynamics constraints. We use a joint-TCN to estimate person-centric 3D poses across frames, and propose a velocity-TCN to estimate the speed of 3D joints to ensure the consistency of the 3D pose estimation in consecutive frames. Finally, to estimate the 3D human poses for multiple persons, we propose a root-TCN that estimates camera-centric 3D poses without requiring camera parameters. Quantitative and qualitative evaluations demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2021

Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks

In monocular video 3D multi-person pose estimation, inter-person occlusion and close interactions can cause human detection to be erroneous and human-joints grouping to be unreliable. Existing top-down methods rely on human detection and thus suffer from these problems. Existing bottom-up methods do not use human detection, but they process all persons at once at the same scale, causing them to be sensitive to multiple-persons scale variations. To address these challenges, we propose the integration of top-down and bottom-up approaches to exploit their strengths. Our top-down network estimates human joints from all persons instead of one in an image patch, making it robust to possible erroneous bounding boxes. Our bottom-up network incorporates human-detection based normalized heatmaps, allowing the network to be more robust in handling scale variations. Finally, the estimated 3D poses from the top-down and bottom-up networks are fed into our integration network for final 3D poses. Besides the integration of top-down and bottom-up networks, unlike existing pose discriminators that are designed solely for single person, and consequently cannot assess natural inter-person interactions, we propose a two-person pose discriminator that enforces natural two-person interactions. Lastly, we also apply a semi-supervised method to overcome the 3D ground-truth data scarcity. Our quantitative and qualitative evaluations show the effectiveness of our method compared to the state-of-the-art baselines.

cs.CV↗

arXiv2021

Linearized Learning Methods with Multiscale Deep Neural Networks for Stationary Navier-Stokes Equations with Oscillatory Solutions

In this paper, we present linearized learning methods to accelerate the convergence of training for stationary nonlinear Navier-Stokes equations. To solve the stationary nonlinear Navier-Stokes (NS) equation, we integrate the procedure of linearization of the nonlinear convection term in the NS equation into the training process of multi-scale deep neural network approximation of the NS solution. Four forms of linearizations are considered. After a benchmark problem, we solve the highly oscillating stationary flows utilizing the proposed linearized learning with multi-scale neural network for complex domains. The results show that multiscale deep neural network combining with the linearized schemes can be trained fast and accurately.

math.NA↗

arXiv2021

Onfocus Detection: Identifying Individual-Camera Eye Contact from Unconstrained Images

Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status (e.g., lying or suffering mental disease) and special purposes (e.g., seeking help or attracting fans). Thus, developing effective onfocus detection algorithms is of significance for assisting the criminal investigation, disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector due to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the OnFocus Detection In the Wild (OFDIW). It consists of 20,623 images in unconstrained capture conditions (thus called ``in the wild'') and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network (ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN. The project (containing both datasets and codes) is at https://github.com/wintercho/focus.

cs.CV↗