Compliments and concerns in reviews are valuable for understanding users shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can on
ly learn latent and uninterpretable text representations. They lack explicit user attention and item property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidence. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.
In this paper, we calculate the $B_cto J/psi$ helicity form factors (HFFs) up to twist-4 accuracy by using the light-cone sum rules approach. After extrapolating those HFFs to the physically allowable $q^2$ region, we investigate the $B^+_c$-meson tw
o-body decays and semi-leptonic decays $B_c^+ to J/psi+(P, V, ell^+ u_ell)$, where $P$ and $V$ stand for a light pseudo-scalar meson and a vector meson, respectively. The branching fractions can be derived by using the CKM matrix element and the $B_c$ lifetime from the Particle Data Group, and we obtain ${cal B}(B_c^+ to J/psi pi^+)=(0.132^{+0.002}_{-0.002})%$, ${cal B}(B_c^+ to J/psi K^+)=(0.010^{+0.000}_{-0.000})%$, ${cal B}(B_c^+ to J/psi rho^+) =(0.755^{+0.030}_{-0.034})%$, ${cal B}(B_c^+ to J/psi K^{ast +})=(0.043^{+0.001}_{-0.001})%$, ${cal B}(B_c^+ to J/psi mu^+ u_mu)=(2.808^{+0.547}_{-0.704})%$ and ${cal B}(B_c^+ to J/psi tau^+ u_tau)=(0.563^{+0.137}_{-0.178})%$. We then obtain ${cal R}_{pi^+/mu^+ u_mu} = 0.047^{+ 0.009}_{-0.012}$ and ${cal R}_{K^+ / pi^+} = 0.075^{+0.005}_{-0.005}$, which agree with the LHCb measured value within $1sigma$-error. We also obtain ${cal R}_{J/psi}=0.200^{+ 0.062}_{-0.081}$, which like other theoretical predictions, are consistent with the LHCb measured value within $2sigma$-error.
In this paper, we investigate the Axion-like Particle inflation by applying the multi-nature inflation model, where the end of inflation is achieved through the phase transition (PT). The events of PT should not be less than $200$, which results in t
he free parameter $ngeq404$. Under the latest CMB restrictions, we found that the inflation energy is fixed at $10^{15} rm{GeV}$. Then, we deeply discussed the corresponding stochastic background of the primordial gravitational wave (GW) during inflation. We study the two kinds of $n$ cases, i.e., $n=404, 2000$. We observe that the magnitude of $n$ is negligible for the physical observations, such as $n_s$, $r$, $Lambda$, and $Omega_{rm{GW}}h^2$. In the low-frequency regions, the GW is dominated by the quantum fluctuations, and this GW can be detected by Decigo at $10^{-1}~rm{Hz}$. However, GW generated by PT dominates the high-frequency regions, which is expected to be detected by future 3DSR detector.
Thermo-optic microheater is indispensable in silicon photonic devices for smart and reconfigurable photonic networks. Much efforts have been made to improve the metallic microheater performance in the past decades. However, because of the metallic na
ture of light absorption, placing the metallic microheater very close to the waveguide for fast response is impractical and has not been done experimentally. Here, we experimentally demonstrate a metallic microheater placed very close to the waveguide based on parity-time (PT) symmetry breaking. The intrinsic high loss of metallic heater ensures the system will operate in the PT-symmetry-broken region, which guarantee the low loss of light in the silicon waveguide. Moreover, heating at a close range significantly reduces the response time. A fast response time of ~1 us is achieved without introducing extra loss. The insertion loss is only 0.1 dB for the long heater. The modulation bandwidth is 280 kHz, which is an order of magnitude improvement when compared with that of the mainstream thermo-optic phase shifters. To verify the capability of large-scale integration, a 1*8 phased array for beam steering is also demonstrated experimentally with the PT-symmetry-broken metallic heaters. Our work provides a novel design concept for low-loss fast-response optical switches with dissipative materials and offers a new approach to enhance the performance of thermo-optic phase shifters.
Financial regulatory agencies are struggling to manage the systemic risks attributed to negative economic shocks. Preventive interventions are prominent to eliminate the risks and help to build a more resilient financial system. Although tremendous e
fforts have been made to measure multi-risk severity levels, understand the contagion behaviors and other risk management problems, there still lacks a theoretical framework revealing what and how regulatory intervention measurements can mitigate systemic risk. Here we demonstrate regshock, a practical visual analytical approach to support the exploration and evaluation of financial regulation measurements. We propose risk-island, an unprecedented risk-centered visualization algorithm to help uncover the risk patterns while preserving the topology of financial networks. We further propose regshock, a novel visual exploration and assessment approach based on the simulation-intervention-evaluation analysis loop, to provide a heuristic surgical intervention capability for systemic risk mitigation. We evaluate our approach through extensive case studies and expert reviews. To our knowledge, this is the first practical systemic method for the financial network intervention and risk mitigation problem; our validated approach potentially improves the risk management and control capabilities of financial experts.
In this paper, we propose a generalized natural inflation (GNI) model to study axion-like particle (ALP) inflation and dark matter (DM). GNI contains two additional parameters $(n_1, n_2)$ in comparison with the natural inflation, that make GNI more
general. The $n_1$ build the connection between GNI and other ALP inflation model, $n_2$ controls the inflaton mass. After considering the cosmic microwave background and other cosmological observation limits, the model can realize small-field inflation with a wide mass range, and the ALP inflaton considering here can serve as the DM candidate for certain parameter spaces.
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-supervised learning algorithms used for image and NLP pretraining, we hypoth
esize that high-quality document embedding should be invariant to diverse paraphrases that preserve the semantics of the original document. With different backbones and contrastive learning frameworks, our study reveals the enormous benefits of contrastive augmentation for document representation learning with two additional insights: 1) including data augmentation in a contrastive way can substantially improve the embedding quality in unsupervised document representation learning, and 2) in general, stochastic augmentations generated by simple word-level manipulation work much better than sentence-level and document-level ones. We plug our method into a classifier and compare it with a broad range of baseline methods on six benchmark datasets. Our method can decrease the classification error rate by up to 6.4% over the SOTA approaches on the document classification task, matching or even surpassing fully-supervised methods.
Unmanned surface vehicles (USVs) have great value with their ability to execute hazardous and time-consuming missions over water surfaces. Recently, USVs for inland waterways have attracted increasing attention for their potential application in auto
nomous monitoring, transportation, and cleaning. However, unlike sailing in open water, the challenges posed by scenes of inland waterways, such as the complex distribution of obstacles, the global positioning system (GPS) signal denial environment, the reflection of bank-side structures, and the fog over the water surface, all impede USV application in inland waterways. To address these problems and stimulate relevant research, we introduce USVInland, a multisensor dataset for USVs in inland waterways. The collection of USVInland spans a trajectory of more than 26 km in diverse real-world scenes of inland waterways using various modalities, including lidar, stereo cameras, millimeter-wave radar, GPS, and inertial measurement units (IMUs). Based on the requirements and challenges in the perception and navigation of USVs for inland waterways, we build benchmarks for simultaneous localization and mapping (SLAM), stereo matching, and water segmentation. We evaluate common algorithms for the above tasks to determine the influence of unique inland waterway scenes on algorithm performance. Our dataset and the development tools are available online at https://www.orca-tech.cn/datasets.html.
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTSs individually
, and do not leverage the dynamic distributions underlying the MTSs, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.
Rapid globalization and the interdependence of humanity that engender tremendous in-flow of human migration towards the urban spaces. With advent of high definition satellite images, high resolution data, computational methods such as deep neural net
work, capable hardware; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. In this paper we propose a novel classification method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. The state-of-the-art is mostly dominated by classification of building structures, building types etc. and largely represents the developed world which are insufficient for developing countries such as Bangladesh where the surrounding is crucial for the classification. Moreover, the traditional methods propose small-scale classifications, which give limited information with poor scalability and are slow to compute. We categorize the urban area in terms of informal and formal spaces taking the surroundings into account. 50 km x 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert. The classification is based broadly on two dimensions: urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four classes: 1) highly informal; 2) moderately informal; 3) moderately formal; and 4) highly formal areas. In total 16 sub-classes were identified. For semantic segmentation, Googles DeeplabV3+ model was used which increases the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used for training and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean IoU.
