Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Implicit Sentiment Learning via Local Sentiment Aggregation

Recent well-known works demonstrate encouraging progress in aspect-based sentiment classiﬁcation (ABSC), while implicit aspect sentiment modelling is still a problem that has to be solved. Our preliminary study shows that implicit aspect sentiments usually depend on adjacent aspects’ sentiments, which indicates we can extract implicit sentiment via local sentiment dependency modeling. We formulate a local sentiment aggregation paradigm ( LSA ) based on empirical sentiment patterns (SP) to address sentiment dependency modelling. Compared to existing methods, LSA is an efﬁcient approach that learns the implicit sentiments in a local sentiment aggregation window, which tackles the efﬁciency problem and avoids the token-node alignment problem of syntax-based methods. Furthermore, we reﬁne a differential weighting method based on gradient descent that guides the construction of the sentiment aggregation window. According to experimental results, LSA is effective for all objective ABSC models, attaining state-of-the-art performance on three public datasets. LSA is an adaptive paradigm and is readily to be adapted to existing models, and we release the code to offer insight to improve existing ABSC models.
Measurements of the branching fractions $\mathcal{B} (\bar{B}{}^0 \to D^{*+} \pi^-)$ and $\mathcal{B} (\bar{B}{}^0 \to D^{*+} K^-)$ and tests of QCD factorization

Using $(771.6 \pm 10.6) \times 10^6$ $B\bar{B}$ meson pairs recorded by the Belle experiment at the KEKB $e^+ e^-$ collider, we report the branching fractions $\mathcal{B}(\bar{B}^0 \to D^{*+} \pi^-) = (2.62 \pm 0.02 \pm 0.09)\times 10^{-3}$ and $\mathcal{B}(\bar{B}^0 \to D^{*+} K^-) = (2.22 \pm 0.06 \pm 0.08)\times 10^{-4}$; the quoted uncertainties are statistical and systematic, respectively. A measurement of the ratio of these branching fractions is also presented, $\mathcal{R}_{K/\pi}=\mathcal{B}(\bar{B} \to D^{*+}K^-) /\mathcal{B}( \bar{B} \to D^{*+}\pi^-) = (8.41 \pm 0.24 \pm 0.13)\times 10^{-2}$, where systematic uncertainties due to the $D^{*+}$ meson reconstruction cancel out. Furthermore, we report a new QCD factorization test based on the measured ratios for $\bar{B} \to D^{*+}h^-$ and $\bar{B} \to D^{*+}\ell^-\nu$ decays at squared momentum transfer values equivalent to the mass of the $h = \pi$ or $K$ hadron. The parameters $|a_1(h)|$ are measured to be $|a_1(\pi)|= 0.884 \pm 0.004 \pm 0.003 \pm 0.016$ and $|a_1(K)|=0.913 \pm 0.019 \pm 0.008 \pm 0.013$, where the last uncertainties account for all external inputs. These values are approximately $15\%$ lower than those expected from theoretical predictions. Subsequently, flavor $SU(3)$ symmetry is tested by measuring the ratios for pions and kaons, $|a_1(K)|^2/ |a_1(\pi)|^2=1.066 \pm 0.042 \pm 0.018 \pm 0.023$, as well as for different particle species. The ratio is consistent with unity and therefore no evidence for $SU(3)$ symmetry breaking effects is found at the $5\%$ precision level.
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth

As a fundamental building block in computer vision, edges can be categorised into four types according to the discontinuity in surface-Reflectance, Illumination, surface-Normal or Depth. While great progress has been made in detecting generic or individual types of edges, it remains under-explored to comprehensively study all four edge types together. In this paper, we propose a novel neural network solution, RINDNet, to jointly detect all four types of edges. Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages. In stage I, RINDNet uses a common backbone to extract features shared by all edges. Then in stage II it branches to prepare discriminative features for each edge type by the corresponding decoder. In stage III, an independent decision head for each type aggregates the features from previous stages to predict the initial results. Additionally, an attention module learns attention maps for all types to capture the underlying relations between them, and these maps are combined with initial results to generate the final edge detection results. For training and evaluation, we construct the first public benchmark, BSDS-RIND, with all four types of edges carefully annotated. In our experiments, RINDNet yields promising results in comparison with state-of-the-art methods. Additional analysis is presented in supplementary material.
Computational Statistics and Data Analysis Discretization-based Direct Random Sample Generation

An efficient Monte Carlo method for random sample generation from high dimensional distributions of complex structures is developed. The method is based on random discretiza-tion of the sample space and direct inversion of the discretized cumulative distribution function. It requires only the knowledge of the target density function up to a multiplica-tive constant and applies to standard distributions as well as high-dimensional distributions arising from real data applications. Numerical examples and real data applications are used for illustration. The algorithms are implemented in statistical software R and a package dsample has been developed and is available online.
Web-based Long-term Spine Treatment Outcome Forecasting

The aging of global population is witnessing increasing prevalence of spinal disorders. According to latest statistics, nearly five percent of the global population is suffering from spinal disorders. To relieve the pain, many spine patients tend to choose surgeries. However, recent evidences reveal that some spine patients can self-heal over time with nonoperative treatment and even surgeries may not ease the pain for some others, which raises a critical question regarding the appropriateness of such surgeries. Furthermore, the complex and time-consuming diagnostic process places a great burden on both clinicians and patients. Due to the development of web technology, it is possible for spine patients to obtain decision making suggestions on the Internet. The uniqueness of web technology, including its popularity, convenience, and immediacy, makes intelligent healthcare techniques, especially Treatment Outcome Forecasting (TOF), able to support clinical decision-making for doctors and healthcare providers. Despite a few machine-learning-based methods have been proposed for TOF, their performance and feasibility are mostly unsatisfactory due to the neglect of a few practical challenges (caused by applying on the Internet), including biased data selection, noisy supervision, and patient noncompliance. In light of this, we propose DeepTOF, a novel end-to-end deep learning model to cope with the unique challenges in web-based long-term continuous spine TOF. In particular, we combine different patient groups and train a unified predictive model to eliminate the data selection bias. Towards robust learning, we further take advantage of indirect but fine-grained supervision signals to mutually calibrate with the noisy training labels. Additionally, a feature selector was co-trained with DeepTOF to select the most important features (i.e., answers/indicators that need to be collected) for inference, thus easing the use of DeepTOF during web-based real-world application. The proposed DeepTOF could bring great benefits to the rehabilitation of spine patients. Comprehensive experiments and analysis show that DeepTOF outperforms conventional solutions by a large margin.
Hierarchical random additive model for wall-bounded flows at high Reynolds numbers

High Reynolds number wall-bounded flows involve dynamically important scales that cover many orders of magnitude in time and space. Fluid motions that dominate momentum transport also cover wide ranges of scales and all of these scales must be accounted for when modeling turbulence near walls. In this review, we summarize recent developments as related to the so-called hierarchical random additive process (HRAP), which models the near-wall momentum-carrying parts of turbulence by exploiting the self-similarity and the tree-like hierarchical organization of attached eddies. The HRAP model follows Townsend (1980 The Structure of Turbulent Shear Flow) and models boundary layer flows as collections of space-filling, self-similar, wall-attached eddies. However, instead of invoking specifically-shaped typical eddy, HRAP isolates the basic scaling properties of the phenomena by parameterizing contributions of near-wall eddies to generic flow quantities using random addends, a superposition of which leads to the real observable (fluid velocity, wall-shear stress, etc). This compact and relatively simple representation proves to be quite useful, leading to scaling predictions of many turbulence statistics. So far, the HRAP model has been used to provide estimates for scalings of single- and two-point velocity central moments, single- and two-point moment generating functions of velocity fluctuations, and variance of wall-shear stress fluctuations as a function of Reynolds number.
D OMAIN -S PECIFIC R ISK M INIMIZATION FOR O UT - OF - D ISTRIBUTION G ENERALIZATION

Recent domain generalization (DG) approaches typically use the hypothesis learned on source domains for inference on the unseen target domain. However, such a hypothesis can be arbitrarily far from the optimal one for the target domain, induced by a gap termed “adaptivity gap”. Without exploiting the domain information from the unseen test samples, adaptivity gap estimation and minimization are intractable, which hinders us to robustify a model to any unknown distribution. In this paper, we first establish a generalization bound that explicitly considers the adaptivity gap. Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target. The other method is minimizing the gap directly by adapting model parameters using online target samples. We thus propose Domain-specific Risk Minimization (DRM). During training, DRM models the distributions of different source domains separately; for inference, DRM performs online model steering using the source hypothesis for each arriving target sample. Extensive experiments demonstrate the effectiveness of the proposed DRM for domain generalization with the following advantages: 1) it significantly outperforms competitive baselines on different distributional shift settings; 2) it achieves either comparable or superior accuracies on all source domains compared to vanilla empirical risk minimization; 3) it remains simple and efficient during training, and 4) it is complementary to invariant learning approaches.
Preferences of Technology Amenities, Satisfaction and Behavioral Intention: The Perspective of Hotel Guests in Hong Kong

ABSTRACT Technology in the hospitality industry is becoming increasingly important nowadays, because it has a close relationship with guest satisfaction and behavioral intention. This research aims to investigate hotel guests’ preferences of in-room technology amenities by different demographic groups, and to find out how technology amenities including robots, artificial intelligence, and service automation, affect hotel guests’ satisfaction, which in turn influence future behavioral intention to choose hotel and destination. This study uses a mixed quantitative and qualitative approach. Data are analyzed using SPSS and NVIVO. Results show that hotel guests’ preferences of technology amenities vary significantly by their education levels and will influence guests’ satisfaction and behavioral intention with regard to both hotel selection and destination selection. This paper appears to be the first attempt to investigate the influence of hotel guests’ technology preferences on their behavioral intention in both the hotel and the tourism destination context. With the expectation to help the hotel industry, managerial implications and suggestions on technology adoption were provided on the basis of data analysis.
iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival Estimation

Time-of-arrival estimation or Estimated Time of Arrival (ETA) has become an indispensable building block of modern intelligent transportation systems. While many efforts have been made for time-of-arrival estimation, most of them have scalability and robustness issues when dealing with real-world large-scale ETA scenarios, where billions of vehicle trajectories and ETA requests have been continuously generating every day. To this end, in this paper, we propose a robust and scalable incremental ETA learning framework, iETA, to continuously exploit spatio-temporal traffic patterns from massive floating-car data and thus achieve better estimation performances. Specifically, we first build an incremental travel time predictor that can be incrementally updated based on newly generated traffic data. The incremental travel time predictor not only reduces the overall learning overhead but also improves the model's robustness toward urban traffic distribution shifts. Then, we propose a historical traffic knowledge consolidation module to preserve critical spatio-temporal knowledge from previous ETA predictors under the incremental learning setting. Moreover, to reduce interference induced by low-quality traffic data, we propose an adversarial training module to improve the learning robustness by proactively mitigating and resisting traffic noise perturbations. Finally, extensive experiments demonstrate the effectiveness and efficiency of the proposed system against state-of-the-art baselines in large-scale ETA scenarios. Most importantly, iETA has been deployed on the Didi Chuxing platform, handling real-time billions of ETA queries every day, and substantially improves the prediction accuracy.
CCT and Cullin1 regulate the TORC1 pathway to promote dendritic arborization in health and disease

The development of cell-type-specific dendritic arbors is integral to the proper functioning of neurons within their circuit networks. In this study, we examine the regulatory relationship between the cytosolic chaperonin CCT, key insulin pathway genes, and an E3 ubiquitin ligase (Cullin1) in homeostatic dendritic development. CCT loss of function (LOF) results in dendritic hypotrophy in Drosophila Class IV (CIV) multidendritic larval sensory neurons, and CCT has recently been shown to fold components of the TOR (Target of Rapamycin) complex 1 (TORC1), in vitro. Through targeted genetic manipulations, we have confirmed that LOF of CCT and the TORC1 pathway reduces dendritic complexity, while overexpression of key TORC1 pathway genes increases dendritic complexity in CIV neurons. Both CCT and TORC1 LOF significantly reduce microtubule (MT) stability. CCT has been previously implicated in regulating proteinopathic aggregation, thus we examined CIV dendritic development in disease conditions as well. Expression of mutant Huntingtin leads to dendritic hypotrophy in a repeat-length-dependent manner, which can be rescued by TORC1 disinhibition via Cullin1 LOF. Together, our data suggest that Cullin1 and CCT influence dendritic arborization through regulation of TORC1 in both health and disease. SIGNIFICANCE The insulin pathway has become an increasingly attractive target for researchers interested in understanding the intersection of metabolism and brain health. We have found connections between the insulin pathway and cytosolic protein maintenance in the development of neuronal dendrites. These pathways converge on the dendritic cytoskeleton, particularly microtubules. Neurons expressing mutant Huntingtin also show defects in dendritic development and the underlying cytoskeleton, and we find that disinhibition of the insulin pathway can rescue dendritic hypotrophy in these neurons. This work advances our understanding of the molecular interactions between the insulin pathway and neuronal development in both health and Huntington’s Disease conditions.
Genomic analysis supports Cape Lion population connectivity prior to colonial eradication and extinction

Extinct Cape lions (Panthera leo melanochaitus) formerly ranged throughout the grassland plains of the “Cape Flats” in what is today known as the Western Cape Province, South Africa. Cape lions were likely eradicated because of overhunting and habitat loss after European colonization. European naturalists originally described Cape lions as “Black-maned lions” and claimed that they were phenotypically distinct. However, other depictions and historical descriptions of lions from the Cape report mixed or light coloration and without black or extensively developed manes. These findings suggest that, rather than forming a distinct population, Cape lions may have had phenotypic and genotypic variation similar to other African lions. Here we investigate extinct Cape lion genome characteristics, population dynamics and genetic distinctiveness prior to their extinction. We generated genomic data from two historic Cape lions to compare to 118 existing high-coverage mitogenomes, and low-coverage nuclear genomes of 53 lions from 13 African countries. We show that, before their eradication, lions from the Cape Flats had diverse mitogenomes and nuclear genomes that clustered with lions from both southern and eastern Africa. Cape lions had high genome-wide heterozygosity and low inbreeding coefficients, indicating that populations in the Cape Flats went extinct so rapidly that genomic effects associated with long-term small population size and isolation were not detectable. Our findings do not support the characterization of Cape lions as phylogeographically distinct, as originally put forth by some European naturalists, but rather highlights how alternative knowledge-systems, e.g., Indigenous perspectives, could potentially further inform interpretations of species’ life histories.
Local adaptation to an altitudinal gradient: the interplay between mean phenotypic trait variation and phenotypic plasticity in Mimulus laciniatus

Organisms can adapt to environmental heterogeneity through two mechanisms: (1) expression of population genetic variation or (2) phenotypic plasticity. In this study we investigated whether patterns of variation in both trait means and phenotypic plasticity along elevational and latitudinal clines in a North American endemic plant, Mimulus laciniatus, were consistent with local adaptation. We grew inbred lines of M. laciniatus from across the species’ range in two common gardens varying in day length to measure mean and plastic trait expression in several traits previously shown to be involved in adaptation to M. laciniatus’s rocky outcrop microhabitat: flowering time, size-related traits, and leaf shape. We examined correlations between the mean phenotype and phenotypic plasticity, and tested for a relationship between trait variation and population elevation and latitude. We did not find a strong correlation between mean and plastic trait expression at the individual genotype level suggesting that they operate under independent genetic controls. We identified multiple traits that show patterns consistent with local adaptation to elevation: critical photoperiod, flowering time, flower size, mean leaf lobing, and leaf lobing plasticity. These trends occur along multiple geographically independent altitudinal clines indicating that selection is a more likely cause of this pattern than gene flow among nearby populations with similar trait values. We also found that population variation in mean leaf lobing is associated with latitude. Our results indicate that both having more highly lobed leaves and greater leaf shape plasticity may be adaptive at high elevation within M. laciniatus. Our data strongly suggest that traits known to be under divergent selection between M. laciniatus and close relative Mimulus guttatus are also under locally varying selection within M. laciniatus.
Dynamic proximity interaction profiling suggests that YPEL2 is involved in cellular stress surveillance

YPEL2 is a member of the evolutionarily conserved YPEL family involved in cellular proliferation, mobility, differentiation as well as senescence and death. However, the mechanism by which YPEL2, or YPEL proteins, mediates its effects is yet unknown. Proteins perform their functions in a network of proteins whose identities, amounts, and compositions change spatiotemporally in a lineage-specific manner in response to internal and external stimuli. We here explored interaction partners of YPEL2 by using dynamic TurboID-coupled mass spectrometry analyses to infer a function for the protein. Our results using inducible transgene expressions in COS7 cells indicate that proximity interaction partners of YPEL2 are largely involved in RNA and mRNA metabolic processes, ribonucleoprotein complex biogenesis, regulation of gene silencing by miRNA, and cellular responses to stress. We showed that YPEL2 interacts with RNA binding protein ELAVL1 and selective autophagy receptor SQSTM1. We also found that YPEL2 participates in events associated with the formation/disassembly of stress granules in response to sodium arsenite an oxidative stress inducer. Establishing a point of departure in the delineation of structural/functional features of YPEL2, our results suggest that YPEL2 may be involved in stress surveillance mechanisms.
Detecting Receptivity for mHealth Interventions

Just-In-Time Adaptive Interventions (JITAI) have the potential to provide effective support for health behavior by delivering the right type and amount of intervention at the right time. The timing of interventions is crucial to ensure that users are receptive and able to use the support provided. Previous research has explored the association of context and user-specific traits on receptivity and built machine-learning models to detect receptivity after the study was completed. However, for effective intervention delivery, JITAI systems need to make in-the-moment decisions about a user's receptivity. In this study, we deployed machinelearning models in a chatbot-based digital coach to predict receptivity for physical-activity interventions. We included a static model that was built before the study and an adaptive model that continuously updated itself during the study. Compared to a control model that sent intervention messages randomly, the machine-learning models improved receptivity by up to 36%. Receptivity to messages from the adaptive model increased over time.