Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning

Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted to improve event correlation reasoning by using pretrained language models and incorporating external knowledge (e.g., discourse relations). Though promising results have been achieved, some challenges still remain. First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well. Second, modeling correlations between events with discourse relations is limited because it can only capture explicit correlations between events with discourse markers, and cannot capture many implicit correlations. To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. Specifically, we first introduce a novel event-level blank infilling strategy as the learning objective to inject event-level knowledge into the pretrained language model, and then design a likelihood-based contrastive loss for fine-tuning the generative model. Instead of using an additional prediction layer, we perform prediction by using sequence likelihoods generated by the generative model. Our approach models correlations between events in a soft way without any external knowledge. The likelihood-based prediction eliminates the need to use additional networks to make predictions and is somewhat interpretable since it scores each word in the event. Experimental results on the multi-choice narrative cloze (MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines. Our code will be available at https://github.com/zhufq00/mcnc.
MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation

Recently, many deep learning based beamformers have been proposed for multi-channel speech separation. Nevertheless, most of them rely on extra cues known in advance, such as speaker feature, face image or directional information. In this paper, we propose an end-to-end beamforming network for direction guided speech separation given merely the mixture signal, namely MIMO-DBnet. Specifically, we design a multi-channel input and multiple outputs architecture to predict the direction-of-arrival based embeddings and beamforming weights for each source. The precisely estimated directional embedding provides quite effective spatial discrimination guidance for the neural beamformer to offset the effect of phase wrapping, thus allowing more accurate reconstruction of two sources' speech signals. Experiments show that our proposed MIMO-DBnet not only achieves a comprehensive decent improvement compared to baseline systems, but also maintain the performance on high frequency bands when phase wrapping occurs.
Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue

Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations. It has been found in different dimensions as acoustic, prosodic, lexical or syntactic. In this work, we explore and utilize the entrainment phenomenon to improve spoken dialogue systems for voice assistants. We first examine the existence of the entrainment phenomenon in human-to-human dialogues in respect to acoustic feature and then extend the analysis to emotion features. The analysis results show strong evidence of entrainment in terms of both acoustic and emotion features. Based on this findings, we implement two entrainment policies and assess if the integration of entrainment principle into a Text-to-Speech (TTS) system improves the synthesis performance and the user experience. It is found that the integration of the entrainment principle into a TTS system brings performance improvement when considering acoustic features, while no obvious improvement is observed when considering emotion features.
Tracking the Dynamics of the Tear Film Lipid Layer

Dry Eye Disease (DED) is one of the most common ocular diseases: over five percent of US adults suffer from DED. Tear film instability is a known factor for DED, and is thought to be regulated in large part by the thin lipid layer that covers and stabilizes the tear film. In order to aid eye related disease diagnosis, this work proposes a novel paradigm in using computer vision techniques to numerically analyze the tear film lipid layer (TFLL) spread. Eleven videos of the tear film lipid layer spread are collected with a micro-interferometer and a subset are annotated. A tracking algorithm relying on various pillar computer vision techniques is developed. Our method can be found at https://easytear-dev.github. io/.
Incompatibility of standard galaxy bias models in general relativity

The standard model for galaxy bias is built in a Newtonian framework, and several attempts have been made in the past to put it in a relativistic framework. The focus of past works was, however, to use the same Newtonian formulation, but to provide its interpretation in a relativistic framework by either fixing a gauge condition or transforming to a local coordinate system. Here we demonstrate that these reverse-engineered approaches do not respect the diffeomorphism symmetry in general relativity, and we need to develop a covariant model of galaxy bias that is diffeomorphism compatible. We consider a simple toy model for galaxy bias and discuss the impact for measuring the primordial non-Gaussianity.
Maximum entropy states for rotating vortex patches

The statistical equilibrium theory of coherent structures in two‐dimensional turbulence is used to study the coalescence and symmetrization of rotating vortex patches. In this theory, most probable states, which are local probability distributions on the fluctuating vorticity field, are characterized by maximizing entropy subject to constraints derived from the conserved quantities for the Euler equations. The merger of a symmetric pair of patches and the axisymmetrization of an elliptical patch are computed by solving the constrained maximum entropy problem. In this way, these generic phenomena, which are usually simulated by direct time integration, are shown to be equilibration processes for the statistical mechanics model. Moreover, macroscopic features of the filamentation generated in these processes are captured by the statistical equilibrium theory. Namely, the delicate balance between the coalescence into a vortex core and the migration of filamentary vorticity away from the core, which is dictated by the conservation of angular impulse, is exhibited by the equilibrium solutions.
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving

—Lidar sensors are costly yet critical for understand- ing the 3D environment in autonomous driving. High-resolution sensors provide more details about the surroundings because they contain more vertical beams, but they come at a much higher cost, limiting their inclusion in autonomous vehicles. Upsampling lidar pointclouds is a promising approach to gain the beneﬁts of high resolution while maintaining an affordable cost. Although there exist many pointcloud upsampling frameworks, a consistent comparison of these works against each other on the same dataset using uniﬁed metrics is still missing. In the ﬁrst part of this paper, we propose to benchmark existing methods on the Kitti dataset. In the second part, we introduce a novel lidar upsampling model, HALS: Height-Aware Lidar Super-resolution. HALS exploits the observation that lidar scans exhibit a height-aware range distribution and adopts a generator architecture with multiple upsampling branches of different receptive ﬁelds. HALS regresses polar coordinates instead of spherical coordi- nates and uses a surface-normal loss. Extensive experiments show that HALS achieves state-of-the-art performance on 3 real-world lidar datasets.
Past and future hydrological simulations of Chao Phraya river basin

Predictions of variations in global and regional hy drological cycles and their response to changes in climate and the environment are key problems for fu ture human life. Therefore, basin-scale hydrologica l forecasts, along with predictions regarding future climate change, are needed in areas with high flood potential. This study forecasts hydrological proces s scenarios in the upper Chao Phraya River basin us ing a distributed hydrological model (DHM) and precipitation scenarios from three general circulation models modified by the H08 model. First, discharge was simulated by the DHM using the observed rainfall from 2007 to 2009 and then, future discharge scenarios f rom2010 to 2040 were forecast.
Computing representations for Lie algebraic networks

Recent work has constructed neural networks that are equivariant to continuous symmetry groups such as 2D and 3D rotations. This is accomplished using explicit Lie group representations to derive the equivariant kernels and nonlinearities. We present three contributions motivated by frontier applications of equivariance beyond rotations and translations. First, we relax the requirement for explicit Lie group representations with a novel algorithm that finds representations of arbitrary Lie groups given only the structure constants of the associated Lie algebra. Second, we provide a self-contained method and software for building Lie group-equivariant neural networks using these representations. Third, we contribute a novel benchmark dataset for classifying objects from relativistic point clouds, and apply our methods to construct the first object-tracking model equivariant to the Poincar\'e group.
Observation of silicon-mediated alleviation of cadmium stress in maize (Zea mays L.) seedlings via LED-induced chlorophyll fluorescence

LED-induced chlorophyll fluorescence analysis is exploited to observe, and monitor the time evolution of silicon-induced alleviation of toxicity in maize (Zea mays L.) seedlings in cadmium contaminated soil. Red, and far-red emissions were examined as a function of cadmium-silicon concentrations, during the 20 days period of the seedlings growing process under stress. The chlorophyll fluorescence spectral analysis provided detection, and evaluation of the damage imposed by the metal stress in the early stages of the plant growing process. The technique also provided the time evolution evaluation of the silicon-induced tolerance enhancement of maize plants to cadmium, which is not viable using conventional in vitro spectral analysis techniques
Impact of emissions cost on the mid-term generation scheduling in the Greek Electricity Market

The EU Emissions Trading Scheme, Europe's main mechanism for implementing EU climate policy, is driving thermal electricity generation units to incorporate their emissions cost into their variable costs. In this paper, we investigate the impact of this incorporation on the mid-term (yearly) performance of the Greek Electricity Market. To carry out our investigation, we iteratively solve the day-ahead market problem for 365 days, assuming that the generation units internalize the emissions cost in their bids. In order to provide a realistic estimate for the market performance, we use a set of input data that is based on a projection of the current data onto the year 2013. We examine three different scenarios for the prices of gas and oil, which are used to fuel some of the thermal generation units, and seven scenarios for the CO2 prices. To enhance the confidence in our results, we perform a sensitivity analysis of the market performance with respect to outages. We conclude by discussing the major insights and potential extensions of this work. (10 pages)
Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India

Abstract Aerosol monitoring is the emerging application field of satellite remote sensing. As a satellite-based indicator of aerosol concentration, aerosol optical depth (AOD) can aid in assessing the crucial effects of aerosols on the global environment. Among various satellite-based aerosol product, Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6), Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol product (1 km resolution) has still untapped potential in Indian regions. Considering the importance of regional validation of such high-resolution aerosol product, the present study attempts to fill this gap by validating MAIAC aerosol estimates (AODMAIAC) in highly polluted districts (Faridabad, Ghaziabad, Gautam Budh Nagar, Gurugram) of National Capital Region (NCR) with heavy aerosol loading using limited AErosol RObotic NETwork (AERONET) observations obtained from AERONET sites at Amity University (AU) and Gual Pahari (GP). Such evaluation of satellite-retrieved aerosol product with ground data confirms its practicality based on retrieval errors (Expected Error (EE) values (EE = 0.05 + 15 %*AOD) (EE: 78.85 % at AU, 73.58 % at GP), root mean square error (RMSE) values (RMSE: 0.15 at AU, 0.24 at GP), and correlation coefficient (R) values (R: 0.86 at AU, 0.73 at GP). The seasonal variation in AOD over the study area from 2010-2019 reveals increasing trend of AOD in the monsoon and post-monsoon season due to natural and anthropogenic factors. In addition to contributing to a holistic assessment of MAIAC aerosol estimates as a recent, high-resolution aerosol product, present results provide a basis for further research into NCR aerosols.
Noise Reduction Using Finite-Length Flexible Segments

In this work, the reduction of acoustic noise in piping systems through the installation of finite flexible segments is explored. A mathematical model describing the relevant parameters is developed. To verify the model, experimental work is undertaken using rubber hoses as flexible segments and air as the contained fluid. The effect of the segment on propagating acoustical energy is studied in terms of its insertion loss. Both theoretical and experimental results indicate that, for light fluid loading, the flexibility of the segment is only important when significant axial shell resonances exist, or when one or both acoustical termination impedances at the ends of the segment are much larger or smaller than the characteristic impedance of the medium. Further, the model indicates that for heavier fluid loading (i.e., when the compressibility of the flexible section is significant in relation to the bulk compliance of the fluid), in addition to higher associated insertion losses, the finite length resonances become more pronounced, and performance is less sensitive to small variations in the termination impedances.
A note on generating correlated binary variables

SUMMARY It is important to be able to generate correlated binary data in an efficient, easily programmed manner for, among other things, the generation of large bootstrap samples. In this note nothing more than the basic ingredient of the uniform random number generator is required for simulating binary data from the most commonly occurring correlation structures.
Algorithms on Clustering, Orienteering, and Conflict -Free Coloring

We present algorithms for three geometric problems—clustering, orienteering, and conflict-free coloring. In the first part, we obtain small coresets for k-median and k-means clustering in general metric spaces and in Euclidean spaces. In Rd , these coresets have size that depend polynomially on d. This leads to more efficient approximation algorithms for k-median and k-means clustering. We use those coresets to maintain a (1 + e)-approximate k-median and k-means clustering of a stream of points in Rd , using O(dk2 e-2 log8 n) space. These are the first streaming algorithms, for those problems, that have space complexity with polynomial dependency on the dimension. We next study the k-median clustering with outliers problem. Here, given a finite point set in a metric space and parameters k and m, we want to remove m points (called outliers), such that the cost of the optimal k-median clustering of the remaining points is minimized. We present the first polynomial time constant factor approximation algorithm for this problem. In the second part, we consider the rooted orienteering problem: Given a set P of n points in the plane, a starting point r ∈ P, and a length constraint B, one needs to find a path starting from r that visits as many points of P as possible and of length not exceeding B. We present the first polynomial time approximation scheme (PTAS) for this problem. The scheme also works in higher (fixed) dimensions. In the last part, we present randomized algorithms for online conflict-free coloring of points in the plane, with respect to intervals, halfplanes, congruent disks, and nearly-equal axis-parallel rectangles. In all these cases, the coloring algorithms use O(log n) colors, with high probability. We also present the first efficient deterministic algorithm for the CF coloring of points in the plane with respect to nearly-equal axis-parallel rectangles, using O(log 12 n) colors.
Large-eddy simulation of transitional flows using a co-located grid

A large-eddy simulation (LES) of a transitional separated flow over a plate with a semi-circular leading at low (<0.2%) and high (5.6%) free-stream turbulence (FST) has been performed, using a co-located grid with the Rhie–Chow pressure smoothing. A numerical trip is used to produce a high FST level and a dynamic subgrid-scale model is also employed in the current study. The entire transition process leading to breakdown to turbulence has been shown clearly by the flow visualisations using instantaneous spanwise vorticities, and the differences between the low- and high-FST cases are clearly visible. Coherent structures are also visualised using isosurfaces of the Q-criterion, and for the high-FST case, the spanwise-oriented quasi-two-dimensional rolls, which are clearly present in the low-FST case, are not visible anymore. Detailed quantitative comparisons between the present LES results and experimental data and the previous LES results at low FST using a staggered grid have been done and a good agreement has been obtained, indicating that the current LES using a co-located grid with pressure smoothing can also predict transitional flows accurately.
Representations of tactile object location in the retrosplenial cortex

Little is known about how animals use tactile sensation to detect important objects and remember their location in a worldbased coordinate system. Here, we hypothesized that retrosplenial cortex (RSC), a key network for contextual memory and spatial navigation, represents the location of objects based on tactile sensation. We studied mice that palpate objects with their whiskers while running on a treadmill in a tactile virtual reality in darkness. Using two-photon Ca2+ imaging, we discovered a population of neurons in agranular RSC that signal the location of tactile objects. Tactile object location responses do not simply reflect the sensory stimulus. Instead, they are highly task- and context-dependent and often predict the upcoming object before it is within reach. In addition, most tactile object location neurons also maintain a memory trace of the object’s location. These data show that RSC encodes the location and arrangement of tactile objects in a spatial reference frame.
Secreted ADAMTS-like 2 promotes myoblast differentiation by potentiating Wnt signaling

The formation of multinucleated contractile myofibers from muscle stem cells during myogenesis is indispensable for skeletal muscle formation. Myogenesis is governed by myogenic regulatory transcription factors, including MYOD. However, very few MYOD- regulated effector proteins were shown to be sufficient to promote myogenesis. Here, we identified an unexpected role for the secreted matricellular protein ADAMTS-like 2 (ADAMTSL2) as a rheostat for Wnt signaling during myogenesis downstream of MYOD. ADAMTSL2 expression was induced during myoblast differentiation and ADAMTSL2 was required for myoblast differentiation. ADAMTSL2 ablation in myogenic precursor cells resulted in aberrant muscle architecture in vivo. The pro-myogenic ADAMTSL2 function was dependent on WNT ligands. Mechanistically, ADAMTSL2 potentiated WNT signaling by binding to WNT ligands and WNT receptors. Finally, we identified a WNT-binding ADAMTSL2 peptide that was sufficient to promote myogenesis. Since ADAMTSL2 was previously described as a negative regulator of TGFβ signaling in fibroblasts, ADAMTSL2 now emerges as a signaling node that could integrate and fine-tune WNT, TGFβ and potentially other signaling pathways within the dynamic microenvironment of differentiating myoblasts during skeletal muscle development and regeneration.
Socioeconomic status and caregiving sensitivity among Chinese childcare centre teachers: adaptive caregiving belief as a mediator and childcare centre type as a moderator

ABSTRACT The critical developmental implications of childcare centre teachers’ caregiving sensitivity have been extensively documented. More efforts are pressing to identify antecedents of such sensitivity and elucidate implicated mechanisms. This study examined the association between socioeconomic status and caregiving sensitivity among Chinese childcare centre teachers, with adaptive caregiving belief as a mediator and childcare centre type as a moderator. Survey data for 1,316 Chinese teachers were used. The results indicated that teachers’ monthly income was positively associated with their caregiving sensitivity via positive associations with their verbal stimulation beliefs. This effect did not vary by childcare centre type. Our findings highlight possible avenues for interventions aimed at promoting caregiving quality in childcare centre setting, such as improving teachers’ adaptive caregiving belief.
Forward … to the nineteenth century: Historiographic concerns about Reviel Netz's ‘The Place of Archimedes in World History’

In this response I argue that Netz’ essay adopts nineteenth-century theses on the history of mathematics, one of the uses of which was to highlight an alleged difference between Europe and “the others”. The debasement of certain facets of mathematical work devoted to numbers and computation has played a key role in perpetuating these theses. Such a devaluation applies notably to decimal place-value numeration systems, which Reviel perceives as representing no “significant contribution to mathematical science. ” I counter this view by showing the continued theoretical impact that numeration systems of this type have had. I also discuss two historiographic operations that I identify in Reviel's argument and whose use requires revision: selecting and purifying. My conclusion emphasizes how in the post World War II period historians such as Lucien Febvre invited their colleagues to focus, in contrast, on the hybrid character of cultural artefacts.
Distribution and ecological risk assessment of heavy metals in the sediments of Changli ecological monitoring area, northwest of Bohai Bay, China

ABSTRACT The concentrations of six heavy metals were determined in the sediments of the Changli ecological monitoring area, northwest of the Bohai Bay. The spatial distribution of the metals was discussed, and the ecological risk was evaluated using the potential ecological risk index (PERI) and secondary phase enrichment factor (KSPEF ). The content ranges of Cu, Pb, Cd, Cr, Ni, and Zn were 1.38–25.78, 11.13–21.96, 0.12–0.54, 15.44–120.60, 0.48–57.11, and 44.21–156.38 mg·kg−1, respectively. Based on the results of the geochemical speciation analysis, the residual phase was the main form for all five metals (Cu, Pb, Cd, Cr, and Zn). Compared to the assessment results of KSPEF, PERI is more suitable for the pollution evaluation of heavy metals. The results indicated that all the sediments pose a moderate to considerable level of risk, except site S14 (low risk), and Cd was found to be the main contributor of the ecological risk due to heavy metals.