Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Language Models Understand and Can be Enhanced by Emotional Stimuli

Emotional intelligence significantly impacts our daily behaviors and interactions. Although Large Language Models (LLMs) are increasingly viewed as a stride toward artificial general intelligence, exhibiting impressive performance in numerous tasks, it is still uncertain if LLMs can genuinely grasp psychological emotional stimuli. Understanding and responding to emotional cues gives humans a distinct advantage in problem-solving. In this paper, we take the first step towards exploring the ability of LLMs to understand emotional stimuli. To this end, we first conduct automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4. Our tasks span deterministic and generative applications that represent comprehensive evaluation scenarios. Our automatic experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts (which we call"EmotionPrompt"that combines the original prompt with emotional stimuli), e.g., 8.00% relative performance improvement in Instruction Induction and 115% in BIG-Bench. In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks (10.9% average improvement in terms of performance, truthfulness, and responsibility metrics). We provide an in-depth discussion regarding why EmotionPrompt works for LLMs and the factors that may influence its performance. We posit that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for human-LLMs interaction.
Four Years of Type Ia Supernovae Observed by TESS: Early-time Light-curve Shapes and Constraints on Companion Interaction Models

We present 307 type Ia supernova (SN) light curves from the first 4 yr of the Transiting Exoplanet Survey Satellite mission. We use this sample to characterize the shapes of the early-time light curves, measure the rise times from first light to peak, and search for companion star interactions. Using simulations, we show that light curves must have noise <10% of the peak flux to avoid biases in the early-time light-curve shape, restricting our quantitative analysis to 74 light curves. We find that the mean power-law index tβ1 of the early-time light curves is β 1 = 1.93 ± 0.57, and the mean rise time to peak is 15.7 ± 3.5 days. The underlying population distribution for β 1 may instead consist of a Gaussian component with mean 2.29, width 0.34, and a long tail extending to values less than 1.0. We find that the data can rarely distinguish between models with and without companion interaction models. Nevertheless, we find three high-quality light curves that tentatively prefer the addition of a companion interaction model, but the statistical evidence for the companion interactions is not robust. We also find two SNe that disfavor the addition of a companion interaction model to a curved power-law model. Taking the 74 SNe together, we calculate 3σ upper limits on the presence of companion signatures to control for orientation effects that can hide companions in individual light curves. Our results rule out common progenitor systems with companions having Roche lobe radii >31 R ⊙ (separations >5.7 × 1012 cm, 99.9% confidence level) and disfavor companions having Roche lobe radii >10 R ⊙ (separations >1.9 × 1012 cm, 95% confidence level). Lastly, we discuss the implications of our results for the intrinsic fraction of single degenerate progenitor systems.
InVAErt networks: a data-driven framework for emulation, inference and identifiability analysis

Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable ﬂexibility offered by data-driven architectures would suggest to extend this representation to other aspects of system synthesis including model inversion and identiﬁability. We introduce inVAErt (pronounced invert ) networks, a comprehensive framework for data-driven analysis and synthesis of parametric physical systems which uses a deterministic encoder and decoder to represent the forward and inverse solution maps, normalizing ﬂow to capture the probabilistic distribution of system outputs, and a variational encoder designed to learn a compact latent representation for the lack of bijectivity between inputs and outputs. We formally investigate the selection of penalty coefﬁcients in the loss function and strategies for latent space sampling, since we ﬁnd that these signiﬁcantly affect both training and testing performance. We validate our framework through extensive numerical examples, including simple linear, nonlinear, and periodic maps, dynamical systems, and spatio-temporal PDEs.
Testing Viable f(T) Models with Current Observations

We perform observational tests on the f(T) gravity with the BAO data (including the BOSS DR 12 galaxy sample, the DR12 Lyα-Forests measurement, the new eBOSS DR14 quasar sample, the 6dFGS, and the SDSS), the CMB distance priors from the Planck 2015, the SNIa data from the joint light-curve analysis, the latest H(z) data, and the local value of the Hubble constant. Six different f(T) models are investigated. Furthermore, the ΛCDM is also considered. All models are compared by using the Akaike information criteria (AIC) and the Bayesian information criteria (BIC). Our results show that the ΛCDM remains to be the most favored model by current observations. However, there are also the Hubble constant tension between the Planck measurements and the local Universe observations and the tension between the CMB data and the H(z) data in the ΛCDM. For f(T) models considered in this paper, half, which can reduce to the ΛCDM, have values of smaller than that of the ΛCDM and can relieve the tensions existing in the ΛCDM. However, they are punished slightly by the BIC due to one extra parameter. Two of six f(T) models, in which the crossing of the phantom divide line can be realized for the equation of state of the effective dark energy and this crossing is shown in this paper to be favored by current observations, are punished by the information criteria. In addition, we find that the logarithmic f(T) model is excluded by cosmological observations.
Arndt and De Morgan Integer Compositions

In 2013, Joerg Arndt recorded that the Fibonacci numbers count integer compositions where the first part is greater than the second, the third part is greater than the fourth, etc. We provide a new combinatorial proof that verifies his observation using compositions with only odd parts as studied by De Morgan. We generalize the descent condition to establish families of recurrence relations related to two types of compositions: those made of any odd part and certain even parts, and those made of any even part and certain odd parts. These generalizations connect to compositions studied by Andrews and Viennot. New tools used in the combinatorial proofs include two permutations of compositions and a statistic based on the signed pairwise difference between parts.
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition

This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while ensuring accuracy, we propose an emotion recognizer augmented by a full-attention topic regularizer, which enables an emotion-related global view when modeling the local context in a conversation. A joint topic modeling strategy is introduced to implement regularization from both representation and loss perspectives. To avoid over-regularization, we drop the constraints on prior distributions that exist in traditional topic modeling and perform probabilistic approximations based entirely on attention alignment. Experiments show that our models obtain more favorable results than state-of-the-art models, and gain convincing robustness under three types of adversarial attacks.
SL: Stable Learning in Source-Free Domain Adaption for Medical Image Segmentation

Deep learning techniques for medical image analysis usually suffer from the domain shift between source and target data. Most existing works focus on unsupervised domain adaptation (UDA). However, in practical applications, privacy issues are much more severe. For example, the data of different hospitals have domain shifts due to equipment problems, and data of the two domains cannot be available simultaneously because of privacy. In this challenge defined as Source-Free UDA, the previous UDA medical methods are limited. Although a variety of medical source-free unsupervised domain adaption (MSFUDA) methods have been proposed, we found they fall into an over-fitting dilemma called"longer training, worse performance. "Therefore, we propose the Stable Learning (SL) strategy to address the dilemma. SL is a scalable method and can be integrated with other research, which consists of Weight Consolidation and Entropy Increase. First, we apply Weight Consolidation to retain domain-invariant knowledge and then we design Entropy Increase to avoid over-learning. Comparative experiments prove the effectiveness of SL. We also have done extensive ablation experiments. Besides, We will release codes including a variety of MSFUDA methods.
Revisiting Event-Based Video Frame Interpolation

Dynamic vision sensors or event cameras provide rich complementary information for video frame interpolation. Existing state-of-the-art methods follow the paradigm of combining both synthesis-based and warping networks. However, few of those methods fully respect the intrinsic characteristics of events streams. Given that event cameras only encode intensity changes and polarity rather than color intensities, estimating optical flow from events is arguably more difficult than from RGB information. We therefore propose to incorporate RGB information in an event-guided optical flow refinement strategy. Moreover, in light of the quasi-continuous nature of the time signals provided by event cameras, we propose a divide-and-conquer strategy in which event-based intermediate frame synthesis happens incrementally in multiple simplified stages rather than in a single, long stage. Extensive experiments on both synthetic and real-world datasets show that these modifications lead to more reliable and realistic intermediate frame results than previous video frame interpolation methods. Our findings underline that a careful consideration of event characteristics such as high temporal density and elevated noise benefits interpolation accuracy.
Facial Video-Based Remote Physiological Measurement via Self-Supervised Learning

Facial video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human facial videos and then measure multiple vital signs (e.g., heart rate, respiration frequency) from rPPG signals. Recent approaches achieve it by training deep neural networks, which normally require abundant facial videos and synchronously recorded photoplethysmography (PPG) signals for supervision. However, the collection of these annotated corpora is not easy in practice. In this paper, we introduce a novel frequency-inspired self-supervised framework that learns to estimate rPPG signals from facial videos without the need of ground truth PPG signals. Given a video sample, we first augment it into multiple positive/negative samples which contain similar/dissimilar signal frequencies to the original one. Specifically, positive samples are generated using spatial augmentation; negative samples are generated via a learnable frequency augmentation module, which performs non-linear signal frequency transformation on the input without excessively changing its visual appearance. Next, we introduce a local rPPG expert aggregation module to estimate rPPG signals from augmented samples. It encodes complementary pulsation information from different face regions and aggregates them into one rPPG prediction. Finally, we propose a series of frequency-inspired losses, i.e., frequency contrastive loss, frequency ratio consistency loss, and cross-video frequency agreement loss, for the optimization of estimated rPPG signals from multiple augmented video samples. We conduct rPPG-based heart rate, heart rate variability, and respiration frequency estimation on five standard benchmarks. The experimental results demonstrate that our method improves the state of the art by a large margin.
Selecting the Motion Ground Truth for Loose-fitting Wearables: Benchmarking Optical MoCap Methods

To aid smart wearable researchers in selecting optimal ground truth methods for motion capture (MoCap) across all loose garment types, we introduce a benchmark: DrapeMoCapBench (DMCB). This benchmark is tailored to assess optical marker-based and marker-less MoCap performance. While high-cost marker-based systems are recognized as precise standards, they demand skin-tight markers on bony areas for accuracy, which is problematic with loose garments. Conversely, marker-less MoCap methods driven by computer vision models have evolved, requiring only smartphone cameras and being cost-effective. DMCB employs real-world MoCap datasets, conducting 3D physics simulations with diverse variables: six drape levels, three motion intensities, and six body type-gender combinations. This benchmarks advanced marker-based and marker-less MoCap techniques, identifying the superior approach for distinct scenarios. When evaluating casual loose garments, both methods exhibit notable performance degradation (>10cm). However, for everyday activities involving basic and swift motions, marker-less MoCap slightly surpasses marker-based alternatives. This renders it an advantageous and economical choice for wearable studies.
Effects of thermal load on wheel–rail contacts: A review

ABSTRACT This article presents a technical review on the effects of thermal loads evolved at the wheel–rail–brake contact interfaces. These dynamic contact interfaces develop heat transfer conditions of widely varied thermal level. Their modeling to identify the sources for a variety of defect formation, observable on wheel tread or rail surface, is very important. The railway system, in general, has to bear axle load, friction load, and thermal load arising from their contact conditions in addition to traction and dynamic loads. The defects arising from the interaction of thermal load and other loadings may be identified as hot spots, shelling, spalling, rolling contact fatigue (RCF), and corrugation. The mechanisms for the formation of such defects are pivoted over the existing thermal environment of dynamic interacting surfaces. This review summarizes the works of early investigations and recent advances in modeling the heat transfer conditions required to estimate the temperature distribution at the contact zone. The heat partitioning method for both drag and stop braking conditions, in the presence of rail chill effect, is emphasized. Thermal gradient, introduced by localized temperature rise in the contact zone, in the presence of variable friction coefficient, promotes the RCF process. These alter the residual stresses in the contact region to cause a structural shakedown, aggravate plastic flow and activates ratchetting phenomenon in rails. The evolution of thermomechanical surface and subsurface fatigue cracks are also discussed for the completeness of this article. The effect of all such defect formation, emerging from thermal loading condition, and their countermeasures for defect mitigation are presented in this review. This abridged technical documentation envisions attracting more research in the area to improve wheel–rail set design and performance standards to extend enhanced safety and comfort to rail transport operation. It is opined that the thermomechanical loading, their effects on promoting defect formation and propagation should be studied in combination instead of the current practice of treating them separately.
Fundamental Parameters of the Eclipsing Binary TU Canis Majoris

We present a spectroscopic and photometric study of the eclipsing binary TU Canis Majoris aimed to obtain their fundamental stellar parameters and evolutionary stage. Our results indicate that the masses, radii, temperatures, and luminosities for the primary and secondary stars are: M 1 = 1.761 ± 0.012 M ⊙ , M 2 = 1.144 ± 0.010 M ⊙ , R 1 = 1.553 ± 0.002 R ⊙ , R 2 = 1.075 ± 0.002 R ⊙ , T 1 = 8014 ± 151 K, T 2 = 6060 ± 100 K, L 1 = 8.913 ± 0.695 L ⊙ , and L 2 = 1.396 ± 0.097 L ⊙ . We estimate an age for the system of τ = 2.11 ± 0.24 × 10 8 years, and a distance of d = 324.81 ± 12.86 pc. In addition, we note that none of the components has filled their respective Roche lobe and both are on the main sequence.
Verification of High-Resolution Medium-Range Precipitation Forecasts from Global Environmental Multiscale Model over China during 2009–2013

Accurate and timely precipitation forecasts are a key factor for improving hydrological forecasts. Therefore, it is fundamental to evaluate the skill of Numerical Weather Prediction (NWP) for precipitation forecasting. In this study, the Global Environmental Multi-scale (GEM) model, which is widely used around Canada, was chosen as the high-resolution medium-term prediction model. Based on the forecast precipitation with the resolution of 0.24° and taking regional differences into consideration, the study explored the forecasting skill of GEM in nine drought sub-regions around China. Spatially, GEM performs better in East and South China than in the inland areas. Temporally, the model is able to produce more precise precipitation during flood periods (summer and autumn) compared with the non-flood season (winter and spring). The forecasting skill variability differs with regions, lead time and season. For different precipitation categories, GEM for trace rainfall and little rainfall performs much better than moderate rainfall and above. Overall, compared with other prediction systems, GEM is applicable for the 0–96 h forecast, especially for the East and South China in flood season, but improvement for the prediction of heavy and storm rainfall and for the inland areas should be focused on as well.
Modern Physics: An Introductory Text

The birth of a new physics: the electron electromagnetic waves the special theory of relativity the general theory of relativity. Quantum theory: the quantum hypothesis the photoelectric effect photons the mechanics of minute particles. The nuclear atom: the structure of the atom the Bohr Model of the atom the quantum mechanical model of the atom electron spin. Interactions of electromagnetic radiation and matter: the passage of radiation through matter molecular spectra fluorescence and phosphorescence. Nuclear physics: the structure of the nucleus nuclear radiations radioactivity nuclear reactions and nuclear energy. Selected applications: the laser the Mossbauer Effect nuclear magnetic resonance the conductivity of electricity through solids invariance, symmetry and conservation laws.
Effects of Stellar-Mass Black Holes on Massive Star Cluster Evolution

Abstract Recent observations have revealed the existence of stellar mass black hole (BH) candidates in some globular clusters (GC) in the Milky Way and in other galaxies. Given that the detection of BHs is challenging, these detections likely indicate the existence of large populations of BHs in these clusters. This is in direct contrast to the past understanding that at most a handful of BHs may remain in old GCs due to quick mass segregation and rapid mutual dynamical ejection. Modern realistic star-by-star numerical simulations suggest that the retention fraction of BHs is typically much higher than what was previously thought. The BH dynamics near the cluster center leads to dynamical formation of new binaries and dynamical ejections, and acts as a persistent and significant energy source for these clusters. We have started exploring effects of BHs on the global evolution and survival of star clusters. We find that the evolution as well as survival of massive star clusters can critically depend on the details of the initial assumptions related to BH formation physics, such as natal kick distribution, and the initial stellar mass function (IMF). In this article we will present our latest results.
On Coherent Structures of Turbulent Open-channel Flow Above a Rough Bed

Present study examines turbulent structures of a rough bed open-channel flow in the context of deterministic approach. Instantaneous velocity field is measured in different hydraulic conditions using two dimensional Particle Image Velocimetry (PIV) in vertical plane and Stereoscopic PIV in horizontal plane. Different techniques and quantities such as swirl strength, two-point and cross-correlations of the swirl strength and fluctuating velocity are estimated to control formation of hairpin vortex. Our observations show in the area far above roughness elements, there is fairly a good agreement with the observations of previous studies for smooth-bed boundary flow. This consistency demonstrates that there is a similarity between outer region of the smooth and rough-bed layer flow. Moreover, some of the observations in the present study such as contourmap of instantaneous vorticity and swirling strength or observed inclination angle in contourmap of two-point correlations of the fluctuating velocity and swirl contourmap can be considered as signatures of the hairpin vortex. Given these observations, due to the lack of direct observation of the hairpin vortex and similarity of the observed features with signatures of other types of coherent structures, it is still too hard to assurly opine that in this condition, hairpin vortex is present.
Un théorème sur les opérateurs linéaires entre espaces de Banach qui se factorisent par un espace de Hilbert

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Node Representation Learning for Multiple Networks: The Case of Graph Alignment

Recent advances in representation learning produce node embeddings that may be used successfully in many downstream tasks (e.g., link prediction), but do not generally extend beyond a single network. Motivated by the prevalence of multi-network problems, such as graph alignment, similarity, and transfer learning, we introduce an elegant and principled node embedding formulation, Cross-network Matrix Factorization or xNetMF, to effectively and efficiently learn embeddings from similarities based on the nodes' structural and attribute identities (if available), which are comparable across networks. Leveraging the Nystr\"om method for low-rank approximation, we achieve significant reduction in space and time compared to the na\"ive approach of directly factorizing the node similarity matrix, with xNetMF running up to 30x faster than existing representation learning methods. We study its effectiveness on the task of network alignment, and introduce REGAL (REpresentation-based Graph ALignment), the first method to use node representation learning for this task. Extensive experiments on a variety of challenging real-world networks with and without attributes show that REGAL outperforms existing network alignment methods by 20 to 30% in accuracy on average, uses attributes up to 40x faster, and scales to networks with millions of nodes each.