Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling Subtle Cues: Backchannel Detection Using Temporal Multimodal Attention Networks

Automatic detection of backchannel has great potential to enhance artificial mediators, which indicate listeners' attention and agreement in human communication. It is often expressed by subtle non-verbal cues that occur briefly and sparsely. Focusing on identifying and locating these subtle cues (i.e., their occurrence moment and the involved body parts), this paper proposes a novel approach for backchannel detection. In particular, our model utilizes temporal- and modality-attention modules to determine and lead the model to pay more attention to both the indicative moment and the accompanying body parts at that specific time. It achieves an accuracy of 68.6% on the testing set in MultiMediate'23 backchannel detection challenge, outperforming the counterparts. Furthermore, we conducted an ablation study to thoroughly understand the contributions of our model. This study underscores the effectiveness of our selection of modality inputs and the importance of the two attention modules in our model.
The Late Jurassic Cleveland-Lloyd Dinosaur Quarry as a Drought-Induced Assemblage

Abstract A comprehensive taphonomic analysis has yielded a novel interpretation for one of the most famous dinosaur quarries in the world. The Cleveland-Lloyd Dinosaur Quarry (CLDQ) traditionally has been interpreted as an attritional predator trap. This scenario is based largely on a remarkable 3:1 predator:prey ratio, dominated by the remains of the theropod Allosaurus fragilis. This study addresses the taphonomy of CLDQ by combining analyses of fossils and entombing sediments along with putative modern analogues. Thousands of bones have been excavated from CLDQ, representing at least 70 individual dinosaurs from a minimum of nine genera. The fossils occur in a 1-m-thick fine-grained calcareous mudstone interpreted as a floodplain ephemeral-pond deposit. The bones show minimal carnivore modification and surface weathering, whereas approximately 1/3 of the elements studied possess pre-depositional fractures and evidence of abrasion. The vast majority of elements are found horizontal to subhorizontal, without a preferred long-axis orientation. The demographic profile of the CLDQ dinosaur assemblage appears to be highly skewed toward subadult individuals. Numerous lines of evidence question the traditional predator-trap hypothesis. Of the alternatives, catastrophic drought appears to be most consistent with available data. Evidence includes a large assemblage of animals in a low-energy ephemeral-pond depositional setting and geologic and biologic evidence of desiccation. Additional support comes from modern drought analogues that frequently result in mass-death assemblages of large vertebrates. Climatic interpretations during Late Jurassic times are consistent with a semiarid environment characterized by periodic drought conditions.
G2-DUN: Gradient Guided Deep Unfolding Network for Image Compressive Sensing

Inspired by certain optimization solvers, the deep unfolding network (DUN) usually inherits a multi-phase structure for image compressive sensing (CS). However, in existing DUNs, the message transmission within and between phases still faces two issues: 1) the roughness of transmitted information, e.g., the low-dimensional representations. 2) the inefficiency of transmitted policy, e.g., simply concatenating deep features. In this paper, by unfolding the Proximal Gradient Descent (PGD) algorithm, a novel gradient guided DUN (G2 -DUN) for image CS is proposed, in which a gradient map is delicately introduced within each phase for providing richer informational guidance at both intra-phase and inter-phase levels. Specifically, corresponding to the gradient descent (GD) of PGD, a gradient guided GD module is designed, in which the gradient map can adaptively guide step size allocation for different textures of input image, realizing a content-aware gradient updating. On the other hand, corresponding to the proximal mapping (PM) of PGD, a gradient guided PM module is developed, in which the gradient map can dynamically guide the exploring of deep textural priors in multi-scale space, achieving the dynamic perception of the proposed deep model. By introducing the gradient map, the proposed message transmission system not only facilitates the informational communication between different functional modules within each phase, but also strengthens the inferential cooperation among cascaded phases. Extensive experiments manifest that the proposed G2 -DUN outperforms existing state-of-the-art CS methods.
Uncertainty-Driven Dynamic Degradation Perceiving and Background Modeling for Efficient Single Image Desnowing

Single-image snow removal aims to restore clean images from heterogeneous and irregular snow degradations. Recent methods utilize neural networks to remove various degradations directly. However, these approaches suffer from the limited ability to flexibly perceive complicated snow degradation patterns and insufficient representation of background structure information. To further improve the performance and generalization ability of snow removal, this paper aims to develop a novel and efficient paradigm from the perspective of degradation perceiving and background modeling. For this purpose, we first analyze two critical properties in real snow images, namely local-region heterogeneity and axial anisotropy. Inspired by them, we propose Dynamic Perceiving for Degraded Regions and Axial-Pooling Attention for Background Structure Modeling, which together couple a new network architecture, dubbed as D2P-BMNet. Our proposed D2P-BMNet offers several key advantages: (i) It can effectively segment regions under the uncertainty map's guidance, and dynamically perceives heterogeneous degradations within various regions. (ii) By utilizing linear attention solely along a horizontal axis, it can effectively model clean scene information that is buried beneath the snow. (iii) D2P-BMNet significantly improves over prior methods across all benchmarks and maintains excellent inference speeds.
Learning Shared Semantic Information from Multimodal Bio-signals for Brain-Muscle Modulation Analysis

This paper presents a novel learning-based algorithm to investigate the high-level shared semantic information between electroencephalography (EEG) and electromyography (EMG) signals, for understanding brain-muscle modulation during movement execution. The proposed algorithm incorporates a spatial encoder that condenses spatial information obtained from EEG/EMG signals into unified temporal tokens using a learnable correlation matrix. These tokens are then encoded and decoded via a siamese temporal encoder and classification head to extract joint semantic information presented in cross-modal signals. Additionally, an analysis pipeline is designed to examine brain-muscle modulation based on the proposed algorithm. Experimental results from a self-collected multimodal bio-signals dataset validate the efficacy of the proposed algorithm in extracting and analyzing high-level latent semantic information shared in EEG and EMG signals, outperforming the state-of-the-art model by 5.35% in accuracy, 4.69% in precision, and 8.65% in recall. Notably, the designed analysis pipeline can also reveal low-level relationships, such as those related to time and space, between multimodal bio-signals. This research provides neuroscientists with a valuable tool for obtaining enhanced insights into brain-muscle modulation.
Graph to Grid: Learning Deep Representations for Multimodal Emotion Recognition

Multimodal emotion recognition based on electroencephalogram (EEG) and compensating physiological signals (e.g., eye tracking) has shown potential in the diagnosis and rehabilitation tracking of depression. Since the multi-channel EEG signals are generally processed as one-dimensional (1-D) graph-like features, existing approaches can only adopt underdeveloped shallow models to recognize emotions. However, these simple models have difficulty decoupling complex emotion patterns due to their limited representation capacity. To address this problem, we propose the graph-to-grid (G2G), a concise and plug-and-play module that transforms the 1-D graph-like data into the two-dimensional (2-D) grid-like data via the numerical relation coding. After that, the well developed deep models, e.g., ResNet can be used to downstream tasks. In addition, G2G simplifies the previous complex multimodal fusion into an input matrix augmentation operation, which greatly reduces the difficulty of model design and parameter tuning. Extensive results on three public datasets (SEED, SEED5 and MPED) indicate that the proposed approach achieves state-of-the-art emotion recognition accuracy in both unimodal and multimodal settings, with good cross-session generalization ability. G2G enables the development of more appropriate multimodal emotion recognition algorithms for follow-up studies. Our code is publicly available at https://github.com/Jinminbox/G2G.
Parameter Exchange for Robust Dynamic Domain Generalization

Agnostic domain shift is the main reason of model degradation on the unknown target domains, which brings an urgent need to develop Domain generalization (DG). Recent advances at DG use dynamic networks to achieve training-free adaptation on the unknown target domains, termed Dynamic Domain Generalization (DDG), which compensates for the lack of self-adaptability in static models with fixed weights. The parameters of dynamic networks can be decoupled into a static and a dynamic component, which are designed to learn domain-invariant and domain-specific features, respectively. Based on the existing arts, in this work, we try to push the limits of DDG by disentangling the static and dynamic components more thoroughly from an optimization perspective. Our main consideration is that we can enable the static component to learn domain-invariant features more comprehensively by augmenting the domain-specific information. As a result, the more comprehensive domain-invariant features learned by the static component can then enforce the dynamic component to focus more on learning adaptive domain-specific features. To this end, we propose a simple yet effective Parameter Exchange (PE) method to perturb the combination between the static and dynamic components. We optimize the model using the gradients from both the perturbed and non-perturbed feed-forward jointly to implicitly achieve the aforementioned disentanglement. In this way, the two components can be optimized in a mutually-beneficial manner, which can resist the agnostic domain shifts and improve the self-adaptability on the unknown target domain. Extensive experiments show that PE can be easily plugged into existing dynamic networks to improve their generalization ability without bells and whistles.
Video Inverse Tone Mapping Network with Luma and Chroma Mapping

\beginabstract With the popularity of consumer high dynamic range (HDR) display devices, video inverse tone mapping (iTM) has become a research hotspot. However, existing methods are designed based on a perceptual non-uniformity color space (e.g., RGB and YC_BC_R), resulting in limited quality of HDR video rendered by these methods. Considering the two key factors involved in the video iTM task: luma and chroma, in this paper, we design an IC_TC_P color space based video iTM model, which reproduces high quality HDR video by processing luma and chroma information. Benefitting from the decorrelated perception of luma and chroma in the IC_TC_P color space, two global mapping networks (INet and TPNet) are developed to enhance the luma and chroma pixels, respectively. However, luma and chroma mapping in the iTM task may be affected by color appearance phenomena. Thus, a luma-chroma adaptation transform network (LCATNet) is proposed to process the luma and chroma pixels affected by color appearance phenomena, which can complement the local details to the globally enhanced luma and chroma pixels. In the LCATNet, either the luma mapping or the chroma mapping is adaptively adjusted according to both the luma and the chroma information. Besides, benefitting from the perceptually consistent property of the IC_T C_P color space, the same pixel errors can draw equal model attentions during the training. Thus, the proposed model can correctly render luma and chroma information without highlighting special regions or designing special training losses. Extensive experimental results demonstrate the effectiveness of the proposed model. \endabstract
A NEW LOWER CRETACEOUS SINOPTERID PTEROSAUR FROM THE WESTERN LIAONING,CHINA

Pterosaurs are special flying reptiles. They are difficult to be preserved as fossils, due to their hollow and very thin bones. They were often found from marine and lagoonal sediments, such as the Solnhofen Limestone of Germany, the Santana Formation of Brazil, and the Cambridge Greensand of England (Unwin, 2001). They were seldom found from terrestrial deposits. Recently, five genera of pterosaurs found from western Liaoning and its peripheral areas have been reported, they are Eosipterus (Ji and Ji, 1997), Dendrorhynchoides (Ji and Ji, 1998; Ji et al ., 1999; see Unwin et al ., 2000), Haopterus (Wang and Lu, 2001), Jeholopterus (Wang et al ., 2002), and Sinopterus (Wang and Zhou, 2002). Some beautifully preserved skeletons with soft tissues and impressions (Wang et al ., 2002; Lu, 2002) provide important information on the study of the distribution, evolution and ecology of pterosaurs. This area may become the first pterosaur locality, in which more specimens are found from terrestrial deposits than Mongolia (Bakhurina and Unwin, 1995; Unwin et al ., 1997; Unwin and Bakhurina, 2000; Unwin et al ., 2000). Herein a small pterodactyloid pterosaur is described. It is the smallest toothless pterosaur found from the Jiufotang Formation so far.
Robust H ∞ filtering for a class of uncertain linear systems with time-varying delay

This paper deals with the problem of delay-dependent robust H∞ filtering for uncertain linear systems with time-varying delay. Two cases of time-varying delays are fully considered; one is the time-varying delay being continuous uniformly bounded while the other is the time-varying delay being differentiable uniformly bounded with delay-derivative bounded by a constant. A stable linear filter is designed to ensure that the filtering error system is asymptotically stable with a prescribed level of H∞ noise attenuation. Based on a new integral inequality, delaydependent sufficient conditions for the existence of such a filter are established in terms of linear matrix inequalities (LMIs). Through deriving these conditions, neither model transformation nor bounding technique for cross terms is employed. Moreover, the relationship between the sufficient conditions for the two cases of time-varying delay is disclosed. Finally, two examples are also given to illustrate the effectiveness of the proposed methodology. 2007 Elsevier Ltd. All rights reserved.
Path Description Dependencies in Feature-Based DLs

All members of the FunDL family of description logics replace roles with partial functions ( features ) and have a concept constructor, called path functional dependency (PFD), for expressing extensions of functional dependencies, useful in object-relational data sources. In this paper, we consider generalizing PFDs to path description dependencies (PDD), by allowing inverse features in them, which result in paths being set-valued. We show that logical consequence for partial-𝒟ℒℱ𝒟ℐ , one of the most expressive dialects of the FunDL family, remains EXPTIME complete for coherent terminologies, when extended with PDDs. As a first application, we consider referring expression types , which are concept descriptions used to identify individuals in query answers. In the new dialect, such a type denotes a set of concept descriptions. As such, one must prove in advance that any interpretation of any member of the set will never have more than one element. We show that this “singularity condition” can be diagnosed as a collection of logical consequence questions wrt a TBox.
Silence is not Golden : Corporate Governance Standards , Transparency , and Performance

We examine corporate governance and disclosure choices in a regulatory environment suited to address the nature of governance as chosen by individual rms. In this environment, companies choose whether to comply with governance standards or to report and explain their non-compliance. By using a voluntary regulatory arena, we can observe the impact of di¤erent corporate characteristics on corporate governance choices. We nd that rm ownership structure and rm growth opportunities a¤ect the decision to deviate from regulatory standards. The disclosed reasons for deviation are often obscure and uninformative. Only those companies which adhere to the recommended standards or those with the highest level of disclosure have better performance. Our ndings suggest that some companies adopt di¤erent corporate governance practices not because of their di¤erent characteristics but to extract private bene ts.
Detection and Recording of Partial Discharges below the So-called Inception Voltage

The problem of insulation damage from partial discharges below the so- called inception voltage has not attracted much attention from the insulation commu- nity. Indications of possible damage exist from previous research. In the present paper, the possibility of existence of such phenomena is investigated with small air gaps and non-uniform electrode arrangements. It is shown that random discharges below in- ception voltage exist, which on occasions can be quite numerous. Such discharges are registered and their implications are discussed.
EVALUATION OF THE BRAY P1 SOIL TEST ON EASTERN RED SOILS IN WISCONSIN

Soil testing for phosphorus (P) is used agronomically to determine the amount of P needed for crop production. Soil test P is also used for determining environmental risks associated with elevated levels of soil P. The Bray P1 soil test method is commonly used in Wisconsin for measuring plant available P and is the required test for regulatory agencies. The Bray P1 extracting solution consists of 0.03 N ammonium fluoride and 0.025 N hydrochloric acid. The ammonium fluoride extracts mostly aluminum bound-P, and no iron-P, and the hydrochloric acid extracts calcium-P (Tandon et al., 1967). Mehlich-3 and Olsen are also widely-used soil test P methods. Mehlich-3 was developed for a wide range of soils including calcareous soils (Wang et al., 2004; Lucero et al., 1998), and Olsen was developed primarily for calcareous soils (Olsen et al., 1954). A calcareous soil is defined as a “soil containing sufficient calcium carbonate (often magnesium carbonate) to effervesce visibly when treated with cold 0.1 N hydrochloric acid” (Brady and Weil, 1999). Mehlich-3 and Bray P1 soil test results are highly correlated in neutral to acid soils with Mehlich-3 extracting slightly more P than Bray P1 in most soils because Mehlich-3 uses a more acidic extracting solution (Tran et al., 1990, Beegle and Oravec, 1990, Lucero et al., 1998; Mallarino, 2003). The Bray P1 soil test method is intended for acid soils, and the validity of its use to predict plant available P levels on the eastern red soils (ERS) in Wisconsin has been questioned due to concerns that the weak acid Bray P1 extracting solution could be neutralized by reaction with carbonates in these soils. Previous studies have found that Bray P1 extracts less P at higher soil pH (Mallarino and Blackmer, 1992; Mallarino, 1997; Atia and Mallarino, 2002) and that Bray P1 does not correlate as well as with Mehlich 3 or Olsen in soils with higher calcium carbonate contents (Hooker et al., 1980; Mallarino, 2003; Hermin et al., 2004). Mallarino (1997) found that Mehlich 3, Olsen, and Bray P1 correlated well with each other until soils reached a calcium carbonate equivalent (CCE) of 4% or higher. Other research indicates that pH alone is not a good indicator of when Bray P will fail; data shows that carbonate content greater than 36 g kg (3.6%) is important (Mallarino and Atia, 2005). Mehlich 3 and Olsen soil test P results are well correlated regardless of soil type.
On analysing sea level rise in the German Bight since 1844

Abstract. In this paper, a methodology to analyse observed sea level rise (SLR) in the German Bight, the shallow south-eastern part of the North Sea, is presented. The paper focuses on the description of the methods used to generate and analyse mean sea level (MSL) time series. Parametric fitting approaches as well as non-parametric data adaptive filters, such as Singular System Analysis (SSA) are applied. For padding non-stationary sea level time series, an advanced approach named Monte-Carlo autoregressive padding (MCAP) is introduced. This approach allows the specification of uncertainties of the behaviour of smoothed time series near the boundaries. As an example, the paper includes the results from analysing the sea level records of the Cuxhaven tide gauge and the Heligoland tide gauge, both located in the south-eastern North Sea. For comparison, the results from analysing a worldwide sea level reconstruction are also presented. The results for the North Sea point to a weak negative acceleration of SLR since 1844 with a strong positive acceleration at the end of the 19th century, to a period of almost no SLR around the 1970s with subsequent positive acceleration and to high recent rates.
Stochastic Modeling of Galileo E1 and E5a Signals

In order to take full advantage of the new Galileo signals E1 and E5a, it is essential that their stochastic characteristics are rigorously modelled. In this paper, various sets of GPS and Galileo measurements, collected at two stations with short separation, were used to study the stochastic characteristics of Galileo E1 and E5a signals. As a by-product, the stochastic characteristics of the legacy GPS P1 code was obtained and then used to verify the developed stochastic model of the Galileo signal. It is shown that the developed stochastic models improve the accuracy of precise point positioning solution and its convergence time. Index Terms—Galileo, GNSS, GPS, Stochastic Characteristics, PPP.
Renormalized Perturbation Theory: a Missing Chapter Received (day Month Year) Revised (day Month Year)

Why a talk on Renormalized Perturbation Theory (RPT) in 2008? The consensus established in the 70’s under the acronym BPHZ is part of elementary particle physicists’ theoretical equipment. Yet, the corresponding literature is hard to penetrate for a mind endowned with good logical connections typically that of a professional mathematician. This state of affairs may be assigned, in parts, to some fuzziness about the connection between the operator version and the functional version (à la Feynman) of quantum mechanics, in this context. In standard textbooks[5], the latter are both usually described in formal terms which are most of the time not subject to any mathematical formalization . Parallel to the establishment of BPHZ, and following the path indicated by E.C. G. Stueckelberg[6][7], N.N. Bogoliubov[1] and coworkers, H. Epstein and
. A cross-sectional survey of 5-year-old children with non-syndromic unilateral cleft lip and palate: The Cleft Care UK study. Part 1: Background and methodology. Orthodontics and Craniofacial Research, 18(Supplement S2), 1-13.

Structured Abstract Objectives – We describe the methodology for a major study investigating the impact of reconﬁgured cleft care in the United Kingdom (UK) 15 years after an initial survey, detailed in the Clinical Standards Advisory Group (CSAG) report in 1998, had informed government recommendations on centralization. Setting and Sample Population – This is a UK