Miracle-dz/UniFAR-FTUs huggingface dataset:
The dud canard: Existence of strong canard cycles in $\mathbb R^3$

In this paper, we provide a rigorous description of the birth of canard limit cycles in slow-fast systems in $\mathbb R^3$ through the folded saddle-node of type II and the singular Hopf bifurcation. In particular, we prove -- in the analytic case only -- that for all $0<\epsilon\ll 1$ there is a family of periodic orbits, born in the (singular) Hopf bifurcation and extending to $\mathcal O(1)$ cycles that follow the strong canard of the folded saddle-node. Our results can be seen as an extension of the canard explosion in $\mathbb R^2$, but in contrast to the planar case, the family of periodic orbits in $\mathbb R^3$ is not explosive. For this reason, we have chosen to call the phenomena in $\mathbb R^3$, the ``dud canard''. The main difficulty of the proof lies in connecting the Hopf cycles with the canard cycles, since these are described in different scalings. As in $\mathbb R^2$, we use blowup to overcome this, but we also have to compensate for the lack of uniformity near the Hopf bifurcation, due to its singular nature; it is a zero-Hopf bifurcation in the limit $\epsilon=0$. In the present paper, we do so by imposing analyticity of the vector-field. This allows us to prove existence of an invariant slow manifold, that is not normally hyperbolic.
Detection of ADHD based on Eye Movements during Natural Viewing

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose. It is known that an individual's viewing behavior, reflected in their eye movements, is directly related to attentional mechanisms and higher-order cognitive processes. We therefore explore whether ADHD can be detected based on recorded eye movements together with information about the video stimulus in a free-viewing task. To this end, we develop an end-to-end deep learning-based sequence model which we pre-train on a related task for which more data are available. We find that the method is in fact able to detect ADHD and outperforms relevant baselines. We investigate the relevance of the input features in an ablation study. Interestingly, we find that the model's performance is closely related to the content of the video, which provides insights for future experimental designs.
Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.
ARAUS: A Large-Scale Dataset and Baseline Models of Affective Responses to Augmented Urban Soundscapes

Choosing optimal maskers for existing soundscapes to effect a desired perceptual change via soundscape augmentation is non-trivial due to extensive varieties of maskers and a dearth of benchmark datasets with which to compare and develop soundscape augmentation models. To address this problem, we make publicly available the ARAUS (Affective Responses to Augmented Urban Soundscapes) dataset, which comprises a five-fold cross-validation set and independent test set totaling 25,440 unique subjective perceptual responses to augmented soundscapes presented as audio-visual stimuli. Each augmented soundscape is made by digitally adding “maskers” (bird, water, wind, traffic, construction, or silence) to urban soundscape recordings at fixed soundscape-to-masker ratios. Responses were then collected by asking participants to rate how pleasant, annoying, eventful, uneventful, vibrant, monotonous, chaotic, calm, and appropriate each augmented soundscape was, in accordance with ISO/TS 12913-2:2018. Participants also provided relevant demographic information and completed standard psychological questionnaires. We perform exploratory and statistical analysis of the responses obtained to verify internal consistency and agreement with known results in the literature. Finally, we demonstrate the benchmarking capability of the dataset by training and comparing four baseline models for urban soundscape pleasantness: a low-parameter regression model, a high-parameter convolutional neural network, and two attention-based networks in the literature.
Dynamic Contrastive Distillation for Image-Text Retrieval

The recent advancement in vision-and-language pretraining (VLP) has significantly improved the performance of cross-modal image-text retrieval (ITR) systems. However, the increasing size of VLP models presents a challenge for real-world deployment due to their high latency, making them unsuitable for practical search scenarios. To alleviate this problem, we present a novel plug-in dynamic contrastive distillation (DCD) framework to compress the large VLP models for the ITR task. Technically, we face the following two challenges: 1) the typical uni-modal metric learning approach is difficult to directly apply to cross-modal tasks due to the limited GPU memory to optimize too many negative samples during handling cross-modal fusion features. 2) it is inefficient to static optimize the student network from different hard samples, which affects distillation learning and student network optimization. We propose a method for multi-modal contrastive learning that balances training costs and effects. Our approach involves using a teacher network to identify hard samples for student networks to learn from, allowing the students to leverage the knowledge from pre-trained teachers and effectively learn from hard samples. To learn from hard sample pairs, we propose dynamic distillation to dynamically learn samples of different difficulties to balance better the difficulty of knowledge and students' self-learning ability. We successfully apply our proposed DCD strategy on two state-of-the-art vision-language pretrained models, i.e., ViLT and METER. Extensive experiments on MS-COCO and Flickr30K benchmarks show the effectiveness and efficiency of our DCD framework. We further provide in-depth analyses and discussions that explain how the performance improves.
Maximum a posteriori estimators in ℓp are well-defined for diagonal Gaussian priors

We prove that maximum a posteriori estimators are well-defined for diagonal Gaussian priors µ on ℓp under common assumptions on the potential Φ. Further, we show connections to the Onsager–Machlup functional and provide a corrected and strongly simplified proof in the Hilbert space case p = 2, previously established by Dashti et al (2013 Inverse Problems 29 095017); Kretschmann (2019 PhD Thesis). These corrections do not generalize to the setting 1⩽p<∞ , which requires a novel convexification result for the difference between the Cameron–Martin norm and the p-norm.
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition

Speech emotion recognition (SER) is an essential part of human-computer interaction. In this paper, we propose an SER network based on a Graph Isomorphism Network with Weighted Multiple Aggregators (WMA-GIN), which can effectively handle the problem of information confusion when neighbour nodes' features are aggregated together in GIN structure. Moreover, a Full-Adjacent (FA) layer is adopted for alleviating the over-squashing problem, which is existed in all Graph Neural Network (GNN) structures, including GIN. Furthermore, a multi-phase attention mechanism and multi-loss training strategy are employed to avoid missing the useful emotional information in the stacked WMA-GIN layers. We evaluated the performance of our proposed WMA-GIN on the popular IEMOCAP dataset. The experimental results show that WMA-GIN outperforms other GNN-based methods and is comparable to some advanced non-graph-based methods by achieving 72.48% of weighted accuracy (WA) and 67.72% of unweighted accuracy (UA).
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their usage in decision-critical contexts like healthcare. To bridge this gap, we propose an interpretable framework to analyze disorder-specific Regions of Interest (ROIs) and prominent connections. The proposed framework consists of two modules: a brain-network-oriented backbone model for disease prediction and a globally shared explanation generator that highlights disorder-specific biomarkers including salient ROIs and important connections. We conduct experiments on three real-world datasets of brain disorders. The results verify that our framework can obtain outstanding performance and also identify meaningful biomarkers. All code for this work is available at https://github.com/HennyJie/IBGNN.git.
Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer

With the development of computer technology, various models have emerged in artificial intelligence. The transformer model has been applied to the field of computer vision (CV) after its success in natural language processing (NLP). Radiologists continue to face multiple challenges in today's rapidly evolving medical field, such as increased workload and increased diagnostic demands. Although there are some conventional methods for lung cancer detection before, their accuracy still needs to be improved, especially in realistic diagnostic scenarios. This paper creatively proposes a segmentation method based on efficient transformer and applies it to medical image analysis. The algorithm completes the task of lung cancer classification and segmentation by analyzing lung cancer data, and aims to provide efficient technical support for medical staff. In addition, we evaluated and compared the results in various aspects. For the classification mission, the max accuracy of Swin-T by regular training and Swin-B in two resolutions by pre-training can be up to 82.3%. For the segmentation mission, we use pre-training to help the model improve the accuracy of our experiments. The accuracy of the three models reaches over 95%. The experiments demonstrate that the algorithm can be well applied to lung cancer classification and segmentation missions.
Cover your bases: asymptotic distributions of the profile likelihood ratio when constraining effective field theories in high-energy physics

We investigate the asymptotic distribution of the profile likelihood ratio (PLR) when constraining effective field theories (EFTs) and show that Wilks’ theorem is often violated, meaning that we should not assume the PLR to follow a \chi^2χ2-distribution. We derive the correct asymptotic distributions when either one or two real EFT couplings modulate observable cross sections with a purely linear or quadratic dependence. We then discover that when both the linear and quadratic terms contribute, the PLR distribution does not have a simple form. In this case we provide a partly-numerical solution for the one-parameter case. Using a novel approach, we find that the constants which define our asymptotic distributions may be obtained experimentally using a profile of the Asimov likelihood contour. Our results may be immediately used to obtain the correct coverage when deriving real-world EFT constraints using the PLR as a test-statistic.
VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

In this paper, we propose a tightly-coupled SLAM system fused with RGB, Depth, IMU and structured plane information. Traditional sparse points based SLAM systems always maintain a mass of map points to model the environment. Huge number of map points bring us a high computational complexity, making it difficult to be deployed on mobile devices. On the other hand, planes are common structures in man-made environment especially in indoor environments. We usually can use a small number of planes to represent a large scene. So the main purpose of this article is to decrease the high complexity of sparse points based SLAM. We build a lightweight back-end map which consists of a few planes and map points to achieve efficient bundle adjustment (BA) with an equal or better accuracy. We use homography constraints to eliminate the parameters of numerous plane points in the optimization and reduce the complexity of BA. We separate the parameters and measurements in homography and point-to-plane constraints and compress the measurements part to further effectively im-prove the speed of BA. We also integrate the plane information into the whole system to realize robust planar feature extraction, data association, and global consistent planar reconstruction. Finally, we perform an ablation study and compare our method with similar methods in simulation and real environment data. Our system achieves obvious advantages in accuracy and efficiency. Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures. The global bundle adjustment is nearly 2 times faster than the sparse points based SLAM algorithm.
Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individuals' physical health. Recently Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision making based on patients' authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications broadening from computer vision to healthcare. This study analyzes unstructured user data on the Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals. This effort will benefit the public health system by automating the detection process and informing appropriate authorities about people who require emergency assistance.
Learning Disentangled Representations for Controllable Human Motion Prediction

Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lower-body motions. However, to achieve this, the state-of-the-art methods require either subsequently learning mapping functions to seek similar motions or training the model repetitively to enable control over the desired portion of body. In this paper, we propose a novel framework to learn disentangled representations for controllable human motion prediction. Our network involves a conditional variational auto-encoder (CVAE) architecture to model full-body human motion, and an extra CVAE path to learn only the corresponding partial-body (e.g., lower-body) motion. Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion. With a single training, our model is able to provide two types of controls for the generated human motions: (i) strictly controlling one portion of human body and (ii) adaptively controlling the other portion, by sampling from a pair of latent spaces. Additionally, we extend and adapt a sampling strategy to our trained model to diversify the controllable predictions. Our framework also potentially allows new forms of control by flexibly customizing the input for the extra CVAE path. Extensive experimental results and ablation studies demonstrate that our approach is capable of predicting state-of-the-art controllable human motions both qualitatively and quantitatively.
Distribution of solids in the rings of the HD 163296 disk: a multiwavelength study

Context. Observations at millimeter wavelengths of bright protoplanetary disks have shown the ubiquitous presence of structures such as rings and spirals in the continuum emission. The derivation of the underlying properties of the emitting material is nontrivial because of the complex radiative processes involved. Aims. In this paper we analyze new observations from the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA) at high angular resolution corresponding to 5 – 8au to determine the dust spatial distribution and grain properties in the ringed disk of HD 163296. Methods. We ﬁt the spectral energy distribution as a function of the radius at ﬁve wavelengths from 0.9 to 9mm, using a simple power law and a physical model based on an analytic description of radiative transfer that includes isothermal scattering. We considered eight dust populations and compared the models’ performance using Bayesian evidence. Results. Our analysis shows that the moderately high optical depth ( τ >1) at λ ≤ 1 . 3mm in the dust rings artiﬁcially lower the millime- ter spectral index, which should therefore not be considered as a reliable direct proxy of the dust properties and especially the grain size. We ﬁnd that the outer disk is composed of small grains on the order of 200 µ m with no signiﬁcant difference between rings at 66 and 100au and the adjacent gaps, while in the innermost ∼ 30au, larger grains ( ≥ mm) could be present. We show that the assumptions on the dust composition have a strong impact on the derived surface densities and grain size. In particular, increasing the porosity of the grains to 80% results in a total dust mass about ﬁve times higher with respect to grains with 25% porosity. Finally, we ﬁnd that the derived opacities as a function of frequency deviate from a simple power law and that grains with a lower porosity seem to better reproduce the observations of HD 163296. Conclusions. While we do not ﬁnd evidence of differential trapping in the rings of HD 163296, our overall results are consistent with the postulated presence of giant planets affecting the dust temperature structure and surface density, and possibly originating a second-generation dust population of small grains.
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows

Optimal transport (OT) provides effective tools for comparing and mapping probability measures. We propose to leverage the flexibility of neural networks to learn an approximate optimal transport map. More precisely, we present a new and original method to address the problem of transporting a finite set of samples associated with a first underlying unknown distribution towards another finite set of samples drawn from another unknown distribution. We show that a particular instance of invertible neural networks, namely the normalizing flows, can be used to approximate the solution of this OT problem between a pair of empirical distributions. To this aim, we propose to relax the Monge formulation of OT by replacing the equality constraint on the push-forward measure by the minimization of the corresponding Wasserstein distance. The push-forward operator to be retrieved is then restricted to be a normalizing flow which is trained by optimizing the resulting cost function. This approach allows the transport map to be discretized as a composition of functions. Each of these functions is associated to one sub-flow of the network, whose output provides intermediate steps of the transport between the original and target measures. This discretization yields also a set of intermediate barycenters between the two measures of interest. Experiments conducted on toy examples as well as a challenging task of unsupervised translation demonstrate the interest of the proposed method. Finally, some experiments show that the proposed approach leads to a good approximation of the true OT.
The rise and sink dependence on the shape of a horizontally wiggling intruder

We investigate the effect of shape and orientation for a horizontally oscillating intruder on its vertical dynamics in a granular medium via Discrete Element Method (DEM) simulations. Five distinct intruder shapes were considered in this study: a disk (OS1), a square (OS2), two rectangles with aspect ratio>1 (OS3) and<1 (OS4) respectively, and an equilateral triangle (OS5). The vertical velocity of the oscillating object was observed to be a function of amplitude and frequency (f) (inverse of time period (T)) of oscillation, and the shape of the object. The dynamics of the motion are modelled with the help of a cavity based model which can empirically produce the regimes observed by incorporating the free fall of the particles filling the cavity.The cavity based model assumes that there is a point on the surface of each oscillating object unique to its shape that any grain crossing it will irreversibly reach the bottom of the cavity. This leads to the creation of bed of particles at the bottom of the cavity which increases intruder's elevation as it oscillates, leading to its vertical rise in the granular medium. When an intruder oscillates much faster than the falling rate of the grains, the intruder cannot rise up and thus sinks due to energy provided by the oscillation to the bottom layer. On the contrary, if the intruder oscillates too slow, the cavity gets completely filled much before it returns. The maximum rise rate is thus observed at the oscillation time period where the cavity filling time is equal to the half time period of oscillation. In addition, we observed a minimum amplitude (Amin) below which the vertical position of the IO remains unaltered for various T. We have also presented the time-averaged pressure P, velocity V, and area fraction {\phi} fields around the intruder shapes.