Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing Act: Acute and Contextual Vestibular Sensations of Cranial Electrotherapy Stimulation Using Survey and Sensor Outcomes in a Non-Clinical Sample

Cranial electrotherapy stimulation (CES) delivers low-intensity electrical currents to the brain to treat anxiety, depression, and pain. Though CES is considered safe and cost-effective, little is known about side effects emerging across different contexts. Our objective was to investigate how varying physical and cognitive demands impact the frequency and intensity of CES vestibular sensations in a sample of healthy young adults. We used a 2 (stimulation: sham, active) × 2 (physical demand: static sway, dynamic sit-to-stand) × 2 (cognitive demand: single-task remain silent, dual-task count backward) repeated measures design. Vestibular sensations were measured with surveys and wearable sensors capturing balance changes. Active stimulation did not influence reported vestibular sensations. Instead, high physical demand predicted more sensation reports. High cognitive demand, but not active stimulation, predicted postural sway unsteadiness. Significant effects of active stimulation on balance were observed only during the dynamic sit-to-stand transitions. In summary, CES induces vestibular sensations only for a specific outcome under certain circumstances. Our findings imply that consumers can safely maximize the benefits of CES while ensuring they are taking steps to minimize any potential side effects by considering their context and circumstances.
Pandemic-Related Post-traumatic Stress Symptomatology in COVID-19 Patients with and without Post-COVID Conditions

Trauma and stressor-related symptoms have been frequently reported during the COVID-19 pandemic. Few studies compare post-traumatic stress symptoms (PTSS) between patients and non-infected controls. Using data from an ongoing natural history study of COVID-19, this study compared PTSS between patients infected with SARS-CoV-2 during the first year of the pandemic and controls. Within the COVID-19 patient cohort, we also compared PTSS between patients with and without post-COVID conditions, also known as post-acute sequelae of SARS-CoV-2 infection (PASC). This study also examined the association of PTSS with trait resilience and prior trauma exposure. PTSS were assessed using the Impact of Event Scaled-Revised (IES-R), which has a validated probable PTSD cutoff (score [≥]33). The results showed that patients (n=131) reported significantly higher IES-R scores than controls (n=82) and had significantly higher odds of having scores indicative of PTSD [AOR: 4.17 p: 0.029]. IES-R scores among PASC patients (n=68) were significantly elevated compared to patients without PASC (n=63) and PASC patients did not have higher odds for probable PTSD [AOR: 2.60; p: 0.14]. Trait resilience was associated with lower PTSS. These findings help characterize the mental health impact of the COVID-19 illness experience and highlight elevated PTSS in patients with persistent post-COVID conditions.
Submicroscopic malaria parasitemia and migration: the hidden weight carried by pregnant women.

Introduction: Submicroscopic malaria parasitemia is a known phenomenon for years, however there are currently not enough existing data to set guidelines for diagnosis and treatment. Due to multiple reasons, pregnant women arrived recently from endemic areas seem to be particularly at risk of submicroscopic malaria parasitemia, which can only be diagnosed by molecular techniques. This study aims to determine the prevalence of submicroscopic malaria parasitemia in pregnant women coming from endemic areas and to pin down its potential consequences on maternal and birth outcomes. Methods: A prospective study on pregnant women that recently arrived from endemic areas for malaria was conducted in the Obstetrics Department of CHU Saint-Pierre (Brussels). Several P. falciparum detection techniques were performed for each participant on 2 maternal (pre and postnatal) and 1 cord blood samples, using both conventional tests and loop-mediated isothermal amplification (LAMP). Placentas were also analyzed to search for placental malaria. Birth outcomes were collected. Patients with and without submicroscopic malaria parasitemia were compared. Results: Maternal and umbilical blood samples were collected in 96 enrolled patients and analyzed by conventional tests and loop mediated isothermal amplification. Among the enrolled patients, 5 (5.2%) had submicroscopic malaria parasitemia. Patients with submicroscopic malaria parasitemia had left their endemic area more recently (p <0.05), had higher reticulocyte counts in both maternal prenatal blood (p <0.02) and cord blood (p <0.02) and had higher lactate dehydrogenase level in cord blood (p <0.05). No impact of submicroscopic malaria parasitemia on birth weight and term of birth was detected. Conclusion: The prevalence of submicroscopic malaria parasitemia in pregnant women arriving in non-endemic countries is non negligible. More data need to be collected to assess the consequences of submicroscopic malaria parasitemia on maternal and birth outcomes.
Evaluation of the contributions of selected non-timber forest products to food security and income generation, in Imo State, Nigeria

The study examined the contributions of three non-timber forest products (NTFPs) to household food security and income generation in rural areas of Imo State. The research methods involved field visits and the use of semi-structured questionnaire. Nine hundred (900) copies of the research instrument were administered to rural households in the State using multi-stage and systematic sampling procedures. Data were analyzed using descriptive statistics, Z-test and Foster-Greer-Thorbecke (FGT) metric. The result revealed that 98% of the respondents consumed the three NTFPs as food and 1.7% used the species for medicinal purposes with only 4.0% utilizing the NTFPs as means of income generations in Imo State. The consumption of the three species (i.e. Pentaclethra macrophylla, Monodora myristica and Gnetum africanum) as foods were highest in Owerri and Okigwe Zones (98.5%) and least in Orlu Zone (96.6%). A higher percentage (6%) of the respondents utilized the species for income generation in Owerri Zone compared to 3% and 2.5% recorded for Orlu and Okigwe Zones respectively. For medicinal purposes, the uses of the three NTFPs were limited with only 2.7%, 1.7% and 0.7% confirming their utilization for the purpose in Okigwe, Orlu and Owerri Zones respectively. There were significant differences in mean annual incomes of those that traded in the NTFPs and those that did not (P<0.05), and those who traded in the NTFPs recorded a higher mean annual income compared to non-NTFPs traders. The poverty severity in Owerri, Orlu and Okigwe Zones were 7%, 9% and 12% respectively, which implies that for the rural households to live a moderately poor life, leaving above 2 USD a day, additional ₦ 10,972.12, ₦ 14,881.08 and ₦ 11,476.83 will be required to the current annual mean income, respectively. Keywords : Consumption, income generation, livelihood, poverty severity, agricultural zones
The moduli space of solutions to the Extended Bogomolny equations on $\Sigma \times \mathbb{R_+}$

We study moduli spaces of solutions to the extended Bogomolny equations on $\Sigma \times \mathbb{R_{+,y}}$ with gauge group $\operatorname{SL}(2,\mathbb{C})$ satisfying the generalized Nahm pole boundary condition as $y\to 0$ and limiting to complex flat connections as $y\to \infty$. Refining the Kobayashi-Hitchin correspondence of \cite{MH2}, we identify these moduli spaces with certain holomorphic lagrangian sub-manifolds inside the moduli space of Higgs bundles.
Self-Labeling the Job Shop Scheduling Problem

In this work, we propose a Self-Supervised training strategy specifically designed for combinatorial problems. One of the main obstacles in applying supervised paradigms to such problems is the requirement of expensive target solutions as ground-truth, often produced with costly exact solvers. Inspired by Semi- and Self-Supervised learning, we show that it is possible to easily train generative models by sampling multiple solutions and using the best one according to the problem objective as a pseudo-label. In this way, we iteratively improve the model generation capability by relying only on its self-supervision, completely removing the need for optimality information. We prove the effectiveness of this Self-Labeling strategy on the Job Shop Scheduling (JSP), a complex combinatorial problem that is receiving much attention from the Reinforcement Learning community. We propose a generative model based on the well-known Pointer Network and train it with our strategy. Experiments on two popular benchmarks demonstrate the potential of this approach as the resulting models outperform constructive heuristics and current state-of-the-art Reinforcement Learning proposals.
Location Sensitive Embedding for Knowledge Graph Embedding

Knowledge graph embedding transforms knowledge graphs into a continuous, low-dimensional space, facilitating inference and completion tasks. This field is mainly divided into translational distance models and semantic matching models. A key challenge in translational distance models is their inability to effectively differentiate between 'head' and 'tail' entities in graphs. To address this, the novel location-sensitive embedding (LSE) method has been developed. LSE innovatively modifies the head entity using relation-specific mappings, conceptualizing relations as linear transformations rather than mere translations. The theoretical foundations of LSE, including its representational capabilities and its connections to existing models, have been thoroughly examined. A more streamlined variant, LSEd, employs a diagonal matrix for transformations to enhance practical efficiency. In tests conducted on four large-scale datasets for link prediction, LSEd either outperforms or is competitive with leading contemporary models.
Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable

There has been considerable recent interest in estimating heterogeneous causal effects. In this paper, we introduce conditional average partial causal effects (CAPCE) to reveal the heterogeneity of causal effects with continuous treatment. We provide conditions for identifying CAPCE in an instrumental variable setting. We develop three families of CAPCE estimators: sieve, parametric, and reproducing kernel Hilbert space (RKHS)-based, and analyze their statistical properties. We illustrate the proposed CAPCE estimators on synthetic and real-world data.
Linear-quadratic optimal control for infinite-dimensional input-state-output systems

We examine the minimization of a quadratic cost functional composed of the output and the final state of abstract infinite-dimensional evolution equations in view of existence of solutions and optimality conditions. While the initial value is prescribed, we are minimizing over all inputs within a specified convex subset of square integrable controls with values in a Hilbert space. The considered class of infinite-dimensional systems is based on the system node approach introduced by Staffans. Thus, our developed approach includes optimal control of a wide variety of linear partial differential equations with boundary control and observation that are not well-posed in the sense that the output continuously depends on the input and the initial value. We provide an application of particular optimal control problems arising in energy-optimal control of port-Hamiltonian systems. Last, we illustrate the our abstract theory by two examples including a non-well-posed heat equation with Dirichlet boundary control and a wave equation on an L-shaped domain with boundary control of the stress in normal direction.
Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training

The rapidly evolving field of robotics necessitates methods that can facilitate the fusion of multiple modalities. Specifically, when it comes to interacting with tangible objects, effectively combining visual and tactile sensory data is key to understanding and navigating the complex dynamics of the physical world, enabling a more nuanced and adaptable response to changing environments. Nevertheless, much of the earlier work in merging these two sensory modalities has relied on supervised methods utilizing datasets labeled by humans.This paper introduces MViTac, a novel methodology that leverages contrastive learning to integrate vision and touch sensations in a self-supervised fashion. By availing both sensory inputs, MViTac leverages intra and inter-modality losses for learning representations, resulting in enhanced material property classification and more adept grasping prediction. Through a series of experiments, we showcase the effectiveness of our method and its superiority over existing state-of-the-art self-supervised and supervised techniques. In evaluating our methodology, we focus on two distinct tasks: material classification and grasping success prediction. Our results indicate that MViTac facilitates the development of improved modality encoders, yielding more robust representations as evidenced by linear probing assessments.
The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models

While large language models (LLMs) are still being adopted to new domains and utilized in novel applications, we are experiencing an influx of the new generation of foundation models, namely multi-modal large language models (MLLMs). These models integrate verbal and visual information, opening new possibilities to demonstrate more complex reasoning abilities at the intersection of the two modalities. However, despite the revolutionizing prospect of MLLMs, our understanding of their reasoning abilities is limited. In this study, we assess the nonverbal abstract reasoning abilities of open-source and closed-source MLLMs using variations of Raven's Progressive Matrices. Our experiments expose the difficulty of solving such problems while showcasing the immense gap between open-source and closed-source models. We also reveal critical shortcomings with individual visual and textual modules, subjecting the models to low-performance ceilings. Finally, to improve MLLMs' performance, we experiment with various methods, such as Chain-of-Thought prompting, resulting in a significant (up to 100%) boost in performance.
Stein EWMA Control Charts for Count Processes

The monitoring of serially independent or autocorrelated count processes is considered, having a Poisson or (negative) binomial marginal distribution under in-control conditions. Utilizing the corresponding Stein identities, exponentially weighted moving-average (EWMA) control charts are constructed, which can be flexibly adapted to uncover zero inflation, over- or underdispersion. The proposed Stein EWMA charts' performance is investigated by simulations, and their usefulness is demonstrated by a real-world data example from health surveillance.
The Bigger the Better? Rethinking the Effective Model Scale in Long-term Time Series Forecasting

Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, distinguished by its focus on extensive input sequences, in contrast to the constrained lengths typical of traditional approaches. While longer sequences inherently convey richer information, potentially enhancing predictive precision, prevailing techniques often respond by escalating model complexity. These intricate models can inflate into millions of parameters, incorporating parameter-intensive elements like positional encodings, feed-forward networks and self-attention mechanisms. This complexity, however, leads to prohibitive model scale, particularly given the time series data's semantic simplicity. Motivated by the pursuit of parsimony, our research employs conditional correlation and auto-correlation as investigative tools, revealing significant redundancies within the input data. Leveraging these insights, we introduce the HDformer, a lightweight Transformer variant enhanced with hierarchical decomposition. This novel architecture not only inverts the prevailing trend toward model expansion but also accomplishes precise forecasting with drastically fewer computations and parameters. Remarkably, HDformer outperforms existing state-of-the-art LTSF models, while requiring over 99\% fewer parameters. Through this work, we advocate a paradigm shift in LTSF, emphasizing the importance to tailor the model to the inherent dynamics of time series data-a timely reminder that in the realm of LTSF, bigger is not invariably better.
Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition

Domain shift poses a significant challenge in Cross-Domain Facial Expression Recognition (CD-FER) due to the distribution variation across different domains. Current works mainly focus on learning domain-invariant features through global feature adaptation, while neglecting the transferability of local features. Additionally, these methods lack discriminative supervision during training on target datasets, resulting in deteriorated feature representation in target domain. To address these limitations, we propose an Adaptive Global-Local Representation Learning and Selection (AGLRLS) framework. The framework incorporates global-local adversarial adaptation and semantic-aware pseudo label generation to enhance the learning of domain-invariant and discriminative feature during training. Meanwhile, a global-local prediction consistency learning is introduced to improve classification results during inference. Specifically, the framework consists of separate global-local adversarial learning modules that learn domain-invariant global and local features independently. We also design a semantic-aware pseudo label generation module, which computes semantic labels based on global and local features. Moreover, a novel dynamic threshold strategy is employed to learn the optimal thresholds by leveraging independent prediction of global and local features, ensuring filtering out the unreliable pseudo labels while retaining reliable ones. These labels are utilized for model optimization through the adversarial learning process in an end-to-end manner. During inference, a global-local prediction consistency module is developed to automatically learn an optimal result from multiple predictions. We conduct comprehensive experiments and analysis based on a fair evaluation benchmark. The results demonstrate that the proposed framework outperforms the current competing methods by a substantial margin.
HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability. Source code is made available at https://github.com/thibault-wch/HOPE-for-mild-cognitive-impairment.
Chiral perturbation theory with vector mesons and the pion form factors

The chiral perturbation theory (ChPT) of pions is extended to include vector mesons as well as pertinent degrees of freedom. By counting the typical momentum scale of vector mesons as order of $Q$ and vector meson masses as of the order of $\Lambda_\chi$, a consistent theory could be obtained. The explicit renormalization procedure of the theory is presented for the form factors of the pion up to one-loop accuracy. The resulting theory prediction for the form factors is in good agreement with the experimental data for a wide range of momentum transfers. The vector-meson dominance mechanism is also discussed in the systematic framework of ChPT.
A Cosmological Tachyon Collider: Enhancing the Long-Short Scale Coupling

The squeezed limit of the primordial curvature bispectrum is an extremely sensitive probe of new physics and encodes information about additional fields active during inflation such as their masses and spins. In the conventional setup, additional fields are stable with a positive mass squared, and hence induce a decreasing signal in the squeezed limit, making a detection challenging. Here we consider a scalar field that is temporarily unstable by virtue of a transient tachyonic mass, and we construct models in which it is embedded consistently within inflation. Assuming IR-finite couplings between the tachyon and the inflaton, we find an exchange bispectrum with an enhanced long-short scale coupling that grows in the squeezed limit parametrically faster than local non-Gaussianity. Our approximately scale-invariant signal can be thought of as a cosmological tachyon collider. In a sizeable region of parameter space, the leading constraint on our signal comes from the cross correlation of $\mu$-type spectral distortions and temperature anisotropies of the microwave background, whereas temperature and polarization bispectra are less sensitive probes. By including anisotropic spectral distortions in the analysis, future experiments such as CMB-S4 will further reduce the allowed parameter space.
Measuring hierarchically-organized interactions in dynamic networks through spectral entropy rates: theory, estimation, and illustrative application to physiological networks

Recent advances in signal processing and information theory are boosting the development of new approaches for the data-driven modelling of complex network systems. In the fields of Network Physiology and Network Neuroscience where the signals of interest are often rich of oscillatory content, the spectral representation of network systems is essential to ascribe the analyzed interactions to specific oscillations with physiological meaning. In this context, the present work formalizes a coherent framework which integrates several information dynamics approaches to quantify node-specific, pairwise and higher-order interactions in network systems. The framework establishes a hierarchical organization of interactions of different order using measures of entropy rate, mutual information rate and O-information rate, to quantify respectively the dynamics of individual nodes, the links between pairs of nodes, and the redundant/synergistic hyperlinks between groups of nodes. All measures are formulated in the time domain, and then expanded to the spectral domain to obtain frequency-specific information. The practical computation of all measures is favored presenting a toolbox that implements their parametric and non-parametric estimation, and includes approaches to assess their statistical significance. The framework is illustrated first using theoretical examples where the properties of the measures are displayed in benchmark simulated network systems, and then applied to representative examples of multivariate time series in the context of Network Neuroscience and Network Physiology.