Miracle-dz/UniFAR-FTUs huggingface dataset:
Postpartum cessation of urban space use by a female baboon living at the edge of the City of Cape Town

Abstract Species with slow life history strategies that invest in few offspring with extended parental care need to adapt their behavior to cope with anthropogenic changes that occur within their lifetime. Here we show that a female chacma baboon (Papio ursinus) that commonly ranges within urban space in the City of Cape Town, South Africa, stops using urban space after giving birth. This change of space use occurs without any significant change in daily distance traveled or social interactions that would be expected with general risk‐sensitive behavior after birth. Instead, we suggest this change occurs because of the specific and greater risks the baboons experience within the urban space compared to natural space, and because leaving the troop (to enter urban space) may increase infanticide risk. This case study can inform methods used to manage the baboons' urban space use in Cape Town and provides insight into how life history events alter individuals' use of anthropogenic environments.
Analysis of Operated Patients with Ocular Trauma: A 10-Year Retrospective Study

Background: Ocular trauma is one of the most common causes of hospitalization in patients with ocular injuries. It imposes many direct and indirect physical and psychological costs on the patient and the community. Materials and Methods: The present descriptive cross-sectional and retrospective study includes all patients with ocular trauma who underwent surgery in the referral center's ophthalmic operating room for 10 years. A checklist was completed for each patient, including demographic information and the variables required for the study. A total of 927 patients who underwent eye surgery due to ocular trauma were eligible for the study. All descriptive data were reported as the mean plus standard deviation for quantitative variables and distribution tables and frequency percentage for qualitative variables. Inferential tests such as independent t-test and Chi-square test were used to evaluate the research questions. Results: This study showed that most ocular traumas occur at a young age and mostly in males. The types of trauma in the studied eyes were divided into penetrating and non-penetrating in different age groups. The results showed that the most common type of surgery was corneal laceration repair, and in all patients, visual acuity improved significantly after surgery. In this study, a total of 81% of patients underwent only one operation. Conclusions: Training and caring for children and adolescents about high-risk behaviors and educating industry professionals to wear goggles, and increase workplace safety can reduce these types of trauma.
Global weak solutions to the generalized Proudman-Johnson equation

We consider the generalized Proudman-Johnson equation, in which an artificial parameter $a$ controlling the impact of convection was introduced to the Proudman-Johnson equation ([33]). In the present paper, we are going to show that there are global weak solutions to the generalized Proudman-Johnson equation for certain parameter $a$'s.
Exploring the association between EEG microstates during resting-state and error-related activity in young children

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates – whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity – during both a go/no-go task and resting-state in 90, 4–8-year-old children. The mean amplitude of the ERN was quantified during the − 64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same − 64 to 108 ms period (i.e., error-related microstate 3), and to greater parent-report-measured anxiety risk. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.
Time- and Concentration-Dependent Effects of the Stem Cells Derived from Human Exfoliated Deciduous Teeth on Osteosarcoma Cells

Background: Stem cells have been proposed to be one of the potent sources for treatment applications. Among diverse types of stem cells, stem cells derived from human exfoliated deciduous teeth (SHEDs) are known as the immature stem cell population, which are easily isolated, fast, and without ethical implications. SHEDs could induce pluripotent stem cells and show differentiation in chondrocytes, adipocytes, osteoblasts, neural cells, hepatocytes, myocytes, odontoblasts, and skin cells. Materials and Methods: In the current study, we investigated the effects of SHED on osteosarcoma cells (Saos-II) following 3 and 5 days indirect coculture. Results: Our results showed that indirect coculture of SHED with Saos-II cells could promote or inhibit Saos-II cells' growth in a concentration (the number of SHED vs. Saos-II cells) and time (days of indirect co-culture) dependent manner. Conclusion: Our findings suggested that, indirectly, SHED co-culture with the Soas-II cells might functions as a tumor suppressor where a higher number of SHEDs are used in the culture in comparison with the one cultured in the absence of/or fewer SHED incubation.
EXACT DISTRIBUTION OF INTERMITTENTLY CHANGING POSITIVE AND NEGATIVE COMPOUND POISSON PROCESS DRIVEN BY AN ALTERNATING RENEWAL PROCESS AND RELATED FUNCTIONS

Alternating renewal processes have been widely used to model social and scientific phenomenal where independent “on” and “off” states alternate. In this paper, we study a model where the value of a process cumulates and declines according to two modes of compound Poisson processes with respect to an underlying alternating renewal process. The model discussed in the present paper can be used as a revenue management model applied to inventory or to finance. The exact distribution of the process is derived as well as the double Laplace transform with respect to the level and time of the process.
Personality Understanding of Fictional Characters during Book Reading

Comprehending characters’ personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived. This leads to a natural problem of situated and fine-grained personality understanding. The problem has not been studied in the NLP field, primarily due to the lack of appropriate datasets mimicking the process of book reading. We present the first labeled dataset PersoNet for this problem. Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books. Experiments and human studies indicate that our dataset construction is both efficient and accurate; and our task heavily relies on long-term context to achieve accurate predictions for both machines and humans.
Groups of F-Type

We consider a class of groups, called groups of F-type, which includes some known and important classes like Fuchsian groups of geometric rank $\ge 3$, surface groups of genus $\ge 2$, cyclically pinched one-relator groups and torus-knot groups, and discuss algebraic and geometric properties of groups of F-type.
Rethinking Data Augmentation for Tabular Data in Deep Learning

Tabular data is the most widely used data format in machine learning (ML). While tree-based methods outperform DL-based methods in supervised learning, recent literature reports that self-supervised learning with Transformer-based models outperforms tree-based methods. In the existing literature on self-supervised learning for tabular data, contrastive learning is the predominant method. In contrastive learning, data augmentation is important to generate different views. However, data augmentation for tabular data has been difficult due to the unique structure and high complexity of tabular data. In addition, three main components are proposed together in existing methods: model structure, self-supervised learning methods, and data augmentation. Therefore, previous works have compared the performance without comprehensively considering these components, and it is not clear how each component affects the actual performance. In this study, we focus on data augmentation to address these issues. We propose a novel data augmentation method, $\textbf{M}$ask $\textbf{T}$oken $\textbf{R}$eplacement ($\texttt{MTR}$), which replaces the mask token with a portion of each tokenized column; $\texttt{MTR}$ takes advantage of the properties of Transformer, which is becoming the predominant DL-based architecture for tabular data, to perform data augmentation for each column embedding. Through experiments with 13 diverse public datasets in both supervised and self-supervised learning scenarios, we show that $\texttt{MTR}$ achieves competitive performance against existing data augmentation methods and improves model performance. In addition, we discuss specific scenarios in which $\texttt{MTR}$ is most effective and identify the scope of its application. The code is available at https://github.com/somaonishi/MTR/.
Stack number and queue number of graphs

In this paper we give an overview of the graph invariants queue number and stack number (the latter also called the page number or book thickness). Due to their similarity, it has been studied for a long time, whether one of them is bounded in terms of the other. It is now known that the stack number is not bounded by the queue number. We present a simplified proof of this result. We also survey the known results about possible stack number bound on the queue number. This preprint is a rework of the bachelor thesis [29].
Unveiling the Potential of Counterfactuals Explanations in Employability

In eXplainable Artificial Intelligence (XAI), counterfactual explanations are known to give simple, short, and comprehensible justifications for complex model decisions. However, we are yet to see more applied studies in which they are applied in real-world cases. To fill this gap, this study focuses on showing how counterfactuals are applied to employability-related problems which involve complex machine learning algorithms. For these use cases, we use real data obtained from a public Belgian employment institution (VDAB). The use cases presented go beyond the mere application of counterfactuals as explanations, showing how they can enhance decision support, comply with legal requirements, guide controlled changes, and analyze novel insights.
An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients

Type 1 diabetes is a serious disease in which individuals are unable to regulate their blood glucose levels, leading to various medical complications. Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels. However, current AP systems lack detection capabilities for exercise-induced glucose intake, which can last up to 4 to 8 hours. This incapability can lead to hypoglycemia, which if left untreated, could have serious consequences, including death. Existing exercise detection methods are either limited to single sensor data or use inaccurate models for exercise detection, making them less effective in practice. In this work, we propose an ensemble learning framework that combines a data-driven physiological model and a Siamese network to leverage multiple physiological signal streams for exercise detection with high accuracy. To evaluate the effectiveness of our proposed approach, we utilized a public dataset with 12 diabetic patients collected from an 8-week clinical trial. Our approach achieves a true positive rate for exercise detection of 86.4% and a true negative rate of 99.1%, outperforming state-of-the-art solutions.
Digital Forensics in the Age of Smart Environments: A Survey of Recent Advancements and Challenges

Digital forensics in smart environments is an emerging field that deals with the investigation and analysis of digital evidence in smart devices and environments. As smart environments continue to evolve, digital forensic investigators face new challenges in retrieving, preserving, and analyzing digital evidence. At the same time, recent advancements in digital forensic tools and techniques offer promising solutions to overcome these challenges. In this survey, we examine recent advancements and challenges in digital forensics within smart environments. Specifically, we review the current state-of-the-art techniques and tools for digital forensics in smart environments and discuss their strengths and limitations. We also identify the major challenges that digital forensic investigators face in smart environments and propose potential solutions to overcome these challenges. Our survey provides a comprehensive overview of recent advancements and challenges in digital forensics in the age of smart environments, and aims to inform future research in this area.
A phase field model for droplets suspended in viscous liquids under the influence of electric fields

In this paper, we propose a Poisson-Nernst-Planck-Navier-Stokes-Cahn-Hillard (PNP-NS-CH)model for an electrically charged droplet suspended in a viscous fluid subjected to an external electric field. Our model incorporates spatial variations of electric permittivity and diffusion constants, as well as interfacial capacitance. Based on a time scale analysis, we derive two approximations of the original model, namely a dynamic model for the net charge and a leaky-dielectric model. For the leaky-dielectric model, we conduct a detailed asymptotic analysis to demonstrate the convergence of the diffusive-interface leaky-dielectric model to the sharp interface model as the interface thickness approaches zero. Numerical computations are performed to validate the asymptotic analysis and demonstrate the model's effectiveness in handling topology changes, such as electrocoalescence. Our numerical results of these two approximation models reveal that the polarization force, which is induced by the spatial variation of electric permittivity in the direction perpendicular to the external electric field, consistently dominates the Lorentz force, which arises from the net charge. The equilibrium shape of droplets is determined by the interplay between these two forces along the direction of the electric field. Furthermore, in the presence of the interfacial capacitance, a local variation of effective permittivity leads to an accumulation of counter-ions near the interface, resulting in a reduction in droplet deformation. Our numerical solutions also confirm that the leaky dielectric model serves as a reasonable approximation of the original PNP-NS-CH model when the electric relaxation time is sufficiently short. The Lorentz force and droplet deformation both decrease when the diffusion of net charge is significant.
NUANCE: Near Ultrasound Attack On Networked Communication Environments

This study investigates a primary inaudible attack vector on Amazon Alexa voice services using near ultrasound trojans and focuses on characterizing the attack surface and examining the practical implications of issuing inaudible voice commands. The research maps each attack vector to a tactic or technique from the MITRE ATT&CK matrix, covering enterprise, mobile, and Industrial Control System (ICS) frameworks. The experiment involved generating and surveying fifty near-ultrasonic audios to assess the attacks' effectiveness, with unprocessed commands having a 100% success rate and processed ones achieving a 58% overall success rate. This systematic approach stimulates previously unaddressed attack surfaces, ensuring comprehensive detection and attack design while pairing each ATT&CK Identifier with a tested defensive method, providing attack and defense tactics for prompt-response options. The main findings reveal that the attack method employs Single Upper Sideband Amplitude Modulation (SUSBAM) to generate near-ultrasonic audio from audible sources, transforming spoken commands into a frequency range beyond human-adult hearing. By eliminating the lower sideband, the design achieves a 6 kHz minimum from 16-22 kHz while remaining inaudible after transformation. The research investigates the one-to-many attack surface where a single device simultaneously triggers multiple actions or devices. Additionally, the study demonstrates the reversibility or demodulation of the inaudible signal, suggesting potential alerting methods and the possibility of embedding secret messages like audio steganography.
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment. A central question boils down to how to efficiently utilize online data collection to strengthen and complement the offline dataset and enable effective policy fine-tuning. Leveraging recent advances in reward-agnostic exploration and model-based offline RL, we design a three-stage hybrid RL algorithm that beats the best of both worlds -- pure offline RL and pure online RL -- in terms of sample complexities. The proposed algorithm does not require any reward information during data collection. Our theory is developed based on a new notion called single-policy partial concentrability, which captures the trade-off between distribution mismatch and miscoverage and guides the interplay between offline and online data.
Truncated Partial-Wave Analysis for $\eta$-photoproduction observables via Bayesian Statistics

A truncated partial-wave analysis is performed for $\eta$-photoproduction using the polarization observables $\sigma_0, \Sigma, T, E, F$ and $G$. Different truncation orders are analyzed for six energy bins within the range of $E^{lab}_{\gamma} \in [750, 1250]$ MeV. Bayesian statistics is combined with truncated partial-wave analysis for the first time to investigate the structure of emerging ambiguities and their relevance in comparison to each other. Marginal distributions for the electromagnetic multipole parameters are presented together with predictions for polarization observables which have not yet been measured, in order to determine promising future measurements able to remove remaining mathematical ambiguities.
Concavity properties for quasilinear equations and optimality remarks

In this paper we study quasiconcavity properties of solutions of Dirichlet problems related to modified nonlinear Schr\"odinger equations of the type $$-{\rm div}\big(a(u) \nabla u\big) + \frac{a'(u)}{2} |\nabla u|^2 = f(u) \quad \hbox{in $\Omega$},$$ where $\Omega$ is a convex bounded domain of $\mathbb{R}^N$. In particular, we search for a function $\varphi:\mathbb{R} \to \mathbb{R}$, modeled on $f\in C^1$ and $a\in C^1$, which makes $\varphi(u)$ concave. Moreover, we discuss the optimality of the conditions assumed on the source.
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors. Methods to solve this \textit{inverse problem} of mapping detector observations to theoretical quantities of the underlying collision are essential parts of many physics analyses at the LHC. We investigate and compare various generative deep learning methods to approximate this inverse mapping. We introduce a novel unified architecture, termed latent variation diffusion models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. We demonstrate the effectiveness of this approach for reconstructing global distributions of theoretical kinematic quantities, as well as for ensuring the adherence of the learned posterior distributions to known physics constraints. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline and 3 times less than traditional latent diffusion models.
Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions

We study robust community detection in the context of node-corrupted stochastic block model, where an adversary can arbitrarily modify all the edges incident to a fraction of the $n$ vertices. We present the first polynomial-time algorithm that achieves weak recovery at the Kesten-Stigum threshold even in the presence of a small constant fraction of corrupted nodes. Prior to this work, even state-of-the-art robust algorithms were known to break under such node corruption adversaries, when close to the Kesten-Stigum threshold. We further extend our techniques to the $Z_2$ synchronization problem, where our algorithm reaches the optimal recovery threshold in the presence of similar strong adversarial perturbations. The key ingredient of our algorithm is a novel identifiability proof that leverages the push-out effect of the Grothendieck norm of principal submatrices.
Energy Loss Prediction in IoT Energy Services

We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is essential for the successful deployment of efficient energy service composition techniques. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment. The predicted battery levels are used to estimate the energy loss. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework. We conducted extensive experiments on real wireless energy datasets to demonstrate that our framework significantly outperforms existing methods.