Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenomenology of bond and ﬂux orders in kagome metals

Despite much experimental and theoretical work, the nature of the charge order in the kagome metals belonging to the family of materials A V 3 Sb 5 ( A = Cs,Rb,K) remains controversial. A crucial ingredient for the identiﬁcation of the ordering in these materials is their response to external perturbations, such as strain or magnetic ﬁelds. To this end, we provide a comprehensive symmetry classiﬁcation of the possible charge orders in kagome materials with a 2 × 2 increase of the unit cell. Motivated by the experimental reports of time-reversal symmetry breaking and rotational anisotropy, we consider the interdependence of ﬂux and bond orders. Deriving the relevant Landau free energy for possible orders, we study the effect of symmetry-breaking perturbations such as strain and magnetic ﬁelds. Our results thus provide a road map for future tests of these intricate orders.
Impacts of forest management and habitat change on declining avian species in West Virginia

IMPACTS OF FOREST MANAGEMENT AND HABITAT CHANGE ON DECLINING AVIAN SPECIES IN WEST VIRGINIA Christina Slover Understanding the impacts of forest management and severe storms on avian communities can assist decision making for specific wildlife and timber management goals. This is important because even carefully managed forests will be influenced by a combination of anthropogenic activities and non-anthropogenic forces. I studied the effects of prescribed burning and severe storm events on vegetation structure and avian community diversity and structure in the Monongahela National Forest in central West Virginia. I observed dramatic responses from vegetation structure to prescribed burning but few changes in avian abundances. I measured further impacts, particularly understory vegetation structure, in our forest stands after the occurrence of two severe storms. I also was able to measure a greater response by my aerial insectivore species to the combination of management and storm impacts. My results suggested that infrequent low-intensity prescribed burning did not cause dramatic changes to avian abundance. However, when severe storm impacts were added to the management regime, the impacts to vegetation became consequential enough that high priority and aerial insectivore bird species were also impacted. Populations of the eastern whip-poor-will (Antrostomus vociferus) are declining due to a range of potential factors including habitat loss, pesticide use and predation. However, because this species is nocturnal, it is poorly studied and neither its ecology nor its demographic status are well measured by traditional bird surveys like the Christmas Bird Count, Breeding Bird Surveys, and point count surveys. Therefore, I studied habitat associations and distribution of eastern whip-poor-wills, to better understand and contextualize their population status and to provide a framework for future research and management. Transect data were analyzed with occupancy models to associate presence of whip-poor-wills with habitat characteristics. I observed habitat associations of and annual differences in both presence and density of whippoor-wills. Whip-poor-wills most frequently occupied areas lower in elevation and mixed forest, herbaceous, as well as wetland cover types. In contrast, high elevation evergreen forest communities had substantially fewer whip-poor-wills. My results suggest that recent recovery of agricultural fields and clearings to forested habitat may be contributing factors to whip-poor-will declines.
Exploring the Factors That Influence Stakeholder Participation in Decision-Making for the Moat System Restoration Project in Tianchang City, China

The driving factors have a critical effect on shaping stakeholder behavior toward participating in decision-making for river restoration initiatives. The participation of stakeholders is a vital determinant for increasing public confidence in the government and enhancing the acceptance of government decisions. Conversely, insufficient stakeholder participation in decision-making may lead to resistance to decisions on river restoration projects. Thus, the primary purpose of this investigation is to shed light on the complex interactions between the various drivers that underpin stakeholder participation in the context of the Moat System Restoration Project (MSRP). The extended Theory of Planned Behavior (TPB) describes the relationships between seven drivers that have positively influenced stakeholder participation behaviors: stakeholder attitude, priority, risk perception, trust in government decisions, motivation, intention, and knowledge. The empirical underpinning of this research was obtained through a questionnaire survey conducted in Tianchang, China, encompassing a sample size of 473. The empirical findings discern that stakeholder attitudes vis-à-vis the MSRP favorably influence stakeholder participation behaviors. Additionally, stakeholder motivation and intention have been discerned as catalysts for heightened stakeholder participation behavior. These findings promise to furnish invaluable insights, benefit forthcoming river restoration initiatives, and equip decision-makers with a profound understanding of strategies to enhance stakeholder participation.
A bioengineered in situ ovary (ISO) supports follicle engraftment and live-births post-chemotherapy

Female cancer patients who have undergone chemotherapy have an elevated risk of developing ovarian dysfunction and failure. Experimental approaches to treat iatrogenic infertility are evolving rapidly; however, challenges and risks remain that hinder clinical translation. Biomaterials have improved in vitro follicle maturation and in vivo transplantation in mice, but there has only been marginal success for early-stage human follicles. Here, we developed methods to obtain an ovarian-specific extracellular matrix hydrogel to facilitate follicle delivery and establish an in situ ovary (ISO), which offers a permissive environment to enhance follicle survival. We demonstrate sustainable follicle engraftment, natural pregnancy, and the birth of healthy pups after intraovarian microinjection of isolated exogenous follicles into chemotherapy-treated (CTx) mice. Our results confirm that hydrogel-based follicle microinjection could offer a minimally invasive delivery platform to enhance follicle integration for patients post-chemotherapy.
Trust criteria for artificial intelligence in health: normative and epistemic considerations

Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that user trust is properly calibrated to a tool’s computational capacities and limitations has both practical and ethical implications, given that overtrust or undertrust can influence over-reliance or under-reliance on algorithmic tools, with significant implications for patient safety and health outcomes. It is, thus, important to better understand how variability in trust criteria across stakeholders, settings, tools and use cases may influence approaches to using AI/ML tools in real settings. As part of a 5-year, multi-institutional Agency for Health Care Research and Quality-funded study, we identify trust criteria for a survival prediction algorithm intended to support clinical decision-making for left ventricular assist device therapy, using semistructured interviews (n=40) with patients and physicians, analysed via thematic analysis. Findings suggest that physicians and patients share similar empirical considerations for trust, which were primarily epistemic in nature, focused on accuracy and validity of AI/ML estimates. Trust evaluations considered the nature, integrity and relevance of training data rather than the computational nature of algorithms themselves, suggesting a need to distinguish ‘source’ from ‘functional’ explainability. To a lesser extent, trust criteria were also relational (endorsement from others) and sometimes based on personal beliefs and experience. We discuss implications for promoting appropriate and responsible trust calibration for clinical decision-making use AI/ML.
THE EVOLUTION OF THE ALTIPLANO-PUNA PLATEAU OF THE CENTRAL ANDES

The enigma of continental plateaus formed in the absence of continental collision is embodied by the Altiplano-Puna, which stretches for 1800 km along the Central Andes and attains a width of 350‐400 km. The plateau correlates spatially and temporally with Andean arc magmatism, but it was uplifted primarily because of crustal thickening produced by horizontal shortening of a thermally softened lithosphere. Nonetheless, known shortening at the surface accounts for only 70‐ 80% of the observed crustal thickening, suggesting that magmatic addition and other processes such as lithospheric thinning, upper mantle hydration, or tectonic underplating may contribute significantly to thickening. Uplift in the region of the Altiplano began around 25 Ma, coincident with increased convergence rate and inferred shallowing of subduction; uplift in the Puna commenced 5‐10 million years later.
Regulatory networks of circRNA- centred ceRNAs in sepsis-induced acute kidney injury

ABSTRACT Sepsis is the primary cause of acute kidney injury (AKI) and is associated with high mortality rates. Growing evidence suggests that noncoding RNAs are vitally involved in kidney illnesses, whereas the role of circular RNAs (circRNAs) in sepsis-induced AKI (SAKI) remains largely unknown. In this present study, caecal ligation and puncture (CLP) in mice was performed to establish an SAKI model. The expression of circRNAs and mRNAs was analysed using circRNA microarray or next-generation sequencing. The results revealed that the expressions of 197 circRNAs and 2509 mRNAs were dysregulated. Validation of the selected circRNAs was performed by qRT-PCR. Bioinformatics analyses and chromatin immunoprecipitation demonstrated that NF-κB/p65 signalling induced the upregulation of circC3, circZbtb16, and circFkbp5 and their linear counterparts by p65 transcription in mouse tubular epithelial cells (mTECs). Furthermore, competitive endogenous RNA (ceRNA) networks demonstrated that some components of NF-κB signalling were potential targets of these dysregulated circRNAs. Among them, Tnf-α was increased by circFkbp5 through the downregulation of miR-760-3p in lipopolysaccharide (LPS)-stimulated mTECs. Knocking down circFkbp5 inhibited the p65 phosphorylation and apoptosis in injured mTECs. These findings suggest that the selected circRNAs and the related ceRNA networks provide new knowledge into the fundamental mechanism of SAKI and circFkbp5/miR-760-3p/Tnf-α axis might be therapeutic targets.
A Fingertip Sensor and Algorithms for Pre-touch Distance Ranging and Material Detection in Robotic Grasping

To enhance robotic grasping capabilities, we are developing new contactless fingertip sensors to measure distance in close proximity and simultaneously detect the type of material and the interior structure. These sensors are referred to as pre-touch dual-modal and dual-mechanism (PDM$^2$) sensors, and they operate using both pulse-echo ultrasound (US) and optoacoustic (OA) modalities. We present the design of a PDM$^2$ sensor that utilizes a pulsed laser beam and a customized ultrasound transceiver with a wide acoustic bandwidth for ranging and sensing. Both US and OA signals are collected simultaneously, triggered by the same laser pulse. To validate our design, we have fabricated a prototype of the PDM$^2$ sensor and integrated it into an object scanning system. We have also developed algorithms to enable the sensor, including time-of-flight (ToF) auto estimation, ranging rectification, sensor and system calibration, distance ranging, material/structure detection, and object contour detection and reconstruction. The experimental results demonstrate that the new PDM$^2$ sensor and its algorithms effectively enable the object scanning system to achieve satisfactory ranging and contour reconstruction performances, along with satisfying material/structure detection capabilities. In conclusion, the PDM$^2$ sensor offers a practical and powerful solution to improve grasping of unknown objects with the robotic gripper by providing advanced perception capabilities.
Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks

Fingerprint traits are widely recognized for their unique qualities and security benefits. Despite their extensive use, fingerprint features can be vulnerable to puppet attacks, where attackers manipulate a reluctant but genuine user into completing the authentication process. Defending against such attacks is challenging due to the coexistence of a legitimate identity and an illegitimate intent. Research on countering puppet attacks is limited, and existing studies are hindered by the requirement of guiding users through the authentication process manually. In this article, we propose PUPGUARD, a solution designed to guard against puppet attacks. This method is based on user behavioral patterns, specifically, the user needs to press the capture device twice successively with different fingers during the authentication process. PUPGUARD leverages both the image features of fingerprints and the timing characteristics of the pressing intervals to establish two-factor authentication. More specifically, after extracting image features and timing characteristics, and performing feature selection on the image features, PUPGUARD fuses these two features into a 1-D feature vector, and feeds it into a one-class classifier to obtain the classification result. This two-factor authentication method emphasizes dynamic behavioral patterns during the authentication process, thereby enhancing security against puppet attacks. To assess PUPGUARD’s effectiveness, we conducted experiments on datasets collected from 31 subjects, including image features and timing characteristics. Our experimental results demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a remarkably low false positive rate (FPR) of 1.89%. Furthermore, we conducted comparative experiments to validate the superiority of combining image features and timing characteristics within PUPGUARD for enhancing resistance against puppet attacks.
Kondo effect in the isotropic Heisenberg spin chain

We investigate the boundary effects that arise when spin-$\frac{1}{2}$ impurities interact with the edges of the antiferromagnetic spin-$\frac{1}{2}$ Heisenberg chain through spin exchange interactions. We consider both cases when the couplings are ferromagnetic or anti-ferromagnetic. We find that in the case of antiferromagnetic interaction, when the impurity coupling strength is much weaker than that in the bulk, the impurity is screened in the ground state via the Kondo effect. The Kondo phase is characterized by the Lorentzian density of states and dynamically generated Kondo temperature $T_K$. As the impurity coupling strength increases, $T_K$ increases until it reaches its maximum value $T_0=2\pi J$ which is the maximum energy carried by a single spinon. When the impurity coupling strength is increased further, we enter another phase, the bound mode phase, where the impurity is screened in the ground state by a single particle bound mode exponentially localized at the edge to which the impurity is coupled. We find that the impurity can be unscreened by removing the bound mode. There exists a boundary eigenstate phase transition between the Kondo and the bound-mode phases, a transition which is characterized by the change in the number of towers of the Hilbert space. The transition also manifests itself in ground state quantities like local impurity density of states and the local impurity magnetization. When the impurity coupling is ferromagnetic, the impurity is unscreened in the ground state; however, when the absolute value of the ratio of the impurity and bulk coupling strengths is greater than $\frac{4}{5}$, the impurity can be screened by adding a bound mode that costs energy greater than $T_0$. When two impurities are considered, the phases exhibited by each impurity remain unchanged in the thermodynamic limit, but nevertheless the system exhibits a rich phase diagram.
ToSkA: Topological Skeleton Analysis for Network-Based Shape Representation and Evaluation of Objects from Cells to Death Stars

Shape analysis and classification are popular methods for biologists, biophysicists and mathematicians investigating relationships between object function and form. Classic shape descriptors, such as sphericity, can be very powerful; however, when evaluating complex shapes, these descriptors can be insufficient for rigorous assessment. Here, we present"ToSkA: Topological Skeleton Analysis"a method to analyse complex objects by representing their shape asymmetries as networks. Using global neighbourhood principles, classic network science metrics and spatial feature embedding we are able to create unique object profiles for classification. It is also possible to track objects over time and extract significantly different shape features between experiments. Importantly, we have incorporated the capacity to measure absolute spatial features of objects (e.g., branch lengths). This adds additional layers of sensitivity to object classification. Furthermore, because topology is an inherent property of system identity, ToSkA is able to identify segmentation errors that alter object topology by observing the emergence or loss of cycles in network representations. Combined, the analytics of ToSkA presented here allow for the flexibility and in-depth shape profiling necessary for complex objects often observed in biological and physical settings where robust, yet precise, system configuration is essential to downstream processes.
Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning

In response to the COVID-19 pandemic, traditional physical classrooms have transitioned to online environments, necessitating effective strategies to ensure sustained student engagement. A significant challenge in online teaching is the absence of real-time feedback from teachers on students learning progress. This paper introduces a novel approach employing deep learning techniques based on facial expressions to assess students engagement levels during online learning sessions. Human emotions cannot be adequately conveyed by a student using only the basic emotions, including anger, disgust, fear, joy, sadness, surprise, and neutrality. To address this challenge, proposed a generation of four complex emotions such as confusion, satisfaction, disappointment, and frustration by combining the basic emotions. These complex emotions are often experienced simultaneously by students during the learning session. To depict these emotions dynamically,utilized a continuous stream of image frames instead of discrete images. The proposed work utilized a Convolutional Neural Network (CNN) model to categorize the fundamental emotional states of learners accurately. The proposed CNN model demonstrates strong performance, achieving a 95% accuracy in precise categorization of learner emotions.
Countering Misinformation via Emotional Response Generation

The proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and ultimately democracy. Previous research has shown how social correction can be an effective way to curb misinformation, by engaging directly in a constructive dialogue with users who spread -- often in good faith -- misleading messages. Although professional fact-checkers are crucial to debunking viral claims, they usually do not engage in conversations on social media. Thereby, significant effort has been made to automate the use of fact-checker material in social correction; however, no previous work has tried to integrate it with the style and pragmatics that are commonly employed in social media communication. To fill this gap, we present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs (linked to debunking articles), accounting for both SMP-style and basic emotions, two factors which have a significant role in misinformation credibility and spreading. To collect this dataset we used a technique based on an author-reviewer pipeline, which efficiently combines LLMs and human annotators to obtain high-quality data. We also provide comprehensive experiments showing how models trained on our proposed dataset have significant improvements in terms of output quality and generalization capabilities.
A Survey on Off-chain Networks: Frameworks, Technologies, Solutions and Challenges

Blockchain has received increasing attention in academia and industry. However, the increasing transaction volumes and limited on-chain storage underscore scalability as a key challenge hindering the widespread adoption of blockchain. Fortunately, off-chain networks that enable transactions outside the blockchain show promising potential to mitigate the scalability challenge. Off-chain solutions that address blockchain scalability hurdles, such as payment channel networks, facilitate secure and fast off-chain transactions, thus relieving the main chain's strain. In this article, we provide a comprehensive review of key technologies, solutions, and challenges of off-chain networks. First, we introduce the background of off-chain networks encompassing design motivation, framework, overview, and application scenarios. We then review the key issues and technologies associated with off-chain networks. Subsequently, we summarize the mainstream solutions for the corresponding key issues. Finally, we discuss some research challenges and open issues in this area.
Multimodal Representation Learning by Alternating Unimodal Adaptation

Multimodal learning, which integrates data from diverse sensory modes, plays a pivotal role in artificial intelligence. However, existing multimodal learning methods often struggle with challenges where some modalities appear more dominant than others during multimodal learning, resulting in suboptimal performance. To address this challenge, we propose MLA (Multimodal Learning with Alternating Unimodal Adaptation). MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process, thereby minimizing interference between modalities. Simultaneously, it captures cross-modal interactions through a shared head, which undergoes continuous optimization across different modalities. This optimization process is controlled by a gradient modification mechanism to prevent the shared head from losing previously acquired information. During the inference phase, MLA utilizes a test-time uncertainty-based model fusion mechanism to integrate multimodal information. Extensive experiments are conducted on five diverse datasets, encompassing scenarios with complete modalities and scenarios with missing modalities. These experiments demonstrate the superiority of MLA over competing prior approaches.