Miracle-dz/UniFAR-FTUs huggingface dataset:
Monotonicity of positive solutions to semilinear elliptic equations with mixed boundary conditions in triangles

This paper investigates semilinear elliptic problems in planar triangles with Dirichlet conditions specified on one side of the boundary and Neumann conditions imposed on the remaining two sides. By employing moving plane method, we establish that the positive solution is monotone in the normal direction of the Dirichlet side when the Neumann vertex is non-obtuse. In the case where the Neumann vertex is obtuse, the positive solution is monotone in the normal direction of the longer Neumann side provided some technical conditions. Furthermore, this monotonicity property extends to the first mixed eigenfunction in triangles through continuity method via domain deformation. It is noteworthy that the maximum of the first positive eigenfunction in a triangle with mixed boundary conditions, consisting of two Neumann sides and one Dirichlet side, is uniquely located on the Neumann side with the greater length. This maximum point coincides with the Neumann vertex if and only if either the Neumann vertex is non-obtuse or the two Neumann sides have equal lengths. This result successfully resolves a specific problem posed within the Polymath project: Polymath7 research thread 1.
Challenges in Understanding the Relationship between Teamwork Quality and Project Success in Large-Scale Agile Projects

A number of methods for large-scale agile development have recently been suggested. Much of the advice in agile methods focuses on teamwork. Prior research has established that teamwork quality influences project success both for traditional software development teams and agile teams. Further, prior studies have also suggested that teamwork quality may play out differently in large projects compared to small. We investigated the relationship between teamwork quality and project success with a survey of 196 project participants across 34 teams in four projects, replicating a previous study on single teams. The new data do not fit the previously established theoretical model, which raises several concerns. The observed effect of teamwork quality on project success operates differently across projects. We discuss possible reasons, which include disagreements on what characterises success in large-scale agile development,"concept drift"of teamwork quality factors, the possibility that interteam factors might have more influence on project success than intrateam factors, and finally, that our study design does not capture all relevant levels and functions. We conclude with a call for more studies on the quality and frequency of interaction between teams in addition to internal team factors to further advance theory and practice within large-scale agile software development.
Entity Linking in the Job Market Domain

In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but yet remains underexplored for the job market domain. Disambiguating skill mentions can help us get insight into the current labor market demands. In this work, we are the first to explore EL in this domain, specifically targeting the linkage of occupational skills to the ESCO taxonomy (le Vrang et al., 2014). Previous efforts linked coarse-grained (full) sentences to a corresponding ESCO skill. In this work, we link more fine-grained span-level mentions of skills. We tune two high-performing neural EL models, a bi-encoder (Wu et al., 2020) and an autoregressive model (Cao et al., 2021), on a synthetically generated mention–skill pair dataset and evaluate them on a human-annotated skill-linking benchmark. Our findings reveal that both models are capable of linking implicit mentions of skills to their correct taxonomy counterparts. Empirically, BLINK outperforms GENRE in strict evaluation, but GENRE performs better in loose evaluation (accuracy@k).
From Sparse to Dense Functional Data: Phase Transitions from a Simultaneous Inference Perspective

We aim to develop simultaneous inference tools for the mean function of functional data from sparse to dense. First, we derive a unified Gaussian approximation to construct simultaneous confidence bands of mean functions based on the B-spline estimator. Then, we investigate the conditions of phase transitions by decomposing the asymptotic variance of the approximated Gaussian process. As an extension, we also consider the orthogonal series estimator and show the corresponding conditions of phase transitions. Extensive simulation results strongly corroborate the theoretical results, and also illustrate the variation of the asymptotic distribution via the asymptotic variance decomposition we obtain. The developed method is further applied to body fat data and traffic data.
GuardFS: a File System for Integrated Detection and Mitigation of Linux-based Ransomware

Although ransomware has received broad attention in media and research, this evolving threat vector still poses a systematic threat. Related literature has explored their detection using various approaches leveraging Machine and Deep Learning. While these approaches are effective in detecting malware, they do not answer how to use this intelligence to protect against threats, raising concerns about their applicability in a hostile environment. Solutions that focus on mitigation rarely explore how to prevent and not just alert or halt its execution, especially when considering Linux-based samples. This paper presents GuardFS, a file system-based approach to investigate the integration of detection and mitigation of ransomware. Using a bespoke overlay file system, data is extracted before files are accessed. Models trained on this data are used by three novel defense configurations that obfuscate, delay, or track access to the file system. The experiments on GuardFS test the configurations in a reactive setting. The results demonstrate that although data loss cannot be completely prevented, it can be significantly reduced. Usability and performance analysis demonstrate that the defense effectiveness of the configurations relates to their impact on resource consumption and usability.
Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration

Machine learning models traditionally assume that training and test data are independently and identically distributed. However, in real-world applications, the test distribution often differs from training. This problem, known as out-of-distribution generalization, challenges conventional models. Invariant Risk Minimization (IRM) emerges as a solution, aiming to identify features invariant across different environments to enhance out-of-distribution robustness. However, IRM's complexity, particularly its bi-level optimization, has led to the development of various approximate methods. Our study investigates these approximate IRM techniques, employing the Expected Calibration Error (ECE) as a key metric. ECE, which measures the reliability of model prediction, serves as an indicator of whether models effectively capture environment-invariant features. Through a comparative analysis of datasets with distributional shifts, we observe that Information Bottleneck-based IRM, which condenses representational information, achieves a balance in improving ECE while preserving accuracy relatively. This finding is pivotal, as it demonstrates a feasible path to maintaining robustness without compromising accuracy. Nonetheless, our experiments also caution against over-regularization, which can diminish accuracy. This underscores the necessity for a systematic approach in evaluating out-of-distribution generalization metrics, one that beyond mere accuracy to address the nuanced interplay between accuracy and calibration.
CONCORD: Towards a DSL for Configurable Graph Code Representation

Deep learning is widely used to uncover hidden patterns in large code corpora. To achieve this, constructing a format that captures the relevant characteristics and features of source code is essential. Graph-based representations have gained attention for their ability to model structural and semantic information. However, existing tools lack flexibility in constructing graphs across different programming languages, limiting their use. Additionally, the output of these tools often lacks interoperability and results in excessively large graphs, making graph-based neural networks training slower and less scalable. We introduce CONCORD, a domain-specific language to build customizable graph representations. It implements reduction heuristics to reduce graphs' size complexity. We demonstrate its effectiveness in code smell detection as an illustrative use case and show that: first, CONCORD can produce code representations automatically per the specified configuration, and second, our heuristics can achieve comparable performance with significantly reduced size. CONCORD will help researchers a) create and experiment with customizable graph-based code representations for different software engineering tasks involving DL, b) reduce the engineering work to generate graph representations, c) address the issue of scalability in GNN models, and d) enhance the reproducibility of experiments in research through a standardized approach to code representation and analysis.
Parametric Near-Field Channel Estimation for Extremely Large Aperture Arrays

Accurate channel estimation is critical to fully exploit the beamforming gains when communicating with extremely large aperture arrays. The propagation distances between the user and receiver, which potentially has thousands of antennas/elements, are such that they are located in the radiative near-field region of each other when considering the Fraunhofer distance of the entire array. Therefore, it is imperative to consider near-field effects to achieve proper channel estimation. This paper proposes a parametric multi-user near-field channel estimation algorithm based on MUltiple SIgnal Classification (MUSIC) method to obtain the essential parameters describing the users' locations. We derive the estimated channel by incorporating the estimated parameters into the near-field channel model. Additionally, we implement a least-squares-based estimation corrector, resulting in a precise near-field channel estimation. Simulation results demonstrate that our proposed scheme outperforms classical least-squares and minimum mean-square error channel estimation methods in terms of normalized beamforming gain and normalized mean-square error.
Polarization properties of the 128 non-repeating fast radio bursts from the first CHIME/FRB baseband catalog

We present a 400-800 MHz polarimetric analysis of 128 non-repeating fast radio bursts (FRBs) from the first CHIME/FRB baseband catalog, increasing the total number of FRB sources with polarization properties by a factor of ~3. Of the 128 sources, 89 FRBs have>6${\sigma}$ linearly polarized detections, 29 FRBs fall below this significance threshold and are deemed linearly unpolarized, and for 10 FRBs the polarization data are contaminated by instrumental polarization. For the 89 polarized FRBs, we find Faraday rotation measure (RM) amplitudes, after subtracting approximate Milky Way contributions, in the range 0.5-1160 rad m$^{-2}$ with a median of 53.8 rad m$^{-2}$. Most non-repeating FRBs in our sample have RMs consistent with Milky Way-like host galaxies and their linear polarization fractions range from ${\leq}$10% to 100% with a median of 63%. The non-repeater RMs and linear polarization fraction distributions are consistent with those of repeating FRBs. We see marginal evidence that non-repeating FRBs have more constraining lower limits than repeating FRBs for the host electron-density-weighted line-of-sight magnetic field strength. We classify the non-repeating FRB polarization position angle (PA) profiles into four archetypes: (i) single component with constant PA (57% of the sample), (ii) single component with variable PA (10%), (iii) multiple components with a single constant PA (22%), and (iv) multiple components with different or variable PAs (11%). We see no evidence for population-wide frequency-dependent depolarization and, therefore, the spread in the distribution of fractional linear polarization is likely intrinsic to the FRB emission mechanism.
On the minimization k-valued logic functions in the class of disjunctive normal forms

The paper considers the representation of k-valued logical functions in the class of disjunctive normal forms. Various classes of monotone functions of k-valued logic are investigated. Theorems are proved on the coincidence of reduced and shortest disjunctive nominal forms of k-valued functions. For a certain class of k-valued monotone functions, we prove an estimate for the number of functions in this class. we prove criteria for the absorption of elementary conjunctions by a first-order neighborhood of disjunctive normal forms of k-valued functions.
Can we constrain warm dark matter masses with individual galaxies?

We study the impact of warm dark matter mass on the internal properties of individual galaxies using a large suite of 1,024 state-of-the-art cosmological hydrodynamic simulations from the DREAMS project. We take individual galaxies' properties from the simulations, which have different cosmologies, astrophysics, and warm dark matter masses, and train normalizing flows to learn the posterior of the parameters. We find that our models cannot infer the value of the warm dark matter mass, even when the values of the cosmological and astrophysical parameters are given explicitly. This result holds for galaxies with stellar mass larger than $2\times10^8 M_\odot/h$ at both low and high redshifts. We calculate the mutual information and find no significant dependence between the WDM mass and galaxy properties. On the other hand, our models can infer the value of $\Omega_{\rm m}$ with a $\sim10\%$ accuracy from the properties of individual galaxies while marginalizing astrophysics and warm dark matter masses.
Place Attachment and Tourist Experience in the Context of Desert Tourism – the Case of Wadi Rum

Abstract This paper aims to explore the relationship between tourist experience and place attachment in a desert tourism experience. The current study was carried out with a sample of international and domestic visitors in Wadi Rum, Southern Jordan. The results show that there is a significant positive correlation between the nearby constructs of the study units such as the four tourist experience dimensions (education, esthetics, entertainment, and escapism), and the two dimensional place attachments measures (place identity and place dependence). Moreover, the findings also indicate that education factor had a significantly positive relationship with place identity, and there was a significantly positive relationship between escapism factor and place dependence. The findings can be used to develop the tourist experience and place attachment in the context of desert tourism experience. They further help tourism destinations managers, planners and marketers to provide appropriate marketing strategies and enrich their offers to desert tourism participants.
Analyzing the Impacts of Urban Expansion on Green Fragmentation Using Constraint Gradient Analysis

Many existing gradient analysis methods are arbitrary or too simple in gradient partitioning and unsuitable for cities with irregular forms. We propose an improved gradient analysis method with urban structural features as spatial constraints to properly partition an urban area into more homogeneous buffers. Taking the Wuhan metropolitan area in China as an example, we use the improved gradient partitioning method and six landscape metrics to characterize urban expansion and green space fragmentation in 1989, 2001, and 2013 and investigate the relationship between urban green fragmentation and urban expansion using correlation analysis and regression modeling. The results show that the fragmentation of built-up area in each buffer generally decreases from 1989 to 2013, but the fragmentation of green space increases. For each time point, with growing distance from urban centers, the fragmentation of built-up area increases and green space fragmentation decreases. This shows that urbanization exerts a significant influence on fragmentation of urban green space from both a temporal and a spatial perspective. Intensive urban expansion moved outward mainly at the cost of agricultural land and resulted in more fragmented green space in the urban fringe. Further quantitative analyses show that the area ratio and fragmentation of the built-up area are statistically negatively correlated with the fragmentation of green space. The improved gradient analysis with spatial constraints can generate more homogeneous ring gradients and then lead to more reasonable results, thereby helping to better understand how urban expansion affects landscape dynamics.
Decentralized Federated Learning: A Survey on Security and Privacy

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possible variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study.
Camouflage Adversarial Attacks on Multiple Agent Systems

The multi-agent reinforcement learning systems (MARL) based on the Markov decision process (MDP) have emerged in many critical applications. To improve the robustness/defense of MARL systems against adversarial attacks, the study of various adversarial attacks on reinforcement learning systems is very important. Previous works on adversarial attacks considered some possible features to attack in MDP, such as the action poisoning attacks, the reward poisoning attacks, and the state perception attacks. In this paper, we propose a brand-new form of attack called the camouflage attack in the MARL systems. In the camouflage attack, the attackers change the appearances of some objects without changing the actual objects themselves; and the camouflaged appearances may look the same to all the targeted recipient (victim) agents. The camouflaged appearances can mislead the recipient agents to misguided actions. We design algorithms that give the optimal camouflage attacks minimizing the rewards of recipient agents. Our numerical and theoretical results show that camouflage attacks can rival the more conventional, but likely more difficult state perception attacks. We also investigate cost-constrained camouflage attacks and showed numerically how cost budgets affect the attack performance.
Changes in Forest Structure and Species Composition during Secondary Forest Succession in the Bolivian Amazon

Changes in forest structure and species diversity throughout secondary succession were studied using a chronosequence at two sites in the Bolivian Amazon. Secondary forests ranging in age from 2 to 40 years as well as mature forests were included, making a total of 14 stands. Fifty plants per forest layer (understory, subcanopy, and canopy) were sampled using the transect of variable area technique. Mean and maximum height, total stem density, basal area, and species number were calculated at the stand level. Species diversity was calculated for each stand and for each combination of forest layer and stand. A correspondence analysis was performed, and the relationship between relative abundance of the species and stand age was modeled using a set of hierarchical models. Canopy height and basal area increased with stand age, indicating that secondary forests rapidly attain a forest structure similar in many respects to mature forests. A total of 250 species were recorded of which ca 50 percent made up 87 percent of the sampled individuals. Species diversity increased with stand age and varied among the forest layers, with the lowest diversity in the canopy. The results of the correspondence analysis indicated that species composition varies with stand age, forest layer, and site. The species composition of mature forests recovered at different rates in the different forest layers, being the slowest in the canopy layer. Species showed different patterns of abundance in relation to stand age, supporting the current model of succession.
SMARCD1 is a “Goldilocks” metastasis modifier

Breast cancer is the most frequently diagnosed cancer worldwide, constituting around 15% of all diagnosed cancers in 2023. The predominant cause of breast cancer-related mortality is metastasis to distant essential organs, and a lack of metastasis-targeted therapies perpetuates dismal outcomes for late-stage patients. However, through our use of meiotic genetics to study inherited transcriptional network regulation, we have identified a new class of “Goldilocks” genes that are promising candidates for the development of metastasis-targeted therapeutics. Building upon previous work that implicated the CCR4-NOT RNA deadenylase complex in metastasis, we now demonstrate that the RNA-binding proteins (RNA-BPs) NANOS1, PUM2, and CPSF4 also regulate metastatic potential. Using cell lines, 3D culture, mouse models, and clinical data, we pinpoint Smarcd1 mRNA as a key target of all three RNA-BPs. Strikingly, both high and low expression of Smarcd1 is associated with positive clinical outcomes, while intermediate expression significantly reduces the probability of survival. Applying the theory of “essential genes” from evolution, we identify an additional 50 genes that span several cellular processes and must be maintained within a discrete window of expression for metastasis to occur. In the case of Smarcd1, small perturbations in its expression level significantly reduce metastasis in laboratory mouse models and alter splicing programs relevant to the ER+/HER2-enriched breast cancer subtype. The identification of subtype-specific “Goldilocks” metastasis modifier genes introduces a new class of genes and potential catalogue of novel targets that, when therapeutically “nudged” in either direction, may significantly improve late-stage patient outcomes.