Miracle-dz/UniFAR-FTUs huggingface dataset:
Tree Stands and Liana Community in Royal Belum State Park, Malaysia

The diversity of lianas and trees were studied in five study sites of 100 x 20 m within the Royal Belum State Park, Malaysia with a view to provide baseline information on their incidence, taxonomy and ecological distributions. The sites include Sungai Kejar, Sungai Papan, Sungai Papan 2, Teluk Gopal and Sungai Kooi with at least 1000 m apart. These plots were further sub-divided into five sub-plots of 20 x 20 m each. Lianas with a diameter at breast height (dbh) ≥ 1 cm and trees with dbh ≥ 10 cm were identified and frequencies of occurrence were determined. Lianas comprising 92 species from 23 families while trees comprising 221 species and 48 families were enumerated. Annonaceae was the richest family of lianas and trees (19 species and 23 species respectively). Connarus (Connaraceae) and Spatholobus (Fabaceae) had the highest number of lianas (six species) whilst Syzygium (Myrtaceae) had the highest number of trees (11 species). There are significant differences in all the diversity indices among the study sites, except between Sungai Papan and Teluk Gopal which were the richest and most diverse in liana species. These two sites also showed high similarity index in liana species (0.50) followed by Sungai Kejar and Sungai Papan 2 (0.37). Sungai Kejar was however observed to have the highest tree species richness. These study sites could be described as very rich with a high diversity of lianas and trees. Although, it is richer in trees than lianas which means that the level of disturbance of the park is very low. Keywords: Annonaceae, Connarus, forests, lianas diversity, Perak, Syzygium
The impacts of precipitating cloud radiative effects on ocean surface evaporation, precipitation, and ocean salinity in coupled GCM simulations

The coupled global climate model (GCM) fidelity in representing upper ocean salinity including near sea surface bulk salinity (SSS) is evaluated in this study, with a focus on the Pacific Ocean. The systematic biases in ocean surface evaporation (E) minus precipitation (P) and SSS are found to be fairly similar in the twentieth century simulations of the Coupled Model Intercomparison Phase 3 (CMIP3) and Phase 5 (CMIP5) relative to the observations. One of the potential causes of the CMIP model biases is the missing representation of the radiative effects of precipitating hydrometeors (i.e., snow) in most CMIP models. To examine the radiative effect of cloud snow on SSS, sensitivity experiments with and without such effect are conducted by the National Center for Atmospheric Research‐coupled Community Earth System Model (CESM). This study investigates the difference in SSS between sensitivity experiments and its relationship with atmospheric circulation, E − P and air‐sea heat fluxes. It is found that the exclusion of the cloud snow radiative effect in CESM produces weaker Pacific trade winds, resulting in enhanced precipitation, reduced evaporation, and a reduction of the upper ocean salinity in the tropical and subtropical Pacific. The latter results in an improved comparison with climatological upper ocean bulk salinity. The introduction of cloud snow also altered the budget terms that maintain the time‐mean salinity in the mixed layer.
Spectral convergence of a semi-discretized numerical system for the Boltzmann equation with uncertainties

In this paper, we study the Boltzmann equation with uncertainties and prove that the spectral convergence of the semi-discretized numerical system holds in a combined velocity and random space, where the Fourier-spectral method is applied for approximation in the velocity space whereas the generalized polynomial chaos (gPC)-based stochastic Galerkin (SG) method is employed to discretize the random variable. Our proof is based on a delicate energy estimate for showing the well-posedness of the numerical solution as well as a rigorous control of its negative part in our well-designed functional space that involves high-order derivatives of both the velocity and random variables. This paper rigorously justifies the statement proposed in [Remark 4.4, J. Hu and S. Jin, J. Comput. Phys., 315 (2016), pp. 150-168].
Architectural Neural Backdoors from First Principles

While previous research backdoored neural networks by changing their parameters, recent work uncovered a more insidious threat: backdoors embedded within the definition of the network's architecture. This involves injecting common architectural components, such as activation functions and pooling layers, to subtly introduce a backdoor behavior that persists even after (full re-)training. However, the full scope and implications of architectural backdoors have remained largely unexplored. Bober-Irizar et al. [2023] introduced the first architectural backdoor; they showed how to create a backdoor for a checkerboard pattern, but never explained how to target an arbitrary trigger pattern of choice. In this work we construct an arbitrary trigger detector which can be used to backdoor an architecture with no human supervision. This leads us to revisit the concept of architecture backdoors and taxonomise them, describing 12 distinct types. To gauge the difficulty of detecting such backdoors, we conducted a user study, revealing that ML developers can only identify suspicious components in common model definitions as backdoors in 37% of cases, while they surprisingly preferred backdoored models in 33% of cases. To contextualize these results, we find that language models outperform humans at the detection of backdoors. Finally, we discuss defenses against architectural backdoors, emphasizing the need for robust and comprehensive strategies to safeguard the integrity of ML systems.
On the Convergence Rate of MCTS for the Optimal Value Estimation in Markov Decision Processes

A recent theoretical analysis of a corrected-version of a Monte-Carlo tree search (MCTS) method, so-called UCT, established an unexpected result, due to a great deal of empirical successes reported from heuristic usage of UCT with relevant adjustments for the problem domains in the literature, that its convergence rate in estimating the expected error relative to the optimal value of a finite-horizon Markov decision process (MDP) at an initial state is polynomial. We strengthen this dispiriting slow-convergence result by arguing within a simpler framework in the perspective of MDP, apart from the usual MCTS description, that just simpler UCB1 applied with the policy set as the arm set is actually competitive with or asymptotically faster than the corrected-version of UCT because of its logarithmic convergence-rate. We also point out that MCTS in general has the worst-case time and space complexities that depend on the state-set size which contradicts the original spirit of MCTS. Unless heuristically used, UCT-based MCTS has yet to have theoretical supports for its applicabilities.
Free Open Source Communities Sustainability: Does It Make a Difference in Software Quality?

Context: Free and Open Source Software (FOSS) communities' ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically. Objective: This study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality. Method: 16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts. Results: Findings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted. Conclusion: Findings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.
Discovering Decision Manifolds to Assure Trusted Autonomous Systems

Developing and fielding complex systems requires proof that they are reliably correct with respect to their design and operating requirements. Especially for autonomous systems which exhibit unanticipated emergent behavior, fully enumerating the range of possible correct and incorrect behaviors is intractable. Therefore, we propose an optimization-based search technique for generating high-quality, high-variance, and non-trivial data which captures the range of correct and incorrect responses a system could exhibit. This manifold between desired and undesired behavior provides a more detailed understanding of system reliability than traditional testing or Monte Carlo simulations. After discovering data points along the manifold, we apply machine learning techniques to quantify the decision manifold's underlying mathematical function. Such models serve as correctness properties which can be utilized to enable both verification during development and testing, as well as continuous assurance during operation, even amidst system adaptations and dynamic operating environments. This method can be applied in combination with a simulator in order to provide evidence of dependability to system designers and users, with the ultimate aim of establishing trust in the deployment of complex systems. In this proof-of-concept, we apply our method to a software-in-the-loop evaluation of an autonomous vehicle.
Digital Footprints of Streaming Devices

These days, there are many ways to watch streaming videos on television. When compared to a standalone smart television, streaming devices such as Roku and Amazon Fire Stick have a plethora of app selections. While these devices are platform agnostic and compatible with smartphones, they can still leave behind crumbs of sensitive data that can cause privacy, security, and forensic issues. In this paper, the authors conduct an experiment with streaming devices to ascertain digital footprints from network traffic and mobile forensics that they leave behind.
Local moduli of continuity for permanental processes that are zero at zero

Let $u(s,t)$ be a continuous potential density of a symmetric L\'evy process or diffusion with state space $T$ killed at $T_{0}$, the first hitting time of $0$, or at $\lambda \wedge T_{0}$, where $\lambda$ is an independent exponential time. Let \[ f(t)=\int_{T} u(t,v)\,d\mu(v), \] where $\mu$ is a finite positive measure on $T$. Let $X_{\alpha}=\{X_{\alpha}(t),t\in T \}$ be an $\alpha-$permanental process with kernel \[ v(s,t)=u(s,t)+f(t). \] Then when $\lim_{t\to 0}u(t,t)=0$, \[ \limsup_{t\downarrow 0}\frac{X_{\alpha}(t )}{u(t,t)\log \log 1/t }\ge 1 ,\qquad \text{a.s. } \] and \[ \limsup_{t\downarrow 0}\frac{X_{\alpha}(t )}{u(t,t)\log \log 1/t }\le 1+C_{u,h} ,\qquad \text{a.s. } \] where $C_{u,\mu}\le |\mu|$ is a constant that depends on both $u$ and $\mu$, which is given explicitly, and is different in the different examples.
Analyzing Currency Fluctuations: A Comparative Study of GARCH, EWMA, and IV Models for GBP/USD and EUR/GBP Pairs

In this study, we examine the fluctuation in the value of the Great Britain Pound (GBP). We focus particularly on its relationship with the United States Dollar (USD) and the Euro (EUR) currency pairs. Utilizing data from June 15, 2018, to June 15, 2023, we apply various mathematical models to assess their effectiveness in predicting the 20-day variation in the pairs' daily returns. Our analysis involves the implementation of Exponentially Weighted Moving Average (EWMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and Implied Volatility (IV) models. To evaluate their performance, we compare the accuracy of their predictions using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. We delve into the intricacies of GARCH models, examining their statistical characteristics when applied to the provided dataset. Our findings suggest the existence of asymmetric returns in the EUR/GBP pair, while such evidence is inconclusive for the GBP/USD pair. Additionally, we observe that GARCH-type models better fit the data when assuming residuals follow a standard t-distribution rather than a standard normal distribution. Furthermore, we investigate the efficacy of different forecasting techniques within GARCH-type models. Comparing rolling window forecasts to expanding window forecasts, we find no definitive superiority in either approach across the tested scenarios. Our experiments reveal that for the GBP/USD pair, the most accurate volatility forecasts stem from the utilization of GARCH models employing a rolling window methodology. Conversely, for the EUR/GBP pair, optimal forecasts are derived from GARCH models and Ordinary Least Squares (OLS) models incorporating the annualized implied volatility of the exchange rate as an independent variable.
Measuring friction from simulations of folded graphene sheets

We run molecular dynamics simulations of folded graphene sheets and present a procedure to measure the sliding friction in these systems based on the rate of decay of a damped-harmonic oscillator. This procedure allowed us to study the affect the size, geometry and the temperature of the graphene sheet had on the ability to propagate the initial fold and the rate at which it settles to a final 'fully-folded' equilibrium state. We offer simple rationalisations for the relationships between the initial geometries of our simulations and the friction values that emerge.
Consistent inclusion of fluctuations in first-order causal and stable relativistic hydrodynamics

We construct, for the first time, a BDNK theory for linear stochastic fluctuations, which is proved to be mathematically consistent, causal, and covariantly stable. The Martin-Siggia-Rose action is shown to be bilocal in most cases, and the noise is not white. The presence of non-hydrodynamic modes induces long-range correlations in the primary fluid variables (temperature, chemical potential, and flow velocity). However, correlators of conserved densities remain localized in space, and coincide with those calculated within fluctuating Isreal-Stewart theory. We show that, in some cases, there is a non-local change of variables that maps the Israel-Stewart action into the BDNK action.
Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain

The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches, and delves into their applications in diverse domains, including healthcare and finance. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed. The paper further investigates the potential convergence of XAI with cognitive sciences, the development of emotionally intelligent AI, and the quest for Human-Like Intelligence (HLI) in AI systems. As AI progresses towards Artificial General Intelligence (AGI), considerations of consciousness, ethics, and societal impact become paramount. The ongoing pursuit of deciphering the mysteries of the brain with AI and the quest for HLI represent transformative endeavors, bridging technical advancements with multidisciplinary explorations of human cognition.
Using remotely sensed data for air pollution assessment

Air pollution constitutes a global problem of paramount importance that affects not only human health, but also the environment. The existence of spatial and temporal data regarding the concentrations of pollutants is crucial for performing air pollution studies and monitor emissions. However, although observation data presents great temporal coverage, the number of stations is very limited and they are usually built in more populated areas. The main objective of this work is to create models capable of inferring pollutant concentrations in locations where no observation data exists. A machine learning model, more specifically the random forest model, was developed for predicting concentrations in the Iberian Peninsula in 2019 for five selected pollutants: $NO_2$, $O_3$ $SO_2$, $PM10$, and $PM2.5$. Model features include satellite measurements, meteorological variables, land use classification, temporal variables (month, day of year), and spatial variables (latitude, longitude, altitude). The models were evaluated using various methods, including station 10-fold cross-validation, in which in each fold observations from 10\% of the stations are used as testing data and the rest as training data. The $R^2$, RMSE and mean bias were determined for each model. The $NO_2$ and $O_3$ models presented good values of $R^2$, 0.5524 and 0.7462, respectively. However, the $SO_2$, $PM10$, and $PM2.5$ models performed very poorly in this regard, with $R^2$ values of -0.0231, 0.3722, and 0.3303, respectively. All models slightly overestimated the ground concentrations, except the $O_3$ model. All models presented acceptable cross-validation RMSE, except the $O_3$ and $PM10$ models where the mean value was a little higher (12.5934 $\mu g/m^3$ and 10.4737 $\mu g/m^3$, respectively).
Synthesizing Strongly Equivalent Logic Programs: Beth Definability for Answer Set Programs via Craig Interpolation in First-Order Logic

We show a projective Beth definability theorem for logic programs under the stable model semantics: For given programs $P$ and $Q$ and vocabulary $V$ (set of predicates) the existence of a program $R$ in $V$ such that $P \cup R$ and $P \cup Q$ are strongly equivalent can be expressed as a first-order entailment. Moreover, our result is effective: A program $R$ can be constructed from a Craig interpolant for this entailment, using a known first-order encoding for testing strong equivalence, which we apply in reverse to extract programs from formulas. As a further perspective, this allows transforming logic programs via transforming their first-order encodings. In a prototypical implementation, the Craig interpolation is performed by first-order provers based on clausal tableaux or resolution calculi. Our work shows how definability and interpolation, which underlie modern logic-based approaches to advanced tasks in knowledge representation, transfer to answer set programming.
Upper crustal structure beneath the northern South Yellow Sea revealed by wide‐angle seismic tomography and joint interpretation of geophysical data

Mapping the crustal structure beneath the South Yellow Sea (SYS), which includes the collision zone between the North China Block and the South China Block, will help to reveal the geologic history and relationship between these geologic blocks. Previous interpretations of the crustal structure of the SYS did not use detailed seismic velocity structure as a constraint. We produced an upper crustal velocity model of the northern SYS using a new wide‐angle seismic dataset. Velocity anomalies resolved by our models correlate well with known features such as sedimentary basins and the tectonic uplifts in the SYS as low‐ and high‐velocity anomalies, respectively. As might be expected, not only major faults and/or lithologic boundaries were found to be associated with large velocity gradients, but also our models exhibited resolvable deeply penetrating vertical low‐velocity anomalies associated with the locations of strike‐slip faults that were poorly resolved in an older seismic reflection profile. Our interpretations of the locations of major faults were based on the stacked seismic reflection profile and velocity modelling of wide‐angle seismic array data and were extended to neighbouring regions by following trends in gravity, magnetic and earthquake location data. Faults traversing the North Basin of the SYS converged with the onshore Jia‐Xiang Fault (JXF), suggesting that the branching of the JXF fault system controlled the formation of the North Basin. The distribution of the strike‐slip faults resolved by our modelling of the JXF leads us to propose a model for the North Basin as a strike‐slip stress regime that is consistent with the known tectonic framework. These models will be useful for future analysis of the regional tectonic evolution of the SYS region. Copyright © 2016 John Wiley & Sons, Ltd.
AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts

To improve language models' proficiency in mathematical reasoning via continual pretraining, we introduce a novel strategy that leverages base language models for autonomous data selection. Departing from conventional supervised fine-tuning or trained classifiers with human-annotated data, our approach utilizes meta-prompted language models as zero-shot verifiers to autonomously evaluate and select high-quality mathematical content, and we release the curated open-source AutoMathText dataset encompassing over 200GB of data. To demonstrate the efficacy of our method, we continuously pretrained a 7B-parameter Mistral language model on the AutoMathText dataset, achieving substantial improvements in downstream performance on the MATH dataset with a token amount reduced by orders of magnitude compared to previous continuous pretraining works. Our method showcases a 2 times increase in pretraining token efficiency compared to baselines, underscoring the potential of our approach in enhancing models' mathematical reasoning capabilities. The AutoMathText dataset is available at https://huggingface.co/datasets/math-ai/AutoMathText. The code is available at https://github.com/yifanzhang-pro/AutoMathText.
An elaborate new proof of Cayley's formula

We construct a bijection between certain Deodhar components of a braid variety constructed from an affine Kac-Moody group of type $A_{n-1}$ and vertex-labeled trees on $n$ vertices. By an argument of Galashin, Lam, and Williams using Opdam's trace formula in the affine Hecke algebra and an identity due to Haglund, we obtain an elaborate new proof for the enumeration of the number of vertex-labeled trees on $n$ vertices.
Towards Robotic Companions: Understanding Handler-Guide Dog Interactions for Informed Guide Dog Robot Design

Dog guides are favored by blind and low-vision (BLV) individuals for their ability to enhance independence and confidence by reducing safety concerns and increasing navigation efficiency compared to traditional mobility aids. However, only a relatively small proportion of BLV individuals work with dog guides due to their limited availability and associated maintenance responsibilities. There is considerable recent interest in addressing this challenge by developing legged guide dog robots. This study was designed to determine critical aspects of the handler-guide dog interaction and better understand handler needs to inform guide dog robot development. We conducted semi-structured interviews and observation sessions with 23 dog guide handlers and 5 trainers. Thematic analysis revealed critical limitations in guide dog work, desired personalization in handler-guide dog interaction, and important perspectives on future guide dog robots. Grounded on these findings, we discuss pivotal design insights for guide dog robots aimed for adoption within the BLV community.