Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantified soil dynamics and spatial fragmentation within the shifting agricultural landscape in southern Cameroon

This research used scientific modeling tools to produce quantitative information on the effects of shifting agriculture on soil and spatial pattern of landscape dynamics in the area. An analysis of farming system led to the development of a conceptual model of the spatio-temporal dynamics of shifting agriculture, including transition matrices of rotational cycles. The study of soil variability showed that 30-35% of the total variance of some topsoil (0-20 cm) properties was due to the influence land use practices. A robust quantitative multi-criteria method was then developed that selected five soil properties (pH, calcium, available phosphorus, bulk density and organic carbon) that are the most sensitive to these agricultural practices. Empirical models of linear/quadratic fractional rational functions were successfully fitted to time series soil variables to derive quantitative measures on temporal changes in soil with land use. Multi-spectral satellite imagery was able to map with 80% accuracy the extension front of shifting agricultural landscape and the most dynamic land cover types (crop fields, young fallows), which shift every season and every year. Data and methods produced are useful for soil quality assessment and spatio-temporal dynamic simulation in order to guide decision-making for sustainable land-use planning and forest resources management.
Object Importance Estimation Using Counterfactual Reasoning for Intelligent Driving

The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It also helps assistive driving systems decide when to alert drivers. We tackle object importance estimation in a data-driven fashion and introduce HOIST - Human-annotated Object Importance in Simulated Traffic. HOIST contains driving scenarios with human-annotated importance labels for vehicles and pedestrians. We in addition propose a novel approach that relies on counterfactual reasoning to estimate an object's importance. We generate counterfactual scenarios by modifying the motion of objects and ascribe importance based on how the modifications affect the ego vehicle's driving. Our approach outperforms strong baselines for the task of object importance estimation on HOIST. We also perform ablation studies to justify our design choices and show the significance of the different components of our proposed approach.
Dynamics of nonlinear scalar field with Robin boundary condition on the Schwarzschild--Anti-de Sitter background

This work concerns the dynamics of conformal cubic scalar field on a Schwarzschild--anti-de Sitter background. The main focus is on understanding how it depends on the size of the black hole and the Robin boundary condition. We identify a critical curve in the parameter space that separates regions with distinct asymptotic behaviours. For defocusing nonlinearity, the global attractor undergoes a pitchfork bifurcation, whereas for the focusing case, we identify a region of the phase space where all solutions blow up in finite time. In the course of this study we observe an interplay between black hole geometry, boundary conditions, and the nonlinear dynamics of scalar fields in asymptotically anti-de Sitter spacetime.
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower-dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet datasets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than 2E-6 seconds and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods.
An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos

In this work, we present an integrated system for spatiotemporal summarization of 360-degrees videos. The video summary production mainly involves the detection of salient events and their synopsis into a concise summary. The analysis relies on state-of-the-art methods for saliency detection in 360-degrees video (ATSal and SST-Sal) and video summarization (CA-SUM). It also contains a mechanism that classifies a 360-degrees video based on the use of static or moving camera during recording and decides which saliency detection method will be used, as well as a 2D video production component that is responsible to create a conventional 2D video containing the salient events in the 360-degrees video. Quantitative evaluations using two datasets for 360-degrees video saliency detection (VR-EyeTracking, Sports-360) show the accuracy and positive impact of the developed decision mechanism, and justify our choice to use two different methods for detecting the salient events. A qualitative analysis using content from these datasets, gives further insights about the functionality of the decision mechanism, shows the pros and cons of each used saliency detection method and demonstrates the advanced performance of the trained summarization method against a more conventional approach.
Bright spectroscopic binaries: III. Binary systems with orbital periods of P > 500 days

We present an analysis of nine bright spectroscopic binaries (HD 1585, HD 6613, HD 12390, HD 39923, HD 55201, HD 147430, HD 195543, HD 202699, HD 221643), which have orbital periods of days. These well‐separated binaries are the last stars of our sample that we observed with the TIGRE telescope obtaining intermediate‐resolution spectra of . We applied the same method as described in our previous publication of this series. For the analysis of the radial velocity curves, we used the toolkit RadVel, which allowed us to determine all orbital parameters. Recently published orbital solutions of some systems from Gaia DR3 agree with our results. However, our solutions have much smaller uncertainties. We determined the basic stellar parameters of the primary stars with our automatic script using iSpec. The parameter determination allowed us to place all nine stars in the Hertzsprung–Russell diagram. We found that all stars have already evolved to the giant phase. A comparison with stellar evolution tracks of the Eggleton code was applied to determine the stellar masses and ages. As a result of our analysis, we were able to estimate the masses of the secondary stars and the orbital inclinations of the binary systems.
Fine-Tuning Language Models for Context-Specific SQL Query Generation

The ability to generate SQL queries from natural language has significant implications for making data accessible to non-specialists. This paper presents a novel approach to fine-tuning open-source large language models (LLMs) for the task of transforming natural language into SQL queries within the retail domain. We introduce models specialized in generating SQL queries, trained on synthetic datasets tailored to the Snowflake SQL and GoogleSQL dialects. Our methodology involves generating a context-specific dataset using GPT-4, then fine-tuning three open-source LLMs(Starcoder Plus, Code-Llama, and Mistral) employing the LoRa technique to optimize for resource constraints. The fine-tuned models demonstrate superior performance in zero-shot settings compared to the baseline GPT-4, with Code-Llama achieving the highest accuracy rates, at 81.58% for Snowflake SQL and 82.66% for GoogleSQL. These results underscore the effectiveness of fine-tuning LLMs on domain-specific tasks and suggest a promising direction for enhancing the accessibility of relational databases through natural language interfaces.
Insights to Molecular and Bulk Mechanical Properties of Glassy Carbon Through Molecular Dynamics Simulation and Mechanical Tensile Testing

With increasing interest in the use of glassy carbon (GC) for a wide variety of application areas, the need for developing fundamental understanding of its mechanical properties has come to the forefront. Further, recent theoretical and modeling works that shed some light on the synthesis of GC through the process of pyrolysis of polymer precursors have highlighted the possibilities of a revisit to investigation of its mechanical properties at a fundamental level. While there are isolated reports on the experimental determination of its elastic modulus, insights into stress-strain behavior of GC material under tension and compression obtained through simulation, either at molecular level or for the bulk material is missing. This current study fills the gap at the molecular level and investigates the mechanical properties of GC using molecular dynamics (MD) simulations which model the atomistic level formation and breaking of bonds using bond-order based reactive force field formulations. The molecular model considered for this simulation has a characteristics 3D cagey structure of 5-, 6-, and 7-membered carbon rings and graphitic domain of a flat graphene-like structure. The GC molecular model was subjected to loading under varying strain rates (0.4/ns, 0.6/ns, 1.25/ns, and 2.5/ns) and varying temperatures (300 - 800 K) in each of the three axes x, y, and z. The simulation showed that GC molecule has distinct stress-strain curves under tension and compression. In tension, MD modeling predicted mean elastic modulus of 5.71 GPa for a single GC molecule with some dependency on strain rates and temperature, while in compression, the elastic modulus was also found to depend on the strain rates as well as temperature and was predicted to have a mean value of 35 GPa
Differentiable Point-based Inverse Rendering

We present differentiable point-based inverse rendering, DPIR, an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end, we adopt point-based rendering, eliminating the need for multiple samplings per ray, typical of volumetric rendering, thus significantly enhancing the speed of inverse rendering. To realize this idea, we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast rendering through point-based splatting while retaining the geometric details and stability inherent to SDF-based representations. The regularized basis-BRDF mitigates the ill-posedness of inverse rendering stemming from limited light-view angular samples. We also propose an efficient shadow detection method using point-based shadow map rendering. Our extensive evaluations demonstrate that DPIR outperforms prior works in terms of reconstruction accuracy, computational efficiency, and memory footprint. Furthermore, our explicit point-based representation and rendering enables intuitive geometry and reflectance editing.
Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks

High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also on the density and distribution of reference user locations used for fingerprinting.
WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words

Training on multiple modalities of input can augment the capabilities of a language model. Here, we ask whether such a training regime can improve the quality and efficiency of these systems as well. We focus on text--audio and introduce Whisbert, which is inspired by the text--image approach of FLAVA (Singh et al., 2022). In accordance with Babylm guidelines (Warstadt et al., 2023), we pretrain Whisbert on a dataset comprising only 100 million words plus their corresponding speech from the word-aligned version of the People's Speech dataset (Galvez et al., 2021). To assess the impact of multimodality, we compare versions of the model that are trained on text only and on both audio and text simultaneously. We find that while Whisbert is able to perform well on multimodal masked modeling and surpasses the Babylm baselines in most benchmark tasks, it struggles to optimize its complex objective and outperform its text-only Whisbert baseline.
Growth patterns of chusquea bamboos in the understory of chilean nothofagus forests and their influences in forest dynamics

Growth Patterns of Chusquea Bamboos in the Understory of Chilean Nothofagus forests andTheir Influences in Forest DynamicsAuthor(s): Thomas T. VeblenSource: Bulletin of the Torrey Botanical Club, Vol. 109, No. 4 (Oct. - Dec., 1982), pp. 474-487Published by: Torrey Botanical SocietyStable URL: http://www.jstor. org/stable/2996488Accessed: 01/12/2010 10:40
UTBoost: A Tree-boosting based System for Uplift Modeling

Uplift modeling refers to the set of machine learning techniques that a manager may use to estimate customer uplift, that is, the net effect of an action on some customer outcome. By identifying the subset of customers for whom a treatment will have the greatest effect, uplift models assist decision-makers in optimizing resource allocations and maximizing overall returns. Accurately estimating customer uplift poses practical challenges, as it requires assessing the difference between two mutually exclusive outcomes for each individual. In this paper, we propose two innovative adaptations of the well-established Gradient Boosting Decision Trees (GBDT) algorithm, which learn the causal effect in a sequential way and overcome the counter-factual nature. Both approaches innovate existing techniques in terms of ensemble learning method and learning objectives, respectively. Experiments on large-scale datasets demonstrate the usefulness of the proposed methods, which often yielding remarkable improvements over base models. To facilitate the application, we develop the UTBoost, an end-to-end tree boosting system specifically designed for uplift modeling. The package is open source and has been optimized for training speed to meet the needs of real industrial applications.
Replica analysis of overfitting in regression models for time to event data: the impact of censoring

We use statistical mechanics techniques, viz. the replica method, to model the effect of censoring on overfitting in Cox's proportional hazards model, the dominant regression method for time-to-event data. In the overfitting regime, Maximum Likelihood parameter estimators are known to be biased already for small values of the ratio of the number of covariates over the number of samples. The inclusion of censoring was avoided in previous overfitting analyses for mathematical convenience, but is vital to make any theory applicable to real-world medical data, where censoring is ubiquitous. Upon constructing efficient algorithms for solving the new (and more complex) RS equations and comparing the solutions with numerical simulation data, we find excellent agreement, even for large censoring rates. We then address the practical problem of using the theory to correct the biased ML estimators {without} knowledge of the data-generating distribution. This is achieved via a novel numerical algorithm that self-consistently approximates all relevant parameters of the data generating distribution while simultaneously solving the RS equations. We investigate numerically the statistics of the corrected estimators, and show that the proposed new algorithm indeed succeeds in removing the bias of the ML estimators, for both the association parameters and for the cumulative hazard.
Quest Complete: the Holy Grail of Gradual Security

Languages with gradual information-flow control combine static and dynamic techniques to prevent security leaks. Gradual languages should satisfy the gradual guarantee: programs that only differ in the precision of their type annotations should behave the same modulo cast errors. Unfortunately, Toro et al. [2018] identify a tension between the gradual guarantee and information security; they were unable to satisfy both properties in the language $\mathrm{GSL}_\mathsf{Ref}$ and had to settle for only satisfying information-flow security. Azevedo de Amorim et al. [2020] show that by sacrificing type-guided classification, one obtains a language that satisfies both noninterference and the gradual guarantee. Bichhawat et al. [2021] show that both properties can be satisfied by sacrificing the no-sensitive-upgrade mechanism, replacing it with a static analysis. In this paper we present a language design, $\lambda_{\mathtt{IFC}}^\star$, that satisfies both noninterference and the gradual guarantee without making any sacrifices. We keep the type-guided classification of $\mathrm{GSL}_\mathsf{Ref}$ and use the standard no-sensitive-upgrade mechanism to prevent implicit flows through mutable references. The key to the design of $\lambda_{\mathtt{IFC}}^\star$ is to walk back the unusual decision in $\mathrm{GSL}_\mathsf{Ref}$ to include the unknown label $\star$ among the runtime security labels. We mechanize the definition of $\lambda_{\mathtt{IFC}}^\star$ in Agda and prove the gradual guarantee. On the technical side, the semantics of $\lambda_{\mathtt{IFC}}^\star$ is the first gradual information-flow control language to be specified using coercion calculi (a la Henglein), thereby expanding the coercion-based theory of gradual typing.
Ruddlesden-Popper chalcogenides push the limit of mechanical stiffness and glass-like thermal conductivity in crystals

Insulating materials featuring ultralow thermal conductivity for diverse applications also require robust mechanical properties. Conventional thinking, however, which correlates strong bonding with high atomic-vibration-mediated heat conduction, led to diverse weakly bonded materials that feature ultralow thermal conductivity and low elastic moduli. One must, therefore, search for strongly-bonded materials in which heat transport is impeded by other means. Here, we report intrinsic, glass-like, ultralow thermal conductivity and ultrahigh elastic-modulus/thermal-conductivity ratio in single-crystalline, BaZrS3-derived, Ruddlesden-Popper phases Ban+1ZrnS3n+1, n = 2, 3. Their key features are strong anharmonicity and intra-unit-cell rock-salt blocks. The latter produce strongly bonded intrinsic superlattices, impeding heat conduction by broadband reduction of phonon velocities and mean free paths and concomitant strong phonon localization. The present study initiates a paradigm of"mechanically stiff phonon glasses".
Impact of twice-a-day transcranial direct current stimulation intervention on cognitive function and motor cortex plasticity in patients with Alzheimer’s disease

Background Non-invasive brain stimulation has improved cognitive functions in patients with Alzheimer’s disease (AD), and some studies suggest a close relationship between cognition and plasticity. However, the clinical benefits of transcranial direct current stimulation (tDCS) in patients still need to be evaluated. Aims This study examined the role of tDCS in improving cognition and whether the improved cognition is related to altered cortical plasticity. Methods 124 patients with AD were randomly assigned to active tDCS (n=63) or sham tDCS (n=61). The tDCS was applied at the dorsolateral prefrontal cortex for 30 treatment sessions across 6 weeks (5 days per week, 2 days off). The Mini-Mental State Examination and the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog) were used for cognition evaluation at baseline, week 2 and week 6. The cortical plasticity was represented by motor-evoked potential (MEP) measured with an electromyogram. Results The results showed that multiple courses of active tDCS can improve the cognitive functions of patients with AD, especially in the memory domain (word recall, recall of test instructions and word recognition). In addition, the damaged MEP level was enhanced following active treatment. In the active tDCS group, the improvements in ADAS-Cog total and subitem (word recall and word recognition) scores were negatively correlated with the enhancement of MEP. Conclusions Our research indicates for the first time that twice-a-day tDCS may improve the cognitive function of patients with AD. This study also suggests that cognitive dysfunction may be related to impaired cortical plasticity, which warrants mechanistic investigations of the relationship between cognition and plasticity in the future. Trial registration number ChiCTR1900021067.
Holmstedt's formula for the K‐functional: the limit case θ0=θ1$\theta _0=\theta _1$

We consider K‐interpolation spaces involving slowly varying functions, and derive necessary and sufficient conditions for a Holmstedt‐type formula to be held in the limiting case θ0=θ1∈{0,1}$\theta _0=\theta _1\in \lbrace 0,1\rbrace$ . We also study the case θ0=θ1∈(0,1)$\theta _0=\theta _1\in (0,1)$ . Applications are given to Lorentz–Karamata spaces, generalized gamma spaces, and Besov spaces.