Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameterized thermal convection in a layered region and the thermal history of the Earth

Parameterized convective schemes for constant viscosity fluids are used to investigate the influence of layering on the relationship between the rate of heat loss from and heat supply to a number of convecting layers of different viscosity and thicknesses. Simple analytic expressions are obtained which accurately predict the behavior to a step decrease in the heat supply, and show that layering considerably increases the response time. The same arguments are applied to a planet consisting of spherical shells. They show that the response of the earth's heat loss to the decay of radioactive elements depends strongly on whether or not mantle convection extends from the core to the surface. If it does so, the surface heat flux is only about 50% greater than the heat generation rate. However if upper and lower mantle convection occurs in two separate shells, as seismological and geochemical arguments suggest, the response time is increased. The present surface heat flux is then principally controlled by the thermal structure of the earth at the time it was formed. The best estimate of the present ratio of the heat loss to heat generation is about two, and this value is consistent with the behavior of an earth model consisting of a two-layered mantle overlying a core.
Hubungan antara hasil dengan perbelanjaan kerajaan Negeri di Malaysia

Objektif utama kajian ini adalah untuk mengkaji hubungan antara pembolehubah fiskal iaitu perbelanjaan dan hasil dengan menggunakan data di peringkat kerajaan negeri. Kaedah ekonometrik siri masa diaplikasikan dalam kajian ini yang terdiri daripada ujian kointegrasi Johansen dun ujian sebab-menyebab Granger untuk menentukan arah hubungan kedua-dua pembolehubah fiskal tersebut. Di samping itu, kaedah panel data juga diaplikasikan dalam kajian ini. Dapatan kajian menunjukkan keputusan yang tidak selaras, yang mana sebilangan besar negeri menepati hipotesis hasil-belanja (misalnya negeri Kedah, Melaka, Pahang, Perak, Sabah, Sarawak dun Terengganu), manakala hipotesis belanja-hasil hanya wujud di negeri Perlis sahaja, dan tiada hubungan antara hasil dengan belanja di negeri Johor, Kelantan dan Pulau Pinang yang menunjukkan bahawa kedua-dua pembolehubah fiskal adalah bersifat nuetral. Hipotesis hubungan sebab-menyebab dua hala hanya berlaku bagi kes negeri Selangor dan Negeri Sembilan sahaja. Di samping itu, hasil ujian panel data dengan menggunakan kesan tetap juga mendapati pembolehubah hasil rnempunyai pengaruh yang penting terhadap perbelanjaan kerajaan negeri. Ini menunjukkan bahawa proses belanjawan kerajaan negeri didominasi di sebelah hasil atau dengan perkataan lain menyokong hipotesis hasil-belanja.
Uncovering global climate fields causing local precipitation extremes

ABSTRACT Earlier studies have focused on establishing the hydroclimatic teleconnection between hydrological extremes and a single or a few preselected coupled oceanic–atmospheric circulation patterns (OACP). In this study, we hypothesise that local (continental-scale) hydrological extremes are, in general, influenced by a concurrent effect of different climate variables from several regions across the globe. The suite of these variables is termed the global climate pattern (GCP) behind the target hydrological event. To explore our hypothesis, we considered monthly Indian rainfall, which is a complex, continental-scale hydrological phenomenon. Extreme events are identified through the standardized precipitation anomaly index (SPAI), used as an indicator of dry and wet events. The global fields of different climate variables—sea surface temperature (SST), surface pressure (SP), air temperature (AT), wind speed (WS) and total precipitable water (TPW)—are explored for possible revelatory patterns. In all, 15 different variables, each designated by a climate anomaly from a distinct zone on the globe, are found to form a potential global climate signal pool, which can be designated as the GCP for Indian rainfall. The combined information extracted from this GCP is proven to be highly efficient in predicting dry and wet events in Indian rainfall. EDITOR Z. W. Kundzewicz; ASSOCIATE EDITOR G. Di Baldassarre
The Impact of Mindfulness-Based Interventions on Objective Physiological Measures of Autonomic Function for Individuals With Medical Conditions: A Review of the Evidence

ABSTRACT Objective Autonomic dysregulation is common in many medical conditions and can have a widespread, negative impact on multiple bodily systems, leading to poorer health outcomes. Thus, addressing autonomic dysregulation as part of a comprehensive treatment plan is important. The goal of this study was to gain a better understanding of the physiological benefits of a mindfulness-based intervention (MBI) for a population with medical conditions, using validated, objective measures of autonomic functioning. Methods We conducted a review of the literature and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols. Studies were included if a) participants were diagnosed with a medical condition, b) an MBI was used, and c) objective pre/post measurements of autonomic nervous system function were collected. Medical conditions were included as a category for this review when a minimum of three articles met the criteria for inclusion. Results Ten articles met the criteria and included oncology, cardiac, and chronic pain conditions. Clinical recommendations were made based on the Clinical Practice Guideline Process Manual, 2017 Edition by the American Academy of Neurology. Conclusions Based on level of evidence, only oncology met the criteria for “possibly effective. ” However, there was some evidence of the benefit of MBIs for all three medical conditions, based on individual study findings.
Clinical applicability of the WaveFront Phase Imaging Sensor in the Keratoconus Disease

Significance: The analysis of the wavefront phase of keratoconus eyes has become increasingly important due to its impact on the assessment of visual quality loss, irrecoverable with conventional optical treatments, and the understanding of morphological changes in corneal structures. Aim: The aim of this work is to quantitatively assess the wavefront phase of keratoconic eyes measured by the novel high-resolution ocular aberrometer t-eyede (based on WaveFront Phase Imaging Sensor). Approach: The measurements with this device were taken on healthy and keratoconic eyes at the Hospital Universitario Fundacion Jimenez Diaz (Madrid, Spain). The wavefront phase recovered is post-processed and displayed in different phase maps, using exclusively the high-pass filter map for this study. From this map, the Root Mean Square, the Peak-to-Valley, and the amplitude value of the predominant obtained from its Fourier Transform are the parameters to distinguish between healthy and keratoconic eyes. Furthermore, the banding pattern observed in most keratoconic eyes is quantitatively analyzed by means of its period and orientation calculated from the Fourier Transform. Results: The Control group was composed of 43 healthy eyes, and the Pathological group presented 43 keratoconic eyes. Regarding the first three parameters used to compare both groups, there were significant statistical differences between them in all parameters, where the values obtained with the keratoconic group were higher than those of the healthy group. Although the values cannot be statistically comparable between stages, they tended to increase according to the severity of the disease. On the other hand, analyzing the banding pattern, the mean value of the period was approximately 50 microns, and most orientations were obliquus, tending to be horizontal rather than vertical. Conclusions: The present study demonstrates that the objective analysis of the high frequencies detected by this wavefront sensor is a potential tool to reliably advise in the keratoconus diagnosis. Furthermore, combining this achievement with the quantitative assessment of the banding pattern would be used to objectively monitor the internal corneal status throughout the disease and its treatment optimization and surgery time.
What's left can't be right - The remaining positional incompetence of contrastive vision-language models

Contrastive vision-language models like CLIP have been found to lack spatial understanding capabilities. In this paper we discuss the possible causes of this phenomenon by analysing both datasets and embedding space. By focusing on simple left-right positional relations, we show that this behaviour is entirely predictable, even with large-scale datasets, demonstrate that these relations can be taught using synthetic data and show that this approach can generalise well to natural images - improving the performance on left-right relations on Visual Genome Relations.
Functional relative error regression under left truncation and right censoring

The nonparametric estimators built by minimizing the mean squared relative error are gaining in popularity for their robustness in the presence of outliers in comparison to the Nadaraya Watson estimators. In this paper we build a relative error regression function estimator in the case of a functional explanatory variable and a left truncated and right censored scalar variable. The pointwise and uniform convergence of the estimator is proved and its performance is assessed by a numerical study in particularly the robustness which is highlighted using the influence function as a measure of robustness.
The homology growth for finite abelian covers of smooth quasi-projective varieties

Let $X$ be a complex smooth quasi-projective variety with a fixed epimorphism $\nu\colon\pi_1(X)\twoheadrightarrow H$, where $H$ is a finitely generated abelian group with $\mathrm{rank}H\geq 1$. In this paper, we study the asymptotic behaviour of Betti numbers with all possible field coefficients and the order of the torsion subgroup of singular homology associated to $\nu$, known as the $L^2$-type invariants. When $\nu$ is orbifold effective, we give explicit formulas of these invariants at degree 1. This generalizes the authors' previous work for $H\cong \Z$.
Optical control of spatially localized red blood cell activity by holographic tweezing

Red blood cells possess unique biomechanical ability to squeeze through capillaries smaller than their size to enable gas and ion exchange. A key signature of their active biomechanics is the out-of-equilibrium fluctuation of the plasma membrane, also known as flickering motion. This active flickering is driven by motor proteins that connect the forces between the spectrin skeleton and the lipid bilayer. However, studying flickering motions in living red blood cells is challenging without altering their physical properties. Here, we implemented a holographic optical tweezer that sculpted a laser beam to create a force field distributed directly along the membrane equatorial contour. We show heterogeneous membrane flickering activity driven by membrane kickers in free-standing cells. Then we inhibited the active kickers by optical forces under minimal invasion, thus benchmarking the active motion against thermal fluctuations. Our work paves the way for optical control of biophysical forces, providing touchless strategies for mechanotransduction in living cells.
DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks

Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred from the history cascades. Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee. However, in their settings, though the diffusion parameters are inferred, they still need users to preset the diffusion model, which can be an intractable problem in real-world practices. In this paper, we reformulate the problem on the attributed network and leverage the node attributes to estimate the closeness between the connected nodes. Specifically, we propose a machine learning-based framework, named DSCom, to address this problem in a heuristic way. Under this framework, we first infer the users' relationship from the diffusion dataset through attention mechanism and then leverage spectral clustering to overcome the influence overlap problem in the lack of exact diffusion formula. Compared to the previous theoretical works, we carefully designed empirical experiments with parameterized diffusion models based on real-world social networks, which prove the efficiency and effectiveness of our algorithm.
Gradients and frequency profiles of quantum re-uploading models

Quantum re-uploading models have been extensively investigated as a form of machine learning within the context of variational quantum algorithms. Their trainability and expressivity are not yet fully understood and are critical to their performance. In this work, we address trainability through the lens of the magnitude of the gradients of the cost function. We prove bounds for the differences between gradients of the better-studied data-less parameterized quantum circuits and re-uploading models. We coin the concept of {\sl absorption witness} to quantify such difference. For the expressivity, we prove that quantum re-uploading models output functions with vanishing high-frequency components and upper-bounded derivatives with respect to data. As a consequence, such functions present limited sensitivity to fine details, which protects against overfitting. We performed numerical experiments extending the theoretical results to more relaxed and realistic conditions. Overall, future designs of quantum re-uploading models will benefit from the strengthened knowledge delivered by the uncovering of absorption witnesses and vanishing high frequencies.
Radarize: Large-Scale Radar SLAM for Indoor Environments

We present Radarize, a self-contained SLAM pipeline for indoor environments that uses only a low-cost commodity single-chip mmWave radar. Our radar-native approach leverages phenomena unique to radio frequencies, such as doppler shift-based odometry, to improve performance. We evaluate our method on a large-scale dataset of 146 trajectories spanning 4 campus buildings, totaling approximately 4680m of travel distance. Our results show that our method outperforms state-of-the-art radar-based approaches by approximately 5x in terms of odometry and 8x in terms of end-to-end SLAM, as measured by absolute trajectory error (ATE), without the need additional sensors such as IMUs or wheel odometry.
AIaaS for ORAN-based 6G Networks: Multi-time scale slice resource management with DRL

This paper addresses how to handle slice resources for 6G networks at different time scales in an architecture based on an open radio access network (ORAN). The proposed solution includes artificial intelligence (AI) at the edge of the network and applies two control-level loops to obtain optimal performance compared to other techniques. The ORAN facilitates programmable network architectures to support such multi-time scale management using AI approaches. The proposed algorithms analyze the maximum utilization of resources from slice performance to take decisions at the inter-slice level. Inter-slice intelligent agents work at a non-real-time level to reconfigure resources within various slices. Further than meeting the slice requirements, the intra-slice objective must also include the minimization of maximum resource utilization. This enables smart utilization of the resources within each slice without affecting slice performance. Here, each xApp that is an intra-slice agent aims at meeting the optimal quality of service (QoS) of the users, but at the same time, some inter-slice objectives should be included to coordinate intra- and inter-slice agents. This is done without penalizing the main intra-slice objective. All intelligent agents use deep reinforcement learning (DRL) algorithms to meet their objectives. We have presented results for enhanced mobile broadband (eMBB), ultra-reliable low latency (URLLC), and massive machine type communication (mMTC) slice categories.
A unified fused Lasso approach for sparse and blocky feature selection in regression and classification

In many applications, sparse and blocky coefficients often occur in regression and classification problems. The fused Lasso was designed to recover these sparse structured features especially when the design matrix encounters the situation of ultrahigh dimension. Quantile loss is well known as a robust loss function in regression and classification. In this paper, we combine quantile loss and fused Lasso penalty together to produce quantile fused Lasso which can achieve sparse and blocky feature selection in both regression and classification. Interestingly, our proposed model has the unified optimization formula for regression and classification. For ultrahigh dimensional collected data, we derive multi-block linearized alternating direction method of multipliers (LADMM) to deal with it. Moreover, we prove convergence and derive convergence rates of the proposed LADMM algorithm through an elegant method. Note that the algorithm can be easily extended to solve many existing fused Lasso models. Finally, we present some numerical results for several synthetic and real world examples, which illustrate the robustness, scalability, and accuracy of the proposed method.
Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving

In the domain of autonomous driving, the offline Reinforcement Learning~(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline datasets. However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets. In this paper, we introduce the saFety-aware strUctured Scenario representatION (FUSION), a pioneering representation learning method in offline RL to facilitate the learning of a generalizable end-to-end driving policy by leveraging structured scenario information. FUSION capitalizes on the causal relationships between the decomposed reward, cost, state, and action space, constructing a framework for structured sequential reasoning in dynamic traffic environments. We conduct extensive evaluations in two typical real-world settings of the distribution shift in autonomous vehicles, demonstrating the good balance between safety cost and utility reward compared to the current state-of-the-art safe RL and IL baselines. Empirical evidence in various driving scenarios attests that FUSION significantly enhances the safety and generalizability of autonomous driving agents, even in the face of challenging and unseen environments. Furthermore, our ablation studies reveal noticeable improvements in the integration of causal representation into the offline safe RL algorithm. Our code implementation is available at: https://sites.google. com/view/safe-fusion/.
Traceability of Water Pollution: An Inversion Scheme Via Dynamic Complex Geometrical Optics Solutions

We investigate the identification of the time-dependent source term in the diffusion equation using boundary measurements. This facilitates tracing back the origins of environmental pollutants. Employing the concept of dynamic complex geometrical optics (CGO) solutions, a variational formulation of the inverse source problem is analyzed, leading to a proof of uniqueness result. Our proposed two-step reconstruction algorithm first determines the point source locations and subsequently reconstructs the Fourier components of the emission concentration functions. Numerical experiments on simulated data are conducted. The results demonstrate that the proposed two-step reconstruction algorithm can reliably reconstruct multiple point sources and accurately reconstruct the emission concentration functions. Additionally, by partitioning the algorithm into online and offline computations, and concentrating computational demand offline, real-time pollutant traceability becomes feasible. This method, applicable in various fields - especially those related to water pollution, can identify the source of a contaminant in the environment, thus serving as a valuable tool in environmental protection.
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data

The integration of multisource remote sensing data and deep learning models offers new possibilities for accurately mapping high spatial resolution forest height. We found that GEDI relative heights (RH) metrics exhibited strong correlation with the mean of the top 10 highest trees (dominant height) measured in situ at the corresponding footprint locations. Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data. MARSNet comprises separate encoders for each remote sensing data modality to extract multi-scale features, and a shared decoder to fuse the features and estimate height. Using individual encoders for each remote sensing imagery avoids interference across modalities and extracts distinct representations. To focus on the efficacious information from each dataset, we reduced the prevalent spatial and band redundancies in each remote sensing data by incorporating the extended spatial and band reconstruction convolution modules in the encoders. MARSNet achieved commendable performance in estimating dominant height, with an R2 of 0.62 and RMSE of 2.82 m, outperforming the widely used random forest approach which attained an R2 of 0.55 and RMSE of 3.05 m. Finally, we applied the trained MARSNet model to generate wall-to-wall maps at 10 m resolution for Jilin, China. Through independent validation using field measurements, MARSNet demonstrated an R2 of 0.58 and RMSE of 3.76 m, compared to 0.41 and 4.37 m for the random forest baseline. Our research demonstrates the effectiveness of a multimodal deep learning approach fusing GEDI with SAR and passive optical imagery for enhancing the accuracy of high resolution dominant height estimation.