Miracle-dz/UniFAR-FTUs huggingface dataset:
Agricultural supply response for sunflower in South Africa (1947–2016): The partial Nerlovian framework approach

The study estimates sunflower supply response in South Africa using time series data from 1947 to 2016, modelled through the Nerlovian Partial Adjustment approach. Short- and long-run price elasticities of 0.238 and 0.313 respectively, suggest that farmers do not easily adjust acreage devoted to sunflower given price changes, indicating the influence of other non-price factors. An adjustment coefficient of 0.272 indicates that the time taken to adjust from the actual to the desired acreage level is slow, at 27% per year. The estimated elasticities provide some scope for using price and non-price incentives to influence sunflower production in the long-run. This could facilitate decision-making by sunflower producers to spearhead internal and external adjustment processes. The study contributes to a growing body of literature on agricultural supply response determinants, thus providing evidence-based macro-economic tools towards agricultural policy-making and reform process.
PERFORMANCE AND NUTRIENT UTILIZATION OF BROILER STARTERS FED DIETS CONTAINING RAW, BOILED OR DEHULLED PIGEONPEA SEEDS (Cajanus cajann)

Two hundred and sixteen 7-day old broilers were used to compare three types of pigeon pea seed meal, namely raw, boiled and dehulled at two levels, 20 and 30% of the diet in a 3x2 factorially designed experiment lasting four weeks. Measurements recorded included weight gain, feed intake, feed conversion ratio (FCR), protein efficiency ratio (PER), nutrient retention and live weight of the broilers at the end of the fourth week. Better results were obtained when pigeon pea seed meal (PPSM) was included at 30% level than at 20% in all performance criteria. The interaction between processing method and level of inclusion was significant (P<0.001) with dehulled PPSM included at 30% (D30) producing best results; while at 20%, dehulled PPSM was not superior to boiled PPSM. In all measurements, broilers fed boiled and dehulled PPSM performed significantly better (P<0.01) than those fed raw seed meal Results of this experiment indicated that broilers fed dehulled pigeon pea seed meal at 30% inclusion level produced best results. Keywords: Pigeonpea, dehulled, performance nutrient utilization, broiler starter
ELEVATED SELENIUM LEVELS IN VEGETABLES, FRUITS, AND WILD PLANTS AFF ECTED BY THE RAŠA COAL MINE WATER CHEMISTRY

Selenium (Se), an essential trace element that is toxic when humans and animals are exposed to it in excess, is ubiquitous in coal. For centuries, superhigh-organic-sulfur (SHOS) Raša coal, enriched in S, Se, U, V, and Mo, was mined and processed across the Mediterranean Raša Bay area, located in the Istrian peninsula (in the northern Adriatic Sea, Croatia). There is concern that Raša coal mine water is contaminating local water, soil, and crops. The aim of this monitoring study was to determine the levels of Se and selected potentially toxic trace (As, Cd, Cu, Cr, Mo, Pb, U, V, and Zn), and minor (Fe and Mn) elements in Raša coal mine water, surface water, and associated vegetables, one fruit, and wild plants. Levels of Se in coal mine water were increased (up to 12 μg/L) compared to the maximum allowed water Se (10 μg/L). Compared to an EU average soil Se (1.15 mg/kg), Raša garden soil showed a 5-fold increase in Se. Compared to Croatian and Greek vegetable Se levels (low to normal), Raša vegetables showed a 20-fold, and a 50-fold increase in Se, respectively. Although approximative only, estimates of daily intake (EDI) of Se for mixed Raša vegetables (n = 21) showed a high level (0.055 mg/day). Namely, recommended dietary allowances (RDA) of Se for females and males are 0.055 mg/day, and 0.070 mg/ day, respectively. The EDI values of the analyzed vegetables contributed to average RDA levels as follows: garlic (183%), turnip (154%), parsley (147%), onion and gourd (76%), lettuce (74%), kale (62%), radicchio (51%), and potato (20%). Although the calculated EDI for the analyzed Raša vegetables was 1/8 the toxic dose (>0.4 mg/day), these results call for further research on the dietary and nutritional status of the residents in terms of Se.
Uncertainty and Sensitivity Analysis in Turbulent Pipe Flow Simulation

In this study, we would like to evaluate and improve the performance of Wall-Modeled Large-Eddy Simulation (WMLES) on the modeling of a pipe ﬂow for which Direct Numerical Simulation (DNS) data is available [1] and considered as a reference for further comparisons. Models used in WMLES may raise problems of accuracy which come from the uncertain values of model parameters and model simpliﬁcations. In this study, we focus ﬁrstly on the impact of the model parameter uncertainties on the simulation results, and then on the reduction of these uncertainties via data calibration. These studies using sampling-based approaches can be unaffordable when coupled with a high-ﬁdelity simulation that requires several CPU hours for a single execution. To reduce the computational cost while maintaining a target accuracy, we propose to build surrogate models based on Gaussian Processes for simulations outputs, and replace the simulator for evaluating the large size sampled sets. For this study, a CFD-UQ methodology is developed which couples our internal UQ tool and a CFD solver. It has been applied on a turbulent pipe ﬂow case that allows us to validate its implementation.
Efficient Empowerment Estimation for Unsupervised Stabilization

Intrinsically motivated artiﬁcial agents learn advantageous behavior without externally-provided rewards. Previously, it was shown that maximizing mutual information between agent actuators and future states, known as the empowerment principle, enables unsupervised stabilization of dynamical systems at upright positions, which is a prototypical intrinsically motivated behavior for upright standing and walking. This follows from the coincidence between the objective of stabilization and the objective of empowerment. Unfortunately, sample-based estimation of this kind of mutual information is challenging. Recently, various variational lower bounds (VLBs) on empowerment have been proposed as solutions; however, they are often biased, unstable in training, and have high sample complexity. In this work, we propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel, which allows us to efﬁciently calculate an unbiased estimator of empowerment by convex optimization. We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches. Speciﬁcally, we show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images. Consequently, our method opens a path to wider and easier adoption of empowerment for various applications. 1
Metropolized Forest Recombination for Monte Carlo Sampling of Graph Partitions

We develop a new Markov chain on graph partitions that makes relatively global moves yet is computationally feasible to be used as the proposal in the Metropolis-Hastings method. Our resulting algorithm can be made reversible and able to sample from a specified measure on partitions. Both of these properties are critical to some important applications and computational Bayesian statistics in general. Our proposal chain modifies the recently developed method called Recombination (ReCom), which draws spanning trees on joined partitions and then randomly cuts them to repartition. We improve the computational efficiency by augmenting the state space from partitions to spanning forests. The extra information accelerates the computation of the forward and backward proposal probabilities. We demonstrate this method by sampling redistricting plans and find promising convergence results on several key observables of interest.
Nuclear charge densities in spherical and deformed nuclei: Toward precise calculations of charge radii

Background: Precise measurements of atomic transitions affected by electron-nucleus hyperfine interactions offer sensitivity to explore basic properties of the atomic nucleus and study fundamental symmetries, including the search for new physics beyond the Standard Model of particle physics. Such measurements impose higher precision requirements on a theoretical description. Purpose: The nuclear charge density is composed of the proton point distribution folded with the nucleonic charge distributions. The latter induce subtle relativistic corrections due to the coupling of nucleon magnetic moments with the nuclear spin-orbit density. We assess the precision of nuclear charge density calculations by studying the behavior of relativistic corrections. Methods: The calculations are performed using Skyrme energy density functionals and density-dependent pairing force. We used the general expression for the spin-orbit form factor that is valid for spherical and deformed nuclei. Results: We studied the impact of various correction terms on the charge radii, fourth radial moments, diffraction radii, and surface thickness of spherical and deformed nuclei. The spin-orbit corrections to charge radial moments and surface thickness show strong shell fluctuations which impact high-precision predictions of isotopic shifts. Conclusions: To establish reliable constraints on the existence of new forces from isotope shift measurements,precise calculations of nuclear charge densities of deformed nuclei are needed. The proper inclusion of the spin-orbit charge density and other correction terms is essential when aiming at extraction of subtle effects which become particularly visible in isotopic trends.
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation

Effective feature representation is key to the predictive performance of any algorithm. This paper introduces a meta-procedure, called Non-Euclidean Upgrading (NEU), which learns feature maps that are expressive enough to embed the universal approximation property (UAP) into most model classes while only outputting feature maps that preserve any model class’s UAP. We show that NEU can learn any feature map with these two properties if that feature map is asymptotically deformable into the identity. We also find that the feature-representations learned by NEU are always submanifolds of the feature space. NEU’s properties are derived from a new deep neural model that is universal amongst all orientation-preserving homeomorphisms on the input space. We derive qualitative and quantitative approximation guarantees for this architecture. We quantify the number of parameters required for this new architecture to memorize any set of input-output pairs while simultaneously fixing every point of the input space lying outside some compact set, and we quantify the size of this set as a function of our model’s depth. Moreover, we show that deep feed-forward networks with most commonly used activation functions typically do not have all these properties. NEU’s performance is evaluated against competing machine learning methods on various regression and dimension reduction tasks both with financial and simulated data.
Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph

Websites use third-party ads and tracking services to deliver targeted ads and collect information about users that visit them. These services put users’ privacy at risk, and that is why users’ demand for blocking these services is growing. Most of the blocking solutions rely on crowd-sourced filter lists manually maintained by a large community of users. In this work, we seek to simplify the update of these filter lists by combining different websites through a large-scale graph connecting all resource requests made over a large set of sites. The features of this graph are extracted and used to train a machine learning algorithm with the aim of detecting ads and tracking resources. As our approach combines different information sources, it is more robust toward evasion techniques that use obfuscation or changing the usage patterns. We evaluate our work over the Alexa top-10K websites and find its accuracy to be 96.1% biased and 90.9% unbiased with high precision and recall. It can also block new ads and tracking services, which would necessitate being blocked by further crowd-sourced existing filter lists. Moreover, the approach followed in this paper sheds light on the ecosystem of third-party tracking and advertising.
Adsorption of uremic toxins over dealuminated zeolites

In this study, adsorption capacities of uremic toxins over Faujasite (HFAU) and Beta (HBEA) have been evaluated by varying the composition of solvent by using water, physiological, and sodium chloride solutions. HFAU was found to be more efficient in adsorption of these molecules. The adsorption results over HFAU were compared in various conditions to understand the adsorption mechanism. Thus, the adsorption mechanism was confirmed also by Fourier transform infrared and X-ray diffraction analysis, and it is found to be through the interaction of creatinine by hydrogen bonding on two types of sites on zeolites. Pseudo-second-order equation described well the adsorption kinetics data. Equilibrium isotherms were determined by Fowler–Guggenheim model. Finally, hydrophobic HFAU zeolite seems to be an efficient adsorbent; it is able to be easily regenerated under air, through retention of these initial adsorption properties.
Layer-adaptive Sparsity for the Magnitude-based Pruning

Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on"how to choose,"the layerwise sparsities are mostly selected algorithm-by-algorithm, often resorting to handcrafted heuristics or an extensive hyperparameter search. To fill this gap, we propose a novel importance score for global pruning, coined layer-adaptive magnitude-based pruning (LAMP) score; the score is a rescaled version of weight magnitude that incorporates the model-level $\ell_2$ distortion incurred by pruning, and does not require any hyperparameter tuning or heavy computation. Under various image classification setups, LAMP consistently outperforms popular existing schemes for layerwise sparsity selection. Furthermore, we observe that LAMP continues to outperform baselines even in weight-rewinding setups, while the connectivity-oriented layerwise sparsity (the strongest baseline overall) performs worse than a simple global magnitude-based pruning in this case. Code: https://github.com/jaeho-lee/layer-adaptive-sparsity
Controllability Issues of Linear Ensemble Systems

We address an open problem in ensemble control: Whether there exist controllable linear ensemble systems over high dimensional parameterization spaces? We provide a negative answer: Any real-analytic linear ensemble system is not $\mathrm{L}^p$-controllable, for $2\le p \le \infty$, if the dimension of its parameterization space is greater than one.
Moisture-dependent physical properties of arecanut kernels

The physical properties of arecanut kernels were determined in the moisture range from 88.91 to 10.51% d.b. The kernel shape can be treated as ovate. As the moisture content decreased from 88.91 to 10.51% d.b. , the dimensions of the major, medium and minor axes decreased by 10.45, 10.49 and 22.84%, respectively. The mean values of roundness and sphericity of the kernel decreased by 5.69 and 8.13%, respectively, as the moisture content decreased from 88.91 to 10.51% d.b. One hundred kernel weights of arecanut were 1.159 and 0.594 kg at moisture contents of 88.91 and 10.51% d.b. , respectively. The bulk and true densities increased linearly whereas the porosity and angle of repose decreased linearly as the moisture content decreased from 88.91 to 10.51% d.b. The static and kinetic coefficients of friction were maximum on mild steel surface and the values on mild steel, galvanized iron, aluminium, stainless steel and hard board decreased curvilinearly from 88.91 to 30.24% d.b. and afterwards the values increased.
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering

Combinations of neural ODEs with recurrent neural networks (RNN), like GRUODE-Bayes or ODE-RNN are well suited to model irregularly-sampled time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregularly-sampled time series data originates from a continuous stochastic processes, the optimal on-line prediction is the conditional expectation given the currently available information. We introduce the Neural Jump ODE (NJ-ODE) that provides a data-driven approach to learn, continuously in time, the conditional expectation of a stochastic process. Our approach models the conditional expectation between two observations with a neural ODE and jumps whenever a new observation is made. We define a novel training framework, which allows us to prove theoretical convergence guarantees for the first time. In particular, we demonstrate the predictive capabilities of our model by proving that, under some regularity assumptions, the output process converges to the conditional expectation process. We provide experiments showing that the theoretical results also hold empirically. Moreover, we experimentally show that our model outperforms one state of the art model in more complex learning tasks and give comparisons on a real-world dataset.
Dirty Pixels: Towards End-to-end Image Processing and Perception

Real-world, imaging systems acquire measurements that are degraded by noise, optical aberrations, and other imperfections that make image processing for human viewing and higher-level perception tasks challenging. Conventional cameras address this problem by compartmentalizing imaging from high-level task processing. As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping, and compression. This pipeline is optimized to obtain a visually pleasing image. High-level processing, however, involves steps such as feature extraction, classification, tracking, and fusion. While this silo-ed design approach allows for efficient development, it also dictates compartmentalized performance metrics without knowledge of the higher-level task of the camera system. For example, today’s demosaicking and denoising algorithms are designed using perceptual image quality metrics but not with domain-specific tasks such as object detection in mind. We propose an end-to-end differentiable architecture that jointly performs demosaicking, denoising, deblurring, tone-mapping, and classification (see Figure 1). The architecture does not require any intermediate losses based on perceived image quality and learns processing pipelines whose outputs differ from those of existing ISPs optimized for perceptual quality, preserving fine detail at the cost of increased noise and artifacts. We show that state-of-the-art ISPs discard information that is essential in corner cases, such as extremely low-light conditions, where conventional imaging and perception stacks fail. We demonstrate on captured and simulated data that our model substantially improves perception in low light and other challenging conditions, which is imperative for real-world applications such as autonomous driving, robotics, and surveillance. Finally, we found that the proposed model also achieves state-of-the-art accuracy when optimized for image reconstruction in low-light conditions, validating the architecture itself as a potentially useful drop-in network for reconstruction and analysis tasks beyond the applications demonstrated in this work. Our proposed models, datasets, and calibration data are available at https://github.com/princeton-computational-imaging/DirtyPixels.
Continuous manipulation of mental representations is compromised in cerebellar degeneration

Here we test the hypothesis that the cerebellum aids in the dynamic transformation of mental representations. We report a series of neuropsychological experiments comparing the performance of individuals with cerebellar degeneration (CD) on cognitive tasks that either entail continuous, movement-like mental operations or more discrete mental operations. In visual cognition, individuals with CD exhibited an impaired rate of mental rotation, an operation hypothesized to require the continuous manipulation of a visual representation. In contrast, individuals with CD showed a normal processing rate when scanning items in visual working memory, an operation hypothesized to require the maintenance and retrieval of representations. In mathematical cognition, individuals with CD were impaired at single-digit addition, an operation hypothesized to require iterative manipulations along a mental number-line; this group was not impaired on arithmetic tasks requiring memory retrieval (e.g., single-digit multiplication). These results, obtained in tasks from two disparate domains, suggest one potential constraint on the contribution of the cerebellum to cognitive tasks. This constraint may parallel the cerebellum’s role in motor control, involving coordinated dynamic transformations in a mental workspace.
Evaluation of Lung Contusion, Associated Injuries, and Outcome in a Major Trauma Center in Shiraz, Southern Iran

Objective Blunt chest trauma as one of the most common injuries in trauma cases can cause significant morbidity and mortality. The purpose of this study was to determine the clinical course of traumatic injuries with an initial diagnosis of a pulmonary contusion in patients. Method In this retrospective study, we evaluated the demographic and clinical features of patients who were referred to a major trauma center in southern Iran. In our study, patients were enrolled with the diagnosis of pulmonary contusion. All included patients were above 16 years of age, with an initial CT scan in favor of pulmonary contusion, while patients not being hospitalized for more than 48 hours were excluded from the study. Results Among the 434 patients included in our study, 366 (84%) were male and the mean age was 41.17 (SD = 17.89). Among them, the majority (80.4%) had right side lung contusion and 47 patients (10.8%) had right rib fracture. The most common injuries were head and neck injury (56.9%) and limbs (30%). In 25% of cases, pulmonary contusion was associated with pneumothorax and 15.8% with hemothorax. Also, 49.6% of patients were transferred to the ICU. The mortality rate in our study was 15.2% (n = 66). Conclusion Although recent advances in pulmonary care and ventilator management have been achieved, there is still considerable morbidity and mortality associated with this condition. Therefore, there is a need to provide a national guideline based on native patient information for better management.