Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of foliar boron application on boron, chlorophyll, phenol, sugars and hormones concentration of olive (Olea europaea L.) buds, leaves, and fruits

ABSTRACT The objective of this study is to investigate the effect of different time and rates of boron (B) foliar application on olive (Olea europaea L.) tree's tissue boron concentration, total phenol, chlorophyll, total soluble sugars, and endogenous hormones. A field experiment was conducted during two successive seasons 2010/2011 and 2011/2012 using 20 years old olive trees cv. Frantoio. The trees are grown in sandy soil planted at 5 × 5 m apart under drip irrigation system at the Nuclear Research Center Experimental Farm, Inshas, El-Qaliubiya Governorate, Egypt. Boron was applied foliarly as boric acid at the following rates (0.0, 100, 200, 300, 400, 500 mg L−1) at flower initiation and after 1 and 2 months from flower initiation. Results showed that boron was significantly effective in increasing leaf, bud, and fruit boron concentration. Total phenol concentration in leaves and buds were significantly highest in the control treatment, significantly decreased as the boron application rate increased. Total chlorophyll, chlorophyll a and b, and total soluble sugars significantly increased as the boron application rate increased and the highest increase was achieved at 200 mg L−1 boron concentration rate. Leaf and bud endogenous indole acetic acid (IAA) and abscisic acid (ABA) were highest in the control treatment; they decreased as the boron application rate increased. However, gibberellic acid (GA3) increased in response to boron treatments compared with the control. The maximum increase was observed at 200 mg L−1 boron rate. We concluded that boron is mobile in olive tree as reproductive organs accumulated more boron than vegetative organs. There is evidence that boron is involved in reduction of phenols, increase in fruit set, and in sugar transport. A balance in endogenous hormones (IAA, GA, ABA) concentrations in olive tree has induced the maximum fruit set and yield.
Neutrino Echos following Black Hole Formation in Core-collapse Supernovae

During a failed core-collapse supernova, the protoneutron star eventually collapses under its own gravitational field and forms a black hole. This collapse happens quickly, on the dynamical time of the protoneutron star, ≲0.5 ms. During this collapse, barring any excessive rotation, the entire protoneutron star is accreted into the newly formed black hole. The main source of neutrinos is now removed and the signal abruptly shuts off over this formation timescale. However, while the source of neutrinos is turned off, the arrival times at an Earth-based detector will depend on the neutrino path. We show here that a modest amount of neutrinos, emitted just prior to the black hole forming, scatter on the infalling material into our line of sight and arrive after the formation of the black hole, up to 15 ms in our model. This neutrino echo, which we characterize with Monte Carlo simulations and analytic models, has a significantly higher average energy (upwards of ∼50 MeV) compared to the main neutrino signal, and for the canonical failed supernova explored here, is likely detectable in  (10 kT) supernova neutrino detectors for Galactic failed supernovae. The presence of this signal is important to consider if using black hole formation as a time post for triangulation or the post black hole timing profile for neutrino mass measurements. On its own, it can also be used to characterize or constrain the structure and nature of the accretion flow.
Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b

Ultrahot Jupiters represent an exciting avenue for testing extreme physics and observing atmospheric circulation regimes not found in our solar system. Their high temperatures result in thermally ionized particles embedded in atmospheric winds interacting with the planet’s interior magnetic field by generating current and experiencing bulk Lorentz force drag. Previous treatments of magnetic drag in 3D general circulation models (GCMs) of ultrahot Jupiters have mostly been uniform drag timescales applied evenly throughout the planet, which neglects the strong spatial dependence of these magnetic effects. In this work, we apply our locally calculated active magnetic drag treatment in a GCM of the planet WASP-76b. We find the effects of this treatment to be most pronounced in the planet’s upper atmosphere, where strong differences between the day and night side circulation are present. These circulation effects alter the resulting phase curves by reducing the hot spot offset and increasing the day–night flux contrast. We compare our models to Spitzer phase curves, which imply a magnetic field of at least 3 G for the planet. We additionally contrast our results to uniform drag timescale models. This work highlights the need for more careful treatment of magnetic effects in atmospheric models of hot gas giants.
The Role of Lookahead and Approximate Policy Evaluation in Policy Iteration with Linear Value Function Approximation

When the sizes of the state and action spaces are large, solving MDPs can be computationally prohibitive even if the probability transition matrix is known. So in practice, a number of techniques are used to approximately solve the dynamic programming problem, including lookahead, approximate policy evaluation using an m -step return, and function approximation. In a recent paper, (Efroni et al. 2019) studied the impact of lookahead on the convergence rate of approximate dynamic programming. In this paper, we show that these convergence results change dramatically when function approximation is used in conjunction with lookout and approximate policy evaluation using an m -step return. Speciﬁcally, we show that when linear function approximation is used to represent the value function, a certain minimum amount of lookahead and multi-step return is needed for the algorithm to even converge. And when this condition is met, we characterize the ﬁnite-time performance of policies obtained using such approximate policy iteration. Our results are presented for two different procedures to compute the function approximation: linear least-squares regression and gradient descent.
3D Hand Pose and Shape Estimation from RGB Images for Improved Keypoint-Based Hand-Gesture Recognition

Estimating the 3D hand pose from a 2D image is a well-studied problem and a requirement for several real-life applications such as virtual reality, augmented reality, and hand-gesture recognition. Currently, good estimations can be computed starting from single RGB images, especially when forcing the system to also consider, through a multi-task learning approach, the hand shape when the pose is determined. However, when addressing the aforementioned real-life tasks, performances can drop considerably depending on the hand representation, thus suggesting that stable descriptions are required to achieve satisfactory results. As a consequence, in this paper we present a keypoint-based end-to-end framework for the 3D hand and pose estimation, and successfully apply it to the hand-gesture recognition task as a study case. Speciﬁcally, after a pre-processing step where the images are normalized, the proposed pipeline comprises a multi-task semantic feature extractor generating 2D heatmaps and hand silhouettes from RGB images; a viewpoint encoder predicting hand and camera view parameters; a stable hand estimator producing the 3D hand pose and shape; and a loss function designed to jointly guide all of the components during the learning phase. To assess the proposed framework, tests were performed on a 3D pose and shape estimation benchmark dataset, obtaining state-of-the-art performances. What is more, the devised system was also evaluated on 2 hand-gesture recognition benchmark datasets, where the framework signiﬁcantly outperforms other keypoint-based approaches; indicating that the presented method is an e ﬀ ective solution able to generate stable 3D estimates for the hand pose and shape.
Multi-Task Triplet Loss for Named Entity Recognition using Supplementary Text

Retail item data contains many different forms of text like the title of an item, the description of an item, item name and reviews. It is of interest to identify the item name in the other forms of text using a named entity tagger. However, the title of an item and its description are syntactically different (but semantically similar) in that the title is not necessarily a well formed sentence while the description is made up of well formed sentences. In this work, we use a triplet loss to contrast the embeddings of the item title with the description to establish a proof of concept. We find that using the triplet loss in a multi-task NER algorithm improves both the precision and recall by a small percentage. While the improvement is small, we think it is a step in the right direction of using various forms of text in a multi-task algorithm. In addition to precision and recall, the multi task triplet loss method is also found to significantly improve the exact match accuracy i.e. the accuracy of tagging the entire set of tokens in the text with correct tags.
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme

Voice conversion is a common speech synthesis task which can be solved in different ways depending on a particular real-world scenario. The most challenging one often referred to as one-shot many-to-many voice conversion consists in copying the target voice from only one reference utterance in the most general case when both source and target speakers do not belong to the training dataset. We present a scalable high-quality solution based on diffusion probabilistic modeling and demonstrate its superior quality compared to state-of-the-art one-shot voice conversion approaches. Moreover, focusing on real-time applications, we investigate general principles which can make diffusion models faster while keeping synthesis quality at a high level. As a result, we develop a novel Stochastic Differential Equations solver suitable for various diffusion model types and generative tasks as shown through empirical studies and justify it by theoretical analysis.
Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking

Recent progress in task-oriented neural dialogue systems is largely focused on a handful of languages, as annotation of training data is tedious and expensive. Machine translation has been used to make systems multilingual, but this can introduce a pipeline of errors. Another promising solution is using cross-lingual transfer learning through pretrained multilingual models. Existing methods train multilingual models with additional code-mixed task data or refine the cross-lingual representations through parallel ontologies. In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks. Specifically, we use parallel and conversational movie subtitles datasets to design cross-lingual intermediate tasks suitable for downstream dialogue tasks. We use only 200K lines of parallel data for intermediate fine-tuning which is already available for 1782 language pairs. We test our approach on the cross-lingual dialogue state tracking task for the parallel MultiWoZ (English -> Chinese, Chinese -> English) and Multilingual WoZ (English -> German, English -> Italian) datasets. We achieve impressive improvements (> 20% on joint goal accuracy) on the parallel MultiWoZ dataset and the Multilingual WoZ dataset over the vanilla baseline with only 10% of the target language task data and zero-shot setup respectively.
Designing the Quantum Channels Induced by Diagonal Gates

The challenge of quantum computing is to combine error resilience with universal computation. Diagonal gates such as the transversal T gate play an important role in implementing a universal set of quantum operations. This paper introduces a framework that describes the process of preparing a code state, applying a diagonal physical gate, measuring a code syndrome, and applying a Pauli correction that may depend on the measured syndrome (the average logical channel induced by an arbitrary diagonal gate). It focuses on CSS codes, and describes the interaction of code states and physical gates in terms of generator coefficients determined by the induced logical operator. The interaction of code states and diagonal gates depends very strongly on the signs of Z-stabilizers in the CSS code, and the proposed generator coefficient framework explicitly includes this degree of freedom. The paper derives necessary and sufficient conditions for an arbitrary diagonal gate to preserve the code space of a stabilizer code, and provides an explicit expression of the induced logical operator. When the diagonal gate is a quadratic form diagonal gate (introduced by Rengaswamy et al.), the conditions can be expressed in terms of divisibility of weights in the two classical codes that determine the CSS code. These codes find application in magic state distillation and elsewhere. When all the signs are positive, the paper characterizes all possible CSS codes, invariant under transversal Z-rotation through π/2l, that are constructed from classical Reed-Muller codes by deriving the necessary and sufficient constraints on l. The generator coefficient framework extends to arbitrary stabilizer codes but there is nothing to be gained by considering the more general class of non-degenerate stabilizer codes.
Mass Transfer and Stellar Evolution of the White Dwarfs in AM CVn Binaries

We calculate the stellar evolution of both white dwarfs (WDs) in AM CVn binaries with orbital periods of P orb ≈ 5–70 minutes. We focus on the cases where the donor starts as a M He < 0.2M ⊙ helium WD and the accretor is a M WD > 0.6 M ⊙ WD. Using Modules for Experiments in Stellar Astrophysics, we simultaneously evolve both WDs assuming conservative mass transfer and angular momentum loss from gravitational radiation. This self-consistent evolution yields important feedback of the properties of the donor on the mass-transfer rate, Ṁ , as well as the thermal evolution of the accreting WD. Consistent with earlier work, we find that the high Ṁ 's at early times forces an adiabatic evolution of the donor for P orb < 30 minutes so that its mass–radius relation depends primarily on its initial entropy. As the donor reaches M He ≈ 0.02–0.03 M ⊙ at P orb ≃ 30 minutes, it becomes fully convective and could lose entropy and expand much less than expected under further mass loss. However, we show that the lack of reliable opacities for the donor’s surface inhibit a secure prediction for this possible cooling. Our calculations capture the core heating that occurs during the first ≈107 yr of accretion and continue the evolution into the phase of WD cooling that follows. When compared to existing data for accreting WDs, as seen by Cheng and collaborators for isolated WDs, we also find that the accreting WDs are not as cool as we would expect given the amount of time they have had to cool.
A Deep Dive into the Accuracy of IP Geolocation Databases and its Impact on Online Advertising

The quest for every time more personalized Internet experience relies on the enriched contextual information about each user. Online advertising also follows this approach. Among the context information that advertising stakeholders leverage, location information is certainly one of them. However, when this information is not directly available from the end users, advertising stakeholders infer it using geolocation databases, matching IP addresses to a position on earth. The accuracy of this approach has often been questioned in the past: however, the reality check on an advertising stakeholder shows that this technique accounts for a large fraction of the served advertisements. In this paper, we revisit the work in the field, that is mostly from almost one decade ago, through the lenses of big data. More specifically, we, i) benchmark two commercial Internet geolocation databases, evaluate the quality of their information using a ground-truth database of user positions containing over 2 billion samples, ii) analyze the internals of these databases, devising a theoretical upper bound for the quality of the Internet geolocation approach, and iii) we run an empirical study that unveils the monetary impact of this technology by considering the costs associated with a real-world ad impressions dataset.
Dynamical Symmetry and the Thermofield State at Large N

We discuss thermofield double QFT at real time, in the large N limit. First, we establish a (dynamical) symmetry, which we argue holds in general for the real-time portion of the Schwinger–Kelydish contour. At large N, this symmetry is seen to generate a one-parameter degeneracy of stationary collective solutions. The construction is explicitly worked out on an example of the O(N) vector QFT. As a nontrivial application, we describe the construction of the corresponding (large N) thermofield double state in real-time collective formalism.
The Fragility of Optimized Bandit Algorithms

Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that designs that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of arm plays, at a rate governed by the Lai-Robbins lower bound. In this paper, we show that when one uses such optimized designs, the regret distribution of the associated algorithms necessarily has a very heavy tail, specifically, that of a truncated Cauchy distribution. Furthermore, for $p>1$, the $p$'th moment of the regret distribution grows much faster than poly-logarithmically, in particular as a power of the total number of arm plays. We show that optimized UCB bandit designs are also fragile in an additional sense, namely when the problem is even slightly mis-specified, the regret can grow much faster than the conventional theory suggests. Our arguments are based on standard change-of-measure ideas, and indicate that the most likely way that regret becomes larger than expected is when the optimal arm returns below-average rewards in the first few arm plays, thereby causing the algorithm to believe that the arm is sub-optimal. To alleviate the fragility issues exposed, we show that UCB algorithms can be modified so as to ensure a desired degree of robustness to mis-specification. In doing so, we also provide a sharp trade-off between the amount of UCB exploration and the tail exponent of the resulting regret distribution.
Fairness guarantee in multi-class classification

Algorithmic Fairness is an established area of machine learning, willing to reduce the influence of hidden bias in the data. Yet, despite its wide range of applications, very few works consider the multi-class classification setting from the fairness perspective. We focus on this question and extend the definition of approximate fairness in the case of Demographic Parity to multi-class classification. We specify the corresponding expressions of the optimal fair classifiers. This suggests a plug-in data-driven procedure, for which we establish theoretical guarantees. The enhanced estimator is proved to mimic the behavior of the optimal rule both in terms of fairness and risk. Notably, fairness guarantees are distribution-free. The approach is evaluated on both synthetic and real datasets and reveals very effective in decision making with a preset level of unfairness. In addition, our method is competitive (if not better) with the state-of-the-art in binary and multi-class tasks.
Measurement of single-cell elasticity by nanodiamond-sensing of non-local deformation

Nano-indentation based on, e.g., atomic force microscopy (AFM), can measure single cell elasticity with high spatial resolution and sensitivity, but relating the data to cell mechanical properties depends on modeling that requires knowledge about the local contact between the indentation tip and the material, which is unclear in most cases. Here we use the orientation sensing by nitrogen-vacancy centers in nanodiamonds to chart the non-local deformation of fixed HeLa cells induced by AFM indentation, providing data for studying cell mechanics without requiring detailed knowledge about the local contact. The competition between the elasticity and capillarity on the cells is observed. We show that the apparent elastic moduli of the cells would have been overestimated if the capillarity is not considered (as in most previous studies using local depth-loading data). We also find reduction of both elastic moduli and surface tensions due to depolymerization of the actin cytoskeleton structure. This work demonstrates that, under shallow indentation, the nanodiamond sensing of non-local deformation with nanometer precision is particularly suitable for studying mechanics of cells.
Sign-RIP: A Robust Restricted Isometry Property for Low-rank Matrix Recovery

Restricted isometry property (RIP), essentially stating that the linear measurements are approximately norm-preserving, plays a crucial role in studying low-rank matrix recovery problem. However, RIP fails in the robust setting, when a subset of the measurements are grossly corrupted with noise. In this work, we propose a robust restricted isometry property, called Sign-RIP, and show its broad applications in robust low-rank matrix recovery. In particular, we show that Sign-RIP can guarantee the uniform convergence of the subdifferentials of the robust matrix recovery with nonsmooth loss function, even at the presence of arbitrarily dense and arbitrarily large outliers. Based on Sign-RIP, we characterize the location of the critical points in the robust rank-1 matrix recovery, and prove that they are either close to the true solution, or have small norm. Moreover, in the over-parameterized regime, where the rank of the true solution is over-estimated, we show that subgradient method converges to the true solution at a (nearly) dimension-free rate. Finally, we show that sign-RIP enjoys almost the same complexity as its classical counterparts, but provides significantly better robustness against noise.
Quantitative phase-contrast computed tomography for biomedical applications

X-ray phase-contrast computed tomography (CT) is an emerging imaging technique that provides enhanced soft-tissue contrast and complementary information compared to conventional attenuation-based X-ray imaging. In recent years, phase-contrast CT became increasingly popular for the three-dimensional investigation of biological samples at large-scale synchrotron radiation facilities. In this thesis, phase-contrast CT utilizing a grating interferometer in commonplace research laboratories is investigated and further developed.
A 4-dimensional light bulb theorem for disks

We give a 4-dimensional light bulb theorem for properly embedded disks, generalizing recent work of Gabai and Kosanovic-Teichner in certain contexts, and extending the 4-dimensional light bulb theorem for 2-spheres due to Gabai and Schneiderman-Teichner. In particular, we provide conditions under which a pair of disks properly embedded in a 4-manifold X (with boundary) are smoothly isotopic rel boundary, if these disks (i) are homotopic rel boundary and (ii) share a common dual in the interior of X. We also provide a new geometric interpretation of the Dax invariant, to aid in its computation.