Miracle-dz/UniFAR-FTUs huggingface dataset:
Variations in Yield along a Phenological Gradient in Bread Wheat (Triticum aestivum L.)

Future food security will depend on crop adaptations to changing environment. Such adaptations can be made through genetic divergence in plants which is prerequisite for identifying superior plant traits and varieties. It is already known that plant varieties with supreme characteristics can play important roles in the enhancement of yield and production. Here, we practically assessed 36 wheat breeding lines (35 lines with Janbaz as check cultivar) across two planting dates (mid- November and mid-December) to determine the genetic differences and inheritance contributing production traits. For this purpose, heritability (h2), genotype by environment interaction (GEI) and correlation coefficients were studied. Data were recorded on days to heading (DH), flag leaf area (FLA), plant height (PH), days to maturity (DM), spike length (SL), grain yield (GY) and harvest index (HI). Pooled ANOVA showed significant differences among genotypes for all traits studied while GEI interactions were significant for some traits finding a significant gap for genotypes. Genotype DN-84 took less number of DH while, V-09136 had minimum values for DH and DM. Interestingly, maximum FLA, PH and SL were observed for a single genotype NR- 408. High GY and HI were recorded for genotype V-07096 and WRIS-12, respectively. Heritability (h2) estimates for DH, PH, DM, SL, ranged between 60 and 74%, illustrating strong flow of genetic makeup among genotypes, while 37 to 54% for FLA, GY and HI. Correlation coefficients were significant for various traits indicating robust association among genotypes. PCA analysis showed that dates clustered the variables differently but in a similar pattern. Based on our findings, we suggest genotypes V-09136, PR-103, NR-400, V-08BT016 and V-07096 for further investigation. Further, it is suggested to find out optimum time of sowing for each variety whereas, late sowing affect crop production.
A Generalized Model to Estimate the Elastic Stiffness Tensor of Mudrocks Based on the Full Strain Tensor APPROVED BY SUPERVISING COMMITTEE:

I develop a three-step framework to model the anisotropic elastic properties of a mechanically compacted mudrock based on the full strain tensor. I model the microstructure as an effective medium representative of locally aligned domains of clay grains and fluid-filled porosity with isolated quartz. Then I predict the orientation of these building blocks due to the application of any strain field. Finally, the previous two steps are combined to determine an effective medium model for the entire mudrock that predicts the elastic stiffness matrix. I focus on the relationship of deformation to porosity reduction and grain alignment in mudrocks. My results show that the application of axial loading leads to the development of elastic anisotropy with stiffnesses increasing more rapidly in the direction perpendicular to loading. These stiffness predictions closely match experimental data on a mudrock specimen from Eugene Island – Gulf of Mexico. I further apply my three-step framework to predict elastic stiffnesses in a synthetic salt basin based on the full strain tensor predicted by an evolutionary poromechanical model. This coupling allows us to predict elastic stiffnesses and anisotropy due to sediment deposition and non-uniaxial salt loading. Accurate estimation of elastic stiffnesses for
Supplementary material for the paper : “ What does LIME really see in images ?

reduces to least squares, with β̂n given in closed-form by β̂n “ pZJWZq ́1ZJWy , with Z P t0, 1unˆd the matrix whose lines are given by the zis and W the diagonal matrix such that Wi,i “ πi. Setting Σ̂n :“ 1 nZ JWZ and Γ̂n :“ 1 nZ JWy, the study of β̂n can be split in two parts: the examination of Σ̂n (Section 1), and then that of Γ̂n (Section 2). We put everything together in Section 3, proving the concentration of β̂n and providing the expression of β . All technical results are collected in Section 4. Finally, additional qualitative results are presented in Section 5.
Factors Related with Underperformance in Reading Proficiency, the Case of the Programme for International Student Assessment 2018

Almost 217 million secondary school students (60% of the world’s adolescents) do not reach minimum levels in reading proficiency at the end of secondary school, according to objective 4.1 of the UN’s Sustainable Development Goals. Therefore, the early and efficient identification of this disadvantage and implementation of remedial strategies is critical for economies. In 2018, the Programme for International Student Assessment (PISA) assessed the reading skills of 15-year-old students in 80 countries and economies. This work introduces a methodology that uses PISA’s data to build logistic regression models to identify the main factors contributing to students’ underperforming reading skills. Results showed that socioeconomic status (SES), metacognition strategies, Information and Communication Technology (ICT) skills, and student–teacher relationships are the most important contributors to low reading abilities.
Generalized Crewther relation and a novel demonstration of the scheme independence of commensurate scale relations up to all orders

Xu-Dong Huang, Xing-Gang Wu, Qing Yu, Xu-Chang Zheng, Jun Zeng, and Jian-Ming Shen 1 Department of Physics, Chongqing University, Chongqing 401331, People’s Republic of China 2 Chongqing Key Laboratory for Strongly Coupled Physics, Chongqing 401331, People’s Republic of China 3 INPAC, Key Laboratory for Particle Astrophysics and Cosmology (MOE), Shanghai Key Laboratory for Particle Physics and Cosmology, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China and 4 School of Physics and Electronics, Hunan University, Changsha 410082, People’s Republic of China (Dated: August 2, 2021)
Anharmonicity-induced excited-state quantum phase transition in the symmetric phase of the two-dimensional limit of the vibron model

In most cases, excited state quantum phase transitions can be associated with the existence of critical points (local extrema or saddle points) in a system’s classical limit energy functional. However, an excited-state quantum phase transition might also stem from the lowering of the asymptotic energy of the corresponding energy functional. One such example takes place in the 2D vibron model, once an anharmonic term in the form of a quadratic bosonic number operator is added to the Hamiltonian. The study of this case in the broken-symmetry phase was presented in Phys. Rev. A. 81 050101 (2010). In the present work, we delve further into the nature of this excited-state quantum phase transition and we characterize it in the, previously overlooked, symmetric phase of the model. Quantum phase transitions (QPTs) are zero-temperature phase transitions that occur when a quantum system ground state undergoes an abrupt variation when a Hamiltonian interaction parameter, named control parameter, goes through a critical value. Such transitions have been observed in numerous quantum systems in different fields: quantum optics, condensed-matter, atomic, nuclear, and molecular systems [1]. In the case of algebraic models, based on Lie algebras and useful in studies of molecular [2, 3], nuclear [4], and hadronic structure [5], the different phases can be mapped to the system dynamical symmetries. A general classification of ground state QPTs in algebraic models can be found in [6] and for an extended treatment see the reviews [7–9] and references therein. In the present work we deal with the two-dimensional limit of the vibron model (2DVM), introduced by Iachello and Oss for the treatment of bending molecular vibrations [10] as collective bosonic excitations (vibrons). The dynamical algebra of the systems is u(3), with two possible dynamical symmetries, associated with the u(2) and so(3) subalgebras and known as the cylindrical and anharmonic oscillator dynamical symmetries [11]. The two subalgebra chains end up in the system symmetry algebra, so(2), which denotes the conservation of the vibrational angular momentum in the molecular case, which lies perpendicular to the plane of the bending motion ∗ Miguel Carvajal: miguel.carvajal@dfa.uhu. es
Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms

We use the resonant dipole-dipole interaction between Rydberg atoms and a periodic external microwave field to engineer XXZ spin Hamiltonians with tunable anisotropies. The atoms are placed in 1D and 2D arrays of optical tweezers, allowing us to study iconic situations in spin physics, such as the implementation of the Heisenberg model in square arrays, and the study of spin transport in 1D. We first benchmark the Hamiltonian engineering for two atoms, and then demonstrate the freezing of the magnetization on an initially magnetized 2D array. Finally, we explore the dynamics of 1D domain wall systems with both periodic and open boundary conditions. We systematically compare our data with numerical simulations and assess the residual limitations of the technique as well as routes for improvements. The geometrical versatility of the platform, combined with the flexibility of the simulated Hamiltonians, opens exciting prospects in the field of quantum simulation, quantum information processing and quantum sensing.
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback

We introduce ChrEnTranslate, an online machine translation demonstration system for translation between English and an endangered language Cherokee. It supports both statistical and neural translation models as well as provides quality estimation to inform users of reliability, two user feedback interfaces for experts and common users respectively, example inputs to collect human translations for monolingual data, word alignment visualization, and relevant terms from the Cherokee English dictionary. The quantitative evaluation demonstrates that our backbone translation models achieve state-of-the-art translation performance and our quality estimation well correlates with both BLEU and human judgment. By analyzing 216 pieces of expert feedback, we find that NMT is preferable because it copies less than SMT, and, in general, current models can translate fragments of the source sentence but make major mistakes. When we add these 216 expert-corrected parallel texts into the training set and retrain models, equal or slightly better performance is observed, which demonstrates indicates the potential of human-in-the-loop learning.
Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning

We use Reinforcement Meta Learning to optimize an adaptive guidance system suitable for the approach phase of a gliding hypersonic vehicle. Adaptability is achieved by optimizing over a range of o -nominal flight conditions including perturbation of aerodynamic coe cient parameters, actuator failure scenarios, and sensor noise. The system maps observations directly to commanded bank angle and angle of attack rates. These observations include a velocity field tracking error formulated using parallel navigation, but adapted to work over long trajectories where the Earth’s curvature must be taken into account. Minimizing the tracking error keeps the curved space line of sight to the target location aligned with the vehicle’s velocity vector. The optimized guidance system will then induce trajectories that bring the vehicle to the target location with a high degree of accuracy at the designated terminal speed, while satisfying heating rate, load, and dynamic pressure constraints. We demonstrate the adaptability of the guidance system by testing over flight conditions that were not experienced during optimization. The guidance system’s performance is then compared to that of a linear quadratic regulator tracking an optimal trajectory.
Practical Attacks on Voice Spoofing Countermeasures

Voice authentication has become an integral part in security-critical operations, such as bank transactions and call center conversations. The vulnerability of automatic speaker verification systems (ASVs) to spoofing attacks instigated the development of countermeasures (CMs), whose task is to tell apart bonafide and spoofed speech. Together, ASVs and CMs form today's voice authentication platforms, advertised as an impregnable access control mechanism. We develop the first practical attack on CMs, and show how a malicious actor may efficiently craft audio samples to bypass voice authentication in its strictest form. Previous works have primarily focused on non-proactive attacks or adversarial strategies against ASVs that do not produce speech in the victim's voice. The repercussions of our attacks are far more severe, as the samples we generate sound like the victim, eliminating any chance of plausible deniability. Moreover, the few existing adversarial attacks against CMs mistakenly optimize spoofed speech in the feature space and do not take into account the existence of ASVs, resulting in inferior synthetic audio that fails in realistic settings. We eliminate these obstacles through our key technical contribution: a novel joint loss function that enables mounting advanced adversarial attacks against combined ASV/CM deployments directly in the time domain. Our adversarials achieve concerning black-box success rates against state-of-the-art authentication platforms (up to 93.57\%). Finally, we perform the first targeted, over-telephony-network attack on CMs, bypassing several challenges and enabling various potential threats, given the increased use of voice biometrics in call centers. Our results call into question the security of modern voice authentication systems in light of the real threat of attackers bypassing these measures to gain access to users' most valuable resources.
Single image deep defocus estimation and its applications

Depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the level of blurriness due to defocus is related to the distance between the object and the focal plane. Based on this principle and the widely used assumption that Gaussian blur is a good model for defocus blur, we formulate the problem of estimating the spatially varying defocus blurriness as a Gaussian blur classification problem. We solved the problem by training a deep neural network to classify image patches into one of the 20 levels of blurriness. We have created a dataset of more than 500000 image patches of size $32\times32$ which are used to train and test several well-known network models. We find that MobileNetV2 is suitable for this application due to its low memory requirement and high accuracy. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel. We compare the proposed method with state-of-the-art techniques and we demonstrate its successful applications in adaptive image enhancement, defocus magnification, and multi-focus image fusion.
New trends in indoor positioning based on WiFi and machine learning: A systematic review

Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.
Single-Leader-Multiple-Followers Stackelberg Security Game With Hypergame Framework

In this paper, we employ a hypergame framework to analyze the single-leader-multiple-followers (SLMF) Stackelberg security game with two typical misinformed situations: misperception and deception. We provide a stability criterion with the help of hyper Nash equilibrium (HNE) to investigate both strategic stability and cognitive stability of equilibria in SLMF games with misinformation. In fact, we find mild stable conditions such that the equilibria with misperception and deception can become HNE. Moreover, we discuss the robustness of the equilibria to reveal whether players have the ability to keep their profits under the influence of some misinformation.
Lunaport: Math, Mechanics & Transport

Issues for transport facilities on the lunar surface related to science, engineering, architecture, and human-factors are discussed. Logistic decisions made in the next decade may be crucial to financial success. In addition to outlining some of the problems and their relations with math and computation, the paper provides useful resources for decision-makers, scientists, and engineers. Key Phrases: Large-scale transport facilities in low-gravity, failsafes, solar power, ergonomics, facility planning, material science, non-rocket propulsion, efficient terminal layout, mathematics, heuristics, neural networks.
Importance of the Ion-Pair Lifetime in Polymer Electrolytes

Ion pairing is commonly considered as a culprit for the reduced ionic conductivity in polymer electrolyte systems. However, this simple thermodynamic picture should not be taken literally, as ion pairing is a dynamical phenomenon. Here we construct model poly(ethylene oxide)–bis(trifluoromethane)sulfonimide lithium salt systems with different degrees of ion pairing by tuning the solvent polarity and examine the relation between the cation–anion distinct conductivity σ+–d and the lifetime of ion pairs τ+– using molecular dynamics simulations. It is found that there exist two distinct regimes where σ+–d scales with 1/τ+– and τ+–, respectively, and the latter is a signature of longer-lived ion pairs that contribute negatively to the total ionic conductivity. This suggests that ion pairs are kinetically different depending on the solvent polarity, which renders the ion-pair lifetime highly important when discussing its effect on ion transport in polymer electrolyte systems.
SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection

Freespace detection is a fundamental component of autonomous driving perception. Recently, deep convolutional neural networks (DCNNs) have achieved impressive performance for this task. In particular, SNE-RoadSeg, our previously proposed method based on a surface normal estimator (SNE) and a data-fusion DCNN (RoadSeg), has achieved impressive performance in freespace detection. However, SNE-RoadSeg is computationally intensive, and it is difficult to execute in real time. To address this problem, we introduce SNE-RoadSeg+, an upgraded version of SNE-RoadSeg. SNE-RoadSeg+ consists of 1) SNE+, a module for more accurate surface normal estimation, and 2) RoadSeg+, a data-fusion DCNN that can greatly minimize the trade-off between accuracy and efficiency with the use of deep supervision. Extensive experimental results have demonstrated the effectiveness of our SNE+ for surface normal estimation and the superior performance of our SNE-RoadSeg+ over all other freespace detection approaches. Specifically, our SNE-RoadSeg+ runs in real time, and meanwhile, achieves the state-of-the-art performance on the KITTI road benchmark. Our project page is at https://www.sne-roadseg.site/sne-roadseg-plus.
Minimal hypersurfaces with cylindrical tangent cones

First we construct minimal hypersurfaces M ⊂ R in a neighborhood of the origin, with an isolated singularity but cylindrical tangent cone C × R, for any strictly minimizing strictly stable cone C in R. We show that many of these hypersurfaces are area minimizing. Next, we prove a strong unique continuation result for minimal hypersurfaces V with such a cylindrical tangent cone, stating that if the blowups of V centered at the origin approach C × R at infinite order, then V = C × R in a neighborhood of the origin. Using this we show that for quadratic cones C = C(S × S), in dimensions n > 8, all O(p+ 1)× O(q + 1)-invariant minimal hypersurfaces with tangent cone C×R at the origin are graphs over one of the surfaces that we constructed. In particular such an invariant minimal hypersurface is either equal to C ×R or has an isolated singularity at the origin.
Feasibility of GNSS-free Localization: A TDoA-based Approach Using LoRaWAN

LoRaWAN has garnered tremendous attention owing to the low power consumption of end nodes, long range, high resistance to multipath, low cost, and use of license-free sub-GHz bands. Consequently, LoRaWAN is gradually replacing Wi-Fi and Bluetooth in sundry IoT applications including utility metering, smart cities, and localization. Localization, in particular, has already witnessed a surge of alternatives to Global Navigation Satellite System (GNSS), based on Wi-Fi, Bluetooth, Ultra Wide Band, 5G, etc. in indoor and low power domains due to the poor indoor coverage and high power consumption of GNSS. With the need for localization only shooting up with dense IoT deployments, LoRaWAN is seen as a promising solution in this context. Indeed, many attempts employing various techniques such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Received Signal Strength Index (RSSI) have been made to achieve localization using LoRaWAN. However, a significant drawback in this scenario is the lack of extensive data on path loss and signal propagation modeling, particularly in Indian cityscapes. Another demerit is the use of GNSS at some stage primarily for time synchronization of gateways. In this work, we attempt to nullify these two disadvantages of LoRaWAN based localization. The first part of this work presents experimental data of LoRaWAN transmissions inside a typical city building to study signal propagation and path loss. The latter part proposes a standalone GNSS-free localization approach using LoRaWAN that is achieved by applying a collaborative, TDoA-based methodology. An additional stationary node is introduced into the network to allow the synchronization of gateways without GNSS. Finally, the distribution of localization error in a triangle of gateways and the effect of timing resolution, time-on-air, and duty cycle constraints on it are investigated.