Miracle-dz/UniFAR-FTUs huggingface dataset:
Drought Stress Priming Improved the Drought Tolerance of Soybean

The capability of a plant to protect itself from stress-related damages is termed “adaptability” and the phenomenon of showing better performance in subsequent stress is termed “stress memory”. While drought is one of the most serious disasters to result from climate change, the current understanding of drought stress priming in soybean is still inadequate for effective crop improvement. To fill this gap, in this study, the drought memory response was evaluated in cultivated soybean (Glycine max). To determine if a priming stress prior to a drought stress would be beneficial to the survival of soybean, plants were divided into three treatment groups: the unprimed group receiving one cycle of stress (1S), the primed group receiving two cycles of stress (2S), and the unstressed control group not subjected to any stress (US). When compared with the unprimed plants, priming led to a reduction of drought stress index (DSI) by 3, resulting in more than 14% increase in surviving leaves, more than 13% increase in leaf water content, slight increase in shoot water content and a slower rate of loss of water from the detached leaves. Primed plants had less than 60% the transpiration rate and stomatal conductance compared to the unprimed plants, accompanied by a slight drop in photosynthesis rate, and about a 30% increase in water usage efficiency (WUE). Priming also increased the root-to-shoot ratio, potentially improving water uptake. Selected genes encoding late embryogenesis abundant (LEA) proteins and MYB, NAC and PP2C domain-containing transcription factors were shown to be highly induced in primed plants compared to the unprimed group. In conclusion, priming significantly improved the drought stress response in soybean during recurrent drought, partially through the maintenance of water status and stronger expression of stress related genes. In sum, we have identified key physiological parameters for soybean which may be used as indicators for future genetic study to identify the genetic element controlling the drought stress priming.
PENGABDIAN KEPADA MASYARAKAT DI USAHA SEPATU MOJO, PELATIHAN PERHITUNGAN HARGA POKOK PRODUKSI (HPP) DALAM MENENTUKAN HARGA JUAL

Perhitungan Harga Pokok Produksi sangat penting dalam penentuan harga jual produk pada beberapa UKM. Sebagian besar UKM menentukan harga jual tidak berdasaarkan perhitunagn Hara Pokok Produksi yang sesuai kaidah akuntansi, terutama dalam penvatatan penyusutan Aktiva Tetap. Usaha Sepatu Mojo yang menjadi mitra kami adalah salah satu UKM yang belum menghitung harga pokok produksi secara tepat. Kegiatan Pengabdian Kepada Masyarakat ini bertujuan memberikan pemahaman, pelatihan dan pendampingan penghitungan Harga Pokok Produksi dalam menentukan harga Jual. Pelaksanaan kegiatan melalui zoom dan pendampingan ke lokasi. Hasil evaluasi kegiatan ini menunjukakn hasil pemahaman yang baik dimana hasil jawaban kuesioner pretest dan posttest mengalami peningkatan, hasil lainnya mitra sudah mampu menghitung Harga Pokok Produksi dan mampu mencatat dalam jurnal dan menentukan harga jual sesuai kaidah akuntansi, serta menyusun Laporan Rugi Laba, dan berhasil mengimplementasikan dalam usaha sepatu mereka
Adversarial Structure Matching for Structured Prediction Tasks

Pixel-wise losses, i.e., cross-entropy or L2, have been widely used in structured prediction tasks as a spatial extension of generic image classification or regression. However, its i.i. d. assumption neglects the structural regularity present in natural images. Various attempts have been made to incorporate structural reasoning mostly through structure priors in a cooperative way where co-occurring patterns are encouraged. We, on the other hand, approach this problem from an opposing angle and propose a new framework, Adversarial Structure Matching (ASM), for training such structured prediction networks via an adversarial process, in which we train a structure analyzer that provides the supervisory signals, the ASM loss. The structure analyzer is trained to maximize ASM loss, or to emphasize recurring multi-scale hard negative structural mistakes usually among co-occurring patterns. On the contrary, the structured prediction network is trained to reduce those mistakes and is thus enabled to distinguish fine-grained structures. As a result, training structured prediction networks using ASM reduces contextual confusion among objects and improves boundary localization. We demonstrate that ASM outperforms its pixel-wise counterpart and commonly used structure priors, GAN, on three different structured prediction tasks, namely, semantic segmentation, monocular depth estimation, and surface normal prediction.
Installing of cosmological constant

An artificial scale of observable cosmological constant is dynamically related to its natural bare value due to an installation of relevant coherent state for the inflationary field in a finite volume of early Universe, because of exponential suppression of probability to find the state with zero number of quanta. Homogeneous quantum fluctuations of the field actually put hard constraints on the total amount of inflation. ar X iv :1 30 8. 13 44 v3 [ gr -q c] 1 4 Ja n 20 14
Learning hetero-synaptic delays for motion detection in a single layer of spiking neurons

The response of a biological neuron depends on the precise timing of afferent spikes. This temporal aspect of the neuronal code is essential in understanding information processing in neurobiology and applies particularly well to the output of neuromorphic hardware such as event-based cameras. However, most artificial neuronal models do not take advantage of this minute temporal dimension and here, we develop a model for the efficient detection of temporal spiking motifs based on a layer of neurons with hetero-synaptic delays. Indeed, the variety of synaptic delays on the dendritic tree allows to synchronize synaptic inputs as they reach the basal dendritic tree. We show this can be formalized as a time-invariant logistic regression which can be trained using labelled data. We apply this model to solve the specific computer vision problem of motion detection, and demonstrate its application to synthetic naturalistic videos transformed into event streams similar to the output of event-based cameras. In particular, we quantify how its accuracy can vary with the total computational load. This end-to-end event-driven computational brick could help improve the performance of future Spiking Neural Network (SNN) algorithms and their prospective use in neuromorphic chips.
Can Machine Learning Apply to Musical Ensembles ?

This paper was presented at the Human Centred Machine Learning Workshop at CHI 2016. Copyright © 2016 by the authors. Abstract In this paper we ask whether machine learning can apply to musical ensembles as well as it does to the individual musical interfaces that are frequently demonstrated at NIME and CHI. While using machine learning to map individual gestures and sensor data to musical output is becoming a major theme of computer music research, these techniques are only rarely applied to ensembles as a whole. We have developed a server-based system that tracks the touch-data of an iPad ensemble and have used such techniques to identify touch-gestures and to characterise ensemble interactions in real-time. We ask whether further analysis of this data can reveal unknown dimensions of collaborative musical interaction and enhance the experience of performers.
On the spectrum of stable secondary nuclei in cosmic rays

The ratio of the fluxes of secondary and primary nuclei in cosmic rays has long been used as an indicator of the grammage traversed in the journey of cosmic ray particles throughout the Galaxy. The basic idea is that primary particles are accelerated in astrophysical sources, such as supernova remnant shocks and eventually propagate in the Galactic volume, occasionally interacting with gas, mainly in the disc of the Galaxy, and there they produce secondary nuclei through spallation. At sufficiently high energy, typically $\gtrsim 100$ GeV/n, the ratio of fluxes of the secondary nucleus to that of the main primary nucleus is found to scale as $E_{k}^{-\delta}$, where $E_{k}$ is the energy per nucleon (a conserved quantity in spallation reactions) and $\delta$ identifies the energy dependence of the diffusion coefficient. The same shock waves that may be responsible for cosmic ray acceleration in the first place also pick up any other charged particle in the upstream, provided being above threshold for injection. The secondary nuclei produced by spallation in the interstellar medium are no exception, hence they also get accelerated. This effect is unavoidable, only its strength may be subject of debate. We compute the spectrum of secondary elements such as boron and lithium taking into account shock reacceleration and compare our predictions with the recent observations of the B/C ratio and preliminary measurements of the boron and lithium flux. Both these sets of data seem to confirm that reacceleration of secondary nuclei indeed plays an important role, thereby affecting the validity of those scaling rules that are often used in cosmic ray physics.
Seventy-One New L and T Dwarfs from the Sloan Digital Sky Survey

We present near-infrared observations of 71 newly discovered L and T dwarfs, selected from imaging data of the Sloan Digital Sky Survey (SDSS) using the i-dropout technique. Sixty-five of these dwarfs have been classified spectroscopically according to the near-infrared L dwarf classification scheme of Geballe et al. and the unified T dwarf classification scheme of Burgasser et al. The spectral types of these dwarfs range from L3 to T7 and include the latest types yet found in the SDSS. Six of the newly identified dwarfs are classified as early to mid-L dwarfs according to their photometric near-infrared colors, and two others are classified photometrically as M dwarfs. We also present new near-infrared spectra for five previously published SDSS L and T dwarfs, and one L dwarf and one T dwarf discovered by Burgasser et al. from the Two Micron All Sky Survey. The new SDSS sample includes 27 T dwarfs and 30 dwarfs with spectral types spanning the complex L-T transition (L7-T3). We continue to see a large (∼0.5 mag) spread in J - H for L3-T1 types and a similar spread in H - K for all dwarfs later than L3. This color dispersion is probably due to a range of grain sedimentation properties, metallicity, and gravity. We also find L and T dwarfs with unusual colors and spectral properties that may eventually help to disentangle these effects.
Psychological Factors Affecting the Willingness to Accept a Possible Tyrosine Kinase Inhibitor (TKI) Discontinuation in Chronic Myeloid Leukaemia (CML) Patients

Purpose Patients with chronic myeloid leukemia (CML) who present a sustained deep molecular response (DMR) for a stable period of time might benefit from discontinuing tyrosine kinase inhibitors (TKIs). A significant number of patients seem able to reach this stage due to the availability of TKIs. However, many patients remain reluctant about TKI discontinuation and may refuse treatment interruption. The purpose of this study was to explore the clinical and psycho-cognitive factors that may influence the decision to discontinue TKI therapy, thereby gaining a better understanding of patients’ viewpoints on TKI discontinuation. Patients and Methods One hundred and nineteen patients diagnosed with CML aged between 34 and 69 were enrolled (67 males and 52 females). Different clinical information and psycho-cognitive aspects such as attitude toward risk behaviours, risk preferences, need for cognitive closure, and tendency to resist to changes were assessed through the administration of a battery of questionnaires. Results A higher tendency toward risk behaviours and the tendency to focus on possible gain in the short term rather than on losses might represent important predictors for the willingness to accept TKI discontinuation. Possible relapses following interruption of the therapy are the most common reason for concern. Furthermore, lower levels of resistance to change and having previously experienced the desire to interrupt the therapy might lead patients to accept a higher probability of relapse risk when facing such a decision. Conclusion TKI discontinuation appears appealing and challenging at the same time for many CML patients, and different factors may influence this decision. Psychology plays a crucial role in assisting physician-patient communication and informed decision-making.
Cosmology with Phase 1 of the Square Kilometre Array Red Book 2018: Technical specifications and performance forecasts

Abstract We present a detailed overview of the cosmological surveys that we aim to carry out with Phase 1 of the Square Kilometre Array (SKA1) and the science that they will enable. We highlight three main surveys: a medium-deep continuum weak lensing and low-redshift spectroscopic HI galaxy survey over 5 000 deg2; a wide and deep continuum galaxy and HI intensity mapping (IM) survey over 20 000 deg2 from $z = 0.35$ to 3; and a deep, high-redshift HI IM survey over 100 deg2 from $z = 3$ to 6. Taken together, these surveys will achieve an array of important scientific goals: measuring the equation of state of dark energy out to $z \sim 3$ with percent-level precision measurements of the cosmic expansion rate; constraining possible deviations from General Relativity on cosmological scales by measuring the growth rate of structure through multiple independent methods; mapping the structure of the Universe on the largest accessible scales, thus constraining fundamental properties such as isotropy, homogeneity, and non-Gaussianity; and measuring the HI density and bias out to $z = 6$ . These surveys will also provide highly complementary clustering and weak lensing measurements that have independent systematic uncertainties to those of optical and near-infrared (NIR) surveys like Euclid, LSST, and WFIRST leading to a multitude of synergies that can improve constraints significantly beyond what optical or radio surveys can achieve on their own. This document, the 2018 Red Book, provides reference technical specifications, cosmological parameter forecasts, and an overview of relevant systematic effects for the three key surveys and will be regularly updated by the Cosmology Science Working Group in the run up to start of operations and the Key Science Programme of SKA1.
Curvaton with nonminimal derivative coupling to gravity: Full perturbation analysis

In our previous paper [K. Feng, T. Qiu, and Y. -S. Piao, Phys. Lett. B 729, 99 (2014)], we have shown a curvaton model where the curvaton has a nonminimal derivative coupling to gravity. Such a coupling could cause scale invariance of the perturbations for a wide range of constant values of the equation of state of the cosmic background at the early time. In this paper, we continue our study by fully analyzing its perturbations up to the third order. Apart from the usual 2-point correlation function that have already been calculated in Feng, Qiu, and Piao [Phys. Lett. B 729, 99 (2014)], we also take into account the 3-point correlation functions, including the pure scalar part, the pure tensor part, and the cross-correlations between scalar and tensor perturbation modes. We find that for the pure scalar part, the 3-point correlation functions can generate non-Gaussianities that fit the PLANCK data very well. For the pure tensor and mixed parts, the shape functions have peaks at squeezed and equilateral limits, respectively, responsible for sizable f(NL)(sqz) and f(NL)(eql), which could be tested by the future observational data.
COMPACT COMPOSITION OPERATORS ON L 1

The composition operator induced by a holomorphic self-map of the unit disc is compact on L1 of the unit circle if and only if it is compact on the Hardy space H2 of the disc. This answers a question posed by Donald Sarason: it proves that Sarason's integral condition characterizing compactness on L1 is equivalent to the asymptotic condition on the Nevanlinna counting function which characterizes compactness on H2 .
Atmospheric Deposition Over the Caribbean Region: Sea Salt and Saharan Dust Are Sources of Essential Elements on the Island of Guadeloupe

Dust emitted from North Africa is transported over long distances and has a strong impact on large areas over the North Tropical Atlantic Ocean. Sea salt emitted by the sea surface is the second source of essential elements transported in the atmosphere and plays a major role in the cycles of alkaline‐earth metals in the ecosystems of tropical North Atlantic Islands. The total atmospheric deposition fluxes were continuously sampled on a weekly basis in Guadeloupe, Lesser Antilles, from March 2015 to August 2018 (41 months). Elemental deposition fluxes, including Al, Ca, K, Mg, Fe, Na, P, S, and Zn, were measured for all samples to provide the first long time series of atmospheric elemental deposition fluxes over the Lesser Antilles region. It is shown that: (a) the three sources of atmospheric deposits in Guadeloupe for the presented elements are sea salt (for K, Ca, Mg, Na, and S), long‐range transported Saharan dust (for Al, Ca, K, and Fe), and biogenic particles (for P and Zn); (b) the average deposition mass fluxes of sea salt and Saharan dust are 17.4 and 11.2 g.m−2.year−1, respectively, without noticeable inter‐annual variations; (c) a pronounced seasonality is found for the Saharan dust deposition, for which maximum flux values are observed between June and July each year and 85% of the annual deposition flux occurs between April and September; (d) the deposition flux of sea salt is strongly correlated to local wind speed, without seasonality.
Global Finite-Time Stabilization of Spacecraft Attitude With Disturbances via Continuous Nonlinear Controller

In this article, a robust continuous controller is investigated to solve the global finite-time attitude stabilization problem of rigid spacecraft system subject to multiple disturbances. The system order is first increased by derivation and the derivative of the actual torque is regarded as the virtual control law to be designed. Then, a super-twisting differentiator is constructed to provide finite-time angular acceleration estimations. Finally, based on the estimated angular accelerations, a discontinuous set stabilization control law is designed as the virtual controller. The actual continuous controller is obtained by integrating the discontinuous one. Rigorous proof and stability analysis are presented based on Lyapunov analysis. Compared with most of the existing finite-time control approaches in spacecraft systems, the proposed continuous control scheme guarantees the finite-time set stability of the closed-loop system. Moreover, the system states can converge to the equilibria even in the presence of derivative-bounded disturbances. Simulation studies of the proposed controller are illustrated to demonstrate the strong disturbance rejection ability, fast convergence rate, and high steady-state precision.
Automatic and Accurate Vision-Based Measurement of Camber and Toe-In Alignment of Vehicle Wheel

Camber and toe-in are important alignment parameters of vehicle wheels, which determine the operation stability and safety during vehicle driving. An automatic and accurate measurement method is proposed for camber and toe-in alignment without vision system calibration. Different from the previous target-based vision measurement (TVM) based on the maximum likelihood estimation (MLE), the relevance vector machine (RVM)-density-based spatial clustering (DBSC) model is proposed to achieve the automatic measurement of camber and toe-in alignment. Furthermore, the camber and toe-in measurement models are established by the maximum a posteriori (MAP) and the alternating direction method of multipliers (ADMM), rather than the traditional MLE. The contrast experiments are performed by the verification instrument, which indicates that the proposed method achieves effectiveness and high accuracy of camber and toe-in measurement.
IQ Signal Demodulation for Noncontact Vital Sign Monitoring Using a CW Doppler Radar: A Review

Using a Doppler radar for noncontact detection is a promising way to monitor vital signs of a subject more conveniently than other common contact medical devices, but has many challenges including null-point issue, dc offset, and nonlinear distortion. The wireless property of a radar system easily affects the Doppler signals, so it needs a demodulation method to obtain the vital sign information. Furthermore, detecting precise information for respiration rate (RR) and heart rate (HR) of the subject needs further signal processing to extract the target signals from the demodulated signals at various interference levels. Hence, this study reviews research on continuous-wave (CW) Doppler radar, signal demodulation and subsequent signal processing to address the problems for noncontact vital sign monitoring.
A Phase Variable Model of Aircraft Brushless Exciter Based on Finite Element Analysis and Its Coupling with the Rotating Rectifier Circuit

This paper presents the phase variable model of brushless exciter employed by aircraft integrated starter/generators. The model uses current-flux linkage functions obtained by finite element analysis. Since the developed model contains the exciter machine and its connected rotating rectifier, a particular attention is paid to adequately considering coupling effects between the exciter and the rotating rectifier which govern operation of the rectifier. One important parameter of this coupling is the inductance which contributes in commutation process of the rectifier (commutation inductance). In this regard, a simple finite element analysis technique is proposed to identify the commutation inductance. Finally, the developed model is validated by comparing its results with finite element analysis.
Research on the Physics–Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles

The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the physics–intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios.