Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of bulk-magnon transport in the temporal evolution of the longitudinal spin-Seebeck effect

We present temporal evolution of the spin Seebeck effect in a YIG|Pt bilayer system. Our findings reveal that this effect is a sub-microseconds fast phenomenon governed by the temperature gradient and the thermal magnons diffusion in the magnetic materials. A comparison of experimental results with the thermal-driven magnon-diffusion model shows that the temporal behavior of this effect depends on the time development of the temperature gradient in the vicinity of the YIG|Pt interface. The effective thermal-magnon diffusion length for YIG|Pt systems is estimated to be around 700nm.
823 OCEAN 5 : the ECMWF Ocean Reanalysis System and its Real-Time analysis component

The ECMWF OCEAN5 system is a new global eddy-permitting ocean-sea ice ensemble reanalysisanalysis system. This Technical Memorandum gives a full description of the OCEAN5 system, with the focus on its Behind-Real-Time (BRT) component, the reanalysis product ORAS5. The OCEAN5 Real-Time (RT) component includes all upgrades developed for ORAS5 and runs daily using the latest observations and forcing fields from the operational Numerical Weather Prediction (NWP). ORAS5 includes 5 ensemble members and covers from 1979 onwards. Compared to ORAS4, the ocean model resolution has been increased to an eddy-permitting resolution (0.25◦) in the horizontal and a near-surface resolution of 1 m in the vertical. ORAS5 also includes a prognostic thermodynamic-dynamic sea-ice model with assimilation of sea-ice concentration data. Both observation data sets and forcing fields have been updated as well. An important novelty in ORAS5 is a generic ensemble generation scheme for perturbing both observations and forcing fields. Other upgrades include revisions to the ensemble-based spin-up and a-priori bias correction scheme. Assessment of ORAS5 and sensitivity experiments suggests that all system components contribute to an improved ocean analysis state, with directly assimilation of temperature and salinity in-situ data as the most influential term. Compared to ORAS4, ORAS5 also shows improved ocean climate variability in terms of Sea Surface Temperature (SST) and sea-level when verified against independent observation data sets. The SST biases in the Gulf Stream region remain large in ORAS5 though, which is associated with a misrepresentation of front positions and overshoot of the northward transport of the Gulf Stream.
Weak Scale Leptogenesis, R-symmetry, and a Displaced Higgs

We present a supersymmetric model of leptogenesis in which the right-handed neutrinos have weak scale masses and O(10-2-10-3) yukawa couplings. The model employs an R-symmetry at the weak scale that forces neutrino masses to be proportional to the gravitino mass. We predict that the lightest right-handed neutrino decays to a displaced Higgs and neutrino and that the NLSP is long-lived on collider scales. These could be striking signatures at the LHC if, for instance, the right-handed neutrino is produced in a cascade decay.
Scheduling Drones to Recharge Electric Vehicles

Drones and Electric Vehicles (EVs) are two technologies that are growing fast and have the potential to revolutionize the transportation sector. In this paper, we take inspiration from a recent patent submitted by Amazon, and we study the problem of scheduling drones that carry a large battery and can partially recharge EVs when a ground charging station is not available. The drones have a limited range, as they are also using an electric motor powered by batteries which need regular recharging. Thus, given a fixed set of drones, the problem that arises is to calculate a schedule for the flying and the recharging of the drones aiming to maximize the number of EVs that are actually serviced. In this vein, we develop a greedy algorithm that uses a heuristic search mechanism. We evaluate our algorithm in a realistic setting and in a plethora of scenarios to verify its effectiveness.
Balanced homodyne detectors in quantum field theory

By examining balanced homodyne defectors (BHDs) from the general quantum-field-theoretical point of view we derive a number of generalizations of the standard analysis of their response. In particular, by allowing for interactions restricted in time (by a smooth function) we present general expressions for BHD output (in which the usual, simplifying limits can but need not be taken). Moreover, we point out the need for nonstrictly monochromatic local oscillators (i.e., the need for ``pulsed'' ones) in order to have well-defined quantum-field-theory (QFT) observables (the products of which, e.g., have finite vacuum expectation values). Furthermore, we show how the analysis of the detectors generalize to situations with BHDs in waveguides, Casimir cavities, or other time-independent but inhomogeneous $ϵ$ and $\ensuremath{\mu}$. The general treatment also allows us to comment on important QFT features of the detector observables such as locality (i.e., commuting for causally separated measurements) and non-null vacuum response. This leads to the conclusion that balanced homodyne-type detectors (and not single photodiodelike ones) are the appropriate tools in testing intriguing QFT predictions such as negative (subvacuum) energy densities. Finally, by recalling results on field-autocorrelation functions for QFT in Casimir cavities we show that interesting effects (large reductions of fluctuations) are to be expected if a version of BHDs were to be placed in such cavities.
Private Sector Presence in Healthcare in India: Econometric Analysis of Patterns and Consequences

The presence of private providers in the Indian healthcare sector remains one of the most debated issues. This article attempts to contribute to this debate from the angle of the ultimate goal of healthcare provision—a healthy population. We explore whether private sector presence (PSP) has improved the general health status of the people. We develop a theoretical argument to hypothesize that PSP in India would lead to better health status through the route of competition-driven quality, which in turn could lead to adverse economic consequences. We use district-level secondary data from government sources to confirm our hypotheses using robust tools of applied econometrics, correcting for serious problems of endogeneity. Constructing a district-level index of PSP, we identify distinct spatial/geographical clusters, explained by socio-economic prosperity as well as demonstration effect. We also find a robust positive association between PSP and general health outcomes, accompanied by an adverse economic consequence of rising catastrophic out-of-pocket expenditure. In terms of policy, the article concludes that rather than restricting the growth of the private sector, the government must strengthen the quality of the existing public healthcare delivery system and ensure effective monitoring and regulation. JEL Codes: 110, 111, 112, 115
The Journal of Systems & Software

With the COVID-19 pandemic, Scrum teams had to switch abruptly from a traditional working setting into an enforced working from home one. This abrupt switch had an impact on software projects. Thus, it is necessary to understand how potential future disruptive events will impact Agile software teams’ ability to deliver successful projects while working from home. To investigate this problem, we used a two-phased Multi-Method study. In the first phase, we uncover how working from home impacted Scrum practitioners through semi-structured interviews. Then, in the second phase, we propose a theoretical model that we test and generalize using Partial Least Squares-Structural Equation Modeling (PLS-SEM) surveying 138 software engineers who worked from home within Scrum projects. We concluded that all the latent variables identified in our model are reliable, and all the hypotheses are significant. This paper emphasizes the importance of supporting the three innate psychological needs of autonomy, competence, and relatedness in the home working environment. We conclude that the ability of working from home and the use of Scrum both contribute to project success, with Scrum acting as a mediator. © 2022TheAuthor(s). PublishedbyElsevierInc.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/).
Ranks of Elliptic Curves and Random Matrix Theory: Modeling families of L -functions

We discuss the idea of a ``family of L-functions'' and describe various methods which have been used to make predictions about L-function families. The methods involve a mixture of random matrix theory and heuristics from number theory. Particular attention is paid to families of elliptic curve L-functions. We describe two random matrix models for elliptic curve families: the Independent Model and the Interaction Model.
Plant-endophyte mediated improvement in physiological and bio-protective abilities of marigold (Tagetes patula)

Endophytic bacteria improve the growth, physiology, and metabolite profile of plants. They are known as potential biocontrol agents of soil-borne diseases. This study evaluated the effects of endophytic bacterial strains on growth, vase life, biochemical attributes, and antioxidant and nematicidal activities of French marigold (Tagetes patula). French marigold seeds were sole and consortium inoculated with three promising endophytic bacterial strains, Burkholderia phytofirmans (PsJN), Enterobacter sp. (MN17), and Bacillus sp. (MN54). The vase life of French marigold was promoted by 66.6% in the individual application of PsJN and 100% in plants treated with consortium compared to the uninoculated control. The shoot and root fresh weights were also increased by 65.9 and 68.7%, with the combined application of all three strains. The total phenolics, flavonoid, and protein contents were higher in consortium treatment with an increase of up to 38.0, 55.9, and 65.9%, respectively, compared to the uninoculated control. Furthermore, combined application of endophytic bacterial strains promoted DPPH radical scavenging, mortality of plant-parasitic nematodes, and ferric reducing antioxidant power activities with increase of up to 278.0, 103.8, and 178.0%, respectively, compared to uninoculated control. An increase in antioxidant activities of ascorbate peroxidase (APX), catalase (CAT), glutathione peroxidase (GPX), and superoxide dismutase (SOD) were observed up to 77.3, 86.0, 91.6, and 102.9%, respectively by combined application of endophytic bacterial strains. So, given the economic importance of floriculture crops, endophytic bacterial isolates studied here have shown a great potential for improving the productivity of cultivated ornamental French marigold.
Zinc toxicity response in Ceratoides arborescens and identification of CaMTP, a novel zinc transporter

Zinc (Zn) is an essential micronutrient for several physiological and biochemical processes. Changes in soil Zn levels can negatively affect plant physiology. Although the mechanism of Zn nutrition has been studied extensively in crops and model plants, there has been little research on steppe plants, particularly live in alkaline soils of arid and semiarid regions. Ceratoides arborescens is used in arid and semiarid regions as forage and ecological restoration germplasm, which is studied can enrich the mechanism of Zn nutrition. The plants were exposed to three different Zn treatments, Zn-deficient (-Zn 0 mM L−1), Zn-normal (Control, 0.015 mM L−1), and Zn-excess (+Zn, 0.15 mM L−1), for 3 weeks. Individual biomass, ion concentrations, photosynthetic system, and antioxidant characteristics were measured. High Zn supply significantly decreased plant biomass and induced chlorosis and growth defects and increased Zn concentration but decreased Fe and Ca concentrations, unlike in controls (p < 0.05). High Zn supply also reduced plant chlorophyll content, which consequently decreased the photosynthesis rate. Increased concentrations of malondialdehyde and soluble sugar and activities of peroxidase and superoxide dismutase could resist the high-level Zn stress. In contrast, low Zn supply did not affect plant growth performance. We also identified a novel protein through RNA transcriptome analysis, named CaMTP, that complemented the sensitivity of a yeast mutant to excessive Zn, which was found to be localized to the endoplasmic reticulum through transient gene expression in Nicotiana benthamiana. The gene CaMTP identified to be highly sensitive to Zn stress is a potential candidate for overcoming mineral stress in dicot crop plants.
The Effect of Culture on Performance Expectancy, Intention, and Trust in Mobile Payment Adoption

This study aims to investigate the role of culture at an individual level in the context of m-payment adoption. Specifically, it examines the effect of uncertainty avoidance and collectivism based on the Hofstede cultural dimensions on three important constructs in technology adoption as well as m-payment adoption which are performance expectancy, behavioural intention and consumer trust. The data was collected in Vietnam which ranked first in m-payment development worldwide in 2019 to test the hypotheses. The findings confirmed the significant impact of cultural variables in the context of mobile payment adoption. The positive and significant impacts of performance expectancy on behavioural intention and consumer trust, and consumer trust on behavioural intention are also confirmed which are aligned to previous studies. This research contributes to the body of literature of not only m-payment adoption, but also the impact of culture in m-payment adoption in particular and technology adoption in general.
The Design and Implementation of a Ray-tracing Algorithm for Signal-level Pulsed Radar Simulation Using the NVIDIA® OptiXTM Engine

This paper presents the design and development of a ray-traced, signal-level pulsed radar simulation algorithm and a novel ray aggregation technique using the NVIDIA® OptiXTM engine for propagation modelling. This is expected to aid radar engineers and researchers in systems design, testing, and training. The program supports the short- and long-range simulation of various radar scenarios using complex target and signal models with a high degree of realism. This was achieved through raytraced radio propagation and geometry modelling, simulating realistic multipath signal interactions with mesh-based objects as opposed to the more traditional point-model approach. The designed algorithm has also been implemented as an open-source ray-tracing module and was verified against an established radar software package.
An Empirical Study of Consumer Attitude Toward Adoption of Online Food Ordering App

The purpose of this paper is to identify the factors influencing the adoption of online food ordering app and the degree of influence of each factor on consumer attitude with moderating effect of technical barriers. This research proposed a conceptual model aimed to test the attitude towards online food ordering app. Relationships among constructs were tested on the basis of 514 surveys collected in India and analyzed with the structural equation modeling approach of partial least squares path modeling. Findings established the positive relationship between effort expectancy (EF), performance expectancy (PE), perceived social norms (PSN), perceived complexity (PCL), perceived compatibility (PCT), perceived relative advantage (PRA), perceived risk (PR) and Attitude towards online food ordering app (ATT). In addition, TB plays a significant moderating role in the relation between PE, PCL, PCT, PR, and ATT. However, no significant moderation effect was found in the relation between EF, PSN, PRA, and ATT.
Looking to relate: observations of a proactive and resilient infant who, with the support of father’s nurturing, helped draw mother out of her unresponsive and disconnected state of mind

ABSTRACT The author describes eleven months of observations of baby Meena, whose mother was unresponsive to and disconnected from her during the first five months of observations after the birth. The observations illustrate the key role and contribution of the baby’s individual temperament in the development of the relationship with the Mother. It is further argued that, in this family, Meena’s father nurtured her and her own proactivity and, critically, her capacity, resilience and motivation assisted in carrying over positive aspects of her relationship with her Father to the relationship with her Mother. This seemed helpful in bringing Mother out of her more cut-off state of mind. Meena and her Mother were able to go on to achieve a reciprocally responsive relationship. These factors also helped Meena not to withdraw or to give up on trying to connect with her mother, as do the infants of unresponsive mothers in the still-face studies [Weinberg, M. K. , & Tronick, E. Z. (1996). Infant affective reactions to the resumption of maternal interaction after the still-face. Child Development, 67(3), 905–914. https://doi.org/10.2307/1131869]. The presence of the observer and the extended family’s attribution of intelligence and communicative intent to Meena, even in utero, are seen as part of her ‘facilitating environment’ [Winnicott, D. W. (2018). The maturational processes and the facilitating environment: Studies in the theory of emotional development. Routledge. first published in 1965 by the Hogarth Press and the Institute of Psycho-Analysis. ].
Torsion of structural shapes

Charts and empirical formulas are provided for the accurate calculation of the torsion constant for the wide flange, I, channel, tee, zee, and angle structural shapes. The differential equation in solving the torsion stress function is replaced by a difference equation, and the stress function is evaluated at nodal points of a fine square mesh net by means of the digital computer. The formulas for the torsion constant make use of existing solutions for the rectangular section but provide accurate correction for the additional torsional rigidity resulting from the juncture of rectangular component parts including the effect of the fillets and the variation in flange thickness of the channel and I-sections. Shear stresses caused by uniform torsion are also evaluated. Calculations for both torsion constant and stress are aided by means of graphical charts.
GradZip: Gradient Compression using Alternating Matrix Factorization for Large-scale Deep Learning

We present a novel gradient compression algorithm, GradZip , to accelerate distributed deep learning by minimizing the communication overhead. Unlike the popular sparsiﬁcation-based compression techniques, GradZip performs dense compression based on alternating matrix-decomposition tailored for deep learning. Therefore, the compressed gradient can be directly communicated and reduced through the standard collective all-reduce available in most high-performance deep learning frameworks. The key insight is that all the learners will have a synchronized tensor from the last all-reduce in training which can be exploited to obtain high-quality gradient factorization very efﬁciently in the subsequent iteration. When GradZip used with an industry-leading all-reduce , it delivers signiﬁcant compression performance: 100 × compression and 5 × training speedup with small accuracy loss of 0 . 22% for ResNet50 with half-precision quantization.
S ep 2 00 2 The Asymptotic Order of the Random kSAT Threshold

Form a randomk-SAT formula onn variables by selecting uniformly and independently m = rn clauses out of all 2 n k possiblek-clauses. TheSatisfiability Threshold Conjectureasserts that for each k there exists a constant rk such that, asn tends to infinity, the probability that the formula is satisfiable tends to 1 if r< rk and to 0 ifr> rk . It has long been known that 2=k < rk < 2. We prove that rk > 2 k 1 ln2 dk, wheredk ! (1+ ln2)=2. Our proof also allows a blurry glimpse of the “geometry” of the set of satisfying truth assignments.
Neural Entrainment to Rhythms of Imagined Syllables

Imagined speech based brain-computer interface (BCI) is of great interest due to its efficiency and user-friendliness for patients with speech impairment. The aim of this work was to study whether different rhythms of imagined syllables could elicit corresponding frequency components on EEG amplitude spectra. Seventeen participants were recruited to take part in the experiments, and performed a control task and four imagery tasks with the presence of periodic pure tones while their EEG signals were recorded. The four imagery tasks included imagining the syllable’ /a/’ every time, every two times, and every three times the periodic pure tones occurred, and imagined twice every three times the periodic pure tones occurred. The experimental results analyzed by Fourier transform indicated that neural entrainment to rhythmic speech imagery can be notably reflected on the EEG amplitude spectra. Clinical Relevance- This work manifested that different rhythms of imagined syllables could be identified from EEG amplitude spectra, which may be beneficial to the development of imagined speech based BCIs.
Language Mapping using tEEG and EEG Data with Convolutional Neural Networks

Tripolar electroencephalography (tEEG) has been found to have significantly better signal-to-noise ratio, spatial resolution, mutual information, and high-frequencies compared to EEG. This paper analyzes the tEEG signals acquired simultaneously with the EEG signals and compares their ability to map language to left and right hemispheres using convolutional neural networks (CNNs). The results show that while the time-domain features of tEEG and EEG signals lead to comparable functional mapping, the frequency domain features are significantly different. The left and right hemisphere classification performances using tEEG are equivalent in time and frequency domains. However, frequency domain classification for EEG results in less accuracy. Clinical Relevance— This technique could quickly, and noninvasively, guide clinicians about language dominance when preparing for resective surgery.
Suspicious Skin Lesion Detection in Wide-Field Body Images using Deep Learning Outlier Detection

During consultation dermatologists have to address hundreds of lesions in a limited amount of time. They will not only evaluate the single lesion of interest but more importantly the context of it. Visually comparing the similarity of the majority of lesions within the same patient provides a strong indication for lesions with significantly differing aspects. Deep learning algorithms are capable to identify such outliers, i.e. images that differ considerably from the expected appearance on a larger cohort, and highlight the main differences in those cases. In the present study we evaluate the use of autoencoders as unsupervised tools to detect suspicious skin lesions based on evaluation of real world data acquired during consultation at the USZ Dermatology Clinic. Clinical Relevance— Deep learning algorithms are showing many promising results in dermatology lesion classification. However the context of the lesion is normally not considered in the analysis which prevents these tools to transition into routine practice. An outlier detector based on real world data would allow a dermatologist or general practitioner to detect the suspicious lesions for further examination. The algorithm would additionally provide useful insights by highlighting the feature differences between the original outlier (malignant lesion) and the lesion reconstructed by the autoencoder