Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological Differentiation in Cross-Cultural Perspective

Cross-cultural studies of psychological differentiation are reviewed with the objectives of: examining the applicability, across cultures, of the main propositions of differentiation theory and the generality of its intracultural supporting data; identifying extensions of the theory, and of its empirical base, suggested by the cross-cultural findings; and delineating problems in the existing data and useful lines of further cross-cultural inquiry. The evidence on self-consistency, age changes and stability indicates that these aspects of differentiation show patterns in other cultures essentially similar to those observed in the original American studies. Numerous cross-cultural studies have sought the sources of individual and group differences in differentiation in family practices, in cultural influences and in ecological pressures. The evidence from these studies suggests that less differentiated functioning, including a more field-dependent perceptual mode, is associated with insistence upon adherence to adult authority, female salience and the absence of strong male role models in the family; “tight” organization and stress upon conformity in society; and sedentary agricultural and pastoral ecological settings. In contrast, more differentiated functioning, including relative field independence, are associated with encouragement of autonomy in the family, “loose” social structure and mobile hunting ecological settings. The small sex differences in field-dependence-independence, beginning in adolescence, repeatedly observed in Western studies are not universally evident in the non-Western data. Sex differences appear to be common in samples at the sedentary agricultural end of the ecological spectrum and less evident in mobile hunting samples. Sex differences also seem to be more prevalent in “tight” than in “loose” social settings.
Examining the psychometric properties of the Controlling Coach Behaviors Scale in Chinese Elite Athletes

Coaching style is key to athletes’ performance and mental well-being. However, few attempts have examined the effects of coaching style on athletes in a Chinese cultural context. Based on previous literature and 23 expert interviews (11 with athletes and 12 with coaches), we rebuilt a 16-items on the Controlling Coach Behaviors Scale. In study 1, 130 provincial team athletes completed a 16-items questionnaire on controlling coach behaviors. The questionnaire items were then screened using exploratory factor analysis and transformed into a formal scale. In study 2, another 560 provincial athletes completed several measures related to coaching style, motivation, subjective vitality, and burnout, and systematic tests were carried out to validate the scale. Study 3 examined the test-retest reliability of the Controlling Coach Behaviors Scale over a 2-week interval. Finally, the present study yielded a nine-item Controlling Coach Behaviors Scale with three dimensions (controlling use of reward, negative conditional regard, and excessive personal control). It suggests that cultural differences played an important role in the communication between athletes and coaches. The new Controlling Coach Behaviors Scale shows good validity and can be used in future research.
Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: a Systematic Literature Review

Objectives: An SLR is presented focusing on text mining based automation of SLR creation. The present review identifies the objectives of the automation studies and the aspects of those steps that were automated. In so doing, the various ML techniques used, challenges, limitations and scope of further research are explained. Methods: Accessible published literature studies that primarily focus on automation of study selection, study quality assessment, data extraction and data synthesis portions of SLR. Twenty-nine studies were analyzed. Results: This review identifies the objectives of the automation studies, steps within the study selection, study quality assessment, data extraction and data synthesis portions that were automated, the various ML techniques used, challenges, limitations and scope of further research. Discussion: We describe uses of NLP/TM techniques to support increased automation of systematic literature reviews. This area has attracted increase attention in the last decade due to significant gaps in the applicability of TM to automate steps in the SLR process. There are significant gaps in the application of TM and related automation techniques in the areas of data extraction, monitoring, quality assessment and data synthesis. There is thus a need for continued progress in this area, and this is expected to ultimately significantly facilitate the construction of systematic literature reviews.
2022 Unit-log-symmetric models: Characterization, statistical properties and application to internet access data

We present the unit-log-symmetric model which is based on the bivariate log-symmetric distribution. It is a ﬂexible family of distributions over the interval (0 , 1). We study mathematical properties like stochastic representations, symmetry, modality, moments, quantile function, entropy and maximum likelihood estimators, giving special attention to the spe-ciﬁc cases of the unit-log-normal, the unit-log-Student-t and the unit-log-Laplace distributions. Simulation results and practical application are also presented.
Biomedical images texture detail denoising based on PDE

Biomedical images denosing based on Partial Differential Equation are well-known for their good processing results. General denosing methods based on PDE can remove the noises of images with gentle changes and preserve more structure details of edges, but have a poor effectiveness on the denosing of biomedical images with many texture details. This paper attempts to make an overview of biomedical images texture detail denosing based on PDE. Subsequently, Three kinds of important image denosing schemes are introduced in this paper: one is image denosing based on the adaptive parameter estimation total variation model, which denosing the images based on region energy distribution; the second is using G norm on the perception scale, which provides a more intuitive understanding of this norm; the final is multi-scale denosing decomposition. The above methods involved can preserve more structures of biomedical images texture detail. Furthermore, this paper demonstrates the applications of those three methods. In the end, the future trend of biomedical images texture detail denosing Based on PDE is pointed out.
Object Detection in Aerial Imagery

Object detection in natural images has achieved remark-able results over the years. However, a similar progress has not yet been observed in aerial object detection due to several challenges, such as high resolution images, instances scale variation, class imbalance etc. We show the performance of two-stage, one-stage and attention based object detectors on the iSAID dataset. Furthermore, we describe some modiﬁcations and analysis performed for different models in two stage detector: introduced weighted attention based FPN, class balanced sampler and density prediction head. in one stage detector: used weighted focal loss and introduced FPN in attention based detector: compare single,multi-scale attention and demonstrate effect of different backbones 1 . Finally, we show a comparative study highlighting the pros and cons of different models in aerial imagery setting.
Photo Rater: Photographs Auto-Selector with Deep Learning

Photo Rater is a computer vision project that uses neural networks to help photographers select the best photo among those that are taken based on the same scene. This process is usually referred to as"culling"in photography, and it can be tedious and time-consuming if done manually. Photo Rater utilizes three separate neural networks to complete such a task: one for general image quality assessment, one for classifying whether the photo is blurry (either due to unsteady hands or out-of-focusness), and one for assessing general aesthetics (including the composition of the photo, among others). After feeding the image through each neural network, Photo Rater outputs a final score for each image, ranking them based on this score and presenting it to the user.
High-fidelity generation of four-photon GHZ states on-chip

Mutually entangled multi-photon states are at the heart of all-optical quantum technologies. While impressive progresses have been reported in the generation of such quantum light states using free space apparatus, high-fidelity high-rate on-chip entanglement generation is crucial for future scalability. In this work, we use a bright quantum-dot based single-photon source to demonstrate the high fidelity generation of 4-photon Greenberg-Horne-Zeilinger (GHZ) states with a low-loss reconfigurable glass photonic circuit. We reconstruct the density matrix of the generated states using full quantum-state tomography reaching an experimental fidelity to the target $|{\text{GHZ}_4}\rangle$ of $\mathcal{F}_{\text{GHZ}_4} (86.0\pm0.4)\,\%$, and a purity of $\mathcal{P}_{\text{GHZ}_4}=(76.3\pm0.6)\,\%$. The entanglement of the generated states is certified with a semi device-independent approach through the violation of a Bell-like inequality by more than 39 standard deviations. Finally, we carry out a four-partite quantum secret sharing protocol on-chip where a regulator shares with three interlocutors a sifted key with up to 1978 bits, achieving a qubit-error rate of $10.87\,\%$. These results establish that the quantum-dot technology combined with glass photonic circuitry for entanglement generation on chip offers a viable path for intermediate scale quantum computation and communication.
Recurrent Narrow Quasiperiodic Fast-propagating Wave Trains Excited by the Intermittent Energy Release in the Accompanying Solar Flare

About the driven mechanisms of the quasiperiodic fast-propagating (QFP) wave trains, there exist two dominant competing physical explanations: they are associated with the flaring energy release or attributed to the waveguide dispersion. Employing Solar Dynamics Observatory/Atmospheric Imaging Assembly 171 Å images, we investigated a series of QFP wave trains composed of multiple wave fronts propagating along a loop system during the accompanying flare on 2011 November 11. The wave trains showed a high correlation in start times with the energy release of the accompanying flare. Measurements show that the wave trains’ phase speed is almost consistent with its group speed with a value of about 1000 km s−1, indicating that the wave trains should not be considered dispersed waves. The period of the wave trains was the same as that of the oscillatory signal in X-ray emissions released by the flare. Thus we propose that the QFP wave trains were most likely triggered by the flare rather than by dispersion. We investigated the seismological application with the QFP waves and then obtained that the magnetic field strength of the waveguide was about 10 G. Meanwhile, we also estimated that the energy flux of the wave trains was about 1.2 × 105 erg cm−2 s−1.
PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks

—Deemed as the third generation of neural network, event-driven Spiking Neural Networks(SNNs) combined with bio-plausible local learning rules make it promising to build low- power, neuromorphic hardware for SNNs. However, because of the non-linearity and discrete property of spiking neural networks, the training of SNN remains difﬁcult and is still under discussion. Originated from gradient descent, backpropagation has achieved stunning success in multi-layer SNNs. Nevertheless, it is assumed to lack of biologically plausibility, while consuming relatively high computational resources. In this paper, we propose a novel learning algorithm inspired by predictive coding theory and show that it can perform supervised learning fully autonomously and successfully as the backpropagation, utilizing only local Hebbian plasticity. Furthermore, this method reaches favorable performance compared to the state-of-the-art multi- layer SNNs: test accuracy of 99.25% for Caltech Face/Motorbike dataset, 84.25% for ETH-80 dataset, 98.1% for the MNIST dataset and 98.5% for Neuromorphic dataset: the N-MNIST. Furthermore, our work provides a new perspective on how supervised learning algorithms directly implemented in spiking neural circuitry be conducted, which may give some new insights into Neuromorphological calculation in neuroscience.
On the Security Vulnerabilities of Text-to-SQL Models

Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL systems that are commonly used to create natural language interfaces to databases. We showed that the Text-to-SQL modules within six commercial applications can be manipulated to produce malicious code, potentially leading to data breaches and Denial of Service attacks. 1 This is the first demonstration that NLP models can be exploited as attack vectors 2 in the wild . In addition, experiments using four open-source language models verified that straightforward backdoor attacks on Text-to-SQL systems achieve a 100% success rate without affecting their performance. The aim of this work is to draw the community’s attention to potential software security issues associated with NLP algorithms and encourage exploration of methods to mitigate against them.
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments

Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitatively distinct neural network models often predict similar representational geometries of typical stimulus sets. We propose a Bayesian experimental design approach to synthesizing stimulus sets for adjudicating among representational models efficiently. We apply our method to discriminate among candidate neural network models of behavioral face dissimilarity judgments. Our results indicate that a neural network trained to invert a 3D-face-model graphics renderer is more human-aligned than the same architecture trained on identification, classification, or autoencoding. Our proposed stimulus synthesis objective is generally applicable to designing experiments to be analyzed by representational similarity analysis for model comparison.
Phase-dependent Supermode Excitation in Photonic Molecules

A photonic molecule (PM) is a miniature diffractive optical structure composed of resonance microcavities called atoms (e.g., cylinders or spheres) supporting a set of high-quality eigenmodes. All atoms in a PM are coupled by the electromagnetic fields of eigenmodes, which form collective supermodes of the whole PM. We consider a particular type of mirror-symmetric PMs being optically excited simultaneously via two light channels (tapered fibers). Based on the numerical simulations, we show that the spectral composition of supermodes in such PM can be effectively manipulated by changing the phase detuning between the optical channels. For a seven-atom silicon microcylinder cyclic-PM is demonstrated the possibility to achieve tenfold intensity amplification/suppression of several supermodes from the Stokes and anti-Stokes bands of PM spectrum.
When Private Blockchain Meets Deterministic Database

Private blockchain as a replicated transactional system shares many commonalities with distributed database. However, the intimacy between private blockchain and deterministic database has never been studied. In essence, private blockchain and deterministic database both ensure replica consistency by determinism. In this paper, we present a comprehensive analysis to uncover the connections between private blockchain and deterministic database. While private blockchains have started to pursue deterministic transaction executions recently, deterministic databases have already studied deterministic concurrency control protocols for almost a decade. This motivates us to propose Harmony, a novel deterministic concurrency control protocol designed for blockchain use. We use Harmony to build a new relational blockchain, namely HarmonyBC, which features low abort rates, hotspot resiliency, and inter-block parallelism, all of which are especially important to disk-oriented blockchain. Empirical results on Smallbank, YCSB, and TPC-C show that HarmonyBC offers 2.0x to 3.5x throughput better than the state-of-the-art private blockchains.
Addendum: The Dwarf Irregular Galaxy Sextans A. II. Recent Star Formation History [Astron. J. 114, 2527 (1997)]

It is well known that di†erent regions of dwarf galaxies can have very di†erent color-magnitude diagrams (CMDs). The implication is that each region has its own star formation history (SFH). An e†ort to understand the spatial variation of the SFH will not only reveal how these galaxies have evolved, but will illuminate the star formation process itself. In Paper I et al. we describe Hubble Space Telescope Wide Field Planetary (Dohm-Palmer 1997a), Camera 2 photometric data of the resolved stars in the nearby (D\ 1.4 Mpc) dwarf irregular galaxy Sextans A (DDO 75, A1008[04). The CMDs produced from this data set are very accurate and align well with stellar evolution model predictions for a low-metallicity system. Especially important is the photometric separation of the blue He-burning (HeB) stars, the bluest part of the so-called blue-loop phase, from the main sequence. Paper II et al. outlines a method for calculating the star formation rate (SFR) (Dohm-Palmer 1997b) based on the blue HeB stars over the past 600 Myr. We have combined the spatial density distribution of the blue HeB stars with the SFR calculations to create a time sequence of SFR maps that can be viewed as a movie, a frame of which is shown in Figure 1. The SFR calculation for this movie used the Padua stellar evolution models for Z\ 0.001 et (Bertelli al. and assumed a Salpeter initial mass function (see for details). The spatial density maps 1994) Paper II were created with a Gaussian convolution kernal with p \ 84 pc. The movie has also been convolved in time, with p \ 30 Myr. The SFR density ranges from 0 to 3700 Myr~1 kpc~2, where one star per M _ convolution beam is roughly 60 Myr~1 kpc~2. Finally, each frame is 1 kpc on a side, at the disM _ tance of Sextans A. The movie begins 700 Myr ago and goes through D10 Myr steps to 100 Myr ago. The oldest star formation appears as a di†use region in the lower part of the image. As time progresses, we see a patch of star formation move in from the bottom left corner. This appears to propagate toward the center of the image, peaking at about 370 Myr. This event then fades away as the young association in the bottom right comes to prominence at 200 Myr. provides a full discussion of the SFH of Sextans Paper II A and the implications of this movie.
ULTRAVIOLET AND OPTICAL EXTINCTIONS FOR THE HERBIG-HARO OBJECTS HH 1 AND HH 2

Based on a comparison of reddening-corrected line ratios with predictions from bow-shock models, an attempt is made to determine which type of reddening curve is the most appropriate for HH1 and HH2. Specifically, the extinction curves for R = 3.1 and R = 5.4 are considered. In order to discriminate between these two curves, the flux ratios I(C IV 1550)/I(semiforbidden C III 1909) and I(C IV 1550)/I(H-beta) are used together with information on E(B-V). The suprising result is that the R = 3.1 curve leads to a better agreement between predicted and observed line flux ratios than the R = 5.4 curve.
Learning Single Image Defocus Deblurring with Misaligned Training Pairs

By adopting popular pixel-wise loss, existing methods for defocus deblurring heavily rely on well aligned training image pairs. Although training pairs of ground-truth and blurry images are carefully collected, e.g., DPDD dataset, misalignment is inevitable between training pairs, making existing methods possibly suffer from deformation artifacts. In this paper, we propose a joint deblurring and reblurring learning (JDRL) framework for single image defocus deblurring with misaligned training pairs. Generally, JDRL consists of a deblurring module and a spatially invariant reblurring module, by which deblurred result can be adaptively supervised by ground-truth image to recover sharp textures while maintaining spatial consistency with the blurry image. First, in the deblurring module, a bi-directional optical flow-based deformation is introduced to tolerate spatial misalignment between deblurred and ground-truth images. Second, in the reblurring module, deblurred result is reblurred to be spatially aligned with blurry image, by predicting a set of isotropic blur kernels and weighting maps. Moreover, we establish a new single image defocus deblurring (SDD) dataset, further validating our JDRL and also benefiting future research. Our JDRL can be applied to boost defocus deblurring networks in terms of both quantitative metrics and visual quality on DPDD, RealDOF and our SDD datasets.
Distributionally Robust Optimal Allocation with Costly Verification

We consider the mechanism design problem of a principal allocating a single good to one of several agents without monetary transfers. Each agent desires the good and uses it to create value for the principal. We designate this value as the agent's private type. Even though the principal does not know the agents' types, she can verify them at a cost. The allocation of the good thus depends on the agents' self-declared types and the results of any verification performed, and the principal's payoff matches her value of the allocation minus the costs of verification. It is known that if the agents' types are independent, then a favored-agent mechanism maximizes her expected payoff. However, this result relies on the unrealistic assumptions that the agents' types follow known independent probability distributions. In contrast, we assume here that the agents' types are governed by an ambiguous joint probability distribution belonging to a commonly known ambiguity set and that the principal maximizes her worst-case expected payoff. We study support-only ambiguity sets, which contain all distributions supported on a rectangle, Markov ambiguity sets, which contain all distributions in a support-only ambiguity set satisfying some first-order moment bounds, and Markov ambiguity sets with independent types, which contain all distributions in a Markov ambiguity set under which the agents' types are mutually independent. In all cases we construct explicit favored-agent mechanisms that are not only optimal but also Pareto-robustly optimal.