Miracle-dz/UniFAR-FTUs huggingface dataset:
Title: Analogy-related Information Can be Accessed by Simple Addition and Subtraction of fMRI Activation Patterns, without Participants Performing any Analogy Task. Abbreviated Title: Analogy-related Information Accessed by Simple Arithmetic in fMRI Patterns

nouns or adjectives in the brain is organized such that analogy-related information can 8 be similarly accessed. Second, the tool words in the stimuli list have lower word frequency 9 compared to words in other categories, and less-frequent words might elicit higher activation 10 values in brain regions such as the left inferior frontal gyrus (Schuster, Hawelka, Hutzler, 11 Kronbichler, & Richlan, 2016). However, at the same time, the tool words were not obscure (e.g. 12 gun, wrench, skillet) and word frequency alone could not account for all of the ranking metrics, 13 in particular the theme metric that compares words within the same category. Third, the 14 categories chosen are quite distinct, and it is challenging to eliminate every confounder that 15 varies between any two categories; for instance, living vs. non-living can also differentiate 16 person words from the others. Again, these confounders cannot drive the significant results in the 17 theme metric. Further studies will be necessary to test whether the current results can be 18 generalized to finer-grained categories. Finally, our word stimuli varied in length, because it was 19 beyond our ability to devise a suitable list of words with the same length. While word length is 20 an important factor in semantic processing and reading (Just et al., 2010; Schuster et al., 2016), 21 its influence has mostly been observed in the occipital pole and lingual/fusiform gyri (Just et al., 22 D ow naded rom httpdirect.m it.edu/nol/article-pdf/doil_a_00045/1927611/nol_a_00045. pdf by gest on 02 Sptem er 2021
Simulating quantum circuits with ZX-calculus reduced stabiliser decompositions

We introduce an enhanced technique for strong classical simulation of quantum circuits which combines the ‘sum-of-stabilisers’ method with an automated simplification strategy based on the ZX-calculus. Recently it was shown that quantum circuits can be classically simulated by expressing the non-stabiliser gates in a circuit as magic state injections and decomposing them in chunks of 2–6 states at a time, obtaining sums of (efficiently-simulable) stabiliser states with many fewer terms than the naive approach. We adapt these techniques from the original setting of Clifford circuits with magic state injection to generic ZX-diagrams and show that, by interleaving this ‘chunked’ decomposition with a ZX-calculus-based simplification strategy, we can obtain stabiliser decompositions that are many orders of magnitude smaller than existing approaches. We illustrate this technique to perform exact norm calculations (and hence strong simulation) on the outputs of random 50- and 100-qubit Clifford + T circuits with up to 70 T-gates as well as a family of hidden shift circuits previously considered by Bravyi and Gosset with over 1000 T-gates.
Most “Young” α-rich Stars Have High Masses but are Actually Old

Recent observations have revealed a population of α-element abundances, enhanced giant stars with unexpected high masses (≳1 M ⊙) from asteroseismic analysis and spectroscopy. Assuming single-star evolution, their masses imply young ages (τ < 6 Gyr) incompatible with the canonical Galactic chemical evolution scenario. Here we study the chemistry and kinematics of a large sample of such α-rich, high-mass red giant branch (RGB) stars drawn from the LAMOST spectroscopic surveys. Using LAMOST and Gaia, we found these stars share the same kinematics as the canonical high-α old stellar population in the Galactic thick disk. The stellar abundances show that these high-α massive stars have α- and iron-peak element abundances similar to those of the high-α old thick-disk stars. However, a portion of them exhibit higher [(N+C)/Fe] and [Ba/Fe] ratios, which implies they have gained C- and Ba-rich materials from extra sources, presumably asymptotic giant branch (AGB) companions. The results support the previous suggestion that these RGB stars are products of binary evolution. Their high masses thus mimic “young” single stars, yet in fact they belong to an intrinsic old stellar population. To fully explain the stellar abundance patterns of our sample stars, a variety of binary evolution channels, such as main-sequence (MS) + RGB, MS + AGB, RGB + RGB, and RGB + AGB, are required, pointing to diverse formation mechanisms of these seemly rejuvenated cannibals. With this larger sample, our results confirm earlier findings that most, if not all, α-rich stars in the Galactic disk seem to be old.
The Role of Explainability in Assuring Safety of Machine Learning in Healthcare

Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against which to assess validity. This problem is exacerbated by the “opaque” nature of ML where the learnt model is not amenable to human scrutiny. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human-interpretable representations of ML models which can help users to gain confidence and build trust in the ML system. However, little work explicitly investigates the role of explainability for safety assurance in the context of ML development. This paper identifies ways in which XAI methods can contribute to safety assurance of ML-based systems. It then uses a concrete ML-based clinical decision support system, concerning weaning of patients from mechanical ventilation, to demonstrate how XAI methods can be employed to produce evidence to support safety assurance. The results are also represented in a safety argument to show where, and in what way, XAI methods can contribute to a safety case. Overall, we conclude that XAI methods have a valuable role in safety assurance of ML-based systems in healthcare but that they are not sufficient in themselves to assure safety.
The influence of foreign languages on L1: the case of intervocalic stop occlusion

The aim of the article is to investigate cross-linguistic influence (CLI) of foreign languages on the L1 also referred to as L1 drift. The influence is measured by means of the degree of intervocalic stop occlusion in all the languages of L1 Polish, L2 Spanish and L3 English multilinguals. Although Polish is a language with no systemic spirantization of intervocalic stops, the assumption is that if L1 Polish is under the influence of L2 Spanish in multilinguals the degree of intervocalic stop occlusion may be lower in the otherwise non-spirantized context in L1 Polish. The degree of intervocalic stop occlusion is calculated with measures of intensity: Cmin, Vmax; but also with measures of relative intensity used in spirantization research: IntDiff and IntRatio. The results show that only Cmin and Vmax effectively capture the influence of L2 on L1, whereas the extent of the influence is conditioned by how well the speakers master spirantization in their L2 Spanish. Finally, as predicted, the L3 does not constitute a source of influence for the L1.
ANALYSIS OF COST OF SALES AND SALES ON NET INCOME

The existence of competition the world of business in Indonesia, triggered because increasingly many types of businesses that arise and thrive in a rapidly so that the level of competition of business is more stringent and more difficult to avoid. Most companies vying to raise the value of the company to maximize profit them because only entities business that have the performance or the performance of the well which will survive. In a competitive business that is increasingly competitive companies are required to increasingly efficient in running their activities especially in the conditions of the economy when these are filled with uncertainty where the crisis economy that hit the world very heavy and damaging every sector of the economy, so the need to optimize the source power are held.
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection

ASVspoof 2021 is the forth edition in the series of bi-annual challenges which aim to promote the study of spoofing and the design of countermeasures to protect automatic speaker verification systems from manipulation. In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previous editions, ASVspoof 2021 introduces a new task involving deepfake speech detection. This paper describes all three tasks, the new databases for each of them, the evaluation metrics, four challenge baselines, the evaluation platform and a summary of challenge results. Despite the introduction of channel and compression variability which compound the difficulty, results for the logical access and deepfake tasks are close to those from previous ASVspoof editions. Results for the physical access task show the difficulty in detecting attacks in real, variable physical spaces. With ASVspoof 2021 being the first edition for which participants were not provided with any matched training or development data and with this reflecting real conditions in which the nature of spoofed and deepfake speech can never be predicated with confidence, the results are extremely encouraging and demonstrate the substantial progress made in the field in recent years.
Double- and Triple-line Spectroscopic Candidates in the LAMOST Medium-Resolution Spectroscopic Survey

The LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS) provides an unprecedented opportunity for detecting multiline spectroscopic systems. Based on the cross correlation function and successive derivatives, we search for spectroscopic binaries and triples and derive their radial velocities (RVs) from the LAMOST-MRS spectra. A Monte Carlo simulation is adopted to estimate the RV uncertainties. After examining over 1.3 million LAMOST DR7 MRS blue-arm spectra, we obtain 3133 spectroscopic binary (SB) and 132 spectroscopic triple (ST) candidates, which account for 1.2% of the LAMOST-MRS stars. Over 95% of the candidates are newly discovered. It is found that all of the ST candidates are on the main sequence, while around 10% of the SB candidates may have one or two components on the red giant branch.
A Network Analysis of Skill Game Dynamics

Casino games can be classified in two main categories, i.e. skill games and gambling. Notably, the former refers to games whose outcome is affected by the strategies of players, the latter to those games whose outcome is completely random. For instance, lotteries are easily recognized as pure gambling, while some variants of Poker (e.g. Texas Hold’em) are usually considered as skill games. In both cases, the theory of probability constitutes the mathematical framework for studying their dynamics, despite their classification. Here, it is worth to consider that when games entail the competition between many players, the structure of interactions can acquire a relevant role. For instance, some games as Bingo are not characterized by this kind of interactions, while other games as Poker, show a network structure, i.e. players interact each other and have the opportunity to share or exchange information. In this paper, we analyze the dynamics of a population composed of two species, i.e. strong and weak agents. The former represents expert players, while the latter beginners, i.e. non-expert ones. Here, pair-wise interactions are based on a very simple game, whose outcome is affected by the nature of the involved agents. In doing so, expert agents have a higher probability to succeed when playing with weak agents, while the success probability is equal when two agents of the same kind face each other. Numerical simulations are performed considering a population arranged in different topologies like regular graphs and in scale-free networks. This choice allows to model dynamics that we might observe on online game platforms. Further aspects as the adaptability of agents are taken into account, e.g. the possibility to improve (i.e. to becomean expert). Results show that complex topologies represent a strong opportunity for experts and a risk for both kinds of agents.
Observation of electroweak production of two jets in association with an isolated photon and missing transverse momentum, and search for a Higgs boson decaying into invisible particles at 13 TeV with the ATLAS detector

This paper presents a measurement of the electroweak production of two jets in association with a Z γ pair, with the Z boson decaying into two neutrinos. It also presents a search for invisible or partially invisible decays of a Higgs boson with a mass of 125 GeV produced through vector-boson fusion with a photon in the ﬁnal state. These results use data from LHC proton–proton collisions at √ s = 13 TeV collected with the ATLAS detector and corresponding to an integrated luminosity of 139 fb − 1 . The event signature, shared by all benchmark processes considered for the measurements and searches, is characterized by a signiﬁcant amount of unbalanced transverse momentum and a photon in the ﬁnal state, in addition to a pair of forward jets. Electroweak Z γ production in association with two jets is observed in this ﬁnal state with a signiﬁcance of 5.2 (5.1 expected) standard deviations. The measured ﬁducial cross-section for this process is 1 . 31 ± 0 . 29 fb. An observed (expected) upper limit of 0.37 (0 . 34 + 0 . 15 − 0 . 10 ) at 95% conﬁdence level is set on the branching ratio of a 125 GeV Higgs boson to invisible particles, assuming the Standard Model production cross-section. The signature is also interpreted in the context of decays of a Higgs boson into a photon and a dark photon. An observed (expected) 95% CL upper limit on the branching ratio for this decay is set at 0.018 (0 . 017 + 0 . 007 − 0 . 005 ), assuming the Standard Model production cross-section for a 125 GeV Higgs boson.
Compton-Thick AGN in the NuSTAR ERA VII. A joint NuSTAR, Chandra, and XMM-Newton Analysis of Two Nearby, Heavily Obscured Sources

We present the joint Chandra, XMM-Newton, and NuSTAR analysis of two nearby Seyfert galaxies, NGC 3081 and ESO 565-G019. These are the only two having Chandra data in a larger sample of 10 low-redshift (z ≤ 0.05), candidates Compton-thick (CT) Active Galactic Nuclei selected in the 15–150 keV band with Swift-BAT that were still lacking NuSTAR data. Our spectral analysis, performed using physically motivated models, provides an estimate of both the line-of-sight (l.o.s.) and average (N H,S ) column densities of the two torii. NGC 3081 has a Compton-thin l.o. s. column density N H,z = [0.58–0.62] × 1024 cm−2, but the N H,S , beyond the CT threshold (N H,S = [1.41–1.78] × 1024 cm−2), suggests a “patchy” scenario for the distribution of the circumnuclear matter. ESO 565-G019 has both CT l.o. s. and N H,S column densities (N H,z > 2.31 × 1024 cm−2 and N H,S > 2.57 × 1024 cm−2, respectively). The use of physically motivated models, coupled with the broad energy range covered by the data (0.6–70 keV and 0.6–40 keV, for NGC 3081 and ESO 565-G019, respectively) allows us to constrain the covering factor of the obscuring material, which is C TOR = [0.63–0.82] for NGC 3081, and C TOR = [0.39–0.65] for ESO 565-G019.
‘I’m Not at All Protected and I Think Other Women Should Know That, That They’re Not Protected Either’: Victim–Survivors’ Experiences of ‘Misidentification’ in Victoria’s Family Violence System

The misidentification of women as predominant aggressors has emerged as a topical issue in family violence research, with feminist scholarship suggesting that such trends may be attributed to a range of factors, including incident-based policing and a misunderstanding of the ways in which women use violence against their partners. Where existing research has primarily focused on policing practices in relation to misidentification, this article explores the impacts of misidentification on the lives of women victim–survivors of family violence in Victoria (Australia), a jurisdiction that has recently seen significant reforms to family violence systems in the wake of the Victorian Royal Commission into Family Violence (2016). Using data from interviews with 32 system stakeholders and survey responses from 11 women who have experienced misidentification in Victoria, this study explores misidentification within the family violence intervention order system. It demonstrates that being misidentified as a predominant aggressor on a family violence intervention order can have a significant impact on women’s lives and their access to safety, highlighting the need for improved policing and court responses to the issue beyond existing reforms.
Multichannel Audio Source Separation with Independent Deeply Learned Matrix Analysis Using Product of Source Models

Independent deeply learned matrix analysis (IDLMA) is one of the state-of-the-art multichannel audio source separation methods using the source power estimation based on deep neural networks (DNNs). The DNN-based power estimation works well for sounds having timbres similar to the DNN training data. However, the sounds to which IDLMA is applied do not always have such timbres, and the timbral mismatch causes the performance degradation of IDLMA. To tackle this problem, we focus on a blind source separation counterpart of IDLMA, independent low-rank matrix analysis. It uses nonnegative matrix factorization (NMF) as the source model, which can capture source spectral components that only appear in the target mixture, using the low-rank structure of the source spectrogram as a clue. We thus extend the DNN-based source model to encompass the NMF-based source model on the basis of the product-of-expert concept, which we call the product of source models (PoSM). For the proposed PoSM-based IDLMA, we derive a computationally efficient parameter estimation algorithm based on an optimization principle called the majorization-minimization algorithm. Experimental evaluations show the effectiveness of the proposed method.
Endophytes and Rhizosphere Fungi from Galam (Melaleuca cajuputi Powell.) which has the Potential to Produce Indole Acetic Acid (IAA)

ABSTRACT Some types of fungi are known to have the ability to produce Indole Acetic Acid (IAA). Fungi can be isolated from the rhizosphere and tissues of various plants, including from the rhizosphere and the root "Galam" (Melaleuca cajuputi Powell.), which grow predominantly in peatlands. Therefore, the purposes of this study were: (a) to isolate and measure the potential of fungi from endophytic and rhizospheric of “Galam”(M. cajuputi) as a producer of IAA hormone, (b) determine the types of fungal interaction that occur and their potential to increase the total IAA hormone produced. This research begins with isolation, purification, isolate screening, analysis of IAA hormone production, data analysis, seed germination test and isolates identification. The result showed that the concentration of IAA produced by Penicillium sp. IRZ15 was 5.86 ± 0.47 μg. mL-1 to 8.46 ± 0.26 μg. mL-1 and Syncephalastrum sp. AG15 is 4.77 ± 0.44 μg. mL-1 to 8.77 ± 0.25 μg. mL-1. Meanwhile, the combination of rhizospheric fungi Penicillium sp. IRZ15 and endophytic fungi Syncephalastrum sp. AG15 does not produce significantly different IAA concentrations (6.42 ± 0.34 μg.mL-1 to 9.19 ± 0.50 μg.mL-1 ) compared to fungi used alone without combinations.
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification

Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations. Traditional methods first devise task-specific operations such as Synonym Substitute, then preset the corresponding parameters such as the substitution rate artificially, which require a lot of prior knowledge and are prone to fall into the sub-optimum. Besides, the number of editing operations is limited in the previous methods, which decreases the diversity of the augmented data and thus restricts the performance gain. To overcome the above limitations, we propose a framework named Text AutoAugment (TAA) to establish a compositional and learnable paradigm for data augmentation. We regard a combination of various operations as an augmentation policy and utilize an efficient Bayesian Optimization algorithm to automatically search for the best policy, which substantially improves the generalization capability of models. Experiments on six benchmark datasets show that TAA boosts classification accuracy in low-resource and class-imbalanced regimes by an average of 8.8% and 9.7%, respectively, outperforming strong baselines.
The Impact of the COVID-19 Pandemic on Social Workers at the Frontline: A Survey of Canadian Social Workers

Abstract Social workers are facing increasingly complex client needs during the coronavirus disease of 2019 (COVID-19) pandemic. Because of the social distancing requirements of the pandemic, social workers have undergone transformative changes in practice with the rapid uptake of virtual technologies. The objective of our study was to understand the experiences of social workers during the first-wave of the COVID-19 pandemic. We conducted a cross-sectional, web-based survey, comprised of close-ended and open-ended questions. Survey participants included social workers who were the members of a provincial social work association in Ontario, Canada. With n = 2,470 participants, the response rate was close to 40 per cent. Descriptive statistics were conducted on the close-ended questions. Two open-ended questions were coded using the thematic analysis. Nine themes were identified on the impact to social worker’s employment status: increased work-load; loss of employment; redeployment to new settings; early retirement; concern for personal health and safety; social workers in private practice seeing fewer clients; personal caregiving responsibilities; limiting recent graduates’ employment potential and social workers experiencing new opportunities. There were five themes on the impact on social work practice: clients with increasing complexities; challenges with transition to virtual care; benefits with transition to virtual care; adapting in-person services and personal well-being.
Human papillomavirus 16 L1 gene methylation as a potential biomarker for predicting anal intraepithelial neoplasia in men who have sex with men (MSM)

The human papillomavirus (HPV) 16 early promoter and L1 gene methylation were quantitatively measured using pyrosequencing assay in anal cells collected from men who have sex with men (MSM) to determine potential biomarkers for HPV-related anal cancer. The methylation patterns of HPV16 genes, including the early promoter (CpG 31, 37, 43, 52, and 58) and L1 genes (CpG 5600, 5606, 5609, 5615, 7136, and 7145), were analyzed in 178 anal samples. The samples were diagnosed as normal, anal intraepithelial neoplasia (AIN) 1, AIN2, and AIN3. Low methylation levels of the early promoter (< 10%) and L1 genes (< 20%) were found in all detected normal anal cells. In comparison, medium to high methylation (≥ 20–60%) in the early promoter was found in 1.5% (1/67) and 5% (2/40) of AIN1 and AIN2-3 samples, respectively. Interestingly, slightly increased L1 gene methylation levels (≥ 20–60%), especially at the HPV16 5’L1 regions CpGs 5600 and 5609, were demonstrated in AIN2-3 specimen. Moreover, a negative correlation between high HPV16 L1 gene methylation at CpGs 5600, 5609, 5615, and 7145 and a percentual CD4 count was found in AIN3 HIV positive cases. When comparing the methylation status of AIN2-3 to that of normal/AIN1 lesions, the results indicated the potential of using HPV16 L1 gene methylation as a biomarker for HPV-related cancer screening.