Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Analysis of Narrative Reports of Psychedelic Drugs

Background: Psychedelic drugs facilitate profound changes in consciousness and have potential to provide insights into the nature of human mental processes and their relation to brain physiology. Yet published scientific literature reflects a very limited understanding of the effects of these drugs, especially for newer synthetic compounds. The number of clinical trials and range of drugs formally studied is dwarfed by the number of written descriptions of the many drugs taken by people. Analysis of these descriptions using machine-learning techniques can provide a framework for learning about these drug use experiences. Methods: We collected 1000 reports of 10 drugs from the drug information website Erowid.org and formed a term-document frequency matrix. Using variable selection and a random-forest classifier, we identified a subset of words that differentiated between drugs. Results: A random forest using a subset of 110 predictor variables classified with accuracy comparable to a random forest using the full set of 3934 predictors. Our estimated accuracy was 51.1%, which compares favorably to the 10% expected from chance. Reports of MDMA had the highest accuracy at 86.9%; those describing DPT had the lowest at 20.1%. Hierarchical clustering suggested similarities between certain drugs, such as DMT and Salvia divinorum. Conclusion: Machine-learning techniques can reveal consistencies in descriptions of drug use experiences that vary by drug class. This may be useful for developing hypotheses about the pharmacology and toxicity of new and poorly characterized drugs.
Low-dose aspirin for prevention of stroke in low-risk patients with atrial fibrillation: Japan Atrial Fibrillation Stroke Trial.

BACKGROUND AND PURPOSE Although the efficacy of anticoagulant therapy for primary prevention of stroke in patients with nonvalvular atrial fibrillation (NVAF) has been established, efficacy of antiplatelet therapy for low-risk patients is disputable in Japanese patients because of the frequent hemorrhagic complications. We examined the efficacy and safety of aspirin therapy in Japanese patients with NVAF in a prospective randomized multicenter trial. METHODS Patients with NVAF were randomized to an aspirin group (aspirin at 150 to 200 mg per day) or a control group without antiplatelet or anticoagulant therapy. Primary end points included cardiovascular death, symptomatic brain infarction, or transient ischemic attack. RESULTS A total of 426 patients were randomized to aspirin group and 445 to no treatment. The trial was stopped earlier because there were 27 primary end point events (3.1% per year; 95% CI, 2.1% to 4.6% per year) in the aspirin group versus 23 (2.4% per year; 95% CI, 1.5% to 3.5% per year) in the control group, suggesting a low possibility of superiority of the aspirin treatment for prevention of the primary end point. In addition, treatment with aspirin caused a marginally increased risk of major bleeding (7 patients; 1.6%) compared with the control group (2 patients; 0.4%; Fisher exact test P=0.101). CONCLUSIONS For prevention of stroke in patients with NVAF, aspirin at 150 to 200 mg per day does not seem to be either effective or safe. Further prospective studies are needed to determine the best preventive therapy for cerebrovascular events in Japanese patients with NVAF.
Das Reallabor als Forschungsprozess und -infrastruktur für nachhaltige Entwicklung: Konzepte, Herausforderungen und Empfehlungen

Die Grose Transformation zur Nachhaltigkeit ist eine gesamtgesellschaftliche Herausforderung, fur deren Bewaltigung auch die Wissenschaft gefordert ist. Das Bundesministerium fur Bildung und Forschung (BMBF) hat daher unter anderem die Fordermasnahme "Nachhaltiges Wirtschaften" (NaWi) ins Leben gerufen, um Wissenschaft und Praxis in ihrer Begegnung dieser Herausforderung zu unterstutzen. Ein neues Format, um diese Wissenschafts-Praxis-Kooperation als Beitrag zu einer nachhaltigen Entwicklung zu gestalten, ist das sogenannte Reallabor. Dort kommen Akteure aus Wissenschaft und Praxis zusammen, um gemeinsam Losungen fur ein realweltliches Nachhaltigkeitsproblem zu erarbeiten und auszuprobieren. Ausgehend von der konzeptionellen und empirischen Reallaborforschung des NaWi-Projekts "Wohlstands-Transformation Wuppertal" (WTW) wurden die Erfahrungen und Erkenntnisse aus den NaWi-Projekten WTW, KInChem und WohnMobil sowie die aktuelle wissenschaftliche Literatur zu Reallaboren synthetisiert. Die hieraus entstandene vorliegende Studie bietet den Leserinnen und Lesern einen umfassenden Uberblick uber den Aufbau und die Umsetzung von Reallaboren. Zunachst werden bisherige Reallaborverstandnisse reflektiert und acht Schlusselkomponenten von Reallaboren prasentiert. Anschliesend wird das Reallabor in seiner Prozess- und seiner Strukturdimension naher beleuchtet. Ein ausfuhrlicher Prozess-Leitfaden zeigt Schritt fur Schritt auf, wie ein Reallabor-Prozess gestaltet werden sollte. Auch die teils neuen Rollen von Wissenschaftlerinnen und Wissenschaftlern in Reallaboren werden analysiert. Schlieslich werden die strukturierenden Elemente eines Reallabors vorgestellt und - wie bereits die Prozessschritte und Rollen - auf die drei NaWi-Projekte angewendet.
Efficient searching with linear constraints

We show how to preprocess a set S of points in Rd into an external memory data structure that efficiently supports linear-constraint queries. Each query is in the form of a linear constraint xd? a0+? d? 1i=1aixi; the data structure must report all the points of S that satisfy the constraint. This problem is called halfspace range searching in the computational geometry literature. Our goal is to minimize the number of disk blocks required to store the data structure and the number of disk accesses (I/Os) required to answer a query. For d=2, we present the first data structure that uses linear space and answers linear-constraint queries using an optimal number of I/Os in the worst case. For d=3, we present a near-linear-size data structure that answers queries using an optimal number of I/Os on the average. We present linear-size data structures that can answer d-dimensional linear-constraint queries (and even more general d-dimensional simplex queries) efficiently in the worst case. For the d=3 case, we also show how to obtain trade-offs between space and query time.
Sperm variables in Cryopreserved Rabbit semen as affected by curcumin nano-formulation added to Tris-egg yolk or Tris-soybean lecithin extender

: This study aimed to assess the impacts of curcumin nano-formulation (CM-NF), type of extender, and their interaction on physical characteristics of cryopreserved rabbit spermatozoa. Ten mature NZW rabbit bucks (6.6–7.8-month-old and 3.45-3.95 kg LBW) were used as semen donors. Pooled semen was diluted with Tris-egg yolk (TEY) or Tris-soybean lecithin (TSBL) extender supplemented with CM-NF (0, 1.5, 2 and 2.5 µg/mL), equilibrated (2h at 5°C), loaded in straws, stored in liquid nitrogen, thawed at 37 o C for 30 s, and evaluated for progressive motility (PM), livability (LS), abnormality (SA), membrane integrity (MI), and acrosome integrity (AI) after dilution, equilibration, and thawing. Results showed that extender type had non-significant effect on all sperm parameters after all cryopreservation processes. In semen diluted with both extenders, CM-NF (1.5 µg/mL) recorded significantly the highest (P<0.05) PM, LS, MI, and AI, and the lowest (P<0.05) SA percentages compared with free-extenders after all cryopreservation processes. Impact of CM-NF (1.5 µg/mL) on all sperm parameters did not differ from that of free-extender. Increasing CM-NF level to 2 µg/mL adversely affect PM, LS, and SA only in post-diluted semen, and had no effects on all sperm characteristics after equilibration and thawing. The interaction effect of extender type with CM-NF level on all sperm was not significant. Conclusively, there were no differences between TEY and TSBL extenders in maintaining rabbit sperm characteristics after cryopreservation. Supplementation of either TEY or TSBL extenders with CM-NF (1.5 µg/mL) can improve the progressive motility, livability, membrane integrity and acrosome integrity in cryopreserved rabbit semen.
Tightly-Coupled Multi-Layer Topologies for 3-D NoCs

Three-dimensional network-on-chip (3-D NoC) is an emerging research topic exploring the network architecture of 3-D ICs that stack several smaller wafers for reducing wire length and wire delay. Although the network topology of 3-D NoC has been explored for a couple of years, there is still only a narrow range of choices. In this paper, we propose a class of 3-D topologies called Xbar-connected network-on-tiers (XNoTs), which consist of multiple network layers tightly connected via crossbar switches. To make the best use of the short delay and high density of inter-wafer links, XNoTs topologies have crossbar switches that connect different layers and their cores. The planar topology on every layer can be independently customized so as to meet the cost-performance requirements, as far as network connectivity is at least guaranteed with the bottom layer. We also propose their routing algorithm, which guarantees deadlock-freedom by restricting the inter-layer packet transfer from a lower-numbered layer to a higher-numbered layer. Path sets at the bottom layer close to the heat sink of the chip can be selectively employed in order to mitigate the heat-dissipation problem of 3-D ICs. Several forms of XNoTs topologies including meshes, tori, and/or trees are created, and they are evaluated in terms of performance, cost, and energy consumption. As a result, we show that even with the flexibilities mentioned above, XNoTs achieve at least as high throughput as existing 3-D topologies for equivalent chip sizes.
Effect of tumor necrosis factor alpha on insulin-dependent diabetes mellitus in NOD mice. I. The early development of autoimmunity and the diabetogenic process

Tumor necrosis factor (TNF) alpha is a cytokine that has potent immune regulatory functions. To assess the potential role of this cytokine in the early development of autoimmunity, we investigated the effect of TNF on the development of insulin-dependent diabetes mellitus (IDDM) in nonobese diabetic (NOD) mice, a spontaneous murine model for autoimmune, insulin-dependent type I diabetes. Treatment of newborn female NOD mice with TNF every other day for 3 wk, led to an earlier onset of disease (10 versus 15 wk of age in control mice) and 100% incidence before 20 wk of age (compared to 45% at 20 wk of age in control phosphate-buffered saline treated female mice). In contrast, administration of an anti-TNF monoclonal antibody, TN3.19. 12, resulted in complete prevention of IDDM. In vitro proliferation assays demonstrated that mice treated with TNF developed an increased T cell response to a panel of beta cell autoantigens, whereas anti-TNF treatment resulted in unresponsiveness to the autoantigens. In addition, autoantibody responses to the panel of beta cell antigens paralleled the T cell responses. The effects mediated by TNF appear to be highly age dependent. Treatment of animals either from birth or from 2 wk of age had a similar effect. However, if treatment was initiated at 4 wk of age, TNF delayed disease onset. These data suggest that TNF has a critical role in the early development of autoimmunity towards beta-islet cells.
WP. ENGLISH ONLY UNITED NATIONS STATISTICAL COMMISSION and ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS

Many different methods for macro editing can be found in the literature but only few of them are based on robust estimates (for example such as BACON-EEM, Epidemic algorithms (EA) and Transformed rank correlation (TRC) methods of Bèguin and Hulliger). However, we can show that outlier detection is only reasonable if robust methods are applied, because the classical estimates are themselves influenced by the outliers. Nevertheless, macro editing is essential to check the multivariate data for possible data problems and it is not deterministic like usual editing using certain rules/constraints. First we review the available multivariate outlier detection methods. In a simulation study, where a subset of the Austrian Structural Business Statistics is simulated, we compare several approaches. Robust methods based on the Minimum Covariance Determinant (MCD) estimator, S-estimators and OGK-estimator as well as BACON-BEM provide the best results in finding the outliers and in providing a low false discovery rate. Many of the discussed methods are implemented in the R package rrcovNA which will be soon available under GPL at CRAN.
Estimation of mental lexicon size with word familiarity database

A familiarity database was developed for about 80,000 Japanese words of which familiarity scores were rated by 32 Japanese adults using a 7-point scale in auditory, visual, and audio-visual modalities. Auditory, visual, and audio-visual stimulus words were selected from the database according to their word familiarity for size estimation of the mental lexicon. Sixty Japanese adults participated in a two-alternative forced-choice task (Know–Don't know) for the stimulus words. The size of the mental lexicon was estimated as the number of words of which familiarity is above a particular word corresponding to 50% point on the fitted logistic curve to “know”-response probability of the stimulus words. The estimated size was about 68,000 for auditory words, and about 66,000 both for visual and audio-visual words when homophones and homographs were included. The results suggest that very small difference in the mental lexicon size among modalities.
Programs Following Total Knee Arthroplasty: A Randomized Controlled Trial

Introduction: Although total knee arthroplasty (TKA) is a frequently performed surgery, a standard rehabilitation approach has not yet been established. The study aimed to investigate the effects of sensorimotor and core stabilization exercises on proprioception, range of motion, balance, and function following TKA. Material and methods: This randomized trial was conducted with 40 female patients (69.38 ± 5.81 years) undergone unilateral TKA. Participants were randomly allocated to either the sensorimotor group (N = 20) or core stabilization group (N = 20). Patients performed exercise programs over 6 week between second and eighth weeks postoperatively. Proprioception, knee and hip range of motion, Knee Injury and Osteoarthritis Outcome Scale, Berg Balance Scale, Timed Up and go test, and 5-times sit-to-stand test were measured on three separate occasions: preoperative (E0), before treatment (E1), and after treatment (E2) during postoperative rehabilitation. Results: A statistically significant improvement was found in both groups for all outcomes between E1 and E2 (p < 0.05). However, the difference between the groups was found only in the KOOS-sportive recreational activities (p < 0.001), favoring the sensorimotor group. Additionally, the treatment programs provided recovery of knee and hip ROM and proprioception (p < 0.05). Conclusions: Core stabilization exercises are effective for improving balance, proprioception, function, and ROM; however, sensorimotor exercises are more effective in the acquisition of sports and recreational activities. Both programs contribute to improvement in rehabilitation through non-operated extremity.
Performance analysis of a BPSK-BPPM UWB physical layer for wireless body area networks

Because of the various application scenarios and use cases they make possible, Body Area Networks (BAN) have been investigated during the recent years by many academic and industrial communities. Among various technologies, Ultra Wide Band (UWB) was considered to be a good candidate for a physical layer in a BAN. Many propagation measurements have been conducted to study the wave propagation around the human body, and then design channel models. Our work aims at evaluating a UWB-Impulse Radio (IR) physical layer for BAN. This physical layer is based on Binary Phase Shift Keying (BPSK) and Binary Pulse Position Modulation (BPPM) modulations and it relies on the last IEEE 802.15. 6 BAN channel models. This analysis presents the Bit or Symbol Error Rate (BER or SER) as a function of the signal to noise ratio (SNR). Computer simulations are conducted using IEEE 802.15. 6 BAN channel models, and the system performances are investigated under coherent and noncoherent receivers.
Integrating Efﬁcient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning

In the realm of computer vision and graphics, accu-001 rately establishing correspondences between geometric 3D 002 shapes is pivotal for applications like object tracking, reg-003 istration, texture transfer, and statistical shape analysis. 004 Moving beyond traditional hand-crafted and data-driven 005 feature learning methods, we incorporate spectral methods 006 with deep learning, focusing on functional maps (FMs) and 007 optimal transport (OT). Traditional OT-based approaches, 008 often reliant on entropy regularization OT in learning-based 009 framework, face computational challenges due to their 010 quadratic cost. Our key contribution is to employ the sliced 011 Wasserstein distance (SWD) for OT, which is a valid fast op-012 timal transport metric in an unsupervised shape matching 013 framework. This unsupervised framework integrates func-014 tional map regularizers with a novel OT-based loss derived 015 from SWD, enhancing feature alignment between shapes 016 treated as discrete probability measures. We also introduce 017 an adaptive reﬁnement process utilizing entropy regularized 018 OT, further reﬁning feature alignments for accurate point-019 to-point correspondences. Our method demonstrates supe-020 rior performance in non-rigid shape matching, including 021 near-isometric and non-isometric scenarios, and excels in 022 downstream tasks like segmentation transfer. The empirical 023 results on diverse datasets highlight our framework’s effec-024 tiveness and generalization capabilities, setting new stan-025 dards in non-rigid shape matching with efﬁcient OT metrics 026 and an adaptive reﬁnement module. 027
On rectiﬁcation of randomly forced ﬂows

Nonlinear rectiﬁcation of the ocean circulation driven by random forcing, which simulates the effect of unresolved eddies, is studied in an idealized closed basin. The results are based on the analysis of randomly forced solutions and linear eigenmodes. Depending on the forcing strength, two rectiﬁcation regimes are found: zonal jets and isolated gyres. It is shown that both regimes are due to nonlinear interactions of resonant basin modes. In the zonal-jet regime, these interactions involve complex interplay between resonant baroclinic modes and some secondary modes. Both Rhines’ scaling for zonal jets and prediction of gyres based on the maximum entropy argument are not conﬁrmed.
Father–Child Longitudinal Relationship: Parental Monitoring and Internet Gaming Disorder in Chinese Adolescents

Although empirical studies have indicated that parents have an important role in preventing Internet gaming disorder in adolescents, longitudinal research on the parental predictors of Internet gaming disorder is lacking. We used a three-wave cross-lagged panel model to explore the reciprocal association between parental monitoring and Internet gaming disorder, and examined the different impacts of mother– and father–child relationships on this association. A sample of 1490 adolescents aged 10–15 years (M = 12.03, SD = 1.59; 45.4% female) completed assessments at all three points. The cross-lagged model revealed that (a) parental monitoring at T1 predicted lower Internet gaming disorder at T2, and greater Internet gaming disorder at T2 predicted lower parental monitoring at T3; (b) father–child relationship had a reciprocal, indirect effect on the relationship between parental monitoring and Internet gaming disorder, while mother–child relationship did not. These findings suggest that the parental effects (e.g., higher parental monitoring and better father–child relationship) might play a vital role in preventing Internet gaming disorder in adolescents.
Expert Systems With Applications

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a ‘‘twinned" Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering ×2 , ×4 , and ×8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as ×8 .
Expert Systems With Applications

The great quantity of music content available online has increased interest in music recommender systems. However, some important problems must be addressed in order to give reliable recommendations. Many approaches have been proposed to deal with cold-start and ﬁrst-rater drawbacks; however, the problem of generating recommendations for gray-sheep users has been less studied. Most of the meth-ods that address this problem are content-based, hence they require item information that is not always available. Another signiﬁcant drawback is the diﬃculty in obtaining explicit feedback from users, necessary for inducing recommendation models, which causes the well-known sparsity problem. In this work, a recommendation method based on playing coeﬃcients is proposed for addressing the above-mentioned shortcomings of recommender systems when little information is available. The results prove that this proposal outperforms other collaborative ﬁltering methods, including those that make use of user attributes.
Self ‐ inferring incomplete multi ‐ view clustering

With the advantage of exploiting complementary and consensus information across multiple views, techniques for Multi ‐ view Clustering have attracted increasing attention in recent years. However, it is common that data on some views is not completed in real ‐ world applications, which brings the challenge of partial mapping between the views. To explore the information hidden in the local geometric structure and recover missing instances through mining the information hidden in existing instances, a self ‐ inferring incomplete multi ‐ view clustering algorithm is proposed. Firstly, the incomplete multi ‐ view data is replenished directly and exploited as variables for inferring the missing instances. And then, a feature graph constraint is united in consensus learning. Besides, a similarity graph learning method is imposed to preserve the local manifold structure. At last, the inferred instances are filled in the missing instances for learning better consensus representation in the iterative process. Extensive experiment results show that this method can improve the clustering performance compared with the state ‐ of ‐ the ‐ art methods.