Miracle-dz/UniFAR-FTUs huggingface dataset:
Recollection of “true” feedback is better than “false” feedback independently of a priori beliefs: an investigation from the perspective of dual-recollection theory

ABSTRACT The processes underlying memory for truth and falsity have been explored and discussed in experimental psychology for over thirty years now. Psychologists have often referred to the Spinozan and Cartesian models about truth-value information “tagging” but, so far, experimental results have been inconsistent. This paper investigates memory for truth and falsity from the new perspective of the dual-recollection theory. We conducted two experiments using the conjoint recognition paradigm and multinomial modelling as a measurement model. Both our experiments confirmed a satisfactory goodness of fit of the data to the dual-recollection multinomial model. In Experiment 1, the context recollection parameter representing memory for feedback information was significantly higher for true than for false statements. This finding was replicated in the second experiment, which controlled the potential impact of participants’ previous knowledge on memory performance. Experiment 2 indicated that the target recollection parameter representing memory for the sentence itself was significantly higher for true than for false sentences solely when participants believed this sentence to be true but not when they perceived it as false before the memory experiment. Our research was the first attempt to look at memory for truth and falsity from the perspective of the recently developed dual-recollection theory.
A Segmented Variable-Parameter ZNN for Dynamic Quadratic Minimization With Improved Convergence and Robustness

As a category of the recurrent neural network (RNN), zeroing neural network (ZNN) can effectively handle time-variant optimization issues. Compared with the fixed-parameter ZNN that needs to be adjusted frequently to achieve good performance, the conventional variable-parameter ZNN (VPZNN) does not require frequent adjustment, but its variable parameter will tend to infinity as time grows. Besides, the existing noise-tolerant ZNN model is not good enough to deal with time-varying noise. Therefore, a new-type segmented VPZNN (SVPZNN) for handling the dynamic quadratic minimization issue (DQMI) is presented in this work. Unlike the previous ZNNs, the SVPZNN includes an integral term and a nonlinear activation function, in addition to two specially constructed time-varying piecewise parameters. This structure keeps the time-varying parameters stable and makes the model have strong noise tolerance capability. Besides, theoretical analysis on SVPZNN is proposed to determine the upper bound of convergence time in the absence or presence of noise interference. Numerical simulations verify that SVPZNN has shorter convergence time and better robustness than existing ZNN models when handling DQMI.
A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Access

In this paper, we propose a model protection method for convolutional neural networks (CNNs) with a secret key so that authorized users get a high classification accuracy, and unau-thorized users get a low classification accuracy. The proposed method applies a block-wise transformation with a secret key to feature maps in the network. Conventional key-based model protection methods cannot maintain a high accuracy when a large key space is selected. In contrast, the proposed method not only maintains almost the same accuracy as non-protected accuracy, but also has a larger key space. Experiments were carried out on the CIFAR-10 dataset, and results show that the proposed model protection method outperformed the previous key-based model protection methods in terms of classification accuracy, key space, and robustness against key estimation attacks and fine-tuning attacks.
Using Constrained Density Functional Theory to Track Proton Transfers and to Sample Their Associated Free Energy Surface

Ab initio molecular dynamics (AIMD) and quantum mechanics/molecular mechanics (QM/MM) methods are powerful tools for studying proton solvation, transfer, and transport processes in various environments. However, due to the high computational cost of such methods, achieving sufficient sampling of rare events involving excess proton motion—especially when Grotthuss proton shuttling is involved—usually requires enhanced free energy sampling methods to obtain informative results. Moreover, an appropriate collective variable (CV) that describes the effective position of the net positive charge defect associated with an excess proton is essential both for tracking the trajectory of the defect and for the free energy sampling of the processes associated with the resulting proton transfer and transport. In this work, such a CV is derived from first principles using constrained density functional theory (CDFT). This CV is applicable to a broad array of proton transport and transfer processes as studied via AIMD and QM/MM simulations.
Repurposing pharmaceutical excipients as an antiviral agent against SARS-CoV-2

Abstract The limited time indorsed to face the COVID-19 emergency and large number of deaths across the globe, poses an unrelenting challenge to find apt therapeutic approaches. However, lead candidate selection to phase III trials of new chemical entity is a time-consuming procedure, and not feasible in pandemic, such as the one we are facing. Drug repositioning, an exploration of existing drug for new therapeutic use, could be an effective alternative as it allows fast-track estimation in phase II–III trials, or even forthright compassionate use. Although, drugs repurposed for COVID-19 pandemic are commercially available, yet the evaluation of their safety and efficacy is tiresome and painstaking. In absence of any specific treatment the easy alternatives such as over the counter products, phytotherapies and home remedies have been largely adopted for prophylaxis and therapy as well. In recent years, it has been demonstrated that several pharmaceutical excipients possess antiviral properties making them prospective candidates against SARS-CoV-2. This review highlights the mechanism of action of various antiviral excipients and their propensity to act against SARs-CoV2. Though, repurposing of pharmaceutical excipients against COVID-19 has the edge over therapeutic agents in terms of safety, cost and fast-track approval trial burdened, this hypothesis needs to be experimentally verified for COVID-19 patients.
Amount, Type, and Timing of Domain-Specific Moderate to Vigorous Physical Activity Among US Adults.

BACKGROUND Surveillance of domain-specific physical activity in the United States is lacking. Thus, the authors describe domain-specific moderate to vigorous physical activity (MVPA) in a nationwide sample of US adults. METHODS Participants from the AmeriSpeak panel (n = 2649; 20-75 y; 50% female) completed the Activities Completed Over Time in 24-Hours previous-day recall. The authors estimated average MVPA duration (in hours per day) overall and in major life domains by sex, age, race/ethnicity, and education. They also described the most commonly reported MVPAs and timing of MVPA during the day. RESULTS Across all life domains, participants reported an average of 2.5 hours per day in MVPA. Most MVPA was accumulated during work (50% of total, 1.2 h/d) and household activities (28%, 0.7 h/d) with less MVPA reported in leisure time (15%, 0.4 h/d). Time reported in MVPA varied by sex, and race/ethnicity (P < .05). Walking at work and for exercise, childcare, and walking for transportation were the most commonly reported domain-specific MVPAs. A greater proportion of MVPA took place in the morning (∼06:00 h) and evening (∼18:00 h). CONCLUSIONS Work and household activities accounted for 78% of overall MVPA reported, while leisure-time MVPA accounted for only 15% of the total. Encouraging MVPA during leisure time and transportation remain important targets for promoting MVPA in US adults.
Counterexample to the conjectured Planckian bound on transport

Nicholas R. Poniatowski, 2, ∗ Tarapada Sarkar, Ricardo P.S. M. Lobo, 4 Sankar Das Sarma, and Richard L. Greene † Maryland Quantum Materials Center and Department of Physics, University of Maryland, College Park, Maryland 20742, USA Department of Physics, Harvard University, Cambridge, MA 02138, USA LPEM, ESPCI Paris, CNRS, PSL University, Paris F-75005, France Sorbonne Université, ESPCI Paris, LPEM, Paris F-75005, France Condensed Matter Theory Center and Joint Quantum Institute, Department of Physics, University of Maryland, College Park, Maryland 20742, USA (Dated: December 23, 2021)
High carbon losses from oxygen‐limited soils challenge biogeochemical theory and model assumptions

Oxygen (O2) limitation contributes to persistence of large carbon (C) stocks in saturated soils. However, many soils experience spatiotemporal O2 fluctuations impacted by climate and land‐use change, and O2‐mediated climate feedbacks from soil greenhouse gas emissions remain poorly constrained. Current theory and models posit that anoxia uniformly suppresses carbon (C) decomposition. Here we show that periodic anoxia may sustain or even stimulate decomposition over weeks to months in two disparate soils by increasing turnover and/or size of fast‐cycling C pools relative to static oxic conditions, and by sustaining decomposition of reduced organic molecules. Cumulative C losses did not decrease consistently as cumulative O2 exposure decreased. After >1 year, soils anoxic for 75% of the time had similar C losses as the oxic control but nearly threefold greater climate impact on a CO2‐equivalent basis (20‐year timescale) due to high methane (CH4) emission. A mechanistic model incorporating current theory closely reproduced oxic control results but systematically underestimated C losses under O2 fluctuations. Using a model‐experiment integration (ModEx) approach, we found that models were improved by varying microbial maintenance respiration and the fraction of CH4 production in total C mineralization as a function of O2 availability. Consistent with thermodynamic expectations, the calibrated models predicted lower microbial C‐use efficiency with increasing anoxic duration in one soil; in the other soil, dynamic organo‐mineral interactions implied by our empirical data but not represented in the model may have obscured this relationship. In both soils, the updated model was better able to capture transient spikes in C mineralization that occurred following anoxic–oxic transitions, where decomposition from the fluctuating‐O2 treatments greatly exceeded the control. Overall, our data‐model comparison indicates that incorporating emergent biogeochemical properties of soil O2 variability will be critical for effectively modeling C‐climate feedbacks in humid ecosystems.
Evolution of Jigsaw ‐ a National Youth Mental Health Service

There has been a global movement towards transformation of youth mental health services, but limited information on the core principles and characteristics of these new services is available. Jigsaw is one such service, established in Ireland in 2006, with the intent of creating change in Ireland's system of mental healthcare for 12–25 year olds. The aim of this paper is to describe the evolution of Jigsaw services, which are now firmly embedded in the Irish system of care for young people, and recognized internationally as an established service network.
YAP facilitates melanoma migration through regulation of actin‐related protein 2/3 complex subunit 5 (ARPC5)

Yes‐associated protein 1 (YAP) and transcriptional coactivator with PDZ‐binding motif (TAZ) are transcriptional coactivators that have been implicated in driving metastasis and progression in many cancers, mainly through their transcriptional regulation of downstream targets. Although YAP and TAZ have shown redundancy in many contexts, it is still unknown whether or not this is true in melanoma. Here, we show that while both YAP and TAZ are expressed in a panel of melanoma cell lines, depletion of YAP results in decreased cell numbers, focal adhesions, and the ability to invade matrigel. Using non‐biased RNA‐sequencing analysis, we find that melanoma cells depleted of YAP, TAZ, or YAP/TAZ exhibit drastically different transcriptomes. We further uncover the ARP2/3 subunit ARPC5 as a specific target of YAP but not TAZ and that ARPC5 is essential for YAP‐dependent maintenance of melanoma cell focal adhesion numbers. Our findings suggest that in melanoma, YAP drives melanoma progression, survival, and invasion.
Synthesis and Characterization of Sn, Ge, and Zr Isomorphous Substituted MFI Nanosheets for Glucose Isomerization to Fructose.

Various metals including Sn, Ge, and Zr have been successfully incorporated into the MFI nanosheets via a one-pot synthesis. The as-synthesized zeolites exhibit high external surface area and mesopore volume without large metal oxides aggregated on zeolite surfaces. Interestingly, the successful introduction of heteroatoms in MFI nanosheets can be confirmed by shifted XRD peaks corresponding to the unit cell expansion due to the replacement of metals into the framework. In addition, the UV-Vis absorbance spectra reveal that at the suitable metal loading the incorporated tetrahedral coordination of metal species in the zeolite framework has been obtained. To illustrate the benefits of the prepared catalysts, the glucose isomerization to fructose was carried out in a water/dioxane system. Obviously, the SnMFI-NS samples, containing the high dispersion of metal isomorphous species demonstrate the outstanding catalytic behavior in term of fructose selectivity (>85 %).
Developing reliably distinguishable color schemes for legends of natural resource taxonomy-based maps

ABSTRACT The legends of natural resource taxonomy-based maps (e.g. soil, geological, geomorphological, vegetation, and land cover/land use) need many different distinguishable colors. The existing methods of color selection for map legends are based on the designer subjectivity, ensuring schemes having few colors. An analysis of the modeling and management of colors in digital applications has led to define an algorithm to calculate an objective colorimetric measure of color difference – “DE*ab ” – based on the perceptually uniform color model CIELAB. The proposed method consists of a set of specific rules for developing hierarchically structured color schemes and a specific procedure for ensuring selection of a large number of reliably distinguishable colors, based on a color difference threshold. The accuracy of color reproduction in printing processes is also taken into account. The method has been applied to develop a standard of colors for soil maps. It contains 63 colors and has been used for developing a soil map having 41 standard colors. A user test of the method results proved that thresholds of 10 DE*ab units and 15 DE*ab units ensure obtaining acceptably distinguishable colors for displaying/printing maps by using high-quality, respectively, current devices. Three datasets that support the research are given.
Learning Hierarchical Visual-Semantic Representation with Phrase Alignment

Effective visual-semantic representation is critical to the image-text matching task. Various methods are proposed to develop image representation with more semantic concepts and a lot of progress has been achieved. However, the internal hierarchical structure in both image and text, which could effectively enhance the semantic representation, is rarely explored in the image-text matching task. In this work, we propose a Hierarchical Visual-Semantic Network (HVSN) with fine-grained semantic alignment to exploit the hierarchical structure. Specifically, we first model the spatial or semantic relationship between objects and aggregate them into visual semantic concepts by the Local Relational Attention (LRA) module. Then we employ Gated Recurrent Unit (GRU) to learn relationships between visual semantic concepts and generate the global image representation. For the text part, we develop phrase features from related words, then generate text representation by learning relationships between these phrases. Besides, the model is trained with joint optimization of image-text retrieval and phrase alignment task to capture the fine-grained interplay between vision and language. Our approach achieves state-of-the-art performance on Flickr30K and MS-COCO datasets. On Flickr30K, our approach outperforms the current state-of-the-art method by 3.9% relatively in text retrieval with image query and 1.3% relatively for image retrieval with text query (based on Recall@1). On MS-COCO, our HVSN improves image retrieval by 2.3% relatively and text retrieval by 1.2% relatively. Both quantitative and visual ablation studies are provided to verify the effectiveness of the proposed modules.
A real-time global re-localization framework for 3D LiDAR SLAM

Simultaneous localization and mapping (SLAM) has been a hot research field in the past years. Against the backdrop of more affordable 3D LiDAR sensors, research on 3D LiDAR SLAM is becoming increasingly popular. Furthermore, the re-localization problem with a point cloud map is the foundation for other SLAM applications. In this paper, a template matching framework is proposed to re-localize a robot globally in a 3D LiDAR map. This presents two main challenges. First, most global descriptors for point cloud can only be used for place detection under a small local area. Therefore, in order to re-localize globally in the map, point clouds and descriptors(templates) are densely collected using a reconstructed mesh model at an offline stage by a physical simulation engine to expand the functional distance of point cloud descriptors. Second, the increased number of collected templates makes the matching stage too slow to meet the real-time requirement, for which a cascade matching method is presented for better efficiency. In the experiments, the proposed framework achieves 0.2-meter accuracy at about 10Hz matching speed using pure python implementation with 100k templates, which is effective and efficient for SLAM applications. Key Words—global re-localization, LiDAR SLAM, template matching
Personalized Chest Computed Tomography

Objectives The purpose of this study was to evaluate the minimum diagnostic radiation dose level for the detection of high-resolution (HR) lung structures, pulmonary nodules (PNs), and infectious diseases (IDs). Materials and Methods A preclinical chest computed tomography (CT) trial was performed with a human cadaver without known lung disease with incremental radiation dose using tin filter-based spectral shaping protocols. A subset of protocols for full diagnostic evaluation of HR, PN, and ID structures was translated to clinical routine. Also, a minimum diagnostic radiation dose protocol was defined (MIN). These protocols were prospectively applied over 5 months in the clinical routine under consideration of the individual clinical indication. We compared radiation dose parameters, objective and subjective image quality (IQ). Results The HR protocol was performed in 38 patients (43%), PN in 21 patients (24%), ID in 20 patients (23%), and MIN in 9 patients (10%). Radiation dose differed significantly among HR, PN, and ID (5.4, 1.2, and 0.6 mGy, respectively; P < 0.001). Differences between ID and MIN (0.2 mGy) were not significant (P = 0.262). Dose-normalized contrast-to-noise ratio was comparable among all groups (P = 0.087). Overall IQ was perfect for the HR protocol (median, 5.0) and decreased for PN (4.5), ID-CT (4.3), and MIN-CT (2.5). The delineation of disease-specific findings was high in all dedicated protocols (HR, 5.0; PN, 5.0; ID, 4.5). The MIN protocol had borderline IQ for PN and ID lesions but was insufficient for HR structures. The dose reductions were 78% (PN), 89% (ID), and 97% (MIN) compared with the HR protocols. Conclusions Personalized chest CT tailored to the clinical indications leads to substantial dose reduction without reducing interpretability. More than 50% of patients can benefit from such individual adaptation in a clinical routine setting. Personalized radiation dose adjustments with validated diagnostic IQ are especially preferable for evaluating ID and PN lesions.
Global Path Planning for Fire-Fighting Robot Based on Advanced Bi-RRT Algorithm*

Successful and efficient path planning is essential for autonomous mobile robot. Traditional Rapidly-Exploring Random Tree(RRT) algorithm usually suffer from long path planning time and strong randomness in specific environment such as depression trap and narrow channel. In order to tackle such problem, in this paper a bidirectional fast search algorithm based on violent matching and regression analysis is proposed for fire-fighting robot. Specifically, by using violent matching strategy, the path can be searched directly when there are few obstacles. In addition, the regression analysis reduces the probability of sampling nodes appearing in the same area. Meanwhile, a bidirectional RRT algorithm is employed to improve the path search efficiency. Finally, the redundant nodes in the random tree are discarded by the method of node backtracking, which reduces the robot control complexity. Several comparison experiment case studies are carried out on Rviz, and the results validate the effectiveness and advantages of our proposed approach.
Exploration of Zoo felids in North-East China for the prevalence and molecular identification of Cryptosporidium spp.

Cryptosporidium spp. is a protozoan having the potential to cause zoonosis in humans and animals. Despite the zoonotic importance of this protozoan parasite, limited data are available about its prevalence in zoo felids in North-Eastern China. Hence, the current study was designed to determine the occurrence and molecular characterization of Cryptosporidium spp. from the fecal samples of captive zoo felids. Fecal samples (N = 244) were collected from different felids from five different zoos of North-Eastern China. 18S rRNA gene was amplified from the genomic DNA using species specific primers in nested polymerase chain reaction (nPCR) and Cryptosporidium parvum and Cryptosporidium spp. was found. The overall prevalence of Cryptosporidium was 9.43% (23/244). The 18S rRNA gene similarity analysis showed that 6 Cryptosporidium isolates were Cryptosporidium parvum and the remaining 17 Cryptosporidium isolates were resembling to a Cryptosporidium spp. , which is similar to Cryptosporidium NEV10. Phylogenetic tree was constructed based on 18S rRNA of Cryptosporidium spp. The similarity of Cryptosporidium parvum was with its other isolates in China, India, Iran, Iraq, Turkey, Czech Republic, Spain and USA while Cryptosporidium NEV10 alike had a close relationship with Turkish isolates. In conclusion, Cryptosporidium was prevailing in feline animals of China zoo and zoo officials are directed to consider their control policy as it can be a cause of zoonosis.