Miracle-dz/UniFAR-FTUs huggingface dataset:
A COMMUNICATION EFFICIENT FEDERATED KERNEL k-MEANS

A federated kernel k-means algorithm is developed in this paper. This algorithm resolves two challenging issues: 1) how to distributedly so lve the optimization problem of kernel k-means under federated settings; 2) how to maintain communication efficiency in the algorithm. To tackle the first chal lenge, a distributed stochastic proximal gradient descent (DSPGD) algorithm is developed to determine an approximate solution to the optimization problem of kernelk-means. To tackle the second challenge, a communication efficient mech anism (CEM) is designed to reduce the communication cost. Besides, the feder ated kernelk-means provides two levels of privacy preservation : 1) users’ local data are not exposed to the cloud server; 2) the cloud server cannot recover users ’ local data from the local computational results via matrix operations. Theoretical analysis shows: 1) DSPGD with CEM converges with an O(1/T ) rate, whereT is the number of iterations; 2) the communication cost of DSPGD with CEM is un related to the number of data samples; 3) the clustering quality of the federated kernel k-means approaches that of the standard kernel k-means, with a(1 + ǫ) approximate ratio. The experimental results show that the federated kerne l k-means achieves the highest clustering quality with the communication cost red uced by more than 60% in most cases.
WHY DOES DECENTRALIZED TRAINING OUTPER-

Distributed Deep Learning (DDL) is essential for large-scale Deep Learning (DL) training. Using a sufficiently large batch size is critical to achieving DDL runtime speedup. In a large batch setting, the learning rate must be increased to compensate for the reduced number of parameter updates. However, a large batch size may converge to sharp minima with poor generalization, and a large learning rate may harm convergence. Synchronous Stochastic Gradient Descent (SSGD) is the de facto DDL optimization method. Recently, Decentralized Parallel SGD (DPSGD) has been proven to achieve a similar convergence rate as SGD and to guarantee linear speedup for non-convex optimization problems. While there was anecdotal evidence that DPSGD outperforms SSGD in the large-batch setting, no systematic study has been conducted to explain why this is the case. Based on a detailed analysis of the DPSGD learning dynamics, we find that DPSGD introduces additional landscape-dependent noise, which has two benefits in the large-batch setting: 1) it automatically adjusts the learning rate to improve convergence; 2) it enhances weight space search by escaping local traps (e.g., saddle points) to find flat minima with better generalization. We conduct extensive studies over 12 stateof-the-art DL models/tasks and demonstrate that DPSGD consistently outperforms SSGD in the large batch setting; and DPSGD converges in cases where SSGD diverges for large learning rates. Our findings are consistent across different application domains, Computer Vision and Automatic Speech Recognition, and different neural network models, Convolutional Neural Networks and Long ShortTerm Memory Recurrent Neural Networks.
Anti-inflammatory and antioxidant activities of flavonoids from the flowers of Hosta plantaginea

Hosta plantaginea was a traditional Chinese medicinal plant used to treat inflammation-related diseases with little scientific validation. Twelve flavonoids, including two new compounds namely plantanones A (1) and B (2), were isolated from the flowers of Hosta plantaginea. Their structures were elucidated by NMR and HRMS as well as comparison with literature data. All of the isolated compounds showed significant inhibitory activities against ovine COX-1 and COX-2 at a concentration of 50 μM, with inhibition ratios from 53.00% to 80.55% for COX-1 and from 52.19% to 66.29% for COX-2. Further detailed testing showed that compounds 1, 2, 4 and 12 inhibited the COX-1 and COX-2 enzymes with IC50 values 12.90–33.37 μM and 38.32–46.16 μM, respectively. Moreover, the antioxidant effects of these isolates against DPPH free radical-scavenging were also evaluated in vitro, and a tight structure-activity relationship was discussed. Our results suggested that the anti-inflammatory and antioxidant activities of H. plantaginea flowers are partly attributed to these flavonoids.
Measurementof Lipase Activity by a DifferentialpH Technique

This is a new electrochemical method for determination of lipase activity in biological fluids, including serum, plasma, and duodenal juice. Advantages of turbidimetric methodsshort reaction time, and small sample and reagent volumes-are combined with those of titrimetric methods: measurement of absolute activity (i.e., no standardization required), saturated substrate conditions, and direct measurement of reaction products. The proposed method is easy, inexpensive, and takes only 3 mm. Precision is good: CV = 3.74% within day and 7.3% between days at the clinicaldecision concentration, CV = 1.86% within day and 4.65% between days for above-normal lipase activities. The standard curve is linear up to 4500 U/L. Results (y) correlate well with those by turbidimetry (c): y = 0.9287x - 65.3 (r = 0.9719). Reference values are between 0 and 130 U/L. AddItIonal Keyphrases: electrochemistry duodenal juice referenceinterval . turbidimetry compared emergency procedure Measurement of lipase activity (EC 3.1.1.3) is considered by many to be the most clinically useful assay in the diagnosis of acute pancreatitis. In fact, in this disease, the increase in serum lipase activity is more specific and frequently more pronounced and prolonged than that of amylase (1-3). Despite these diagnostic advantages, lipase measurement has not been favored for several methodological reasons: long analysis time, nonspecificity of some of the substrates used, and difficulties related to use of an emulsified substrate. Among the different methods developed to overcome these problems, most are modifications of the Cherry-Crandall procedure (4), in which fatty acids released from an olive oil emulsion are titrated, either with use of an indicator (5-7) or electrometrically (8). These methods are time consuming, having incubation times from 1 to 24 h, and therefore not suitable for emergency determinations. A better method is the kinetic determination of
Gated CNNs for Nuclei Segmentation in H&E Breast Images

Nuclei segmentation using deep learning has been achieving high accuracy using U-Net and variants, but a remaining challenge is distinguishing touching and overlapping cells. In this work, we propose using gated CNN (GCNN) networks to obtain sharper predictions around object boundaries and improve nuclei segmentation performance. The method is evaluated in over 1000 multicentre diverse H&E breast cancer images from three databases and compared to baseline U-Net and R2U-Net.
Influence of DNA extraction methods and 16S rRNA targeted hypervariable regions on microbial diversity profiling of dental plaque and saliva

: The application of high-throughput sequencing has revolutionized our ability to investigate the microbiota composition of complex oral ecosystems. One such approach is through 16S rRNA sequencing. Deciphering this diverse microbial community using such approach is more accurate than traditional culture-based methods. Numerous factors such as primer selection, DNA extraction methods, and sequencing platforms may compromise the results. Therefore, these factors need to be considered carefully for studies such as microbial diversity analysis and microbial abundance determination. Here, we evaluated the effect of two DNA extraction methods (soil and swab DNA extraction kits) and targeted 16S rRNA hypervariable regions (V1–V3 and V3–V4) on plaque and saliva sample types to investigate the diverse microbes in oral samples of ﬁve Chinese citizens. Both of the extraction methods were successful in determining the relative abundance of microbes; however, relative abundance percentage of the species reported by the soil extraction method with the hypervariable region V1–V3 is higher in both sample types. The relative taxonomical abundance of microbes at OTU level of species represented by the V3–V4 region is lower; nonetheless, the number of unshared species identiﬁed is higher using this region. Both of the hypervariable regions identiﬁed unshared species in plaque and saliva sample types. Thus, choice of DNA extraction methods and hypervariable regions greatly inﬂuence the representation of oral microbial diversity in plaque and saliva. This study provides a guide to future studies to portray and quantify the human oral microbiota.
Reply on RC2

Thank you for the very detailed review and the valuable suggestions for our study. We will soon go through all of them, revising the manuscript accordingly or explaining the reasons otherwise. Meanwhile, we would like to briefly explain the novelty of our study, the choice of the SCOPE model and some assumptions in our study that justify not having used an urban hydrological model currently available to predict ET.
Differential Influence of Ventromedial Prefrontal Cortex Lesions on Neural Representations of Schema and Semantic Category Knowledge

Abstract Prior knowledge, such as schemas or semantic categories, influences our interpretation of stimulus information. For this to transpire, prior knowledge must first be reinstated and then instantiated by being applied to incoming stimuli. Previous neuropsychological models implicate the ventromedial prefrontal cortex (vmPFC) in mediating these functions for schemas and the anterior/lateral temporal lobes and related structures for categories. vmPFC, however, may also affect processing of semantic category information. Here, the putative differential role of the vmPFC in the reinstatement and instantiation of schemas and semantic categories was examined by probing network-level oscillatory dynamics. Patients with vmPFC damage (n = 11) and healthy controls (n = 13) were instructed to classify words according to a given schema or category, while electroencephalography was recorded. As reinstatement is a preparatory process, we focused on oscillations occurring 500 msec prior to stimulus presentation. As instantiation occurs at stimulus presentation, we focused on oscillations occurring between stimulus presentation and 1000 msec poststimulus. We found that reinstatement was associated with prestimulus, theta and alpha desynchrony between vmPFC and the posterior parietal cortex for schemas, and between lateral temporal lobe and inferotemporal cortex for categories. Damage to the vmPFC influenced both schemas and categories, but patients with damage to the subcallosal vmPFC showed schema-specific deficits. Instantiation showed similar oscillatory patterns in the poststimulus time frame, but in the alpha and beta frequency bands. Taken together, these findings highlight distinct but partially overlapping neural mechanisms implicated in schema- and category-mediated processing.
The Mediating Role of Organizational Citizenship Behavior and Voice Behavior in the Relationship Between Proactive Personality and Job Performance

This study aims to evaluate the mediating role of organizational citizenship behavior on the impact of proactive personality on job performance as well as the mediating impact of employee voice behavior on the effect of proactive personality on organizational citizenship behavior. The statistical population of this study included 447 private hospital employees. Data was analyzed by structural equation method using SPSS AMOS program. The mediation functions were calculated using a process macro. As a result of the study, it was discovered that proactive personality has a positive effect on job performance, with organizational citizenship actions acting as a mediating factor. In addition, it has been found that proactive personality has a positive effect on organizational citizenship behavior and in this relationship employee voice behavior has a mediating role.
Partitioning Entropy with Action Mechanics: Predicting Chemical Reaction Rates and Gaseous Equilibria of Reactions of Hydrogen from Molecular Properties

Our intention is to provide easy methods for estimating entropy and chemical potentials for gas phase reactions. Clausius’ virial theorem set a basis for relating kinetic energy in a body of independent material particles to its potential energy, pointing to their complementary role with respect to the second law of maximum entropy. Based on this partitioning of thermal energy as sensible heat and also as a latent heat or field potential energy, in action mechanics we express the entropy of ideal gases as a capacity factor for enthalpy plus the configurational work to sustain the relative translational, rotational, and vibrational action. This yields algorithms for estimating chemical reaction rates and positions of equilibrium. All properties of state including entropy, work potential as Helmholtz and Gibbs energies, and activated transition state reaction rates can be estimated, using easily accessible molecular properties, such as atomic weights, bond lengths, moments of inertia, and vibrational frequencies. We conclude that the large molecular size of many enzymes may catalyze reaction rates because of their large radial inertia as colloidal particles, maximising action states by impulsive collisions. Understanding how Clausius’ virial theorem justifies partitioning between thermal and statistical properties of entropy, yielding a more complete view of the second law’s evolutionary nature and the principle of maximum entropy. The ease of performing these operations is illustrated with three important chemical gas phase reactions: the reversible dissociation of hydrogen molecules, lysis of water to hydrogen and oxygen, and the reversible formation of ammonia from nitrogen and hydrogen. Employing the ergal also introduced by Clausius to define the reversible internal work overcoming molecular interactions plus the configurational work of change in Gibbs energy, often neglected; this may provide a practical guide for managing industrial processes and risk in climate change at the global scale. The concepts developed should also have value as novel methods for the instruction of senior students.
Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be

When seeing a new object, humans can immediately recognize it across different retinal locations: the internal object representation is invariant to translation. It is commonly believed that Convolutional Neural Networks (CNNs) are architecturally invariant to translation thanks to the convolution and/or pooling operations they are endowed with. In fact, several studies have found that these networks systematically fail to recognise new objects on untrained locations. In this work, we test a wide variety of CNNs architectures showing how, apart from DenseNet-121, none of the models tested was architecturally invariant to translation. Nevertheless, all of them could learn to be invariant to translation. We show how this can be achieved by pretraining on ImageNet, and it is sometimes possible with much simpler data sets when all the items are fully translated across the input canvas. At the same time, this invariance can be disrupted by further training due to catastrophic forgetting/interference. These experiments show how pretraining a network on an environment with the right `latent' characteristics (a more naturalistic environment) can result in the network learning deep perceptual rules which would dramatically improve subsequent generalization.
High-risk alcohol consumption may increase the risk of SARS-CoV-2 seroconversion: a prospective seroepidemiologic cohort study among American college students

Aims: To estimate the associations between high-risk alcohol consumption and (1) SARS-CoV-2 seroconversion, (2) self-reported new SARS-CoV-2 infection, and (3) symptomatic COVID-19. Design: Prospective cohort Setting: Indiana University Bloomington (IUB), a public university of 34,660 students in southern Indiana. Participants: At the beginning of the fall 2020 semester, we randomly sampled N=1,267 IU undergraduate students, aged 18 years or older and residing in Monroe County, IN. Measurements: Primary exposure was high-risk alcohol consumption measured with the AUDIT questionnaire. We used an AUDIT score of 8 or more as the cut-off score when detecting high-risk alcohol consumption. Primary outcome was SARS-CoV-2 seroconversion, assessed with two SARS-CoV-2 antibody tests, at baseline and endline. Secondary outcomes were a) self-reported new SARS-CoV-2 infection at the study endline, and b) self-reported symptomatic COVID-19 at baseline. Findings: Prevalence of high-risk alcohol consumption was 34%. We found that students with high-risk alcohol consumption status had 2.34 [95% CI: (1.29, 4.24)] times the risk of SARS-CoV-2 seroconversion and 1.89 [95% CI: (1.08, 3.32)] times the risk of self-reporting a positive SARS-CoV-2 infection, compared to students with no such risk. Moreover, students with high-risk alcohol consumption were 18% more likely to develop symptomatic COVID-19, though this association was not statistically significant. Similar results were found after adjusting for sex at birth, race, and year in school. Findings from sensitivity analyses corroborated these results and suggested potential for a dose-response relationship. Conclusions: In this sample of American college students, high-risk alcohol consumption was associated with higher risk for SARS-CoV-2 seroconversion/infection. These findings could have implications for colleges' reopening planning in fall 2021.
Cooperative Circumnavigation and Tracking of Irregular Shape Target Supervisors

Collective behaviour happens not only among humans but also across several species of the animal kingdom. It depends on local interactions among individuals, which coordination is driven by the spread of a general information. By taking inspiration from biological systems, researchers and engineers are trying to reproduce the cooperative behaviour of humans and animals like ants and birds, giving birth to multi-vehicle systems, with the aim of solving several problems. A multi-vehicle system is composed of interconnected vehicles coordinated to act collectively towards global objective. When controlling such systems, the goal is to obtain a coordinated behaviour through local interactions among vehicles and the surrounding environment. The aim of the degree project is to perform a multi-vehicle circumnavigation and tracking of an irregular shape target, by implementing estimation and control algorithms. The degree project is based on the licentiate thesis [1] performed by PhD student Joana Filipa Gouveia Fonseca, concerning the multi-vehicle target tracking. Thementioned thesis has been developed in particular tomonitor and study colonies of algae which grow out of control and produce toxic or harmful effects. These are known as harmful algal blooms, or HABs. They appear frequently and have a substantial negative effect, mainly on the environment but also on humans, causing large-scale mortality of fish, mammals and birds. Through this master thesis, we aim at the achievement of a solution to the HABs problem, by applying the collective behaviour concept to a multi-agent system. Starting from the literature, a preliminary work has been developed in Matlab/Simulink to get familiar with the algorithms presented in the licentiate thesis. Afterwards, it is introduced the estimation and control’s algorithm, developed in a
Isolation and characterization of secondary metabolites from apolar fraction of Scabiosa sicula and evaluation of their antioxidant activities

In this study, the identification and characterization of Scabiosa sicula dichloromethane extract constituents were discussed for the first time. Moreover, this study is also providing an overview of the phytochemistry of Mediterranean medicinal plants. The phytochemical investigation of S. sicula dichloromethane extract was performed by HPLC and resulted in isolation of six compounds and five of them were identified as phenolic compounds and one as triterpene. The structures of the isolated compounds determined as luteolin-6-C-glucoside, luteolin-7-O-glucoside, caffeic acid, ursolic acid, 4-O-caffeoylquinic acid and 3,5-O-dicaffeoylquinic acid using spectroscopic analysis, including NMR and HR-MS techniques. Moreover, TEAC and DPPH assays were performed to evaluate the antioxidant activity of S. sicula’s dichloromethane extract. TEAC value was defined as the concentration of Trolox solution with the same amount of antioxidant potential found in the 1 mg/mL solution of the tested extract resulted to be 1.21 ± 0.01 mg/mL. DPPH activity was calculated as (4.42 μg/mL) while the corresponding methanol extract antiradical activity was 3.34 ± 0.01 μg/mL. The contribution to literature with this study is to increase the depth of knowledge about S. sicula on secondary metabolites isolated from apolar fraction and the results of this study will help reveal the medical potential of these species.
T HE UNREASONABLE EFFECTIVENESS OF THE CLASS - REVERSED SAMPLING IN TAIL SAMPLE MEMORIZA - TION

Long-tailed visual recognition poses significant challenges to traditional machine learning and emerging deep networks due to its inherent class imbalance. A common belief is that tail classes with few samples cannot exhibit enough regularity for pattern extraction. What makes things worse, the limited cardinality may lead to low exposure of tail classes in the training stage. Re-sampling methods, especially those who naively enlarge the exposure frequency, eventually fail with head classes under-represented and tail classes overfitted. Arguing that long-tailed learning involves a trade-off between head class pattern extraction and tail class memorizing, we first empirically identify the regularity of classes under long-tailed distributions and find that regularity of the same training samples will be sharply decreased with the reduction of class cardinality. Motivated by the recent success of a series works on the memorization-generalization mechanism, we propose a simple yet effective training strategy by switching from instance-balanced sampling to class-reversed sampling to memorize tail classes without seriously damaging the representation of head classes. Closely afterwards, we give the theoretical generalization error upper bound to prove that classreversed sampling is better than instance-balanced sampling during the last training stage. In our experiments, the proposed method can reach the state-of-the-art performance more efficiently than current methods, on several datasets. Further experiments also validate the superior performance of the proposed sampling strategy, implying that the long-tailed learning trade-off could be effectively tackled only in the memorization stage with a small learning rate and over-exposure of tail samples.