Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Lead Work Teams: Facilitation Skills

Introduction. TEAMWORK IN EVOLVING ORGANIZATIONS. The New Workplace. From Teams to Teamwork. A FACILITATIVE STYLE OF LEADERSHIP. How to L.E. A.D. From Controlling to Facilitating. Facilitation Ignites Team Spirit. The New Team Leader. FACILITATING PRODUCTIVE TEAM COMMUNICATION. Facilitating One on One. Facilitating Communication of Distance Teams. FACILITATING TEAM MEETINGS. Leading a Participative Team Meeting. Planning and Focusing the Meeting. Encouraging Participation. Recording People's Ideas. Managing Group Process. Reaching Consensus and Closure. References and Bibliography. About the Author. Index.
Industrial management approaches for improving material control in building services works

Purpose – Given increasing user aspirations for comfort, connectivity, health, safety and security, the building services subsector now plays an even greater role in construction. It also contributes heavily to construction debris. Hypothesising that improved materials control can help reduce construction debris considerably, this paper aims to investigate relevant critical production shortcomings that generate construction debris in building services works.Design/methodology/approach – Relevant previous research studies on main causes of waste are first reviewed. Findings from a brainstorming exercise at a focus group meeting provide the basis for a series of structured face‐to‐face interviews with experienced site supervisors. Subsequent analysis of the interview findings, together with deductive reasoning, leads to the development of the proposed improvement strategies.Findings – The most significant causes of production shortcomings identified in the study are poor coordination of processes or trades,.
Performance of smart piezoceramic patches in health monitoring of a RC bridge

This paper presents the results of a health monitoring study, carried out during the destructive load testing of a prototype reinforced concrete (RC) bridge. The bridge was made up of cement-concrete reinforced with steel rods, and represented a popular class of road bridges in which regular health monitoring is a very important issue during the service life. The bridge was instrumented with piezoceramic transducer (PZT) patches, which were electrically excited at high frequencies, of the order of kHz, and the real part of admittance (conductance) was extracted as a function of the exciting frequency. The patches were scanned for the acquisition of this signature at various stages during the loading process. The signatures of the patches located in the vicinity of the damage were found to have undergone drastic changes, while those farther away were less affected. Damage was quantified in non-parametric terms using the root mean square of the deviation in signatures with respect to the baseline signature of the healthy state. This non-parametric index was found to correlate well with the damage progression in the structure.
A Key Regulator For Iron Homeostasis in chronic hepatitis C.

Background: Hepcidin is a small, cysteine-rich cationic peptide produced by hepatocytes. There is a single human hepcidin gene; whose essential role in iron homeostasis was confirmed by identifying homozygous frameshift or nonsense mutations in affected individuals with severe Juvenile hemochromatosis. IL-6 may be the mediator of hepcidin induction by inflammation. Hypoferremia is a common response to systemic infections or generalized inflammatory disorders, anemia of chronic disease occurs in patients with acute and chronic immune activation and represents an important clinical problem. Aim of the work: The study will attempt to determine the hepatic hepcidin expression levels in patients with chronic hepatitis C virus infection. Patients and methods: Fifty patients with chronic hepatitis C virus infection (CHCV), their age between (20- 55) years, selected from the National Hepatology and Tropical Medicine Research Institute were included in this study, before interferon and Ribavirin therapy, and ten healthy individuals were included to serve as controls. All the patients and controls were subjected to the following history, clinical examination, abdominal ultrasonography and collection of blood samples for routine laboratory investigations. CBCs and serological assay for serum ferritin, iron, transferrin (s-TFR) levels, Liver biopsy for hepcidin mRNA levels and iron deposits in liver by (PCR) polymerase chain reaction. All subjects gave written informed consent for enrolment in the study, which was approved by the Research Ethical Committee of the General Organization for Teaching Hospitals and Institutes. Liver biopsy was taken from healthy subjects during abdominal surgery. Results: Our results revealedthat hepatic hepcidin expression is considered highly valid marker in case of CHCV infection. Conclusion: Our study concluded thatthere’s a highly significant inverse correlation between hepcidin versus liver iron, serum iron and serum transferrin but there’s no significant correlation versus ferritin. Recommendations: Hepcidin measuring and manipulating hepcidin levels will, in the future, have a role in diagnosing and treating any number of iron related disorders.Hepcidin itself has antimicrobial properties of uncertain importance so that careful clinical trials will be required to define appropriate indications of hepcidin antagonists.
Which Industries Are Most Science-Based? (Japanese)

Many industrial innovations would not have occurred without substantial delay if there had not been academic research behind them, and the linkage between industrial innovation and academic research is now gaining much more attention because of the increasing complexity of technologies. The authors have shown that a particularly high degree of science linkage is observed in the area of biotechnology, based on an analysis of the number of citations of scientific papers in patents (science linkage) by type of technology (RIETI DP 04-E-034). Building on this previous analysis, the present research project aims to provide quantitative evidence showing the degree to which Japanese technology development is dependent on science by type of industry. The data on the degree of science dependence is extremely important from the viewpoint of formulating science and technology policies, such as support for scientific research, as well as in implementing industrial policies such as those designed to promote new businesses. Conventional discussions of science dependence, however, have mostly been based on the ratio of research and development (R&D) expenditures to total sales. In contrast, our analysis of science linkage directly examines how technology development in each industry is influenced by science. This enables us to get a clear picture of science-based industries. The analysis shows a high degree of science linkage in the pharmaceutical, food, and chemical industries, with the remaining industries showing a relatively low linkage. This result indicates that the characteristics of technology development differ substantially depending on the type of industry, and therefore that such differences need to be taken into account in formulating industrial policies.
Patents and R&D: Searching for a Lag Structure

This paper extends earlier work on the RD in the second,the unobserved past series are assumed to have followed a low order autoregression. Neither approach yields strong evidence of a long lag. The available sample, though numerically large,turns out not to be particularly informative on this question. It does reconfirm, however, a significant effect of R&D on patenting (with most of it occurring in the first year or two) and the presence of rather wide and semi-permanent differences among firms in their patenting policies.
Binary perceptron: efficient algorithms can find solutions in a rare well-connected cluster

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in finding solutions at low density. This phenomenon has been justified numerically by the existence of subdominant and dense connected regions of solutions, which are accessible by simple learning algorithms. In this paper, we establish formally such a phenomenon for both the symmetric and asymmetric binary perceptrons. We show that at low constraint density (equivalently for overparametrized perceptrons), there exists indeed a subdominant connected cluster of solutions with almost maximal diameter, and that an efficient multiscale majority algorithm can find solutions in such a cluster with high probability, settling in particular an open problem posed by Perkins-Xu in STOC'21. In addition, even close to the critical threshold, we show that there exist clusters of linear diameter for the symmetric perceptron, as well as for the asymmetric perceptron under additional assumptions.
Generalized parton distributions of the proton from lattice QCD

Generalized parton distributions (GPDs) are among the most fundamental quantities for describing the internal structure of hadrons. In this work, we present results on isovector GPDs of the proton obtained within lattice Quantum Chromodynamics. We use the quasi-distribution formalism and perform the calculations on an ensemble of $N_f = 2 + 1 + 1$ twisted mass fermions, with pion mass $M_\pi=260$~MeV and lattice spacing $a\simeq 0.093$~fm. Results are presented for unpolarized, helicity, and transversity GPDs at zero and nonzero skewness with controlled statistical uncertainties. Comparisons with their forward limit show qualitative features anticipated from model calculations.
Revealing the Global Linguistic and Geographical Disparities of Public Awareness to Covid-19 Outbreak through Social Media

The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30th, 2020, seeking to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Can the changing pandemic awareness predict the Covid-19 outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track the awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more significant disparities in awareness than European countries, and awareness in the eastern part of Europe was higher than in central Europe. Finally, the Ratio index could accurately predict global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-30, 35-42, and 17 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research.
Data selection for efficient model update in federated learning

The Federated Learning (FL) workflow of training a centralized model with distributed data is growing in popularity. However, until recently, this was the realm of contributing clients with similar computing capability. The fast expanding IoT space and data being generated and processed at the edge are encouraging more effort into expanding federated learning to include heterogeneous systems. Previous approaches distribute light-weight models to clients are rely on knowledge transfer to distil the characteristic of local data in partitioned updates. However, their additional knowledge exchange transmitted through the network degrades the communication efficiency of FL. We propose to reduce the size of knowledge exchanged in these FL setups by clustering and selecting only the most representative bits of information from the clients. The partitioned global update adopted in our work splits the global deep neural network into a lower part for generic feature extraction and an upper part that is more sensitive to this selected client knowledge. Our experiments show that only 1.6% of the initially exchanged data can effectively transfer the characteristic of the client data to the global model in our FL approach, using split networks. These preliminary results evolve our understanding of federated learning by demonstrating efficient training using strategically selected training samples.
Rate of Convergence of Polynomial Networks to Gaussian Processes

We examine one-hidden-layer neural networks with random weights. It is well-known that in the limit of infinitely many neurons they simplify to Gaussian processes. For networks with a polynomial activation, we demonstrate that the rate of this convergence in 2-Wasserstein metric is O ( n − 12 ) , where n is the number of hidden neurons. We suspect this rate is asymptotically sharp. We improve the known convergence rate for other activations, to power-law in n for ReLU and inverse-square-root up to logarithmic factors for erf. We explore the interplay between spherical harmonics, Stein kernels and optimal transport in the non-isotropic setting.
Non-radial modes in classical Cepheids what to look for in spectroscopy?

Recent photometric observations of first-overtone classical Cepheids and RR Lyrae stars have led to the discovery of additional frequencies showing a characteristic period ratio of 0.60-0.65 with the main pulsation mode. In a promising model proposed by Dziembowski (2016), these signals are suggested to be due to the excitation of non-radial modes with degrees 7, 8 and 9 (Cepheids) or 8 and 9 (RR Lyrae). Such modes usually have low amplitudes in photometric data. Spectroscopic time series offer an unexplored and promising way forward. We simulated time series of synthetic line profiles for a representative first-overtone classical Cepheid model and added a low-amplitude non-radial mode. We studied sets of spectra with dense sampling and without noise, so-called ’perfect’ cases, as well as more realistic samplings and signal-to-noise levels. Besides the first-overtone mode and the non-radial mode, also the harmonics of both modes and combination signals were often detected, but a sufficiently high sampling and signal-to-noise ratio prove essential. The amplitudes of the non-radial mode and its harmonic depend on the azimuthal order m. The inclination is also an important factor determining the detectability of the non-radial mode and/or its harmonic. We compared the results obtained for the predicted high degrees with those for lower-degree modes. Finally, we studied the sampling requirements for detecting the non-radial mode. Our findings can be used to plan a spectroscopic observing campaign tailored to uncover the nature of these mysterious modes.
Blind Extraction of Target Speech Source Guided by Supervised Speaker Identification via X-vectors

—This manuscript proposes a novel robust procedure for extraction of a speaker of interest (SOI) from a mixture of audio sources. The estimation of the SOI is blind, performed via independent vector extraction. A recently proposed constant separating vector (CSV) model is employed, which improves the estimation of moving sources. The blind algorithm is guided towards the SOI via the frame-wise speaker identiﬁcation, which is trained in a supervised manner and is independent of a speciﬁc scenario. When processing challenging data, an incorrect speaker may be extracted due to limitations of this guidance. To identify such cases, a criterion non-intrusively assessing quality of the estimated SOI is proposed. It utilizes the same model as the speaker identiﬁcation; no additional training is therefore required. Using this criterion, the “deﬂation” approach to extraction is presented. If an incorrect source is estimated, it is subtracted from the mixture and the extraction of the SOI is performed again from the reduced mixture. The proposed procedure is experimentally tested on both artiﬁcial and real-world datasets containing challenging phenomena: source movements, reverberation, transient noise or microphone failures. The presented method is comparable to the state-of-the-art blind algorithms on static mixtures; it is more accurate for mixtures containing source movements. Compared to fully supervised methods, the proposed procedure achieves a lower level of accuracy but requires no scenario-speciﬁc data for the training.
Tight Bounds for Differentially Private Anonymized Histograms

In this note, we consider the problem of differentially privately (DP) computing an anonymized histogram, which is defined as the multiset of counts of the input dataset (without bucket labels). In the low-privacy regime $\epsilon \geq 1$, we give an $\epsilon$-DP algorithm with an expected $\ell_1$-error bound of $O(\sqrt{n} / e^\epsilon)$. In the high-privacy regime $\epsilon<1$, we give an $\Omega(\sqrt{n \log(1/\epsilon) / \epsilon})$ lower bound on the expected $\ell_1$ error. In both cases, our bounds asymptotically match the previously known lower/upper bounds due to [Suresh, NeurIPS 2019].
Maillard Sampling: Boltzmann Exploration Done Optimally

The PhD thesis of Maillard (2013) presents a rather obscure algorithm for the $K$-armed bandit problem. This less-known algorithm, which we call Maillard sampling (MS), computes the probability of choosing each arm in a \textit{closed form}, which is not true for Thompson sampling, a widely-adopted bandit algorithm in the industry. This means that the bandit-logged data from running MS can be readily used for counterfactual evaluation, unlike Thompson sampling. Motivated by such merit, we revisit MS and perform an improved analysis to show that it achieves both the asymptotical optimality and $\sqrt{KT\log{T}}$ minimax regret bound where $T$ is the time horizon, which matches the known bounds for asymptotically optimal UCB. %'s performance. We then propose a variant of MS called MS$^+$ that improves its minimax bound to $\sqrt{KT\log{K}}$. MS$^+$ can also be tuned to be aggressive (i.e., less exploration) without losing the asymptotic optimality, a unique feature unavailable from existing bandit algorithms. Our numerical evaluation shows the effectiveness of MS$^+$.
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices

Graph classification has a wide range of applications in bioinformatics, social sciences, automated fake news detection, web document classification, and more. In many practical scenarios, including web-scale applications, labels are scarce or hard to obtain. Unsupervised learning is thus a natural paradigm for these settings, but its performance often lags behind that of supervised learning. However, recently contrastive learning (CL) has enabled unsupervised computer vision models to perform comparably to supervised models. Theoretical and empirical works analyzing visual CL frameworks find that leveraging large datasets and task relevant augmentations is essential for CL framework success. Interestingly, graph CL frameworks report high performance while using orders of magnitude smaller data, and employing domain-agnostic graph augmentations (DAGAs) that can corrupt task relevant information. Motivated by these discrepancies, we seek to determine why existing graph CL frameworks continue to perform well, and identify flawed practices in graph data augmentation and popular graph CL evaluation protocols. We find that DAGA can destroy task-relevant information and harm the model’s ability to learn discriminative representations. We also show that on small benchmark datasets, the inductive bias of graph neural networks can significantly compensate for these limitations, while on larger graph classification tasks commonly-used DAGAs perform poorly. Based on our findings, we propose better practices and sanity checks for future research and applications, including adhering to principles in visual CL when designing context-aware graph augmentations. For example, in graph-based document classification, which can be used for better web search, we show task-relevant augmentations improve accuracy by up to 20.
Predictive replica placement for mobile users in distributed fog data stores with client-side markov models

Mobile clients that consume and produce data are abundant in fog environments and low latency access to this data can only be achieved by storing it in their close physical proximity. To adapt data replication in fog data stores in an efficient manner and make client data available at the fog node that is closest to the client, the systems need to predict both client movement and pauses in data consumption. In this paper, we present variations of Markov model algorithms that can run on clients to increase the data availability while minimizing excess data. In a simulation, we find the availability of data at the closest node can be improved by 35% without incurring the storage and communication overheads of global replication.
Correlation of microstructural and multicaloric properties of suction ‐ cast Ni ‐ Mn ‐ In

Novel multicaloric cooling utilizing the giant caloric response of Ni‐Mn‐based metamagnetic shape‐ memory alloys to different external stimuli such as magnetic field, uniaxial load and hydrostatic pressure is a promising candidate for energy‐efficient and environmentally‐friendly refrigeration. However, the role of microstructure when several external fields are applied simultaneously or sequentially has been scarcely discussed in literature. Here, we synthesized ternary Ni‐Mn‐In alloys by suction casting and analyzed the microstructural influence on the response to magnetic fields and uniaxial load. SEM‐EBSD reveals a distinct core‐shell microstructure with a radially symmetric <001> solidification texture resulting in a two‐step martensitic transformation. In correlation with temperature‐dependent XRD a significant effect of grain orientation on the stress‐induced martensitic transformation is demonstrated. The influence of microstructure on the magnetic‐field‐induced transformation dynamics is studied by strain measurements in static and pulsed fields. Temperature‐ stress and temperature‐magnetic field phase diagrams are established and single caloric performances are characterized in terms of ΔsT and ΔTad. The cyclic ΔTad values are compared to the ones achieved in the multicaloric exploiting‐hysteresis cycle. It turns out that a tailored microstructure and the combination of both stimuli enable outstanding caloric effects in moderate external fields which can significantly exceed the single caloric performances. In particular for Ni‐Mn‐In, the maximum cyclic effect in magnetic fields of 1.9 T is increased by more than 200 % to ‐4.1 K when a moderate sequential stress of 59 MPa is applied. Our results illustrate the crucial role of microstructure for multicaloric cooling using Ni‐Mn‐based metamagnetic shape‐memory alloys.
Divide-and-Conquer Hard-thresholding Rules in High-dimensional Imbalanced Classification

In binary classification, imbalance refers to situations in which one class is heavily underrepresented. This issue is due to either a data collection process or because one class is indeed rare in a population. Imbalanced classification frequently arises in applications such as biology, medicine, engineering, and social sciences. In this manuscript, for the first time, we theoretically study the impact of imbalance class sizes on the linear discriminant analysis (LDA) in high dimensions. We show that due to data scarcity in one class, referred to as the minority class, and high-dimensionality of the feature space, the LDA ignores the minority class yielding a maximum misclassification rate. We then propose a new construction of a hard-thresholding rule based on a divide-and-conquer technique that reduces the arezu.mojiri@math.iut. ac.ir abbas.khalili@mcgill.ca hamadani@cc.iut. ac.ir