Miracle-dz/UniFAR-FTUs huggingface dataset:
DETECTING DEPRESSION IN ALZHEIMER’S DISEASE AND MCI BY SPEECH ANALYSIS

It is estimated that 30% of Alzheimer’s patients suffer from depression. Since this condition can lead to further cognitive decline and suffering, its detection is essential to alleviate MCI (mild cognitive impairment) or AD symptoms in patients. This paper presents a machine learning method aimed at identifying MCI and Alzheimer’s disease (AD) patients suffering from depression, using different features extracted from their speech. 276 participants (mean age 70.9 years) are selected from DementiaBank’s Pitt Corpus for this research. The interviewer’s voice and the silences are removed from the audio records as a preprocessing task. Several audio features are extracted from the patient's speech to achieve this task. For instance, MFCC’s, Spectral Centroid, Spectral Roll-Off Point, and others. We trained and compared three families of classifiers (SVM, Random Tree, and Random Forest) through two experiments, one using Spectral feature variants and MFCC, and the other using only MFCC features. A third experiment is conducted for comparison with the literature review. In all cases, we used a bootstrapping method to solve the sampling bias, i.e., 70% of the patients not suffering from depression. From the results, the MFCC feature set was more appropriate for tree-based classifiers than SVM, in which the Random Tree classifier reported the highest classification performance (91.3%). Meanwhile, the other feature sets were more appropriate for SVM than the tree-based classifiers, where SVM reported an 89.1 classification accuracy, with 91.1% recall.
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality

Goal-oriented Reinforcement Learning, where the agent needs to reach the goal state while simultaneously minimizing the cost, has received significant attention in real-world applications. Its theoretical formulation, stochastic shortest path (SSP), has been intensively researched in the online setting. Nevertheless, it remains understudied when such an online interaction is prohibited and only historical data is provided. In this paper, we consider the offline stochastic shortest path problem when the state space and the action space are finite. We design the simple value iteration-based algorithms for tackling both offline policy evaluation (OPE) and offline policy learning tasks. Notably, our analysis of these simple algorithms yields strong instance-dependent bounds which can imply worst-case bounds that are near-minimax optimal. We hope our study could help illuminate the fundamental statistical limits of the offline SSP problem and motivate further studies beyond the scope of current consideration.
The design and fabrication of a temperature diagnosis system for the intelligent rotating spindle of industry 4.0

Abstract Nearly all machinery depends on high-speed rotation to convert kinetic energy into output power. However, high-speed rotation generates heat which needs to be removed to prevent operational anomaly. Prolonged overheating will result in decreased accuracy and reliability of the machine and may even cause serious damage. It is therefore valuable to monitor the heat generation in each and every part of a rotating shaft while it is operating. Internal sensors can be installed to determine temperature distribution and measures can be taken to remove heat from places which reach high temperature to prolong the life of the shaft and its precision. However, to measure temperatures in a high-speed rotating body, it is necessary to provide a means of transferring the signal from the sensors to the measuring device because leads cannot be used. This paper describes a temperature measuring device that uses a slip ring to transfer temperature sensor signals from a rotating spindle to the measuring instruments. Successful measurement of the internal temperatures of a rotating component is very useful. In event of a temperature anomaly, an alarm can be given which allows action to be taken to avert both damage to the machine itself and the output of defective production.
The GRAFT-VERSUS-HOST DISEASE IN CHILDREN WHO HAVE RECEIVED A CORD-BLOOD OR BONE MARROW TRANSPLANT FROM AN HLA-IDENTICAL SIBLING

as an alternative for stem-cell risk of the conditioning regimen; the type of prophylaxis against GVHD; the use or nonuse of granulocyte colony-stimulating factor, gran-ulocyte–macrophage colony-stimulating factor, or both to hasten the recovery of neutrophils; and the dose of nucleated cells infused. Only factors significantly associated with an outcome (P<0.05) were retained in the final models. The absence of a factor from the final models indicates that there was no significant association with the risk of the outcome described. and covariate, the assumption of proportional hazards was tested with use of time-dependent covariates. When the test indicated differential effects over (nonproportional hazards), we constructed models in which the interval after transplantation was divided into two periods, and we used the maxi- mized partial-likelihood method to find the most appropriate break-point. 20 For these variables, we determined the relative risks for each of the periods. A forward stepwise method of selecting variables at a significance level of less than 0.05 was used to identify covariates other than the type of graft that were associated with outcome. In each step, the covariate for the type of graft was in- cluded in the model. First-order interactions between the type of graft and significant covariates were considered. We tested the over- all effects of covariates using Wald’s test. To determine the effects of the transplantation center, we tested a random-effects model 21 ; there was no evidence that center-related effects confounded our previous findings. All P values are two-sided.
ISOLATION AND CHARACTERIZATION OF TWO IMMUNOLOGICALLY DISTINCT FORMS OF a-AMYLASE AND A 13-AMYLASE FROM SEEDS OF GERMINATED SORGHUM BICOLOR (L.) MOENCH

Two immunologically distinct forms of or-amylase named 0t-1 and ct-2 and a 15-amylase were isolated from germinated sorghum by affinity-, hydroxylapatiteand hydrophobic interaction chromatography. The two a-amylases showed similar molecular weights of 41,500 42,700 as determined by electrophoresis and gel permeation chromatography. The two enzymes have similar amino acid compositions except for differences in Ser, Val, lie, and Cys. They have slightly different isoelectric points of 4.65 for ct-I (major form) and 5.1 for ct-2. at-1 and ct-2 amylases contain 0.7% and 5.7% carbohydrate by weight respectively and they exhibit different kinetic properties and substrate specificities. Crossed immunoelectrophoresis and immunodiffusion of sorghum ct-1 amylase against monospecific anti-barley ct-2 amylase showed immunological identity between barley malt a-2 and sorghum malt a-1 amylase. Non-identity was seen between sorghum ct-2 and the barley malt amylase. Sorghum ct-1 and ~t-2 amylase show partial immunological identity, a-1 amylase could only be detected in trace amounts in ungerminated sorghum.
Review of Biomimetic Self-assembly of Nanomaterials in Morphology and Performance Control

: Nano materials exhibit excellent properties compared with macro scale materials due to their special properties at nano scale. It exhibits good application prospects in the fields of optics, magnetism, electricity and thermology. The biomimetic self-assembly technology of nano materials, which simulates the life activities in vivo and makes the nano materials spontaneously form stable structures based on the noncovalent bond interaction, has become one of the main methods to prepare nano materials. This paper reviews the different biomimetic self-assembly synthesis strate-gies for nanomaterials in morphology and performance control. The characteristics of biomimetic self-assembled nanomaterials are summarized. This paper also introduced the application of self-assembly technology in sensors, surface Raman scattering, biomedical and other fields. In addition, the prospects for the development of biomimetic self-assembly technology of nanomaterials are prospected.
Task-specific Temporal Node Embedding

Graph embedding aims to learn a representation of graphs’ nodes in a latent low-dimensional space. The purpose is to encode the graph’s structural information. While the majority of real-world networks are dynamic, literature generally focuses on static networks and overlooks evolution patterns. In a previous article entitled "TemporalNode2vec: Temporal Node Embedding in Temporal Networks", we introduced a dynamic graph embedding method that learns continuous time-aware vertex representations. In this paper, we adapt TemporalNode2vec to tackle especially the node classiﬁcation-related tasks. Overall, we prove that task-speciﬁc embedding improves data eﬃciency signiﬁcantly comparing to task-agnostic embedding.
GC-MS Analysis of the in vitro leaf derived Callus Extracts of Ichnocarpus frutescens (L)W.T.Aiton

: Medicinal plants are the treasure house of secondary metabolites. Production of seconmdary metabolites through micro propagation technology has immense application in new drug discoveries. Ichnocarpus frutescens (L.) W.T. Aiton - is a latex secreting medicinal herb commonly known as black creeper or Krishna Sariva belonging to the family Apocyanaceae.The present study was aimed at induction of callus from the leaf explants of I. frutescens using Murashige and Skoog (MS) medium supplemented with various combinations of plant growth regulators. Maximum amount of green compact mass of callus was proliferated from leaf explants on MS medium fortified with 3mg/L NAA + 2mg/L BAP. The main objective of the present study was to investigate the secondary metabolites in different extracts of callus by GC-MS analysis. To our best knowledge, no literature is available and this is the first report on GC–MS analysis of leaf derived callus extracts, which showed the presence of different phytocompounds out of which, Stigmasterol, Hexadecanoic acid, Beta-Sitosterol, Stigmasta-3,5-dien-7-one and Heptacosane are the major compounds that found to possess a wide range of biological activities. This protocol could be used to identify the secondary metabolites from leaf derived callus of the I. frutescens and further studies have to be carried out for the isolation and characterization of novel compounds for the formulation of new pharmaceutical drugs and also for its commercial production which will be used to cure some important ailments.
Establishment and Corroboration of a Thermodynamically Consistent Helium-3 Vapour-Pressure Scale

The helium-3 vapour-pressure scale (VPS) of the ITS-90 (Equation (6a) T 90 ( p ) in the text, with T 90 being the ITS-90 temperature value and p the vapour pressure) deviates systematically from thermodynamic temperature T below about 1.2 K. The difference T 90 - T increases with de-creasing temperature up to about 1 mK at 0.65 K. This fact has been demonstrated in three different ways by two independent groups in each case. The results obtained were the basis for establishing and corroborating a new, thermodynamically consistent helium-3 VPS, called PTB-2006.
COUPLED STRESS-FLOW PROPERTIES OF ROCK JOINTS FROM HYDRAULIC FIELD TESTING

This thesis describes a method for in situ determination of the coupled stress-flow properties of rock joints by a combination of hydraulic tests in single boreholes and coupled numerical modelling. The method was applied to sub-horizontal joints intersecting a vertical borehole at Luleå University of Technology, Sweden. The rock type at the site is granite and the joints are located at a depth of 80 to 417 metres. A stress-transmissivity relationship for joints is derived based on an empirical stress-normal displacement model and a linear relationship between normal displacement and hydraulic aperture. There are three key parameters defining this relationship; i) initial hydraulic aperture, ii) effective normal stress and iii) joint normal stiffness. Each of these three parameters can be determined from a combination of hydraulic field tests and numerical simulation with a coupled hydromechanical finite element program. Initial hydraulic aperture was determined by a hydraulic pulse test. The pulses were of short duration with a fast pressure decay and apertures as small as a few microns could be measured. Modelling results show that if fracture flow dominates, then the storativity is dominated by the mechanical deformation of the joint. For a short duration pulse test used in this study, the deformation of the joint and thus the storativity is restricted by the stiffness of the ambient rock. Therefore, the field test could be evaluated for a fixed value of Young's modulus in the model. The test could also be evaluated by conventional curve fitting to homogenous radial flow solutions for a fixed fracture storativity of 3.5e-8. A known storativity makes die determination of hydraulic aperture of the joint less subjective. Joint normal stiffness was determined from a step-pressure test, where the well pressure was increased in steps up to pressure above the virgin stress normal across the joint. At each pressure level, the effective stress inside the joint is reduced and the joint aperture increases. The aperture change is measured through the injection flow rate. However, injection flow rate is dependent on the hydraulic aperture while the mechanical aperture is unknown. Therefore, the back-calculated stiffness is a "hydraulic stiffness" relating the change in effective stress to the change in hydraulic aperture. Hydraulic fracturing and cyclic hydraulic jacking (step-pressure) tests were used to determine the virgin stress normal to the joints. Fracture closure and virgin normal stress was determined from the zero-flow pressure in a well pressure versus well flow diagram. The zero-flow pressure is clearly defined and is in-
Time-dependent Probabilistic Tsunami Hazard Analysis for Western Sumatra, Indonesia, Using Space-Time Earthquake Rupture Modelling and Stochastic Source Scenarios

We develop a novel framework of time-dependent probabilistic tsunami hazard analysis (PTHA) and apply it to Western Sumatra, Indonesia, where future tsunamigenic events are anticipated in the Mentawai region of the Sunda 10 subduction zone. An earthquake rupture model taking into account the spatiotemporal interaction of major megathrust segments is used to simulate future tsunamigenic earthquakes. The earthquake rupture process of the segments is characterized by a multivariate Bernoulli model with interarrival times following a Brownian passage‐time distribution and the dependency between segments specified by a spatial correlation function. We calibrate this model with historical ruptures of the Mentawai thrust in the last 450 years. A total of ≥ 100,000 time-dependent earthquake rupture cases are then 15 coupled with a stochastic tsunami simulation method to evaluate tsunami hazards. We generate a total of 6,300 stochastic tsunami source models from six magnitude scenarios between M 7.75 and M 9.0 and obtain time-dependent PTHA results for seven different periods (1, 5, 10, 20, 30, 50 and 450 years). We further compare the time-dependent PTHA results with a time-independent PTHA approach to investigate the influence of the spatiotemporal earthquake rupture model. The space-time interaction model successfully generates annual seismic moment rates consistent with the observations. Moreover, the 20 model can capture the uncertainty of future time-dependent tsunami hazards. On the other hand, the time-independent approach produces slightly higher hazard estimates than the time-dependent model for long-term hazard assessments (> 450 years).
A Near-Optimal Approach to Edge Connectivity-Based Hierarchical Graph Decomposition

Driven by applications in graph analytics, the problem of efficiently computing all k -edge connected components ( k -ECCs) of a graph G for a user-given k has been extensively and well studied. It is known that the k -ECCs of G for all possible values of k form a hierarchical structure. In this paper, we study the problem of efficiently constructing the hierarchy tree for G which compactly encodes the k -ECCs for all possible k values in space linear to the number of vertices n. All existing approaches construct the hierarchy tree in O (δ( G ) × T KECC ( G ) ) time, where δ( G ) is the degeneracy of G and T KECC ( G ) is the time complexity of computing all k -ECCs of G for a specific k value. To improve the time complexity, we propose a divide-and-conquer approach running in O ((log δ( G )) × T KECC ( G ) ) time, which is optimal up to a logarithmic factor. However, a straightforward implementation of our algorithm would result in a space complexity of O (( m + n ) log δ( G )). As main memory also becomes a scarce resource when processing large-scale graphs, we further propose techniques to optimize the space complexity to 2 m + O ( n log δ( G )), where m is the number of edges in G. Extensive experiments on large real graphs and synthetic graphs demonstrate that our approach outperforms the state-of-the-art approaches by up to 28 times in terms of running time, and by up to 8 times in terms of main memory usage. As a by-product, we also improve the space complexity of computing all k -ECCs for a specific k to 2 m + O ( n ).
Work Engagement Investigation of Zakat Management Professional in East Java

: Employee disengagement was found to cause a decline in organizational productivity and incur a cost of $ 300 billion in 2004 in America (Gallup Management Journal, 2005). Therefore, there is an awareness of the organization to increase employee engagement, especially in profit organization. The studies of employee engagement are rarely investigated not for profit organization, although this context is interesting to study since most of its employees are volunteers but they have worked for decades and employees believe that their work is part of the mandate. Based on that reasons, this study wants to investigate employee engagement condition of professional zakat management in East Java. By using descriptive data analysis, the study investigated data from 230 workers that were selected randomly from total 285 professional zakah population in East Java. This study also used work engagement measurements that consist of vigour, dedication, and absorption. The result shows that in general work engagement condition of zakah management professional is in good condition. However, from three indicators, dedication has the highest value when compared to vigour and absorption. While absorption is found to be the lowest scaled indicators.
Radical chemistry at a UK coastal receptor site – Part 1: observations of OH, HO 2 , RO 2 , and OH reactivity and comparison to MCM model predictions

OH, HO 2 , total and partially-speciated RO 2 , and OH reactivity ( k’ OH ) were measured during the July 2015 ICOZA (Integrated Chemistry of OZone in the Atmosphere) project that took place at a coastal site in North Norfolk, UK. Maximum measured daily OH, HO 2 , and total RO 2 radical concentrations were in the range 2.6–17 × 10 6 , 0.75–4.2 × 10 8 , and 2.3–8.0 × 10 8 molecule cm −3 , respectively. k' OH ranged from 1.7 to 17.6 s −1 with a median value of 4.7 s −1 . ICOZA data were split by 25 wind direction to assess differences in the radical chemistry between air that had passed over the North Sea (NW–SE sectors) or major urban conurbations such as London (SW sector). A photostationary steady-state (PSS) calculation underpredicted daytime OH in NW–SE air by ~35%, whereas agreement (~15%) was found within instrumental uncertainty (~26% at 2 σ ) in SW air. A box model using MCMv3.3. 1 chemistry was in better agreement with the OH measurements, but it overpredicted HO 2 observations in NW–SE air in the afternoon by a factor of ~2–3, although slightly better agreement was found for HO 2 in 30 SW air (factor of ~1.4–2.0 underprediction). The box model severely underpredicted total RO 2 observations in both NW–SE and SW air by factors of ~8–9 on average. Measured radical and k’ OH levels and measurement-to-model ratios displayed strong dependences on NO mixing ratios. The PSS calculation could capture OH observations at high NO but underpredicted the observations at low NO. The box model overpredicted HO 2 concentrations at low NO in NW–SE air, whereas in SW air, the measurements and model results were in agreement across the full NO range. The box model underpredicted total RO 2 at all
Transfer Learning for Bayesian HPO with End-to-End Landmark Meta-Features

Hyperparameter optimization (HPO) is a crucial component of deploying machine learning models, however, it remains an open problem due to the resource-constrained number of possible hyperparameter evaluations. As a result, prior work focuses on exploring the direction of transfer learning for tackling the sample inefﬁciency of HPO. In contrast to existing approaches, we propose a novel D eep K ernel Gaussian Process surrogate with L andmark M eta-features (DKLM) that can be jointly meta-trained on a set of source tasks and then transferred efﬁ-ciently on a new (unseen) target task. We design DKLM to capture the similarity between hyperparameter conﬁgurations with an end-to-end meta-feature network that embeds the set of evaluated conﬁgurations and their respective performance. As a result, our novel DKLM can learn contextualized dataset-speciﬁc similarity representations for hyperparameter conﬁgurations. We experimentally validate the performance of DKLM in a wide range of HPO meta-datasets from OpenML and demonstrate the empirical superiority of our method against a series of state-of-the-art baselines.
The Emergence of Gender Associations in Child Language Development

Gender associations have been a long-standing research topic in psychological and social sciences. Although it is known that children learn aspects of gender associations at a young age, it is not well understood how they might emerge through the course of development. We investigate whether gender associations, such as the association of dresses with women and bulldozers with men, are reflected in the linguistic communication of young children from ages 1-5. Drawing on recent methods from machine learning, we use word embeddings derived from large text corpora including news articles and web pages as a proxy for gender associations in society, and we compare those with the gender associations of words uttered by caretakers and children in children's linguistic environment. We quantify gender associations in childhood language through gender probability, which measures the extent to which word usage frequencies in speech to and by girls and boys are gender-skewed. By analyzing 4,875 natural conversations between children and their caretakers in North America, we find that frequency patterns in word usage of both caretakers and children correlate strongly with the gender associations captured in word embeddings through the course of development. We discover that these correlations diminish from the 1970s to the 1990s. Our work suggests that early linguistic communication and social changes may jointly contribute to the formation of gender associations in childhood.