Miracle-dz/UniFAR-FTUs huggingface dataset:
DenseCNN: A Densely Connected CNN Model for Alzheimer's Disease Classification Based on Hippocampus MRI Data

Alzheimer's Disease (AD) is a common type of dementia, affecting human memory, language ability and behavior. Hippocampus is an important biomarker for AD diagnosis. Previous hippocampus-based biomarker analyses mainly focused on volume, texture and shape of the bilateral hippocampus. 3D convolutional neural networks (CNNs) can understand and extract complex morphology features from Magnetic resonance imaging (MRI) and have recently been developed for hippocampus-based AD classification. However, existing CNN models often have highly complex structures and require large amounts of training data. Here we propose an accurate and lightweight Densely Connected 3D convolutional neural network (DenseCNN) for AD classification based on hippocampus segments. DenseCNN was trained on 746 and tested on 187 pairs of hippocampus from Alzheimer's Disease Neuroimaging Initiative (ADNI) databases. DenseCNN has an average accuracy of 0.898, sensitivity of 0.985, specificity of 0.852, and area under curve (A UC) of0.979, which are better than or comparable to state-of-art approaches.
Towards motor evaluation of Parkinson's Disease Patients using wearable inertial sensors

Parkinson's disease (PD) patients require frequent office visits where they are assessed for health state changes using Unified Parkinson's Disease Rating Scale (UPDRS). Inertial wearable sensor devices present a unique opportunity to supplement these assessments with continuous monitoring. In this work, we analyze kinematic features from sensor devices located on feet, wrists, lumbar and sternum for 35 PD subjects as they performed walk trials in two clinical visits, one for each of their self-reported ON and OFF motor states. Our results show that a few features related to subject's whole-body turns and pronation-supination motor events can accurately infer cardinal features of PD like bradykinesia and posture instability and gait disorder (PIGD). In addition, these features can be measured from only two sensors, one located on the affected wrist and one on the lumbar region, thus potentially reducing patient burden of wearing sensors while supporting continuous monitoring in out of office settings.
A Reliable Machine Learning Approach applied to Single-Cell Classification in Acute Myeloid Leukemia

Machine Learning research applied to the medical field is increasing. However, few of the proposed approaches are actually deployed in clinical settings. One reason is that current methods may not be able to generalize on new unseen instances which differ from the training population, thus providing unreliable classifications. Approaches to measure classification reliability could be useful to assess whether to trust prediction on new cases. Here, we propose a new reliability measure based on the similarity of a new instance to the training set. In particular, we evaluate whether this example would be selected as informative by an instance selection method, in comparison with the available training set. We show that this method distinguishes reliable examples, for which we can trust the classifier's prediction, from unreliable ones, both on simulated data and in a real-case scenario, to distinguish tumor and normal cells in Acute Myeloid Leukemia patients.
Visual Analytics to Combat Selection Bias in Retrospective EHR Data Analyses

Introduction Retrospective analyses of electronic health records (EHRs) and other health data sources are increasingly common as investigators seek to employ data collected during routine health care delivery to learn about health practices and outcomes. The data analysis process typically includes cohort selection, in which a group of patients (and their corresponding data) are identified from within a health organization’s overall population of patients. Such cohorts are typically defined using sets of inclusion and/or exclusion criteria that are applied as filters within a data query; several interactive tools (such as i2b2 [1] and our own Cadence cohort selection system [2]) have been developed to facilitate this cohort definition process.
Photo and cathodoluminescence of Si/SiO2 nanoparticles produced by laser ablation

Photo and cathodoluminescence of Si/SiO 2 nanoclusters produced by laser ablation is reported. The study was made on untreated nanopowders (size 2-10 nm, 64% of primary nanoparticles < 5 nm), aged 18 months in normal atmospheric conditions. Photoluminescence spectra at room temperature and 77 K revealed a broadband spectrum (two bands 1.8 and 2.5 eV) in an approximate range from 400 nm to more than 800 nm. The maximum intensity emission of the main band, measured at room temperature (RT), is centered at ∼ 690 nm. At 77 K the photoluminescence emission peak increased by a factor of 3, and intensities for shorter wavelengths increased too (the luminescence color changed from red to orange). The cathodoluminescence spectra measured at 5 kV-30 kV, presents also two band components, 1.75 and 2.5 eV. When the accelerating voltage is increased, the main peak intensity (1.75 eV) increases dramatically, without changing the shape of the emission profile, whereas the second band intensity remains unchanged.
Comparative analyses of residential building efficiency for AC and DC distribution networks

The escalating prevalence of rooftop solar PVs and DC powered home appliances are the driving forces for the research in the field of DC distribution at residential level. The current research work presents comparative analyses of AC and DC distribution systems considering various scenarios for the specific purpose of efficiency/energy savings. A modern Bakersfield CA, USA home is considered for the analyses. The loads are classified according to the power demand. Rooftop solar PVs are also included in each residential building. Mathematical equations are devised for the efficiency analysis of residential buildings powered with AC as well as DC. The results reveal strong dependence of the efficiency values on the utilization/types of loads, efficiencies of the power electronic converters (PECs), solar capacity and seasonal conditions, as a function of the time of day. It is concluded that AC system presents better efficiency values as compared to the DC counterpart except during the time periods when solar power is available and when the penetration of variable speed drive (VSD) based loads is high.
Biological hazards that can affect the health of the workers handling hospital waste: a systematic review protocol

T. F. B. X. Silvaa, H. Costab, M. Ribeiroc, J. C. Guedesd aFaculty of Engineering of University of Porto, Porto, PT (up201802228@fe.up.pt) ORCID: 0000-0001-7168-4161, bFaculty of Engineering of University of Porto, Porto, PT ( hcosta@fe.up.pt) ORCID: 0000-0003-0329-9004, cFaculty of Engineering of University of Porto, Porto, PT (marcelomontebellogmail.com), dAssociated Laboratory for Energy, Transports and Aeronautics (PROA/LAETA), Faculty of Engineering of University of Porto, Porto, PT (jccg@fe.up.pt) ORCID: 0000-0003-2367-2187
eScholarship@UMassChan eScholarship@UMassChan

A reliable and searchable knowledge database of adverse drug reactions (ADRs) is highly important and valuable for improving patient safety at the point of care. In this paper, we proposed a neural multi-task learning system, NeuroADR, to extract ADRs as well as relevant modifiers from free-text drug labels. Specifically, the NeuroADR system exploited a hierarchical multi-task learning (HMTL) framework to perform named entity recognition (NER) and relation extraction (RE) jointly, where interactions among the learned deep encoder representations from different subtasks are explored. Different from the conventional HMTL approach, NeuroADR adopted a novel task decomposition strategy to generate auxiliary subtasks for more inter-task interactions and integrated a new label encoding schema for better handling discontinuous entities. Experimental results demonstrate the effectiveness of the proposed system.
Heterogeneous StringStability ofUnidirectionally InterconnectedMIMOLTISystems ?

One-dimensional formations of unidirectionally interconnected heterogeneous, multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems are studied in terms of the spatial propagation of initial conditions, disturbances and reference inputs. The proposed mixed frequency-domain and complex state-space formulation of the string leads to the stability theory of switching and uncertain polytopic systems, and so the tools available there can be adopted for the analysis of interconnected systems. It is shown based on this analogy that in certain important cases string stability conditions for homogeneous and heterogeneous interconnected systems coincide. In addition to presenting general string (in)stability conditions, the necessity of introducing the notion of string performance is demonstrated. Special attention is devoted to interconnected rank one systems, i.e., systems whose transfer matrices are of rank one. The topic is motivated by car following problems for which the available analysis tools fail to provide appropriate heterogeneous string stability conditions. A cooperative adaptive cruise control (CACC) example is presented to illustrate the usefulness of the approach.
Polygenic mediation analysis of Alzheimer's disease implicated intermediate amyloid imaging phenotypes

Mediation models have been employed in the study of brain disorders to detect the underlying mechanisms between genetic variants and diagnostic outcomes implicitly mediated by intermediate imaging biomarkers. However, the statistical power is influenced by the modest effects of individual genetic variants on both diagnostic and imaging phenotypes and the limited sample sizes ofimaging genetic cohorts. In this study, we propose a polygenic mediation analysis that comprises a polygenic risk score (PRS) to aggregate genetic effects ofa set ofcandidate variants and then explore the implicit effect ofimaging phenotypes between the PRS and disease status. We applied our proposed method to an amyloid imaging genetic study of Alzheimer's disease (AD), identified multiple imaging mediators linking PRS with AD, and further demonstrated the promise of the PRS on mediator detection over individual variants alone.
Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning

Information extraction (IE), the distillation of specific information from unstructured data, is a core task in natural language processing. For rare entities (<1% prevalence), collection of positive examples required to train a model may require an infeasibly large sample of mostly negative ones. We combined unsupervised- with biased positive-unlabeled (PU) learning methods to: 1) facilitate positive example collection while maintaining the assumptions needed to 2) learn a binary classifier from the biased positive-unlabeled data alone. We tested the methods on a real-life use case of rare (<0.42%) entity extraction from medical malpractice documents. When tested on a manually reviewed random sample of documents, the PU model achieved an area under the precision-recall curve of0.283 and Fj of 0.410, outperforming fully supervised learning (0.022 and 0.096, respectively). The results demonstrate our method's potential to reduce the manual effort required for extracting rare entities from narrative texts.
Non-inferential Probabilistic Method for designing Typical Species of Northern Greece Oak Forest Natura 2000 Habitat Types

Conservation status of the Natura 2000 Habitat Types is assessed through parameters that include the presence or absence of Typical Species. The EEC and the Greek application guidance documents do not mention a clear definition of «Typical Species» and most of the Habitat Types’ Typical Species lists published for the whole Greece have common species with others. Moreover, they can contain till 48 species without indication on which species is more typical than another for a Habitat type. Taking the example of oak forests habitat types, this paper exposes a method to find the Typical Species which are sufficiently linked with a Habitat Type to be exclusive. It also exposes the way to give ecological profiles for the Typical Species, and by that way to inform on habitat types ecology. This permitted to propose a list of maximum 8 ranked Typical Species for three Habitat Types and a new partial organization of the North Greece oak forest’s Natura 2000 Habitat Types.
Learning to Explain Machine Learning

Explainable AI (XAI) methods yield human-understandable, often post-hoc descriptions of a machine learning (ML) model’s behavior. Evaluation metrics for XAI methods fall within readily-measurable dimensions such as ﬁdelity of the explanation to the underlying ML model, various forms of human comprehensibility, computational overhead, and others. We argue that—given ML models’ role as only one piece of larger, deployed sociotechnical systems—these metrics alone do not enable the selection of an appropriate XAI method, or methods, for a speciﬁc use case. Indeed, it is necessary to include additional context , related to the user of the system as well as the downstream impact of the ML model. Inspired by prior work in human-computer interaction and computational social choice, we propose a learning-based framework for the selection of XAI methods that are tailored to each user and context.
Continuous Spatial Query Processing

In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of location-based services in business as well as daily applications such as navigation, targeted advertising, and location-based social networking. Continuous spatial queries serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of safe region. A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements.
Interactive comment on “PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)” by S. Härer et al

Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition 5 of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2. 0 ✿✿ . 1 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow10 affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown ✿✿✿✿ show that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches. 15
Adding “Student Voice” to the Mix: Perception Surveys and State Accountability Systems

For the past two decades, student perception surveys have become standard tools in data collection efforts. At the state level, however, “student voice” is still used sparingly. In this study, we examine the ways in which including student survey results might alter state accountability determinations. Reconstructing the accountability system in Massachusetts, we draw on a unique set of student survey data, which we add to the state’s formula at a maximally feasible dosage in order to determine new school ratings. As we find, student survey data shift school accountability ratings in small but meaningful ways and appear to enhance functional validity. Student survey results introduce information about school quality that is not captured by typical accountability metrics, correlate moderately with test score growth, and are not predicted by student demographic variables.
Pikachu: Serverless Message Passing

Serverless computing pioneered by AWS Lambda becomes popular due to its appealing fine-grain billing and auto-scaling properties. Recent research has demonstrated that moving big data processing ( e.g. , data analytics, video processing) to serverless will bring huge benefits. However, this research heavily relies on non-serverless components such as S3 and Redis to pass messages between function instances during big data processing, which is non-efficient and costly. In this paper, we argue that current serverless platforms lack the features of direct message passing and addressability that are necessary to implement distributed data processing algorithms in an efficient manner. To resolve these two missing features, we designed a new serverless platform, Pikachu , that allows function instances to find and communicate with each other directly. The experiment results show that Pikachu achieves up to 1070 . 4 times shorter messaging passing time than using Redis. We open source the code of Pikachu and evaluation scripts at GitHub [11].
Performance Optimization of Brushless DC Motors Mitigating Voltage Dips, Swells and Short Interruptions in Weak Grids

Normally, BLDC motor drives a sensitive load which is severely influenced by disturbances such as voltage dips, swells, and short interruptions in weak grids. The main objective of this work is to investigate the performance of BLDC motors when they are tied to weak grids. Thus, this paper presents a study on the performance of BLDC motor considering disturbed utility conditions. Also, an efficient solution to guarantee a soft operation of a BLDC motor driving a sensitive load is presented. A buckboost DC converter is merged with the BLDC drive inverter to be able to mitigate voltage dips as well as voltage swells. A short interruption of one or two phases of the grid voltage can also be mitigated. The BLDC motor is controlled via a cascade PI-controller layout that has an outer loop for the speed control and inner loop for the DC voltage control. The PI controllers’ gains are optimized by using the ant-colony optimization technique and then implemented in a simulation model using MATLAB/SIMULINK. Six cases of disturbances have been simulated including balanced and unbalanced dips, swells, and short interruption of one or two phases of the grid. Simulation results with various disturbances show the ability of the proposed controller to mitigate the grid disturbances with good transient and steady-state responses.
The Effects of Perceived Justice and Emotions on Service Recovery Satisfaction on Indonesian B2B and C2C E-commerce Customers

This study aims to analyze the effects of justice theory and emotions on service recovery satisfaction of Indonesian e-commerce customers. The factors used in this study are distributive justice, procedural justice, interactional justice, as cognitive aspect. Positive emotions and negative emotions are the factors used as affective aspect. This study used quantitative approach in the form of online survey. The total amount of data used in this study are 601 data. The respondents are Indonesian B2C or C2C e-commerce customer who filed a complaint directly to the B2C or C2C e-commerce at least once and receives a response at least once. Covariance-based structural equation modelling is used to analyze the data. The analysis results show that distributive justice, procedural justice, interactional justice, and positive emotions affect service recovery satisfaction. The results of this study can help Indonesian B2C & C2C e-commerce to understand service recovery from the perspective of customers, so satisfactory service recovery can be implemented.
25. PERMEABILITY VERSUS DEPTH IN THE UPPER OCEANIC CRUST: IN SITU MEASUREMENTS IN DEEP SEA DRILLING PROJECT HOLE 504B, EASTERN EQUATORIAL PACIFIC

In situ permeabilities measured within the upper kilometer of oceanic crust in Hole 504B on the south flank of the Costa Rica Rift decrease exponentially from 10~ to 10" m in Layer 2A (the upper 150 m) to 10 to 10" m in Layer 2B (150-550 m into basement) and to 10" 7 m and lower in Layer 2C (deeper than 550 m). We estimate the permeability (k) versus depth (z) to vary as k(z) = 0.11 e~ × 10" m. If this permeability versus depth function is representative of the oceanic crust in general, then hydrothermal convection would be vertically stratified, with the most vigorous circulation confined to the shallowest pillow basalt layers of the crust. Use of a full suite of geophysical l o g s including borehole televiewer imagery and multichannel, full waveform sonic, nuclear, and variable spacing electrical resistivity logs—allows us to characterize the scale and interconnectedness of fractures, and the degree of infilling of these fractures by alteration minerals. In Hole 504B, we observe a relationship between permeability and fracture porosity, determined from the geophysical logs. If fractures with similar aspect ratios are encountered at other oceanic sites, prediction of the permeability of the oceanic crust to within an order of magnitude is possible from geophysical logs. In Hole 395A in the Atlantic, for example, in situ permeability measurements yielded values similar to those predicted by the logging relationships established in Hole 504B.