Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Non-functional Requirements in Software Effort Estimation: Systematic Review and Experimental Results

Software effort estimation is a critical step in software project management, and its main driver are requirements. Some algorithmic methods use as inputs only functional requirements and others take also into account non-functional requirements. The goal of this study is to understand the correlation of using non-functional requirements on the accuracy of software effort estimation algorithmic methods. A systematic literature review was conducted to learn which non-functional requirements are used, how they are used, and their effects on estimation accuracy. The systematic review shows that only 33% of 39 algorithmic methods use non-functional requirements. However, the investigation on its correlation with estimation accuracy was not conclusive from published results. In order to address this issue, a quasi-experiment was conducted on publicly available datasets. This experiment shows that the use of non-functional requirements results in a reduction of about 30% in the estimation error, with statistically significant confidence.
Point-of-Care Testing: A Prospective, Randomized Clinical Trial of Efficacy in Coagulopathic Cardiac Surgery Patients

Introduction: The current investigation aimed to study the efficacy of hemostatic therapy guided either by conventional coagulation analyses or point-of-care (POC) testing in coagulopathic cardiac surgery patients. Methods: Patients undergoing complex cardiac surgery were assessed for eligibility. Those patients in whom diffuse bleeding was diagnosed after heparin reversal or increased blood loss during the first 24 postoperative hours were enrolled and randomized to the conventional or POC group. Thromboelastometry and whole blood impedance aggregometry have been performed in the POC group. The primary outcome variable was the number of transfused units of packed erythrocytes during the first 24 h after inclusion. Secondary outcome variables included postoperative blood loss, use and costs of hemostatic therapy, and clinical outcome parameters. Sample size analysis revealed a sample size of at least 100 patients per group. Results: There were 152 patients who were screened for eligibility and 100 patients were enrolled in the study. After randomization of 50 patients to each group, a planned interim analysis revealed a significant difference in erythrocyte transfusion rate in the conventional compared with the POC group [5 (4;9) versus 3 (2;6) units [median (25th and 75th percentile)], P < 0.001]. The study was terminated early. The secondary outcome parameters of fresh frozen plasma and platelet transfusion rates, postoperative mechanical ventilation time, length of intensive care unit stay, composite adverse events rate, costs of hemostatic therapy, and 6-month mortality were lower in the POC group. Conclusions: Hemostatic therapy based on POC testing reduced patient exposure to allogenic blood products and provided significant benefits with respect to clinical outcomes.
Understanding Machine-learned Density Functionals

Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density. The properties of different kernels and methods of cross-validation are explored, and highly accurate energies are achieved. Accurate {\em constrained optimal densities} are found via a modified Euler-Lagrange constrained minimization of the total energy. A projected gradient descent algorithm is derived using local principal component analysis. Additionally, a sparse grid representation of the density can be used without degrading the performance of the methods. The implications for machine-learned density functional approximations are discussed.
Criminal Victims, Victimized Criminals, or Both? A Deeper Look at the Victim-Offender Overlap

Offenders are more likely than non-offenders to be victims, and victims are more likely than non-victims to be offenders. The overlap between offenders and victims is not well understood in criminology, and in the economics of crime the stylized empirical fact is even widely ignored. The paper gives a survey of leading theoretical interpretations and empirical results. It summarizes findings from criminology and focuses on economic explanations, where rational choice, behavioral economics, as well as bounded and ecological rationality are discussed. The paper presents new econometric evidence based on German survey data covering victimization experiences and criminal activities. Using recursive bivariate Probit modeling, econometric results confirm that victimization depends on offending but not vice versa. Among the joint covariates of the bivariate system, broken homes, criminal records of parents and personal indebtedness turn out as highly relevant factors of offending behavior, whereas individual victimization risks are significantly linked to education, employment and size of peer groups.
A Competition to Push the Dependability of Low-Power Wireless Protocols to the Edge

A large number of low-power wireless communication protocols has been proposed in the last decade by both academia and industry in an attempt to deliver information in an increasingly reliable, timely, and energy-efficient manner. However, their level of dependability has rarely been benchmarked under the same settings and environmental conditions. In this paper we present the execution and results of a competition aimed to evaluate the dependability of state-of-the-art low-power wireless protocols under the same settings, and push their performance to the limit. We define a scenario emulating the operation of a wireless sensor network in industrial environments rich with radio interference and compare the end-to-end dependability of systems based on protocol strategies ranging from adaptive and time-slotted frequency-hopping to multi-modal routing and flooding. To increase fairness and realism, we allow the developers of the competing protocols to interact with the benchmarking infrastructure and optimize the protocol parameters for the scenario at hand. We achieve this by designing, implementing, and employing D-Cube, a low-cost tool that allows to accurately measure key dependability metrics such as end-to-end delay, reliability, and power consumption, as well as to graphically visualize their evolution in real-time. This interaction with the benchmarking infrastructure and the competitiveness of the event have incited the developers to push the performance of their protocols to the limit and reach impressive results.
Training Data Modification for SMT Considering Groups of Synonymous Sentences

Generally speaking, statistical machine translation systems would be able to attain better performance with more training sets. Unfortunately, well-organized training sets are rarely available in the real world. Consequently, it is necessary to focus on modifying the training set to obtain high accuracy for an SMT system. If the SMT system trained the translation model, the translation pair would have a low probability when there are many variations for target sentences from a single source sentence. If we decreased the number of variations for the translation pair, we could construct a superior translation model. This paper describes the effects of modification on the training corpus when consideration is given to synonymous sentence groups. We attempt three types of modification: compression of the training set, replacement of source and target sentences with a selected sentence from the synonymous sentence group, and replacement of the sentence on only one side with the selected sentence from the synonymous sentence group. As a result, we achieve improved performance with the replacement of source-side sentences.
Estimation of neuronal responses from fMRI data

In this paper we describe a deconvolution technique for estimation of the neuronal signal from an observed hemodynamic responses in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Additionally, we enhance the cubature Kalman filter with a variational Bayesian approach for adaptive estimation of the measurement noise covariance.
Supplementary Information Text

In Vitro Callus Production. Induction of in vitro callus production was performed as previously described (Iwase et al., 2011). Etiolated hypocotyls were excised from mutant and wild type seedlings 4 days after germination. Mutant and wild type explants were cultured for 30 days side-by-side on MS medium supplemented with Gamborg’s vitamins, 2% glucose, 0.05% 2-(N-morpholino)ethanesulfonic acid (MES), pH 5.8 with KOH, 0.8% phytoagar, and the indicated concentrations of NAA and kinetin.
Femtosecond laser-based formation of electrically conductive silicon carbide on PDMS

We experimentally demonstrated formation of electrically conductive structures by modifying native polydimethylsiloxane (PDMS) with femtosecond laser. By irradiating femtosecond laser pulses to native PDMS, black structures with electrical conductivity were formed. Analyses using X-ray diffraction (XRD) show that the formed structures were composed of β-silicon carbide (β-SiC). Our technique enables the spatially selective formation of β-SiC on the surface of PDMS, leading to open a novel route to develop a simple method to fabricate flexible or stretchable MEMS devices with SiC microstructures.
Double-blind placebo-controlled randomized clinical trial on the use of paracetamol for performing mammography

Background: This study was conducted within the Goias Mastology Research Network. To verify the possibility of diminishing pain, and discomfort during the mammography using analgesic administration. Methods: Randomized, double-blinded, placebo controlled trial, testing paracetamol to diminish the pain, and discomfort during mammography. Three hundred patients who came for screening mammography were randomized for this study. A questionnaire with 2 parts was used: the first had questions that concerned the patient identification, and factors related to the pain during mammography; and the second asked about the scale of discomfort (no discomfort; uncomfortable; very uncomfortable; intolerable), and the pain (analogical linear scale) during the mammography. Each patient received 1000 mg of paracetamol, or placebo. Afterwards each patient filled out the second part of the questionnaire. Six patients were excluded from the analysis; this resulted in 149 in the paracetamol group, and 145 in the placebo group. Results: The 2 groups were homogenous concerning the mean of the ages, weight, height, and breast size. The mean of the pain was 3.5 in the paracetamol, and 2.8 in the placebo group (P = .12). There were fewer women experiencing mild pain in the paracetamol group when compared with those in placebo group (relative risk [RR] 0.76, confidence interval [CI] 95% 0.52–0.98). There was no significant difference between the 2 groups, according to the degrees of discomfort (P = .69). Conclusion: The use of paracetamol can reduces the mild pain for women undergoing mammography.
SuRF: Practical Range Query Filtering with Fast Succinct Tries

We present the Succinct Range Filter (SuRF), a fast and compact data structure for approximate membership tests. Unlike traditional Bloom filters, SuRF supports both single-key lookups and common range queries: open-range queries, closed-range queries, and range counts. SuRF is based on a new data structure called the Fast Succinct Trie (FST) that matches the point and range query performance of state-of-the-art order-preserving indexes, while consuming only 10 bits per trie node. The false positive rates in SuRF for both point and range queries are tunable to satisfy different application needs. We evaluate SuRF in RocksDB as a replacement for its Bloom filters to reduce I/O by filtering requests before they access on-disk data structures. Our experiments on a 100 GB dataset show that replacing RocksDB's Bloom filters with SuRFs speeds up open-seek (without upper-bound) and closed-seek (with upper-bound) queries by up to 1.5× and 5× with a modest cost on the worst-case (all-missing) point query throughput due to slightly higher false positive rate.
Design, Simulation and Fabrication of A Novel MEMS Based Pulsometer

A novel pulsometer was successfully developed using microelectromechanical systems (MEMS) based silicon-on-glass (SOG) technology for biomedical applications. The sensor was modelled and simulated in COMSOL Multiphysics® for pressures ranging from 0 to 40 mmHg. The capability of the fabricated pulsometer to detect movements in x and z-axis directions was investigated. The simulation results demonstrated displacement changes as high as of 98% and 36% in the x and z-axis directions, respectively for 40 mmHg, which correspond to typical radial blood pressure (rBP) on the wrist. In addition, an average capacitance change of 1 nF was experimentally obtained in the x-axis direction, from −5 V to 5 V. The response of the pulsometer is analyzed and presented in this paper.
Metabolomics analysis identifies sex-associated metabotypes of oxidative stress and the autotaxin–lysoPA axis in COPD

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and a leading cause of mortality and morbidity worldwide. The aim of this study was to investigate the sex dependency of circulating metabolic profiles in COPD. Serum from healthy never-smokers (healthy), smokers with normal lung function (smokers), and smokers with COPD (COPD; Global Initiative for Chronic Obstructive Lung Disease stages I–II/A–B) from the Karolinska COSMIC cohort (n=116) was analysed using our nontargeted liquid chromatography–high resolution mass spectrometry metabolomics platform. Pathway analyses revealed that several altered metabolites are involved in oxidative stress. Supervised multivariate modelling showed significant classification of smokers from COPD (p=2.8×10−7). Sex stratification indicated that the separation was driven by females (p=2.4×10−7) relative to males (p=4.0×10−4). Significantly altered metabolites were confirmed quantitatively using targeted metabolomics. Multivariate modelling of targeted metabolomics data confirmed enhanced metabolic dysregulation in females with COPD (p=3.0×10−3) relative to males (p=0.10). The autotaxin products lysoPA (16:0) and lysoPA (18:2) correlated with lung function (forced expiratory volume in 1 s) in males with COPD (r=0.86; p<0.0001), but not females (r=0.44; p=0.15), potentially related to observed dysregulation of the miR-29 family in the lung. These findings highlight the role of oxidative stress in COPD, and suggest that sex-enhanced dysregulation in oxidative stress, and potentially the autotaxin–lysoPA axis, are associated with disease mechanisms and/or prevalence. Oxidative stress and the autotaxin–lysoPA axis evidence sex-associated metabotypes in the serum of COPD patients http://ow.ly/kAeE309MpdI
Quantitative Tissue Measurements in Transoral Robotic Surgery

Transoral robotic surgery (TORS) represents a new treatment paradigm for patients with obstructive sleep apnea (OSA). TORS overcomes the limitations of suboptimal access associated with the conventional transoral approach particularly when the tongue base (BOT) is involved. Preliminary results suggest higher cure rates compared with both conventional transoral and external surgical techniques yet with significantly less morbidity [1]. Flexible thulium laser fibres are typically used during TORS for bloodless tissue resection and vaporization to de-bulk tissue volume. However, it is not possible to measure the volume of tissue reduction in vivo. The surgeon uses subjective evaluation to qualitatively estimate the desired tissue volume which needs to be removed. The extent of tongue base reduction required can significantly impact on surgical success and patient morbidity including pain, dysphagia and aspiration.
The Role of Bmp2 in the Maturation and Maintenance of the Murine Knee Joint

Bone morphogenetic proteins (BMPs) are key regulators of skeletal development, growth, and repair. Although BMP signaling is required for synovial joint formation and is also involved in preserving joint function after birth, the role of specific BMP ligands in adult joint homeostasis remains unclear. The purpose of this study was to define the role of Bmp2 in the morphogenesis and maintenance of the knee joint. To do this, we first created Bmp2‐LacZ and Gdf5‐LacZ knock‐in mice and compared their expression patterns in the developing and postnatal murine knee joint. We then generated a knockout mouse model using the Gdf5‐cre transgene to specifically delete Bmp2 within synovial joint‐forming cells. Joint formation, maturation, and homeostasis were analyzed using histology, immunohistochemistry, qRT‐PCR, and atomic force microscopy (AFM)‐based nanoindentation to assess the cellular, molecular, and biomechanical changes in meniscus and articular cartilage. Bmp2 is expressed in the articular cartilage and meniscus of the embryonic and adult mouse knee in a pattern distinct from Gdf5. The knee joints of the Bmp2 knockout mice form normally but fail to mature properly. In the absence of Bmp2, the extracellular matrix and shape of the meniscus are altered, resulting in functional deficits in the meniscus and articular cartilage that lead to a progressive osteoarthritis (OA) like knee pathology as the animals age. These findings demonstrate that BMP activity provided by Bmp2 is required for the maturation and maintenance of the murine knee joint and reveal a unique role for Bmp2 that is distinct from Gdf5 in knee joint biology. © 2018 American Society for Bone and Mineral Research.
Sleep, mood and sociability in a healthy population

Sleep and mood problems have a considerable public health impact with serious societal and significant financial effects. In this work, we study the relationship between these factors in the everyday life of healthy young adults. More importantly, we look at these factors from a social perspective, studying the impact that couples have on each other and the role that face-to-face interactions play. We find that there is a significant bi-directional relationship between mood and sleep. More interestingly, we find that the spouse's sleep and mood may have an effect on the subject's mood and sleep. Further, we find that subjects whose sleep is significantly correlated with mood tend to be more sociable. Finally, we observe that less sociable subjects show poor mood more often than their more sociable contemporaries. These novel insights, especially those involving sociability, measured from quantified face-to-face interaction data gathered through smartphones, open up several avenues to enhance public health research through the use of latest wireless sensing technologies.
Linking discourse modes and situation entity types in a cross-linguistic corpus study

The main contribution of this paper is a cross-linguistic empirical analysis of two interacting levels of linguistic analysis of written text: situation entity (SE) types, the semantic types of situations evoked by clauses of text, and discourse modes (DMs), a characterization of passages at the sub-document level. We adapt an existing annotation scheme for SEs in English to be used for German data, with a detailed discussion of the most important differences. We create the first parallel corpus annotated for SEs, and the first DM-annotated corpus. We find that: (a) the adapted scheme is supported by evidence from a large-scale experimental study; (b) SEs mainly correspond to each other in parallel text, and a large part of the mismatches are systematic; (c) the DM annotation task can be performed intuitively with reasonable agreement; and (d) the annotated DMs show the predicted differences in the distributions of SE types.
Elimination of established tumors with nanodisc-based combination chemoimmunotherapy

We report a new nanoparticle system for chemoimmunotherapy that elicits robust antitumor immunity against established tumors. Although immune checkpoint blockade has shown initial success for various cancers, only a small subset of patients benefits from this therapy. Some chemotherapeutic drugs have been reported to induce antitumor T cell responses, prompting a number of clinical trials on combination chemoimmunotherapy. However, how to achieve potent immune activation with traditional chemotherapeutics in a manner that is safe, effective, and compatible with immunotherapy remains unclear. We show that high-density lipoprotein–mimicking nanodiscs loaded with doxorubicin (DOX), a widely used chemotherapeutic agent, can potentiate immune checkpoint blockade in murine tumor models. Delivery of DOX via nanodiscs triggered immunogenic cell death of cancer cells and exerted antitumor efficacy without any overt off-target side effects. “Priming” tumors with DOX-carrying nanodiscs elicited robust antitumor CD8+ T cell responses while broadening their epitope recognition to tumor-associated antigens, neoantigens, and intact whole tumor cells. Combination chemoimmunotherapy with nanodiscs plus anti–programmed death 1 therapy induced complete regression of established CT26 and MC38 colon carcinoma tumors in 80 to 88% of animals and protected survivors against tumor recurrence. Our work provides a new, generalizable framework for using nanoparticle-based chemotherapy to initiate antitumor immunity and sensitize tumors to immune checkpoint blockade.
Online Scaling of NFV Service Chains Across Geo-Distributed Datacenters

Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on virtualized platforms, for significant cost reduction and ease of management. Such virtual network functions (VNFs) commonly constitute service chains, to provide network services that traffic flows need to go through. Efficient deployment of VNFs for network service provisioning is a key to realize the NFV goals. Existing efforts on VNF placement mostly deal with offline or one-time placement, ignoring the fundamental, dynamic deployment and scaling need of VNFs to handle practical time-varying traffic volumes. This work investigates dynamic placement of VNF service chains across geo-distributed datacenters to serve flows between dispersed source and destination pairs, for operational cost minimization of the service chain provider over the entire system span. An efficient online algorithm is proposed, which consists of two main components: 1) A regularization-based approach from online learning literature to convert the offline optimal deployment problem into a sequence of one-shot regularized problems, each to be efficiently solved in one time slot and 2) An online dependent rounding scheme to derive feasible integer solutions from the optimal fractional solutions of the one-shot problems, and to guarantee a good competitive ratio of the online algorithm over the entire time span. We verify our online algorithm with solid theoretical analysis and trace-driven simulations under realistic settings.
RDFization of Japanese Electronic Dictionaries and LOD

This paper describes the practice and the reality of OWL conversion of Japanese WordNet and Japanese dictionary IPAdic. The outcomes of OWL conversion are linked to DBpedia Japanese dataset using lexical word matching. The difficulty originating from the specialty of Japanese, which is shareable by non-English languages, is focused. The potential of LOD in linguistics is also discussed. The goal of our study on Linguistics by LOD is to provide an open and rich environment in linguistics that propels multi-lingual studies for linguistics researchers and bottom-up style ontology buildings for ontologists.