Miracle-dz/UniFAR-FTUs huggingface dataset:
SmartPub: A Platform for Long-Tail Entity Extraction from Scientific Publications

This demo presents SmartPub, a novel web-based platform that supports the exploration and visualization of shallow meta-data (e.g., author list, keywords) and deep meta-data--long tail named entities which are rare, and often relevant only in specific knowledge domain--from scientific publications. The platform collects documents from different sources (e.g. DBLP and Arxiv), and extracts the domain-specific named entities from the text of the publications using Named Entity Recognizers (NERs) which we can train with minimal human supervision even for rare entity types. The platform further enables the interaction with the Crowd for filtering purposes or training data generation, and provides extended visualization and exploration capabilities. SmartPub will be demonstrated using sample collection of scientific publications focusing on the computer science domain and will address the entity types Dataset (i.e. dataset presented or used in a publication), and Methods (i.e. algorithms used to create/enrich/analyse a data set)
Protein Kinase Cζ Abrogates the Proapoptotic Function of Bax through Phosphorylation*

Protein kinase Cζ (PKCζ) is an atypical PKC isoform that plays an important role in supporting cell survival but the mechanism(s) involved is not fully understood. Bax is a major member of the Bcl-2 family that is required for apoptotic cell death. Because Bax is extensively co-expressed with PKCζ in both small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) cells, it is possible that Bax may act as the downstream target of PKCζ in regulating survival and chemosensitivity of lung cancer cells. Here we discovered that treatment of cells with nicotine not only enhances PKCζ activity but also results in Bax phosphorylation and prolonged cell survival, which is suppressed by a PKCζ specific inhibitor (a myristoylated PKCζ pseudosubstrate peptide). Purified, active PKCζ directly phosphorylates Bax in vitro. Overexpression of wild type or the constitutively active A119D but not the dominant negative K281W PKCζ mutant results in Bax phosphorylation at serine 184. PKCζ co-localizes and interacts with Bax at the BH3 domain. Specific depletion of PKCζ by RNA interference blocks nicotine-stimulated Bax phosphorylation and enhances apoptotic cell death. Intriguingly, forced expression of wild type or A119D but not K281W PKCζ mutant results in accumulation of Bax in cytoplasm and prevents Bax from undergoing a conformational change with prolonged cell survival. Purified PKCζ can directly dissociate Bax from isolated mitochondria of C2-ceramide-treated cells. Thus, PKCζ may function as a physiological Bax kinase to directly phosphorylate and interact with Bax, which leads to sequestration of Bax in cytoplasm and abrogation of the proapoptotic function of Bax.
Towards an Unsupervised Entrainment Distance in Conversational Speech using Deep Neural Networks

Entrainment is a known adaptation mechanism that causes interaction participants to adapt or synchronize their acoustic characteristics. Understanding how interlocutors tend to adapt to each other's speaking style through entrainment involves measuring a range of acoustic features and comparing those via multiple signal comparison methods. In this work, we present a turn-level distance measure obtained in an unsupervised manner using a Deep Neural Network (DNN) model, which we call Neural Entrainment Distance (NED). This metric establishes a framework that learns an embedding from the population-wide entrainment in an unlabeled training corpus. We use the framework for a set of acoustic features and validate the measure experimentally by showing its efficacy in distinguishing real conversations from fake ones created by randomly shuffling speaker turns. Moreover, we show real world evidence of the validity of the proposed measure. We find that high value of NED is associated with high ratings of emotional bond in suicide assessment interviews, which is consistent with prior studies.
Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion

Accurate age estimation from a facial image is quite challenging, since physical age and apparent age can be quite different, and this difference is dependent on gender, ethnicity, and many other factors. Multitask deep learning is one of the approach to improve age estimation by employing auxiliary tasks, such as gender recognition, that are related to the primary task. However, in traditional multitask learning for age estimation, the relationship between the primary and auxiliary tasks is difficult to describe; how the auxiliary tasks enhance the model for the primary objective is ambiguous. In this letter, we propose a conditional multitask learning method that architecturally factorizes an age variable into gender-conditioned age probabilities in a deep neural network. The lack of accurate training labels with discrete age values is another critical limitation to training age estimation models. Therefore, we propose a label expansion method that increases the number of accurate labels from weakly supervised categorical labels. To verify the generality of the proposed method, we perform intensive experiments on the publicly available MORPH-II and FG-NET datasets. The proposed methods outperform state-of-the art methods in both age estimation and gender recognition accuracy. These performance gains are verified on well-known deep network architectures—VGG-16, CASIA-WebFace, and Alexnet—to confirm the proposed methods generality.
Uptakes and images of 38K in rabbit heart, kidney, and brain.

UNLABELLED The purpose of this study was to evaluate the kinetics and image quality of positron-emitting 38K (half-life, 7.6 min) and high-resolution small-animal PET in the heart, kidney, and brain of rabbits. METHODS Studies were performed with 18 closed-chest anesthetized rabbits at baseline and during infusions of adenosine (0.2 mg/kg/min) and propranolol (0.5-1.0 mg/kg intravenously) using high-resolution small-animal PET. 38K was injected intravenously and dynamic PET imaging of the heart, kidney, or brain was performed for 3 min. Colored microspheres were injected into the left ventricle to measure organ blood flow. Arterial blood was withdrawn directly from the femoral artery, and, after the animals were killed, 38K activities in each organ were measured directly with a well counter. Uptake of 38K was calculated by dividing the 38K activities in each organ by the integral of the input function. The extraction fraction of 38K was estimated by dividing the uptake of 38K in each organ by the organ blood flow, measured by microspheres. RESULTS The left ventricular myocardium and kidney were clearly visualized, but there was no visual 38K uptake in the brain. For the heart, kidney, and brain, respectively, average blood flow was 2.91 +/- 1.29, 5.49 +/- 0.71, and 0.57 +/- 0.11 mL/min/g, and the extraction fraction of 38K at baseline was 0.55 +/- 0.13, 0.48 +/- 0.13, and 0.022 +/- 0004. The Renkin-Crone model fit the relation between myocardial extraction and flow under a wide range of myocardial blood flow (r = 0.89). CONCLUSION 38K is a suitable tracer for noninvasively showing the potassium kinetics of the heart, kidney, and brain by PET imaging.
CNN-based Chemical-Protein Interactions Classification

In this study, we present a Convolutional Neural Network (CNN) based model for the extraction and classification of different groups of interactions between chemical and protein pairs for the Text Mining Chemical-Protein Interactions (CHEMPROT) task of BioCreative VI . We used wordembeddings and distance embeddings to represent a potential relation. Our system obtained 0.68 F-measure on the CHEMPROT development set. Keywords—CNN; relation classification; chemical-protein interactions; word-embeddings; deep learning
Array antenna characteristics enhancement for electronic scanning radar application: Parasitic patches and multi layer techniques

This paper presents characteristics improvement of patch array antenna used for electronic scanning radar application. This basic array antenna, designed on low cost substrate FR-4 and fed by microstrip technique, consists of 8 patches and operates at frequency 3GHz. However, this structure has two disadvantages a low gain value by dint of using a lossy material FR4 as a substrate, and a narrow bandwidth which is due to microstrip antenna limitations. Hence, the objective of this work is to increase the gain and the bandwidth of this structure. Therefore two techniques will be proposed separately in this paper: parasitic patches and multi layer substrate. For the first one, it consists of adding a defined number of parasitic patches, the distance between driven and parasitic patches will be evaluated to ensure strong coupling between them. For the second technique, a second layer of FR4 coated with an annealed copper of 0.035mm is added to the substrate. An air gap of 0.04x is used to separate between both FR-4 layers. The design and simulation of the array antenna and the modified structures will be performed using Advanced Design System (ADS) software. Simulation results of each technique will be compared first to basic array antenna simulation results and compared thereafter to each other.
How Many Hops Can Self-Backhauled Millimeter Wave Cellular Networks Support?

This paper considers the following question for viable wide-area millimeter wave cellular networks. What is the maximum extended coverage area of a single fiber site using multi-hop relaying, while achieving a minimum target per user data rate? We formulate an optimization problem to maximize the minimum end-to-end per user data rate, and exploit unique features of millimeter wave deployments to yield a tractable solution. The mesh network is modeled as a k−ring urbancanyon type deployment, where k is the number of hops back to the fiber site. The total number of relays per fiber site grows as k. We consider both integrated access-backhaul (IAB) and orthogonal access-backhaul (OAB) resource allocation strategies, as well as both half and full duplex base stations (BSs). With a few validated simplifications, our results are given as simple closed-form expressions that are easy to evaluate even for large networks. Several design guidelines are provided, including on the choice of routing and scheduling strategy, the maximum allowable self-interference in full duplex relays and role of dual connectivity to reduce load imbalance across BSs. For example, we show that usually there is very little gain to IAB (as considered for 5G) as opposed to tunable OAB (using separate spectrum for access and backhaul links); the latter being significantly simpler to implement.
Sequence analysis Improved spliced alignment from an information theoretic approach

Motivation: mRNA sequences and expressed sequence tags represent some of the most abundant experimental data for identifying genes and alternatively spliced products in metazoans. These transcript sequences are frequently studied by aligning them to a genomic sequence template. For existing programs, error-prone, polymorphic and cross-species data, as well as non-canonical splice sites, still present significant barriers to producing accurate, complete alignments. Results:We took a novel approach to spliced alignment that meaningfully combined information from sequence similarity with that obtained from PSSM splice site models. Scoring systems were chosen to maximize theirpowerofdiscrimination,anddynamicprogramming(DP)was employed to guarantee optimal solutionswould be found. The resultant program, EXALIN, performed better than other popular tools tested under a wide range of conditions that included detection of microexons and human–mouse cross-species comparisons. For improved speed with only a marginal decrease in splice site prediction accuracy, EXALIN could perform limited DP guided by a result from BLASTN. Availability: The source code, binaries, scripts, scoring matrices and splice site models for human, mouse, rice andCaenorhabditis elegans utilized in this study are posted at http://blast.wustl. edu/exalin. The software (scripts, source code and binaries) is copyrighted but free
Syntactic Patterns Improve Information Extraction for Medical Search

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both neural and linear) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited and of the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.
Robust Traffic-Sign Detection and Classification Using Mobile LiDAR Data With Digital Images

This study aims at building a robust method for detecting and classifying traffic signs from mobile LiDAR point clouds and digital images. First, this method detects traffic signs from mobile LiDAR point clouds with regard to a prior knowledge of road width, pole height, reflectance, geometrical structure, and traffic-sign size. Then, traffic-sign images are segmented by projecting the detected traffic-sign points onto the digital images. Afterward, the segmented traffic-sign images are normalized for automatic classification with a given image size. Finally, a traffic-sign classifier is proposed based on a supervised Gaussian–Bernoulli deep Boltzmann machine model. We evaluated the proposed method using datasets acquired by a RIEGL VMX-450 system. The traffic-sign detection accuracy of 86.8% was achieved; through parameter sensitivity analysis, the overall performance of traffic-sign classification achieved a recognition rate of 93.3%. The computational performance showed that our method provides a promising solution to traffic-sign detection and classification using mobile LiDAR point clouds and digital images.
Power-Decoupling of a Multiport Isolated Converter for an Electrolytic-Capacitorless Multilevel Inverter

This paper presents a generalized power-decoupling control scheme using a multiport isolated bidirectional converter for a multilevel inverter, which has multiple dc links inside. In the proposed method, a single power-decoupling capacitor is needed for all the dc links in the multilevel inverter cell. First, a prototype of the power-decoupling concept of individual H-bridge cells in the multilevel inverter is proposed, using a separate power-decoupling circuit. Then, a more advanced one-step power-decoupling method is proposed. The lifetime and reliability of the multilevel inverter is improved as film capacitors replace the large capacitance electrolyte capacitors. A multi-input ports/single output voltage-fed dual half-bridge converter (MDHB) is used for the power-decoupling circuit. A steady-state analysis for the peak and root mean square of the MDHB current is carried out for the loss breakdown. The currents are functions of the switching frequency, phase shift, leakage inductance, turn ratio, and output voltage, which make the multiport transformer design complex. A design methodology is proposed, which takes into account the design of the copper and core losses as functions of the switching frequency and number of turns. Furthermore, a special winding method for the input port is illustrated to obtain identical leakage inductances for the uniform current distribution in the multiport transformer. The proposed MDHB employs a current-sensorless power-decoupling control that contributes to the spontaneous ripple rejection of all the dc links without individual link current information, as well as to the cost and size reduction. Hence, the ripple-rejection controller is independent of the control configuration of the multilevel inverter, and also available for universal applications of various inverter topologies. Since the primary-input ports of MDHB share a single magnetic core for interfacing the ripple power to the unified secondary ripple capacitor, the controller design becomes difficult in considering the dynamic interaction among the ports, along with the average-voltage control loop design. In this paper, the dynamic analysis and controller design procedure of the circuit is also presented. The power decoupling is achieved even when the ripple frequency is other than the double frequency of the inverter output, since the single-pole transfer function of the small-signal model of the MDHB allows sufficient phase margin, along with high bandwidth. The proposed power-decoupling method for the multilevel inverter is validated with the help of simulation and 1.2-kW hardware prototype experimental results.
Online insomnia treatment and the reduction of anxiety symptoms as a secondary outcome in a randomised controlled trial: The role of cognitive-behavioural factors

Objective: Insomnia and anxiety commonly co-occur, yet the mechanisms underlying this remain unclear. The current paper describes the impact of an Internet-based intervention for insomnia on anxiety, and explores the influence of two cognitive-behavioural constructs – dysfunctional beliefs about sleep and sleep-threat monitoring. Methods: A large-scale, 9-week, two-arm randomised controlled trial (N = 1149) of community-dwelling Australian adults with insomnia and elevated yet subclinical depression symptoms was conducted, comparing a cognitive behavioural therapy–based online intervention for insomnia (Sleep Healthy Using The Internet) with an attention-matched online control intervention (HealthWatch). Symptoms of anxiety were assessed at pretest, posttest, and 6-month follow-up. Dysfunctional beliefs about sleep and sleep threat monitoring were assessed only at pretest. Results: Sleep Healthy Using The Internet led to a greater reduction in anxiety symptoms at both posttest (t724.27 = –6.77, p < 0.001) and at 6-month follow-up (t700.67 = –4.27, p < 0.001) than HealthWatch. At posttest and follow-up, this effect was found to moderated by sleep-threat monitoring (t713.69 = –2.39, p < 0.05 and t694.77 = –2.98, p < 0.01 respectively) but not by dysfunctional beliefs about sleep at either posttest or follow-up (t717.53 = –0.61, p = 0.55 and t683.79 = 0.22, p = 0.83 respectively). Participants in the Sleep Healthy Using The Internet condition with higher levels of sleep-threat monitoring showed a greater reduction in anxiety than those with lower levels from pretest to posttest, (t724.27 = –6.77, p < 0.001) and through to 6-month follow-up (t700.67 = –4.27, p < 0.001). This result remained after controlling for baseline anxiety levels. Conclusion: The findings suggest that online cognitive behavioral therapy interventions for insomnia are beneficial for reducing anxiety regardless of people’s beliefs about their sleep and insomnia, and this is particularly the case for those with high sleep-threat monitoring. This study also provides further evidence for cognitive models of insomnia.
Expression of granulosa cell microRNAs, AVEN and ATRX are associated with human blastocyst development

A greater understanding of the key molecules associated with embryo development during human‐assisted reproduction is imperative for the development of advanced diagnostics. Previous studies have shown that follicular microRNAs (miRNAs) are reliable markers of the polycystic ovarian syndrome (PCOS). Leveraging the utility of miRNAs in PCOS, the aim of this study was to identify miRNAs in human granulosa cells that may be indicative of blastocyst development. Granulosa cells and oocytes were collected from the first follicle aspirated from patients undergoing oocyte retrieval for in vitro fertilization or intracytoplasmic sperm injection. The development of isolated oocytes was recorded, and granulosa cell samples in this study were separated as follows. Group 1—BLAST: granulosa cells from follicles containing an oocyte that fertilized and developed into a blastocyst, and Group 2—FERT: granulosa cells from oocytes that fertilized but failed to reach blastocyst. A panel of 84 miRNAs, related to development and cellular differentiation, was assessed between the two groups using a miScript PCR array. Fourteen miRNAs and one snoRNA were differentially expressed between the groups. In addition, two downstream candidate protein biomarkers, ATRX and AVEN, were also found to be differentially expressed between the groups. The findings of this pilot study reveal follicular abnormalities on a molecular level, which may affect oocyte competence and its potential to develop successfully as an embryo. We encourage additional studies to confirm and expand on our findings and to determine the usefulness of granulosa‐borne miRNAs, ATRX, and AVEN as biomarkers.
The Algorithm Selection Competition Series 2015-17

The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance and thus, it leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection Competition series 2015 to 2017. The results of these competitions show how the state of the art improved over the years. Although performance in some cases is very promising, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.
In vivo immunogenicity of bovine bone removed by a novel decellularization protocol based on supercritical carbon dioxide

Abstract Trauma or infections associated critical bone defects lead to a huge economic burden in the healthcare system worldwide. Recent advances in tissue engineering have led to potential new strategies for the repair, replacement, and regeneration of bone defects, especially in biomaterials and decellularization protocols from xenogenic tissues. However, the complexity in bone structure and mechanical environment limits the synthesis of artificial bone with biomaterials. Thus, the purpose of our study is to develop a natural bone scaffold with great immunocompatibility. We combined decellularization techniques base on SC-CO2 to decellularize bovine bone. In order to study the immune response of mice to materials, the histology, spleen index, immune cells contents and in vitro proliferative performance, cytokine and immunoglobulin light chain expression of mice were characterized. Compared with the fresh bone group, the immune responses of decellularized group were significantly reduced. In conclusion, decellularization via this method can achieve a decellularized scaffold with great immunocompatibility. Our findings suggest the potential of using decellularized BB as a scaffold for bone bioengineering. Graphical Abstract
Calf circumference: clinical validation for evaluation of muscle mass in the elderly.

OBJECTIVE To validate calf circumference as a technology for assessing muscle mass in the elderly. METHOD Cross-sectional study with 132 elderly people from Goiânia, Goiás, Brazil. Decreased muscle mass was determined by the skeletal muscle mass index (IME) using Dual Energy X-Ray Absortometry (DEXA). The cutoff circumferences (CC) cutoff points to indicate muscle mass decrease were estimated by ROC curve, sensitivity, specificity and accuracy. RESULTS The most accurate cut-off points for detecting decreased muscle mass in the elderly were 34 cm for men (sensitivity: 71.5%, specificity: 77.4%) and 33 cm for women (sensitivity: 80.0%; specificity: 84.6%). CONCLUSION CC can be used as a measure for early identification of muscle mass decrease in routine evaluations of the elderly in primary care.