Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA-Seq Provides Insights into VEGF-Induced Signaling in Human Retinal Microvascular Endothelial Cells: Implications in Retinopathy of Prematurity

The pathophysiology of retinopathy of prematurity (ROP) is postulated to first involve delayed intraretinal vascularization, followed by intravitreal neovascularization (IVNV). Although intravitreal agents that reduce the bioactivity of vascular endothelial growth factor (VEGF) are used to treat IVNV, concerns exist regarding their effects on intraretinal vascularization. In an experimental ROP model, VEGF receptor 2 (VEGFR2) knockdown in retinal endothelial cells reduced IVNV and promoted intraretinal vascularization, whereas knockdown of a downstream effector, signal transducer and activator of transcription 3 (STAT3) in retinal endothelial cells only reduced IVNV. In this study, we tested the hypothesis that the different pathways involved in VEGF-triggered VEGFR2 signaling and VEGF-triggered STAT3 signaling in retinal endothelial cells would allow us to delineate signaling pathways involved in IVNV from those involved in intraretinal vascularization in ROP. To address our hypothesis, we used RNA-sequencing and pathway enrichment analysis to determine changes in the transcriptome of cultured human retinal microvascular endothelial cells (HRMECs). Of the enriched pathways, inactivation of oncostatin M signaling was predicted by either KDR or STAT3 knockdown in the presence of VEGF. Activation of kinetochore metaphase signaling was predicted by KDR knockdown, whereas inactivation was predicted by STAT3 knockdown in the presence of VEGF. Inactivation of signaling by the Rho family of GTPases was predicted by KDR knockdown, but activation was predicted by STAT3 knockdown in the presence of VEGF. Taken together, our data identified unique signaling pathway differences between VEGF-triggered VEGFR2 and VEGF-triggered STAT3 in HRMECs that might have implications in ROP.
SenSE: A Toolkit for Semantic Change Exploration via Word Embedding Alignment

Lexical Semantic Change (LSC) detection, also known as Semantic Shift, is the process of identifying and characterizing variations in language usage across different scenarios such as time and domain. It allows us to track the evolution of word senses, as well as to understand the difference between the language used in distinct communities. LSC detection is often done by applying a distance measure over vectors of two aligned word embedding matrices. In this demonstration, we present SenSE, an interactive semantic shift exploration toolkit that provides visualization and explanation of lexical semantic change for an input pair of text sources. Our system focuses on showing how the different alignment strategies may affect the output of an LSC model as well as on explaining semantic change based on the neighbors of a chosen target word, while also extracting examples of sentences where these semantic deviations appear. The system runs as a web application (available at http://sense.mgruppi.me), allowing the audience to interact by configuring the alignment strategies while visualizing the results in a web browser.
Metabolome and transcriptome profiling reveal regulatory network and mechanism of flavonoid biosynthesis during color formation of Dioscorea cirrhosa L.

Dioscorea cirrhosa is a plant that is used as a dye as well as in medicine. Many metabolites with pharmacological activity exist in the tubers of D. cirrhosa. However, little is known about the mechanism regulating biosynthesis in these metabolites. In this study, transcriptome and metabolome profiling were performed in four color tubers. A total of 531 metabolites, including 62 flavonoids, were identified. Epicatechin and proanthocyanin B2 were the key metabolites that exhibited high content levels in the four tubers. These metabolites were divided into nine classes with distinct change patterns. A total of 22,865 differentially expressed genes (DEGs) were identified by transcriptome analysis. Among these DEGs, we identified 67 candidate genes related to the flavonoid biosynthesis pathway and three genes that played pivotal roles in proanthocyanin (PA) synthesis. A weighted gene co-expression network analysis (WGCNA) revealed that the two modules, “MEblue” and “MEblack,” were two key gene sets strongly associated with phenylpropanoid and flavonoid biosynthesis. We also found that the plant hormone signal transduction biological process exhibited activity in the late stage of tuber color formation. Additionally, we identified 37 hub transcript factors related to flavonoid biosynthesis, of which 24 were found to be highly associated with flavonoid pathway genes. In addition to the MYB-bHLH-WD40 (MBW) genes, we found that the plant hormone gene families exhibited high expression levels. This study provides a reference for understanding the synthesis of D. cirrhosa tuber metabolites at the molecular level and provides a foundation for the further development of D. cirrhosa related plant pigments as well as its further use in the pharmaceutical industry.
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data

Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns, regulation restrictions, and commercial competition. Federated Learning is the de-facto standard for collaborative training of machine learning models over many distributed edge devices without the need for centralization. Nevertheless, training graph neural networks in a federated setting is vaguely defined and brings statistical and systems challenges. This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature. We provide convergence guarantees and empirically demonstrate the efficacy of our framework on a variety of non-I.I. D. distributed graph-level molecular property prediction datasets with partial labels. Our results show that SpreadGNN outperforms GNN models trained over a central server-dependent federated learning system, even in constrained topologies.
Battery Storage Allocation in Unbalanced Distribution Systems with EV Demand Uncertainty

This paper presents an optimization study for allocating battery energy storage systems (BESS) in unbalanced distribution systems. The paper tackles a problem where there is high penetration of residential photovoltaic (PV) systems and electric vehicle (EV) charging, which may lead to phase imbalance and overload the distribution system. A mixed-integer linear programming (MILP) model is developed for modifying the distribution system by installing BESSs in the network to mitigate the negative effects of EV charging. A robust scenario-based method is adopted to account for the spatio-temporal uncertainty of residential electric vehicle charging, load demand, and PV generation. The optimization problem is solved using an algorithm that combines the Alternating Direction Method of Multipliers (ADMM) with a cutting plane procedure. The effectiveness of the proposed method is demonstrated on the IEEE 123-bus system. Numerical results show that the proposed method can effectively allocate BESSs while considering a large number of scenarios to represent the uncertainty.
QoS-aware Dynamic Network Slicing and VNF Embedding in Softwarized 5G Networks

The inclusion of Network Function Virtualization (NFV) and Software-Defined Networking in next-generation communication networks (e.g., 5G and beyond) influence Telecommunication Service Providers (TSPs) to deploy Virtual Network Functions (VNFs) to improve network performance without in-curring additional usage of network resources. The softwarization and virtualization of network resources uplift the Network Slicing (NS) concept to optimally place the VNF instances over Service Function Chains (SFCs) for superior service delivery. Limited network capacity and storage work as a major hindrance toward better Quality-of-Service (QoS), so optimal VNF embedding over SFCs becomes an obligatory aspect. In this work, we have studied the VNF embedding problem (VNF-EP) over SFC instances for Softwarized 5G Networks. We have proposed a novel approach for dynamic VNF sharing over multiple SFCs, considering the flow requests of SFC for individual Network Slice. The inter-slice co-ordinations are done considering the common service requests among independent and heterogeneous Slices. The mathematical formulation follows a MILP based optimization approach that optimally controls the VNF sharing to increase network efficiency and hardware usage. This flexible and shareable VNF embedding approach (FlexShare-VNF) results in significant energy-efficient service delivery in a low latency environment, and our performance evaluation also supports the claim. This approach will be extremely helpful for smooth network up-gradation in resource-restricted environments for future network advancements.
Wideband Array for BGAN Portable Terminals

A circularly-polarized antenna array with sequentially rotated elements is proposed for Broadband Global Area Network L-band portable terminals. The antenna employs a 2×2 array of air-spaced annular-ring loaded circular patches. The measured results show a 3 dB axial-ratio bandwidth better than 70% (1113 - 2314 MHz) and a 10 dB S11 bandwidth of 56.7% (1170- 2095 MHz). The measured peak gain is 13.6 dBic.
Cricket Event Recognition and Classification from Umpire Action Gestures using Convolutional Neural Network

—The advancement of hardware and deep learning technologies has made it possible to apply these technologies to a variety of fields. A deep learning architecture, the Convolutional Neural Network (CNN), revolutionized the field of computer vision. One of the most popular applications of computer vision is in sports. There are different types of events in cricket, which makes it a complex game. This task introduces a new dataset called SNWOLF for detecting Umpire postures and categorizing events in cricket match. The proposed dataset will be a preliminary help, it was assessed in system development for the automatic generation of highlights from cricket sport. When it comes to cricket, the umpire has the authority to make crucial decisions about on-field incidents. The referee signals important incidents with hand signals and gestures that are one-of-a-kind. Based on detecting the referee's stance from the cricket video referee action frame, it identifies most frequently used events classification: SIX, NO BALL, WIDE, OUT, LEG BYE, and FOUR. The proposed method utilizes Convolutional Neural Networks (CNNs) architecture to extract features and classify identified frames into Umpire postures of six event classes. Here created a completely new dataset of 1040 images of Umpire Action Images containing these six events. Our method train CNNs classifier on 80% images of SNWOLF dataset and tested on 20% of remaining images. Our approach achieves an average overall accuracy of 98.20% and converges on very low cross-entropy losses. The proposed system is a influential answer for generation of cricket sport highlights.
Morbidity and Mortality in Heart Transplant Candidates Supported With Mechanical Circulatory Support: Is Reappraisal of the Current United Network for Organ Sharing Thoracic Organ Allocation Policy Justified?

Background— Survival of patients on left ventricular assist devices (LVADs) has improved. We examined the differences in risk of adverse outcomes between LVAD-supported and medically managed candidates on the heart transplant waiting list. Methods and Results— We analyzed mortality and morbidity in 33 073 heart transplant candidates registered on the United Network for Organ Sharing (UNOS) waiting list between 1999 and 2011. Five groups were selected: patients without LVADs in urgency status 1A, 1B, and 2; patients with pulsatile-flow LVADs; and patients with continuous-flow LVADs. Outcomes in patients requiring biventricular assist devices, total artificial heart, and temporary VADs were also analyzed. Two eras were defined on the basis of the approval date of the first continuous-flow LVAD for bridge to transplantation in the United States (2008). Mortality was lower in the current compared with the first era (2.1%/mo versus 2.9%/mo; P<0.0001). In the first era, mortality of pulsatile-flow LVAD patients was higher than in status 2 (hazard ratio [HR], 2.15; P<0.0001) and similar to that in status 1B patients (HR, 1.04; P=0.61). In the current era, patients with continuous-flow LVADs had mortality similar to that of status 2 (HR, 0.80; P=0.12) and lower mortality compared with status 1A and 1B patients (HR, 0.24 and 0.47; P<0.0001 for both comparisons). However, status upgrade for LVAD-related complications occurred frequently (28%) and increased the mortality risk (HR, 1.75; P=0.001). Mortality was highest in patients with biventricular assist devices (HR, 5.00; P<0.0001) and temporary VADs (HR, 7.72; P<0.0001). Conclusions— Mortality and morbidity on the heart transplant waiting list have decreased. Candidates supported with contemporary continuous-flow LVADs have favorable waiting list outcomes; however, they worsen significantly once a serious LVAD-related complication occurs. Transplant candidates requiring temporary and biventricular support have the highest risk of adverse outcomes. These results may help to guide optimal allocation of donor hearts.
Automatic Detection of Adverse Drug Reactions from Online Health Forums

Adverse drug reaction is a serious health problem that has a crucial impact on patient’s morbidity, mortality and quality of life. This paper investigates the problem of detecting adverse drug reactions of anti-epileptic drugs from patients’ reviews in online health forums. To this end, a lexicon-based methodology is proposed and applied to a dataset of patients’ reviews collected from two online health forums. Following this methodology, the dataset is preprocessed using natural language processing techniques and the adverse reactions of anti-epileptic drugs are extracted with the aid of Consumer Health Vocabulary and a lexicon of adverse reactions. After that, proportional reporting ratio is applied to quantify the correlation between each drug and all adverse reactions and then identify the lists of adverse reactions for each drug. The detected lists of adverse reactions are validated quantitatively against a widely known database of adverse drug reactions called Side Effect Resource. The validation results provide empirical evidence on the effectiveness of the proposed methodology for the detection the adverse reactions from online health forums.
A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs

Recent advances in efficient planning in deterministic or stochastic high-dimensional domains with continuous action spaces leverage backpropagation through a model of the environment to directly optimize action sequences. However, existing methods typically do not take risk into account when optimizing in stochastic domains, which can be incorporated efficiently in MDPs by optimizing a nonlinear utility function of the return distribution. We bridge this gap by introducing Risk-Aware Planning using PyTorch (RAPTOR), a novel unified framework for risk-sensitive planning through end-to-end optimization of commonly-studied risk-sensitive utility functions such as entropic utility, mean-variance optimization and CVaR. A key technical difficulty of our approach is that direct optimization of general risk-sensitive utility functions by backpropagation is impossible due to the presence of environment stochasticity. The novelty of RAPTOR lies in leveraging reparameterization of the state distribution, leading to a unique distributional perspective of end-to-end planning where the return distribution is utilized for sampling as well as optimizing risk-aware objectives by backpropagation in a unified framework. We evaluate and compare RAPTOR on three highly stochastic MDPs, including nonlinear navigation, HVAC control, and linear reservoir control, demonstrating the ability of RAPTOR to manage risk in complex continuous domains according to different notions of risk-sensitive utility.
A Low-Power DRAM Transmitter With Phase and Current-Mode Amplitude Equalization to Improve Impedance Matching

This brief presents a single-ended transmitter for a low-power DRAM that switches equalization modes to provide improved matching to channel impedance and reduced energy consumption. The transmitter’s voltage-mode driver responds to the incoming data by selecting between phase equalization and current-mode amplitude equalization. The impedance matching with the channel is further improved by reducing the variation in on-resistance exhibited by the driver. A prototype chip that uses supply voltages of 0.5 V for the driver, and 1.05 V for rest of the blocks, was fabricated in a 65 nm CMOS process and occupied 0.0698 mm2. This prototype can compensate for a channel loss of 11.8 dB at a data-rate of 12 Gb/s while consuming 1.30 pJ/bit.
An ICN-Based Data Marketplace Model Based on a Game Theoretic Approach Using Quality-Data Discovery and Profit Optimization

In the age of data and machine learning, massive amounts of data produced throughout our society can be rapidly delivered to various applications through a broad spectrum of cloud services. However, the spectrum of applications has vastly different data quality requirements and Willingness-To-Pay(WTP), creating a general and complex problem matching consumer quality requirements and budgets with providers’ data quality and price. This paper proposes the Information-Centric Networking(ICN)-based data marketplace to foster quality-data trading service to address the challenge above. We embed a WTP mechanism into an ICN-based data broker service running on cloud computing; therefore, a data consumer can request its desired data with a data name and quality requirement. By specifying nominal WTPs, data consumers can acquire data of the desired quality at the range of maximum nominal WTP. At the same time, a data broker can offer data of a suitable quality based on the profit-optimized price and the proposed service quality using ground-truth accuracy trained by data. We demonstrate that the data broker's profit can be almost doubled by using the optimal data size and budget determined by considering the one-leader-multiple-followers Stackelberg game. These results show that a value-added data brokering service can profitably facilitate data trading.
Articulatory properties of Estonian palatalization by Russian L1 speakers Shortened title: Estonian palatalization by Russian speakers

We studied the articulatory and temporal properties of Estonian palatalization by Russian L1 Estonian L2 speakers and compared the results to Estonian native speakers. The tongue movement of 24 Russian L1 and 21 Estonian L1 speakers were recorded with an electromagnetic articulograph. The speakers articulated phonologically contrastive word pairs and i-stemmed nouns where the final consonant was palatalized. Previous research has shown that palatalization is generally realized by raising the tongue dorsum towards the hard palate. Consequently, the tongue becomes more anterior and wider. This rising of the tongue also lengthens the duration of the segments. The results showed that Russian L1 speakers’ tongue dorsum was higher and more anterior when palatalizing consonants and the subsequent vowels. Compared to native speakers, their tongue was retracted and lower. Russian L1 speakers’ duration of palatalized consonants and the vowels that preceded them were significantly shorter than in the control group. They did not use duration to differentiate palatalization in word pairs as native speakers do. Kokkuvõte. Anton Malmi, Pärtel Lippus, Einar Meister: Vene emakeelega kõnelejate Eesti palatalisatsiooni artikulatoorsed ja temporaalsed tunnused. Artiklis uuritakse, kuidas vene emakeelega kõnelejad hääldavad eesti keele palatalisatsiooni. Varasemad uurimused on näidanud, et konsonanti palataliseeritakse keeleselja tõstmisega kõva suulae poole. Sellega kaasneb ka eespoolsem keele asend ja laiem keele jaotus suulaes ning hääliku pikem kestus. Uurimismaterjal koosnes sõnadest, kus palatalisatsioon eristas tähendust ja i-tüvelistest nimisõnadest, mis lõppesid pika palataliseeritud konsonandiga. Testsõnu sisaldavaid lauseid salvestasime elektromagnetartikulograafiga. Katses osales 24 vene emakeelega ja 21 eesti emakeelega kõnelejat. Tulemused näitasid, et vene emakeelega kõnelejate keel oli palataliseerimisele omaselt kõrgemal ja eespoolsem. Eestlastega võrreldes oli nende keel aga madalamal ja tagapoolsem. Vene emakeelega kõnelejad ei kasutanud palatalisatsioonipaarides kestust eristava tunnusena nagu emakeelsed kõnelejad seda teevad. Nende vokaalid ja konsonandid olid palatalsatsioonipaarides ja i-tüvelistes nimisõnades lühemad kui emakeelsetel kõnelejatel.
Silence or Outbreak - a Real-Time Emergent Topic Identification System (RealTIS) for Social Media

This paper presents RealTIS, a Real-time emergent Topic Identification System for user-generated content on the web via social networking services such as Twitter, Weibo, and Facebook. Without user intervention, our proposed RealTIS system can efficiently collect necessary social media posts, construct a quality topic summarization from the vast sea of data, and then automatically identify whether the emerging topics will be out-breaking or just fading into silence. RealTIS uses a time-sliding window to compute the statistics about the basic structure (motifs) variation of the propagation network for a specific topic. These statistics are then used to predict unusual shifts in correlations, make early warning and detect outbreak. Besides, this work also illustrates the mechanism by which our proposed system makes early warning happen.
Optical Cutting Tool Wear Monitoring by 3D Geometry Reconstruction

Cutting tool wear needs to be monitored closely to ensure good quality of machined parts. However, manual inspection is both expensive and time consuming, therefore there is a need for automated monitoring methods. We present a technique that can reconstruct the cutting tool surface in 3D, allowing a spatial estimation of the tool wear with high accuracy. The reconstruction allows an automated direct monitoring method that estimates at any time the cutting tool condition, avoiding conversion work and major quality issues. The optical measurement setup consists of a hardware triggered line scan camera that registers the spinning cutting tool’s shadow inflicted by a collimated backlight. We show how to leverage the 1D line scan signal acquired at varying cutting heights of the tool into a full 3D reconstruction. The progression of tool wear may thus be monitored by comparing the reconstructed shape to previous measurements. To this end we show a methodology for tool wear quantification. Additionally, to assess the measurement technique, an accuracy analysis with ground truth geometry was performed. The technique was applied to multiple degrading drilling tools. By automation of the cutting tool health monitoring, retrofitting this technology on a conventional machining center would transform it into an Industry 4.0 compatible (smart) machining center utilizing off-the-shelf optical equipment with moderate costs.