Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual Tennis Coach using Pose Estimation

: Tennis enjoys a considerable following in India, although it is limited to urban areas but still it is counted among the most popular sports in India. India has produced a number of tennis players, who have achieved international recognition. However, Coaching for this sport is expensive making it unaffordable for many. A method for creating virtual coaches is presented which uses tenser flow posenet algorithm. A database is developed which contains the images of top tennis players. After applying machine learning algorithms on it, the data is able to segregate good posture from the bad ones. The player is provided with an option to compare his game with the game of top ATA players and the end result is shown in visual format to make it more user friendly. The system works on real time data.
Strategic Experimentation and Information Design in Dynamic Contests: An Experimental Study

Many real-world innovation contests and R&D races have an end goal that might be infeasible. Participants learn about feasibility from their own experimentation and also from observing the progress (or lack thereof) of their competitors. If participants incorrectly learn that the goal is infeasible, they quit the contest and abandon an innovation that could have been achieved. In this paper, we design a novel real-effort experiment to show that a contest designer can avert this undesirable outcome through her choice of information mechanism. By allowing participants to monitor each other’s progress, either fully or partially, she signiﬁcantly increases the chances that the innovation is obtained when it is indeed feasible and when the common prior belief about infeasibility is high. We show that competitor behavior and contest outcomes are sensitive to the timing at which information is released, and we discuss how different information mechanisms affect the designer’s payoffs and participants’ earnings. Our results provide the ﬁrst experimental test of the role of information in environments that combine strategic experimentation and dynamic competition, and offer concrete guidelines for how practitioners and applied researchers should select a contest’s information mechanism in order to maximize the chances of innovation.
Mercurial Caches: OS Support for Low Power DRAM

DRAM is a signiﬁcant consumer of power, accounting for a large percentage of total power in servers. While many hardware technologies to improve DRAM power efﬁciency exist, achieving energy proportionality for memory is still difﬁcult. Modern operating systems lack the mechanisms and abstractions to use low-power technologies. For example, they use idle memory to aggressively cache ﬁle data, and they allow physical memory to become fragmented, making it impossible to turn off memory banks without losing data. In this paper, we describe mercurial caches , which are an operating system mechanism to achieving energy-proportionality for memory. Mercurial caches provide a copy-in/out interface to low powered memory and act as a new level between the ﬁle cache and storage. We show how mercurial caches can be integrated in current operating systems and demonstrate that they provide substantial power savings when memory is idle with minimal performance overhead. Through an analytical model, we demonstrate that it can provide DRAM energy savings proportional to the DRAM under active usage.
XRF – New Applications in Sensor-Based-Sorting Using X-ray Fluorescence

In recent decades, waste management has developed from simply waste disposal toward waste treatment, waste recovery and recycling. Even though waste collection has reached a high standard and many different waste streams are collected separately, an after-treatment is required. Each waste stream is more or less polluted with undesired types of materials making the treatment of collected waste in certain plants obligatory. BT-Wolfgang Binder GmbH is a general contractor specialized in mineral and environmental technology and develops different sensor-based sorting machines with technologies such as Near Infrared Spectroscopy, Infrared-Transmission and Line Scan Cameras as well as X-ray fluorescence analysis.
Brain Tumor Detection Techniques: Brief Survey

Brain tumor is nothing but an uncontrollable and abnormal growth of cells within the brain. Most research in developed countries shows that main cause of death of people having brain tumor is incorrect detection of brain tumor. It is one of the most dangerous diseases and therefore it should be detected quickly and accurately. This can be done by using automatic tumor detection techniques on medical images. Generally, MRI or CT scan that is directed into intracranial cavity produces the complete image of the brain tumor. Magnetic Resonance Imaging (MRI), a highly developed technique of medical imaging, is used to visualize internal structure of human body without any surgery. For the accurate detection of brain tumor, segmentation of MRI image is important. Classification of tumor, through segmented MR image, into normal and abnormal MRI brain images, is a difficult task due to complexity and alteration in tumor tissue characteristics like its location, size, gray level intensities and shape. In this paper, review of various techniques of automatic detection of brain tumor using Magnetic Resonance Image (MRI) is proposed.
CIKM AnalytiCup 2017 - Lazada Product Title Quality Challenge: A Bag of Features for Short Text Classification

This technical report describes our solution in the CIKM AnalytiCup competition held in conjunction with the 26th International Conference on Information and Knowledge Management (CIKM 2017). The task at hand is to determine the quality of product tittles created by public users on the e-commerce platform Lazada. In this report, we present an insightful analysis on the dataset. The observations found are used to derive extensive set of features covering lexical, syntactic and semantic aspects of a title. Our solution for the problem is straightforward and effective. We use Gradient Boosting Tree with all the designed features. Furthermore, 2-level stacking is utilized to further improve the performance
Socially-Equitable Interactive Graph Information Fusion-based Prediction for Urban Dockless E-Scooter Sharing

Urban dockless e-scooter sharing (DES) has become a popular Web-of-Things (WoT) service and widely adopted globally. Despite its early commercial success, conventional mobility demand and supply prediction based on machine learning and subsequent redistribution may favor advantaged socio-economic communities and tourist regions, at the expense of reducing mobility accessibility and resource allocation for historically disadvantaged communities. To address this unfairness, we propose a socially-Equitable Interactive Graph information fusion-based mobility flow prediction system for Dockless E-scooter Sharing (EIGDES). By considering city regions as nodes connected by trips, EIGDES learns and captures the complex interactions across spatial and temporal graph features through a novel interactive graph information dissemination and fusion structure. We further design a novel model learning objective with metrics that capture both the mobility distributions and the socio-economic factors, ensuring spatial fairness in the communities’ resource accessibility and their experienced DES prediction accuracy. Through its integration with the optimization regularizer, EIGDES jointly learns the DES flow patterns and socio-economic factors, and returns socially-equitable flow predictions. Our in-depth experimental study upon more than 2,122,270 DES trips from three metropolitan cities in North America has demonstrated EIGDES’s effectiveness in accurate prediction of DES flow patterns with substantial reduction of mobility unfairness.
The Yellow Tool - Making Yellow Pages More Social and Visible

Yellow Pages, although a well established tool of Knowledge Management, in many cases do not fully exploit their potential as an instrument for Knowledge Identification and Networking: the entries (profiles) are not maintained as they should and not used as they could. Why is this the case and how could the Yellow Pages approach be further developed to provide higher benefits? In the context of our action research in the domain of Knowledge Cooperation, our approach for answering these questions has been to undertake the development of a Knowledge Management instrument for competence analysis, visualisation and interaction called "Yellow Tool", a simple but powerful social tool integrating (on a Moodle platform) a wiki, a knowledge map, a discussion board and a chat & phone facility. The Yellow Tool is constituted by three components: 1. Yellow Pages, a collection of competence profiles in a wiki; 2. Yellow Map, a knowledge asset diagram visualizing the competence profiles with a city map metaphor; 3. Yellow Talk, a moderated conversation event, for talking about the competence profiles both asynchronously on a discussion board and synchronously by means of chat and phone conferences. The main feature of our Yellow Tool is that it makes competence profiles more social and visible. By focusing on social orientation and visibility, the Yellow Tool creates an incentive and motivates people to get in contact with each other and we expect that this will lead both to a higher engagement in maintenance and to a more active use in daily work.
Exploring the use of 3D point cloud data for improved plant stress rating

Currently, automated canopy stress classification for field crops rely on single-perspective, two-dimensional (2D) photographs, typically top view imaging via UAV. However, plant stress symptoms may appear throughout the canopy, and a single viewpoint photograph may not capture the entire region affected by the stress. Recent developments in efficient, large scale, 3D point cloud capture of agricultural fields open up the possibility of more comprehensive stress identification and rating. We hypothesized that utilizing the 3D point cloud will allow multi-perspective construction of plant canopy, and subsequent training of more accurate plant stress identification and its rating in field. We utilize a RGB 3D point cloud of a field where a diversity panel of soybean under Iron Deficiency chlorosis (IDC) stress was grown. We explore both multiview projection as well as area-preserving map projection methods to obtain parameterized 2D images depicting the complete 3D canopy surface. This approach allowed us to create models agnostic to canopy size/shape, while allowing us to leverage pretrained deep learning models – trained on 2D image data. Our preliminary results are promising, and we continue to finetune these machine learning pipelines for classifying plant
Characterization of Digoxin Uptake in Sandwich-Cultured Human Hepatocytes

Digoxin is a commonly used drug to treat congestive heart failure. With its narrow therapeutic index, patients are susceptible to drug-drug interaction-mediated cardiotoxicity. Digoxin is primarily renally cleared; however, a significant component of clearance is due to multidrug resistance 1 (MDR1)-mediated transport into bile. Digoxin is reported to be actively transported into human hepatocytes by the organic anion transporting polypeptide 1B3 (OATP1B3); however, further characterization has not been fully described. The purpose of the present study was to investigate the hepatic uptake mechanisms of [ 3 H]digoxin using sandwich-cultured human hepatocytes (SCHH) and transporter-expressing cells. Digoxin uptake in SCHH involves both a saturable (carrier-mediated) process and a passive (non-saturable) process. At low concentrations, the saturable component exhibited an apparent K m of 2.39 μ M and a V max of 4.49 pmol/min/mg protein. The calculated passive diffusion clearance was 1.25 µL/min/mg protein. Uptake of [ 3 H]digoxin in SCHH was not inhibited by a variety of substrates or inhibitors for OATP1B1, OATP1B3, OATP2B1, organic anion transporter 2 (OAT2), organic cation transporter 1 (OCT1) and monocarboxylate transporter 8 (MCT8). Cytochalasin B, which inhibits glucose transporters (GLUT), did not significantly inhibit digoxin uptake, whereas the flavonoids quercetin and rutin inhibited uptake by ~50%. Nonlabeled digoxin inhibited [ 3 H]digoxin uptake by ~50%. Studies with OATP-transfected HEK cells or oocytes showed that digoxin is not a substrate of OATP1B1, 2B1 or 1B3. In conclusion, the data suggest that digoxin uptake in SCHH involves both saturable and passive processes. The saturable process is mediated by
Sensitization to Gibberellin-Regulated Protein (Peamaclein) Among Italian Cypress Pollen–Sensitized Patients

Asero R1, Abbadessa S2, Aruanno A3, Barilaro G4, Barzaghi C4, Bignardi D5, Bilò MB6, Borro M7, Bresciani M8, Busa M9,10, Buzzulini F11, Cavaliere C12, Cecchi L13, Ciccarelli A14, Cortellini G15, Cucinelli F16, Deleonardi G17, Emiliani F18, Farsi A13, Ferrarini E19, Franchini M20, Ingrassia A21, Lippolis D15, Losappio L22, Marra AM4, Martini M23, Masieri S24, Mauro M25, Mazzolini M26, Muratore L27, Murzilli F16, Nucera E3, Pastorello EA22, Pinter E28, Polillo BR29, Pravettoni V30, Quercia O18, Rizzi A3, Russello M25, Sacerdoti C31, Scala E32, Scala G14, Scarpa A10, Schroeder J22, Uasuf CG33, Villalta D11, Yang B28, Mistrello G34, Amato S34, Lidholm J35
Supplementary Materials

Figure S1: Class distribution of the IN118K-LT dataset. We construct the IN-118K dataset by sampling a uniform subset from the ImageNet train split. Differently, the IN-118KLT is a long-tailed version of the ImageNet train split. The classes follow the Pareto distribution with power value α = 6 (see Figure S1). OI-118K is obtained by randomly sampling 118K images from the OpenImages-v4 train split. Note that all datasets are constructed to be of similar size, which facilitates an apples-to-apples comparison.
Development of a mathematical model to monitoring the velocity of subsidence of charge material column in the blast furnace based on the parameters of gas pressure in the furnace tract

A problem of estimating the velocity of subsidence of a column of charge materials using non-contact methods was considered. This is important because the level of furnace charge materials and the velocity of their subsidence are main indicators of melting intensity determining the furnace productivity. The design of a blast furnace and its blast path were described and existing methods and means of controlling the velocity of charge materials in the blast furnace were analyzed. A mathematical model was presented for estimating the velocity of subsidence of charge materials in a blast furnace based on the magnitude and fluctuations of gas pressure along the furnace shaft height. The model is based on the fact that the furnace gases rise up in the furnace shaft through elementary channels in the column of charge materials consisting of a combination of capacitances and resistances. Volume of capacities and values of resistance of elementary channels are constantly changing. This changes hydraulic resistance to gas movement in the blast furnace. The system of differential equations describes the dependence of the amplitude of pressure fluctuations on the amplitude of change in coefficients of resistance and frequency of pressure fluctuations on the frequency of change in coefficients of resistance. The experimental data on velocity of the column of charge materials and fluctuations in the pressure differential in the furnace were processed and their significant relationship was shown to confirm the previous theoretical study results. To assess the model adequacy, the simulation method was used. The results of the simulation model work were confirmed by experimental data. The developed mathematical model can be introduced into production. This will make it more economical and safer through better and more predictable control and improved flexibility in operation under different production conditions.
Scientific Chart Summarization: Datasets and Improved Text Modeling

Chart ﬁgures usually convey the key message in a multimodal document. Understanding charts automatically and making charts more accessible becomes indispensable in the information era. In this paper, we study the chart summarization problem in which the goal is to generate sentences that describe the salient information in a chart image. To obtain training examples, we leverage image-caption pairs in multiple scien-tiﬁc areas. We create a dataset of single-chart images from research papers in PubMed Central (PMC) and arXiv. Most recent vision-and-language works focus on natural images. Several challenges in structured images such as charts are under-explored. One key property of charts is that the text components (e.g., legends and axis names) carry important information. In our proposed model, we not only use a standard visual encoder but also a text encoder to encode a chart image. The visual and textual representations are connected to a large pre-trained language decoder via pre-embedding and cross-attention approaches, respectively. Experimental results show that the proposed model is signiﬁcantly better than an image captioning baseline.
Actionneurs piézoélectriques ultrasoniques miniatures pour la réalisation de moteurs sphériques compacts

Multi-degree-of-freedom angular motors are used in numerous applications, such as robot articulations, omnidirectional wheels or the stabilization of optical components. Nevertheless, the classical method used to build such platforms limits the miniaturization of these devices. The use of a single spherical rotor is a solution which facilitates the realization of compact multi-degree-of-freedom angular systems. In this work, we propose a piezoelectric ultrasonic actuator adapted to the fabrication of compact spherical motors. The novelty of the developed actuator is the adapted contact surface that allows for an extended contact area and thus the complete guidance of a spherical rotor. The state of the art of single rotor spherical systems is presented along with a presentation of the theoretical considerations useful for the modeling and construction of ultrasonic motors. A complete model for the design of ultrasonic motors is also proposed, which describes the geometry of the actuator and its vibration modes, the analysis of the contact using a force transfer model and the electromechanical conversion using semi-analytical methods. An experimental prototype is proposed along with a characterization of the actuator performance. Experimental data is compared to the theoretical model and validates the proposed design method. Parameters such as electrical amplitude, preloading force and the extent of the contact area are analyzed. Although in this case we demonstrate the application of our design methodology to the specific case of a curved multi-mode actuator, the proposed methodology is general and can be adapted to the design of other ultrasonic systems.
Introducing context awareness in multi-target tracking using re-identification methodologies

In this paper, re-identification techniques are exploited to add context awareness to a multi-target tracker and enhance its tracking performance, in an online manner. To achieve that, targets are labeled as independent, occluders or occluded ones, based on the completeness of their appearance information. For each category, a different tracking strategy is employed to achieve the optimal results. In cases of tracking failure, an online automated re-identification technique is proposed, to alleviate multiple identity assignments to the same target. Experimental evaluation conducted on the CAVIAR and PETS 2009 datasets shows that the proposed mechanism enhances tracking performance compared to a baseline tracker and achieves competitive performance with state of the art methods.
Oxylipin profiling identifies a mechanistic signature of metabolic syndrome: results from two independent cohorts

Background. Metabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Integratively phenotyping the molecular pathways involved in MetS would help to deeply characterize its pathophysiology and to better stratify the risk of cardiometabolic diseases. Oxylipins are a superfamilly of lipid mediators regulating most biological processes involved in cardiometabolic health. Methods. A high-throughput validated mass spectrometry method allowing the quantitative profiling of over 130 oxylipins was applied to identify and validate the oxylipin signature of MetS in two independent case/control studies involving 476 participants. Results. We have uncovered and validated an oxylipin signature of MetS (coined OxyScore) including 23 oxylipins and having high performances of classification and replicability (cross-validated AUCROC of 89%, 95% CI: 85%-93% and 78%, 95% CI: 72%-85% in the Discovery and Replication studies, respectively). Correlation analysis and comparison with a classification model incorporating both the oxylipins and the MetS criteria showed that the oxylipin signature brings consistent and complementary information supporting its clinical utility. Moreover, the OxyScore provides a unique mechanistic signature of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways that may help identify patients at higher risk of cardiometabolic diseases. Conclusion. Oxylipin profiling identifies a mechanistic signature of metabolic syndrome that may help to enhance MetS phenotyping and ultimately to better predict the risk of cardiometabolic diseases via a better patient stratification.
Titanium dioxide dental implants surfaces related oxidative stress in bone remodeling: a systematic review

Background Titanium dioxide dental implants have a controversial effect on reactive oxygen species (ROS) production. ROS is necessary for cellular signal transmission and proper metabolism, but also has the ability to cause cell death as well as DNA, RNA, and proteins damage by excessive oxidative stress. This study aimed to systematically review the effect of titanium dioxide dental implant-induced oxidative stress and its role on the osteogenesis-angiogenesis coupling in bone remodeling. Methods This systematic review was performed conforming to preferred reporting items for systematic review and meta-analysis (PRISMA) model. Four different databases (PubMed, Science Direct, Scopus and Medline databases) as well as manual searching were adopted. Relevant studies from January 2000 till September 2021 were retrieved. Critical Appraisal Skills Programme (CASP) was used to assess the quality of the selected studies. Results Out of 755 articles, only 14 which met the eligibility criteria were included. Six studies found that titanium dioxide nanotube (TNT) reduced oxidative stress and promoted osteoblastic activity through its effect on Wnt, mitogen-activated protein kinase (MAPK) and forkhead box protein O1 (FoxO1) signaling pathways. On the other hand, three studies confirmed that titanium dioxide nanoparticles (TiO2NPs) induce oxidative stress, reduce ostegenesis and impair antioxidant defense system as a significant negative correlation was found between decreased SIR3 protein level and increased superoxide (O2•-). Moreover, five studies proved that titanium implant alloy enhances the generation of ROS and induces cytotoxicity of osteoblast cells via its effect on NOX pathway. Conclusion TiO2NPs stimulate a wide array of oxidative stress related pathways. Scientific evidence are in favor to support the use of TiO2 nanotube-coated titanium implants to reduce oxidative stress and promote osteogenesis in bone remodeling. To validate the cellular and molecular cross talk in bone remodeling of the present review, well-controlled clinical trials with a large sample size are required.