SIRIS-Lab/unlabelled-sti-corpus huggingface dataset:
Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms

Information Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the state-of-the-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario.
Conquering a new paradigm for addressing ion detection in real scenarios

ConquerIons is seeking a definitive solution for the detection of ions in real scenarios by innovatively addressing the limitations of traditional ion-sensing concepts in terms of effectiveness, sensitiveness, robustness and downscaled platforms. The project proposes a new generation of voltammetric, amperometric and optical ion sensors based on calibration-free selective membranes inspired by conventional membranes used in the well-established potentiometry field but with a thickness at the nanoscale. The uniqueness of the dynamic electrochemical tuning of ion-transfer processes across these nano-membranes opens up a revolutionary approach that allows for the monitoring of ion concentration in a sample with unprecedented analytical performance. Thus, the project combines efforts from the synthetic chemistry, material science, nanoscience, electrochemistry and analytical chemistry disciplines resulting in a universal concept for the reliable detection of ions. ConquerIons is based on a research approach carefully designed mainly considering the following challenges: (i) the use of novel materials to afford a robust and universal detection; (ii) the implementation of thin-layer concepts of electrochemical and/or optical-sensing platforms to achieve reduced limits of detection and calibration-free methodology; (iii) the translation of the developed concepts to the single-entity scale (i.e. single nanoparticles) towards the study of ion-transfer processes employing, for the first time, ‘nanocoulometry’ readout; and (iv) exploitation of sensors from the laboratory context based on relevant cases as the next step towards the final use as daily smart informers operating in real scenarios. This latter purpose will lead to a series of extraordinary analytical applications from a long-term perspective related to very distinct fields, such as cell-scale therapies and speciation of trace ion levels in environmental analysis.
APPARATUSES AND METHODS FOR MONITORING STRESS IN STEEL CATENARY RISERS

Apparatuses and methods of monitoring stress on steel catenary risers (SCR) are described, wherein a multi-strand optical cable extending down the length of the riser is provided and a plurality of optical fiber strain sensors are installed on the outer surface of the riser, said sensors being connected to the optical cable. The sensors may be pre-installed on new risers or post-installed on existing risers using remotely operated vehicles.
Multilingualism enhances immigration acceptance through increased intergroup quality contact: A proof of concept in New Jersey and Spain

Multilingualism can potentially increase empathy and facilitate contact between a given country's own nationals and immigrants. A proof of concept exercise was conducted with students from the US (N = 112) and Spain (N = 107), and a small nonstudent sample (N = 22) also from Spain. The effect of the number of languages spoken on immigration acceptance was assayed in all three samples using a questionnaire based on the European Social Survey, and empathy and contact with immigrants were additionally determined in the Spanish samples. The results showed that multilingual students were significantly more accepting of immigrants than monolinguals in the samples from both the US and Spain. The number of languages spoken was a significant mediator between contact with immigrants and immigration acceptance. Empathy was significantly correlated with the acceptance of immigrants from poor countries, without any apparent connection to the number of languages spoken. The results emphasize the importance of multilingualism in improving crosscultural attitudes by increasing the quality of contact with immigrants. Learning the languages spoken by immigrants could be explored as a method for facilitating positive contact between groups in host societies.
Real organ generation

MTM S.r.l has developed a game-changing platform comprising 1) an algorithm able capture 2D images and convert them into 3D geometries ( we have already validated our solution on Cardiac Imaging), in order to generate hyper realistic ( more than 10X resolution respect of the best 3D medical imaging today present in the market) models/mockups, ready to be used in VR and 3D printing allowing an unprecedent medical training approach, as well as a simulation of real patients’ condition on which perform pre-operative simulation in healthcare environment; 2) a sophisticated software able to manage multiple actions: such as loading 2D image parts produced by medical scanning devices (organs and tissues), generating 3D models from 2D images elaborated by our algorithm, enable the user (thanks to our intuitive GUI – Graphical User Interface) to visualise the 3D model, (choosing from different views, differentiate surfaces, anatomical sections, layers, regions of interests), recognising anomalies, tagging the mutations and the morphology aberrations, supporting and collecting extensive libraries of 2D images and 3D models, enabling an unprecedent new way to train students and surgeons during critical preparation to surgery.
ROG’s business model. We already mapped our value chain and plan each phase of our path to exploitation. Our revenue model will be a combination based on: ROG software platform licensing and sales from personalized processing of 3D image services (The specialized paid services will instead involve the personalized processing of 3D images, the tagging performed by semi-automated expert systems and the possibility of having training sessions on the platform).  
As a result of the maturation strategy and the higher potential value of our system, we will see growth in revenues and profits that will enable us to increase our headcount to 12 by 2026 with a raw return on investment (RoI) of 2.29%.
Machine learning based software toolkit for automated identification in atomic-resolution operando nanoscopy

In recent years, the analysis of large data sets is becoming increasingly important in the fields of material science and engineering. There is a strong demand for real-time automated identification algorithms in electron microscopy (EM) for the analysis of atomic-structure, phases, and defects. Unfortunately, it is non-trivial to obtain or extract meaningful scientific information from raw EM output digital data. It requires a tedious process of filtering/fitting and the expertise of a seasoned microscopist. With the rapid development of information technology and computer science, automated computer-assisted analysis of electron microscopy images/data is becoming a reality. In the past decade, different techniques have been developed and applied to digital data analysis. Meanwhile, the rapid development of novel microscopy techniques and instrumentation, e.g. in situ/operando and pixelated detector-based techniques, require high-speed data execution and analysis. Currently, several groups worldwide are concentrating their efforts into implementing machine learning and deep learning algorithms for image/data analysis. However, this is still a very undeveloped direction in the field of electron microscopy for materials science, especially in Europe. According to the Digital Transformation Monitor, artificial intelligence-based technologies will play a major role in future economy. The ability to analyse levels of data that are beyond human comprehension will allow business to personalize experiences, customize products and services and identify growth opportunities with a speed and accuracy that has never been possible before. The objective of this PoC is to generate an innovative software package that enables the analysis of large sets of EM data (i) at high throughput with (ii) low costs, in (iii) a standardized approach and (iv) under operando conditions, based on advanced machine learning algorithms.
Powered smart card with a biometric one time password system

CardLab and QuardLock’s joint vision is to bring a disruptive, highly secure smart card (QuardCard) to the financial payments market, which is currently facing major increases in fraud levels. QuardCard combines, for the first time: (i) a smart card (all data is kept inside the card and only encrypted keys are released); (ii) a biometric fingerprint – highly accurate and impossible to copy; and (iii) dynamic one-time-password (OTP), generated by the user's fingerprint.

As such, the card only works with the rightful owner’s fingerprint and will improve drastically the security of online and physical transactions. The card will be marketable in two generations/versions:
• 1st generation QuardCard for the generation of OTPs through user’s fingerprint for e-banking authentication and e-commerce transactions;
• 2nd generation QuardCard combining generation of OTPs with capacity for physical transactions (card payments or cash withdrawal), replacing the need for a conventional credit card.

Due to the certification compliance of the current prototype, both generations will be ready for mass production upon completion of the QuardCard project. However, financial institutions do not easily replace existing solutions by novel technologies. Ensuring and demonstrating a maximum level of user security is a key differentiating factor to gain access to the market. Given QuardCard´s value proposition, combined with the demonstration and certification activities planned for Phase 2, we will be able to enable subsequent market entry.

The consortium gathers two Danish SMEs: CardLab (QuardCard manufacturer) has an established network of clients and QuardLock holds the patent and know-how to use biometrics to generate an OTP. Both companies will benefit from the project through a cross-license agreement where each receive a share of QuardCard revenues and royalties.
Integrating biology and behaviour for precision stratification of mental disorders

In many areas of medicine, biomarkers have revolutionised diagnosis and personalised treatment allocation (‘precision medicine’). Psychiatry lags behind: disorders are still diagnosed by symptoms and no biomarkers have been found. However, addressing this is a formidable task because of a lack of analysis tools to understand the complex disruptions of mental disorders at multiple levels –from neurobiology to behaviour– and to tackle their extreme heterogeneity at every level. 

My vision is to provide a set of principled, next generation analysis tools to stratify mental disorders on the basis of biomarkers derived from population-scale neuroimaging and quantitative measures of behaviour from smartphone-based digital phenotyping.

I will build generative models to chart variation in brain organisation across massive neuroimaging samples from more than 40,000 individuals based on ‘brain growth charting’ methodology I have pioneered. This will provide a universal platform to understand shared and distinct mechanisms of mental disorders at the level of the individual, simulate clinical brain states and test putative interventions using synthetic data. 

I will develop innovative machine learning tools to: (1) learn latent dynamics of digital phenotyping measures; (2) parse the dynamic interplay between brain systems and the behaviours they underpin; (3) integrate complementary information from distinct data modalities and (4) stratify mental disorders in a way that cuts across diagnostic classifications and accommodates different mechanisms converging on the same symptoms. 

These innovations will have far-reaching impact; here, I will showcase them by predicting trajectories of resilience and risk in major depression and bipolar disorder which are a leading cause of worldwide disease burden. This will bring precision medicine within reach for psychiatry allowing early, personalized intervention, preventative treatments and a better understanding of disorder entities.
Improved Synthesis of Global Irradiance with One-Minute Resolution for PV System Simulations

High resolution global irradiance time series are needed for accurate simulations of photovoltaic (PV) systems, since the typical volatile PV power output induced by fast irradiance changes cannot be simulated properly with commonly available hourly averages of global irradiance. We present a two-step algorithm that is capable of synthesizing one-minute global irradiance time series based on hourly averaged datasets. The algorithm is initialized by deriving characteristic transition probability matrices (TPM) for different weather conditions (cloudless, broken clouds and overcast) from a large number of high resolution measurements. Once initialized, the algorithm is location-independent and capable of synthesizing one-minute values based on hourly averaged global irradiance of any desired location. The one-minute time series are derived by discrete-time Markov chains based on a TPM that matches the weather condition of the input dataset. One-minute time series generated with the presented algorithm are compared with measured high resolution data and show a better agreement compared to two existing synthesizing algorithms in terms of temporal variability and characteristic frequency distributions of global irradiance and clearness index values. A comparison based on measurements performed in Lindenberg, Germany, and Carpentras, France, shows a reduction of the frequency distribution root mean square errors of more than 60% compared to the two existing synthesizing algorithms.
Safety evaluation of high-piled wharf based on fuzzy analytic hierarchy process

Many factors affect the safety of high-piled wharf.The traditional safety assessment method for the high-piled wharf mainly relies on the test results of the qualitative analysis,and seldom on the accurate quantitative evaluation.In order to analyze the safety conditions of high-piled wharf accurately,this paper carries out a research on the safety assessment with reference to the safety assessment method for bridges,in which the safety assessment is divided into visual inspection assessment and bearing capacity assessment.Through the fuzzy analytic hierarchy process,the safety degree of the pier is obtained.The method is concise and can reflect accurately the safety situation of the existing high-pile pier.It provides a foundation for the management,maintenance and reinforcement of wharves.
Knowledge construction in the community of fishing women in the region of Guaraqueçaba – PR

In the knowledge dynamics, women's craft communities form a great ba: knowledge-sharing space. The objective of this article is to establish the dynamics of the process of knowledge construction and professional belonging in the Community of Practice of professional artisanal fisherwomen, in the region of Guaraqueçaba – PR – Brazil – in the light of the Actor-Network Theory, with an approximative approach to the SECI MODEL, a classic in the field of Knowledge Management. Applied, exploratory-descriptive and explanatory research was used as a methodology, with observant participation and mixed methods in the treatment of data, favoring the life stories, communal interactions and day-to-day activities of the involved fisherwomen. Findings: The network of human and non-human elements, the process of knowledge creation, was mapped establishing a comparison between the model proposed in this community and the SECI Model. It was confirmed that the SECI Model also applies outside formal organizations. In the analysis of communities of practice, the concept of communities of practice was expanded, in this context, to a community of women's craft. Research limitations: The shyness of the fisherwomen, the rainy weather not allowing for research or fieldwork, the slow pace of time on the way life passes in those communities; all these, require an adaptation from outsiders
Random Number Generation From Intermittent Optical Chaos

We propose a method to generate physical random numbers based on intermittent optical chaos. Intermittent chaotic output is produced in a semiconductor laser subjected to optical feedback embedded in a photonic integrated circuit. This dynamics is characterized by a temporal waveform organized in a succession of laminar regions of low amplitude and bursts of high amplitude. The temporal randomness ruling the alternation of successive laminar regions and bursts is used as an entropy source to generate sequences of random bits. We compare the performances of the exclusive OR and reverse methods implemented in the bit generation process and evaluate the quality of the random bits with the Rabbit test of TestU01 for different bit sequences lengths.
SYSTEM AND METHOD FOR PERFORMING WELLBORE FRACTURE OPERATIONS

Methods for performing oilfield operations are provided. The methods involve performing a fracture operation. The fracture operation involves generating fractures and a fracture network about the wellbore. The fracture network includes a plurality of fractures and a plurality of matrix blocks positioned thereabout. The fractures are intersecting and hydraulically connected. The matrix blocks are positioned about the plurality of fractures. The method also involves generating flow rate through the fracture network, generating a fluid distribution based on the fracture network, and performing a production operation comprising generating a production rate from the fluid distribution.
Qualification of innovative floating substructures for 10mw wind turbines and water depths greater than 50m.

The focus of the project will be on floating wind turbines installed at water depths from 50m to about 200m. The consortium partners have chosen to focus on large wind turbines (in the region of 10MW), which are seen as the most effective way of reducing the Levelized Cost of Energy (LCOE). The objective of the proposed project is two-fold: 1. Optimize and qualify, to a TRL5 level, two (2) substructure concepts for 10MW turbines.  The chosen concepts will be taken from an existing list of four (4) TRL>4 candidates currently supporting turbines in the region of 5MW. The selection of the two concepts will be made based on technical, economical, and industrial criteria. An existing reference 10MW wind turbine design will be used throughout the project. 2. More generally, develop a streamlined and KPI-based methodology for the design and qualification process, focusing on technical, economical, and industrial aspects. This methodology will be supported by existing numerical tools, and targeted development and experimental work. It is expected that resulting guidelines/recommended practices will facilitate innovation and competition in the industry, reduce risks, and indirectly this time, contribute to a lower LCOE. End users for the project deliverables will be developers, designers and manufacturers, but also decision makers who need to evaluate a concept based on given constraints. The proposed project is expected to have a broad impact since it is not led by single group of existing business partners, focusing on one concept only. On the contrary, it will involve a strong consortium reflecting the value chain for offshore wind turbines: researchers, designers, classification societies, manufacturers, utilities. This will ensure that the project's outcomes suit the concrete requirements imposed by end-users.
НАТЯЖИТЕЛЬ ЦЕПИ ДВИГАТЕЛЯ АВТОМОБИЛЯ

Полезная модель относится к устройствам, обеспечивающим нормальную эксплуатацию машин и установок, а именно к приспособлениям для изменения натяжения цепей, и может найти применение в двигателях автомобиля.Техническим результатом является: упрощение конструкции в сравнении с прототипом, упрощение работы с устройством при разборке, обеспечение надежности плотной усадки скобы внутри пружины. Кроме того, обеспечивается повышенный срок службы скобы и пружины в сравнении с прототипом за счет снижения трения пружины о скобу.Указанный технический результат достигается за счет того, что заявлен натяжитель цепи двигателя автомобиля, содержащий цилиндрический корпус, в центральном отверстии корпуса подвижно вдоль него установлен шток, в средней части штока по его длине выполнена выемка с зубьями, взаимодействующими с установленной на оси в прорези корпуса поворотной подпружиненной собачкой, один конец штока взаимодействует с установленным в центральном отверстии корпуса ползуном, на котором расположена пружина натяжителя, ползун и пружина расположены в гнезде, образованном центральным отверстием корпуса между торцом штока и уступом, который выполнен на конце центрального отверстия, и в этот уступ упирается одним своим концом пружина, где ползун представляет собой скобу, концы усов которой упираются в края пружины, а скоба выполнена из упругого материала такой длины, что ее головка свободно проходит через отверстие наружу корпуса, где зафиксирована чекой, отличающийся тем, что пружина выполнена из металла, а скоба выполнена из металла с возможностью сохранения формы, где у скобы в разобранном виде расстояние между ее усами имеет плавное уширение от головки к концам усов, причем концы усов загнуты и упираются в конец штока без охвата концов пружины.
APPARATUS FOR DELIVERING A DEVICE TO A HOLLOW ORGAN

An apparatus for delivery of a device into a hollow organ and a method of delivery are provided. The apparatus includes an elongated tube having proximal and distal openings and being configured for carrying the device on a distal portion thereof. The apparatus further includes a tubular cover for covering at least a portion of the device when mounted on the elongated tube, the tubular cover being radially elastic and axially non-elastic. The tubular cover is retrievable into the elongated tube through the distal opening, such that when the device is mounted on the elongated tube and covered by the tubular cover, retrieval of the tubular cover into the elongated tube uncovers the device for delivery into the hollow organ.