SIRIS-Lab/unlabelled-sti-corpus huggingface dataset:
Precision gravity: from the lhc to lisa

The nascent field of gravitational wave (GW) science will be an interdisciplinary subject, enriching different branches of physics, yet the associated computational challenges are enormous. Faithful theoretical templates are a compulsory ingredient for successful data analysis and reliable physical interpretation of the signals. This is critical, for instance, to study the equation of state of neutron stars, the nature of black holes, and binary formation channels. However, while current templates for compact binary sources may be sufficient for detection and crude parameter estimation, they are too coarse for precision physics with GW data. We then find ourselves in a situation in which, for key processes within empirical reach, theoretical uncertainties may dominate. To move forward, profiting the most from GW observations, more accurate waveforms will be needed.
I have played a pioneering role in the development and implementation of a new formalism, known as the ‘effective field theory approach’, which has been instrumental for the construction of the state-of-the-art GW template bank. The goal of my proposal is thus to redefine the frontiers of analytic understanding in gravity through the effective field theory framework. Even more ambitiously, to go beyond the current computational paradigm with powerful tools which have been crucial for `new-physics' searches at the Large Hadron Collider. 
The impact of the high-accuracy calculations I propose to undertake will be immense: from probes of dynamical spacetime and strongly interacting matter, to the potential to discover exotic compact objects and ultra-light particles in nature. Furthermore, GW observations scan gravity in a regime which is otherwise unexplored. Consequently, the coming decade will tell whether Einstein's theory withstands precision scrutiny. In summary, my program will provide novel techniques and key results that will enable foundational investigations in physics through GW precision data.
Thor – technical and human infrastructure for open research

Five years ago, a global infrastructure to uniquely attribute to researchers their scientific artefacts (articles, data, software…) appeared technically and socially infeasible. Since then, DataCite has minted over 3.5m unique identifiers for data. ORCID has deployed an open solution for identification of contributors with over 850,000 registrants in less than 2 years. 

THOR will leverage these emerging global infrastructures to support the H2020 goal to make every researcher ‘digital’ and increase creativity and efficiency of research, while bridging the R&D divide between developed and less-developed regions. We will establish interoperability between existing resources, linking digital identifiers across platforms and propagating attribution information.

We will integrate PID services across the research lifecycle and data publishing workflows in four advanced research communities, and then roll-out core services and service building blocks for the wider community. These open resources will foster an open and sustainable e-infrastructure across stakeholders to avoid duplications, give economies of scale, richness of services and the ability to respond rapidly to opportunities for innovation.

THOR is not just relevant to the EINFRA-7-1024 Call, but will become a pervasive element of the EINFRA family of e-Infrastructure resources over the next 3 years. It will allow data-management and curation services to exploit knowledge of data location and attribution; provide robust and persistent mechanism for linking literature and data; enable search and resolving services and generate incentives for Open Science; deliver provenance and attribution mechanisms to underpin data exchange; and provide minting and resolving services for data citation workflows.

Its impact will enable third-party services, no-profit and commercial, to leverage the scholarly record.
Low-Cost printing of high-performance thermoplastics for structural applications

The technology known as Fused Filament Fabrication (FFF) is one of the oldest known methods for 3D printing of objects.  In fact, it was the first technology to be widely commercialized, and still has, by far, the largest presence on the market.  In addition, printers based on FFF technology commonly have prices far less than comparable designs using photo polymerization or sintering technologies.  However, to date, FFF has been largely limited to polymers with fairly low melting points: less than 280°C.  This has limited its success from an industrial standpoint, where high-performance polymers such as ULTEM (poly etherimide) and PEEK are needed in order to provide thermal and mechanical performance in extremely demanding scenarios.  In the aerospace market, PEEK has become the reference high-performance engineering thermoplastic. PEEK is an attractive material due to its high thermal and mechanical performance, coupled with its low density (about one third that of titanium) . It is therefore being widely investigated for replacing titanium and other metals in aerospace applications, in order to reduce overall weight.
Current solutions for additive manufacturing using PEEK require investment is extremely costly systems.  PEEK-capable industrial-grade machines need not be astronomical, however.  In this context, the main ambition of the project will be to provide the Topic Manager with access to a complete FFF supply chain: industrial-grade PEEK FFF printers at a price point ten times less than current industrial-grade FFF printers, along with a supply of high-quality PEEK filament tested and validated on the printer and in accordance with the Topic Manager’s performance specifications.  In addition, the PEEK materials will be accompanied by a high-temperature compatible breakaway support matrix material custom-designed for PEEK usage.
Global EDF-Based On-Line Energy-Aware Real-Time Scheduling Algorithm in Multi-Core Systems

As the energy consumption of multi-core systems becomes increasingly prominent, meeting the time constraints while reducing as much as possible system energy consumption is still an urgent problem in real-time energy-aware scheduling in multi-core system. Most existing works have assumptions on priori information of real-time tasks, but in real applications the tasks' property be received only when the tasks arrive. Therefore, based on the general task model with no priori to tasks' properties, this paper proposes a global EDF-based on-line energy-aware scheduling algorithm for hard real-time tasks in multi-core system. The proposed algorithm can reduce the execution speed of task in multi-core system and reach a reasonable compromise between real-time constraints and energy savings, as it introduces a speed scale factor for utilizing the slack time and combines dynamic power management with dynamic voltage/frequency scaling techniques. The algorithm implementsdynamic voltage/frequency scaling only in each context switch time and task completion time, with the smaller computational complexity, and easier to be included in real-time operating system. Experimental results show that the algorithm can be well applied to different kinds of dynamic voltage/frequency scaling on chip, and compared with Global EDF algorithm, it gain more energy savings in all cases, which can improve energy savings about 15% to 20% at most and about 5% to 10% at least.
Faucet handle with angled interface

A faucet assembly including a handle that rotates about a rotational axis extending perpendicular to an angled interface for adjusting a water parameter.
Application of gas chromatography-tandem mass spectrometry (GC/MS/MS) for the analysis of deuterium enrichment of water

Incorporation of deuterium from deuterium oxide ((2) H2 O) into biological components is a commonly used approach in metabolic studies. Determining the dilution of deuterium in the body water (BW) pool can be used to estimate body composition. We describe three sensitive GC/MS/MS methods to measure water enrichment in BW. Samples were reacted with NaOH and U-(13) C3 -acetone in an autosampler vial to promote deuterium exchange with U-(13) C3 -acetone hydrogens. Headspace injections were made of U-(13) C3 -acetone-saturated air onto a 30-m DB-1MS column in electron impact-mode. Subjects ingested 30 ml (2) H2 O, and plasma samples were collected. BW was determined by standard equation. Dual-energy X-ray absorptiometry scans were performed to calculate body mass, body volume and bone mineral content. A four-compartmental model was used to estimate body composition (fat and fat free mass). Full-scan experiments generated an m/z 45 peak and to a lesser extent an m/z 61 peak. Product fragment ions further monitored included 45 and 46 using selected ion monitoring (Method1), the 61 > 45 and 62 > 46 transition using multiple reaction monitoring (MRM; Method2) and the neutral loss, 62 > 45, transition (Method3). MRM methods were optimized for collision energy (CE) and collision-induced dissociation (CID) argon gas pressure with 6 eV CE and 1.5 mTorr CID gas being optimal. Method2 was used for final determination of (2) H2 O enrichment of subjects because of lower natural background. We have developed a sensitive method to determine (2) H2 O enrichment in BW to enable measurement of FM and FFM.
Normalization of multimodal brain networks for integral and predictive mapping of neurological disorders

Modern network science has introduced exciting new opportunities for understanding the brain as a complex system of interacting units in both health and disease and across the human lifespan. Despite the rapidly growing interdisciplinary science of complex networks, which spans the range from genetic and metabolic networks all the way up to social and economic systems, it remains a formidable challenge to identify the most representative and shared brain alterations caused by a specific disorder (e.g., Alzheimer’s disease), namely ‘disorder signature’, in a population of brain networks, let alone multi-modal brain networks where each brain network is derived from a particular neuroimaging modality (e.g., functional or diffusion magnetic resonance imaging (fMRI or dMRI)). Such integral signature can be revealed by what I name as a multimodal connectional brain template (CBT), which would constitute an unprecedented contribution to network neuroscience, rooted in firstly learning brain connectivity normalization and secondly foreseeing its evolution. During this fellowship, I will develop NormNets, a novel technique leveraging the power of geometric deep-learning to meet this challenge by normalizing a population of multimodal brain networks. Particularly, NormNets tools will substantially advance the field of network neuroscience by estimating not only an integral but also a predictive mapping of neurological disorders. In addition to the multi-disciplinary high-quality training I will receive at both host and secondment institutions, this fellowship will remarkably consolidate and accelerate my career on the international landscape scene in the new cross-disciplinary area of “geometric deep learning & integration connectomics” I will pioneer during this fellowship. With the endorsement of international multi-sectoral stakeholders, open NormNets resources will impact on and contribute towards the development of connectomics-rooted predictive precision medicine.
Presence, distribution, and diversity of iron-oxidizing bacteria at a landfill leachate-impacted groundwater surface water interface

We examined the presence of iron-oxidizing bacteria (IOB) at a groundwater surface water interface (GSI) impacted by reduced groundwater originating as leachate from an upgradient landfill. IOB enrichments and quantifications were obtained, at high vertical resolution, by an iron/oxygen opposing gradient cultivation method. The depth-resolved soil distribution profiles of water content, Fe(2+), and total Fe indicated sharp gradients within the top 10 cm sediments of the GSI, where the IOB density was the highest. In addition, the vertical distribution of iron-reducing bacteria at the same sampling site mirrored the IOB distribution. Clone libraries from two separate IOB enrichments indicated a stratified IOB community with clear differences at short vertical distances. Alpha- and Betaproteobacteria were the dominant phylotypes. Clones from the near-surface sediment (1-2 cm below ground surface) were dominated by members of the Bradyrhizobiaceae and Comamonadaceae; clones from the deeper sediments were phylogenetically more diverse, dominated by members of the Rhodocyclaceae. The iron deposition profiles indicated that active iron oxidation occurred only within the near-to-surface GSI sediments. The match between the iron deposition profiles and the IOB abundance profiles strongly hints at the contribution of the IOB community to Fe oxidation in this Fe-rich GSI ecosystem.
Frequency-difference electrical impedance tomography: Phantom imaging experiments

Frequency-difference electrical impedance tomography (fdEIT) using a weighted voltage difference has been proposed as a means to provide images of admittivity changes at different frequencies. This weighted difference method is an effective way to extract anomaly information while eliminating background effects by unknown boundary geometry, uncertainty in electrode positions and other systematic measurement artefacts. It also properly handles the interplay between conductivity and permittivity in measured boundary voltage data. Though the proposed fdEIT algorithm is promising for applications such as detection of hemorrhagic stroke and breast cancer, more validation studies are needed. In this paper, we performed two-and three-dimensional numerical simulations and phantom experiments. Backgrounds of imaging objects were either saline or carrot pieces suspended in saline. We used carrot pieces to simulate a more realistic frequency-dependent admittivity distribution. Test objects were banana, potato or conductive gel with known admittivity spectra. When the background was saline, both simple and weighted difference approaches produced reasonably accurate images. The weighted difference method yielded better images from two-dimensional imaging objects with background of carrot pieces. For the three-dimensional head-shaped phantom, the advantage of the weighted frequency difference method over the simple difference method is not as obvious as in the case of the two-dimensional phantom. It is unclear if this is due to measurement errors or limitations in the linear algorithm. Further refinement and validation of the frequency difference image reconstructions are currently in progress.
Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review

While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities.We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API.Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%).U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity.
Samson: integrating computational nanoscience

Computer-aided design of macrosystems has greatly contributed to reduce production costs of cars, planes, etc., by replacing drawing boards and physical mock-ups with virtual modeling and simulation tools. Similarly, computational nanoscience is expected to have an increasingly essential impact on biology, chemistry, physics, material science, electronics, etc., by complementing experiments to reduce research and development cycles, lower costs, increase quality, etc.

Unfortunately, while numerous software tools have already been developed, they are currently very fragmented, domain specific, and a significant effort is still needed to address the needs emerging from the convergence trends in nanoscience. Indeed, boundaries that used to clearly delimitate application domains (biology, chemistry, etc.) are now becoming blurred: nanoparticles are being functionalized with antibodies, nanomechanisms are built out of DNA strands, etc. 

My group has been developing SAMSON, a software platform for modeling and simulation of nanosystems, with three main design characteristics in mind: generality, adaptivity and openness. Unlike existing commercial tools, which are mostly closed and “monolithic”, we believe the open architecture of SAMSON, combined with the distribution model enabled by SAMSON Connect, an associated online platform to distribute SAMSON Elements (modules for SAMSON), is the most appropriate foundation to integrate computational tools and help move forward nanoscience and nanotechnology.

The goal of this Proof of Concept proposal is to explore the potential of SAMSON as a software platform for federating computational nanoscience able to sustain a viable business model.
Rotational hybridization, and control of alignment and orientation in triatomic ultralong-range Rydberg molecules

We explore the electronic structure and rovibrational properties of an ultralong-range triatomic Rydberg molecule formed by a Rydberg atom and a ground state heteronuclear diatomic molecule. We focus here on interaction of Rb($27s$) Rydberg atom with KRb($N=0$) diatomic polar molecule. There's significant electronic hybridization of Rb($n=24$, $l\ge 3$) degenerate manifold. The polar diatomic molecule is allowed to rotate in the electric fields generated by the Rydberg electron and core as well as an external field. We investigate the metamorphosis of the Born-Oppenheimer potential curves, essential for the binding of the molecule, with varying electric field and analyze the resulting properties such as the vibrational structure and the alignment and orientation of the polar diatomic molecule.
Composition for forming a silicon-containing resist underlayer film and patterning process using the same

The present invention provides a composition for forming a silicon-containing resist underlayer film, wherein the composition contains: a component (A) comprising at least one or more compounds selected from the group consisting of a polymer having repeating units shown by the following general formulae (1-1a) and (1-1b) and being capable of generating a phenolic hydroxyl group, a hydrolysate of the polymer, and a hydrolysis-condensate of the polymer, and a component (B) which is a silicon-containing compound obtained by hydrolysis-condensation of a mixture containing, at least, one or more hydrolysable silicon compounds represented by the following general formula (2) and one or more hydrolysable silicon compounds represented by the following general formula (3). There can be provided a composition for forming a resist underlayer film applicable not only to a resist pattern obtained in a negative development but also to a resist pattern obtained in a conventional positive development, and a patterning process using this composition.  R 11 m11 R 12 m12 R 13 m13 Si(OR 14 ) (4-m11-m12-m13) (2)  Si(OR 15 ) 4 (3)
Epidemiology and distribution of tick-borne encephalitis

Tick-borne encephalitis (TBE) is the main tick-borne virus infection in Eurasia. It is prevalent across the entire continent from Japan to France and occurs in endemic foci. Expansion of prevalence in areas including northern Russia, Sweden, and Finland has been observed in recent years. Ticks are the most important vectors and may transmit the TBE virus to animals and humans. TBE can also be transmitted to humans in milk containing the virus. TBE has been implicated as a travel-acquired illness and there are isolated reports of its occurrence in countries outside the known areas of prevalence. Therefore, TBE should be included in the differential diagnosis for all central nervous system diseases inside or outside endemic areas.
A joint catalogue

This project is going to ensure the electronic retrieval of the catalogue data that are part of the bibliographic wealth owned by the partner bodies, in order to set up a single OPAC or metaOPAC to be freely consulted through the web. At present the whole bibliographic wealth of Trieste University can be accessed through the web, but after consulting different catalogues. The project is going to unify all catalogues, joining them with those - already computerized or to be computerized - belonging to the participating bodies, and setting up a single OPAC query mask, both in Italian and in Slovene. The OPAC is managed through a couple of dedicated servers, which can restore the service immediately in case of hardware problems; the joint OPAC will foster the circulation of the bibliographic material, increasing the number of interlibrary lending transactions. The project is divided into two stages: the organization of a single OPAC to access all computerized data and the beginning of mutual interlibrary lending; the completion of bibliographic data by entering non-computerized data, whilst ascertaining the accuracy of the bibliographic description of the automatically retrieved data.
Efficient and proven verification of unreliable hardware executions of classic algorithms

Lowering voltage and frequency guardbands of CPU, DRAM, cache, or interconnect lowers power and latency, but increases the risk of silent data corruptions in even formally verified hardware and software. Researchers have been developing systems that use unreliable hardware, combined with software checkers executed on reliable hardware, to gain high performance with no risk. Deterministic checkers for many important algorithms are asymptotically and practically more efficient than the original problem solvers, e.g. for systems of linear equations, satisfiability, linear programming, sorting, 3SUM, graph matching and others. Writing a correct checker is hard, since often intricate corner cases get overlooked. Our system, SoundCheck, helps reduce burden on programmers by automatically proving soundness and completeness of checkers with bounded verification in the Sketch program synthesis language. Verified checkers are emitted as efficient C++ code and shown to have low overhead which results in a net performance improvement with no risk. Thesis Supervisor: Saman Amarasinghe Title: Professor