Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameterized Explanations for Investor / Company Matching

Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can significantly help reduce costs, and eliminate human biases and errors. However, limited sample size of financial data-sets and the need for not only good recommendations, but also explaining why a particular recommendation is being made, makes this a challenging problem. In this work we propose a representation learning based recommendation engine that works extremely well with small datasets and demonstrate how it can be coupled with a parameterized explanation generation engine to build an explainable recommendation system for investor-company matching. We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task. We also highlight how explainability helps with real-life adoption of our system.
Determination of the generalized parton distributions through the analysis of the world electron scattering data considering two-photon exchange corrections

Hadi Hashamipoura,∗ Muhammad Goharipourb,a,† K. Azizib,c,a,‡ and S.V. Goloskokovd§ School of Particles and Accelerators, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran Department of Physics, University of Tehran, North Karegar Avenue, Tehran 14395-547, Iran Department of Physics, Doğuş University, Acıbadem-Kadıköy, 34722 Istanbul, Turkey Bogoliubov Laboratory of Theoretical Physics, Joint Institute for NuclearResearch, Dubna 141980, Moscow region, Russia (Dated: November 4, 2021)
Deep learning for identification and face, gender, expression recognition under constraints

Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully connected layers, FC6 and FC7 respectively, in the structure of the VGG19 network provide robust features with each of these two layers containing 4096 features. The main objective of this work is to test the ability of deep learning based automated computer system to identify not only persons, but also to perform recognition of gender, age, and facial expressions such as eye smile. Our experimental results indicate that we obtain high accuracy for all the tasks. The best recorded accuracy values are up to 99.95% for identifying persons, 99.9% for gender recognition, 99.9% for age recognition and 80.9% for facial expression (eye smile) recognition.
Design methodology for universal line input boost power factor correction magnetics

This study presents a design methodology for inductors employed in single-phase power factor correction (PFC) boost converters operating in continuous conduction mode (CCM). A simulation tool based on the proposed design methodology performs an automatic selection of magnetic core taking into account the standard limits for conducted electromagnetic interference (EMI), single- or multi-layer winding pattern and the limit of temperature rise on the inductor core. Through a sweep simulation, the algorithm chooses the switching frequency and the input current ripple that minimise the magnetic volume of boost inductor considering the characteristics of some powder cores (sendust, molypermalloy and high-flux). The design methodology is confirmed by experimental results obtained from a 650 W universal line input PFC prototype, considering a switching frequency of 70 kHz and an input current ripple of 40%.
The Klarna Product Page Dataset: A Realistic Benchmark for Web Representation Learning

This paper tackles the under-explored problem of DOM tree element representation learning. We advance the ﬁeld of machine learning-based web automation and hope to spur further research regarding this crucial area with two contributions. First, we adapt several popular Graph-based Neural Network models and apply them to embed elements in website DOM trees. Second, we present a large-scale and realistic dataset of webpages. By providing this open-access resource, we lower the entry barrier to this area of research. The dataset contains 51 , 701 manually labeled product pages from 8 , 175 real e-commerce websites. The pages can be rendered entirely in a web browser and are suitable for computer vision applications. This makes it substantially richer and more diverse than other datasets proposed for element representation learning, classiﬁcation and prediction on the web. Finally, using our proposed dataset, we show that the embeddings produced by a Graph Convolutional Neural Network outperform representations produced by other state-of-the-art methods in a web element prediction task.
The Young Stellar Population, Distance, and Cloud–Cloud Collision Induced Star Formation Scenario of the Trifid Nebula

The Trifid Nebula is a young, nearby star-forming region where star formation is proposed to have been triggered by cloud–cloud collision (CCC), based on observations of molecular clouds. It offers a unique opportunity to test whether the CCC hypothesis is supported by the spatial distribution and star formation chronology of young stars. We present the first study of the optically visible pre-main sequence (PMS) population of the region using riHα imaging and Gaia astrometry. Combined with an analysis of young stellar objects (YSOs) using infrared imaging, we capture the spatial distribution and star formation chronology of the young stellar population. From the analysis, 15 Flat/Class I YSOs, 46 Class II YSOs, and 41 accreting PMS stars are identified (diskless/non-accreting sources are not included in the analysis). The distance based on Gaia parallaxes is ∼1250 pc, significantly closer than previously reported. The Class II YSOs and PMS stars (∼1.5 Myr old) are spread toward the edge of the molecular clouds. They are slightly younger than the estimated crossing time of ∼2.7 Myr and closer to the estimated dynamical age ∼0.85 Myr. Younger Class I YSOs are more concentrated spatially. There exists a cavity devoid of young stars where the two clouds overlap. This evidence suggests that the current generation of stars formed after the collision of two clouds ∼1 Myr ago, and this result can be corroborated using future spectroscopic studies.
Improved iteration complexities for overconstrained p-norm regression

In this paper we obtain improved iteration complexities for solving ℓp regression. We provide methods which given any full-rank A ∈ ℝn × d with n ≥ d, b ∈ ℝn, and p ≥ 2 solve minx ∈ ℝd ||A x − b||p to high precision in time dominated by that of solving Op(dp−2/3p−2) linear systems in A⊤D A for positive diagonal matrices D. This improves upon the previous best iteration complexity of Op(np−2/3p−2) (Adil, Kyng, Peng, Sachdeva 2019). As a corollary, we obtain an O(d1/3є−2/3) iteration complexity for approximate ℓ∞ regression. Further, for q ∈ (1, 2] and dual norm q = p/(p−1) we provide an algorithm that solves ℓq regression in O(dp−2/2p−2) iterations. To obtain this result we analyze row reweightings (closely inspired by ℓp-norm Lewis weights) which allow a closer connection between ℓ2 and ℓp regression. We provide adaptations of two different iterative optimization frameworks which leverage this connection and yield our results. The first framework is based on iterative refinement and multiplicative weights based width reduction and the second framework is based on highly smooth acceleration. Both approaches yield Op(dp−2/3p−2) iteration methods but the second has a polynomial dependence on p (as opposed to the exponential dependence of the first algorithm) and provides a new alternative to the previous state-of-the-art methods for ℓp regression for large p.
On the Optimization of Multiple Applications for Sensor Networks

Wireless Sensor Networks are an important example of networked embedded systems, and they have a key role to play in the development and materialization of the concept of an Internet of Things. Most current deployments of sensor networks are very application-specific and only target a particular goal. This negatively impacts the cost-effectiveness of the network, thereby reducing the incentive to deploy a sensing infrastructure in the first place. Multi-purpose sensor networks can overcome these limitations, where they can be used for more than one application simultaneously. To promote the role of wireless sensor networks as an infrastructure technology, support for multiple independent applications is essential, such that different users can concurrently submit their applications to accomplish diverse goals. In this dissertation, we address various challenges that arise when the support of multiple applications is enabled on a sensor network, mainly with respect to application development, installation and execution. First, we propose a holistic programming framework called Nano-CF that is built on top of the Nano-RK operating system that allows independent users to deploy applications at a network level, and coordinates network activity. Then, we propose various approaches to reduce the resource consumption at different levels in a sensor network. Most sensor networking applications are designed for sampling one or more sensors, conduct signal processing on the sensed data, and communicate this data with other devices in the network. With more than one such application executing on the network, it is highly probable that some redundancies occur across applications. In this work, we identify and eliminate these redundancies through a compile-time approach that identifies the temporal overlap across the execution of the applications. Moreover, we augment the Nano-CF framework with a hierarchical task-assignment scheme that selectively eliminates the redundancies while simultaneously conforming to the resource constraints of the sensor node and the application requirements. Finally, we propose a network-level scheme called Network-Harmonized Scheduling that coordinates the packet transmissions in a simple and distributed way such that the radio can be used efficiently in a multi-hop network with multiple applications releasing packets periodically.
Open-Source CFD Analysis of Multi-domain Unsteady Heating with Natural Convection

The paper presents the capabilities of open-source programs used for numerical simulation of fluid flow and heat transfer phenomena. These tools are applied for unsteady problems with natural convection and, in particular, the heating of an automotive headlight. To perform these studies, a solver was created using an open-source C++ libraryOpenFOAM. The implementation problems encountered were, inter alia, dealing with multi-domain computation and coupling of sub-domains. The presented simulation results clearly show that open-source software can readily be applied also to engineering problems and can compete with commercial packages.
Metal core piezoelectric ceramic fiber rosettes for acousto-ultrasonic source localization in plate structures

This paper presents an approach for acousto-ultrasonic source (passive damage or impact) localization based on metal core piezoelectric fiber (MPF) rosettes. MPF is a new ty pe piezoelectric ceramic device with small size and has high directivity to sense Lamb waves. The response of surface-bonded MPF sensors to Lamb-wave fields is explored theoretical ly and experimentally. The good directional properties of MPF are used to determine the direction of propagation of the acoustic waves by mounting three MPF sensors in a rosette configuratio n. Two suitably spaced rosettes are used to locate the source of the ultrasound by taking the intersection of the directio n given by each rosette. The performance of the rosettes for source location is validated on an aluminum plate.
To cope with drought: two forage grass species – Festuca arundinacea and F. glaucescens can activate similar survival strategies although they differ with molecular response

Background: Photosynthesis is among the primary processes affected by drought and its disturbances result with the reduction of growth, reactive oxygen species (ROS) overproduction, and alterations in antioxidant system activity. Our study was performed on two closely related forage grasses: Festuca arundinacea and F. glaucescens. Two genotypes within each species significantly differing with the potential of drought resistance: high drought resistant (HDR) and low drought resistant (LDR), were used. The research involved: (i) the analysis of gene expression at transcript and protein levels for the selected enzymes of Calvin cycle (fructose-1,6-bisphosphate aldolase (pFBA), phosphoglycerate kinase (PGK) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH)) and (ii) the activity of pFBA, as a protein marker of the Calvin cycle, (iii) the analysis of gene expression at protein level for the selected antioxidant enzymes (glutathione reductase (GR), glutathione peroxidase (GPX), L-ascorbate peroxidase (APX), catalase (CAT), and superoxide dismutase (SOD)), (iv) the measurements of physiological parameters describing a plant’s physiological status under control, drought and re-watering conditions (relative water content (RWC), electrolyte leakage (EL), lipid peroxidation, chlorophyll fluorescence, gas exchange, and ROS level). Results: Our analysis clearly showed that physiological reactions to water deficit were similar in both HDR and both LDR genotypes of Festuca arundinacea and F. glaucescens but the species revealed significant differences in the potential to tolerate tissue dehydration, what was correlated with distinct expression of the Calvin cycle enzymes under drought stress.Conclusions: The maintenance of stable efficiency of dark phase of photosynthesis seems to be crucial for drought tolerance and recovery in F. glaucescnes, whereas acquisition of drought tolerance in F. arundinacea and F. glaucescens does not involve marked changes at the protein level in the enzymatic antioxidant system.
Troubleshooting MOOCs: The Case of a Massive Open Online Course at a College of Education

This paper presents a case study involving the establishment of a massive open online course (MOOC) at a college of education. MOOCs are seen to represent an approach to learning that should be of interest to educators preparing their learners (the teachers of tomorrow) for life and work in a knowledge society. First, MOOCs are considered in relation to the pedagogical theory of connectivism. Then the process of setting up a specific MOOC is analyzed in terms of aspects of organizational culture that are necessary for technological innovations to be accepted in an institution. Gaps between the perceived context and the reality on the ground have necessitated a repurposing of the primary ideas and the original plan. These developments constitute the object of study in this research in progress, and form the basis for this presentation.
Dissatisfaction Responses of Tourists in the Havana World Heritage Site

This paper aims to assess the relationships between tourists’ negative evaluation of key management areas in the cultural city, their overall satisfaction and future intentions. More specifically, this paper proposes a covariance-based structural equations model (CB-SEM) to assess the influence of tourists’ dissatisfaction caused by failures in tourist and cultural services, hygiene and infrastructure on their overall satisfaction with the cultural trip and their intentions to repeat it. Using data collected from 1500 tourists visiting Old Havana on a cultural trip, this paper confirms that a negative opinion on the management of hygiene, cleanliness and crowding is dominant in explaining cultural tourists’ lower levels of satisfaction. In addition, this was found to be the only factor with a negative impact on tourists’ intentions to revisit the city in the future. The results are useful for helping managers of cultural destinations to distinguish the areas that are most relevant in their attempt to promote satisfaction and loyalty in the context of cultural tourism.
Układy napędowe pojazdów elektrycznych

W artykule omowiono nowe technologie stosowane w ukladach napedowych pojazdow elektrycznych. Praca zawiera zalezności teorii ruchu pojazdu, nowe rozwiązania w konstrukcji silnikow trakcyjnych. Przedstawiono rowniez zagadnienia ukladow zasilania bateryjnego i magazynow energii z superkondensatorami. Pokazano metody sterowania zapewniające optymalizacje rozdzialu energii pomiedzy akumulatorami a magazynem energii. Zaprezentowano rozwiązania ukladow energoelektronicznych napedow trakcyjnych oraz zagadnienia sterowania silnikami napedowymi z wykorzystaniem energoelektroniki i techniki cyfrowej.
Enriching relationships: Community engagement toward sustainable coastal futures

Coastal communities face the social, cultural and environmental challenges of managing rapid urban and industrial development, expanding tourism, and sensitive ecological environments. Enriching relationships between communities and universities through a structured engagement process can deliver integrated options towards sustainable coastal futures. This process draws on the embedded knowledge and values of all participants in the relationship, and offers a wide and affordable range of options for the future. This paper reviews lessons learnt from two projects with coastal communities, and discusses their application in a third. Queensland University of Technology has formed collaborative partnerships with industry in Queensland's Wide Bay-Burnett region to undertake a series of planning and design projects with community engagement as a central process. Senior students worked with community and produced design and planning drawings and reports outlining future options for project areas. A reflective approach has been adopted by the authors to assess the engagement process and outcomes of each project to learn lessons to apply in the next. Methods include surveying community and student participants regarding the value they place on process and outcomes respectively in planning for a sustainable future. All project participants surveyed have placed high importance on the process of engagement, emphasising the value of developing relationships between all project partners. The quality of these relationships is central to planning for sustainable futures, and while the outcomes the students deliver are valued, it is as much for their catalytic role as for their contents. Design and planning projects through community engagement have been found to develop innovative responses to the challenges faced by coastal communities seeking direction toward sustainable futures. The enrichment of engagement relationships and processes has an important influence on the quality of these design and planning responses.
RESEARCH ON THE INFLUENCE OF GROWING FACTORS ON THE CYANOBACTERIA BIOMASS TO OBTAIN BIODIESEL

Now days renewable biofuels are needed to displace petroleum derived transport fuels, which contribute to global warming and are of limited availability. Biodiesel is a potential renewable fuel that has attracted the most attention. Biodiesel from cyanobacteria seems to be a renewable biofuel that have the potential to displace petroleum-derived transport fuels, to accumulate significant amounts of lipids without affecting supply of food and also can use non-arable land. Cyanobacteria has attracted the attention of researchers because unlike crop plants their energy is converted to lipids and not fibers in the process of photosynthesis, so they are able to synthesize vegetable oils in large quantities, which can be converted into biofuels through transesterification, but there is necessary a rigorous evaluation to determine whether this type of fuel is competitive to the fossil fuels or not. This paper presents an overview of the possibility to obtaine biodiesel from cyanobacteria. The final aim of the study is to control growing factors, aiming to develop procedures to increase the biomass of cyanobacteria and fat content which could serve as a source of biodiesel production. A main objective is to develop techniques to achieve productivity closer to the photosynthesis maximum efficiency.
First and second moments of the size distribution of bond percolation clusters on regular graphs

Abstract Motivated by network resilience and insurance premiums in the context of cyber security, we derive universal upper bounds for the first and second moments of the size of bond percolation clusters on finite regular graphs. Thinking of the clusters as dynamical objects coupled with branching processes gives a first set of bounds that are accurate when the probability of an edge being open is small. Estimating the number of isolated vertices, we also obtain a second set of bounds that are accurate when the probability of an edge being closed is small. As an illustration, we apply our results to the first three Platonic solids.
Pressure-induced structural transition, metallization, and topological superconductivity in PdSSe

Pressure not only provides a powerful way to tune the crystal structure of transition metal dichalcogenides (TMDCs) but also promotes the discovery of exotic electronic states and intriguing phenomena. Structural transitions from the quasi-two-dimensional layered orthorhombic phase to three-dimensional cubic pyrite phase, metallization, and superconductivity under high pressure have been observed experimentally in TMDCs materials PdS2 and PdSe2. Here, we report a theoretical prediction of the pressure-induced evolutions of crystal structure and electronic structure of PdSSe, an isomorphous intermediate material of the orthorhombic PdS2 and PdSe2. A series of pressure-induced structural phase transitions from the layered orthorhombic structure into an intermediate phase, then to a cubic phase are revealed. The intermediate phase features the same structure symmetry as the ambient orthorhombic phase, except for drastic collapsed interlayer distances and striking changes of the coordination polyhedron. Furthermore, the structural phase transitions are accompanied by electronic structure variations from semiconductor to semimetal, which are attributed to bandwidth broaden and orbital-selective mechanisms. Especially, the cubic phase PdSSe is distinct from the cubic PdS2 and PdSe2 materials by breaking inversion and mirror-plane symmetries, but showing similar superconductivity under high pressure, which is originated from strong electron-phonon coupling interactions concomitant with topologically nontrivial Weyl and high-fold Fermions. The intricate interplay between lattice, charge, and orbital degrees of freedom as well as the topologically nontrivial states in these compounds will further stimulate wide interest to explore the exotic physics of the TMDCs materials.