Miracle-dz/UniFAR-FTUs huggingface dataset:
Relevance of heterokaryosis for adaptation and azole-resistance development in Aspergillus fumigatus

Aspergillus fumigatus causes a range of diseases in humans, some of which are characterized by fungal persistence. Aspergillus fumigatus, being a generalist saprotroph, may initially establish lung colonization due to its physiological versatility and subsequently adapt through genetic changes to the human lung environment and antifungal treatments. Human lung-adapted genotypes can arise by spontaneous mutation and/or recombination and subsequent selection of the fittest genotypes. Sexual and asexual spores are considered crucial contributors to the genetic diversity and adaptive potential of aspergilli by recombination and mutation supply, respectively. However, in certain Aspergillus diseases, such as cystic fibrosis and chronic pulmonary aspergillosis, A. fumigatus may not sporulate but persist as a network of fungal mycelium. During azole therapy, such mycelia may develop patient-acquired resistance and become heterokaryotic by mutations in one of the nuclei. We investigated the relevance of heterokaryosis for azole-resistance development in A. fumigatus. We found evidence for heterokaryosis of A. fumigatus in patients with chronic Aspergillus diseases. Mycelium from patient-tissue biopsies segregated different homokaryons, from which heterokaryons could be reconstructed. Whereas all variant homokaryons recovered from the same patient were capable of forming a heterokaryon, those from different patients were heterokaryon-incompatible. We furthermore compared heterokaryons and heterozygous diploids constructed from environmental isolates with different levels of azole resistance. When exposed to azole, the heterokaryons revealed remarkable shifts in their nuclear ratio, and the resistance level of heterokaryons exceeded that of the corresponding heterozygous diploids.
Investigation of Applying Machine Learning for Watch-List Filtering in Anti-Money Laundering

Financial institutions must meet international regulations to ensure not to provide services to criminals and terrorists. They also need to continuously monitor financial transactions to detect suspicious activities. Businesses have many operations that monitor and validate their customer’s information against sources that either confirm their identities or disprove. Failing to detect unclean transaction(s) will result in harmful consequences on the financial institution responsible for that such as warnings or fines depending on the transaction severity level. The financial institutions use Anti-money laundering (AML) software sanctions screening and Watch-list filtering to monitor every transaction within the financial network to verify that none of the transactions can be used to do business with forbidden people. Lately, the financial industry and academia have agreed that machine learning (ML) may have a significant impact on monitoring money transaction tools to fight money laundering. Several research work and implementations have been done on Know Your Customer (KYC) systems, but there is no work on the watch-list filtering systems because of the compliance risk. Thus, we propose an innovative model to automate the process of checking blocked transactions in the watch-list filtering systems. To the best of our knowledge, this paper is the first research work on automating the watch-list filtering systems. We develop a Machine Learning - Component (ML-Component) that will be integrated with the current watch-list filtering systems. Our proposed ML-Component consists of three phases; monitoring, advising, and take action. Our model will handle a known critical issue, which is the false-positives (i.e., transactions that are blocked by a false alarm). Also, it will minimize the compliance officers’ effort, and provide faster processing time. We performed several experiments using different ML algorithms (SVM, DT, and NB) and found that the SVM outperforms other algorithms. Because our dataset is nonlinear, we used the polynomial kernel and achieved higher accuracy for predicting the transactionś decision, and the correlation matrix to show the relationship between the numeric features.
Export activity, R&D investment, and foreign ownership: does it matter for productivity?

PurposeThe paper examines the impact of export activity on productivity and how this effect is moderated by R&D investment and foreign ownership.Design/methodology/approachA time-lag effect is taken into account when examining the proposed model. Data are collected from the Annual Industrial Survey of the National Bureau of Statistics of China. A dataset containing 117,340 firms across the sample period (2001–2007) are used to test the hypotheses.FindingsThe results indicate that while R&D investment plays a significant role in strengthening the positive effect of export activity on a firm's productivity, foreign ownership surprisingly has a negative moderating role.Originality/valueScholarly interest in the links between export activity and productivity is on the rise. However, the bulk of research has been focused on understanding the effects of export activity on productivity at the country or industry level. Little has been done at the firm level. Another gap in the literature is that the mechanism through which the impact of export activity can be leveraged to enhance the firm's productivity has been largely ignored. To address these issues, the study adopts the learning-by-exporting theory to examine the relationship between export and productivity at the firm-level and how R&D investment and foreign ownership may explain how learning can be leveraged to enhance the firm's productivity. Finally, these relationships are examined in the context of firms from an emerging market, China, which is especially relevant for the learning-by-exporting argument used in this study.
Zoom TM and Enhance: Super Resolution

High-resolution images are aesthetically pleasing and have a natural look and feel to them. Too often, though, we are stuck with grainy low-resolution images that are inadequate for use in websites and presentations. This can be catastrophic if the only surviving photo of your subject has poor resolution. Given the increasing importance of video communication, we also desire better video compression. A process that allows users to send downsampled video, then upsample compressed video that is received from the network, would save large amounts of network transactions. An advanced understanding of super resolution in still images might help this effort. While it is possible to improve the resolution of images using interpolation techniques such as bicubic and nearestneighbor, these methods do not improve the resolution as much as one may hope. This motivates the use of a convolutional neural network to improve the resolution of images. Previous work by Wang et al. [3] demonstrates that a variety of neural network designs can solve the problem of implementing super resolution. Work by Dong et al. [1] reports that a fairly simple convolutional neural network can perform super resolution and outperform bicubic interpolation. This prior work shows us that we are not only able to solve this problem, but also solve it using computer vision. We experimented with a neural network similar to the one presented by Dong et al. in [1] in order to demonstrate that it could achieve better performance than bicubic interpolation. Our final network, which we trained using a subset of the flickr2k dataset provided by [2], involved preupsampling, three convolution layers, and two ReLU nonlinearity layers. We ran trials with many different combinations of values for hyperparameters and slight changes in the architecture to decide that this network allowed us to achieve the best results, which indeed were better than the results achieved by bicubic interpolation. 2. Approach
Thermopower in the 
Ba1−δM2+xRu4−xO11
 
(M=Co, Mn, Fe)
 magnetic hexagonal ruthenates

The magnetism, magnetotransport, and Seebeck coefﬁcients ( S ) for three ruthenates Ba 1 − δ M 2 + x Ru 4 − x O 11 ( δ = 0 . 06; M = Mn, Co; x = 0 . 4) and Sr 1 − δ M 2 + x Ru 4 − x O 11 ( δ = 0 . 02; M = Fe; x = 0 . 7) compositions have been studied. Their crystallographic structures contain three metal sites, edge-sharing octahedra forming kagome lattices, face-shared octahedra with the shortest Ru( M )-Ru( M ) distance, and M O 5 trigonal bipyramids. These three compositions have been selected for their transport behavior exhibiting small resistivity values ( ∼ m (cid:3) cm) together with a complex ferrimagnetic behavior, with localization increasing from M = Co to M = Fe. This enabled the thermopower to be measured in hexagonal ruthenates in which the conducting kagome layers are more or less diluted by three different magnetic cations substituted for Ru. The positive Seebeck coefﬁcient of the three compounds is found to increase up to 750 K to values in the range of 22 to 35 μ V K –1 . Such values, similar to those of perovskite ruthenates, reveal a Seebeck coefﬁcient dominated by the Ru network at high temperature whatever the foreign magnetic cation is. In addition, below about 50 K, the values of S are very small for M = Mn and Co, and the S ( T ) curves of the Ba 1 − δ M 2 . 4 Ru 3 . 6 O 11 compounds exhibit similarities with that of ruthenium metal. This is interpreted by shorter Ru-Ru distances as compared with perovskite ruthenates allowing a metallic direct exchange. The ferrimagnetism associated with the M cation does not seem to play a major role in transport, as there is almost no impact of the magnetic ordering on thermopower and electrical resistivity and the values of magnetoresistance remain very small, reaching at most − 1% in 9 T at 5 K for M = Mn, and − 0.4% at T C for M = Co. The present results obtained in these phases containing hexagonal Ru networks show that Hund’s metal model developed to describe the thermopower of perovskite ruthenates with a Ru square lattice can have a broader range of validity.
Bacterial endosymbionts protect beneficial soil fungus from nematode attack

Significance Soil is a complex and competitive environment, forcing its inhabitants to develop strategies against competitors, predators, and pathogens. Identifying and understanding the molecular mechanisms has translational value for medicine, ecology, and agriculture. In this study, we show that a member of important soil-dwelling fungi (Mortierella) forms a tight alliance with toxin-producing bacteria (Mycoavidus) that live within the fungal hyphae and protect their host from nematode attack. This discovery is relevant since Mortierella species correlate with healthy soils and are used as plant growth–promoting fungi in agriculture. Unraveling an ecological role for fungal endosymbionts in Mortierella, our results contribute to the understanding of a mainspring in fungal–endobacterial symbioses and open the possibility for the development of new biocontrol agents. Fungi of the genus Mortierella occur ubiquitously in soils where they play pivotal roles in carbon cycling, xenobiont degradation, and promoting plant growth. These important fungi are, however, threatened by micropredators such as fungivorous nematodes, and yet little is known about their protective tactics. We report that Mortierella verticillata NRRL 6337 harbors a bacterial endosymbiont that efficiently shields its host from nematode attacks with anthelmintic metabolites. Microscopic investigation and 16S ribosomal DNA analysis revealed that a previously overlooked bacterial symbiont belonging to the genus Mycoavidus dwells in M. verticillata hyphae. Metabolic profiling of the wild-type fungus and a symbiont-free strain obtained by antibiotic treatment as well as genome analyses revealed that highly cytotoxic macrolactones (CJ-12,950 and CJ-13,357, syn. necroxime C and D), initially thought to be metabolites of the soil-inhabiting fungus, are actually biosynthesized by the endosymbiont. According to comparative genomics, the symbiont belongs to a new species (Candidatus Mycoavidus necroximicus) with 12% of its 2.2 Mb genome dedicated to natural product biosynthesis, including the modular polyketide-nonribosomal peptide synthetase for necroxime assembly. Using Caenorhabditis elegans and the fungivorous nematode Aphelenchus avenae as test strains, we show that necroximes exert highly potent anthelmintic activities. Effective host protection was demonstrated in cocultures of nematodes with symbiotic and chemically complemented aposymbiotic fungal strains. Image analysis and mathematical quantification of nematode movement enabled evaluation of the potency. Our work describes a relevant role for endofungal bacteria in protecting fungi against mycophagous nematodes.
Analysis and Design of Ultra-Wideband Antennas in the Spectral and Temporal Domains

Formal research on Ultra-wideband (UWB) technology started in the U.S. in 2002; where its definition was formulated and the frequency allocation in that country established. However, the first ultra-wideband transmission was done during the confirmation of Maxwell's equations by Heinrich Hertz in 1886, the first ever reported radio transmission. Sparks or very short pulses were sent and received at a distance away from its source. UWB antennas have existed since those times, but technology has been mostly focused on the transmission of continuous wave narrowband signals. The largest problematic encountered in today's UWB antennas is that characterization methods were, since the beginning, meant to describe narrowband antennas. The frequency characteristics of these antennas are constant over their operational bandwidth, hence they can be fully characterized in the frequency domain. UWB antennas, on the other hand, are meant to transmit pulsed signals. Analyzing them only in the frequency domain is not enough to fully evaluate their performance, as pulse distortion is an important parameter that should be controlled. An extended literature review is included in the first part of this thesis, where the available UWB antenna characterization methods are evaluated. Many limitations were encountered, showing that the available techniques do not analyze simultaneously the most important parameters of an UWB antenna. A new characterization method is proposed: the System Fidelity Factor (SFF). Its main purpose is to incorporate frequency, time and space characteristics of a two-antennas system to compare UWB antennas in an efficient way. This is achieved with the correlation between the received and the input pulses, quantifying the distortion produced by the system. The SFF is an interesting tool because both simulations and measurements can be done in a simple and straight-forward manner, using tools that are commonly available in any antenna lab. Any combination of antennas can be analyzed and the free-space channel can be easily replaced with any other environment. Several UWB antennas were designed and used to prove the efficiency of the SFF. A good agreement between the proposed simulation techniques and the measurements was achieved. The antennas are novel designs specially developed for UWB applications under the American or European regulations. The last part of the thesis presents these antennas and analyzes them using the SFF to compare their performance.
An Introduction to the System and Culture of the College Entrance Examination of China

On June 7 and 8, 2006, more than 9.5 million high school graduates throughout China took the annual college entrance examination (CEE) (Dai Jiagan, 2006). Students took four of five subject tests over the two-day period: a Chinese language test and a mathematics test during the morning and afternoon, respectively, of June 7; and either a synthesis test for the liberal arts track or a synthesis test for the science and technology track during the morning of June 8, and a foreign language test during the afternoon of June 8. The morning tests were 2.5 hours long while those given in the afternoon were 2 hours. Although the first subject test of this year’s CEE, the Chinese language test, was scheduled to start at 9 a.m., the author observed parents, relatives, and examinees gathered outside the campus gate of the People’s University Affiliated Middle School in Beijing by 7:30 a.m. Eavesdropping on various conversations revealed a wide spectrum of wishes: “Do not be too nervous, OK? You will do fine,” “Don’t be too careless. And read the items carefully,” “You have worked hard for an entire year and the real tests are here. Make yourself and us proud,” and “Work on the test items carefully. Your future depends on these tests. I know that you will do well. ” Outside the campus gate stood a row of large white display boards reminding examinees of the test regulations, examinee obligations, and examples of violations. An ambulance was parked beyond the signs in a corner of the entrance square, ready to tend to any emergency, and an air-conditioned recreational vehicle welcomed anyone wishing to seek temporary refuge from the early summer heat. Uniformed campus security guards checked the test registration and personal identification cards of examinees filing into the restricted areas of the campus. The only items permitted were bicycles for personal transportation and test kits containing pens, pencils, erasers, and rulers. Before reaching their designated test rooms, examinees walked along a shaded path lined with large wooden boards displaying both welcomes and reminders. During the CEE season, there is widespread public support for examinees. News reports have described police running red lights in order to transport an examinee to a test site on time (Law-star.com, 2005), helping examinees to fetch their identification cards from home (Dahew.com, 2005), and even helping, with permission, to get into an optometrist’s office to fit a pair of glasses for an examinee (People.com.cn, 2004). The author saw an illegally parked car around a corner of the entrance square with a sign saying “CEE Examinee, please pardon me. ” It was not ticketed, even with a heavy traffic police presence. Long after 9:30 a.m. scores of parents could still be seen sitting on the sidewalk outside of the middle school, anxious to see their children upon completion of the exams.
Prediction of Compressive Strength using Genetic Programming Involving NDT Results

Compressive strength of concrete is major parameter to assess the overall quality of concrete as other mechanical prosperities are directly related to the compressive strength. It can be determined using the destructive (DT) and non-destructive testing (NDT) methods. The destructive testing method is carried out by crushing the specimen to failure while the non-destructive is carried out without destroying the concrete specimen. The destructive method is time taking process and required equipment’s and power. Whereas the NDT methods like the rebound (Schmitz) hammer and Ultrasonic Pulse velocity (UPV) are most popular because they are handy, quicker and easy to use. Though the NDT methods are much quicker; their values are more of an approximation than exact compressive strength values. They are also machine specific, hence a calibration curve is provided by supplier which may not be reliable. The Indian code recommends about 25% variation in results, which is very high. The newly developed soft computing techniques like ANN, Fuzzy logic, Genetic programming etc. may be used to prepare a better numerical model correlating DT and NDT results. Hence the aim of the present study is to propose a model correlating the compressive strength obtained from destructive and non-destructive methods by using Genetic Programming. The whole work involves casting of 100 cubes of 150mm size belonging to of different grades of concrete. They were tested under compression following DT and NDT methods. These data were used for modelling ie. (70% for training and 30% for testing ) in GP. The modelling is done two ways, first by using variables as weight and Rebound values and secondly by using weight, rebound values and UPV values. The models obtained were found to be in good agreement with actual values imparting 6.744 % and 7.4434% error respectively.
DEEP LEARNING APPLIED TO LOCATING FERROMAGNETIC FOREIGN BODIES IN HUMANS

Rogozinski, Marcos; Barbosa, Carlos Roberto Hall Barbosa (Advisor); Costa Monteiro, Elisabeth (Co-advisor). Deep learning applied to locating ferromagnetic foreign bodies in humans. Rio de Janeiro, 2021. 95p. Dissertação de Mestrado – Programa de Pós-Graduação em Metrologia, Pontifícia Universidade Católica do Rio de Janeiro. Ferromagnetic foreign bodies accidentally inserted in patients usually need to be surgically removed. The methods conventionally employed for locating foreign bodies are often ineffective due to the low accuracy in determining the position of the object and pose risks arising from the exposure of medical staff and patients to ionizing radiation during long-term procedures. New methods using SQUID sensors successfully located foreign bodies in an innocuous and noninvasive way, but they have the drawback of presenting high cost and low portability. This work is part of new research that seeks to bring greater portability and low cost in locating foreign bodies in the human body using GMI and GMR sensors. The main objective of this work is to evaluate and apply the use of Deep Learning in the development of a portable and manual device based on a GMR sensor, including position tracking and orientation of this device from images of known patterns obtained by a camera integrated to the device and the solution of the inverse magnetic problem from the obtained magnetic mapping. The techniques presented are capable of tracking the device with good accuracy and detecting the localization of the foreign body with similar or better results than those obtained in previous works, depending on the parameter. The results obtained are promising as a basis for future developments.
A Symmetric Cipher Response-Based Cryptography Engine Accelerated Using GPGPU

Many low-powered devices, such as those in the Internet of Things (IoT), require high levels of security. One shortfall of cryptographic systems is the storage of private key information in non-volatile memory that an opponent can read. Client devices can generate private keys on-demand using a physically unclonable function (PUF) to obviate this problem. However, low-powered devices may not have the computational resources to correct for the error in the PUF relative to the initially recorded PUF challenge. Response-based cryptography (RBC), when combined with encrypting schemes such as the Advanced Encryption Standard (AES), addresses this problem by having a secure server perform a search over the key space starting from a client device’s initially recorded challenge. We propose an RBC engine based on symmetric ciphers that uses graphics processing units (GPUs). We use the GPU to perform a massively parallel search over the key space to authenticate the client’s key(s). The computational requirements for executing the search and authenticating the user within a time threshold, T, increase exponentially. This limits the classes of computers that are able to perform the search. To address this problem, we employ a scheme that generates subkeys from the PUF. This increases the granularity of computational capabilities that are able to perform the RBC search within the selected $T=5$ s authentication threshold. We compare our algorithm, GRBC, to an OpenSSL-based MPI reference implementation executed on up to 512 CPU cores. Our approach using the GPU achieves superior key search throughput over the CPU.