Miracle-dz/UniFAR-FTUs huggingface dataset:
Presence of Papillomavirus DNA sequences in the canine transmissible venereal tumor (CTVT)

Background The canine transmissible venereal tumor (CTVT) or Sticker’s sarcoma is a neoplastic disease affecting dogs. This disease is presented as a tumoral mass in the genital organs of both, male and female individuals. Up to date, there is no clear evidence indicating a viral agent as the causative mediator for CTVT development. Purpose The present work aims to analyze 21 samples from canines with CTVT for molecular identification of Papillomavirus DNA sequences. In addition, microbiological analysis, cytologic and histopathologic evaluations were also performed. Results All patients showed no biochemical and microbiological alterations. Molecular analysis demonstrated the viral DNA presence in the samples using different primer sets. The MY primers amplified a 450 bp band in seven out of 21 samples (33%). The PVF and Fap64 primer set, targeting the L1 sequence of Canine Papillomavirus (CPV), showed positivity in 16 out of 21 samples (76%). Conclusion These results support the possible causative association between CPV and CTVT; nevertheless, additional studies are required to uphold such statement. This work presents evidence indicating that a viral agent might be involved in the pathogenesis of CTVT and set the bases for a better understanding of the CTVT pathobiology.
Testing a model of subjective well-being: The roles of optimism, psychological vulnerability, and shyness

This research extended current positive psychological research by investigating the mediating effect of shyness on the association between optimism, psychological vulnerability and subjective well-being. Two hundred fifty-five volunteer university students (131 females and 124 males, mean age = 23.14) participated in the study. The structural equation modeling revealed shyness fully mediated the effect of psychological vulnerability on subjective well-being, while shyness partially mediated the effect of optimism on subjective well-being. Also, bootstrapping procedures confirmed that psychological vulnerability was indirectly associated with subjective well-being through shyness, whereas optimism was directly associated with subjective well-being. The implications for future studies and the limitations of the study were discussed in the context of the relevant literature.
Correct-and-Memorize: Learning to Translate from Interactive Revisions

State-of-the-art machine translation models are still not on par with human translators. Previous work takes human interactions into the neural machine translation process to obtain improved results in target languages. However, not all model-translation errors are equal – some are critical while others are minor. In the meanwhile, the same translation mistakes occur repeatedly in a similar context. To solve both issues, we propose CAMIT, a novel method for translating in an interactive environment. Our proposed method works with critical revision instructions, therefore allows human to correct arbitrary words in model-translated sentences. In addition, CAMIT learns from and softly memorizes revision actions based on the context, alleviating the issue of repeating mistakes. Experiments in both ideal and real interactive translation settings demonstrate that our proposed CAMIT enhances machine translation results significantly while requires fewer revision instructions from human compared to previous methods1.
Shortest piecewise linear path Trajectory ( initial guess ) Energy-optimized trajectory Scenario Ship model Step 1 Step 2 Step 3 A ? Trajectory generation Optimal control

We consider warm-started optimized trajectory planning for autonomous surface vehicles (ASVs) by combining the advantages of two types of planners: an A implementation that quickly finds the shortest piecewise linear path, and an optimal control-based trajectory planner. A nonlinear 3-degree-of-freedom underactuated model of an ASV is considered, along with an objective functional that promotes energy-efficient and readily observable maneuvers. The A algorithm is guaranteed to find the shortest piecewise linear path to the goal position based on a uniformly decomposed map. Dynamic information is constructed and added to the A-generated path, and provides an initial guess for warm starting the optimal control-based planner. The run time for the optimal control planner is greatly reduced by this initial guess and outputs a dynamically feasible and locally optimal trajectory.
N ov 2 00 7 Half BPS states in AdS 5 ×

We study a class of solutions of IIB supergravity which are asymptotically AdS5× Y p,q. They have an R×SO(4)×SU(2)×U(1) isometry and preserve half of the 8 supercharges of the background geometry. They are described by a set of second order differential equations that we have found and analysed in a previous paper, where we studied 1/8 BPS states in the maximally supersymmetric AdS5 ×S5 background. These geometries correspond to certain chiral primary operators of the N = 1 superconformal quiver theories, dual to IIB theory on AdS5 × Y p,q. We also show how to recover the AdS5 × Y p,q backgrounds by suitably doubling the number of preserved supersymmetries. We then solve the differential equations perturbatively in a large AdS5 radius expansion, imposing asymptotic AdS5 × Y p,q boundary conditions. We compute the global baryonic and mesonic charges, including the R-charge. As for the computation of the mass, i.e. the conformal dimension ∆ of the dual field theory operators, which is notoriously subtle in asymptotically AdS backgrounds, we adopt the general formalism due to Wald and collaborators, which gives a finite result, and verify the relation ∆ = 3R/2, demanded by the N = 1 superconformal algebra.
2 What Makes a Good Loss Function ?

We consider the problem of learning a loss function which, when minimized over a training dataset, yields a model that approximately minimizes a validation error metric. Though learning an optimal loss function is NP-hard, we present an anytime algorithm that is asymptotically optimal in the worst case, and is provably efficient in an idealized “easy” case. Experimentally, we show that this algorithm can be used to tune loss function hyperparameters orders of magnitude faster than state-of-the-art alternatives. We also show that our algorithm can be used to learn novel and effective loss functions on-the-fly during training.
Better Code Hub Maintainability Combine datasets Energy Commits Baseline Commits

Energy efficiency is a crucial quality requirement for mobile applications. However, improving energy efficiency is far from trivial as developers lack the knowledge and tools to aid in this activity. In this paper we study the impact of changes to improve energy efficiency on the maintainability of Android applications. Using a dataset containing 539 energy efficiencyoriented commits, we measure maintainability – as computed by the Software Improvement Group’s web-based source code analysis service Better Code Hub (BCH) – before and after energy efficiency-related code changes. Results show that in general improving energy efficiency comes with a significant decrease in maintainability. This is particularly evident in code changes to accommodate the Power Save Mode and Wakelock Addition energy patterns. In addition, we perform manual analysis to assess how real examples of energy-oriented changes affect maintainability. Our results help mobile app developers to 1) avoid common maintainability issues when improving the energy efficiency of their apps; and 2) adopt development processes to build maintainable and energy-efficient code. We also support researchers by identifying challenges in mobile app development that still need to be addressed.
DnaJ molecules as potential effectors in Meloidogyne arenaria. An unexplored group of proteins in plant parasitic nematodes

ABSTRACT Plant pathogenic organisms secrete proteins called effectors that recognize, infect and promote disease within host cells. Bacteria, like Pseudomona syringae, use effectors with DnaJ function to disrupt plant defenses. DnaJ proteins (also called Hsp40) are a group of co-chaperone molecules, which assist in the folding of proteins. Despite the described role of DnaJs as effectors in several groups of pathogens, this group of proteins has never been correlated with the infection process in plant parasitic nematodes. In this study, we analyze the importance of DnaJ for plant parasitic nematodes. To do that, we compare the number of DnaJ proteins in nematodes with different lifestyles. Then, we predict the secreted DnaJ proteins in order to detect effector candidates. We found that Meloidogyne species have more secreted DnaJs than the rest of the nematodes analyzed in the study. Particularly, M. arenaria possess the highest proportion of secreted DnaJ sequences in comparison to total DnaJ proteins. Furthermore, we found in this species at least five sequences with a putative nuclear localization signal, three of them with a serine rich region with an unknown function. Then, we chose one of these sequences (MG599854) to perform an expression analysis. We found that MG599854 is over-expressed from 3 days post inoculation onwards in tomato plants. Moreover, MG599854 seems to be enough to produce cell death in Nicotiana benthamiana under transient expression conditions. In concordance with our results, we propose that DnaJ proteins are a potential source of effector proteins in plant parasitic nematodes.
J ul 2 01 3 Ameba-inspired Self-organizing Particle Systems ( Extended Abstract )

Particle systems are physical systems of simple computatio nal particles that can bond to neighboring particles and use these bonds to move from one spot to another (non-occupie d) spot. These particle systems are supposed to be able to self-organize in order to adapt to a desired shape without any central control. Self-organizing particle systems hav e many interesting applications like coating objects for mon itoring and repair purposes and the formation of nano-scale devices for surgery and molecular-scale electronic struct u es. While there has been quite a lot of systems work in this area, especially in the context of modular self-reconfigura ble robotic systems, only very little theoretical work has b een done in this area so far. We attempt to bridge this gap by propo sing a model inspired by the behavior of ameba that allows rigorous algorithmic research on self-organizing p article systems.
Tissue Engineering; Current Status & Futuristic Scope

Almost 30 years have passed since the term ‘tissue engineering’ was created to represent a new concept that focuses on the regeneration of neotissues from cells with the support of biomaterials and growth factors. This interdisciplinary engineering has attracted much attention as a new therapeutic means that may overcome the drawbacks involved in the current artificial organs and organ transplantation that have also been aiming at replacing lost or severely damaged tissues or organs. However, the tissues regenerated by tissue engineering and widely applied to patients are still minimal, including skin, bone, cartilage, capillary, and periodontal tissues. What are the reasons for such slow advances in clinical applications of tissue engineering? This article gives a brief overview of the current state of tissue engineering, covering the fundamentals and applications. The fundamentals of tissue engineering involve cell sources, scaffolds for cell expansion and differentiation, as well as carriers for growth factors. Animal and human trials are a major part of the applications. Based on these results, some critical problems to be resolved for the advances of tissue engineering are addressed from the engineering point of view, emphasizing the close collaboration between medical doctors and biomaterials scientists.
Mechanism of ribosome translocation along messenger RNA

In cells, helicase translocation along nucleic acid is essential for many biological processes. However, so far, the mechanism of this translocation is not fully understood. Recent studies show that helicase might translocate through two processes, active process and passive process, with different translocation rate. In this study, a mechanochemical model including such two processes is presented for ribosome translocation along messenger RNA during translation. In which, each of the two processes consists of two sub-processes, translocation factor binding sub-process and the following mechanochemical sub-process in which ribosome makes a forward translocation step. Ribosome switches stochastically between these two processes with external force dependent rates. By this model, we found that, with the increase of external force, the mean translocation rate of ribosome increases from one lower limit to another upper limit, and both of these two limits increase with concentrations of the translocation factors. Under high external force, ribosome translocates mainly through active process. At saturating concentration of translocation factors, the translocation is limited by mechanochemical sub-processes.
Enhanced skyrmion motion via strip domain wall

When magnetic skyrmions move under spin-orbit torque in magnetic nanowires, they experience a skyrmion Hall effect, which pushes them towards the nanowire edge where they risk being annihilated; this puts an upper limit on how fast they can be driven. However, the same magnetic multilayer harboring skyrmions can sustain a N\'eel-type strip domain wall along the nanowire length, potentially keeping the skyrmions separated from the edge. Here we study the interplay between current driven skyrmions and domain walls and find that they increase the annihilation current and allow the skyrmions to move faster. Based on the Thiele formalism, we confirm that the emergent longitudinal repulsive force and the modified energy landscape linked to the domain wall are responsible for the enhanced skyrmion motion. Furthermore, we identify that the longitudinal repulsive force emerges because of the broken axisymmetry in the local magnetization in front of the skyrmion. Our study uncovers key aspects in the interplay between two topological magnetic textures from different homotopy groups and may inspire new device concepts.
Domain A Source Feature extractor Subnetwork Source Classification Subnetwork Task A Domain B Target Feature extractor Subnetwork Target Classification Subnetwork Task B Backpropagation Transfer Learning Frozen Network A Network

Visual defect assessment is a form of anomaly detection. This is very relevant in finding faults such as cracks and markings in various surface inspection tasks like pavement and automotive parts. The task involves detection of deviation/divergence of anomalous samples from the normal ones. Two of the major challenges in supervised anomaly detection are the lack of labelled training data and the low availability of anomaly instances. Semi-supervised methods which learn the underlying distribution of the normal samples and then measure the deviation/divergence from the estimated model as the anomaly score have limitations in their overall ability to detect anomalies. This paper proposes the application of network-based deep transfer learning using convolutional neural networks (CNNs) for the task of anomaly detection. Single class SVMs have been used in the past with some success, however we hypothesize that deeper networks for single class classification should perform better. Results obtained on established anomaly detection benchmarks as well as on a real-world dataset, show that the proposed method clearly outperforms the existing state-of-the-art methods, by achieving a staggering average area under the receiver operating characteristic curve value of 0.99 for the tested datasets which is an average improvement of 41% on the CIFAR10, 20% on MNIST and 16% on Cement Crack datasets. Keywords-anomaly detection; transfer learning; deep learning; convolutional neural networks
Distinguishers for Reduced Round Ascon, DryGASCON, and Shamash Permutations

Ascon, DryGASCON, and Shamash are submissions to NIST’s lightweight cryptography standardization process and have similar designs. We analyze these algorithms against subspace trails, truncated differentials, and differential-linear distinguishers. We provide probability one 4-round subspace trails for DryGASCON-256, 3-round subspace trails for DryGASCON-128, and 2-round subspace trails for Shamash permutations. Moreover, we provide the first 3.5-round truncated differential and 5-round differential-linear distinguisher for DryGASCON128. Finally, we improve the time and data complexity of the 4-round differential-linear attack on Ascon.
State-of-the-Art Routing Protocols for Delay Tolerant Networks

Advances in Micro-Electro-Mechanical Systems (MEMS) have revolutionized the digital age to a point where animate and inanima te objects can be used as a communication channel. In addition, the ubiquit y of mobile phones with increasing capabilities and ample resources me ans people are now effectively mobile sensors that can be used to sense the e nvironment as well as data carriers. These objects, along with their devic es, form a new kind of networks that are characterized by frequent disconn ections, resource constraints and unpredictable or stochastic mobility patt erns. A key underpinning in these networks is routing or data dissemination p rotocols that are designed specifically to handle the aforementioned charact eristics. Therefore, there is a need to review state-of-the-art routing pro toc ls, categorize them, and compare and contrast their approaches in terms of d elivery rate, resource consumption and end-to-end delay. To this end, thi s paper reviews 63 unicast, multicast and coding-based routing protocols t hat are designed specifically to run in delay tolerant or challenged networks . We provide an extensive qualitative comparison of all protocols, high light their experimental setup and outline their deficiencies in terms of desig n and research methodology. Apart from that, we review research that aims t o exploit studies on social networks and epidemiology in order to improve r outing protocol performance. Lastly, we provide a list of future researc h directions.
Emotional and behavioural problems in childhood and risk of overall and cause-specific morbidity and mortality in middle-aged Finnish men

Laura Kauhanen Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland laura.kauhanen@uef.fi Janne Leino Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland Hanna-Maaria Lakka Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland Kuopio National Institute of Health and Welfare, Helsinki John Lynch School of Population Health and Clinical Practice, University of Adelaide Department of Social Medicine, University of Bristol Jussi Kauhanen Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland Prevention Research Center, Pacific Institute of Research and Evaluation, Berkeley, CA, USA
Toward New Unit-Testing Techniques for Shared-Memory Concurrent Programs

Following advances in hardware engineering (multi-core processors) and software engineering (agile practices), there is now a large demand for unit-testing techniques for concurrent code. This paper presents the motivation, problem, proposed solution, first results, and open challenges of an early-stage research project (2019–2022) that aims to develop innovative such techniques. Founded on existing work on coordination models and languages, the project's idea is to use a combination of domain-specific language, compilation, and model-checking to build a fully automated framework for unit-testing concurrency.
Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression

Automatic identification system (AIS) is an important part of perfecting terrestrial networks, radar systems and satellite constellations. It has been widely used in vessel traffic service system to improve navigational safety. Following the explosion in vessel AIS data, the issues of data storing, processing, and analysis arise as emerging research topics in recent years. Vessel trajectory compression is used to eliminate the redundant information, preserve the key features, and simplify information for further data mining, thus correspondingly improving data quality and guaranteeing accurate measurement for ensuring navigational safety. It is well known that trajectory compression quality significantly depends on the threshold selection. We propose an Adaptive Douglas-Peucker (ADP) algorithm with automatic thresholding for AIS-based vessel trajectory compression. In particular, the optimal threshold is adaptively calculated using a novel automatic threshold selection method for each trajectory, as an improvement and complement of original Douglas-Peucker (DP) algorithm. It is developed based on the channel and trajectory characteristics, segmentation framework, and mean distance. The proposed method is able to simplify vessel trajectory data and extract useful information effectively. The time series trajectory classification and clustering are discussed and analysed based on ADP algorithm in this paper. To verify the reasonability and effectiveness of the proposed method, experiments are conducted on two different trajectory data sets in inland waterway of Yangtze River for trajectory classification based on the nearest neighbor classifier, and for trajectory clustering based on the spectral clustering. Comprehensive results demonstrate that the proposed algorithm can reduce the computational cost while ensuring the clustering and classification accuracy.
Osseous manifestations of non-Hodgkin's lymphoma in Human Immunodeficiency Virus (HIV) infection and Acquired Immunodeficiency Syndrome (AIDS)

BACKGROUND: Lymphoma is the second most common Acquired Human Immunodeficiency Syndrome (AIDS)-associated neoplasm, the commonest being Kaposi sarcoma. The diagnosis and treatment of bone lymphoma still remains a challenge in our environment. MATERIALS AND METHODS: This is a retrospective chart review of 105 patients who were diagnosed histopathologi-cally with lymphoma involving the bone over a period of 11 years in six local hospitals. RESULTS: Forty-five (42.9%) patients with bone lesions and who were positive for Human Immunodeficiency Virus (HIV) formed the cohort. Clinical manifestations varied from swelling and pain in all patients to pathological fractures (n = 5, 11.1%) and paraplegia (n = 11, 24.4%) due to cord compression. Common sites included the spine, pelvis, femur, humerus, ribs, tibia and mandible. Treatment included chemotherapy and radiation, splinting or surgical fixation of pathological fractures and decompression of the spine with or without fusion. CONCLUSION: Bone lymphoma should be considered in any HIV-positive patient presenting with unexplained swelling and pain. The radiological picture may mimic bone infections like chronic pyogenic osteomyelitis, tuberculosis and fungal infections, other systemic disorders like Paget's disease, blood-related tumours such as leukaemia and other primary and metastatic bone neoplasms.
Convergence of Bregman Multi-Stage Algorithm

The multi-stage algorithm (MSA) has been widely used, especially in the area of sparse-related problems. Although several convergence results of MSA were established in the earlier literature, the convergence of the generated iterative points is still unclear. In this paper, we revisit the convergence results with some Bregman distance. With the help of Kurdyka-Łojasiewicz property, we prove that this algorithm converges to a critical point of the objective function. An extension about the singular values minimizations is made and the convergence is also presented.