Miracle-dz/UniFAR-FTUs huggingface dataset:
Deformation of MR images using a local linear transformation

A fully automatic method to deform medical images is proposed. The procedure is based on the application of a set of consecutive local linear transformations at fixed landmarks, generating a global non-linear deformation. Continuity is guaranteed by a smooth change form the landmark point to the neighborhood, which is a homotopy between an affine transformation and the identity map. Landmarks are distributed uniformly throughout both reference and target images and their density is increased to reach the desired similarity between both images. A hybrid genetic optimization algorithm is used to search for the transformation parameters by maximizing the normalized mutual information. It is shown, by means of the transformation of a circle into a triangle and vice versa, that the method has the capability to generate either sharp of smooth deformations. For magnetic resonance images, it is proved that the successive application of the local linear transformations allows us to increase the similarity between geometrically deformed images and target. The results suggest that the method can be applied to a wide range of non-rigid image registration problems.
Susceptibility to antibiotics in isolates of Lactobacillus plantarum RAPD‐type Lp299v, harvested from antibiotic treated, critically ill patients after administration of probiotics

Recultured Lactobacillus plantarum 299v‐like strains were tested regarding antibiotic susceptibility, and no decrease was detected. Antibiotics are frequently used to treat patients in intensive care units (ICUs) and are associated with a significant risk of selection of resistant bacterial strains. In particular, it is possible that genetic transfer of antibiotic resistance to the resident gastrointestinal flora, as well as to administered probiotics, may be increased in the ICU setting. The aim of the present investigation was to detect possible changes in antimicrobial susceptibility in reisolates of the probiotic strain Lactobacillus plantarum 299v (Lp299v) given to antibiotic treated, critically ill patients. Lp299v‐like strains were identified in cultures of biopsies and fecal samples from 32 patients given the probiotic strain enterally in two previous ICU studies. The patients received a variety of antibiotics. Isolates with the same genomic RAPD profile (RAPD‐type) as Lp299v were obtained to enable monitoring of antibiotic susceptibility by E‐tests. Forty‐two isolates, collected throughout the course of illness, were tested against 22 different antibiotics. No obvious decrease in susceptibility was found for 21 of the tested antibiotics. There was a tendency toward decreased susceptibility to ampicillin. The stable antibiotic susceptibility profiles of the Lp299v‐like isolates studied here suggests this probiotic is less likely to acquire resistance when administered to critically ill patients treated with broad‐spectrum antibiotics.
Character-Level Models versus Morphology in Semantic Role Labeling

Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill for high-level semantic analysis tasks, such as semantic role labeling (SRL). In this work, we train various types of SRL models that use word, character and morphology level information and analyze how performance of characters compare to words and morphology for several languages. We conduct an in-depth error analysis for each morphological typology and analyze the strengths and limitations of character-level models that relate to out-of-domain data, training data size, long range dependencies and model complexity. Our exhaustive analyses shed light on important characteristics of character-level models and their semantic capability.
Reaction of Banana Hybrids (Phase-II) for Resistance to Meloidogyne incognita

Plant parasitic nematodes are one of the major biotic stresses affecting banana production. Breeding works carried out at the Department of Fruit crops, Tamil Nadu Agricultural University (TNAU), India. The potential diploids and hybrids developed were crossed with commercial triploids to develop primary tetraploids and improved diploids. The susceptible check cultivar used was Rasthali (AAB), while the resistant reference cultivar used Pisang Lilin (AA). Banana suckers of uniform size and weight were collected, pared and planted in earthen part containing 5 kg sterilized pot mixture. Egg masses of M. incognita were picked from roots, allowed to hatch in a beaker of distilled water and the hatched juveniles (J2) were inoculated in the rhizosphere of the hybrids by soil injection method @ 5,000 nematodes/pot. Same set of replicated banana hybrids were also maintained as uninoculated check. The reactions of nineteen new synthetic banana phase II hybrids to Meloidogyne incognita was studied under field conditions as well as in controlled inoculation tests in pots. Hybrid H 531 (Poovan x Pisang Lilin) was found to be resistant and six hybrids, H-02-34, H-03-05, H-03-13, H-04-12, H-04-24 and NPH-02-01 were found to be toterant to the root-knot nematode, Meloidogyne incognita while the remaining were rated as susceptible and highly susceptible ones. Total phenols and PO, PPO, PAL and enzymatic activity of the hybrids in defense mechanism in response to nematode invasion indicated higher activities in resistant genotypes compare to susceptible ones. Hybrid H 531 had the maximum biochemical content and enzyme activity among the hybrids included in this study. The resistant and tolerant hybrids had enhanced contents of total phenol, PO, PPO and PAL.
Eigenvalue bounds for the signless p-Laplacian Elizandro

We consider the signless p-Laplacian Qp of a graph, a generalisation of the quadratic form of the signless Laplacian matrix (the case p = 2). In analogy to Rayleigh’s principle the minimum and maximum of Qp on the p-norm unit sphere are called its smallest and largest eigenvalues, respectively. We show a PerronFrobenius property and basic inequalites for the largest eigenvalue and provide upper and lower bounds for the smallest eigenvalue in terms of a graph parameter related to the bipartiteness. The latter result generalises bounds by Desai and Rao and, interestingly, at p = 1 upper and lower bounds coincide. Mathematics Subject Classifications: 05C50, 05C40, 15A18
Accurate characterization of luminescent coupling effects with voltage and light bias adjustment

In this work we report a novel method to directly characterize the luminescent coupling efficiency of multijunction solar cells. Our method uses small-signal light bias, which enables the measurement of luminescent coupling efficiency under different voltage and light bias, which is unachievable by traditional luminescent efficiency measurement methods. With this method, we further demonstrate the light and voltage dependence of the luminescent coupling efficiency and showed that the luminescent coupling efficiency at the maximum power point can be 40-60% higher than that at the short-circuit point. This work provides a useful tool for multijunction solar cell characterization, and potentially leads to a deeper understanding of luminescent coupling effects, enabling more efficient solar cell design.
Myocarditis Associated with Immune Checkpoint Inhibitors: An Expert Consensus on Data Gaps and a Call to Action

Immune checkpoint inhibitors are a class of drugs that trigger the immune system to recognize and fight cancer cells; however, related immune‐related adverse events are a challenge. This commentary describes a workshop convened by Project Data Sphere to determine best approaches in the treatment of myocarditis, with the goal of minimizing the risk of this adverse reaction in patients with cancer who are treated with immune checkpoint inhibitors.
Study on OS Fingerprinting and NAT / Tethering based on DNS Log Analysis

OS fingerprinting and NAT detection are considered important in various researches like network troubleshooting, deployment of services. Previous passive approaches usually require raw network traffic, which is often difficult to deploy. In this paper, a novel method is designed to fingerprint the OS and classify the NAT only using DNS log. Features of the Windows, MacOS/iOs, Android and Linux operating systems can be automatically extracted from labelled DNS log. With these features a simple classifier can fingerprint the OS types of these devices accurately. We apply this algorithm on data set from a large scale network. Analysis also reveals that nowadays the Windows operating systems are widely used in tethering or NAT, which is contrary to our previous knowledge.
The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations

We concentrate on machine learning techniques used for profiled sidechannel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of them help in mounting successful attacks when the data is highly imbalanced. Especially, the results with the SMOTE technique are encouraging, since we observe some scenarios where it reduces the number of necessary measurements more than 8 times. Next, we provide extensive results on comparison of machine learning and side-channel metrics, where we show that machine learning metrics (and especially accuracy as the most often used one) can be extremely deceptive. This finding opens a need to revisit the previous works and their results in order to properly assess the performance of machine learning in side-channel analysis.
Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems

Freshwater ecosystems are amongst the most threatened ecosystems on Earth. Currently, climate change is one of the most important drivers of freshwater transformation and its effects include changes in the composition, biodiversity and functioning of freshwater ecosystems. Understanding the capacity of freshwater species to tolerate the environmental fluctuations induced by climate change is critical to the development of effective conservation strategies. In the last few years, epigenetic mechanisms were increasingly put forward in this context because of their pivotal role in gene–environment interactions. In addition, the evolutionary role of epigenetically inherited phenotypes is a relatively recent but promising field. Here, we examine and synthesize the impacts of climate change on freshwater ecosystems, exploring the potential role of epigenetic mechanisms in both short‐ and long‐term adaptation of species. Following this wrapping‐up of current evidence, we particularly focused on bringing together the most promising future research avenues towards a better understanding of the effects of climate change on freshwater biodiversity, specifically highlighting potential molecular targets and the most suitable freshwater species for future epigenetic studies in this context.
Fusion of Domain-Specific and Trainable Features for Gender Recognition From Face Images

The popularity and the appeal of systems which are able to automatically determine the gender from face images are growing rapidly. Such a great interest arises from the wide variety of applications, especially in the fields of retail and video surveillance. In recent years, there have been several attempts to address this challenge, but a definitive solution has not yet been found. In this paper, we propose a novel approach that fuses domain-specific and trainable features to recognize the gender from face images. In particular, we use the SURF descriptors extracted from 51 facial landmarks related to eyes, nose, and mouth as domain-dependent features, and the COSFIRE filters as trainable features. The proposed approach turns out to be very robust with respect to the well-known face variations, including different poses, expressions, and illumination conditions. It achieves state-of-the-art recognition rates on the GENDER-FERET (94.7%) and on the labeled faces in the wild (99.4%) data sets, which are two of the most popular benchmarks for gender recognition. We further evaluated the method on a new data set acquired in real scenarios, the UNISA-Public, recently made publicly available. It consists of 206 training (144 male, 62 female) and 200 test (139 male, 61 female) images that are acquired with a real-time indoor camera capturing people in regular walking motion. Such experiment has the aim to assess the capability of the algorithm to deal with face images extracted from videos, which are definitely more challenging than the still images available in the standard data sets. Also for this data set, we achieved a high recognition rate of 91.5%, that confirms the generalization capabilities of the proposed approach. Of the two types of features, the trainable COSFIRE filters are the most effective and, given their trainable character, they can be applied in any visual pattern recognition problem.
Design and Use of Interactive Social Stories for Children with Autism Spectrum Disorder (ASD).

The current study aimed to design technology-supported interactive social stories to teach social skills to children with autism spectrum disorder (ASD). A design-based research was implemented with children with ASD along with the participation of their mothers, teachers, peers and field experts. An iterative remediation process was followed through the feedback of participants. The data collection process was further supported through researcher notes, interviews, video-recorded observations and log files. Three cycles of remediation were employed to improve the interactive materials, which lead them to be perceived as pleasant, interesting, engaging and user-friendly. It was observed that children gained useful non-target skills as well as the target behaviors. Interactive stories were particularly helpful in raising satisfaction as they provided children with the opportunity to make their own learning decisions.
A new method for accelerating impossible differential cryptanalysis and its application on LBlock

Impossible differential cryptanalysis, the extension of differential cryptanalysis, is one of the most efficient attacks against block ciphers. This cryptanalysis method has been applied to most of the block ciphers and has shown significant results. Using structures, key schedule considerations, early abort, and pre-computation are some common methods to reduce complexities of this attack. In this paper, we present a new method for decreasing the time complexity of impossible differential cryptanalysis through breaking down the target key space into subspaces, and extending the results on subspaces to the main target key space. The main advantage of this method is that there is no need to consider the effects of changes in the values of independent key bits on each other. Using the 14-round impossible differential characteristic observed by Boura et al. at ASIACRYPT 2014, we implement this method on 23-round LBlock and demonstrate that it can reduce the time complexity of the previous attacks to 271.8 23-round encryptions using 259 chosen plaintexts and 2 73 blocks of memory.
Ovarian cancer statistics, 2018

In 2018, there will be approximately 22,240 new cases of ovarian cancer diagnosed and 14,070 ovarian cancer deaths in the United States. Herein, the American Cancer Society provides an overview of ovarian cancer occurrence based on incidence data from nationwide population‐based cancer registries and mortality data from the National Center for Health Statistics. The status of early detection strategies is also reviewed. In the United States, the overall ovarian cancer incidence rate declined from 1985 (16.6 per 100,000) to 2014 (11.8 per 100,000) by 29% and the mortality rate declined between 1976 (10.0 per 100,000) and 2015 (6.7 per 100,000) by 33%. Ovarian cancer encompasses a heterogenous group of malignancies that vary in etiology, molecular biology, and numerous other characteristics. Ninety percent of ovarian cancers are epithelial, the most common being serous carcinoma, for which incidence is highest in non‐Hispanic whites (NHWs) (5.2 per 100,000) and lowest in non‐Hispanic blacks (NHBs) and Asians/Pacific Islanders (APIs) (3.4 per 100,000). Notably, however, APIs have the highest incidence of endometrioid and clear cell carcinomas, which occur at younger ages and help explain comparable epithelial cancer incidence for APIs and NHWs younger than 55 years. Most serous carcinomas are diagnosed at stage III (51%) or IV (29%), for which the 5‐year cause‐specific survival for patients diagnosed during 2007 through 2013 was 42% and 26%, respectively. For all stages of epithelial cancer combined, 5‐year survival is highest in APIs (57%) and lowest in NHBs (35%), who have the lowest survival for almost every stage of diagnosis across cancer subtypes. Moreover, survival has plateaued in NHBs for decades despite increasing in NHWs, from 40% for cases diagnosed during 1992 through 1994 to 47% during 2007 through 2013. Progress in reducing ovarian cancer incidence and mortality can be accelerated by reducing racial disparities and furthering knowledge of etiology and tumorigenesis to facilitate strategies for prevention and early detection. CA Cancer J Clin 2018;68:284–296. © 2018 American Cancer Society.
Mechanism Underlying Tissue Cryotherapy to Combat Obesity/Overweight: Triggering Thermogenesis

Background Local adipose tissue (AT) cooling is used to manage obesity and overweight, but the mechanism is unclear. The current view is that acute local cooling of AT induces adipocyte cell disruption and inflammation (“cryolipolysis”) that lead to adipocyte cell death, with loss of subcutaneous fat being recorded over a prolonged period of weeks/months. A contrasting view is that AT loss via targeted cryotherapy might be mediated by thermogenic fat metabolism without cell disruption. Methods In this retrospective study of individuals presenting for cryotherapy to the Clinic BioEsthetic, Paris, France, we recorded waist circumference, body weight, and body mass index (BMI) by direct measurement and by whole-body dual-energy X-ray absorptiometric scanning. In select individuals, blood analysis of markers of inflammation and fat mobilization was performed before and after the procedure. Results We report that (i) single sessions of tissue cryotherapy lead to significant loss of tissue volume in the time frame of hours and (ii) multiple daily procedures lead to a cumulative decline in AT, as assessed by waist circumference, body weight, and BMI, confirmed by whole-body dual-energy X-ray absorptiometric scanning. In addition, (iii) blood analysis following tissue cryotherapy found no significant changes in biochemical parameters including markers of inflammation. Moreover, (iv) calculations of heat extracted and of compensatory weight loss taking place through thermogenesis are substantially consistent with the observed loss of AT. Conclusions These findings argue that cold-induced thermogenesis (“cryothermogenesis”) rather than adipocyte disruption underlies the reduction in AT volume, raising the prospect that more intensive cryotherapy may be a viable option for combating obesity and overweight.
Polymers of Limonene Oxide and Carbon Dioxide: Polycarbonates of the Solar Economy

Limonene epoxide (1,2-limonene oxide) readily reacts with carbon dioxide inserted in a ring-opening copolymerization reaction and forms polycarbonates of exceptional chemical and physical properties. Both poly(limonene carbonate) and poly(limonene dicarbonate) can be synthesized using low-cost Zn or Al homogeneous catalysts. This study addresses selected relevant questions concerning the technical and economic feasibility of limonene and carbon dioxide polymers en route to the bioeconomy.
A novel human posture estimation using single depth image from Kinect v2 sensor

In this paper, we propose an approach to estimate general posture of the human-body. We perform various 2D scans of the body to carry out their Fast-Fourier-Transform(FFT) to extract features which can be fed to a 2-layer feed-forward neural-network. This approach can offer an effective procedure given limited resources which are usually available for deep learning approaches. In comparison with the literature, our proposed method doesn't require any specific skeleton points of the human body, results in a reduced level of computational complexities. We compared our method with the state-of-the-art and the results show an increased level of classification accuracy. The training dataset is captured from a single subject moving with various postures. If the test-data is also captured from the same subject, then the overall level of accuracy is 98.2% while if the test images are captured from different subjects then the accuracy of the estimation would be near 91.5%. We also test our method in the restricted field of view where parts of the subject's body are not in the field of sensor view, the result shows that in this case, the accuracy is as high as 79.1%.
A Survey on SLA Management for Cloud Computing and Cloud-Hosted Big Data Analytic Applications

Cloud computing is a new generation of computing based on the layered model which provides different services to cloud customer. Conceptually, cloud computing offers a scalable platform for Big Data Analytic Applications (BDAAs) which can elastically provision resources based on data growth and complexity of analytic applications. This complexity of analysis increases with vast volumes of data in big enterprises which prefer effective and fast decision-making. Therefore, the importance of Service Level Agreement (SLA) is appeared which clarifies the roles between a customer (i.e., cloud user or Big Data analyst) and a provider for particular service provision comes. The main components of the SLAs are Quality of Service parameters which must be monitored to achieve a set of Service Level Objectives (SLOs) and detect violations. Indeed, many SLA management approaches have been developed as solutions for preventing SLA violations to avoid costly penalties. Consequently, many interesting solutions developed works of SLA violation management in cloud technology and cloud-hosted BDAAs. A survey about the existed works in terms of idea, strengths and weaknesses is introduced in this paper. Meanwhile, the challenges and new research directions in this area which require further investigation will be discussed to provide a comprehensive overview and big-picture.