Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterising Arm Recovery in People with Severe Stroke (CARPSS): protocol for a 12-month observational study of clinical, neuroimaging and neurophysiological biomarkers

Introduction In individuals with early (indexed ≤7 days poststroke) and severe upper limb paresis (shoulder abduction and finger extension score of <5 out of 10), our objectives are to: (1) determine if biomarkers of brain structure and function collected at <1 month poststroke explain who will experience clinically important recovery over the first 12 months poststroke; (2) compare stroke survivors’ perceptions of personally meaningful recovery to clinically important recovery; and (3) characterise the trajectory of change in measures of motor function, brain structure and function. Methods and analysis Prospective observational study with an inception cohort of 78 first-time stroke survivors. Participants will be recruited from a single, large tertiary stroke referral centre. Clinical and biomarker assessments will be completed at four follow-up time points: 2 to 4 weeks and 3, 6 and 12 months poststroke. Our primary outcome is achievement of clinically important improvement on two out of three measures that span impairment (Fugl-Meyer Upper Limb, change ≥10 points), activity (Motor Assessment Scale item 6, change ≥1 point) and participation (Rating of Everyday Arm-use in the Community and Home, change ≥1 point). Brain biomarkers of structure and function will be indexed using transcranial magnetic stimulation and MRI. Multilevel modelling will be performed to examine the relationship between clinically important recovery achieved (yes/no) and a priori defined brain biomarkers related to the corticospinal tract and corpus callosum. Secondary analyses will compare stroke survivor’s perception of recovery, as well as real-world arm use via accelerometry, to the proposed metric of clinically meaningful recovery; and model trajectory of recovery across clinical, a priori defined biomarkers and exploratory variables related to functional connectivity. Ethics and dissemination Approved by the hospital and university ethics review boards. Results will be disseminated through peer-reviewed publications and conference presentations. Trial registration number NCT02464085.
Decolorization by Fusion

Decolorization aims at converting color images into grayscale images while preserving their original contrast and color discriminability. In this paper, we introduce an original fusion-based decolorization approach. Our algorithm employs as inputs the three color channels <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>, and an additional input related to the Helmholtz-Kohlrausch effect. To blend those inputs, we adopt a multiscale fusion strategy to prevent the artifacts arising from a pixel-wise application of weight maps and use several weight maps that respectively control saliency, exposure, and saturation. The new operator has been tested successfully on a large data set of both natural and synthetic images. It is competitive compared with modern optimization-based methods in terms of decolorized image visual quality while offering the advantage of being computationally simple, and temporally consistent when decolorizing video sequences.
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures

We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we give new results on the benefits of multi-generator architecture of GANs. We show that the minimax gap shrinks to $\epsilon$ as the number of generators increases with rate $\widetilde{O}(1/\epsilon)$. This improves over the best-known result of $\widetilde{O}(1/\epsilon^2)$. At the core of our techniques is a novel application of Shapley-Folkman lemma to the generic minimax problem, where in the literature the technique was only known to work when the objective function is restricted to the Lagrangian function of a constraint optimization problem. Our proposed Stackelberg GAN performs well experimentally in both synthetic and real-world datasets, improving Fr\'echet Inception Distance by $14.61\%$ over the previous multi-generator GANs on the benchmark datasets.
Late-Life Depression

A 71-year-old man, whose wife died 6 months previously, presents with foot pain from diabetic neuropathy, poor sleep, lack of energy, and increasing frustration about his inability to “keep his diabetes under control. ” On examination, he also notes lack of interest, decreased appetite, a weight loss of 4.5 kg (10 lb) over the past 3 months, and intermittent thoughts that he would be better off dead. How should his case be managed?
Stellar: network attack mitigation using advanced blackholing

Network attacks, including Distributed Denial-of-Service (DDoS), continuously increase in terms of bandwidth along with damage (recent attacks exceed 1.7 Tbps) and have a devastating impact on the targeted companies/governments. Over the years, mitigation techniques, ranging from blackholing to policy-based filtering at routers, and on to traffic scrubbing, have been added to the network operator's toolbox. Even though these mitigation techniques provide some protection, they either yield severe collateral damage, e.g., dropping legitimate traffic (blackholing), are cost-intensive, or do not scale well for Tbps level attacks (ACL filtering, traffic scrubbing), or require cooperation and sharing of resources (Flowspec). In this paper, we propose Advanced Blackholing and its system realization Stellar. Advanced blackholing builds upon the scalability of blackholing while limiting collateral damage by increasing its granularity. Moreover, Stellar reduces the required level of cooperation to enhance mitigation effectiveness. We show that fine-grained blackholing can be realized, e.g., at a major IXP, by combining available hardware filters with novel signaling mechanisms. We evaluate the scalability and performance of Stellar at a large IXP that interconnects more than 800 networks, exchanges more than 6 Tbps traffic, and witnesses many network attacks every day. Our results show that network attacks, e.g., DDoS amplification attacks, can be successfully mitigated while the networks and services under attack continue to operate untroubled.
Hierarchical routing and cross-layer mechanisms for improving video streaming quality of service over mobile wireless ad hoc networks

This thesis dissertation addresses the problem of providing video streaming services over mobile wireless ad hoc networks. This sort of network represents a hostile environment for this kind of real- time data transmission to the extent that obtaining a good quality of viewer experience is challenging and still under study. Besides the research point of view, providing high-quality multimedia services is decisive for the practical usability and feasibility of wireless ad hoc networks so that service providers can broaden the range of services offered. So far, mobile wireless ad hoc networks have been used to provide network connection among users who could not have connectivity otherwise. However, quality expectations and requirements have been increased notably, fostered by the advent of real-time multimedia applications over mobile devices. Due to the considerable processing and bandwidth constraints underlying these types of devices, coupled with their ability to move freely, it becomes a difficult task to achieve an acceptable quality of service throughout the entire video transmission. Thus, the contribution of this thesis work is twofold. On the one hand, the main problems and limitations that may be encountered and should be faced when deploying real-time services over mobile wireless ad hoc networks are analyzed and discussed. Bandwidth constraints and node mobility are portrayed as the major causes that prevent good quality of service and smooth video playback. On the other hand, following then the aim of improving video streaming quality, this thesis proposes practical solutions that involve diverse routing and cross-layer techniques. One of the proposed approaches focuses on hierarchical routing. Hierarchical arrangement of network nodes may reduce packet interference as well as offer a structured architecture that reduces control traffic overhead. Particularly, the proposed hierarchical routing protocol aims at providing scalability when the number of nodes grows, while maintaining complexity as low as possible. The resulting reduction in packet losses and video playback interruptions finally enhances the quality of received video streams. Furthermore, on the basis that the nodes in an ad hoc network are willing to perform routing tasks, every node could become essential for the proper network operation and routing performance. In tune with this philosophy, a new cross-layer mechanism for recovering lost packets is proposed. By overhearing packets over the wireless shared medium, any node in the surrounding area of the destination endpoint can altruistically retransmit those video packets that have not been correctly received at destination. Moreover, due to the video awareness and frame prioritization algorithm considered in this proposal, it becomes very convenient for real-time video streaming services. The results show that the presented mechanism succeeds in improving video quality and user experience, especially when packet losses are caused due to the mobility of the destination node.
Robustness of Reflection Symmetry Detection Methods on Visual Stresses in Human Perception Perspective

Symmetry is one of the most frequently observed fundamental regularities in the visual characteristic of real-world objects. The human brain has been trained to respond quickly to symmetry patterns, organizing them as salient clues for the unique description of objects. Recently, automatic symmetry detection methods have been widely introduced in computer vision and graphics fields for 2-D and 3-D object data, including reflection, translation, and rotation symmetry patterns. Researchers have invented features inspired by a human vision system and have adopted deep learning approaches. On the other side, traditional performance evaluations have been conducted on a unified test data set containing random degrees of diverse visual challenges. However, they ignore observing the insight of usability and practicality of the methods in higher level tasks, such as object recognition. In this paper, we carefully organize the visual stress data set for reflection symmetry detection evaluation proposing a novel evaluation framework. The state-of-the-art reflection symmetry detection methods are re-evaluated and analyzed in human perception perspective.
Robot Learning Using Learning Classifier Systems Approach

Efforts to develop highly complex and adaptable machines that meet the ideal of mechanical human equivalents are now reaching the proof-of concept stage. Enabling a human to efficiently transfer knowledge and skills to a machine has inspired decades of research. I present a learning mechanism in which a robot learns new tasks using genetic-based machine learning technique, learning classifier system (LCS). LCSs are rule-based systems that automatically build their ruleset. At the origin of Holland’s work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowed with a generalization property. Indeed, from a Reinforcement Learning point of view, LCSs can be seen as learning systems building a compact representation of their problem. More recently, LCSs have proved efficient at solving automatic classification tasks (Sigaud et al., 2007). The aim of the present contribution is to describe the state-of the-art of LCSs, emphasizing recent developments, and focusing more on the application of LCS for Robotics domain. In previous robot learning studies, optimization of parameters has been applied to acquire suitable behaviors in a real environment. Also in most of such studies, a model of human evaluation has been used for validation of learned behaviors. However, since it is very difficult to build human evaluation function and adjust parameters, a system hardly learns behavior intended by a human operator. In order to reach that goal, I first present the two mechanisms on which they rely, namely GAs and Reinforcement Learning (RL). Then I provide a brief history of LCS research intended to highlight the emergence of three families of systems: strength-based LCSs, accuracy-based LCSs, and anticipatory LCSs (ALCSs) but mainly XCS as XCS, is the most studied LCS at this time. Afterward, in section 5, I present some examples of existing LCSs which have LCS applied for robotics. The next sections are dedicated to the particular aspects of theoretical and applied extensions of Intelligent Robotics. Finally, I try to highlight what seem to be the most promising lines of research given the current state of the art, and I conclude with the available resources that can be consulted in order to get a more detailed knowledge of these systems.
JSI Sound - a Machine-Learning Tool in Orange for Simple Biosound Classification

The recent advances in machine learning (ML) and sound processing have considerably expanded the possibilities provided by such techniques. The next step are open-access tools in existing ML toolkits for general sound ML tasks. We present JSI Sound , one of the first tools of this kind, developed within the Orange data mining software. The input for the tool is a series of labelled recordings. The first step allows choosing proper filtering and segmentation of the input sound. In the next step, a series of frequency and time domain features are extracted us-ing extensive feature extraction libraries. Once the features are extracted, the data is ready to be used with the standard ML widgets implemented in Orange. Up to date, we evaluated the tool on five different domains: emotion recognition from speech, chronic heart failure detection form heart sounds, and species classifications from biosounds including bee, bird, and frog species. The evaluation re-sults show high performance on all five domains.
Slot Machine Gambling and Testosterone: Evidence for a “Winner–Loser” Effect?

The “winner–loser effect” refers to a phenomenon in testosterone research where the outcome of a social competition induces increases (wins) and/or decreases (losses) in testosterone levels. Here, we sought to test to what extent changes in testosterone occur in response to gambling behavior. More specifically, we hypothesized that the winner–loser effect would extend to slot machine gambling as a solitary (noncompetitive) gambling activity in players who “anthropomorphized” the slot machine, thus treating the machine as a human opponent. Male participants (n = 113) were recruited into a quasi-experimental design involving 15 min of authentic slot machine gambling, incentivized by a $10 cash bonus for participants who finished in profit. In addition to salivary measures of testosterone, salivary cortisol and self-reported anthropomorphization of the slot machine were tested as potential moderators. Contrary to predictions, winning and losing slot machine sessions did not exert significant differential effects on testosterone, and this pattern was not moderated by cortisol levels or slot machine anthropomorphization. Exploratory analyses tested relationships between subjective gambling experiences in the sessions and testosterone change. Higher positive affect and flow predicted greater testosterone declines from pre- to postgambling. The testosterone results add to a growing literature on the boundary conditions of the winner–loser effect and inform future studies on testosterone reactivity in relation to gambling and disordered gambling. The tendency to anthropomorphize slot machines is a neglected cognitive distortion in gambling that merits further study.
The emergence of online teaching practices: a socio-material analysis

The aim of this article was to analyse relations between online teaching practices and their virtual material arrangements. Two higher education online settings were studied using an online ethnographic approach in which observation of the teaching process was of central importance. The first setting was a course in education carried out on itslearning© (a learning management system) and the second setting was a language course in Second Life® (a virtual world). A socio-material perspective based on practice theory was used in the analysis, and the focal point was the co-constitutive relation between teaching practices and material arrangements in online settings. A number of relations between practice and arrangement were identified and analysed in the results section. It is argued that the relation between online arrangements and practice is not fixed and determined beforehand, but emerges and alters as the teaching unfolds.
Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag)

Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient information and complete blood cell counts (CBC) to identify individuals at elevated risk of colorectal cancer for intensified screening. The purpose of this study was to determine whether ColonFlag is also able to predict the presence of high risk adenomatous polyps at colonoscopy. This study was conducted at a large colon cancer screening center in Calgary, Alberta. The study population included asymptomatic individuals between the ages of 50 and 75 who underwent a screening colonoscopy between January 2013 and June 2015. All subjects had at least one CBC result within the year prior to colonoscopy. Based on age, sex, red blood cell parameters, inflammatory cells and platelets, the ColonFlag algorithm generated a score from 0 to 100. We compared the ability of the ColonFlag test result to discriminate between individuals who were found to have a high risk polyp and those with a normal colonoscopy. Among the 17,676 individuals who underwent a screening colonoscopy there were 1,014 found to have a high risk precancerous lesion (5.7%) and 60 were found to have colorectal cancer (0.3%). At a specificity of 95%, the odds ratio for a positive ColonFlag was 2.0 for those with an advanced precancerous lesion compared with those with a normal colonoscopy. The odds ratio did not vary according to patient subgroup, colorectal cancer location or stage. ColonFlag is a passive test that can use routine blood test results to help identify individuals at elevated risk for high risk precancerous polyps as well as frank colorectal cancer. These individuals may be targeted in an effort to achieve greater compliance with conventional screening tests.
A 1 , 3 = / l = glucanosyltransferase isolated from the cell wall of Aspergillus fumigatus is a homologue of the yeast Bgl 2 p

A 1,3-/l-glucanosyltransferase which introduces intrachain 1,6-/l linkages into 1,3-/l=glucan was isolated from the cell wall of Aspergillus fumigatus. The biochemical and molecular characterization of the A. hnigatus transferase showed it was homologous to the Sacchammyces cerevisiae and Candida albicans transferase BglZp. A null mutant constructed in A. fumigatus by gene replacement did not show a distinct phenotype from the parental strain. The putative function of this major cell-wall-associated protein is discussed.
Control of Interface Defects for Efficient and Stable Quasi‐2D Perovskite Light‐Emitting Diodes Using Nickel Oxide Hole Injection Layer

Metal halide perovskites (MHPs) have emerged as promising materials for light‐emitting diodes owing to their narrow emission spectrum and wide range of color tunability. However, the low exciton binding energy in MHPs leads to a competition between the trap‐mediated nonradiative recombination and the bimolecular radiative recombination. Here, efficient and stable green emissive perovskite light‐emitting diodes (PeLEDs) with an external quantum efficiency of 14.6% are demonstrated through compositional, dimensional, and interfacial modulations of MHPs. The interfacial energetics and optoelectronic properties of the perovskite layer grown on a nickel oxide (NiOx) and poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate hole injection interfaces are investigated. The better interface formed between the NiOx/perovskite layers in terms of lower density of traps/defects, as well as more balanced charge carriers in the perovskite layer leading to high recombination yield of carriers are the main reasons for significantly improved device efficiency, photostability of perovskite, and operational stability of PeLEDs.
Construction of High-Density Genetic Maps and Detection of QTLs Associated With Huanglongbing Tolerance in Citrus

Huanglongbing (HLB), or citrus greening, is the most devastating disease in citrus worldwide. Commercial citrus varieties including sweet orange (Citrus sinensis) are highly susceptible to HLB, and trifoliate orange (Poncirus trifoliata, a close Citrus relative) is widely considered resistant or highly tolerant to HLB. In this study, an intergeneric F1 population of sweet orange and trifoliate orange was genotyped by Genotyping-by-Sequencing, and high-density SNP-based genetic maps were constructed separately for trifoliate orange and sweet orange. The two genetic maps exhibited high synteny and high coverage of the citrus genome. Progenies of the F1 population and their parents were planted in a replicated field trial, exposed to intense HLB pressure for 3 years, and then evaluated for susceptibility to HLB over 2 years. The F1 population exhibited a wide range in severity of HLB foliar symptom and canopy damage. Genome-wide QTL analysis based on the phenotypic data of foliar symptom and canopy damage in 2 years identified three clusters of repeatable QTLs in trifoliate orange linkage groups LG-t6, LG-t8 and LG-t9. Co-localization of QTLs for two traits was observed within all three regions. Additionally, one cluster of QTLs in sweet orange (linkage group LG-s7) was also detected. The majority of the identified QTLs each explained 18–30% of the phenotypic variation, indicating their major role in determining HLB responses. These results show, for the first time, a quantitative genetic nature yet the presence of major loci for the HLB tolerance in trifoliate orange. The results suggest that sweet orange also contains useful genetic factor(s) for improving HLB tolerance in commercial citrus varieties. Findings from this study should be very valuable and timely to researchers worldwide as they are hastily searching for genetic solutions to the devastating HLB crisis through breeding, genetic engineering, or genome editing.