Miracle-dz/UniFAR-FTUs huggingface dataset:
The lived experiences of radiographers in Gauteng

Purpose: Evidence suggests that there is a shortage of radiographers globally. This has a detrimental effect on service delivery and patient care received. There has been a call for research to be conducted in the radiography setting in order to address staff retention in the profession. Objective: To explore and describe the lived experiences of radiographers in a Gauteng setting. Method: A qualitative, exploratory, descriptive and contextual design was utilised. A purposive sample consisted of diagnostic radiographers in Gauteng, some practicing and others that have left the profession completely. A qualitative research paradigm was used, using focus group interviews to collect data until data saturation was reached. Nineteen (n= 19) radiographers participated in the focus group interviews. Results: Three themes emerged from the data: experiences that facilitated a positive radiographic experience; experiences that inhibit a positive radiographic experience, and factors that enhance a positive radiographic experience. Conclusion: The study revealed that the participants (radiographers) in Gauteng are committed to providing excellent patient care and that they value positive interpersonal relationships with their colleagues. They experienced a lack of professional recognition and believe the radiographic profession is plagued by stagnation. The results further revealed that the participants were willing to work towards creating a positive self-image and were committed to introducing more autonomy into the profession.
A Neural Approach to Discourse Relation Signal Detection.

Previous data-driven work investigating the types and distributions of discourse relation signals, including discourse markers such as 'however' or phrases such as 'as a result' has focused on the relative frequencies of signal words within and outside text from each discourse relation. Such approaches do not allow us to quantify the signaling strength of individual instances of a signal on a scale (e.g. more or less discourse-relevant instances of 'and'), to assess the distribution of ambiguity for signals, or to identify words that hinder discourse relation identification in context ('anti-signals' or 'distractors'). In this paper we present a data-driven approach to signal detection using a distantly supervised neural network and develop a metric, Delta s (or 'delta-softmax'), to quantify signaling strength. Ranging between -1 and 1 and relying on recent advances in contextualized words embeddings, the metric represents each word's positive or negative contribution to the identifiability of a relation in specific instances in context. Based on an English corpus annotated for discourse relations using Rhetorical Structure Theory and signal type annotations anchored to specific tokens, our analysis examines the reliability of the metric, the places where it overlaps with and differs from human judgments, and the implications for identifying features that neural models may need in order to perform better on automatic discourse relation classification.
Copy-number variants as modulators of common disease susceptibility

Copy-number variations (CNVs) have been associated with rare and debilitating genomic syndromes but their impact on health later in life in the general population remains poorly described. Assessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white UK Biobank participants with replication in the Estonian Biobank. We identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. Even after correcting for these signals, a higher CNV burden increased risk for 18 disorders, mainly through the number of deleted genes, suggesting a polygenic CNV architecture. Number and identity of genes disturbed by CNVs affected their pathogenicity, with many associations being supported by colocalization with both common and rare single nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacts renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Together, our results shed light on the prominent role of CNVs in determining common disease susceptibility within the general population and provide actionable insights allowing to anticipate later-onset comorbidities in carriers of recurrent CNVs
Elastic Far-Field Decay from Dislocations in Multilattices

We precisely and rigorously characterise the decay of elastic fields generated by dislocations in crystalline materials, focusing specifically on the role of multilattices. Concretely, we establish that the elastic field generated by a dislocation in a multilattice can be decomposed into a continuum field predicted by a linearised Cauchy-Born elasticity theory, and a discrete and nonlinear core corrector representing the defect core. We demonstrate both analytically and numerically the consequences of this result for cell size effects in numerical simulations.
Phylogenies increase power to detect highly transmissible viral genome variants

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a significant threat to global health. Viral genome sequences, combined with statistical models of sequence evolution, may provide a critical tool for early detection of these strains. Using a novel statistical model that links transmission rates to the entire viral genome sequence, we study the power of phylogenetic methods---using a phylogenetic tree relating viral samples---and count-based methods---using case-counts of variants over time---to detect increased transmission rates, and to identify causative mutations. We find that phylogenies in particular can detect novel variants very soon after their origin, and may facilitate the development of early detection systems for outbreak surveillance.
Nanostructures: a platform for brain repair and augmentation

Nanoscale structures have been at the core of research efforts dealing with integration of nanotechnology into novel electronic devices for the last decade. Because the size of nanomaterials is of the same order of magnitude as biomolecules, these materials are valuable tools for nanoscale manipulation in a broad range of neurobiological systems. For instance, the unique electrical and optical properties of nanowires, nanotubes, and nanocables with vertical orientation, assembled in nanoscale arrays, have been used in many device applications such as sensors that hold the potential to augment brain functions. However, the challenge in creating nanowires/nanotubes or nanocables array-based sensors lies in making individual electrical connections fitting both the features of the brain and of the nanostructures. This review discusses two of the most important applications of nanostructures in neuroscience. First, the current approaches to create nanowires and nanocable structures are reviewed to critically evaluate their potential for developing unique nanostructure based sensors to improve recording and device performance to reduce noise and the detrimental effect of the interface on the tissue. Second, the implementation of nanomaterials in neurobiological and medical applications will be considered from the brain augmentation perspective. Novel applications for diagnosis and treatment of brain diseases such as multiple sclerosis, meningitis, stroke, epilepsy, Alzheimer's disease, schizophrenia, and autism will be considered. Because the blood brain barrier (BBB) has a defensive mechanism in preventing nanomaterials arrival to the brain, various strategies to help them to pass through the BBB will be discussed. Finally, the implementation of nanomaterials in neurobiological applications is addressed from the brain repair/augmentation perspective. These nanostructures at the interface between nanotechnology and neuroscience will play a pivotal role not only in addressing the multitude of brain disorders but also to repair or augment brain functions.
Development of a silver nanoparticle ink for fine line patterning using gravure offset printing

A newly developed silver nanoparticle ink for gravure offset printing has been prepared through the thermal decomposition reaction of silver oxalate. The resistivity of silver thin films made of the ink was estimated to be 3.1 µΩ cm by sintering at 250 °C, which is comparable to that of bulk silver. In addition, we achieved the patterning of fine lines with a width of 20 µm using the combination of the ink and gravure offset printing. The prepared ink could be used to fabricate various printed electronic circuits in the near future.
A Generalized Algorithm for Multi-Objective RL and Policy Adaptation

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple competing objectives whose relative importance (preferences) is unknown to the agent. While this alleviates dependence on scalar reward design, the expected return of a policy can change significantly with varying preferences, making it challenging to learn a single model to produce optimal policies under different preference conditions. We propose a generalized version of the Bellman equation to learn a single parametric representation for optimal policies over the space of all possible preferences. After this initial learning phase, our agent can quickly adapt to any given preference, or automatically infer an underlying preference with very few samples. Experiments across four different domains demonstrate the effectiveness of our approach.1
Automated synthesis of normative systems

Normative systems (norms) have been widely proposed as a technique for coordinating multi-agent systems. The automated synthesis of norms for coordination remains an open and complex problem, which we tackle in this paper. We propose a novel mechanism called IRON (Intelligent Robust On-line Norm synthesis mechanism), for the on-line synthesis of norms. IRON aims to synthesise conflict-free norms without lapsing into over-regulation. Thus, IRON produces norms that characterise necessary conditions for coordination, without over-regulation. In addition to defining the norm synthesis problem formally, we empirically show that IRON is capable of synthesising norms that are effective even in the presence of non-compliance behaviours in a system. Source URL: https://www.iiia. csic.es/en/node/54481 Links [1] https://www.iiia. csic.es/en/staff/javier-morales [2] https://www.iiia. csic.es/en/staff/maite-l%C3%B3pez-s%C3%A1nchez [3] https://www.iiia. csic.es/en/staff/juan-rodr%C3%ADguez-aguilar [4] https://www.iiia. csic.es/en/staff/michael-wooldridge [5] https://www.iiia. csic.es/en/staff/wamberto-w-vasconcelos [6] https://www.iiia. csic.es/en/bibliography? f[keyword]=562 [7] https://www.iiia. csic.es/en/bibliography? f[keyword]=444 [8] https://www.iiia. csic.es/en/bibliography? f[keyword]=499
PRINCIPAL CONGRUENCES OF PSEUDOCOMPLEMENTED

Principal congruences of pseudocomplemented dis- tributive lattices are characterized. This characterization is used to give a new proof of the congruence extension property and to show that the meet of two principal congruences is always principal in an equational class of pseudocomplemented distributive lattices if and only if the class is a subclass of the class of Stone algebras.
DClaims: A Censorship Resistant Web Annotations System

The web plays a critical role in informing modern democracies. A study [1] by the Reuters Institute for the Study of Journalism at the University of Oxford, conducted in 2016, reveals that the Internet makes 53% to 84% of the primary sources of information used by UK citizens aged 18-44. Unfortunately, we have become more aware of often lack of credibility, falsehood and incompleteness of that very same information – which can have serious consequences. Democracies are fed by public opinion which in turn is shaped by the information individuals receive. Campaigns of disinformation have the power to start wars [2], influence government elections [3], endanger human lives [4] and even jeopardise the future of the human species [5]. Therefore, it is essential to allow people to have access to reliable sources of information – to the point of the identification and classification of low-quality information has become one of the most active areas of research[6], [7], [8], [9]. There have been several attempts to improve the reliability of information on the Web. Fact-checking platforms and social networks are two. The problem with these platforms is that they too can be censored. There are multiple instances of state censorship of information. There is evidence of efforts by governments of at least 13 countries (including China, Egypt, Turkey, United Kingdom, Russia and France) to either block access to these platforms or censor usergenerated content [10], [11]. Google, Facebook and Twitter publish annual reports of the number of government requests of remove content 1 2 3. Besides being vulnerable to censorship, the information created by these platforms is scattered across multiple profile pages and websites. An alternative way to achieve the goal of informing would be to display the information directly on the source webpage. Web annotations4 is a new way of interacting with information on the web, which allows for users to make and share annotations of web pages, just like they would annotate a physical notepad. It empowers endusers to highlight text, create sticky notes or comment specific parts of a web page. A typical usage scenario consists of a user visiting a news webpage article which shows portions of
Automated extraction of 3D vector topographic feature line from terrain point cloud

Abstract This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm.
Visual-Semantic Graph Attention Network for Human-Object Interaction Detection

In scene understanding, machines benefit from not only detecting individual scene instances but also from learning their possible interactions. Human-Object Interaction (HOI) Detection tries to infer the predicate on atriplet. Contextual information has been found critical in inferring interactions. However, most works use features from single object instances that have a direct relation with the subject. Few works have studied the disambiguating contribution of subsidiary relations in addition to how attention might leverage them for inference. We contribute a dual-graph attention network that aggregates contextual visual, spatial, and semantic information dynamically for primary subject-object relations as well as subsidiary relations. Graph attention networks dynamically leverage node neighborhood information. Our network uses attention to first leverage visual-spatial and semantic cues from primary and subsidiary relations independently and then combines them before a final readout step. Our network learns to use primary and subsidiary relations to improve inference: encouraging the right interpretations and discouraging incorrect ones. We call our model: Visual-Semantic Graph Attention Networks (VS-GATs). We surpass state-of-the-art HOI detection mAPs in the challenging HICO-DET dataset, including in long-tail cases that are harder to interpret. Code, video, and supplementary information is available at http://www.juanrojas. net/VSGAT.
M3F: Multi-Modal Continuous Valence-Arousal Estimation in the Wild

In this paper, we propose a multi-modal multi-feature (M$^{3}F$) approach for in-the-wild valence-arousal estimation. In the proposed M$^{3}F$ framework, we fuse both visual features from videos and acoustic features from the audio tracks to estimate the valence and arousal. We follow a CNN-RNN paradigm, where the spatio-temporal visual features are extracted with a 3D convolutional network and/or a pretrained 2D convolutional network, and a bidirectional recurrent neural network. We evaluated the M$^{3}F$ framework on the validation set provided by the Affective Behavior Analysis in-the-wild (ABAW) Challenge, held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020, and it significantly outperforms the baseline method.
Necessary and Sufficient Stability Criterion and Stabilization for Positive 2‐D Continuous‐Time Systems with Multiple Delays

This paper addresses the stability and control problem of the linear positive two‐dimensional (2‐D) continuous‐time systems in Roesser model with multiple time delays. The contribution lies in two aspects. First, a simple novel proof is provided to establish necessary and sufficient conditions of asymptotic stability for 2‐D continuous delayed systems. It turns out that the magnitude of delays has no any impact on the stability of these systems, which is completely determined by the system matrices. Second, a necessary and sufficient condition for the existence of state‐feedback controllers is proposed for general delayed 2‐D systems, which ensures the non‐negativity and the stability of the resulting closed‐loop systems. Two examples are given to validate the proposed methods.
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

Online real-time bidding (RTB) is known as a complex auction game where ad platforms seek to consider various influential key performance indicators (KPIs), like revenue and return on investment (ROI). The trade-off among these competing goals needs to be balanced on a massive scale. To address the problem, we propose a multi-objective reinforcement learning algorithm, named MoTiAC, for the problem of bidding optimization with various goals. Specifically, in MoTiAC, instead of using a fixed and linear combination of multiple objectives, we compute adaptive weights overtime on the basis of how well the current state agrees with the agent's prior. In addition, we provide interesting properties of model updating and further prove that Pareto optimality could be guaranteed. We demonstrate the effectiveness of our method on a real-world commercial dataset. Experiments show that the model outperforms all state-of-the-art baselines.
Recurrent Instance Segmentation using Sequences of Referring expressions (Supplementary Material)

The goal of visual grounding with referring expressions is to localize specific objects in an image by means of a natural language description of each object. Most existing approaches rely on scored bounding box proposals to determine the correct localization region with a bounding box. To obtain a more precise result, instance segmentation was proposed, which produces segmentation masks for the described objects.
Enhanced Performance in Trajectory Tracking of a Ball and Plate System using Fractional Order Controller

ABSTRACT This paper presents a comparative analysis of performance between integer order proportional derivative (IOPD) and fractional order proportional derivative (FOPD) controllers for the trajectory tracking of a ball in ball and plate system. The controllers are designed minimising integral square error using Bat Algorithm, Gravitational Search Algorithm, and Firefly Algorithm. Effectiveness of the controllers is tested by the ability of the ball to track a circular, square, cardioid, and star shaped trajectories. Results verify that FOPD outperforms IOPD when tuned by the same algorithm. Comparisons with reported works show that the proposed controllers provide fairly improved results.
Bidirectional Recurrent Neural Network Language Model: Cross Entropy Churn Metrics for Defect Prediction Modelling

Software Defect Prediction (SDP) plays an active area in many research domain of Software Quality of Assurance (SQA). Many existing research studies are based on software traditional metric sets and defect prediction models are built in machine language to detect the bug for limited source code line. Inspired by the above existing system. In this paper, defect prediction is focused on predicting defects in source code. The aim of this dissertation is to enhance the quality of the software for precise prediction of defects. So, that it helps the developer to find the bug and fix the issue, to make better use of a resource which reduces the test effort, minimize the cost and improve the quality of software. A new approach is introduced to improve the prediction performance of Bidirectional RNNLM in Deep Neural Network. To build the defect prediction model a defect learner framework is proposed and first it need to build a Neural Language Model. Using this Language Model it helps to learn to deep semantic features in source code and it train & test the model. Based on language model it combined with software traditional metric sets to measure the code and find the defect. The probability of language model and metric set Cross-Entropy with Abstract Syntax Tree (CE-AST) metric is used to evaluate the defect proneness and set as a metric label. For classification the metric label K-NN classifier is used. BPTT algorithm for learning RNN will provide additional improvement, it improves the predictions performance to find the dynamic error.
CCID_A_281364 973..986

1PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People’s Republic of China; 2Second Orthopedic Ward, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People’s Republic of China; 3Graduate School, Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 4Department of Forensic Medicine, Kunming Medical University, Kunming, Yunnan, People’s Republic of China
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

This section provides insight for why existing methods do not encourage the discovery state-covering skills from a theoretical lens. This is achieved by analyzing the reward function of these methods, and studying its asymptotic behavior for known and novel states. Our main result shows that the agent receives larger rewards for visiting known states than discovering new ones. The following subsections contain the derivation of this result, and Figure 6 provides a numerical example on a gridworld environment.