Miracle-dz/UniFAR-FTUs huggingface dataset:
Agreement Between Occupational Therapists and Clients with Stroke on Three Outcome Measures

In recent years, the occupational therapy profession has been under increased pressure to document the outcomes of interventions in order 10 improve the quality of client care and to provide evidence of the effectiveness and efficiency of services to recipients and sponsors. The literature to date has focused primarily on establishing therapy outcomes from a therapist perspective. However, attention must also be paid to the views of our consumers regarding the outcomes of therapy. The purpose of this study was to determine whether clients and therapists agree regarding client scores on three outcome measures. The study included 4 occupational therapists and 20 clients with stroke who rated clients' status in terms of personal activities of daily living, instrumental activities of daily living and quality of life, at admission of clients as well as discharge from rehabilitation. The results indicated that there was generally only moderate agreement between therapists' and clients' ratings at both admission.
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare

Distributed representations have been used to support downstream tasks in healthcare recently. Healthcare data (e.g., electronic health records) contain multiple modalities of data from heterogeneous sources that can provide complementary information, alongside an added dimension to learning personalized patient representations. To this end, in this paper we propose a novel unsupervised encoder-decoder model, namely Mixed Pooling Multi-View Attention Autoencoder (MPVAA), that generates patient representations encapsulating a holistic view of their medical profile. Specifically, by first learning personalized graph embeddings pertaining to each patient's heterogeneous healthcare data, it then integrates the non-linear relationships among them into a unified representation through multi-view attention mechanism. Additionally, a mixed pooling strategy is incorporated in the encoding step to learn diverse information specific to each data modality. Experiments conducted for multiple tasks demonstrate the effectiveness of the proposed model over the state-of-the-art representation learning methods in healthcare.
Geometric flow description of minimal surfaces

We introduce a description of a minimal surface in a space with boundary, as the world hypersurface that the entangling surface traces. It does so by evolving from the boundary to the interior of the bulk under an appropriate geometric flow, whose parameter is the holographic coordinate. We specify this geometric flow for arbitrary bulk geometry. In the case of pure AdS spaces, we implement a perturbative approach for the solution of the flow equation around the boundary. We systematically study both the form of the perturbative solution as well as its dependence on the boundary conditions. This expansion is sufficient for the determination of all the divergent terms of the holographic entanglement entropy, including the logarithmic universal terms in odd spacetime bulk dimensions, for an arbitrary entangling surface, in terms of the extrinsic geometry of the latter.
Resolved observations at 31 GHz of spinning dust emissivity variations in ρ Oph

The ρ Oph molecular cloud is one of the best examples of spinning dust emission, first detected by the cosmic background imager (CBI). Here, we present 4.5 arcmin observations with CBI 2 that confirm 31 GHz emission from ρ Oph W, the PDR exposed to B-type star HD 147889, and highlight the absence of signal from S1, the brightest IR nebula in the complex. In order to quantify an association with dust-related emission mechanisms, we calculated correlations at different angular resolutions between the 31 GHz map and proxies for the column density of IR emitters, dust radiance, and optical depth templates. We found that the 31 GHz emission correlates best with the PAH column density tracers, while the correlation with the dust radiance improves when considering emission that is more extended (from the shorter baselines), suggesting that the angular resolution of the observations affects the correlation results. A proxy for the spinning dust emissivity reveals large variations within the complex, with a dynamic range of 25 at 3σ and a variation by a factor of at least 23, at 3σ, between the peak in ρ Oph W and the location of S1, which means that environmental factors are responsible for boosting spinning dust emissivities locally.
PreProFit: Pressure Profile Fitter for galaxy clusters

Galaxy cluster analyses based on high-resolution observations of the Sunyaev–Zeldovich (SZ) effect have become common in the last decade. We present PreProFit, the first publicly available code designed to fit the pressure profile of galaxy clusters from SZ data. PreProFit is based on a Bayesian forward-modelling approach, allows the analysis of data coming from different sources, adopts a flexible parametrization for the pressure profile, and fits the model to the data accounting for Abel integral, beam smearing, and transfer function filtering. PreProFit is computationally efficient, is extensively documented, has been released as an open source Python project, and was developed to be part of a joint analysis of X-ray and SZ data on galaxy clusters. PreProFit returns χ2, model parameters and uncertainties, marginal and joint probability contours, diagnostic plots, and surface brightness radial profiles. PreProFit also allows the use of analytic approximations for the beam and transfer functions useful for feasibility studies.
Man-in-the-Middle Attacks Against Machine Learning Classifiers Via Malicious Generative Models

Deep Neural Networks (DNNs) are vulnerable to deliberately crafted adversarial examples. In the past few years, many efforts have been spent on exploring query-optimisation attacks to find adversarial examples of either black-box or white-box DNN models, as well as the defending countermeasures against those attacks. In this article, we explore vulnerabilities of DNN models under the umbrella of Man-in-the-Middle (MitM) attacks, which have not been investigated before. From the perspective of an MitM adversary, the aforementioned adversarial example attacks are not viable anymore. First, such attacks must acquire the outputs from the models multiple times before actually launching attacks, which is difficult for the MitM adversary in practice. Second, such attacks are one-off and cannot be directly generalised onto new data examples, which decreases the rate of return for the attacker. In contrast, using generative models to craft adversarial examples on the fly can mitigate the drawbacks. However, the adversarial capability of the generative models, such as Variational Auto-Encoder (VAE), has not been extensively studied. Therefore, given a classifier, we investigate using a VAE decoder to either transform benign inputs to their adversarial counterparts or decode outputs from benign VAE encoders to be adversarial examples. The proposed method can endue more capability to MitM attackers. Based on our evaluation, the proposed attack can achieve above 95 percent success rates on both MNIST and CIFAR10 datasets, which is better or comparable with state-of-the-art query-optimisation attacks. In the meantime, the attack is <inline-formula><tex-math notation="LaTeX">$10^4$</tex-math><alternatives><mml:math><mml:msup><mml:mn>10</mml:mn><mml:mn>4</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="wang-ieq1-3021008.gif"/></alternatives></inline-formula> times faster than the query-optimisation attacks.
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning

Federated learning is a key scenario in modern large-scale machine learning. In that scenario, the training data remains distributed over a large number of clients, which may be phones, other mobile devices, or network sensors and a centralized model is learned without ever transmitting client data over the network. The standard optimization algorithm used in this scenario is Federated Averaging (FedAvg). However, when client data is heterogeneous, which is typical in applications, FedAvg does not admit a favorable convergence guarantee. This is because local updates on clients can drift apart, which also explains the slow convergence and hard-to-tune nature of FedAvg in practice. This paper presents a new Stochastic Controlled Averaging algorithm (SCAFFOLD) which uses control variates to reduce the drift between different clients. We prove that the algorithm requires significantly fewer rounds of communication and benefits from favorable convergence guarantees.
Cascading Machine Learning to Attack Bitcoin Anonymity

Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network. In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation. Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities.
BACKUS: Comprehensive High-Performance Research Software Engineering Approach for Simulations in Supercomputing Systems

High-Performance Computing (HPC) platforms enable scientific software to achieve breakthroughs in many research fields such as physics, biology, and chemistry, by employing Research Software Engineering (RSE) techniques. These include 1) novel parallelism paradigms such as Shared Memory Parallelism (with e.g. OpenMP 4.5); Distributed Memory Parallelism (with e.g. MPI 4); Hybrid Parallelism which combines them; and Heterogeneous Parallelism (for CPUs, co-processors and accelerators), 2) introducing advanced Software Engineering concepts such as Object Oriented Parallel Programming (OOPP); Parallel Unit testing; Parallel I/O Formats; Hybrid Parallel Visualization; and 3) Selecting the Best Practices in other necessary areas such as User Interface; Automatic Documentation; Version Control and Project Management. In this work we present BACKUS: Comprehensive High-Performance Research Software Engineering Approach for Simulations in Supercomputing Systems, which we found to fit best for long-lived parallel scientific codes.
Integrating Temporal and Spatial Attentions for VATEX Video Captioning Challenge 2019

This notebook paper presents our model in the VATEX video captioning challenge. In order to capture multi-level aspects in the video, we propose to integrate both temporal and spatial attentions for video captioning. The temporal attentive module focuses on global action movements while spatial attentive module enables to describe more fine-grained objects. Considering these two types of attentive modules are complementary, we thus fuse them via a late fusion strategy. The proposed model significantly outperforms baselines and achieves 73.4 CIDEr score on the testing set which ranks the second place at the VATEX video captioning challenge leaderboard 2019.
Hysterectomy at a tertiary hospital in South Africa : indications, pathology and complications

This study is a retrospective audit of women undergoing a hysterectomy at Groote Schuur Hospital, Cape Town between 1 January 2007 and 31 December 2007. Objectives: To review the relationship between the clinical indication for hysterectomy and the histopathological findings and to assess the rate of complications following hysterectomy. Results: Three hundred and forty elective hysterectomies were carried out in 2007 at Groote Schuur Hospital. Case notes were obtained for 335 patients. Total abdominal hysterectomy was the most common type of hysterectomy and was performed in 218 out of 335 (65.1%) surgeries. Vaginal hysterectomies accounted for 19.4% (n = 65), subtotal hysterectomies for 9.5% (n = 32), Wertheims hysterectomies for 4.5% (n = 15) and laparoscopic-assisted hysterectomies for 1.5% (n = 5) of all the other surgical procedures for hysterectomy. The most common indication for hysterectomy was menorrhagia secondary to fibroids (23% of women), followed by abnormal bleeding in 14.9% of the patients. Endometrial cancer was the indication in 13.4%, prolapse in 11.3% and cervical cancer in 10.7% of the women undergoing hysterectomy. Fibroids were found on histopathological assessment in 62.8% of women, but were judged to be the pathology responsible for the symptoms leading to consultation in only 35.3% of the patients. There were significantly more symptomatic fibroids among the black African women than any other population group (p<0.001). In 14.3% of hysterectomies no cause for the symptoms could be identified by histopathological examination. Of the 43 women with abnormal menstrual bleeding, no causative pathology could be identified in 11 of them (23%). Un ive rsi ty of Ca pe To wn
Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation

Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain. This is particularly the case for dialogue systems, where we want to be able to seamlessly include new domains into the conversation. Therefore, it is crucial for generation models to share knowledge across domains for the effective adaptation from one domain to another. In this study, we exploit a tree-structured semantic encoder to capture the internal structure of complex semantic representations required for multi-domain dialogues in order to facilitate knowledge sharing across domains. In addition, a layer-wise attention mechanism between the tree encoder and the decoder is adopted to further improve the model’s capability. The automatic evaluation results show that our model outperforms previous methods in terms of the BLEU score and the slot error rate, in particular when the adaptation data is limited. In subjective evaluation, human judges tend to prefer the sentences generated by our model, rating them more highly on informativeness and naturalness than other systems.
Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light

We propose the concept of a multi-frame GAN (MFGAN) and demonstrate its potential as an image sequence enhancement for stereo visual odometry in low light conditions. We base our method on an invertible adversarial network to transfer the beneficial features of brightly illuminated scenes to the sequence in poor illumination without costly paired datasets. In order to preserve the coherent geometric cues for the translated sequence, we present a novel network architecture as well as a novel loss term combining temporal and stereo consistencies based on optical flow estimation. We demonstrate that the enhanced sequences improve the performance of state-of-the-art feature-based and direct stereo visual odometry methods on both synthetic and real datasets in challenging illumination. We also show that MFGAN outperforms other state-of-the-art image enhancement and style transfer methods by a large margin in terms of visual odometry.
A Deep Learning Based Chatbot for Campus Psychological Therapy

In this paper, we propose Evebot, an innovative, sequence to sequence (Seq2seq) based, fully generative conversational system for the diagnosis of negative emotions and prevention of depression through positively suggestive responses. The system consists of an assembly of deep-learning based models, including Bi-LSTM based model for detecting negative emotions of users and obtaining psychological counselling related corpus for training the chatbot, anti-language sequence to sequence neural network, and maximum mutual information (MMI) model. As adolescents are reluctant to show their negative emotions in physical interaction, traditional methods of emotion analysis and comforting methods may not work. Therefore, this system puts emphasis on using virtual platform to detect signs of depression or anxiety, channel adolescents' stress and mood, and thus prevent the emergence of mental illness. We launched the integrated chatbot system onto an online platform for real-world campus applications. Through a one-month user study, we observe better results in the increase in positivity than other public chatbots in the control group.
Distilled embedding: non-linear embedding factorization using knowledge distillation

Word-embeddings are a vital component of Natural Language Processing (NLP) systems and have been extensively researched. Better representations of words have come at the cost of huge memory footprints, which has made deploying NLP models on edge-devices challenging due to memory limitations. Compressing embedding matrices without sacrificing model performance is essential for successful commercial edge deployment. In this paper, we propose Distilled Embedding, an (input/output) embedding compression method based on low-rank matrix decomposition with an added non-linearity. First, we initialize the weights of our decomposition by learning to reconstruct the full word-embedding and then fine-tune on the downstream task employing knowledge distillation on the factorized embedding. We conduct extensive experimentation with various compression rates on machine translation, using different data-sets with a shared word-embedding matrix for both embedding and vocabulary projection matrices. We show that the proposed technique outperforms conventional low-rank matrix factorization, and other recently proposed word-embedding matrix compression methods.
Cascaded Multicycle Terahertz-Driven Ultrafast Electron Acceleration and Manipulation

Terahertz (THz)-based electron acceleration and manipulation has recently been shown to be feasible and to hold tremendous promise as a technology for the development of next-generation, compact electron sources. Previous work has concentrated on structures powered transversely by short, single-cycle THz pulses, with mm-scale, segmented interaction regions that are ideal for acceleration of electrons in the sub- to few-MeV range where electron velocities vary significantly. However, in order to extend this technology to the multi-MeV range, investigation of approaches supporting longer interaction lengths is needed. Here, we demonstrate first steps in electron acceleration and manipulation using dielectrically-lined waveguides powered by temporally long, narrowband, multi-cycle THz pulses that co-propagate with the electrons. This geometry offers centimeter-scale single-stage interaction lengths and offers the opportunity to further increase interaction lengths by cascading acceleration stages that recycle the THz energy and rephase the interaction. We prove the feasibility of THz-energy recycling for the first time by demonstrating acceleration, compression and focusing in two sequential Al2O3-based dielectric capillary stages powered by the same multi-cycle THz pulse. Since the multi-cycle energy achievable using laser-based sources is currently a limiting factor for the maximum electron acceleration, THz energy recycling provides a key enabling factor for reaching relativistic energies with existing sources.
Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

Hybrid analog/digital processing is crucial for millimeter-wave (mmWave) MIMO systems due to its ability to balance the gain and cost. Despite fruitful recent studies, the optimal beamforming/combining method remains unknown for a practical multiuser, broadband mmWave MIMO equipped with low-resolution phase shifters and low-resolution analog-to-digital converters (ADCs). In this paper, we leverage artificial intelligence techniques to tackle this problem. Particularly, we propose a neural hybrid beamforming/combining (NHB) MIMO system, where the various types of hybrid analog/digital mmWave MIMO systems are transformed into a corresponding autoencoder (AE) based neural networks. Consequently, the digital and analog beamforming/combiners are obtained by training the AE based new model in an unsupervised learning manner, regardless of particular configurations. Using this approach, we can apply a machine learning-based design methodology that is compatible with a range of different beamforming/combing architectures. We also propose an iterative training strategy for the neural network parameter updating, which can effectively guarantee fast convergence of the established NHB model. According to simulation results, the proposed NHB can offer a significant performance gain over existing methods in terms of bit error rate (BER). Moreover, NHB can fast formulate the neural network parameters as channel changed, which is believed more promising for practice due to its better flexibility and compatibility.
Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey

Massive machine-type communications (mMTC) is one of the main three focus areas in the 5th generation (5G) of wireless communications technologies to enable connectivity of a massive number of Internet of things (IoT) devices with little or no human intervention. In conventional human-type communications (HTC), due to the limited number of available channel resources and orthogonal resource allocation techniques, users get a transmission slot by making scheduling/connection requests. The involved control channel signaling, negligible with respect to the huge transmit data, is not a major issue. However, this may turn into a potential performance bottleneck in mMTC, where huge number of devices transmit short packet data in a sporadic way. To tackle the limited radio resources and massive connectivity challenges, non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows multiple users to simultaneously transmit their data over the same channel resource. This is achieved by employing user-specific signature sequences at the transmitting devices, which are exploited by the receiver for multi-user data detection. Due to its massive connectivity potential, NOMA has also been considered to enable grant-free transmissions especially in mMTC, where devices can transmit their data whenever they need without the scheduling requests. The existing surveys majorly discuss different NOMA schemes, and exploit their potential, in typical grant-based HTC scenarios, where users are connected with the base station, and various system parameters are pre-defined in the scheduling phase. Different from these works, this survey provides a comprehensive review of the recent advances in NOMA from a grant-free connectivity perspective. Various grant-free NOMA schemes are presented, their potential and related practical challenges are highlighted, and possible future directions are thoroughly discussed at the end.