Miracle-dz/UniFAR-FTUs huggingface dataset:
Labella

Labella is an augmented system that employs non-traditional on-body interactions and explores the potential of using a mobile device as an interface to discover and learn about hidden parts of the body (figure 1). The system is designed to support pelvic fitness in women. It blends wearable and mobile technologies with modern lifestyle by combining a pair of underwear for embodied playful interaction, and a mobile phone as a tool for embodied discovery. The mobile app connects to the female body through the recognition of bespoke printed markers on specialist underwear (figure 2). We explore AR as a tool to help reveal the body in a playful way. We designed a system that aims to provide an enhanced experience to women in gaining awareness of their pelvic floor and to promote pelvic fitness. We use humour to address this topic of taboo and explore how embodied technology can contribute to make learning more accessible. By augmenting perception, our altered reality device makes visible the inside out (figure 3). It also shows potential to enhance engagement in intimate care practices.
AverageExplorer

This paper proposes an interactive framework that allows a user to rapidly explore and visualize a large image collection using the medium of average images. Average images have been gaining popularity as means of artistic expression and data visualization, but the creation of compelling examples is a surprisingly laborious and manual process. Our interactive, real-time system provides a way to summarize large amounts of visual data by weighted average(s) of an image collection, with the weights reflecting user-indicated importance. The aim is to capture not just the mean of the distribution, but a set of modes discovered via interactive exploration. We pose this exploration in terms of a user interactively "editing" the average image using various types of strokes, brushes and warps, similar to a normal image editor, with each user interaction providing a new constraint to update the average. New weighted averages can be spawned and edited either individually or jointly. Together, these tools allow the user to simultaneously perform two fundamental operations on visual data: user-guided clustering and user-guided alignment, within the same framework. We show that our system is useful for various computer vision and graphics applications.
Long-Term Demographic and Spatio-Temporal Trends of Indo-Pacific Bottlenose Dolphin (Tursiops aduncus) Bycatch in Bather Protection Nets off KwaZulu-Natal, South Africa

Bather protection nets have been in place off the coast of KwaZulu-Natal (KZN), South Africa, since the 1950’s. Besides sharks, they also catch a number of other marine vertebrates, including dolphins, the majority of which are Indo-Pacific bottlenose dolphins Tursiops aduncus. Previous analyses of dolphin bycatch in the nets indicated the potential impacts on the local populations, but a lack of information on population structure has to-date hindered a more detailed assessment. A recent re-assessment of the status and population delineations of T. aduncus off South Africa prompted a re-examination of demographic, spatial, and temporal patterns of its catches in bather protection nets over a 36-year period (January 1980 to December 2015). In total, 1169 dolphins were caught, including a slightly greater number of females and juveniles than other sex-classes, raising concern about the potential long-term effects on population demographics. More dolphins were caught off the North than the South coast. Temporal trends indicate that the bycatch during June and July every year (peak Sardine Run period) as a percentage of overall dolphin bycatch has been steadily decreasing from 39.8% in 1980 to 13% in 2015. A large inter-annual fluctuation can be seen, probably as a result of the inter-annual intensity of the Sardine Run. Although our results do not indicate a long-term decline of the individual populations as previously predicted, revised abundance estimates and data on the demographics and movement patterns of the animals are urgently required to accurately assess the impact of bycatch on the Indo-Pacific bottlenose dolphin populations off KZN and to advise conservation and management decisions going forward.
Negative pressure rooms and COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes 2019 novel coronavirus disease (COVID-19), has rapidly developed into a global pandemic and public health emergency. The transmission and virulence of this new pathogen have raised concern for how best to protect healthcare professionals while effectively providing care to the infected patient requiring surgery. Although negative pressure rooms are ideal for aerosol-generating procedures, such as intubation and extubation, most operating theatres are generally maintained at a positive pressure when compared with the surrounding areas. This article compares negative and positive pressure rooms and the advantages of a negative pressure environment in optimising clinical care and minimising the exposure of patients and health care professionals to SARS-CoV-2.
The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders

Objectives: To provide advice and guidance regarding the management of mood disorders, derived from scientific evidence and supplemented by expert clinical consensus to formulate s that maximise clinical utility. Methods: Articles and information sourced from search engines including PubMed, EMBASE, MEDLINE, PsycINFO and Google Scholar were supplemented by literature known to the mood disorders committee (e.g. books, book chapters and government reports) and from published depression and bipolar disorder guidelines. Relevant information was appraised and discussed in detail by members of the mood disorders committee, with a view to formulating and developing consensus-based recommendations and clinical guidance. The guidelines were subjected to rigorous consultation and external review involving: expert and clinical advisors, key stakeholders, professional bodies and specialist groups with interest in mood disorders. Results: The Royal Australian and New Zealand College of Psychiatrists mood disorders clinical practice guidelines 2020 (MDcpg2020) provide up-to-date guidance regarding the management of mood disorders that is informed by evidence and clinical experience. The guideline is intended for clinical use by psychiatrists, psychologists, primary care physicians and others with an interest in mental health care. Conclusion: The MDcpg2020 builds on the previous 2015 guidelines and maintains its joint focus on both depressive and bipolar disorders. It provides up-to-date recommendations and guidance within an evidence-based framework, supplemented by expert clinical consensus. Mood disorders committee: Gin S Malhi (Chair), Erica Bell, Darryl Bassett, Philip Boyce, Richard Bryant, Philip Hazell, Malcolm Hopwood, Bill Lyndon, Roger Mulder, Richard Porter, Ajeet B Singh and Greg Murray.
Production and quality evaluation of instant lassi

Instant lassi was prepared from cows' milk standardized to 4% fat and 8.5% solids-nonfat and heated to 85°C for 30 min, followed by cooling at 37°C and adding 50% v/v lactic acid to adjust the pH to 3.4, 3.6 and 3.8. The sugar was added at the rate of 8%, 10% and 12% and mixed thoroughly in a Waring blender. The synthetic flavours vanilla, strawberry and pineapple were also added at the rate of 0.2% and stored at refrigeration temperature. The sensory evaluation of flavoured instant lassi adjusted to pH 3.8 and 12% sugar indicated no influence in colour, appearance or overall acceptability of compared to control. Among the different flavours used, pineapple-flavoured instant lassi scored highest for colour, appearance and overall acceptability. Both pH and sugar had significant effects, flavoured instant lassi having a higher specific gravity and lower viscosity than the control.
Medical treatment of SUNCT and SUNA: a prospective open-label study including single-arm meta-analysis

Introduction The management of short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing (SUNCT) and short-lasting unilateral neuralgiform headache attacks with cranial autonomic symptoms (SUNA) remains challenging in view of the paucity of data and evidence-based treatment recommendations are missing. Methods In this single-centre, non-randomised, prospective open-label study, we evaluated and compared the efficacy of oral and parenteral treatments for SUNCT and SUNA in a real-world setting. Additionally, single-arm meta-analyses of the available reports of SUNCT and SUNA treatments were conducted. Results The study cohort comprised 161 patients. Most patients responded to lamotrigine (56%), followed by oxcarbazepine (46%), duloxetine (30%), carbamazepine (26%), topiramate (25%), pregabalin and gabapentin (10%). Mexiletine and lacosamide were effective in a meaningful proportion of patients but poorly tolerated. Intravenous lidocaine given for 7–10 days led to improvement in 90% of patients, whereas only 27% of patients responded to a greater occipital nerve block. No statistically significant differences in responders were observed between SUNCT and SUNA. In the meta-analysis of the pooled data, topiramate was found to be significantly more effective in SUNCT than SUNA patients. However, a higher proportion of SUNA than SUNCT was considered refractory to medications at the time of the topiramate trial, possibly explaining this isolated difference. Conclusions We propose a treatment algorithm for SUNCT and SUNA for clinical practice. The response to sodium channel blockers indicates a therapeutic overlap with trigeminal neuralgia, suggesting that sodium channels dysfunction may be a key pathophysiological hallmark in these disorders. Furthermore, the therapeutic similarities between SUNCT and SUNA further support the hypothesis that these conditions are variants of the same disorder.
Effect of the Joule heat-based WG-12 active carbon modification process on the sorption of Cr(III)

The activated carbons used in water technology are most frequently used to remove organic compounds. They can also be used in the adsorption of mineral pollution, among others for heavy metals. The adsorption capacities of activated carbons in relation to these pollutants are slight and their concentrations in water are also low. Heavy metal removal efficiency can be increased by modifying activated carbons. In the article the results of activated carbon modification with the usage of Joule heat are presented. The modification was carried out in the reactor with the height h = 25 cm and diameter d = 5.5 cm filled with activated carbon. At two sides of the reactor electrodes were placed which were joined with a direct current generator. The flow of the current through the deposit was accompanied by incensement of activated carbon temperature. The modification took place during the flow of carbon dioxide while heating and/or cooling down of this deposit. The effects of modification were evaluated on the bas.
A 1.5-μJ/Task Path-Planning Processor for 2-D/3-D Autonomous Navigation of Microrobots

Autonomous microrobots have been utilized in a wide range of applications. Energy-efficient, real-time path planning for navigation is essential. This work presents a path-planning processor for 2-D/3-D autonomous navigation. Energy and latency are minimized through algorithm-architecture optimization. The processor utilizes the rapidly exploring random tree (RRT) algorithm to ensure efficient planning on maps that have higher dimensions and a higher resolution. Dual-tree planning, branch extension, and parallel expansion are adopted in order to reduce both computational complexity and memory requirements. A prune-and-reuse strategy is also adopted so as to quickly respond to dynamic scenarios. An array of processing engines (PEs) is deployed in order to enable parallel expansion. The number of PEs is minimized through latency analysis. Low-complexity implementation for the PE is proposed while maintaining a high performance. Fabricated in a 40-nm CMOS technology, the chip integrates 2M logic gates in an area of 3.65 mm<sup>2</sup>. The processor supports path-planning tasks for both 2-D and 3-D maps, with latencies of less than 1 and 10 ms, respectively. For a 2-D map that has 100 <inline-formula> <tex-math notation="LaTeX">$\mathbf {\times }$ </tex-math></inline-formula> 100 grids, the proposed processor dissipates 1.5 <inline-formula> <tex-math notation="LaTeX">$\boldsymbol{\mu }$ </tex-math></inline-formula>/task at a clock frequency of 200 MHz from a 0.9-V supply. Compared with the state-of-the-art designs, the proposed path-planning processor achieves a 1467<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> shorter processing latency based on an energy dissipation that is 2133<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> lower, despite the capability for larger maps.
Optimal Precoder Selection for Spatially Multiplexed Multiple-Input Multiple-Output Systems With Maximum Likelihood Detection: Exploiting the Concept of Sphere Decoding

In this paper, a computationally efficient implementation technique for optimal precoder selection in spatially multiplexed (SM) multiple-input multiple-output (MIMO) systems with maximum-likelihood detection at the receiver is proposed. The techniques previously developed for suboptimal precoder selection were based on the lower bounds of the free distances of precoders to reduce the processing time. However, the use of these techniques leads to significant declines in error performance when the number of spatial streams approaches the number of receiving antennas. At the same time, to achieve optimal performance, the conventional optimal precoder selection technique can be employed; however, it has a long processing time due to exhaustive search. Thus, in this paper, we propose a precoder selection technique that maintains an optimal performance without the prohibitive processing time of the conventional optimal precoder selection. The processing time can be reduced by the following: (1) exploiting the symmetric structure of quadrature amplitude modulation (QAM) constellations, thereby reducing the search space; (2) adopting the concept of sphere decoding (SD); (3) eliminating the last stage of SD; and (4) performing an SD-like process in a selective manner. Both the optimal performance and reduction in the processing time realized by the proposed technique are confirmed via simulation.
The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

As an important tool of social network analysis, network representation learning also called network embedding maps the network to a latent space and learns low-dimensional and dense real vectors of nodes, while preserving the structure and internal attributes of network. The learned representations or embedding vectors can be used for node clustering, link prediction, network visualization and other tasks for network analysis. Most of the existing network representation learning algorithms mainly focus on the preservation of micro or macro network structure, ignoring the mesoscopic community structure information. Although a few network embedding methods are proposed to preserve the community structure, they all ignore the prior information about communities. Inspired by the semi-supervised community detection in complex networks, in this article, a novel Semi-Supervised DeepWalk method(SSDW) is proposed for network representation learning, which successfully preserves the community structure of network in the embedding space. Specifically, a semi-supervised random walk sampling method which effectively integrates the pairwise constraints is proposed. By doing so, the SSDW model can guide the transition probability in the random walk process and obtain the node context sequence in line with the prior knowledge. The experimental results on eight real networks show that comparing with the popular network embedding methods, the node representation vectors integrating pairwise constraints into the random walk process can obtain higher accuracy on node clustering task, and the results of link prediction, network visualization tasks indicate that the semi-supervised model SSDW is more discriminative than unsupervised ones.
A Scalable Architecture for CNN Accelerators Leveraging High-Performance Memories

As FPGA-based accelerators become ubiquitous and more powerful, the demand for integration with High-Performance Memory (HPM) grows. Although HPMs offer a much greater bandwidth than standard DDR4 DRAM, they introduce new design challenges such as increased latency and higher bandwidth mismatch between memory and FPGA cores. This paper presents a scalable architecture for convolutional neural network accelerators conceived specifically to address these challenges and make full use of the memory's high bandwidth. The accelerator, which was designed using high-level synthesis, is highly configurable. The intrinsic parallelism of its architecture allows near-perfect scaling up to saturating the available memory bandwidth.
Short Text Embedding Autoencoders With Attention-Based Neighborhood Preservation

Shortness and sparsity often plague short text representation for clustering and classification. A popular solution is to extract meaningful low-dimensional embeddings as short text representation via various Dimensionality Reduction technology. However, the existing methods, such as topic models and neural networks, discover low-dimensional embeddings from the whole training sets without considering the geometrical information of short text manifold, resulting in an inability to provide a discriminative embedding of short text. In this paper, we propose a manifold-regularized method, namely Short Texts Embedding AutoEncoders (STE-AEs), aiming to incorporate the semantics from the neighborhood into a regularization training of AutoEncoders (AEs) to extract discriminative low-dimensional short text embeddings. STE-AEs first determines semantics neighborhood via an attention-based weighted matching distance and then preserves the local geometrical structure by incorporating a minimization of the weighted cross-entropy of nearby texts’ embeddings into a regularization training of AEs. Finally, the encoder can act as a parametrized mapping function between observations and embeddings. Furthermore, based on the activation values of the encoder for the training set, STE-AEs employs a regression model of Random Forest (RF) to determine the feature importance so as to find certain informative and readable words for embeddings interpretation. Through extensive experiments on three real-world short text corpuses, the evidence demonstrate that STE-AEs can capture the intrinsic discriminative explanatory factors, improving the performance of short text clustering and classification. Moreover, some understandable words can be efficiently discovered to promote the interpretability of low-dimensional embeddings.
Stability- and Sensitivity-Analysis of a Sensorless Controlled Synchronous Reluctance Machine using the Back-EMF Model

In this paper the position sensorless operation of a synchronous reluctance machine (SynRM) in the medium and high speed range is discussed. The Back-EMF method is the commonly used sensorless method in this speed range. It estimates the flux-linkage of the synchronous motor which is rigidly connected to the rotor position. In the case of the SynRM without permanent magnets the flux-linkage is only caused by the stator current, which leads to a high dependency of the Back-EMF method on the operating point. This results in the fact that no optimal MTPA-operation of the machine is possible, because of stability issues of this sensorless method. In the following work, the stability and the sensitivity to parameter fluctuations of the Back-EMF method is analysed dependent on the current-angle in the rotor fixed reference frame.
Analysis and Design of a Modified Virtual Synchronous Generator Control Strategy for Single-phase Inverter Application

This paper proposed a modified virtual synchronous generator (VSG) control strategy for single-phase inverter application. In order to achieve zero-error tracking for reactive power output, an improved reactive-voltage control approach for the outer layer to emulate the characteristics of synchronous generator behavior. A doubleclosed-loop control strategy based on state observer and inductor current based on virtual impedance for power decoupling is also introduced for output voltage regulation. Finally, experimental results based on 3kVA prototype verify that the proposed control strategy, and the seamless transfer between grid-connected mode and island mode can be achieved.
A 5.5nW Voltage Reference Circuit

This paper proposed a nano-watt voltage reference circuit was implemented in a 0.18um CMOS process with trim techniques. In order to reduce power consumption, a MOS-Only Voltage Reference is presented, which is based on the threshold voltage, However, the deviation of Vref because of process variation is large. We use the difference of Vth instead of Vth to improve the stability of output voltage at different process corner. The simulation results show that under 27°C and 0.5V supply voltage, the output reference voltage is 236mV, the temperature coefficient is 30.8 ppm/°C over temperature range of 125°C (-40°C to 85°C) and only consume 5.5nW at 0.5V supply voltage.
Speech Enhancement Based on Modulation-Domain Parametric Multichannel Kalman Filtering

Recently we presented a modulation-domain multichannel Kalman filtering (MKF) algorithm for speech enhancement, which jointly exploits the inter-frame modulation-domain temporal evolution of speech and the inter-channel spatial correlation to estimate the clean speech signal. The goal of speech enhancement is to suppress noise while keeping the speech undistorted, and a key problem is to achieve the best trade-off between speech distortion and noise reduction. In this paper, we extend the MKF by presenting a modulation-domain parametric MKF (PMKF) which includes a parameter that enables flexible control of the speech enhancement behaviour in each time-frequency (TF) bin. Based on the decomposition of the MKF cost function, a new cost function for PMKF is proposed, which uses the controlling parameter to weight the noise reduction and speech distortion terms. An optimal PMKF gain is derived using a minimum mean squared error (MMSE) criterion. We analyse the performance of the proposed MKF, and show its relationship to the speech distortion weighted multichannel Wiener filter (SDW-MWF). To evaluate the impact of the controlling parameter on speech enhancement performance, we further propose PMKF speech enhancement systems in which the controlling parameter is adaptively chosen in each TF bin. Experiments on a publicly available head-related impulse response (HRIR) database in different noisy and reverberant conditions demonstrate the effectiveness of the proposed method.
Microservices: architecture, container, and challenges

Microservices are emerging as a new computing paradigm which is a suitable complementation of cloud computing. Microservices will decompose traditional monolithic applications into a set of fine-grained services, which can be independently developed, tested, and deployed. However, there are many challenges of microservices. This paper provides a comprehensive overview of microservices. More specifically, firstly, we systematically compare traditional monolithic architecture, service-oriented architecture (SOA), and microservices architecture. Secondly, we give an overview of the container technology. Finally, we outline the technical challenges of microservices, such as performance, debugging and data consistency.