Miracle-dz/UniFAR-FTUs huggingface dataset:
The Tromsøo heart study. Methods and main results of the cross-sectional study.

The mortality from coronary heart disease (CHD) in Norway increased rapidly during 1951-70, the highest mortality rates as well as the most rapid increases being found in Northern Norway. Several surveys of CHD were then planned, one of them is reported here. All men, 20-49 years of age, living in the municipality of Tromsoo, Troms county, were called up for examination. In total, 6595 men, 74.4% of those invited, were examined. Cholesterol, triglyceride and Hb values, BP, body weight and height, the percentage of smokers, and cigarette consumption have been tabulated according to area, age, work schedule, health condition, physical activity and ethnic background. The results suggest that the relatively high mortality from CHD in Northern Norway is associated with high serum cholesterol concentrations as well as a relatively high prevalence of smoking. During the screening there were indications of changes in dietary habits in the municipality, presumably as a result of accompanying publicity.
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images

Computer-vision hospital systems can greatly assist healthcare workers and improve medical facility treatment, but often face patient resistance due to the perceived intrusiveness and violation of privacy associated with visual surveillance. We downsample video frames to extremely low resolutions to degrade private information from surveillance videos. We measure the amount of activity-recognition information retained in low resolution depth images, and also apply a privately-trained DCSCN super-resolution model to enhance the utility of our images. We implement our techniques with two actual healthcare-surveillance scenarios, hand-hygiene compliance and ICU activity-logging, and show that our privacy-preserving techniques preserve enough information for realistic healthcare tasks.
Tidal-traffic-aware routing and spectrum allocation in elastic optical networks

With the growing popularity of 5G mobile communications, cloud and fog computing, 4K video streaming, etc., population distribution and migration have increasing influence on traffic distribution in metro elastic optical networks (EONs). Traffic distribution is further diversified according to people's tendency to use network services in different places at different times. We use the term "tidal traffic" to represent traffic distribution with strong disequilibrium in the time and space dimensions. Note that tidal traffic can potentially result in low bandwidth utilization and weak service capability, as network resources are not allocated properly. To address this problem, in this study, we first analyze traffic characteristics of access networks and metro networks, and mathematically formulate an onion tidal traffic model (OTTM) in EONs. Second, based on the traditional routing and spectrum allocation (RSA) algorithm, which provides end-to-end connection by allocating frequency slot resources in EONs, we propose two algorithms to enhance bandwidth efficiency based on the OTTM model. We call them pre-detour RSA (PD-RSA) and pre-detour k-shortest paths RSA (PDK-RSA). Next, we analyze the shortcomings of a benchmark algorithm named min-hop k-shortest paths RSA (MHK-RSA) under tidal traffic, and compare PD-RSA and PDK-RSA with MHK-RSA via simulation. Simulation results show that PD-RSA and PDK-RSA can effectively reduce at least 26% and 18% of blocking probability, respectively, compared to MHK-RSA with the same time complexity.
Overcoming P-Glycoprotein–Mediated Drug Resistance with Noscapine Derivatives

The antitussive agent noscapine has been shown to inhibit the proliferation of cancer cells by disruption of tubulin dynamic. However, the efficacy of several anticancer drugs that inhibit tublin dynamics (vinca alkaloids and taxanes) is reduced by the multidrug resistance phenotype. These compounds are substrates for P-glycoprotein (P-gp)–mediated extrusion from cells. Consequently, the antiproliferative activity of noscapine and a series of derivatives was measured in drug-sensitive and drug-resistant cells that overexpress P-gp. None of the noscapine derivatives displayed lower potency in cells overexpressing P-gp, thereby suggesting a lack of interaction with this pump. However, the cellular efflux of a fluorescent substrate by P-gp was potently inhibited by noscapine and most derivatives. Further investigation with purified, reconstituted P-gp demonstrated that inhibition of P-gp function was due to direct interaction of noscapine derivatives with the transporter. Moreover, coadministration of vinblastine with two of the noscapine derivatives displayed synergistic inhibition of proliferation, even in P-gp–expressing resistant cell lines. Therefore, noscapine derivatives offer a dual benefit of overcoming the significant impact of P-gp in conferring multidrug resistance and synergy with tubulin-disrupting anticancer drugs.
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs

Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a result, the key application characteristics such as frames-per-second (FPS) are ignored in favor of simply counting GOPs, and results on accuracy, which is critical to application success, are often not even reported. In this work, we adopt an algorithm-hardware co-design approach to develop a ConvNet accelerator called Synetgy and a novel ConvNet model called DiracDeltaNet. Both the accelerator and ConvNet are tailored to FPGA requirements. DiracDeltaNet, as the name suggests, is a ConvNet with only $1\times 1$ convolutions while spatial convolutions are replaced by more efficient shift operations. DiracDeltaNet achieves competitive accuracy on ImageNet (89.0% top-5), but with 48× fewer parameters and 65× fewer OPs than VGG16. We further quantize DiracDeltaNet's weights to 1-bit and activations to 4-bits, with less than 1% accuracy loss. These quantizations exploit well the nature of FPGA hardware. In short, DiracDeltaNet's small model size, low computational OP count, ultra-low precision and simplified operators allow us to co-design a highly customized computing unit for an FPGA. We implement the computing units for DiracDeltaNet on an Ultra96 SoC system through high-level synthesis. Our accelerator's final top-5 accuracy of 88.2% on ImageNet, is higher than all the previously reported embedded FPGA accelerators. In addition, the accelerator reaches an inference speed of 96.5 FPS on the ImageNet classification task, surpassing prior works with similar accuracy by at least 16.9×.
Deep Determinantal Point Processes

Determinantal point processes (DPPs) have attracted significant attention as an elegant model that is able to capture the balance between quality and diversity within sets. DPPs are parameterized by a positive semi-definite kernel matrix. While DPPs have substantial expressive power, they are fundamentally limited by the parameterization of the kernel matrix and their inability to capture nonlinear interactions between items within sets. We present the deep DPP model as way to address these limitations, by using a deep feed-forward neural network to learn the kernel matrix. In addition to allowing us to capture nonlinear item interactions, the deep DPP also allows easy incorporation of item metadata into DPP learning. Since the learning target is the DPP kernel matrix, the deep DPP allows us to use existing DPP algorithms for efficient learning, sampling, and prediction. Through an evaluation on several real-world datasets, we show experimentally that the deep DPP can provide a considerable improvement in the predictive performance of DPPs, while also outperforming strong baseline models in many cases.
Relation Classification via LSTMs Based on Sequence and Tree Structure

The goal of relation classification is to recognize the relationship between two marked entities in a sentence. It is a crucial constituent in natural language processing. Up till the present moment, most previous neural network models for this task either focus on using the handcrafted syntactic features or learning semantic representations of raw word sequences, they have no capacity for encoding the whole sentence representation including syntax and semantic information. In general, information of syntax and semantics can both have significant effect on classifying relation. Based on this idea, we propose a novel two-channel neural network architecture with attention mechanism in the paper to handle this task. First, we employ bidirectional sequence long short-term memory (LSTM) channel to capture the semantic information and acquire syntactic knowledge by utilizing tree structure LSTM channel. Second, sentence-level attention mechanism for word sequences is used to determine which parts of the sentence are most influential component. Eventually, we conduct experiments on two real-world datasets: the Wikipedia and the SemEval-2010 Task8 dataset. The experimental results on datasets demonstrate that our method can make better use of the information contained in sentences and achieves impressive improvements on relation classification as compared with the existing methods.
Distal Radius Fractures: Evaluation of Closed Reduction and Percutaneous Kirschner Wire Pinning

Abstract Objective Closed reduction and percutaneous pinning (CRPP) is losing popularity as a treatment modality for distal radius fractures. However, in select cases, CRPP may have advantages relative to open reduction and internal reduction. We aimed to retrospectively assess the outcomes after CRPP for the treatment of distal radius fractures. Study Design This is a retrospective cohort study. Methods We used billing records to identify all skeletally mature patients with a distal radius fracture who were treated with CRPP by a single surgeon at a level I trauma center in an urban city in the United States. We assessed the medical charts and recorded demographics, trauma and treatment characteristics, radiographic characteristics, and outcomes. Results All patients had a good or excellent range of motion regarding forearm rotation, and almost 80% had good or excellent range of motion regarding flexion or extension of their wrist. One patient had a concern for pin tract infection, and one had subcutaneous migration of a pin, which were both treated by pin removal. Conclusion CRPP is a good option in patients with few and sizeable fracture fragments in patients with a distal radius fracture, and it should be considered as an effective tool to restore radiographic parameters and functional outcomes.
Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models

Snow albedo plays an important role in the global climate system. There are notable missing data and error uncertainties in the current remote sensing snow albedo products that are attributed to the limits of remote-sensing technology. Due to the uncertainties of meteorological factors and the differences in various forward model simulation methods, snow albedo forward simulations also have considerable uncertainties. This paper suggests a long-time-series reconstruction of snow albedo utilizing a forward radiation-transferring model and a remote-sensing retrieval model together with multisource remotely sensed data and meteorological data. The key to this paper is to estimate snow information for areas lacking data utilizing a forward model for snow albedo with clear physical mechanisms. The estimated snow information can be used as reliable data for snow albedo reconstructions. The results indicate that the long time series of snow albedo data obtained by coupling the snow albedo retrieval model and forward simulation model is highly accurate. The mean absolute error, root mean square error, Pearson’s correlation coefficient (R), and Nash–Sutcliffe efficiency coefficient of the observed and reconstructed snow albedos are 0.11, 0.14, 0.79, and 0.69, respectively. The reconstructed snow albedo data are underestimated by only 11% relative to the in situ snow surface albedo measurements. In the alpine mountain regions, the proposed method has a simulation accuracy that is 6% greater than that of the MOD10A1 SAD. This paper provides an effective reconstruction solution that improves the accuracy of estimations of snow albedo and fills gaps in the data.
Energy-Efficient Data Collection Over AUV-Assisted Underwater Acoustic Sensor Network

Most applications of underwater cyber-physical system (UCPS) demand reliable data collection in an efficient and timely manner. However, the energy-constrained characteristic on acoustic communication makes it challenging to collect data. In this paper, we study the energy-efficient data collection for UCPS over autonomous underwater vehicle (AUV) assisted underwater acoustic sensor network. The sensor nodes perceive local physical parameters, whose lifetime is strictly restricted by local power. The AUV that has relatively sufficient energy is adopted to retrieve data from sensor nodes. To balance energy consumption and prolong underwater network lifetime, the energy optimization problem is formulated as minimizing the sum of edge lengths of a particular graph. Then, a min-weighted rigid graph based topology optimization scheme is developed for sensor deployment. With the optimized network topology, a local routing decision algorithm is developed for each sensor node to relay the collected data to the data collectors (sinks). A dynamic value-based path planning strategy is also designed for the AUV to dynamically visit data collectors to maximize the value of information (VoI) within a given time. Finally, simulation results demonstrate that the network lifetime with the topology optimization scheme can be prolonged, and the dynamic value-based path planning strategy can improve the VoI on AUV.
DIFFERENCES IN UPPER BODY POSTURE AND POSTURAL MUSCLE ACTIVATION IN FEMALES WITH LARGER BREAST SIZES

Breast hypertrophy is a common medical condition whose morbidity has increased over recent decades. Symptoms of breast hypertrophy often include musculoskeletal pain in the neck, back and shoulders, painful shoulder grooves from bra straps, and numerous psychosocial health burdens. It is postulated that the size and weight of hypertrophic breasts alters the body’s center of gravity, leading to secondary effects on the musculoskeletal system [1].
Trajectory PHD and CPHD Filters

This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD (CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters. Contrary to the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory estimates from first principles. The TPHD filter is derived by recursively obtaining the best Poisson multitrajectory density approximation to the posterior density over the alive trajectories by minimising the Kullback-Leibler divergence. The TCPHD is derived in the same way but propagating an independent identically distributed (IID) cluster multitrajectory density approximation. We also propose the Gaussian mixture implementations of the TPHD and TCPHD recursions, the Gaussian mixture TPHD (GMTPHD) and the Gaussian mixture TCPHD (GMTCPHD), and the <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-scan computationally efficient implementations, which only update the density of the trajectory states of the last <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> time steps.
A Theoretical Synthesis of Coupling Matrix by Eigen Mode Expansion Method and Householder Transform

This paper presents a new theoretical synthesis method for the coupling matrix strictly based on the classical network synthesis theory. While the classical network synthesis theory based on the real rational functions is a well-established rigorous process, the coupling matrix method becomes popular, and is being widely used because of its flexible design capabilities. The coupling matrix method has, however, relatively ambiguous adaptability to the classical scheme, although many studies have been carried out to relate them to each other. This paper shows that the Eigen mode expansion method in corporation with the orthogonal symmetric transform by the Householder method enables an exact correspondence between them. To generalize and extend this process further, the Householder method is modified to be applicable to general cross coupling configurations. Finally, some design examples are provided.
A Class E Digital Transmitter for 16-APSK

A digital transmitter architecture is described composed of two independent class E amplifiers with different saturate output powers (POUT). Their output ports are connected directly together without a switch or combiner. The two amplifiers are enabled or disabled by alternatively biasing their gates in synch with the envelope of the input signal. The transmitter is optimised for 16 state amplitude phase shift keying (16-APSK). Under simulation, the two class E amplifiers were optimised for a POUT of 13.6 dBm and 22.7 dBm. 71.4% and 78.2% power added efficiency (PAE) was achieved respectively. The same transistor was used for both amplifiers and the different POUT defined by the quality factor (Q) of their output bandpass filter (BPF). A practical implementation achieved 49.8% and 45.6% PAE at POUTS of 14.0 dBm and 20.3 dBm respectively. With a 16-APSK signal, 46.9% PAE was achieved at 19.8 dBm POUT, with an error vector magnitude (EVM) of 5.7%.
A Cell Atlas for the Mouse Brain

Despite vast numbers of studies of stained cells in the mouse brain, no current brain atlas provides region-by-region neuron counts. In fact, neuron numbers are only available for about 4% of brain of regions and estimates often vary by as much as 3-fold. Here we provide a first 3D cell atlas for the whole mouse brain, showing cell positions constructed algorithmically from whole brain Nissl and gene expression stains, and compared against values from the literature. The atlas provides the densities and positions of all excitatory and inhibitory neurons, astrocytes, oligodendrocytes, and microglia in each of the 737 brain regions defined in the AMBA. The atlas is dynamic, allowing comparison with previously reported numbers, addition of cell types, and improvement of estimates as new data is integrated. The atlas also provides insights into cellular organization only possible at this whole brain scale, and is publicly available.
Detection of Botnet Multi-Stage Attack By Using Alert Correlation Model

---------------------------------------------------ABSTRACT------------------------------------------------------Network Intrusion Detection Systems (NIDS) are considered as one of the essential mechanisms to ensure reliable security. Intrusive model is used in signature-based NIDS by defining attack patterns and applying signature-matching on incoming packets. However, detection of novel and multi-stage attacks are not efficiently achieved by the signature-based systems. This is due to lack of mechanism to perform sophisticated analysis to identify relationship between attack events. Hence, the systematic analysis of attack initiation has become a stressing demand in current research. Alerts correlation techniques have been widely used to provide intelligent and stateful detection methodologies. This is to understand attack steps and predict the expected sequence of events. However, most of the proposed systems are based on rule –based mechanisms which are tedious and error prone. Other methods are based on statistical modeling; these are unable to identify causal relationships between the events.In this paper, we have identified the limitations of the current techniques and propose a model for alert correlation that overcomes the shortcomings. An improved “require/provide” model is presented which established a cooperation between statistical and knowledge-based model, to achieve higher detection rate with the minimal false positives. A knowledge-based model with vulnerability and extensional conditions provide manageable and meaningful attack graphs. The proposed model has been implemented in real-time and has successfully generated security events on establishing a correlation between attack signatures. The system has been evaluated to detect one of the most serious multi-stage attacks in cyber crime Botnet. Zeus Botnet is analyzed within the realm of simulated malicious activities normally used by cyber criminals. The system has efficiently established a correlation in attack behaviors and has generated an attack map. The map can be used to discretely analyze the correlated attack activities which in other case may go undetected thus facilitating the multi-stage attack recognition process.
Building of the real-time data transfer system for Siberian NOAA image database

Tohoku University (TU) in Sendai Japan and the Siberian Branch of the Russian Academy of Science (SBRAS) in Novosibirsk Russia are jointly participating in an academic exchange program. One of the main themes is to build the NOAA Image Database by using data received in Siberia because there is about 5,000 km distance between them. To send NOAA data on real-time, we have together constructed the direct computer network named VSAT system between Novosibirsk and Sendai by using Russian communication satellite in 1998. The speed of data transfer is 64 kbps each other. This system automatically receives NOAA data in Novosibirsk and automatically sends them after compression. We report the results of the communication system for transferring data. The authors describe the efficiency of the network. The operating ratio is 90% or more through one year. The Siberian NOAA Image Database and a Mongolian NOAA Image database use the VSAT system.
Women’s experiences of a randomised controlled trial of a specialist psychological advocacy intervention following domestic violence: A nested qualitative study

Background Women’s experience of domestic violence and abuse (DVA) is associated with mental illness which may not be addressed by domestic violence advocacy. The study aimed to compare the experiences of women receiving a psychological intervention with women receiving usual advocacy in a randomized controlled trial (PATH: Psychological Advocacy Towards Healing), to illuminate the trial results by exploring women’s experiences of benefits and difficulties. Methods A qualitative study nested within the PATH trial, based in two DVA agencies in the UK. A purposive sample of thirty-one intervention and usual care participants were interviewed up to three interviews over a year. Thematic analysis was carried out, incorporating concepts from the Trans-Theoretical Model of change. Findings The PATH trial reports a clinically relevant improvement in mental health outcomes for women receiving the intervention compared to usual advocacy. The qualitative study reveals which elements of the intervention were beneficial or problematic, which outcomes were most meaningful and relevant to participants and highlights reasons for variations in adherence. Women valued the educational, psychological and emotional elements of the intervention, they felt safe to explore repressed emotions for the first time and experienced a reduction in self-blame, improved sense of identity and greater self-esteem. They also incorporated new skills and self-help techniques to enable sustainable change. Women receiving usual advocacy reported un-met needs for psychological and emotional support. Adherence was affected by women’s ‘psychological ‘readiness’ to engage, the competing demands of practical issues such as housing insecurity, legal proceedings or the availability of child care, and breaks in the continuity of professional care. Conclusions Continuity and regularity of sessions with a trained specialist worker was key to women’s recovery. Individual assessment of ‘readiness’ would optimise the timing of delivery to maximise adherence and benefit.