Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Orthogonal Pilot Designs for Joint Channel Estimation and Collision Detection in Grant-Free Access Systems

In this paper, we consider a densely deployed phantom cell system providing a low latency network for massive connectivity. Non-orthogonal pilot designs serve as a promising solution to support a large number of users, but fast collision detection at the receiver is needed for low latency networks. Recently, a nice solution based on an on–off type non-orthogonal pilot design with collision detection capability has been proposed. It can serve more users than the orthogonal pilot design but at the cost of degraded channel estimation performance compared to the orthogonal optimum pilot design. We propose a new non-orthogonal pilot design with collision detection capability and improved channel estimation performance. An optimum threshold-based detection criterion is developed. We further show that a dynamically calculated optimum threshold-based detection outperforms a fixed threshold-based detection. Next, we investigate non-orthogonal pilot designs for fractional bandwidth allocation. We propose two new non-orthogonal pilot designs for physical resource block based resource allocation. Both of the new designs support fast collision detection at the receiver. Performance evaluation results show that the proposed schemes provide equivalent or better channel estimation performance and support much more users than the orthogonal pilots defined in the current 4G standards. Finally, we explore sparse channel estimation with compressed sensing technique. We prove several propositions regarding compressed sensing based estimation performance. Using these propositions, two novel orthogonal pilot designs are developed for sparse channel estimation with optimized performances. Finally, utilizing these orthogonal pilot sets, we propose a non-orthogonal pilot design with collision detection capability for sparse channel estimation.
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality

Deep learning has shown high performances in various types of tasks from visual recognition to natural language processing, which indicates superior flexibility and adaptivity of deep learning. To understand this phenomenon theoretically, we develop a new approximation and estimation error analysis of deep learning with the ReLU activation for functions in a Besov space and its variant with mixed smoothness. The Besov space is a considerably general function space including the Holder space and Sobolev space, and especially can capture spatial inhomogeneity of smoothness. Through the analysis in the Besov space, it is shown that deep learning can achieve the minimax optimal rate and outperform any non-adaptive (linear) estimator such as kernel ridge regression, which shows that deep learning has higher adaptivity to the spatial inhomogeneity of the target function than other estimators such as linear ones. In addition to this, it is shown that deep learning can avoid the curse of dimensionality if the target function is in a mixed smooth Besov space. We also show that the dependency of the convergence rate on the dimensionality is tight due to its minimax optimality. These results support high adaptivity of deep learning and its superior ability as a feature extractor.
Analysis of Flux-Reversal Permanent-Magnet Machines With Different Consequent-Pole PM Topologies

This paper comprehensively studies the electromagnetic performance of flux-reversal permanent-magnet (FRPM) machines with different consequent-pole PM (CPM) topologies. Four CPM topologies are first introduced and classified by different numbers of PM pieces and PM locations on stator teeth. Then, the distribution and working harmonics of air-gap flux density of each CPM topology are analyzed and compared, from which the CPM topology with the highest torque density is identified. The influence of critical design parameters on machine performance is also parametrically investigated. By comparing the torque performance of CPM topologies with their surface-mounted PM counterparts, the advantages of CPM topologies, i.e., torque improvement and magnet volume reduction, are clearly revealed. Four FRPM prototypes are manufactured and tested to verify the analyses.
Investigation of Losses for a Concentrated Winding High-Speed Permanent Magnet-Assisted Synchronous Reluctance Motor for Washing Machine Application

In this paper, improvement of torque quality and magnetic losses of a concentrated winding high-speed permanent magnet (PM)-assisted synchronous reluctance motor (PMaSRM) that can operate in the wide speed range are investigated. Optimization studies have been carried out to enhance the torque quality. In addition, ac copper losses in the stator coils and iron losses in laminations at high frequencies have been investigated. Since the PMaSRM can operate in a wide constant power-speed range, the field weakening performance has also been investigated. The motor performance results of the PMaSRM at both no-load and on-load conditions are examined by using Flux 2-D FEA software. A prototype motor based on the simulations has been built and validated in order to show the feasibility of such technology for white good applications.
HIV infection has a profound effect on hematological factors but not on electrolyte profile of Malawian adults presenting with uncomplicated malaria and severe malaria

Aim Although malaria and HIV infections independently affect the electrolyte and hematologic profiles, little is known of how these profiles are affected in individuals coinfected with malaria and HIV. We therefore conducted this study to investigate the electrolyte and hematologic profiles of Malawian adults presenting with either uncomplicated malaria (UM), severe malaria (SM), and those presenting with HIV and UM or HIV and SM. Methods Study participants were recruited at Queen Elizabeth Central Hospital, and malaria infection was confirmed by rapid diagnostic test and malaria slides, and full blood count, HIV, and wet chemistries were analyzed. Results Sodium, potassium, calcium, and chloride levels of all 4 study groups were similar to those of healthy controls. Both HIV-infected groups (UM and SM) had lower red blood cell counts and lower hemoglobin concentration than the reference range. Platelet counts were lower in both HIV-uninfected SM cases (64×109/L) and in the HIV-infected SM cases (66×109/L) compared to the reference range (115–290×109/L). HIV− UM cases had higher proportion and absolute counts of neutrophils and white blood cell counts compared to the HIV+ UM cases. Conclusion HIV infection did not affect the electrolyte profile of Malawian adults presenting with UM or SM but had an effect on red blood cells, Hb concentration, neutrophils, and platelet counts.
Approximate Computing for Long Short Term Memory (LSTM) Neural Networks

Long Short Term Memory (LSTM) networks are a class of recurrent neural networks that are widely used for machine learning tasks involving sequences, including machine translation, text generation, and speech recognition. Large-scale LSTMs, which are deployed in many real-world applications, are highly compute intensive. To address this challenge, we propose AxLSTM, an application of approximate computing to improve the execution efficiency of LSTMs. An LSTM is composed of cells, each of which contains a cell state along with multiple gating units that control the addition and removal of information from the state. The LSTM execution proceeds in timesteps, with a new symbol of the input sequence processed at each timestep. AxLSTM consists of two techniques—Dynamic Timestep Skipping (DTS) and Dynamic State Reduction (DSR). DTS identifies, at runtime, input symbols that are likely to have little or no impact on the cell state and skips evaluating the corresponding timesteps. In contrast, DSR reduces the size of the cell state in accordance with the complexity of the input sequence, leading to a reduced number of computations per timestep. We describe how AxLSTM can be applied to the most common application of LSTMs, viz., sequence-to-sequence learning. We implement AxLSTM within the TensorFlow deep learning framework and evaluate it on 3 state-of-the-art sequence-to-sequence models. On a 2.7 GHz Intel Xeon server with 128 GB memory and 32 processor cores, AxLSTM achieves $ {1.08\times -1.31 \times }$ speedups with minimal loss in quality, and $ {1.12 \times -1.37 \times }$ speedups when moderate reductions in quality are acceptable.
The Adam–Gibbs relation and the TIP4P/2005 model of water

ABSTRACT We report a numerical test of the Adam–Gibbs relation for the TIP4P/2005 model of water. The configurational entropy is here evaluated as the logarithm of the number of different basins in the potential energy landscape sampled in equilibrium conditions. Despite the non-monotonic behaviour which characterise the density dependence of the diffusion coefficient, the Adam–Gibbs relation is satisfied within the numerical precision in a wide range of densities and temperatures. We also show that expressions based on the excess entropy (the logarithm of the number of sampled microstates in phase space) fail in the region of densities where a tetrahedral hydrogen bond network develops. GRAPHICAL ABSTRACT
Pruned DFT-Spread FBMC: Low PAPR, Low Latency, High Spectral Efficiency

We propose a novel modulation scheme which combines the advantages of filter bank multi-carrier (FBMC)-offset quadrature amplitude modulation and single-carrier frequency-division multiple access (SC-FDMA). On the top of a conventional FBMC system, we develop a novel precoding method based on a pruned discrete Fourier transform (DFT) in combination with one-tap scaling. The proposed technique has the same peak-to-average power ratio as SC-FDMA but does not require a cyclic prefix and has much lower out-of-band emissions. Furthermore, our method restores complex orthogonality, and the ramp-up and ramp-down period of FBMC is dramatically decreased, allowing low latency transmissions. Compared to pure SC-FDMA, the computational complexity of our scheme is only two times higher. Simulations over doubly selective channels validate our claims, further supported by a downloadable MATLAB code. Note that pruned DFT-spread FBMC can equivalently be interpreted as a modified SC-FDMA transmission scheme. In particular, the requirements on the prototype filter are less strict than in conventional FBMC systems.
Pilot studies evaluating the nongenotoxic rodent carcinogens phenobarbital and clofibrate in the rat Pig‐a assay

The Pig‐a assay is an emerging and promising in vivo method to determine mutagenic potential of chemicals. Since its development in 2008, remarkable progress has been made in harmonizing and characterizing the test procedures, primarily using known mutagenic chemicals. The purpose of the present study was to evaluate specificity of the Pig‐a assay using two nongenotoxic and well‐characterized rodent liver carcinogens, phenobarbital and clofibrate, in male F344/DuCrl rats. Daily oral administration of phenobarbital or clofibrate at established hepatotoxic doses for 28 days resulted in substantial hepatic alterations, however, did not increase the frequency of Pig‐a mutation markers (RETCD59‐ and RBCCD59‐) compared to vehicle control or pre‐exposure (Day −5) mutant frequencies. These results are consistent with the existing literature on the nonmutagenic mode of action (MoA) of phenobarbital and clofibrate liver tumors. The present study contributes to the limited, but expanding evidence on the specificity of the Pig‐a assay and further for the investigations of carcinogenic MoAs, i.e., mutagenic or nonmutagenic potential of chemicals. Environ. Mol. Mutagen. 60:42–46, 2019. © 2018 Wiley Periodicals, Inc.
Managing HCV treatment failure and the potential of resistance testing in informing second-line therapy options

ABSTRACT Introduction: Direct acting antivirals have completely changed the landscape of the treatment of chronic hepatitis C. The management of the few patients who relapse to direct acting antivirals requires a careful analysis of the chances to achieve therapeutic success with a second antiviral course. In this context, the usefulness of viral resistances testing, able to detect resistance-associated substitutions in the viral sequence, is at present a matter of debate. Areas covered: The role of resistance associated substitutions is examined through the evaluation of the data from clinical trials that have assessed the impact of viral resistances on the treatment outcome. Special attention has been paid on the data from re-treatment studies. Expert commentary: The treatment failure in chronic hepatitis C is still a possible event. Therefore, additional real-world clinical data on relapse rates and on the relapse management are welcome to definitely address the clinical guidelines. At present, the testing of viral resistances is an exquisite tool for the choice of the re-treatment schedule. In the near future, widespread use of the most recently registered direct acting antivirals with high barrier to resistance will probably weaken the need of resistance testing as a support in clinical decisions.
Avaliação de Desempenho de Virtualização Baseada em Contêiner

Virtualization is an important technology used in large data centers and the availability of on-demand resources. It has gained adherence across industry segments and is widely studied to deliver significant cost performance. As such, containers have gained prominence as a new form of virtualization. Unlike a hypervisor-based system, containers share the same host operating system to create multiple isolated instances of virtualized programs. Containers perform is equal to or greater than the performance of a hypervisor-based system, however, they have different architecture and implementation. Thus, performance evaluation of the containers is fundamental for adoption and creation of new projects, since containers have gained adhesion from large companies. Resumo. A virtualização tem ganhado adesão e suporte em vários segmentos da indústria e é amplamente estudada para oferecer o desempenho desejado a um custo significativo. Os contêineres ganharam destaque como uma nova forma de virtualização. Ao contrário de um sistema baseado em hipervisor, os contêineres compartilham o mesmo sistema operacional para criar várias instâncias isoladas de programas virtualizados. Estes possuem desempenho similar ou superior ao de uma máquina virtual, no entanto, têm arquitetura e implementação distintas. Dessarte, avaliar seu desempenho é fundamental para a adoção e criação de novos projetos, uma vez que contêineres têm ganhado a adesão de grandes empresas.
Short-term and long-term cost-effectiveness of a pedometer-based exercise intervention in primary care: a within-trial analysis and beyond-trial modelling

Objectives A short-term and long-term cost-effectiveness analysis (CEA) of two pedometer-based walking interventions compared with usual care. Design (A) Short-term CEA: parallel three-arm cluster randomised trial randomised by household. (B) Long-term CEA: Markov decision model. Setting Seven primary care practices in South London, UK. Participants (A) Short-term CEA: 1023 people (922 households) aged 45–75 years without physical activity (PA) contraindications. (b) Long-term CEA: a cohort of 100 000 people aged 59–88 years. Interventions Pedometers, 12-week walking programmes and PA diaries delivered by post or through three PA consultations with practice nurses. Primary and secondary outcome measures Accelerometer-measured change (baseline to 12 months) in average daily step count and time in 10 min bouts of moderate to vigorous PA (MVPA), and EQ-5D-5L quality-adjusted life-years (QALY). Methods Resource use costs (£2013/2014) from a National Health Service perspective, presented as incremental cost-effectiveness ratios for each outcome over a 1-year and lifetime horizon, with cost-effectiveness acceptability curves and willingness to pay per QALY. Deterministic and probabilistic sensitivity analyses evaluate uncertainty. Results (A) Short-term CEA: At 12 months, incremental cost was £3.61 (£109)/min in ≥10 min MVPA bouts for nurse support compared with control (postal group). At £20 000/QALY, the postal group had a 50% chance of being cost saving compared with control. (B) Long-term CEA: The postal group had more QALYs (+759 QALYs, 95% CI 400 to 1247) and lower costs (−£11 million, 95% CI −12 to −10) than control and nurse groups, resulting in an incremental net monetary benefit of £26 million per 100 000 population. Results were sensitive to reporting serious adverse events, excluding health service use, and including all participant costs. Conclusions Postal delivery of a pedometer intervention in primary care is cost-effective long term and has a 50% chance of being cost-effective, through resource savings, within 1 year. Further research should ascertain maintenance of the higher levels of PA, and its impact on quality of life and health service use. Trial registration number ISRCTN98538934; Pre-results.
Lead-Acid Battery Modeling Over Full State of Charge and Discharge Range

The discharge behavior of electrochemical solid state batteries can be conveniently studied by means of electrical analogical models. This paper builds on one of the best known models proposed in the literature for lead-acid electrochemistry (the Ceraolo's model) by formulating an alternative third-order model and implementing a methodology to compute all model parameters (using particle swarm and nonlinear programming optimization) with increased precision and full usability over the whole range of possible states of charge and discharge currents. The developed methodology is used efficiently to model all commercial lead-acid batteries and enable their integration into simulation software for the optimized design of energy systems using energy storage.
One-Pot Synthesis and Surface Modification of Lauric-Acid-Capped CoFe2O4 Nanoparticles

This paper presents a facile one-pot method to synthesize monodisperse lauric-acid-capped cobalt ferrite nanoparticles (CoFe<sub>2</sub>O<sub>4</sub> NPs)and investigates their potential application as drug carriers for loading and controlled release of doxorubicin (DOX). The lauric-acid-capped CoFe<sub>2</sub>O<sub>4</sub> NPs with a particle size of 15.3 ± 1.7 nm were synthesized via one-pot reaction. Water-soluble lauric-acid-capped CoFe<sub>2</sub>O<sub>4</sub> NPs were obtained through surface modification with cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS). Furthermore, the performance of water-soluble lauric-acid-capped CoFe<sub>2</sub>O<sub>4</sub> NPs for drug delivery was investigated with DOX hydrochloride. The DOX release percentages of CTAB and SDS modified lauric-acid-capped CoFe<sub>2</sub>O<sub>4</sub> NPs are 56.1% and 48.2%, respectively. These results indicated that the modified lauric-acid-capped CoFe<sub>2</sub>O<sub>4</sub> NPs could be utilized as a promising candidate for cancer chemotherapy by making use of their pH-sensitive drug-release properties.
Topology-Reconfigurable Capacitor Matrix for Encrypted Dynamic Wireless Charging of Electric Vehicles

This paper proposes a new topology-reconfigurable capacitive compensation network, aiming to achieve the energy encryption of the contactless charging for multiple electric vehicles (EVs). The proposed variable topology of compensation network consists of an M × N capacitor matrix, where capacitors can be connected either in series or in parallel by controlling switches. Then, the proposed capacitor matrix can be formed in the series-parallel and the series-parallel–series-parallel topologies according to the adjustment of the resonant frequency. In such ways, the proposed topology-reconfigurable capacitor matrix can significantly extend the capacitive compensating range while reducing the number of capacitors, thus effectively enhancing the security performance of multiple-objective contactless charging systems. Finally, this paper gives the simulated and experimental results to verify the theoretical analysis, which can further illustrate the feasibility of the proposed topology-reconfigurable capacitor matrix for the energy encryption of EV contactless charging systems.
Deceit Identification Test on EEG Data Using Deep Belief Network

With the increasing rate of crime, deceit identification has become an issue of concern. The main task here is to classify the EEG data recorded for lie detection, into innocent and guilty. Although various methods have been developed in past to classify EEG data, deep belief nets are rarely used. This paper uses a deep learning technique using the restricted Boltzmann machine with wavelet to obtain the time and frequency domain information of signals. A deep belief network is developed with four RBMs stacked together. Softmax regression is utilized at output layer to classify EEG data into guilty and innocent. An experiment has been performed on EEG data recorded by performing a “Concealed Information Test”. The test consists of a mock crime scenario where some relevant and irrelevant images are presented in front of subjects. The EEG signals generated after flashing these images are recorded and analyzed.
Utilization of Pheromone in Production Scheduling by Negotiation and Cooperation Among Customers

This paper proposes a scheduling method imitating a negotiation among customers ordering products and a cooperating method among them. Combinatorial Auction is used to imitate the negotiation. Auction goods are the right to use a machine. A bidder is the customer and makes a bid. Timeslots for product processing, are included in the bid. The proposed cooperating method uses a pheromone to memorize negotiation records. Bidders deposit pheromones on auctioned goods included in bids. When a bidder makes a bid, the bidder prefers goods deposited own pheromone and avoids goods deposited other bidders' pheromones to obtain a schedule which leads to an agreement among all customers. Computational experiments are conducted to discuss the effectiveness of the proposed method.
Forest Structure Characterization From SAR Tomography at L-Band

Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical structure of forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D forest monitoring. However, the link between 3-D radar reflectivity and 3-D forest structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical forest structure from tomographic SAR data at L-band. For this, forest structure is parameterized into a set of a horizontal and a vertical structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate forest site of Traunstein in Germany. The obtained horizontal and vertical structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the forest structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework.