Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral and Energy Efficiency Analysis of mmWave Communications With Channel Inversion in Outband D2D Network

Device-to-device (D2D) communications is a promising approach for wireless communications in terms of energy and spectrum efficiency. However, such communications increase interferences in a cellular network. In this paper, we propose to lower these interferences by using millimeter wave directional antennas. To analyze the impact of these techniques, we introduce mathematical sectored antenna models that are deduced from mmWave antenna radiation patterns. Moreover, recent studies consider a constant transmit power for the devices. Nevertheless, modern communications use power control techniques to mitigate energy consumption and interferences. The main contribution of our work is the consideration of channel inversion, which is more realistic than the commonly used transmit model. Most works dealing with conventional D2D communications propose to use stochastic geometry to model a D2D-enabled network in order to evaluate the impact of interferences and noise on the various links. The objective of this paper is to analyze the SINR and the energy efficiency of outband D2D links for UEs equipped with directional mmWave antennas. To do so, we implement an energy efficiency calculation that considers both directional antennas and channel inversion. We propose to highlight the advantages and drawbacks of directional mmWave antennas in outband D2D for diverse antenna designs and different environments.
Energy-Delay Trade-Offs in Instruction Register File Design

In order to decrease latency and energy consumption, processors use hierarchical memory systems to store temporally and spatially related instructions close to the core. Instruction register file (IRF) is an energy-efficient solution for the lowest level in the instruction memory hierarcy. Being compiler-controlled, it removes the area and energy overheads involved in cache tag checking and adds flexibility in the separation of the instruction fetch and execution. In this paper, we systematically evaluate for the first time the effect of three IRF design variations on energy and delay against an unoptimized baseline IRF. Having instruction fetch and decode with IRF in the same pipeline stage allows minimal delay branching, but results in low operating clock frequency and impaired energy delay product compared to splitting them into two stages. Assuring instruction presence in IRF before execution with software reduces the area and increases maximum clock frequency compared to assurance with hardware, but requires compiler analysis. With a proposed compiler-analyzed instruction placement and co-designed hardware implementation, energy consumption with the best IRF variant is reduced by 9% on average with EEMBC Coremark and CHStone benchmaks. The energy delay product is improved by 23% when compared to the baseline IRF approach.
Bearing Fault Diagnosis Using an Extended Variable Structure Feedback Linearization Observer

The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer (FLO). The traditional feedback linearization observer is stable; however, this technique suffers from a lack of robustness. The proposed variable structure technique was used to improve the robustness of the fault estimation while reducing the uncertainties in the feedback linearization observer. The effectiveness of the proposed FLO procedure for the identification of outer, inner, and ball faults was tested using the Case Western University vibration dataset. The proposed model outperformed the variable structure observer (VSO), traditional feedback linearization observer (TFLO), and proportional-integral observer (PIO) by achieving average performance improvements of 5.5%, 8.5%, and 18.5%, respectively.
Physical profiles of elite, sub-elite, regional and age-group netballers

ABSTRACT Physical profile data from elite, sub-elite, regional, under 21s (U21), under 19s (U19) and under 17s (U17) (n = 845) players from a state netball association in Australia were analysed. Within season changes were examined for the elite and sub-elite players. Longitudinal changes were examined for the elite across four consecutive netball seasons. Elite were significantly older (24.3 ± 3.4years), taller (182.4 ± 7.2cm) and heavier (73.42 ± 6.95kg) than other playing levels (p < 0.001, ES 0.49–3.26) and had higher vertical jump (VJ) data compared to all groups (p < 0.001, ES 0.47–0.93). U17’s were significantly faster than elite, sub-elite and U19 players over 5m (p < 0.05, ES 0.36–0.58) while elite were faster over 20m compared to all groups (p < 0.01, ES 0.45–0.72). Elite achieved a greater distance (1350.8m; p < 0.05, ES 0.32–0.50) in the Yo-YoIRT1 compared to the sub-elite, regional, U19 and U17. VJ height significantly increased from the 2014 (51.6 ± 4.8cm) to the 2017 season (59.6 ± 6.3cm) for the elite players (p < 0.01, ES 1.18). Yo-YoIRT1 test scores increased significantly between the pre-season and in-season phases (p < 0.05, ES 0.17).
The Complexity and Entropy Analysis for Service Game Model Based on Different Expectations and Optimal Pricing

The internet has provided a new means for manufacturers to reach consumers. On the background of the widespread multichannel sales in China, based on a literature review of the service game and multichannel supply chain, this paper builds a multichannel dynamic service game model where the retailer operates an offline channel and the manufacturer operates an online channel and offers customers the option to buy online and pick up from the retailer’s store (BOPS). The manufacturer and the retailer take maximizing the channel profits as their business objectives and make channel service game under optimal pricing. We carry on theoretical analysis of the model and perform numerical simulations from the perspective of entropy theory, game theory, and chaotic dynamics. The results show that the stability of the system will weaken with the increase in service elasticity coefficient and that it is unaffected by the feedback parameter adjustment of the retailer. The BOPS channel strengthens the cooperation between the manufacturer and the retailer and moderates the conflict between the online and the offline channels. The system will go into chaotic state and cause the system’s entropy to increase when the manufacturer adjusts his/her service decision quickly. In a chaotic state, the system is sensitive to initial conditions and service input is difficult to predict; the manufacturer and retailer need more additional information to make the system clear or use the method of feedback control to delay or eliminate the occurrence of chaos.
Computer-Aided Diagnosis System for Alzheimer's Disease Using Fuzzy-Possibilistic Tissue Segmentation and SVM Classification

We describe a computer-aided diagnosis (CAD) system for discriminating patients suffering from Alzheimer's disease (AD) dementia and healthy patients. It is based on: 1) a clustering process to assess white matter, gray matter and cerebrospinal fluid volumes from noisy anatomical magnetic resonance (MR) and functional positron emission tomography (PET) brain images11The MR and PET data used in this work were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). ; 2) a classification process that distinguishes the brain images of normal and AD patients. The clustering stage consists of three steps: First, the fuzzy c-means (FCM) algorithm is used for a fuzzy partition of the initial class centroids. Second, fuzzy tissue maps are computed using a possibilistic C-means (PCM) algorithm that uses the FCM partition to obtain the final image clusters. The final segmentation is then made to delimit the brain tissue volumes. For the classification stage, a support vector machine (SVM) is used with different kernel functions. Validating the proposed CAD system on the MRI and PET images of 45 AD and 50 healthy brains, of subjects aged between 55 and 90 years, shows better sensitivity, specificity and accuracy in comparison to three alternative approaches, namely FCM, PCM and VAF (Voxels-As-Features). The accuracy rates for the noisiest images (20% of noise) were 75% for MRI and 73% for PET scan, compared to 71 % and 70,2%, 68.5% and 67%, and 65 % and 64.7 % with the three other approaches, respectively.
Implementation of DNN on a RISC-V Open Source Microprocessor for IoT devices

Logarithmic Quantization [1] and feature extraction enable us to reduce model parameters to a great extent. Based on these methods, we have implemented a small sized DNN on a RISC-V microprocessor with RAM of only 16KB. We also propose a feature extraction algorithm which outperforms the original fully connected neural network and reduces inputs by 12.25 $\times$ at the same time. MNIST [2] dataset is used as our training samples and Chainer [8] is used to train the network. As the result, we reduced weights size by nearly 86 $\times$ from 49.625KB to 0.578KB which make it possible to store these weights in arrays and load them directly into the RAM.
Unranking Combinations Using Gradient-Based Optimization

Combinations of m out of n are ubiquitous to model a wide class of combinatorial problems. For an ordered sequence of combinations, the unranking function generates the combination associated to an integer number in the ordered sequence. In this paper, we present a new method for unranking combinations by using a gradient-based optimization approach. Exhaustive experiments within computable allowable limits confirmed the feasibility and efficiency of our proposed approach. Particularly, our algorithmic realization aided by a Graphics Processing Unit (GPU) was able to generate arbitrary combinations within 0.571 seconds and 8 iterations in the worst case scenario, for n up to 1000 and m up to 100. Also, the performance and efficiency to generate combinations are independent of n, being meritorious when n is very large compared to m, or when n is time-varying. Furthermore, the number of required iterations to generate the combinations by the gradient-based optimization decreases with m in average, implying the attractive scalability in terms of m. Our proposed approach offers the building blocks to enable the succinct modeling and the efficient optimization of combinatorial structures.
Performance Gains From Cooperative MIMO Radar and MIMO Communication Systems

In this letter, the coexistence of a multiple-input multiple-output (MIMO) radar and a distributed MIMO communication systems is studied. Target returns contributed from both the radar transmitters and communication transmitters are employed to complete the radar task, leading to a hybrid active-passive MIMO radar network. For the communication task, not only are the communication signals received directly from communication transmitters, but also those bounced off from the target are exploited to extract useful information. The target localization Cramer–Rao bound and mutual information are derived for the radar and communication systems, respectively. It is shown that there is a performance gain due to the cooperation between the radar and communication systems.
Hand-Held 3-D Reconstruction of Large-Scale Scene With Kinect Sensors Based on Surfel and Video Sequences

This letter presents a hand-held complex large-scale scene reconstruction method with Kinect sensors based on surfel and video sequences. The feature point method simultaneous localization and mapping (SLAM) is employed to estimate the pose of the camera, and then bundle adjustment by combining 2-D and 3-D feature points is used to optimize camera pose. Also, the surfel model is employed to construct deformation maps for the fusion and optimization of point clouds, and finally, an accurate precise 3-D map can be obtained. The main contribution of this letter is that: 1) by using the SLAM method to obtain camera pose as the initial value of optimization, the problem of insufficient memory and low efficiency of the structure form motion method can be well solved; 2) sparsely textured regions can be reconstructed better by using bundle adjustment by combining 2-D and 3-D feature points; and 3) dense 3-D reconstruction of large scenes can be achieved, and the reconstructed 3-D models are more elaborate. Finally, experimental results show that this proposed method can be applied to a variety of complex large-scale scenes, and can obtain accurate precise 3-D model. This presented 3-D reconstruction method can be widely used in the fields of human–computer interaction, consumer electronics, and virtual reality.
An Amorphous Alloy Magnetic-Bus-Based SiC NPC Converter With Inherent Voltage Balancing for Grid-Connected Renewable Energy Systems

This paper presents an amorphous alloy magnetic-bus-based neutral point clamped (NPC) converter for grid-connected renewable generation systems. In the proposed system, the amorphous alloy high-frequency high-power density multi-winding magnetic bus generates balanced dc supplies for the five-level (5L) NPC converter for high-quality power conversion. Compared to the traditional NPC converter topologies, the proposed magnetic-bus-based architecture does not require any control circuit for voltage balancing of the series connected capacitors. The magnetic bus inherently overcomes galvanic isolation issues and may reduce the size of the boosting inductor. In this paper, a finite control set model predictive control algorithm is derived to control the grid-connected 5L-NPC inverter for multilevel voltage synthesizing, while achieving the user-defined active and reactive power values. To verify the proposed concept, a simulation model is developed and analyzed in MATLAB/Simulink environment. To validate the technology, a scale d-down prototype test platform is developed in the laboratory with silicon carbide switching devices, which achieves high blocking voltage, low power dissipation, high switching frequency, and high-temperature operation. Based on the simulation and the experimental results, it is expected that the proposed converter will have a great potential for widespread application in renewable generation systems including superconducting generator-based wind turbines.
Complexity of the Basic Reproduction Number (R0)

The basic reproduction number (R0), also called the basic reproduction ratio or rate or the basic reproductive rate, is an epidemiologic metric used to describe the contagiousness or transmissibility of infectious agents. R0 is affected by numerous biological, sociobehavioral, and environmental factors that govern pathogen transmission and, therefore, is usually estimated with various types of complex mathematical models, which make R0 easily misrepresented, misinterpreted, and misapplied. R0 is not a biological constant for a pathogen, a rate over time, or a measure of disease severity, and R0 cannot be modified through vaccination campaigns. R0 is rarely measured directly, and modeled R0 values are dependent on model structures and assumptions. Some R0 values reported in the scientific literature are likely obsolete. R0 must be estimated, reported, and applied with great caution because this basic metric is far from simple.
Can “Stay Online” Reduce Our Work Performance?

Due to the prevalence of mobile devices, more and more people are getting used to switch among different electronic devices during work, such as computers, mobile phones, iPads. Some argue that the multitasking ability allows them to complete multiple tasks at the same time and improve work efficiency. As opposed to this type of view, other people think that due to the limited capacity of attention and memory, switching among different interfaces damages the performance of each individual task. However, simply staying online does not provide such severe consequence. In order to investigate the effect of “stay online” or switching among different electronic interfaces on performances, this study explored the effects of memory maintaining and updating by using the memory updating paradigm. The results showed that the maintenance of other objects delayed the process of memory updating. Moreover, as the number of items increased, users' performances were damaged along with increased reaction time and reduced accuracy. Interestingly, when the number of items maintained in working memory reached a certain level, participants' response time would no longer increase, and subjects could not switch among these items correctly either. These results indicated that only “stay online”, with the presence of other media, would reduce the work performance, but if users did not switch among different items, the impact of media numbers would not affect the efficiency of work. But due to the switching cost, if people need to switch among different media in the work, their performances must be damaged, which might result in extended working hours and increased errors.
The Price for Programmability in the Software Data Plane: The Vendor Perspective

The killer features of the next-generation 5G mobile standard, including mobile edge computing and network slicing, will be very difficult to support with traditional fixed-function network appliances. Rather, the 5G core will depend on programmable switches, which allow packet processing functionality to be reconfigured on the fly in order to deploy virtualized network functions and service chains instantaneously. With 5G on the close horizon, it has become crucial to identify the price for programmability in the software data plane, considering the expected complexity and scale of the next-generation mobile core. In this paper, we report on a multi-year data-plane scalability study we have conducted for a large mobile vendor. Our results paint a rather pessimistic picture on the current landscape of the programmable software data plane. We find that the prominent programmable switches either do not provide all the features necessary to implement 5G telco pipelines efficiently or struggle to meet the scale, and the performance operators have come to expect from conventional fixed-function appliances. The only exception, ESwitch, remains proprietary. We call for further work on data-plane scalability and sketch some directions for future research.
Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods

Background Electronic health records (EHRs) are important data resources for clinical studies and applications. Physicians or clinicians describe patients’ disorders or treatment procedures in EHRs using free text (unstructured) clinical notes. The narrative information plays an important role in patient treatment and clinical research. However, it is challenging to make machines understand the clinical narratives. Objective This study aimed to automatically identify Chinese clinical entities from free text in EHRs and make machines semantically understand diagnoses, tests, body parts, symptoms, treatments, and so on. Methods The dataset we used for this study is the benchmark dataset with human annotated Chinese EHRs, released by the China Conference on Knowledge Graph and Semantic Computing 2017 clinical named entity recognition challenge task. Overall, 2 machine learning models, the conditional random fields (CRF) method and bidirectional long short-term memory (LSTM)-CRF, were applied to recognize clinical entities from Chinese EHR data. To train the CRF–based model, we selected features such as bag of Chinese characters, part-of-speech tags, character types, and the position of characters. For the bidirectional LSTM-CRF–based model, character embeddings and segmentation information were used as features. In addition, we also employed a dictionary-based approach as the baseline for the purpose of performance evaluation. Precision, recall, and the harmonic average of precision and recall (F1 score) were used to evaluate the performance of the methods. Results Experiments on the test set showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom, body part, and treatment simultaneously. With regard to overall performance, CRF and bidirectional LSTM-CRF achieved a precision of 0.9203 and 0.9112, recall of 0.8709 and 0.8974, and F1 score of 0.8949 and 0.9043, respectively. The results also indicated that our methods performed well in recognizing each type of clinical entity, in which the “symptom” type achieved the best F1 score of over 0.96. Moreover, as the number of features increased, the F1 score of the CRF model increased from 0.8547 to 0.8949. Conclusions In this study, we employed two computational methods to simultaneously identify types of Chinese clinical entities from free text in EHRs. With training, these methods can effectively identify various types of clinical entities (eg, symptom and treatment) with high accuracy. The deep learning model, bidirectional LSTM-CRF, can achieve better performance than the CRF model with little feature engineering. This study contributed to translating human-readable health information into machine-readable information.
Avant-Doc: Intersections of Documentary and Avant-Garde Cinema

Table of Contents: Introduction Annette Michelson Robert Gardner Ed Pincus (and Jane Pincus, Lucia Small) Alfred Guzzetti Ross McElwee Nina Davenport Leonard Retel Helmrich Jonathan Caouette Pawel Wojtasik Michael Glawogger Susana de Sousa Dias Alexander Olch (on The Windmill Movie) Amie Siegel (on DDR/DDR) Arthur and Jennifer Smith (on Ice Bears of the Beaufort) Betzy Bromberg (on Voluptuous Sleep) Jen Proctor (on A Movie by Jen Proctor) Jane Gillooly (on Suitcase of Love and Shame) Godfrey Reggio (on Visitors) Todd Haynes Sensory Ethnography Ilisa Barbash and Lucien Castaing-Taylor (on In and Out of Africa and Sweetgrass) Lucien Castaing-Taylor (on his installation work and on Harvard's Sensory Ethnography Lab) Lucien Castaing-Taylor and Verena Paravel (on Leviathan) Stephanie Spray and Pacho Velez (on Manakamana) Filmographies Bibliographies
A Comparative Study of the Role of Australia and New Zealand in Sustainable Dairy Competition in the Chinese Market after the Dairy Safety Scandals

After the melamine milk scandal in 2008, China’s global imports of dairy products soared, especially after FTAs had been established with Australia and New Zealand. The dairy products of the two countries have a unique competitive trading advantage in the Chinese market. However, at a time when Chinese consumers are increasingly dependent on imported dairy products, a succession of whey protein scandals affecting New Zealand’s dairy products in 2013 had a negative psychological impact on Chinese importers and consumers, and this even affected the import status of New Zealand dairy imports to the Chinese market. The present paper, based on the United Nations Comtrade Harmonized System, studies the role of Australia and New Zealand in China’s dairy market. It calculates the trade competitiveness index, revealing the relative competitive advantages of Australia and New Zealand, and investigates the impact of the dairy products from these countries on China’s imports from the rest of the world across six dairy sectors in the period 1992–2017. We find that, under the food safety laws, the relative dairy import prices, milk scandals, and Free Trade Agreements, together with the competitive advantages of Australia and New Zealand, had a varied impact on the corresponding Chinese dairy imports across the relevant sectors in the context of China food safety laws after the melamine milk scandal. These findings acknowledge Australia and New Zealand’s competitiveness in the international dairy trade, and also lead to suggestions regarding their competitiveness and sustainable development in the Chinese market.