Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient characterization of electrically evoked responses for neural interfaces

Future neural interfaces will read and write population neural activity with high spatial and temporal resolution, for diverse applications. For example, an artificial retina may restore vision to the blind by electrically stimulating retinal ganglion cells. Such devices must tune their function, based on stimulating and recording, to match the function of the circuit. However, existing methods for characterizing the neural interface scale poorly with the number of electrodes, limiting their practical applicability. This work tests the idea that using prior information from previous experiments and closed-loop measurements may greatly increase the efficiency of the neural interface. Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina. Three key calibration steps were optimized: spike sorting in the presence of stimulation artifacts, response modeling, and adaptive stimulation. For spike sorting, exploiting the similarity of electrical artifact across electrodes and experiments substantially reduced the number of required measurements. For response modeling, a joint model that captures the inverse relationship between recorded spike amplitude and electrical stimulation threshold from previously recorded retinas resulted in greater consistency and efficiency. For adaptive stimulation, choosing which electrodes to stimulate based on probability estimates from previous measurements improved efficiency. Similar improvements resulted from using either non-adaptive stimulation with a joint model across cells, or adaptive stimulation with an independent model for each cell. Finally, image reconstruction revealed that these improvements may translate to improved performance of an artificial retina.
Nowcasting: An R Package for Predicting Economic Variables Using Dynamic Factor Models

The nowcasting package provides the tools to make forecasts of monthly or quarterly economic variables using dynamic factor models. The objective is to help the user at each step of the forecasting process, starting with the construction of a database, all the way to the interpretation of the forecasts. The dynamic factor model adopted in this package is based on the articles from Giannone et al. (2008) and Banbura et al. (2011). Although there exist several other dynamic factor model packages available for R, ours provides an environment to easily forecast economic variables and interpret results.
Phased Array Digital Beamforming Algorithms and Applications

Phased Array Digital Beamforming Algorithms and Applications David Moyle Marsh Department of Electrical and Computer Engineering, BYU Master of Science With the expansion of unmanned aircraft system (UAS) technologies, there is a growing need for UAS Traffic Management (UTM) systems to promote safe operation and development. To be successful, these UTM systems must be able to detect and track multiple drones in the presence of clutter. This paper examines the implementation of different algorithms on a compact, X-band, frequency modulated continuous wave (FMCW) radar in an effort to enable more accurate detection and estimation of drones. Several algorithms were tested through post processing on actual radar data to determine their accuracy and usefulness for this system. A promising result was achieved through the application of pulse-Doppler processing. Post processing on recorded radar data showed that a moving target indicator successfully separated a target from clutter. An improvement was also noted for the implementation of phase comparison monopulse which accurately estimated angle of arrival (AOA) and required fewer computations than digital beamforming. The second part of this thesis explains the work done on an adaptive broadband, real time beamformer for RF interference (RFI) mitigation. An effective communication system is reliable and can counteract the effects of jamming. Beamforming is an appropriate solution to RFI. To assist in this process FPGA firmware was developed to prepare signals for frequency domain beamforming. This system allows beamforming to be applied to 150 MHz of bandwidth. Future implementation will allow for signal reconstruction after beamforming and demodulation of a communication signal.
Oral Hygiene Performance among a Sample of Sudanese Orthodontic Patients

Background: Proper oral hygiene performance is a prerequisite to prevent complications from increased food debris and plaque retention during the treatment period with fixed orthodontic appliance. Objective: To assess oral hygiene performance of Sudanese patients with fixed orthodontic appliance. Materials and Methods: A descriptive cross-sectional study of 500 orthodontic patients. Data collected through personal interview, using a well-structured questionnaire regarding oral hygiene maintainer during the fixed orthodontic treatment. Results: Half of the sample brushes their teeth twice a day. 46% maintained oral hygiene using a toothbrush and Interdental cleaning-aids. 73% brushes their teeth both vertically and horizontally and 44% used Interdental cleaningaids daily. 51% preferred the use of Interdental brushes. 38% of the patients attended orthodontic clinics regularly for a check-up and professional cleaning. Most of the patients (81%) taught by their orthodontist to maintain a proper oral hygiene, (61%) were instructed to use fluoridated toothpaste or mouthwash and (79%) of the patients were reported to follow the orthodontist’s instructions for maintaining proper oral hygiene during treatment. Conclusion: Acceptable oral hygiene maintenance reported in Sudanese patients with fixed orthodontic appliance. Recommendation: Great emphasis should be considered to improve the awareness and knowledge of disadvantage of improper oral hygiene maintenance during treatment with fixed orthodontic appliance.
Fitting a Computational Model of Perceptual Inference to Principal Component Weights of ERP Responses

The mismatch negativity (MMN), a well-studied electrophysiological response to irregularities in the sensory input stream, has often been used to examine how the brain learns the statistics of its environment. This response has also been found to be systematically altered in clinical populations such as patients with schizophrenia. These deviations in electrophysiology, however, cannot easily be linked to inter-individual differences in cognitive processing style due to the lack of direct behavioral readouts, which limits the paradigm’s usefulness for cognitive science and computational psychiatry. To bridge this gap, we present a pipeline for inferring parameters of a generative model of learning and inference, the Hierarchical Gaussian Filter (HGF), given EEG recordings obtained as part of the auditory MMN paradigm. Our pipeline includes a data-driven feature selection step as well as a proposal for mapping belief updates to the EEG features.
Secrecy Performance of Small-Cell Networks With Transmitter Selection and Unreliable Backhaul Under Spectrum Sharing Environment

We investigate the secrecy performance of an underlay small-cell cognitive radio network under unreliable backhaul connections. The small-cell network shares the same spectrum with the primary network, ensuring that a desired outage probability constraint is always met in the primary network. To improve the security of the small-cell cognitive network, we propose three sub-optimal small-cell transmitter selection schemes, namely sub-optimal transmitter selection, minimal interference selection, and minimal eavesdropping selection. Closed-form expressions of the non-zero secrecy rate, secrecy outage probability, and ergodic secrecy capacity are provided for the schemes along with asymptotic expressions. We also propose an optimal selection scheme and compare performances with the sub-optimal selection schemes. Computable expressions for the non-zero secrecy rate and secrecy outage probability are presented for the optimal selection scheme. Our results show that by increasing the primary transmitter's power and the number of small-cell transmitters, the system performance improves. The selection scheme, the backhaul reliability, and the primary user quality-of-service constraint also have a significant impact on secrecy performance.
Image Smoothing Via Gradient Sparsity and Surface Area Minimization

Image smoothing is a very important topic in image processing. Among these image smoothing methods, the L0 gradient minimization method is one of the most popular ones. However, the L0 gradient minimization method suffers from the staircasing effect and over-sharpening issue, which highly degrade the quality of the smoothed image. To overcome these issues, we use not only the L0 gradient term for finding edges, but also a surface area based term for the purpose of smoothing the inside of each region. An alternating minimization algorithm is suggested to efficiently solve the proposed model, where each subproblem has a closed-form solution. Leveraging the introduced surface area term, the proposed method can effectively alleviate the staircasing effect and the over-sharpening issue. The superiority of our method over the state-of-the-art methods is demonstrated by a series of experiments.
Efficient data management and control over WSNs using SDN‐enabled aerial networks

The recent developments in collaborative search, acquisition, and tracking have hoisted the geographical barrier. The network between unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) is one such collaboration, which comprises battery‐powered static sensor nodes that act as sources and sinks and UAVs that act as relays. This collaborative network presents with opportunities and advantages, but at the same time, configuration of such networks is an arduous task. The WSN nodes are characterized by constant depleting power. Their network itself requires constant management and reconfiguration. These requisites can be slaked through the formation of an efficient data dissemination algorithm, which acclimates according to the network state. Considering this, a data dissemination approach is presented in this paper, which constructs a virtual topology predicated on the charge of WSN nodes utilizing software‐defined networks (SDNs) through UAVs. The topology is constantly monitored and reconfigured when required. The aerial nodes are equipped with multiple‐input multiple‐output (MIMO) antennas in order to facilitate simultaneous communication with the ground nodes, the base station, and the SDN controller. An efficient sleep timer and backoff counter strategies are also utilized by the proposed approach. The SDN controller facilitates the topology formation and maintenance of a sleep timer and a backoff counter. The proposed model is compared with clustered hierarchical layouts and hexagonal cell layouts through the network simulations. The results suggest significant improvements in the proposed model for various metrics, such as lifetime, delay, latency, delivery ratio, and throughput in comparison with the existing solutions.
AVERAGE RISK COLORECTAL CANCER SCREENING: UNDERSTANDING THE CONSEQUENCES OF INTRODUCING COMPETING DEMANDS FOR LIMITED COLONOSCOPY RESOURCES

BACKGROUND: Colorectal cancer is the second leading cause of cancer death in Canada, accounting for 12% of all cancer deaths. Survival following a colorectal cancer diagnosis is dependent upon early identification and access to effective therapies, as the disease is highly treatable if detected and treated early in its course, usually prior to the onset of symptoms. Population-level average risk colorectal cancer screening has become an important strategy in the control of colorectal cancer. However, the implications of screening strategies for already constrained colonoscopy services are not well understood. METHOD: A review of population-based colorectal cancer screening strategies and programs in Canada, including an overview of screening modalities and implications for colonoscopy services. RESULTS: While an important component of colorectal cancer control strategies, the advent of provincial screening programs is expected to significantly increase the demand for already overburdened colonoscopy services. Further, colonoscopy services are shared by follow-up screening, diagnostic and surveillance patients, each with varying risks of disease. The effects of competing demands for these limited resources are not well understood, despite being critical for optimal resource planning. DISCUSSION: Much of the evaluation of average risk colorectal cancer screening to date has focused on clinical and cost effectiveness. While support for screening has generally been provided by randomised trials and observational studies, there is relatively little information regarding the effects of operational decision making on system performance or the potential for an overwhelmed system to limit the effectiveness of screening and cause harm to higher risk patients.
Irradiation test and hardness design for mobile rescue robot in nuclear environment

Purpose The paper aims to improve the radiation-proof capability of the self-designed mobile robot with a 7-DOF manipulator, enabling the long-playing inspection and intervention under high-dose radiation environment. In this context, gamma-ray irradiation test for electronic components and specific hardness design have also been specifically presented and discussed. Design/methodology/approach The study’s hardness design mainly focuses on shielding protection, distance protection and time protection. Irradiation test is first carried out to investigate irradiation resistance of each electronic module. Then, modular deployment and shielding calculation are completed for the point-type nuclear accidents, respectively, to achieve a robust anti-radiation design scheme. Finally, the field experiment is conducted to validate system effectiveness and good mobility, and operational practices are acquired for the realization of time protection. Findings Coupled with modular redeployment and shielding design, irradiation results illustrate the effectiveness of robotic anti-radiation design. Meanwhile, experiences and reformed measures from the field exercise implement efficient operation and radiological time protection. Research limitations/implications Considering the huge risks of high-dose source exposure, the radiation-resistance of the overall system cannot be verified in the field experiment. Fortunately, irradiation test and modular shielding calculation are conducted as a minimal validation. Practical implications The proposed anti-radiation design methods and the irradiated results can be applied to many other nuclear vehicles and manipulators for the feasible multi-layer protection and excellent mobility. Originality/value A nuclear intervention robot with specific hardness design is presented in detail in this paper. Enlightened by the idea of shielding and distance protection, a large number of electronic modules with multiple types and structures are treated and compared in irradiation experiments, while modular redeployment and retrofitting are completed to reduce irradiated damages. To achieve the effect of time protection, mobility performance and operational practices are discussed and validated in the field experiment based on the mobile system.
Temporal Attention Networks for Multitemporal Multisensor Crop Classification

Crop classification based on multitemporal multisensor remote sensing imagery has a great significance. As more and more Earth observation satellites are launched, it becomes easier to obtain increasingly dense multitemporal data, and the category patterns hidden in multitemporal data can be more and more finely mined and expressed. However, the traditional classification methods treat the features of different temporal periods consistently, so the classification performances are not well enough, especially for the categories with subtle phenological differences. In this paper, we propose a temporal-attention CNN-GRU (Convolutional Neural Networks and Gated Recurrent Unit Networks) approach to distinguish subtle crop differences, and the temporal attention mechanism introduced in the model can achieve the effect of enhancing phenological differences and suppressing phenological similarity. Firstly, we use the GRU networks to model the temporal correlation of the multitemporal data. And then, the temporal attention layer utilize a query module to retrieve “what is the important information” over the whole temporal sequence, so we can obtain the attention weights for each temporal period. In the experiments, multitemporal samples are collected from Sentinel-2A/B and Landsat-8. Due to the different spatial resolutions of multiband images, transposed convolution which can extract the raw spatial structure information is used for multiband features fusion. Experimental results on multitemporal data show that the proposed approach achieve the best performance compared with conventional methods, especially for the categories with similar phenological laws.
Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching

In this work, we propose an aggregation method to combine the Bidirectional Encoder Representations from Transformer (BERT) with a MatchLSTM layer for Sequence Matching. Given a sentence pair, we extract the output representations of it from BERT. Then we extend BERT with a MatchLSTM layer to get further interaction of the sentence pair for sequence matching tasks. Taking natural language inference as an example, we split BERT output into two parts, which is from premise sentence and hypothesis sentence. At each position of the hypothesis sentence, both the weighted representation of the premise sentence and the representation of the current token are fed into LSTM. We jointly train the aggregation layer and pre-trained layer for sequence matching. We conduct an experiment on two publicly available datasets, WikiQA and SNLI. Experiments show that our model achieves significantly improvement compared with state-of-the-art methods on both datasets.
Study on Web Content Extraction Techniques

Nowadays, the explosive growth of the World Wide Web generates tremendous amount of web data and consequently web data mining has become an important technique for discovering useful information and knowledge. Web mining is a vivid research area closely related to Information Extraction (IE). Automatic content extraction from web pages is a challenging yet significant problem in the fields of information retrieval and data mining. Web Content mining refers to the discovery of useful information from web content such as text, images videos etc. Web content extraction is the process of organizing data instances into groups whose members are similar in some way. Content Extraction helps the user to easily select the topic of interest. Web Content Ming technology is useful in management information system. Web content mining extracts or mines useful information or knowledge from web page contents. This paper aims to study on web content extraction techniques.
A 3rd-order 1-bit Σ-Δ modulator with a 2-tap FIR filter embedded

This paper presents a low-voltage 3 rd -order single-bit switched-capacitor Σ-Δ modulator implemented in a 40-nm CMOS technology. In the modulator, a 2-tap FIR (finite impulse response) filter is employed in the feedback loop to reduce the integrator output swings. With the help of digital assisted techniques, the number of the sampling capacitors in the first integrator is reduced to mitigate the performance deterioration caused by capacitor mismatch. Besides, the inverter-based amplifiers with dynamic-biased structure are proposed to reduce the power consumption. The proposed modulator is sampled at 25.6 MHz over a bandwidth of 100 kHz. The modulator achieves a max SNR (signal-to-noise ratio) of 92.1 dB, a max SNDR (signal-to-noise and distortion ratio) of 87.3 dB, and a DR (dynamic range) of 88.1 dB under the supply voltage of ±0.45 V while consuming a total power consumption of 790 μW.
Parallel algorithms

The subject of this chapter is the design and analysis of parallel algorithms. Most of today's algorithms are sequential, that is, they specify a sequence of steps in which each step consists of a single operation. These algorithms are well suited to today's computers, which basically perform operations in a sequential fashion. Although the speed at which sequential computers operate has been improving at an exponential rate for many years, the improvement is now coming at greater and greater cost. As a consequence, researchers have sought more cost-e ective improvements by building \parallel" computers { computers that perform multiple operations in a single step. In order to solve a problem e ciently on a parallel machine, it is usually necessary to design an algorithm that speci es multiple operations on each step, i.e., a parallel algorithm. As an example, consider the problem of computing the sum of a sequence A of n numbers. The standard algorithm computes the sum by making a single pass through the sequence, keeping a running sum of the numbers seen so far. It is not di cult however, to devise an algorithm for computing the sum that performs many operations in parallel. For example, suppose that, in parallel, each element of A with an even index is paired and summed with the next element of A, which has an odd index, i.e., A[0] is paired with A[1], A[2] with A[3], and so on. The result is a new sequence of dn=2e numbers that sum to the same value as the sum that we wish to compute. This pairing and summing step can be repeated until, after dlog2 ne steps, a sequence consisting of a single value is produced, and this value is equal to the nal sum. The parallelism in an algorithm can yield improved performance on many di erent kinds of computers. For example, on a parallel computer, the operations in a parallel algorithm can be performed simultaneously by di erent processors. Furthermore, even on a single-processor computer the parallelism in an algorithm can be exploited by using multiple functional units, pipelined functional units, or pipelined memory systems. Thus, it is important to make a distinction between