Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Pose and Correspondence Determination Combining Softassign and Orthogonal Iteration

Pose estimation with unknown correspondences between 3D object points and 2D image points is known as the simultaneous pose and correspondence determination problem in the field of computer vision. It currently is still diffcutlt to solve particularly with the appearance of occlusion and cluster. In this paper, we present a new iterative algorithm for the pose estimation of an 3D object without any additional 3D-2D corresondence infromation. Our method combines SoftAssign algorithm for derterming the correspondence and OI (orthogonal iteration) algorithm for computing the pose. An assignment matrix which describes the correspondence is first introduced to the objective function of OI algorithm, and the simultaneous pose and correspondence determination problem is formulated as that of minimizing the weighted object space collinearity error. The pose and correspondence are evolved from an initial pose to an optimum value by nesting the two algorithms into one deterministic annealing process. Simulation and experimental results demonstrate that the proposed method is computationally more efficient and more accurate than the state-of-art methods.
The analysis and validation of landform stability using unmanned aerial vehicles

Mine closure relies on proof of best practice in both design and performance of rehabilitation. Field techniques have been the traditional approach for producing detailed supporting empirical evidence for mine closure. Although field sampling provides a detailed snapshot of key performance criteria within small areas, these areas themselves may not be representative of the overall performance of rehabilitation. Additionally, their limited scale may miss broader spatial characteristics that could further strengthen arguments for relinquishment. Remote sensing is a complimentary approach to field sampling that can produce an entire census of a rehabilitation site at a reduced scale. However, uncertainty still surrounds the adoption of a remote sensing approach, and whether such techniques can capture key performance indicators accurately and consistently. This paper provides both a demonstration of capacity, and quantification of accuracy, of remotely sensed data analytics for the production of empirical evidence to support mine closure management. Using rehabilitated landforms in the Western Australian Goldfields as case studies, remote sensing was adopted in two supporting roles: the validation of landform construction, and the ongoing monitoring of landform performance. The geometry of constructed landform surfaces was measured through photogrammetric techniques and assessed against design specifications. Ongoing monitoring assessed both vegetative colonisation and relative stability of established landform surfaces. Coupled together, the broader scale impact of non-compliant areas upon local rehabilitation performance was explored and discussed. Underpinning these data analytics is the accuracy of remotely sensed data. The quantification of uncertainty within the data was derived through a comparison against precision field measurements. Quantification of this uncertainty allowed the establishment of confidence intervals on derived measurements. Furthermore, the impact of changing environmental complexity upon analysis performance was quantified. This allowed for the modelling of compensation factors that dynamically counterbalance the increased uncertainty of complex environments. The result of the study demonstrates the capacity for a remote sensing approach to empirically support mine closure and relinquishment.
Closing the GAP: Group-Aware Parallelization for Online Selection of Candidates with Biased Evaluations

The secretary problem and its variants select candidates in an online setting while assuming access to their true rankings (or utilities) as they arrive. Motivated by the existence of implicit bias in evaluations of candidates from different demographic groups (e.g., gender, age, race), we consider the biased secretary problem, where instead of observing the true utility $w(e)$ of each candidate $e$, the algorithm only observes a biased estimate $\tilde{w}(e)$. The true utilities of the candidates are observed only after the entire sequence of candidates has been revealed. The goal is to be competitive with respect to an offline oracle that knows true utilities. We first assume that the bias factors are group-dependent (e.g., dependent on race, age, gender, nationality): for each candidate $e$ in group $G_i$, the observed utility is $\tilde{w}(e) = w(e)/\beta_i$. We posit that a desirable and fair property for selection of candidates is ranked demographic parity: for any $p$, the probability of selecting a candidate in the top $p$th percentile in any group is similar across demographic groups of equal size. We show that group-agnostic methods can be suboptimal both in terms of fairness and utility, and propose group-aware parallelization, \textsc{Gap}, as an effective tool to get provably competitive algorithms. We show that \textsc{Gap} can be tweaked for fairly general stochastic and adversarial settings by adapting techniques in the secretary problem literature. We then propose a more individualized model of bias, where due to implicit bias and limitations of testing, the algorithm only observes an interval $[\ell_e,u_e]$ containing the true utility of candidate $e$. In this setting, we only have pair-wise comparisons between candidates when their observed intervals do not overlap. We then provide an algorithm for this setting which is order-optimal and it extends to the case when elements can only be compared with respect to a partial order.
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network

Leveraging external knowledge is an emerging trend in machine comprehension task. Previous work usually utilizes knowledge graphs such as ConceptNet as external knowledge, and extracts triples from them to enhance the initial representation of the machine comprehension context. However, such method cannot capture the structural information in the knowledge graph. To this end, we propose a Structural Knowledge Graph-aware Network(SKG) model, constructing sub-graphs for entities in the machine comprehension context. Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph. Experiments show that SKG achieves state-of-the-art performance on the ReCoRD dataset.
Evaluation of PPC / mBio Inc.’s Biochip Reader Integrated Circuit Reader System for Diagnosis of HIV / HCV Infection: Preliminary Method

The technological advances of recent decades in immunodiagnostic techniques have enabled the development of methods capable of detecting the antigen-antibody complex with high efficiency and reliability. However, the cost of such procedures has remained high; they are not easy to handle for an unskilled professional, nor provide immediate results for multiple samples. Precision Photonics Corporation (PPC), with m/Bio Inc. and in partnership with the University of San Diego (UCSD), USA, created an integrated circuit reader (Biochip Reader), which is low cost, fast and easy to handle, based on an optical systems and multiplex arrangements for the detection of biological multi-markers at the same time. This study aimed to evaluate the operation of the integrated circuit reader system (Biochip Reader), the protocols and the results of the multiplex tests for antibodies against HIV and HCV in partnership with the Federal University of Bahia (UFBA). We tested a total of 65 samples in the Biochip reader, and they showed 100% of sensitivity and specificity when compared to the results obtained by ELISA for HIV and HCV. Nevertheless, the protocols and the results obtained in Bahia presented slide instability, such as the formation of crystals and trehalose residues, unnecessary steps and manipulation of the slides. This fact led to changing the protocol and improving the prototype. However, more tests with new biomarkers are needed to validate the method.
News2vec: News Network Embedding with Subnode Information

With the development of NLP technologies, news can be automatically categorized and labeled according to a variety of characteristics, at the same time be represented as low dimensional embeddings. However, it lacks a systematic approach that effectively integrates the inherited features and inter-textual knowledge of news to represent the collective information with a dense vector. With the aim of filling this gap, the News2vec model is proposed to allow the distributed representation of news taking into account its associated features. To describe the cross-document linkages between news, a network consisting of news and its attributes is constructed. Moreover, the News2vec model treats the news node as a bag of features by developing the Subnode model. Based on the biased random walk and the skip-gram model, each news feature is mapped to a vector, and the news is thus represented as the sum of its features. This approach offers an easy solution to create embeddings for unseen news nodes based on its attributes. To evaluate our model, dimension reduction plots and correlation heat-maps are created to visualize the news vectors, together with the application of two downstream tasks, the stock movement prediction and news recommendation. By comparing with other established text/sentence embedding models, we show that News2vec achieves state-of-the-art performance on these news-related tasks.
Self-Interference Cancellation Scheme for Secure AF Satellite Communication Based on FH-MWFRFT

This letter focuses on the self-interference cancellation problem for a secure two-way amplify-and-forward (AF) satellite communication system with eavesdropper at both the transmitter and the receiver. Several multi-antenna earth stations (ESs) aim to exchange messages through a multi-antenna satellite in two hops. We apply the frequency hopping multiple-term weighted fractional fourier transform (FH-MWFRFT) with different transform orders at the earth stations in the first hop to eliminate the self-interference existing in the two-way communication and enhance the security. The second hop of the communication is secured by null space beamforming, which steer the signal to the null space of the eavesdroppers’ channel. We demonstrate the improvement in secrecy capacity brought by the self-interference cancellation scheme via various simulation results and discussions. The simulations are carried out under the conditions of perfect channel state information (CSI) and imperfect CSI to verify the robustness of the proposed scheme.
Robust OFDM Modem for a Swarm of Underwater Autonomous Vehicles

In this article, a filter bank is coupled with a CFAR detector to guarantee an efficient frame detection in presence of Doppler shift. This work is realized in scope of the NEMOSENS project, which aims to produce autonomous underwater vehicles (AUVs) able to communicate and move in a network thanks to UA modems. Many adverse phenomenons occur in the context of underwater acoustic. Most harmful effects for underwater acoustic communication (UAC) are the multi-path nature of the environment, the Doppler spread and the noise variability. The proposed method reduces the number of lost frames and gives a rough estimate of delay and Doppler shift. The followed approach is supported by simulations with simplified hypotheses, but the interest of this approach is also shown in real sea experiments.
Similarity Based Auxiliary Classifier for Named Entity Recognition

The segmentation problem is one of the fundamental challenges associated with name entity recognition (NER) tasks that aim to reduce the boundary error when detecting a sequence of entity words. A considerable number of advanced approaches have been proposed and most of them exhibit performance deterioration when entities become longer. Inspired by previous work in which a multi-task strategy is used to solve segmentation problems, we design a similarity based auxiliary classifier (SAC), which can distinguish entity words from non-entity words. Unlike conventional classifiers, SAC uses vectors to indicate tags. Therefore, SAC can calculate the similarities between words and tags, and then compute a weighted sum of the tag vectors, which can be considered a useful feature for NER tasks. Empirical results are used to verify the rationality of the SAC structure and demonstrate the SAC model’s potential in performance improvement against our baseline approaches.
SFM And Semantic Information Based Online Targetless Camera-LIDAR Self-Calibration

In this paper we propose an end-to-end, automatic, online camera-LIDAR calibration approach, for application in self driving vehicle navigation. The main idea is to connect the image domain and the 3D space by generating point clouds from camera data while driving, using a structure from motion (SfM) pipeline, and use it as the basis for registration. As a core step of the algorithm we introduce an object level alignment to transform the generated and captured point clouds into a common coordinate system. Finally, we calculate the correspondences between the 2D image domain and the 3D LIDAR point clouds, to produce the registration. We evaluated the method in various different real life traffic scenarios.
Wake-up Radios for Wireless Sensor Networks

: For successful data delivery, the destination nodes should be listening to the medium to receive the data when the sender node starts the data communication. To achieve this synchronization, there are different rendezvous schemes among which the most energy-efficient scheme is use of wake-up receivers. Current hardware technologies of wake-up receivers enable us to evaluate them as a promising solution for wireless sensor networks (WSNs). In this work, the benefits achieved with wake-up receivers are investigated along with the challenges observed. In addition, an overview of state-of-the-art hardware and networking protocol proposals are also presented. As the wake-up receivers offer new opportunities, new potential application areas are also presented and discussed.
Energy Minimization Based Alternate Sampling and Clustering for Geometric Model Fitting

The preference analysis approach is widely used in solving multimodel fitting problems, but it does not combine the potential spatial correlation in the data. In this paper, we propose a novel method for introducing spatial information to optimize the results of preference analysis through energy minimization. The method consists of two steps: 1) T-linkage is performed based on localized window sampling to obtain the initial clustering results; and 2) the alternate sampling and clustering framework is applied until the energy is stable. The main contribution is that we introduce the spatial information of the data points in the preference analysis by minimizing an energy function, in order to make the inlier preferences more distinguishable and improve the clustering. The experimental results confirm that the proposed method compares favorably with the current state of the art.
Development and Preliminary Validation of Image-enabled Process Metrics for Assessment of Open Surgery Suturing Skill

Suturing is a fundamental surgical skill required in a variety of operations, ranging from wound repair to delicate vascular reconstruction. It is essential that surgeons master requisite suturing skills so that he or she can deliver safe and effective care to patients. Due to an increased emphasis on standardized medical training, tools and methods are needed to provide objective assessment and feedback during the learning process. In this thesis, a new surgical simulator for assessment and training of open surgery suturing skill is introduced. The suturing simulator system design, force-based, motion-based, image-based and image-enabled metrics for skill assessment, and a preliminary study of resident and attending surgeons are presented. The simulator collects synchronized force, motion, video and touch data during radial continuous suturing. The synchronized data is used to extract metrics for suturing skill assessment. The simulator has a camera positioned underneath the suturing membrane, enabling visual tracking of the needle during suturing. Needle tracking data enables extraction of meaningful metrics for both the process and the product of the suturing task. To better simulate surgical conditions, the height of the simulator and the depth of the membrane are both adjustable. The metrics were motivated by insight provided to us from practicing vascular surgeons. These metrics were based on the physics of needle insertion forces, the maxim to follow the curve of the needle while driving through tissue, and minimizing lateral forces and motions that induce tear.
User Association and Resource Allocation in Full-Duplex Relay Aided NOMA Systems

To support ubiquitous connectivity and the rising demand of tele-traffic in the Internet of Things (IoT), we amalgamate nonorthogonal multiple access (NOMA) and full-duplex (FD) techniques to propose a new hybrid NOMA (FDH-NOMA) framework. We formulate an ergodic sum rate maximization problem under statistic channel state information (CSI). To simplify optimization, we derive upper and lower bounds to the ergodic sum rate and decompose the rate upper bound maximization problem into two subproblems, namely, user association and resource allocation. Since these two subproblems are coupled, we design an iterative algorithm for successively optimizing ergodic sum rate. In user association, we propose the mode selection criterion and NOMA mode pairing scheme, whereas for resource allocation, we optimize both interpair and intrapair resource allocation. Our numerical results demonstrate the strength and the effectiveness of our proposed FDH-NOMA framework and optimization algorithm.
Low-Complexity Non-uniform Constellation Demapping Algorithm for Broadcasting System

This paper presents a novel low-complexity soft demapping algorithm for two-dimensional non-uniform spaced constellations (2D-NUCs) and massive order one-dimensional NUCs (1D-NUCs). NUCs have been implemented in a wide range of new broadcasting systems to approach the Shannon limit further, such as DVB-NGH, ATSC 3.0 and NGB-W. However, the soft demapping complexity is extreme due to the substantial distance calculations. In the proposed scheme, the demapping process is classified into four cases based on different quadrants. To deal with the complexity problem, four groups of reduced subsets in terms of the quadrant for each bit are separately calculated and stored in advance. Analysis and simulation prove that the proposed demapper only introduces a small penalty under 0.02dB with respect to Max-Log-MAP demapper, whereas a significant complexity reduction ranging from 68.75\% to 88.54\% is obtained.
Evaluation of Optimum NPRACH Performance in NB-IoT Systems

– Narrowband-internet of Things (NB-IoT) is a low power wide area network that contributes strongly to the revolution and expansion of the internet of things (IoT). Here, we aim at improving the performance and reliability of NB-IOT. Given than random access is one of the most influential characteristics of NB-IoT system performance, a detailed study of the NB-IOT random access was conducted, taking into account random access channel (NPRACH) periodicity, number of collisions, number of users, random access preamble repetitions and time taken for all system users to finalize a random access procedure. Simulation results have shown that appropriately choosing NPRACH periodicity values can efficiently achieve uplink channel optimization between its two signals NPRACH and NPUSCH with an acceptable number of collisions and a high number of delivered packets. Short periodicities (40ms and 80ms) show optimal results in reducing the number of collisions for both low and high traffic while all users win NPRACH and start to send data to the base station after approximately 2 second. However, short periodicities waste more uplink resources in random access, minimizing resources remaining for data transmission. Medium-length periodicities (such as 160ms and 320ms) show a promising performance that balances the two uplink physical channels. Our results demonstrate that doubling the number of users (from 40 to 80) is not a considerable factor in choosing NPRACH periodicity with different repetition values. We further showed how to maximize the system performance by selecting certain NPRACH periodicity value for every preamble repetition scheme.
Crime Data Analysis in Python using K - Means Clustering

: Crime is one of the biggest hurdle in today’s world and its increasing at the fire pace which is a major cause for concern. There is a need to monitor and keep track of all the crimes so that it can be used by police department to investigate the cases easily and quickly. In this experiment, a machine learning K-means algorithm is used to predict and analyze the crime in the town of Chicago. The dataset of crimes in Chicago is available at the kaggle website, that is used as the dataset to make prediction and to visualize the patterns and trends of different crimes. The other purpose of this project is to evaluate how much k – means algorithm is feasible to determine and solve the current problem.
Screening for Speech and Language Delays and Disorders in Children Age 5 Years or Younger: A Systematic Review for the U.S. Preventive Services Task Force

Citation To evaluate the evidence on screening and treating children for speech and language delays or disorders for the U.S. Preventive Services Task Force (USPSTF). Screening for speech and language delays and disorders in children age 5 years or younger: a systematic review for the U.S. Preventive Services Task Force. Rockville: Agency for Healthcare Research and Quality (AHRQ). Evidence Synthesis No. 120. 2015
Multi-sensory integration in biological and artificial systems through an hourglass network architecture

Multi-sensory integration is a fundamental problem for any embodied cognitive system – both biological and artificial. We pursue a network diffusion approach to model the flow of evoked activity, initiated by stimuli at primary sensory regions. In particular, we apply the Asynchronous Linear Threshold (ALT) diffusion model on the mesoscale cortical connectome of the mouse. The ALT model captures how evoked activity at a given cortical region ripples through the rest of the cortex. Our results show that a small number of regions (the Claustrum being at the top of the list) integrate almost all sensory information paths, suggesting that the cortex relies on an “hourglass architecture” to integrate and compress sensory information before utilizing that lower-dimensionality representation in association areas and higher cognitive tasks.