Miracle-dz/UniFAR-FTUs huggingface dataset:
Mask R-CNN for Ear Detection

Ear detection is an important step in ear recognition pipeline as it makes or breaks the system. However, in the literature there is arguably the lack of ear detection approaches available. This poses a problem for opening ear recognition system to wider use and applications in commercial systems. To tackle this problem we present the use of Mask R-CNN for pixel-wise ear detection. Furthermore, we directly compare our approach to one of the previous best performing pixel-wise ear detection approach by using the same dataset and protocol. Our results with intersection over union score of 79.24% on AWE dataset show the superiority of our approach and present a viable approach for future use in ear recognition pipelines.
Design Considerations for High Voltage SiC Power Devices: An Experimental Investigation into Channel Pinching of 10kV SiC Junction Barrier Schottky (JBS) Diodes

This paper reports on the design, fabrication and electrical characteristics of 10kV 4H-SiC JBS diodes. Both Ni and Ti JBS diodes were fabricated on $7\times 10^{14}\ \mathbf{cm}^{-3}$ doped, $95\boldsymbol{\mu} \mathbf{m}$-thick n-type epi on 6-inch 4H-SiC n+ substrates. SEM imaging and SIMS profiling were conducted to investigate the unexpected high knee voltages from Ti JBS diodes. The “extensive” straggle by implanted ions was discovered from this investigation, which was attributed from the low background doping concentration in the drift layer.
Primal-Dual Subgradient Algorithm for Distributed Constraint Optimization Over Unbalanced Digraphs

This paper investigates the distributed convex optimization problem with coupled inequality constraints over unbalanced digraphs depicted as row-stochastic matrices, where each agent in the network only has access to its local information, while the local constraint functions of all the agents are coupled in inequality constraints. To solve this kind of problems, a novel distributed iterative algorithm is proposed via consensus scheme and the projected primal-dual subgradient method. Different from the previous related results, our algorithm can not only deal with coupling inequality constraints but also conquer the unbalanced topology caused by directed graphs. Moreover, it is proved that under certain assumptions, the optimal solution of the problem can be asymptotically obtained by performing the designed algorithm. Finally, the numerical examples are presented to further illustrate the efficacy of the proposed approach.
UAV-Assisted C-RAN with Rate Splitting Under Base Station Breakdown Scenarios

The flexibility and mobility of unmanned aerial vehicles (UAVs) make them a viable candidate in improving connectivity and coverage of cloud-radio access networks (CRANs). To investigate this aspect and facilitate efficient interference mitigation, this paper studies a C-RAN in the downlink, where base stations (BSs) are assisted by UAVs in scenarios where a subset of BSs break down. BSs and UAVs apply the concept of rate-splitting (RS) which divides messages into a common and a private part. Under this strategy, we formulate a weighted sum-rate maximization problem subject to fronthaul capacity and peak transmit power constraints. This non-convex optimization problem is then solved via a low complexity convex-concave procedure (CCP) algorithm. Simulation results suggest that our proposed RS method in an UAV-embedded C-RAN system yields significant improvement in achievable sum-rate over schemes which adopt treating interference as noise as a receiving strategy. Further, the scenario where UAVs are assigned for common messages and BSs for private message is the preferred RS allocation choice.
People Counting in a Public Event—Use Case: Free-to-Ride Bus

In the case of traditional bus travel, there are many ways to gather statistics on bus passengers. Paid tickets can directly contain information about the stop at which the passengers came aboard and where they got off. Alternatively, stamping or reading travel cards can be used to obtain similar statistics. In the case of a free-to-ride bus service, these simple methods are generally not available. Yet, for further route and capacity planning, producing exact – or at least informative – statistics is crucial. This paper presents a real-life use case of collecting statistics about bus passengers on a free-to-ride bus route at a large public event in the summer of 2018 in Pori, Finland. The use case utilized cost-effective and off-the-shelf components such as the Raspberry Pi 3 computer, position sensors, cameras, and the Open Source Computer Vision Library version 3. The hardware and software components of the system, which was based on image analysis and shape detection, as well the results of the study, are explained in this paper. Furthermore, this paper presents a discussion on the challenges faced while developing and implementing the system.
Explosives detection with hard-wired moths

A prototype system that uses moths to detect explosives was designed, assembled, and tested. It compares the electromyographic signals of moths trained to respond or not respond to a target explosive vapor in order to determine whether or not explosive devices, such as bombs or landmines, are present. The device was designed to be portable by making it lightweight, battery-powered, and energy efficient. The prototype performed successfully during testing.
Detection And Identification Of Lower-Limb Bones In Biplanar X-RAY Images With Arbitrary Field Of View And Various Patient Orientations

Correctly detecting and identifying bones in radiographic images are the first stages of every orthopaedic procedure. This apparently simple task is mainly performed by human operators before performing more complex operations. Automatic detection and identification of bones in radiographic images, especially if the field of view and the position of the patient are not known a priori, remains a challenging task. In this paper, lower-limb bones are automatically detected and identified on biplanar X-ray images with varying fields of view and two main orientations of the patient with respect to the imaging device. The proposed method uses data augmentation to improve the training of a deep learning method to identify the lower-limb bones. We used 30 biplanar radiographs with varying fields of view to validate the proposed method. We obtained a global accuracy (mean $\pm {std})$ of $96.75 \pm 0.01$% and a Dice coefficient of $93.85\pm 0.02$%, proving the usefulness of the proposed method.
Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization

The ability of medical image analysis deep learning algorithms to generalize across multiple sites is critical for clinical adoption of these methods. Medical imaging data, especially MRI, can have highly variable intensity characteristics across different individuals, scanners, and sites. However, it is not practical to train algorithms with data from all imaging equipment sources at all possible sites. Intensity normalization methods offer a potential solution for working with multi-site data. We evaluate five different image normalization methods on training a deep neural network to segment the prostate gland in MRI. Using 600 MRI prostate gland segmentations from two different sites, our results show that both intra-site and inter-site evaluation is critical for assessing the robustness of trained models and that training with single-site data produces models that fail to fully generalize across testing data from sites not included in the training.
A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System

The problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energy storage system (ESS) and photovoltaic (PV) system. A smart charging management system is first established. The charging optimization problem is formulated as a cost minimization problem. Then, grey wolf optimizer (GWO) is introduced as a method to find the optimal solution. Considering the constraint conditions in the optimization problem, an improved binary grey wolf optimizer (IBGWO) is proposed, which can improve the convergence speed and optimization accuracy. Finally, a real-time EV charging scheduling strategy based on short-term PV power prediction and IBGWO is proposed. Several cases are simulated to analyze the performance of the proposed strategy. The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms. Moreover, the proposed strategy can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.
Lightweight Group Secret Key Generation Leveraging Non-Reconciled Received Signal Strength in Mobile Wireless Networks

Physical-layer secret key generation (PSKG) leveraging reciprocal wireless channel has been widely studied in pairwise communication scenarios as it addresses the key distribution problem in traditional security mechanisms. However, PSKG encounters the challenges of low key agreement, information leakage and high system cost when it is extended to produce a secret key for a group of users. In this paper, we propose a lightweight Group secret key generation approach leveraging Non-reconciled received Signal Strength (GNSS). Specifically, pairwise bit sequences with high similarity are firstly extracted and then a group key is securely shared with the protection of these pairwise bit sequences, which prevents the information leakage and sophisticated information reconciliation. Detailed algorithms of GNSS for star and chain networks are presented. Numerical and experimental results demonstrate that GNSS can provide high key agreement, thus enabling key generation technology to be used in a group communication scenario.
Joint compensation of IQ imbalance and frequency offset in OFDM systems

Zero-IF receivers are gaining interest because of their potential to enable low-cost OFDM terminals. However, zero-IF receivers introduce IQ imbalance which may have a huge impact on the performance. Rather than increasing design time or component cost to decrease the IQ unbalance, an alternative is to tolerate the IQ imbalance and compensate it digitally. Current solutions require extra analog hardware or converge too slowly for bursty communication. Moreover, the tremendous impact of a frequency offset on the IQ estimation/compensation problem is not considered. In this paper, we analyze the joint IQ imbalance-frequency offset estimation and propose a low-cost, highly effective, all-digital compensation scheme. For large IQ imbalance (/spl isin/ = 10%, /spl Delta//spl phi/ = 10/spl deg/) and large frequency offsets, our solution results in an average implementation loss below 0.5 dB. It therefore enables the design of low-cost, low-complexity OFDM receivers.
Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images

Several features have been developed to characterize the land cover in synthetic aperture radar (SAR) data with speckle noise. Feature extraction has become an essential task for SAR image processing. However, how to preserve the original intrinsic structural information and enhance the discriminant ability to reduce the impact of noise is still a challenge in this area. In this paper, using a clustering method to maintain the nonlocal information in images and tensors with the ability to preserve spatial neighborhood structure information, a new cluster-based tensorial semisupervised discriminant analysis (CTSDA) method is proposed for feature extraction of SAR images. In the CTSDA, the block clustering algorithm is employed to generate several high-order clustering tensors of multifeature SAR images, which preserves the intrinsic nonlocal spatial information and neighborhood structure. In the multiple manifold structures of the cluster tensors, the improved discriminant analysis enhances the feature discrimination by considering the local structure and labeled and unlabeled information through the Laplace matrix, and the fusion of tensor algebraic analysis and improved discriminant analysis produces multiple new projection directions of the cluster tensors. Finally, feature extraction is achieved by rearranging the projected cluster tensors. The experimental results on the simulated SAR data and four real SAR images demonstrate the superiority of the proposed method over several state-of-the-art approaches.
Tuberculosis Lesions in CT Images Inferred using 3D-CNN and Multi-Task Learning

Tuberculosis (TB) is an infectious disease that very frequently damage the lungs and that has a high incidence and mortality rate. The longitudinal assessment of pulmonary affectation is a clear need to boost the development of novel drugs and to control the spread of the disease. In this manuscript, we train a computational model able to infer TB manifestations present in each lung lobe of x-ray Computer Tomography (CT) scans by employing the associated radiologist reports as ground truth. We do so instead of using the classical manually delimited segmentation masks. The proposed learning strategy adapts the V-Net model, which allow us to employ full 3D volumes in order to obtain fine grain features. The model is successfully optimized by a novel loss function for multi-task learning. The loss function employs the model uncertainty to weight the regression and binary tasks. Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules and F1-scores above 0.85 for the most prevalent TB lesions (i.e., Conglomerations, cavitations, consolidations, trees in bud) when considering the whole lung.
Instability of a tandem network and its propagation under RED

Random early detection (RED) mechanism has been proposed to control the average queue size at the bottlenecks inside the network. It has been shown that the interaction between a RED gateway and TCP connections can lead to a rich set of nonlinear phenomena in single bottleneck cases. In this paper we extend this model and study the interaction of TCP connections with RED gateways in a simple tandem network, using a nonlinear first-order discrete-time model. We demonstrate, using bifurcation diagrams, that the nonlinear behavior of TCP can result in both smooth and non-smooth bifurcations, leading to chaos. We show that the instabilities can be induced at both bottlenecks while fixing the parameters at the other, thus demonstrating the propagation of instability. Moreover, we show that locally sufficient conditions for stability based on single node analysis are not sufficient for global network stability.
A novel method for determining Young's modulus of thin films by micro-strain gauges

This work presents a novel method for determining Young's modulus of thin films with compact micromachined test structures and without using any extra load. The test structures comprise of a pair of micro-strain gauges and a cantilever beam. An analytical model is derived to extract the Young's modulus of test structures. Notably, both Young's modulus and residual stress can be simultaneously determined; thus we can correlate Young's modulus and residual stress. The micromachined test structures employed in the measurement are made of low-pressure chemical-vapor deposition (LPCVD) undoped polycrystalline silicon films. A conventional surface-sacrificial layer micromachining technique with one mask is used to fabricate the structures. The obtained Young's modulus is reduced a little as residual stress increases. The average value of the obtained Young's modulus is 169 G Pa, and the residual stress calibrated from the strain gauge is 211 MPa.
On error exponents in quantum hypothesis testing

On error exponents in the simple quantum hypothesis testing problem, a noncommutative analogue of the Hoeffding bound (W. Hoeffding, 1991) is presented. If the hypotheses, namely two density operators, are mutually commutative, the bound is identical with the classical counter part and thus is optimal. As a by-product, a simple proof of the direct part of the quantum Stein's lemma [F. Hiai (1991)] [T. Ogawa (2000)] is also given.
Student misconceptions in signals and systems and their origins

We report on our ongoing investigation on student misconceptions and their origins within the signals and systems module taught in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology. Signals and Systems, as taught in Aeronautics and Astronautics at MIT, consist of two parts. The first part, offered in the Fall semester, covers introductory linear circuits; the second part, offered in the Spring semester, covers the analysis of generic continuous-time linear time-invariant systems. During Fall 2002, we conducted clinical interviews to assess student understanding of introductory linear circuits. Fifty-four sophomore students enrolled in Signals and Systems volunteered to take part in this study. The interview transcripts were analyzed, physical and mathematical misconceptions were identified, and their sources were examined based on diSessa 's theory of intuitive knowledge, and Chi and Slotta 's ontological categorization. In this paper, we report on our results and suggest how this understanding can be used to develop more effective pedagogical instruments designed to enhance student learning.
Metallicity gradients in small and nearby spiral galaxies

Spectra of H ii regions obtained with Gemini/GMOS are used to derive the radial metallicity gradients of four small, low-mass spiral galaxies. The analysis of the outer disc of one of them, NGC 1058, uncovers the characteristic flattening found in similar extended disc galaxies. After combining these data with published long-slit observations of nearby spiral galaxies, no evidence for a dependence of the disc scale length-normalized metallicity gradients with stellar mass is found, down to log (M⋆/M⊙) ∼ 8.5. The abundance gradients derived from these observations are compared to predictions from recent cosmological simulations of galaxy evolution, finding that in several cases the simulations fail to reproduce the mean steepening of the gradients, expressed in dex kpc−1, with decreasing stellar mass for present-day galaxies, or do not extend to sufficiently small stellar masses for a meaningful comparison. The mean steepening of the abundance gradients (in dex kpc−1) with decreasing disc scale length is in qualitative agreement with predictions from the inside-out model of Boissier & Prantzos, although the predicted slopes are systematically steeper than observed. This indicates the necessity of including processes such as outflows and radial mixing in similar models of galactic chemical evolution. Published spatially resolved metallicity and photometric data of dwarf irregular galaxies suggest that significant, but transitory, metallicity gradients can develop for systems that have experienced recent (t < 100 Myr) enhanced star formation in their inner discs.
How should we score athletes and candidates: geometric scoring rules

Scoring in Multi-Event Tournaments How much is a first place worth? Is the athlete who came first once and third twice better than the athlete who came second three times? Different sports value these positions differently: (60, 54, 48) in IBU biathlon, (25, 18, 15) in F1 racing, (8, 7, 6) in Diamond League athletics. Are these choices based on anything? Is there even a rational way to choose a scoring vector of arbitrary length to rank any number of athletes? In “How should we score athletes and candidates: Geometric scoring rules,” authors A.Y. Kondratev, E. Ianovski, and A.S. Nesterov investigate two approaches to how the problem of choosing a scoring vector for a tournament can be reduced to the choice of a single parameter: an axiomatic approach based on eliminating spoilers and an optimization approach based on maximizing the expected quality of the winner. Intriguingly, the vectors generated by the second approach are uncannily similar to those used in real sporting events.
Topic Modeling in Embedding Spaces

Abstract Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the etm models each word with a categorical distribution whose natural parameter is the inner product between the word’s embedding and an embedding of its assigned topic. To fit the etm, we develop an efficient amortized variational inference algorithm. The etm discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation, in terms of both topic quality and predictive performance.
GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed this http URL scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. Here we proposeGraphSAINT, a graph sampling based inductive learning method that improves training efficiency in a fundamentally different way. By a change of perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling process from the forward and backward propagation of training, and extend GraphSAINT with other graph samplers and GCN variants. Comparing with strong baselines using layer sampling, GraphSAINT demonstrates superior performance in both accuracy and training time on four large graphs.
Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs. Cloud Detection and Learning

A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The devices in each cell are connected to an Edge Node (EN), which may also have a finite-capacity fronthaul link to a central processor. In contrast to conventional information-agnostic protocols, the devices transmit using a Type-Based Multiple Access (TBMA) protocol that is tailored to enable the estimate of the field of correlated QoIs in each cell based on the measurements received from IoT devices. In this paper, this form of information-centric radio access is studied for the first time in a multi-cell F-RAN model with edge or cloud detection. Edge and cloud detection are designed and compared for a multi-cell system. Optimal model-based detectors are introduced and the resulting asymptotic behavior of the probability of error at cloud and edge is derived. Then, for the scenario in which a statistical model is not available, data-driven edge and cloud detectors are discussed and evaluated in numerical results.