Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic value of chest ultrasound in children with cystic fibrosis – Pilot study

Cystic fibrosis (CF) is one of the most common genetic disorders in the Caucasian population. The disease has a progressive course and leads to reduced life quality and life expectancy. Standard diagnostic procedures used in the monitoring of CF patients, include methods exposing patients to the ionizing radiation. With increasing life expectancy in CF the cumulative dose of ionising radiation increases, prompting clinicians’ search for safer imaging studies. Despite its safety and availability lung ultrasound (LUS) is not routinely used in the diagnostic evaluation of CF patients. The aim of the study was to evaluate the diagnostic value of LUS in children with CF compared to chest X-ray, and to assess the diagnostic value of the recently developed LUS score - CF-USS (Cystic Fibrosis Ultrasound Score). LUS was performed in 48 CF children aged from 5 to 18 years (24 girls and 24 boys). LUS consisted in the assessment of the pleura, lung sliding, A-line and B-line artifacts, “lung rockets”, alveolar consolidations, air bronchogram and pleural effusion. Chest radiography was performed in all patients and analyzed according to the modified Chrispin-Norman score. LUS was analyzed according to CF-USS. Correlation between the CF-USS and the modified Chrispin-Norman scores were moderate (R=0.52, p=0.0002) and strong in control studies. In 75% of patients undergoing LUS, small areas of subpleural consolidations were observed, not visible on X-rays. At the same time, LUS was not sensitive enough to visualize bronchial pathology, which plays an important role in assessing the disease progression. Conclusions LUS constitutes an invaluable tool for the diagnosis of subpleural consolidations. CF-USS results correlate with conventional x-ray modified Chrispin–Norman score. LUS should be considered an accessory radiographic examination in the monitoring of CF patients, and CF-USS may provide clinicians with valuable information concerning the disease progression.
Analysis of H-polarized Plane Wave Scattering by Inhomogeneous Dielectric Cylinder of Arbitrary Cross Section

A 2D problem of scattering of H-polarized plane wave by a dielectric cylinder consisting of a homogeneous core of circular cross-section and an inhomogeneous cover of arbitrary cross section is considered. A modification of the hybrid projection method is developed for its solution including the case when the core is a perfectly conducting cylinder. Some results of application of the method for calculation of the characteristics of scattering by a homogeneous elliptic cylinder, a perfectly conducting circular cylinder with homogeneous and inhomogeneous plasma shell, as well as a half-cylindrical Maxwell fish-eye lens are presented and discussed. The results corresponding to the cases of homogeneous permittivity are compared to the reference data obtained with use of the method of auxiliary sources.
3D Dynamic MRI for Pelvis Observation - a First Step

In the context of pelvic floor diseases understanding we advocate a process to observe the bladder over time in 3D. Thanks to multiplanar MRI acquisitions and adequate volume constructions, several volumes per seconds can show a bladder undergoing deformations under strain conditions. To our knowledge it is the first time such observation is performed. In addition we propose some deformation estimation clues that may be useful to pathology characterization and clinical diagnosis.
Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

To solve the problem of low efficiency, the complexity of the interactive operation, and the high degree of manual intervention in existing methods, we propose a novel approach based on the sparse voxel octree and 3D convolution neural networks (CNNs) for segmenting and classifying tooth types on the 3D dental models. First, the tooth classification method capitalized on the two-level hierarchical feature learning is proposed to solve the misclassification problem in highly similar tooth categories. Second, we exploit an improved three-level hierarchical segmentation method based on the deep convolution features to conduct segmentation of teeth-gingiva and inter-teeth, respectively, and the conditional random field model is used to refine the boundary of the gingival margin and the inter-teeth fusion region. The experimental results show that the classification accuracy in Level_1 network is 95.96%, the average classification accuracy in Level_2 network is 88.06%, and the accuracy of tooth segmentation is 89.81%. Compared with the existing state-of-the-art methods, the proposed method has higher accuracy and universality, and it has great application potential in the computer-assisted orthodontic treatment diagnosis.
Predicting Residual Cancer Burden In A Triple Negative Breast Cancer Cohort

Analysis and interpretation of stained histopathology sections is one of the main tools in cancer diagnosis and prognosis. In addition to the information which is typically extracted by trained pathologists, there is also information that is not yet exploited, simply because we do not yet understand the impact of all cellular and tissular features that could be predictive of outcome. In this paper, we address a question that can currently not be solved by pathologists: the prediction of treatment efficiency for Triple Negative Breast Cancer (TNBC) patients from biopsy data.
A Sparse Stochastic Method With Optimal Convergence Rate for Optimizing Pseudo-Linear Performance Measures

Optimizing pseudo-linear performance measures has gained increasing interest in recent years. Compared with the existing works focusing on L2-norm, it is more challenging to design the model with L1-norm, especially, when data are of large scale. To address the issue, in this paper, we propose a sparse stochastic method to optimize the pseudo-linear metrics. The algorithm begins by formulating the problem as a cost-sensitive classification with L1 regularization, then composite objective mirror descent (COMID) is adopted for the inner optimization, which can obtain a sparse solution with promising performance. However, the original COMID method only has <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(log(T)/T)$ </tex-math></inline-formula> convergence rate with the strong convex objective. To this end, a simple yet efficient polynomial-decay average strategy is suggested. We prove that with this strategy, the proposed algorithm can not only achieve the optimal convergence rate of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(1/T)$ </tex-math></inline-formula> but also obtain the classifier with higher sparsity. The empirical studies on several public benchmark data sets demonstrate the superior performance of the proposed algorithm in terms of both efficiency and sparsity.
Age-Based Path Planning and Data Acquisition in UAV-Assisted IoT Networks

To support emerging networked monitoring and data analytics applications, timely delivery of fresh information is of critical importance for unmanned aerial vehicle (UAV) assisted data collection in Internet of Things (IoT) networks. Age of information (AoI) is proposed as a performance metric to measure the data freshness at destination since the sampling at data source. In this paper, we employ the concept of age to study a UAV path planning and data acquisition problem by jointly considering the data acquisition mode selection, energy consumption at each node, and age evolution of collected information. To solve the formulated problem, we propose a solution framework based on dynamic programming approach. Through simulation results, we show that the performance of our proposed solution is competitive when compared with traditional flight time-optimal strategy.
Realizing all Index Generation Functions by the Row-Shift Method

We propose a method that allows the realization of all index generation functions using flexible decomposition charts. It is based on the first-fit decreasing heuristic used by Tarjan and Yao to store sparse matrices. We show that the first-fit-decreasing heuristic can yield nonminimal tables in the case of functions that do not satisfy the harmonic decay property. We show that an index generation function representation that just satisfies the harmonic decay property, called the perfect harmonic decay sequence, allows a simple matrix approach for calculating an error matrix, that describes the degree to which a given function representation departs from a perfect harmonic decay sequence. This gives insight into how function representations can be changed to realize the harmonic decay criteria. We also show the existence of sparse function representations for which no compression is possible. In such a case, we can still implement the corresponding index generation function, but it requires the largest resources possible.
High Accuracy Patch-Level Classification of Wireless Capsule Endoscopy Images Using a Convolutional Neural Network

Wireless capsule endoscopy (WCE) is a technology used to record colored internal images of the gastrointestinal (GI) tract for the purpose of medical diagnosis. It transmits a large number of frames in a single examination cycle, which makes the process of analyzing and diagnosis of abnormalities extremely challenging and time-consuming. In this paper, we propose a technique to automate the abnormality detection in WCE images following a deep learning approach. The WCE images are split into patches and input to a convolutional neural network (CNN). A trained deep neural network is used to classify patches to be either malign or benign. The patches with abnormalities are marked on the WCE image output. We obtained an area under receiver-operating-characteristic curve (AUROC) value of about 98.65% on a publicly available test data containing nine abnormalities.
shallowCNN-LE: A shallow CNN with Laplacian Embedding for face anti-spoofing

Identity authentication based on face recognition has been significantly improved due to the outstanding ability of face detection, thus it plays an important role in society. However, face recognition system might be deceived by malicious face spoof attacks raising risk from both safety and property. The algorithm to accurately detect face anti-spoofing in identity authentication system is becoming crucial. In this paper, a shallow convolutional neural network with laplacian embedding (shallowCNN-LE) is proposed for face anti-spoofing. Two different types of features are concatenated to accurately detect the face liveness, including depth features and dynamic texture features. First, the developed shallow CNN model contains four layers which make the model faster. Second, we integrate dynamic texture features extracted by using the dual tree complex wavelet transform (DT-CWT) with the depth features as input features to feed into the proposed model. Finally, we propose a laplacian embedding algorithm, which can maintain the inter-class discrimination and penalize the distance of intra-class. When embedding the laplacian loss with the softmax loss, the proposed method can obtain much more discriminative features, which is helpful to detect face anti-spoofing. Experimental results on public databases of CASIA FASD, Replay attack and MSU USSA database demonstrate that our proposed method outperforms the state-of-the-art methods for face anti-spoofing detection.
Modelling the radiometric response of a dynamic, 3D structural model of wheat in the optical and microwave domains

A dynamic 3D structural model of wheat is used to simulate growth stages of a wheat canopy. The 3D information regarding canopy structure output by this model is used to drive two models of canopy radiometric response, one in the optical and one in the microwave domain. The radiometric response of the canopy in the optical and microwave domains is simulated and the sensitivity of the canopy response to variation in the canopy structural and radiometric parameters is examined. The modelled canopy response is compared to field-measured hyperspectral reflectance in the optical, and airborne synthetic aperture radar (E-SAR) backscatter measurements in the microwave. The modelled signal is shown to agree in general with observations. It is demonstrated that the major growth development stages of the wheat canopy which impact the remote sensing signal in the optical and microwave domains can be modelled in this way. This method is shown to be suitable for exploring the impact of canopy structure on remotely sensed measurements.
On the power efficiency of sensory and ad-hoc wireless networks

This paper discusses the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For AWGN channels, it is well known that power efficiency is attained in the low SNR regime. In this paper we show that for a random sensory, or ad-hoc, wireless network with n users (nodes), with high probability converging to one as n grows, the power efficiency scales at least by a factor of /spl radic/n. In other words, each user in a wireless channel with n nodes can support the same communications rate as a single user system, but by expending only 1//spl radic/n the energy.
ASAP: A Decentralized Slot Reservation Policy for Dynamic 6TiSCH Networks in Industrial IoT

In Industrial Internet of Things (IIoT) networks, end-nodes may disconnect or become no longer reachable, due to manifold causes, such as battery discharge, network interference and/or packet collisions. Overall, this could lead to low Quality of Service (QoS) levels. To satisfy application requirements, i.e., reliability, it is mandatory to continuously monitor connectivity and properly managing disconnections, without causing signaling overhead. The optimization of the exchange of messages can be pursued thanks to dedicated communication slots, eventually rescheduled in cases of deletion, replacement or dynamic reassignments. This solution lowers energy consumption while accounting for a number of improvements in network operative conditions. In this work, network formation optimization mechanisms for IPv6 over the TSCH mode of IEEE 802.15. 4 (6TiSCH) networks are analyzed in real use cases in which the number of nodes in the network varies over time. Once the network was fully connected, a performance analysis has been carried out in order to measure typical networks parameters, such as latencies and Packet Loss Ratio (PLR). The study has proven the effectiveness of dedicated slots dynamic assignment. In particular, it has been measured a sensible reduction of latencies and losses of information. The obtained results pave the way towards robotics-aided set up of Internet of Things (IoT) networks in industrial contexts.
Vessel Extraction Using Crossing-Adaptive Minimal Path Model With Anisotropic Enhancement And Curvature Constraint

In this work, we propose a new minimal path model with a dynamic Riemannian metric to overcome the shortcuts problem in vessel extraction. The invoked metric consists of a crossing-adaptive anisotropic radius-lifted tensor field and a front freezing indicator. It is able to reduce the anisotropy of the metric on the crossing points and steer the front evolution by freezing the points causing high curvature of a geodesic. We validate our model on the DRIVE and IOSTAR datasets, and the segmentation accuracy is 0.861 and 0.881, respectively. The proposed method can extract the centreline position and vessel width efficiently and accuracy.
The Evolution of Submissive Clusters in the Spatial Snowdrift Game

The zero-determinant strategies can make an individual unilaterally enforce a linear relationship with her and coplayer’s payoffs. Traditionally, researchers focus on the evolution of zero-determinant strategies based on the Prisoner’s dilemma game. In this paper, in terms to the famous snowdrift game, we investigate the zero-determinant strategies in the regular graph. The spatial structure can hinder the spread of cooperative strategy in snowdrift game. The submissive strategy is a subset of zero-determinant strategies, which can provide less benefit to defectors than that of cooperative strategy. We find that the spatial structure can promote the emergence of submissive strategy although the payoff relationship between submissive strategy and defective strategy is also snowdrift-like. Moreover, from the perspective of formation pattern, we analyze the underlying mechanism about the evolution of zero-determinant strategies in the regular graph, which may provide some clues to understand cooperative dynamics and design protocols in the networked multi-agent systems.
Period adding bifurcations in dynamic pricing processes

Price information enables consumers to anticipate a price and to make purchasing decisions based on their price expectations, which are critical for agents with pricing decisions or price regulations. A company with pricing decisions can aim to optimise the short-term or the long-term revenue, each of which leads to different pricing strategies thereby different price expectations. Two key ingredients play important roles in the choosing of the short-term or the long-term optimisation objectives: the maximal revenue and the robustness of the chosen pricing strategy against market volatility. However the robustness is rarely identified in a volatile market. Here, we investigate the robustness of optimal pricing strategies with the short-term or long-term optimisation objectives through the analysis of nonlinear dynamics of price expectations. Bifurcation diagrams and period diagrams are introduced to compare the change in dynamics of the optomal pricing strategies. Our results highlight that period adding bifurcations occur during the dynamic pricing processes studied. These bifurcations would challenge the robustness of an optimal pricing strategy. The consideration of the long-term revenue allows a company to charge a higher price, which in turn increases the revenue. However, the consideration of the short-term revenue can reduce the occurrence of period adding bifurcations, contributing to a robust pricing strategy. For a company, this strategy is a robust guarantee of optimal revenue in a volatile market; for consumers, this strategy avoids rapid changes in price and reduce their dissatisfaction of price variations.
Adaptive Trajectory Tracking Control of a Parallel Ankle Rehabilitation Robot With Joint-Space Force Distribution

This paper proposes an adaptive trajectory tracking control strategy implemented on a parallel ankle rehabilitation robot with joint-space force distribution. This device is redundantly actuated by four pneumatic muscles (PMs) with three rotational degrees of freedom. Accurate trajectory tracking is achieved through a cascade controller with the position feedback in task space and force feedback in joint space, which enhances training safety by controlling each PM to be in tension in an appropriate level. At a high level, an adaptive algorithm is proposed to enable movement intention-directed trajectory adaptation. This can further help to improve training safety and encourage human–robot engagement. The pilot tests were conducted with an injured human ankle. The statistical data show that normalized root mean square deviation (NRMSD) values of trajectory tracking are all less than 2.3% and the PM force tracking being always controlled in tension, demonstrating its potential in assisting ankle therapy.
Advanced Thermal Solution for High Performance Server System Energy Efficiency

As data explosion dramatically accelerating from IoT (Internet of Thing)edge devices, high performance server has been dominating deployment in the recent CSPs (Cloud Service Providers)data center for conquering the challenge of huge amount of data processing on cloud computing. Microprocessor architecture advance, continues to involve with more core count, higher frequency, as well as larger on-die memory cache size, pursuing for higher computing performance while trade off with more power consumption. Nowadays, conventional thermal solutions for high performance high power processor in server system have been reaching the limit even with maximum optimization and luxurious design. The conventional air cooling solution is limited by the mainboard spatial keep-out zone and high dense placement, while liquid cooling has not been widely adopted yet by CSPs due to reliability and economical solution readiness issue. In this paper, an advanced thermal solution based on thermosiphon technology for Intel high performance server processors with shadowed placement is detail introduced with Baidu engineering design practice and data center deployment. This solution directly contributed to server system achieving a 12 times improvement on OPEX (Operating Expense)comparing to conventional solutions. Even when considering its relatively higher CAPEX (Capital Expenditure), calculation shows that the overall TCO (Total Cost of Ownership)is lower than traditional solutions starting from 0.36 year after field deployment benefited from energy saving.
Towards Measuring Global DDoS Attack Capacity

In today's Internet, distributed denial-of-service (DDoS) attacks play an ever-increasing role and constitute a risk to any commercial, military or governmental entity that has a presence on the Internet or simply has an Internet connection. To address this threat on all levels, decision-makers have to rely on trustworthy information regarding attack capacity, sources, and the largest contributors. The lack of this information limits the ability of technicians, policymakers, and other relevant decision-makers to remediate the issue as efficiently as possible. This research introduces a methodology for measuring the properties of individual devices participating in such attacks. These properties include rate limiting, amplification factor, and speed, which allows the calculation of each device's actual contribution to the attack capacity. This methodology was implemented as a proof of concept for the NTP protocol and the results indicate that it has promising potential. Individual measurements aggregated together provide insights into particular abused protocols: all the protocols together could provide the global DDoS attack capacity.
A fault-list generation algorithm for the evaluation of system coverage

The expanding size and complexity of dependable computing systems has increased their cost and at the same time complicated the process of estimating dependability attributes such as fault coverage and detection latency. One approach to estimating such parameters is to employ fault injection, however algorithms are needed to generate a list of faults to inject. Unlike randomly selected faults, a fault list is needed which guarantees to cause either system failure or the activation of mechanisms which cover the injected fault. This research effort has developed an automated technique for selecting faults to use during fault injection experiments. The technique is general in nature and can be applied to any computing platform. The primary objective of this research effort was the development and implementation of the algorithms to generate a fault set which exercises the fault detection and fault processing aspects of the system. The end result is a completely automated method for evaluating complex dependable computing systems by estimating fault coverage and fault detection latency.