Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multiple Instance Learning Approach to Electrophysiological Muscle Classification for Diagnosing Neuromuscular Disorders Using Quantitative EMG

Neuromuscular disorder is a broad term that refers to diseases that impair muscle functionality either by affecting any part of the nerve or muscle. Electrodiagnosis of most neuromuscular disorders is based on the electrophysiological classification of involved muscles which in turn, is performed by inferring the structure and function of the muscles by analyzing electromyographic (EMG) signals recorded during low to moderate levels of contraction. The functional unit of muscle contraction is called a motor unit (MU). The morphology and physiology of the MUs of an examined muscle are inferred by extracting motor unit potentials (MUPs) from the EMG signals detected from the muscle. As such, electrophysiological muscle classification is performed by first characterizing extracted MUPs and then aggregating these characterizations. The task of classifying muscles can be represented as an instance of a multiple instance learning (MIL) problem. In the MIL paradigm a bag of instances shares a label and the instance labels are hidden, contrary to standard supervised learning, where each training instance is labeled. In MIL-based muscle classification, the instances are the MUPs extracted from the EMG signals of the analyzed muscle and the bag is the muscle. Detecting and counting the MUPs indicating a specific category of neuromuscular disorder can result in accurately classifying the examined muscle. As such, three major issues usually arise: how to infer MUP labels without full supervision; how the cardinality relationships between MUP labels contribute to predict the muscle label; and how the muscle as a whole entity is classified. In this thesis, these three challenges are addressed. To this end, an MIL-based muscle classification system is proposed that has five major steps: 1) MUPs are represented using morphological, stability, and novel near fiber parameters as well as spectral features extracted from wavelet coefficients. This representation helps to analyze MUPs from a variety of aspects. 2) MUP feature selection using unsupervised similarity preserving Laplacian score which is independent of any learning algorithm. Hence, the features selected in this work can be used in other electrophysiological muscle classification systems. 3) MUP clustering using a novel clustering algorithm
A Cross-Domain Recommendation Algorithm for D2D Multimedia Application Systems

Device-to-device communication is the key technology of the fifth generation mobile communication, which can support mobile multimedia applications. In recent years, mobile multimedia applications, however, have produced a lot of data, which leads to severe information overload. So recommendation models for multimedia application system have become very popular, which aim to overcome the information overload problems by inferring user preference according to user behaviors, such as rating matrices. However, rating matrices are very sparse and skewed since users are always unwilling to rate items, especially those they do not like. Previous recommendation algorithms cannot address the sparsity and skewed distribution challenges effectively. We propose a cross-domain recommendation algorithm based on feature transfer and imbalanced classification in this paper. First, the original recommendation problem is formulated as a rough imbalanced classification problem in the target domain, which takes user and item location as the feature vector and their rating as the label. Then, useful user and item features are generated to alleviate the sparsity in the target domain. More specifically, extra user features are extracted with a Funk-SVD model from use-side auxiliary domains, and item features are retrieved from Wikipedia. Finally, an imbalanced classification model (AdaBoost.NC) is employed to solve the obtained imbalanced classification problem, which can effectively overcome the skewed rating distribution. We conduct extensive experiments and compare the proposed algorithm with various state-of-the-art single- and cross-domain recommendation algorithms. The experimental results show that the proposed algorithm has advantages in terms of four different evaluation metrics.
A Novel Approach on Suggesting Travel Route by Efficient Watchword

With the fame of web-based social networking (e.g., Facebook ), clients can undoubtedly share their registration records and photographs amid their treks. In perspective of the gigantic number of client verifiable portability records in web-based social networking, we plan to find venture out encounters to encourage trip arranging. When arranging a trek, clients dependably have speciﬁc inclinations with respect to their outings. Rather than confining clients to restricted question alternatives, for example, areas, exercises, or eras, we consider discretionary portrayals as watchwords about customized necessities. In addition, an assorted and agent set of suggested travel courses is required. Earlier works have expounded on mining and positioning existing courses from registration information. To address the issue for programmed trip association, we guarantee that more highlights of Spots of Intrigue (POIs) ought to be extricated. Along these lines, in this paper, we propose an efﬁcient Catchphrase mindful Agent Travel Course structure that utilizations learning extraction from clients' verifiable portability records and social associations. Expressly, we have planned a watchword extraction module to group the POI-related labels, for successful coordinating with question catchphrases. We have additionally planned a course recreation calculation to develop course hopefuls that fulﬁll the necessities. To give beﬁtting question comes about, we investigate Agent Horizon ideas, that is, the Horizon courses which best depict the exchange offs among various POI highlights. To assess the viability and efﬁciency of the proposed calculations, we have led broad investigates genuine area based informal community datasets, and the examination comes about demonstrate that our strategies do in fact illustrate great execution contrasted with best in class works.
Hedonic Games with Social Context

htmlabstractHedonic games are coalition formation games in which coalitions are created as a result of the strategic interaction of independent players. To this day, the literature on non-cooperative hedonic games has considered totally selfish players; our aim is that of defining and studying a new model in which, given a social graph, players also care about the happiness of their friends: we call this class of games social context hedonic games (SCHGs). We consider Nash equilibria of SCHGs, and study their existence, convergence and performance with respect to the classical notions of price of anarchy and price of stability. In particular, we provide an exact potential function for SCHGs implying the existence and convergence to Nash equilibria, and we prove tight or asymptotically tight bounds on the price of anarchy and the price of stability of SCHGs.
Clinical Relevance of Partial-Volume Effect: Dependence on Lesion size and Shape

This study sought to systematically assess the influence of partial-volume effect (PVE) and segmentation on PET quantitative measures, hinging on lesion geometry. How this affects, e.g., prevalent maximum standardised uptake values, $SUV_{\max}$, and potentially impact clinical applications, e.g., response evaluation has yet to be fully discerned. From PET simulations with open-source software of variable-sized ellipsoidal lesions inserted in an anthropomorphic phantom, images of two contrasts and resolutions were generated. $SUV_{\max}$ and volumetric indices extracted with six different segmentations were compared for variability and test-retest repeatability. The study showed similar or larger shape dependent variability and lower repeatability in $SUV_{\max}$ than $SUV_{m\mathrm{e}an}$. Alternative volumetric indices might provide more robust measures but require better contouring than common thresholding. Thus findings suggested significant impact of PVE and segmentation in clinically relevant lesion sizes that can bias interpretation of SUV changes.
Adaptive plasticity in the gametocyte conversion rate of malaria parasites

Sexually reproducing parasites, such as malaria parasites, experience a trade-off between the allocation of resources to asexual replication and the production of sexual forms. Allocation by malaria parasites to sexual forms (the conversion rate) is variable but the evolutionary drivers of this plasticity are poorly understood. We use evolutionary theory for life histories to combine a mathematical model and experiments to reveal that parasites adjust conversion rate according to the dynamics of asexual densities in the blood of the host. Our model predicts the direction of change in conversion rates that returns the greatest fitness after perturbation of asexual densities by different doses of antimalarial drugs. The loss of a high proportion of asexuals is predicted to elicit increased conversion (terminal investment), while smaller losses are managed by reducing conversion (reproductive restraint) to facilitate within-host survival and future transmission. This non-linear pattern of allocation is consistent with adaptive reproductive strategies observed in multicellular organisms. We then empirically estimate conversion rates of the rodent malaria parasite Plasmodium chabaudi in response to the killing of asexual stages by different doses of antimalarial drugs and forecast the short-term fitness consequences of these responses. Our data reveal the predicted non-linear pattern, and this is further supported by analyses of previous experiments that perturb asexual stage densities using drugs or within-host competition, across multiple parasite genotypes. Whilst conversion rates, across all datasets, are most strongly influenced by changes in asexual density, parasites also modulate conversion according to the availability of red blood cell resources. In summary, increasing conversion maximises short-term transmission and reducing conversion facilitates in-host survival and thus, future transmission. Understanding patterns of parasite allocation to reproduction matters because within-host replication is responsible for disease symptoms and between-host transmission determines disease spread.
Pose2Seg: Detection Free Human Instance Segmentation

The standard approach to image instance segmentation is to perform the object detection first, and then segment the object from the detection bounding-box. More recently, deep learning methods like Mask R-CNN perform them jointly. However, little research takes into account the uniqueness of the "human" category, which can be well defined by the pose skeleton. Moreover, the human pose skeleton can be used to better distinguish instances with heavy occlusion than using bounding-boxes. In this paper, we present a brand new pose-based instance segmentation framework for humans which separates instances based on human pose, rather than proposal region detection. We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion. Furthermore, there are few public datasets containing many heavily occluded humans along with comprehensive annotations, which makes this a challenging problem seldom noticed by researchers. Therefore, in this paper we introduce a new benchmark "Occluded Human (OCHuman)", which focuses on occluded humans with comprehensive annotations including bounding-box, human pose and instance masks. This dataset contains 8110 detailed annotated human instances within 4731 images. With an average 0.67 MaxIoU for each person, OCHuman is the most complex and challenging dataset related to human instance segmentation. Through this dataset, we want to emphasize occlusion as a challenging problem for researchers to study.
A −40 to 250°C Triple Modular Redundancy Temperature Sensor for Turbofan Engines

Turbofan engines using distributed control architecture require sensors and electronic instrumentation located in the engine compartment to simplify system design, improve reliability, and ease signal multiplexing. Designing a wide range temperature sensor which works reliably regardless of process variation can be extremely challenging. Using a bandgap-like topology with PiN diodes as the sensing element, this work proposes a −40 to 250 °C temperature sensor in the IMS $0.35\ \mu \mathbf{m}$ SOI technology. To circumvent the process variation challenge, a triple modular redundancy is proposed for the sensing element combined with a weighted voter and digital circuitry calibration. The proposed sensing element supplies the analog-to-digital conversion (ADC) with a $V_{REF}$ having a typical-mean temperature coefficient of 62 ppm/°C, 24 ppm/°C for the best corner, and 213 ppm/°C for the worst corner. An average $V_{CTAT}$ sensitivity is estimated at 0.282 mV/°C among all corners. After a 2-point temperature calibration, the ADC quantization step presented a negligible variation over temperature, approximately 1.0 mV among all corners, assuring a 0.28 °C of temperature inaccuracy.
Early impact of fixed orthodontic therapy on gingival health status in relation to weight status

Background: fixed orthodontic appliances deleterious influence on gingival health is well documented. Association between weight status and gingival health is presented in many studies. This study aimed to evaluate how early the impact of fixed orthodontic therapy on patients` gingival health, and if there are differences of that impact among different weight status groups. Materials and Methods: Sample consisted of 54 patients (25 males, 29 females; age limits are 16 -18 years) going under the course of treatment with fixed orthodontic appliance. Patients were categorized according to their Body Mass Index (BMI) into 3 weight status groups considering WHO charts in 2007 (underweight, normal weight, overweight and obese), then determination of each patient`s gingival health status was through the criteria of the gingival index (GI) by Loe and Silness in 1963 which modified by Loe in 1967. Records of gingival index for all patients who met specific criteria were taken in three time points [before bonding (1st visit), 2 weeks after bonding (2nd visit), and 4 weeks after bonding (3rd visit)]. Also BMI of the patients were checked at each of the three visits. Results: There was a significant increase in gingival index for all BMI weight status groups after just two weeks of treatment, and the increase continues during the 3rd visit, with no significant difference in impact among weight status groups. Conclusions: oral health preventive measures should be applied rapidly and equally to all patients treated with fixed orthodontic appliances, without taking their BMI weight status in consideration.
Study on Coverage of Full Frequency Reuse in FFR Systems Based on Outage Probability

A fractional frequency reuse (FFR) system is an inter-cell interference coordination scheme used in cellular networks. In FFR systems, the available bandwidth is partitioned into orthogonal subbands such that the users near the cell center adopt subbands of a frequency reuse (FR) factor equal to one (i.e., Full FR), and the users near the cell edge adopt the subbands of an FR factor greater than one (i.e., Partial FR). The proper design of Full FR coverage, which is used to distinguish Full FR regions from Partial FR regions, plays a critical role in FFR system performance. This paper studies the optimal Full FR coverage that maximizes system throughput in the downlink in multiple-input multiple-output (MIMO) cellular networks. For MIMO systems, orthogonal space–time block codes are considered. We analytically compare the outage probabilities of Full FR and Partial FR for a given user’s location, where the outage probability is evaluated through small-scale multipath fading. By doing so, subject to the constraint that a given target outage probability (quality-of-service) is satisfied, the optimal Full FR coverage is analyzed as a function of base station (BS) power. We prove that the optimal Full FR coverage is a non-increasing function of BS power when the powers of all BSs in the network are scaled up or down at the same rate. This result offers insight into the design of Full FR coverage in relation to BS power; we gain insight into the complicated relationship between crucial FFR design parameters.
A Compact Multi-Beam End-Fire Circularly Polarized Septum Antenna Array for Millimeter-Wave Applications

In this paper, a novel circularly polarized end-fire septum antenna based on substrate-integrated-waveguide (SIW) is proposed in the 60-GHz band. By introducing four symmetrical tapered curved air slots in three stacked open-ended SIW layers, the antenna can obtain an operating bandwidth from 57.2 to 63.4 GHz for both AR < 3 dB and <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -21$ </tex-math></inline-formula> dB. Besides, the half-power-beam-width and 3-dB axial-ratio (AR) beam-width are all wider than 79°, and a stable gain of 7.57 dBic with a variation of 0.15 dBic can be achieved in the working band. A <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> SIW Butler matrix based on SIW with three same substrates is then designed. The modified two-layer phase shifters realized by the different path distance are employed in the folded Butler matrix. By combining the antenna element and the Butler matrix together, a four-beam antenna array with circular polarization wave is realized. The proposed array has been fabricated and measured. The measurement results show that the antenna gain up to 11.01 dBic, −10 dB impedance and isolation bandwidth from 57.8 to 62.5 GHz (except at around 58.3 GHz), stable radiation pattern and a wide angular range of ±37° can be obtained. The measured AR values of the main lobe range of four beams are all less than 5 dB from 58 to 60 GHz.
Synthesis and Anti‐HIV Activity of Guanine Modified Fluorinated Acyclic Nucleoside Phosphonate Derivatives

The preparation of an unprecedented series of nucleobase modified 3‐fluoro‐2‐(phosphonomethoxy)propyl (FPMP) acyclic nucleosides in both their (R) and (S) enantiomerically pure forms is described. The synthesis focuses on a Mitsunobu alkylation reaction to construct the C−N(9) bond between a chiral fluorinated side‐chain residue and 6‐ or 7‐modified guanine analogs. Prodrugs of FPMP‐7‐deazaguanine were also synthesized by derivatization of the corresponding phosphonic acid functionality with (bis)diamyl aspartate amidate groups, leading to moderate activity against human immunodeficiency virus type 1 (HIV‐1).
Evaluating perceptions of risk in mosquito experts and identifying undocumented pathways for the introduction of invasive mosquito species into Europe

In several reported cases of the entry of invasive mosquito species (IMSs) into Europe, the introduction was associated with a specific pathway of introduction or dispersal. The identification of potential pathways for the introduction of IMSs and evaluations of the importance of the different pathways are key to designing proper surveillance strategies to promptly detect and control introductions in non‐infested areas. The main goals of the present study were to identify other, previously undocumented, pathways of introduction into Europe, and to identify mosquito experts' perceptions regarding control measures against IMS introductions via different documented pathways. At the European Mosquito Control Association (EMCA) conference in Montenegro in March 2017, a questionnaire was distributed among meeting participants to collect expert data. Results showed that ground transportation (by cars, trucks, etc.), passive natural dispersal and the shipping of used tyres are perceived as the most likely pathways. Introduction via aircraft did not appear to be well known and was not perceived as probable. This study shows that there were no pathways unknown to European experts that could lead to cryptic introductions into the experts' countries. Furthermore, the findings demonstrated that the perceived efficacy of surveillance and control is key to overcoming the constraints experienced and to supporting the implementation of actions against introductions.
PIK3IP1/TrIP restricts activation of T cells through inhibition of PI3K/Akt

Phosphatidylinositol-3 kinases (PI3Ks) modulate numerous cellular functions, including growth, proliferation and survival. Dysregulation of the PI3K pathway can lead to autoimmune disease and cancer. PIK3IP1 (or Transmembrane Inhibitor of PI3K – TrIP) is a novel transmembrane regulator of PI3K. TrIP contains an extracellular kringle domain and an intracellular “p85-like” domain with homology to the inter-SH2 domain of the regulatory subunit of PI3K. Although TrIP has been shown to bind to the p110 catalytic subunit of PI3K in fibroblasts, the mechanism by which TrIP functions is poorly understood. We show that both the kringle and “p85-like” domains are necessary for TrIP inhibition of PI3K. We also demonstrate that TrIP protein is down-modulated from the surface of T cells to allow T cell activation. In addition, we present evidence that the kringle domain may modulate TrIP function by binding an as-yet-unidentified ligand. Using an inducible knockout mouse model that we developed, we show that TrIP-deficient T cells exhibit more robust T cell activation, show a preference for Th1 polarization and can mediate clearance of Listeria monocytogenes infection faster than WT mice. Thus, TrIP is an important negative regulator of T cell activation and may represent a novel target for immune modulation therapies.
Dose reconstruction from PET images in carbon ion therapy: a deconvolution approach

Dose and range verification have become important tools to bring carbon ion therapy to a higher level of confidence in clinical applications. Positron emission tomography is among the most commonly used approaches for this purpose and relies on the creation of positron emitting nuclei in nuclear interactions of the primary ions with tissue. Predictions of these positron emitter distributions are usually obtained from time-consuming Monte Carlo simulations or measurements from previous treatment fractions, and their comparison to the current, measured image allows for treatment verification. Still, a direct comparison of planned and delivered dose would be highly desirable, since the dose is the quantity of interest in radiation therapy and its confirmation improves quality assurance in carbon ion therapy. In this work, we present a deconvolution approach to predict dose distributions from PET images in carbon ion therapy. Under the assumption that the one-dimensional PET distribution is described by a convolution of the depth dose distribution and a filter kernel, an evolutionary algorithm is introduced to perform the reverse step and predict the depth dose distribution from a measured PET distribution. Filter kernels are obtained from either a library or are created for any given situation on-the-fly, using predictions of the -decay and depth dose distributions, and the very same evolutionary algorithm. The applicability of this approach is demonstrated for monoenergetic and polyenergetic carbon ion irradiation of homogeneous and heterogeneous solid phantoms as well as a patient computed tomography image, using Monte Carlo simulated distributions and measured in-beam PET data. Carbon ion ranges are predicted within less than 0.5 mm and 1 mm deviation for simulated and measured distributions, respectively.