Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.
Temporal variability of euphausiid concentrations in a Nova Scotia shelf basin using a bottom-mounted acoustic Doppler current profiler

The usefulness of bottom-mounted, upward-looking sonars for long-term quantitative zooplankton monltonng was demonstrated util~zing a 49 d record of acoustic volume backscattering strengths collected by a 150 kHz RDI Acoustic Doppler Current Prof~ler in LaHave Basin off Nova Scotla, Canada Varlatlons in the character and Intensity of 2 dlstlnct acoustic scattenng honzons distlnguished by dlffenng dlurnal migration patterns were noted One honzon confined to the top 50 m and only present during the early parts of the recording was tentatively ascribed to ~uveni le fish Another strongly migrating horizon identified wlth the euphausild Meganyctlphanes norveglca was studied in detail M norvegica in daytime were broadly distnbuted at about 150 nl depth At night they were uniformly dlstnbuted in the upper 50 m A 30% decrease In nighttime integrated backscattenng levels was consistent with euphausud accumulation in the acoustically unobservable near-surface region Average target strengths decllned S1 dB dunng active vertical rmgration This observation, ~nterpreted by acoustic scattenng theory, constrained average euphausiid inclinations to llttle more than 30" dunng upward swimmng Column ~ntegrated population densities were roughly 290 m-2 Use of an lnchned beam sonar geometry should result in backscattering levels less sensitive to organism onentahon than use of a conventional verhcally onented sonar beam
Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training

Cognitive training has shown promising results for delivering improvements in human cognition related to attention, problem solving, reading comprehension and information retrieval. However, two frequently cited problems in cognitive training literature are a lack of user engagement with the training programme, and a failure of developed skills to generalise to daily life. This paper introduces a new cognitive training (CT) paradigm designed to address these two limitations by combining the benefits of gamification, virtual reality (VR), and affective adaptation in the development of an engaging, ecologically valid, CT task. Additionally, it incorporates facial electromyography (EMG) as a means of determining user affect while engaged in the CT task. This information is then utilised to dynamically adjust the game's difficulty in real-time as users play, with the aim of leading them into a state of flow. Affect recognition rates of 64.1% and 76.2%, for valence and arousal respectively, were achieved by classifying a DWT-Haar approximation of the input signal using kNN. The affect-aware VR cognitive training intervention was then evaluated with a control group of older adults. The results obtained substantiate the notion that adaptation techniques can lead to greater feelings of competence and a more appropriate challenge of the user's skills.
Effects of Phytase Transgenic Corn on Growth Performance and Calcium and Phosphorus Utilization in Broilers

The goal of this study was to determine the effects of phytase transgenic corn (PTC) in low phosphorus (P) cornsoybean meal diets on growth performance, and P and calcium (Ca) utilization by broilers. Six hundred and forty 1day-old male broiler chicks were randomly divided into four groups, with eight replicates of 20 birds in each group, and fed for 42 days. Four treatments consisted of a positive control (PC), a negative control (NC), a PTC group and an exogenous phytase (EP) group. The PC group was fed with basal diet containing adequate amount of P. The NC group had 50% of the dicalcium phosphate supplement in the basal diet. The PTC group had the same inorganic P as the NC diet, but was substituted PTC for normal corn, so that the diet contained 500 phytase units (FTU) per kg of diet. The EP group was fed a diet identical to the NC diet but supplemented with 500 FTU of EP/kg of diet. The experiment showed that: there were no significant differences between the PTC and the PC groups, although a more favorable feed/gain occurred from the PTC group. The average daily feed intake and the average daily gain in the PC, PTC and EP groups were higher than those in the NC group (P<0.05). The Ca and P contents of tibia ash and serum in the PTC group were similar to those in the PC group, except for a higher tibia P percentage in the PC group at day 21 (P<0.05). These values were slightly higher than those in the EP group, except for a lower content of tibia P of the PTC group on day 42; the apparent retention of total phosphorus of the PTC and EP groups from day 17 to 21 was higher than that in the PC group (P<0.05). In conclusion, PTC as a source of phytase can replace the EP in broiler diets, especially in the starter phase of broiler chicks. The use of PTC could reduce the inclusion of inorganic P, improve the retention rate of P, and reduce pollution of the environment by farm animals without affecting the growth performance of broilers.
Efficient Bayesian inversion for shape reconstruction of lithography masks

Abstract. Background: Scatterometry is a fast, indirect, and nondestructive optical method for quality control in the production of lithography masks. To solve the inverse problem in compliance with the upcoming need for improved accuracy, a computationally expensive forward model that maps geometry parameters to diffracted light intensities has to be defined. Aim: To quantify the uncertainties in the reconstruction of the geometry parameters, a fast-to-evaluate surrogate for the forward model has to be introduced. Approach: We use a nonintrusive polynomial chaos-based approximation of the forward model, which increases speed and thus enables the exploration of the posterior through direct Bayesian inference. In addition, this surrogate allows for a global sensitivity analysis at no additional computational overhead. Results: This approach yields information about the complete distribution of the geometry parameters of a silicon line grating, which in return allows for quantifying the reconstruction uncertainties in the form of means, variances, and higher order moments of the parameters. Conclusions: The use of a polynomial chaos surrogate allows for quantifying both parameter influences and reconstruction uncertainties. This approach is easy to use since no adaptation of the expensive forward model is required.
Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys

We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys’ Data Release 8. We use deep residual neural networks, building on previous work presented by Huang et al. These surveys together cover approximately one-third of the sky visible from the Northern Hemisphere, reaching a z-band AB magnitude of ∼22.5. We compile a training sample that consists of known lensing systems as well as non-lenses in the Legacy Surveys and the Dark Energy Survey. After applying our trained neural networks to the survey data, we visually inspect and rank images with probabilities above a threshold. Here we present 1210 new strong lens candidates.
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality

Sparse principal component analysis (PCA) is a popular dimensionality reduction technique for obtaining principal components which are linear combinations of a small subset of the original features. Existing approaches cannot supply certifiably optimal principal components with more than $p=100s$ covariates. By reformulating sparse PCA as a convex mixed-integer semidefinite optimization problem, we design a cutting-plane method which solves the problem to certifiable optimality at the scale of selecting k=10s covariates from p=300 variables, and provides small bound gaps at a larger scale. We also propose two convex relaxations and randomized rounding schemes that provide certifiably near-exact solutions within minutes for p=100s or hours for p=1,000s. Using real-world financial and medical datasets, we illustrate our approach's ability to derive interpretable principal components tractably at scale.
Seeds of Life in Space (SOLIS). IX. Chemical Segregation of SO2 and SO toward the Low-mass Protostellar Shocked Region of L1157

We present observations of SO and SO2 lines toward the shocked regions along the L1157 chemically rich outflow, taken in the context of the Seeds of Life in Space IRAM Northern Extended Millimeter Array Large Program, and supported by data from the Submillimeter Array and IRAM-30 m telescope at 1.1–3.6 mm wavelengths. We simultaneously analyze, for the first time, all of the brightest shocks in the blueshifted lobe, namely, B0, B1, and B2. We found the following. (1) SO and SO2 may trace different gas, given that the large(-scale) velocity gradient analysis indicates for SO2 a volume density ( ) denser than that of the gas emitting in SO by a factor up to an order of magnitude. (2) Investigating the 0.1 pc scale field of view, we note a tentative gradient along the path of the precessing jet. More specifically, decreases from the B0–B1 shocks to the older B2. (3) At a linear resolution of 500–1400 au, a tentative spatial displacement between the two emitting molecules is detected, with the SO peak closer (with respect to SO2) to the position where the recent jet is impinging on the B1 cavity wall. Our astrochemical modeling shows that the SO and SO2 abundances evolve on timescales less than about 1000 years. Furthermore, the modeling requires high abundances (2 × 10−6) of both and S/H injected in the gas phase due to the shock occurrence, so prefrozen OCS only is not enough to reproduce our new observations.
Theories of Massive Gravity in 2+1 Dimensions

This thesis is dedicated to the study of theories of massive gravity. The formulation of higher spin gauge field theories, along with a Chern-Simons (CS) like term for fields of higher spins is presented. Through this setup, general features of theories describing massless/massive fields of higher spin in arbitrary dimensions is discussed. In 2+1 dimensions, the existence of multiple mass-generating mechanisms i.e. metric-based masses and topological masses, offers the possibility for gauge bosons to acquire mixed masses. This scenario is first introduced in a theory of photons where both Proca mass term and CS mass term is simultaneously present. The mass-mixing which occurs in this theory is further analysed through the St\"uckelberg formalism. Motivated by these results, a theory of massive gravity where gravitons obtain masses from both Fierz-Pauli mass term and CS mass term is studied. This theory allows for 3 propagating massive graviton modes, and their masses have undergone considerable mixing. This mass-mixing is made explicit through a St\"uckelberg analysis. The bimetric theory of gravity is a non-trivial generalization of the theory of General Relativity. This theory provides a consistent non-linear theory of massive gravity which is studied in detail. In this thesis, the bimetric theory of gravity is extended in 2+1 dimensiosn to the theory of Topologically Massive Bimetric Gravity (TMBG). In the theory of TMBG, the mass-mixing which arises from the interaction of the two metrics and their corresponding CS terms occurs at the nonlinear stage itself. For the version of TMBG studied in this thesis, the linearized theory shows that there are 3 massive graviton modes, which corresponds to the same 3 modes found earlier. Finally, quantum loop corrections to the graviton propagator from massive photons (which acquire mass from both CS and Proca mechanism) is calculated.
Phosphorus uptake and translocation in salt-stressed melon plants

Summary Melon seedlings ( Cucumis melo L. cv.Galia) were grown hydroponically to study the effect of salinity (80 mmol/LNaCl) on phosphate (Pi) uptake and translocation at two levels of Pi (25 μmol/L and 1 mmol/L). Net uptake rates of Pi were determined by depletionof the medium and by plant content. Salinity decreased Pi uptake at low Pi (high affinity uptake mechanism), 25 μmol/L, although no specific competitive inhibition of Pi uptake by Cl − was observed. When plants were grown with high Pi (1 mmol/L), the uptake of Pi through the low affinity system was increased by 80 mmol/L NaCl. Salinity also reduced the phosphorus flux, as Pi, through the xylem. It is hypothesised that high levels of NaCl decrease the mobility of Pi stored in vacuoles, and as a result, inhibit export from this storage compartment to other parts of the plant.
Inertial rupture of ultrathin liquid films

Theory and numerical simulations of the Navier-Stokes equations are used to unravel the inertia-driven dewetting dynamics of an ultrathin film of Newtonian liquid deposited on a solid substrate. A classification of the film thinning regimes at finite Ohnersorge numbers is provided, unifying previous findings. We reveal that, for Ohnesorge numbers smaller than one, the final approach to the rupture singularity close to the molecular scales is controlled by a balance between liquid inertia and van der Waals forces leading to a self-similar asymptotic regime with $h_{\text{min}} \propto \tau^{2/5}$, where $h_{\text{min}}$ is the minimum film thickness and $\tau$ is the time remaining before rupture. The flow exhibits a three-region structure comprising an irrotational core delimited by a pair of boundary layers at the wall and at the free surface. A potential-flow description of the irrotational core is provided, which is asymptotically matched with the viscous layers, allowing us to present a complete parameter-free asymptotic description of inertia-driven film rupture.
Inter-specific Relationships Among Some Taxa of Brassicaceae (L) Based on Macromorphology, Lamina Architecture, Stomatography and Palynological Criteria

The present study included the investigation of morphological attributes viz. whole plant, leaf architecture, stomatographic and palynological criteria of some taxa of Brassicaceae to clarify the diversity and the diagnostic importance of these characters. The sum of character states of the investigated taxa were subjected to a numerical analysis using NTSYS-PC program (version 2.02). The generated dendrogram clarified that the taxa under investigation are divided into two main series, three clusters and five groups. The similarities and dissimilarities between the investigated taxa were interpreted. The specific relationships were discussed and compared with some of the current systems of classification. The aim of the present study is to study the interspecific relationships through the investigation of selected morphological characters of the studied taxa and for a numerical evaluation of such characters.
A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices

Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications (apps) provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the security threats of attackers. Consequently, a last line of defense on mobile devices is necessary and much-needed. In this paper, we propose an effective Android malware detection system, MobiTive, leveraging customized deep neural networks to provide a real-time and responsive detection environment on mobile devices. MobiTive is a pre-installed solution rather than an app scanning and monitoring engine using after installation, which is more practical and secure. Although a deep learning-based approach can be maintained on server side efficiently for malware detection, original deep learning models cannot be directly deployed and executed on mobile devices due to various performance limitations, such as computation power, memory size, and energy. Therefore, we evaluate and investigate the following key points: (1) the performance of different feature extraction methods based on source code or binary code; (2) the performance of different feature type selections for deep learning on mobile devices; (3) the detection accuracy of different deep neural networks on mobile devices; (4) the real-time detection performance and accuracy on different mobile devices; (5) the potential based on the evolution trend of mobile devices’ specifications; and finally we further propose a practical solution (MobiTive) to detect Android malware on mobile devices.
Measurement and classification of genetic diversity in okra (Abelmoschus esculentus)

Summary Eighteen accessions of okra of diverse ecological background were evaluated for genetic variability through the techniques of coefficient of racial likeness (CRL) and principal coordinate analysis (PCO). The variation patterns among the accessions were classified by using the techniques of metroglyph analysis and single-linkage cluster analysis. CRL and PCO produced similar results on the diversity of the accessions but the grouping of the accessions by metroglyph analysis and SLCA produced different results. The usefulness of metroglyph analysis depends on the amount of variation accounted for by the two principal characters on which the two co-ordinate axes are constructed. The techniques are compared.
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner. Yet, in most applications of FL, such as keyboard prediction, labeling data requires virtually no additional efforts, which is not generally the case. In reality, acquiring large-scale labeled datasets can be extremely costly, which motivates research works that exploit unlabeled data to help build machine learning models. However, to the best of our knowledge, few existing works aim to utilize unlabeled data to enhance federated learning, which leaves a potentially promising research topic. In this paper, we identify the need to exploit unlabeled data in FL, and survey possible research fields that can contribute to the goal.
Exploring the turbulent velocity gradients at different scales from the perspective of the strain-rate eigenframe

Abstract Expressing the evolution equations for the filtered velocity gradient tensor (FVGT) in the strain-rate eigenframe provides an insightful way to disentangle and understand various processes such as strain self-amplification, vortex stretching and tilting, and to consider their properties at different scales in the flow. Using data from direct numerical simulation of the forced Navier–Stokes equation, we consider the relative importance of local and non-local terms in the FVGT eigenframe equations across the scales using statistical analysis. The eigenframe rotation rate (that drives vorticity tilting) is shown to exhibit highly non-Gaussian fluctuations even at large scales due to kinematic effects, but dynamically, the anisotropic pressure Hessian plays a key role. The anisotropic pressure Hessian conditioned on the eigenvalues and principal vorticity components exhibits highly nonlinear behaviour at low values of normalized local gradients, with important modelling implications. We derive a generalization of the Lumley triangle that allows us to show that the pressure Hessian has a preference for two-component axisymmetric configurations at small scales, with a transition to a more isotropic state at larger scales. Correlations between the sub-grid stress and other terms in the eigenframe equations are considered, highlighting the coupling between the sub-grid and nonlinear amplification terms, with the sub-grid term playing an important role in regularizing the system. These results provide useful guidelines for improving Lagrangian models of the FVGT, since current models fail to capture a number of subtle features observed in our results.
Simultaneous State and Unknown Input Estimation for Continuous-discrete Stochastic Systems

This paper considers the simultaneous state and unknown input estimation for continuous-discrete stochastic systems. Two types of approaches (with and without modeling of unknown inputs) which can address this issue are investigated. A novel continuous recursive four-step Kalman filter is proposed and its asymptotic stability condition is established. A novel one-step unknown input Kalman filter is proposed and has guaranteed stability when the number of unknown inputs is equal to that of the measurements. The design of unknown input Kalman filters and observers is unified. Furthermore, an adaptive augmented Kalman filter which requires the modeling of unknown inputs is introduced. The estimation error covariance of the recursive four-step Kalman filter and the adaptive augmented Kalman filter is analyzed and compared. Finally, simulation results demonstrate the effectiveness of the proposed approaches.
Stationary Entanglement between Light and Microwave via Ferromagnetic Magnons

It is shown how to generate stationary entanglement between light and microwave in a hybrid opto‐electro‐magnonical system which mainly consists of a microwave cavity, a yttrium iron garnet (YIG) sphere, and a nanofiber. The optical modes in nanofiber can evanescently be coupled to whispering gallery modes, that are able to interact with magnon mode via spin–orbit interaction, in YIG sphere, while the microwave cavity photons and magnons are coupled through magnetic dipole interaction simultaneously. Under reasonable parameter regimes, pretty amount of entanglement can be generated, and it also shows persistence against temperature. The present work is expected to provide a new perspective for building more advanced and comprehensive quantum networks along with magnons for fast‐developing quantum technologies and for studying the macroscopic quantum phenomena.